All Papers

Yin, Wei and Liu, Li and Fu, Xiaodong and Liu, Lijun and Peng, Wei

High-quality garment representation is both a challenge and a key factor in constructing generalized 3D humans from a single-view image. Existing techniques often perform poorly when handling complex garments, primarily due to two critical challenges: (1) Single-view images lack complete information about the garments, limiting the completeness and realism of the reconstruction results. (2) The mo ...

Luan, Bozhi and Feng, Hao and Chen, Hong and Wang, Yonghui and Zhou, Wengang and Li, Houqiang

The advent of Large Multimodal Models (LMMs) has fueled extensive research due to their sophisticated reasoning capabilities. However, for understanding text-rich images, challenges persist in fully leveraging the potential of LMMs, and existing methods struggle with effectively processing high-resolution images. Addressing this, we introduce TextCoT, a training-free Chain-of-Thought framework tha ...

Cai, Yali and Qiao, Peng and Li, Dongsheng

Text-driven Talking Head Generation (THG) marks a significant advancement in the video production industry by enabling the creation of realistic talking head videos with minimal data input. While previous research has explored few-shot talking head synthesis, these methods often fall short in terms of lip-sync consistency and expression diversity, both of which are essential for practical applicat ...

Zhang, Wei and Jin, Peng and Zou, Chunyu and Peng, Han and Zhang, Ziyao and Chen, Jie and Gao, Wen

Text-video retrieval plays a pivotal role in cross-modal tasks, aiming to match textual descriptions with corresponding video content accurately. Existing methods often employ fine-grained feature matching to improve retrieval accuracy, but such approaches consume extensive computational resources. Conversely, coarse-grained feature matching between entire sentences and videos offers computational ...

Lu, Zhenxuan and Xu, Zhihua and Yang, Zhijing and Gao, Feng and Lu, Yongyi and Wang, Keze and Chen, Tianshui

Speech-Preserving Facial Expression Manipulation (SPFEM) is an innovative technique aimed at altering facial expressions in images and videos while retaining the original mouth movements. Despite advancements, SPFEM still struggles with accurate lip synchronization due to the complex interplay between facial expressions and mouth shapes. Capitalizing on the advanced capabilities of Audio-Driven Ta ...

Gray, Andrew

Since 2023, a significant number of published scholarly papers show signs of having been edited using AI tools. Many of these will have been cases where it was used for copyediting or translation, but some have been substantially or entirely generated by large language models—and readers cannot easily tell which is which. AI tools, meanwhile, are also being used to review papers and as intermediar ...

Vaishya, Raju and Gupta, Brij Mohan and Vaish, Abhishek and Kambhampati, Srinivas S. B. and Poduval, Murali and Shekhawat, Sudhir and Bansal, Madhu

Background: The Indian Journal of Orthopaedics (IJO) has played a pivotal role in disseminating Indian orthopaedic research. However, a comprehensive bibliometric assessment of its publication trends, citation impact, and research landscape has been lacking. Methods: A bibliometric and scientometric analysis was conducted on all Indian-authored articles published in the IJO and indexed in Scopus f ...

Martuscelli, Patrícia Nabuco

Refugees often end up separated from their families when they cross international borders. While the right to family life and unity is enshrined in different international human rights documents, the right to family reunification is contested. This paper discusses the right to family reunification for refugees in Latin America by explaining family reunification as a human right in the region and t ...

Zhou, Zhenyu and Liao, Qing and Liu, Tianrui and Luo, Lei and Liu, Xinwang and Zhu, En

Metric-based methods, such as ProtoNet, excel in few-shot image classification by encouraging similarity to class prototypes. However, prototypes built from limited samples often capture only partial class information, limiting performance. Recent distribution estimation-based methods attempt to enhance performance by leveraging similar base class distributions. Yet, these approaches struggle when ...

Vaishya, Raju and Gupta, Brij Mohan and Vaish, Abhishek and Kambhampati, Srinivas B S and Jain, Vijay Kumar and Shekhawat, Sudhir

Background and Aims: This study performed a comprehensive bibliometric analysis comparing Indian and international highly-cited papers (HCPs) in the Indian Journal of Orthopaedics (IJO). Methods: The study used a bibliometric design to analyze HCPs published in the IJO between 2007 and 2024, a period selected based on the journal's Scopus coverage. The Scopus database was used for its comprehensiv ...

Gong, Lejun and Wei, Ziyi and Tian, Li and Deng, Jie and Sun, Guozi

In this article, we introduce PingTactics, a multimodal dataset tailored for table tennis action recognition and tactical analysis. Derived from professional table tennis matches, the dataset comprises annotated video clips capturing detailed player actions, positional dynamics, and scoring outcomes. A key feature of PingTactics is its comprehensive temporal annotation framework, which includes se ...

Karipek, Yusuf Ziya

The burgeoning body of research on transnational repression examines how states exert control over their own emigrant citizens beyond national borders. In this discussion, a timely question warrants attention: how do states assert control through deliberate regulation of consular services on expatriate citizens and what impact does it create on them? Drawing on 27 in-depth interviews and extensive ...

Sidhu, Jashanjot Singh and Bentaleb, Abdelhak

The QUIC transport protocol represents a significant evolution in web transport technologies, offering improved performance and reduced latency compared to traditional protocols like TCP. Given the growing number of QUIC implementations, understanding their performance, particularly in video streaming contexts, is essential. This article presents a comprehensive analysis of various QUIC implementa ...

Jia, Zhihao and Liu, Hao and Zhang, Shuo and Zhang, Yonghua and Lu, Yang and Huang, Ziqing and Liu, Shiguang

Point clouds play an essential role in 3D visual media applications. Point Cloud Quality Assessment (PCQA) is vital to improving the subjective experience, but lacking an effective geometric characterization hinders its consistency with subjective perception. To tackle this problem, this article introduces a No-Reference Multi-Modal Point Cloud Quality Assessment (NR-PCQA) approach driven by adapt ...

Wei, Chuyuan and He, Anning and Zhuo, Shengda and Wang, Changdong and Huang, Shuqiang

Graph Neural Networks (GNNs) have demonstrated strong representation learning capabilities in recommender systems, particularly under the contrastive learning paradigm, where the construction of positive and negative sample pairs effectively captures latent relations between users and items, thereby significantly enhancing recommendation performance. However, existing graph contrastive learning me ...

Jiang, Wei and Zhai, Yongqi and Yang, Jiayu and Gao, Feng and Wang, Ronggang

Recent advances in Learned Image Compression (LIC) have achieved remarkable performance improvements over traditional codecs. Notably, the MLIC series—LICs equipped with multi-reference entropy models—have substantially surpassed conventional image codecs such as Versatile Video Coding (VVC) Intra. However, existing MLIC variants suffer from several limitations: performance degradation at high bit ...

Lyko, Tomasz and Elkhatib, Yehia and Ramdhany, Rajiv and Race, Nicholas

Quality of Experience (QoE) is a key component in adaptive bitrate (ABR) streaming. Whilst the effects of delivery disruptions—such as changes in video quality or rebuffering—have been extensively studied, the impact of playback rate variations remains relatively unexplored. Existing work has examined the QoE impact of playback rate in isolation, without comparing it to other common ABR streaming ...

Wang, Zequn and Wang, Tianshi and Li, Fengling and Li, Jingjing and Zhu, Lei

Deep hashing has proven remarkable effectiveness for large-scale cross-modal retrieval, yet its performance is highly vulnerable to supervisory noise, such as mismatched cross-modal correspondences and incorrect category labels. Such noise is prevalent in real-world scenarios, where correspondence mismatches and label inaccuracies often coexist, posing significant challenges for learning accurate ...

Olufson, Hannah and Do, Huyen and Noble, Bridget and Power, Gary and Moore, Janette and Ong, Ruby and Harding, Scott and Robertson, Samantha and Gunawardena, Thilini and Ellick, Jennifer and Young, Adrienne

Introduction: Hospital nutrition care is usually supported by offering written, paper‐based information to patients and carers. However, there is a need for evidence‐based online information, such as videos, to support education. We aimed to co‐design nutrition information videos for current and recently discharged hospital patients and share our process for clinicians and researchers undertaking ...

Huang, Pengyu and Wu, Jingjing and Guo, Yanrong and Hong, Richang

Infrared Object Tracking (IOT) is challenging due to the low contrast of infrared images, which limits effective spatial feature extraction. Although recent works have explored frequency-domain information, their utilization remains insufficient, and fusion strategies either retain redundancy or fail to fully explore distinctive differences, thus limiting complementary enhancement. To overcome thi ...

Carte, Sean and Moodley, Sagren and Moonasar, Anushie

In 2024, the Durban University of Technology (DUT) Library conducted a comprehensive review of its library system to assess whether its current platform, Future of Libraries Is Open (FOLIO) hosted by EBSCO, and its discovery tool, EBSCO Discovery Service (EDS), aligned with its evolving needs. The institution had been using the current system for three years, but the slow development of important ...

Yi, Han and Zheng, Zhedong and Xu, Xiangyu and Chua, Tat-Seng

The challenge of text-to-3D generation lies in accurately and efficiently crafting 3D objects based on natural language descriptions, a capability that promises a substantial reduction in manual design efforts and offers an intuitive interface for user interaction with digital environments. Despite recent advancements, effective recovery of fine-grained details and efficient optimization of high-r ...

Liu, Gang and Zhang, Boying and Ming, Yue

3D human avatar modeling is the key technology in the field of digital entertainment. However, existing methods still face significant challenges in handling complex human poses and recovering fine geometric details. In order to solve the above problems, a 3D Gaussian realistic human avatar modeling method based on the Poincar\'{e} ball and the triplane representation is proposed. Our method utili ...

Miao, Hui and Guo, Yuanfang and Zhang, Leo Yu and Zhou, Jiantao and Wang, Yunhong

With advances in generation techniques, malicious users can easily generate deepfake videos, which can cause severe social problems and trust issues. Therefore, deepfake video detection has received increasing attention in recent years. Given that forgery clues are often subtle and imperceptible, effective detection relies heavily on multi-grained learning. However, existing approaches fail to sys ...

Zhang, Donglin and Shi, Weixiang and Wu, Xiao-Jun and Kittler, Josef

With the explosive growth of video data, video moment retrieval (VMR) has attracted increasing attention due to its ability to localize semantically relevant moments in untrimmed videos. However, existing VMR approaches usually rely on annotated video-text correspondences or temporal annotations, both of which require significant human effort and are costly to scale. Even worse, the inherent subje ...

Yin, Yifang and Cao, Jinming and Liu, Zhenguang and Wang, Guanfeng and Xiang, Shili and Zimmermann, Roger

Source-free domain adaptive semantic segmentation aims at adapting a model trained on the source domain to the target domain without requiring access to the source data. Self-training has emerged as a leading approach to address this challenging problem. However, without robust denoising mechanisms to reduce the noise in pseudo labels, it still easily fall into biased estimates. Most existing meth ...

COTTE, JORGE

Zhang, Wei and Chen, Juan and Zhu, En and Cheng, Wenhong and Li, Yunpeng and Li, Yuhan and Wang, Yanbo J

Clinical depression diagnosis relies heavily on both verbal and non-verbal cues in patient interviews, yet existing automated methods often operate as black-box models and fail to provide trustworthy explanations, limiting their clinical applicability. Moreover, depression datasets commonly impose strict privacy constraints that prohibit access to raw audio–visual data, and the deployment of large ...

Song, Zetian and Zhang, Jiaqi and Duan, Wenhong and Zhang, Yuhuai and Zhang, Xinfeng and Ma, Siwei

Representing the Neural Radiance Field (NeRF) with the Explicit Voxel Grid (EVG) is a promising direction for improving NeRFs. However, the EVG representation is not efficient for storage and transmission because of the tremendous memory cost. Existing methods for compressing EVG mainly inherit the methods designed for neural network compression, such as pruning and quantization, which do not take ...

Tan, Jican and Peng, Jinjia and Zhang, Songyu and Wang, Zhen and Wang, Huibing

Lifelong Person Re-Identification (LReID) seeks to continuously train models across multiple target domains, enabling effective generalization in both known and unseen domains. Achieving a balance between “plasticity” (the ability to adapt to new knowledge) and “stability” (the capacity to prevent forgetting) is crucial in lifelong learning. However, most existing LReID methods primarily focus on ...

Tao, Yong and Zhang, Xinming

Visible-infrared person re-identification (VI-ReID) aims to match pedestrian images captured under visible and infrared modalities, which suffer from significant domain discrepancies. Existing approaches either synthesize cross-modal images or learn modality-invariant representations, yet often encounter semantic degradation or limited alignment capacity. Recent vision-language models leverage tex ...

Zanetti, Cristiano

This essay proposes that the only publication of the Accademia del Cimento, referred to as Saggi, had as one of its main goals the celebration of the House of Medici's paternity of cutting-edge experiments and instruments during the reign of Grand Duke Ferdinando II. These included Ferdinando II's thermometers and hygrometers, Torricelli's experiment and barometer, and Galileo's pendulum as a cloc ...

Gao, Honghao and Feng, Dubin and Wang, Ye and Pan, Zhihao and Xu, Yueshen and Alkhamees, Bader Fahad

3D object detection plays a pivotal role in facilitating comprehensive scene understanding in autonomous driving systems. One of its key challenges is to achieve accurate perception in complex environments. Compared with LiDAR systems and stereo-vision approaches, monocular camera-based solutions are more cost-effective and easier to deploy. However, the absence of depth in monocular images hinder ...

Abdari, Ali and Falcon, Alex and Serra, Giuseppe

Metaverse environments offer immersive, multimedia-rich experiences with growing relevance in education, entertainment, and cultural applications. The ability to grasp the contents of these environments, consisting of many rooms or subspaces, is a key building block for implementing Metaverse retrieval systems. However, current methods remain limited, as they are not designed to separate local (e. ...

Fu, Chunyang and Li, Ge and Gao, Wei and Wang, Shiqi and Li, Zhu and Liu, Shan

Recently, deep learning has significantly advanced the performance of point cloud geometry compression. However, the learning-based lossless attribute compression of point clouds with varying densities is under-explored. In this article, we develop a learning-based framework, namely DALD-PCAC that leverages Levels of Detail (LoD) to tailor for point cloud lossless attribute compression. We develop ...

Biabani, Mohammadreza and Azadfallah, Mohammad and Izadyar, Soheila and Ghamsari, Morteza Sasani and Mohseni, Sabrine Tavakoli

Incorporation of planchettes and use of films that are capable of taking part in color-forming reactions are the common methods in fabricating security paper documents. In this paper, novel nanofibrillated cellulose (NFC)-based films with high performance in optical properties were developed for use in anti-counterfeiting applications. To enhance dyeability of NFC with eco-friendly cationic berber ...

Han, Shuo and Yao, Rui and Hao, Huili and Feng, Qian and Zhu, Hancheng and Zhao, Jiaqi and Zhou, Yong

Video Shadow Detection (VSD) is critical yet challenging, primarily due to ambiguous shadow boundaries and the presence of confusing shadow-like non-shadow regions, which existing methods struggle to resolve effectively by limited temporal modeling. We propose the Dual Sparse Long-Short Term Transformer Network (DSLSTT-Net), a novel framework designed to enhance feature learning by integrating rob ...

Albrecht, Donna

The article traces the evolution of the American driver's license from its origins as a simple paper card to a sophisticated form of identification integral to national security. Initially introduced in the early 20th century to regulate the growing presence of automobiles, driver's licenses began as basic documents but gradually incorporated personal data and security features such as photos, mag ...

Baldrati, Alberto and Morelli, Davide and Cornia, Marcella and Bertini, Marco and Cucchiara, Rita

Fashion illustration is a crucial medium for designers to convey their creative vision and transform design concepts into tangible representations that showcase the interplay between clothing and the human body. In the context of fashion design, computer vision techniques have the potential to enhance and streamline the design process. Departing from prior research primarily focused on virtual try ...

Li, Lei and Liu, Fuqiang and Wang, Yanni and Wang, Junyuan

3D reconstruction faces challenges such as geometric warping and structural ambiguity, particularly in intricate topologies, heavy occlusions and complex backgrounds. These problems are partly attributed to excessive feature entanglement, which induces semantic confusion and spatial ambiguity. To address these limitations, we propose an occlusion sensitivity-guided semantic-disentangled Mamba-CNN ...

Song, Chuanle and Huang, Wenjin and Jiao, Han and Li, Junfeng and Huang, Yihua

Image captioning is a cross-modal text generation task aimed at understanding the relationships among various objects in an image. Therefore, accurately expressing object–object relations remains a key bottleneck for transformer-based image captioning. Prior methods usually inject semantic and geometric relations once and keep them fixed while only updating visual features, creating a mismatch—evo ...

Pankaj, Ajeet Kumar and Jha, Manish K. and Farhad, Saima and Wani, Ibrahim

Governing migration and migrants is a contentious process across the country, and it has become more problematic in politically sensitive states like Manipur. The police regularly conduct document checks of labour migrants in various localities of Manipur to verify and confirm their legal status, which leads to their social, economic and psychological insecurity. While this is a routine law-enforc ...

افشین حمدی پور and رسول ،زوارقی and حمیده جوادی پور

Background and Objective: Air pollution significantly exacerbates the burden of respiratory disease, particularly in the Middle East. This study aimed to conduct a scientometric analysis of publications on air pollution and respiratory diseases in Middle Eastern countries. Materials and Methods: In this scientometric study, we analyzed 19811 documents on air pollution and respiratory diseases in M ...

I. Petrukha; Y. Kurliak; N. Stulova

In recent years, large language models (LLMs) have showcased significant advancements in code generation. However, most evaluation benchmarks are primarily oriented towards Python, making it difficult to evaluate other programming languages, such as Swift, with high quality. By examining widely established multilingual benchmarks like HumanEval-Xl and MultiPL-E, we identified critical issues speci ...

Wai-Yip Lum, Vincent and Kin-Fu Yip, Michael

This paper aims to utilize historical newspapers through the application of computer vision and machine/deep learning to extract the headlines and illustrations from newspapers for storytelling. This endeavor seeks to unlock the historical knowledge embedded within newspaper contents while simultaneously utilizing cutting-edge methodological paradigms for research in the digital humanities (DH) re ...

Varnum, Kenneth J. and DiPasquale, Joanna

The article discusses the growing significance of artificial intelligence (AI), particularly generalized artificial intelligence (GenAI), in libraries and their operations. It highlights the proactive engagement of libraries with GenAI technologies to shape their future, rather than passively waiting for these tools to emerge. A special issue focused on GenAI is planned for September 2026, invitin ...

Scott, David R. and Ayers, Sheilah C. and Read, Kevin B.

Objective: Funding bodies such as Canada's Tri-Agency have implemented requirements for grant recipients to encourage improved research data management (RDM) practices and data sharing. Consequently, RDM and data sharing have become a higher priority for researchers and stakeholders supporting the research process, including librarians. Health sciences research can present special challenges to th ...

Koay, Ying-Yin and Wong, Zi Wen Vivien and Chen, Fanyu and Tan, Yan-Ling and Yiew, Thian-Hee

Although the empirical research in energy footprint have been ballooned in size, bibliometric research of energy of footprint remains limited. Using VOSviewer, we wish to contribute a bibliometric assessment of emerging research trends on energy footprint from 2001 to 2023. Publication characteristics, author keywords, authors collaboration, institutions, and countries were all examined. This anal ...

Topal, Ibrahim Halil

In recent years, there has been a growing interest in bibliometric analyses of specific research topics or academic journals. However, scant attention was paid to the latter. Given the considerable impact of bibliometric studies on guiding prospective research, this study aimed to map the bibliometric characteristics of Lexicography publications since the inception of Lexicography: Journal of ASIA ...

Liu, Xinyu and Zhang, Zhen and Tan, Chao and Ai, Yinquan and Liu, Hao and Li, Yuan and Yang, Jin and Song, Yongyan

Background: Single-cell RNA sequencing (scRNA-seq) has revolutionized cellular heterogeneity analysis by decoding gene expression profiles at individual cell level, while machine learning (ML) has emerged as core computational tool for clustering analysis, dimensionality reduction modeling and developmental trajectory inference in single-cell transcriptomics(SCT). Although 3,307 papers have been p ...

MOISA, GABRIEL

The grand celebration of a century since Romania gained its independence following the 1877-1878 War of Independence was one of the important objectives of the communist regime in Romania and of a part of Romanian historiography engaged in its festive and protochronistic interpretation. One of the goals was to strongly affirm the desire for total independence of Romanians present throughout histor ...

Erdoğdu, Fatih and Kara, Mehmet and Gökoğlu, Seyfullah and Telci, Serkan

This study aims to investigate the trends and topics in Computer-Supported Collaborative Learning (CSCL) research papers from the emergence to the present. For this purpose, all documents on the Scopus database (5775 papers) were gathered and analyzed through bibliometric analysis and Latent Dirichlet Allocation (LDA) analysis, one of the topic modeling-based machine learning methods. The titles, ...

Jähnert, Martin

This paper studies the development of intensity schemes within the framework of the old quantum theory. It investigates how these schemes emerged in a complex process involving empirical observation, data analysis and conceptual reconfiguration and became essential tools for predicting the intensities of multiplets in the absence of a well-formed quantum theory of radiation. By applying the concep ...

Lang, Jing

Paper documents gradually deteriorate during long-term storage, accompanied by acidification and a decline in mechanical strength. To achieve both deacidification and mechanical strengthening, sodium tetra borate (Na₂B4O₇ ) was used as a deacidification agent, and two starch products (enzymatically hydrolyzed starch, quaternary ammonium cationic starch) served as strengthenin ...

McPherson, Lynne and Canosa, Antonia and Gatwiri, Kathomi and Anderson, Donnah and Day, Kylie and Gilligan, Robbie and Graham, Anne and Mitchell, Janise and Moore, Tim and Vosz, Meaghan

Therapeutic residential care (TRC) is a mode of delivering out‐of‐home care (OOHC) that can help meet the needs of some of Australia's most vulnerable young people and their families. TRC programmes aim to support young people to develop positive relationship experiences in a safe and stable environment. Given that TRC is a relatively new model of intervention, to date, the alignment between its a ...

Shillingsburg, Peter L.

The goals of scholarly editing are limited by what can be accomplished in reality. What can be hoped for or aimed at may be the inspiration, but not the goal, of scholarly editing. Well-argued disagreements among scholars demonstrate that variation in interests, methods, and values for documents, texts, works, history and art, both place perfection out of bounds and valuable triumphs within reach. ...

Hossen, Mohammad Shahadat and Pauzi, Hezzrin Mohd

This bibliometric analysis endeavors to bridge a gap in existing research on social support for the older adults, focusing on the theme of ageing. The primary objective is to provide a comprehensive overview of scholarly advancement of previous research papers in this domain, employing a bibliometric approach. A dataset inclusive of publications up to February 2024 from Scopus database has been co ...

Abdelwahab, Siddig Ibrahim and Taha, Manal Mohamed Elhassan and Farasani, Abdullah and Jerah, Ahmed Ali and Abdullah, Saleh M. and Oraibi, Bassem and Babiker, Yasir and Alfaifi, Hassan Ahmad and Alzahrani, Amal Hamdan and Alamer, Ahmed S. and Altherwi, Tawfeeq and Ibrahim, Ibrahim Abdel Aziz and Hassan, Waseem

The Journal of Religion and Health (JORH) has significantly contributed to the intersection of spirituality and healthcare since its inception in 1961. We analyzed the top 100 most cited papers published in JORH, which may provide insights into authorship patterns, institutional affiliations, and publication dynamics. The data was retrieved from the Scopus database. Details about the annual growth ...

Delgado‐Martínez, Carlos M. and Kolb, Melanie and Pascual-Ramírez, Fermín and Mendoza, Eduardo

Water availability strongly influences the ecology of terrestrial birds and mammals. It will likely play an increasing role as a limiting factor as climate change and human demand make water availability scarcer. However, we lack a knowledge synthesis describing our current understanding of the use of water sources, particularly for wildlife hydration. To provide a comprehensive overview of the av ...

Kannazarova, Zulfiya and Khadjiyeva, Zukhra and Abdikairov, Bekmurat and Juliev, Mukhiddin and Sharipov, Zayniddin and Berdimuratov, Parakhat

Landslides are among the major environmental hazards, with large-scale socio-economic and environmental impacts, that jeopardize socio-economic well-being in the countries of the Commonwealth of Independent States (CIS). Landslides are caused by the interaction of several complex factors, including local or regional geology, geomorphology, topography, and seismic motions. The factors that can trig ...

R. Varghese; S. M.

In recent years, the You Only Look Once (YOLO) series of object detection algorithms have garnered significant attention for their speed and accuracy in real-time applications. This paper presents YOLOv8, a novel object detection algorithm that builds upon the advancements of previous iterations, aiming to further enhance performance and robustness. Inspired by the evolution of YOLO architectures ...

Fetvacı, Emine

The arts of cutting and joining paper (katʿı and vas·s·¯ale) were much prized at the early-modern Ottoman court in Istanbul. This essay takes a single folio of katʿı prepared by the Ottoman paper joinery master (vas·s·¯al) Kalender Efendi (d. 1616) as a starting point from which to explore the essential role of paper in the administration and unification of the Ottoman Empire, shaping the state's ...

Taws, Richard

In France, the last decade of the eighteenth century saw an explosion in papers of diverse kinds, put to new political uses. However, in the aftermath of the French Revolution, paper was also instrumentalised to articulate histories of revolutionary participation, as well as changing ideas about revolutionary identification. Some of these were coded as virtuous: paper objects found a place at the ...

Siddique, Asheesh Kapur

This essay considers the material and visual cultural dynamics through which the modern state emerged during the Age of Revolutions in the Atlantic world. It centres upon the emergent rhetoric of the 'written constitution' in Revolutionary North America and the new United States, arguing that this mode of constitution-making represented not the invention that many claimed it to be, but instead a r ...

Gagné, John

This essay charts the way that ideas of nature, life and disease generated new approaches to Europe's documentary culture between roughly 1650 and 1950. Training its focus on the medium of paper, specifically the essay, reveals conceptual intersections between caretakers of libraries and archives and naturalists or physicians whose focus was the entomology or bacteriology. The recognition of the f ...

Rodgers, Suzanne

In this research, I explore the potential of a material‐led, embodied pedagogical approach to cultivate diverse modes of thinking, knowing and becoming within a pre‐GCSE curriculum. Drawing from my experiences as both an artist and educator, I acknowledge the transformative power inherent in recognising the agency of all forms of matter, whether human or non‐human. Through the implementation of a ...

Brisman, Shira and Fowler, Caroline

This special issue examines paper as it became the definitive material for both artistic practice and jurisprudence. European paper made from pulped rags and linens to create the mythic white empty grounds for design was foundational to early modern European ideas about both sovereignty and the creative act. The materiality of paper—as a surface created from refuse—also introduced a form of ecolog ...

A. Świerkowska; J. Echavarria; L. Schulz; M. Schulz

High Performance Computing-Quantum Computing (HPCQC) integration presents a promising yet challenging opportunity, particularly in the area of quantum circuit compilation and optimization, requiring further advancements in the field of Quantum Computing (QC). To address this, we introduce the Munich Quantum Compiler, a key component of the Munich Quantum Software Stack (MQSS). This compiler employ ...

J. Li; D. Lia; T. Luo; J. Zhou

The smart grid architecture, which represents a deep integration of information technology and power systems, brings many conveniences to people. However, due to the highly open communication network and complex information interaction environment, it also faces more security risks. Existing intrusion detection algorithms based on machine learning cannot cope with the increasing features in the En ...

Borgohain, Dhruba Jyoti and Bhardwaj, Raj Kumar and Verma, Manoj Kumar

This study aims to analyze India's publications on organic chemistry and related fields at the micro, meso, and macro levels. The study attempts to map and visualize the publications in organic chemistry from 2016 to 2020 to identify the country co-authorship, author co-authorship, bibliographic coupling of authors, keyword co-occurrence, etc. Performance analysis techniques incorporating publicat ...

Hovious, Amanda and Congwen Wang

Despite ongoing efforts to improve database accessibility, aggregated database vendors concede that they do not have complete control over document accessibility. Instead, they point to the responsibility of journal publishers to deliver articles in an accessible format. This may increase the likelihood that users with disabilities will encounter articles that are not compatible with a screen read ...

Hanbin Mao and Jixin Chen

Research output can be evaluated with productivity and impact, which are quantified by the numbers of publications (N) and citations N_c, respectively. The h-index (H) unifies both factors. However, as an extensive variable, it grows with quantity of research output and favors senior researchers over juniors. In this report, by analyzing the data of the world top 2% scientists (n = 179,5 ...

C. Jimenez; C. J. Sparrey; M. Narimani

The purpose of this study was to use machine learning (ML) algorithms to identify tissue damage based on the mechanical outputs of computational models of spinal cord injury (SCI). Three datasets corresponding to gray matter, white matter, and the combination of gray and white matter tissues were used to train the models. These datasets were built from the comparison of histological images taken f ...

M. L. Collins; T. C. Davies

Emotions are an important contributor to human self-expression and well-being. However, many populations express their emotions differently from what is considered "typical". Previous literature has indicated a possible relationship between emotion and eye-movement. The objective of this paper is to further explore this proposed relationship by identifying specific features of eye-movement that re ...

Paltridge, Brian and Starfield, Sue

This paper examines the PhD by publication in the humanities and social sciences in US, UK, Canadian and Australian universities. A set of PhDs by publication from each of these countries were collected for the study. The theses were analysed to see to what extent they fitted, or not, with discussions of thesis types described in previous research into the PhD by publication. Interviews with stude ...

R. Alam; A. D. Aguirre; C. M. Stultz

QT prolongation often leads to fatal arrhythmia and sudden cardiac death. Antiarrhythmic drugs can increase the risk of QT prolongation and therefore require strict post-administration monitoring and dosage control. Measurement of the QT interval from the 12-lead electrocardiogram (ECG) by a trained expert, in a clinical setting, is the accepted method for tracking QT prolongation. Recent advances ...

Giri, Rabishankar and Das, Anup Kumar

This study critically examines the publications that appeared in the inaugural eleven volumes of Journal of Scientometric Research (JSciRes). The journal publishes 324 research communications under ten categories along with 16 editorials and 60 book reviews. Analysis of research communications shows that diversity plays the pivotal role in the evolution of the journal. Diversity of not only contri ...

S. Y. -C. Chen

This paper introduces the QDQN-DPER framework to enhance the efficiency of quantum reinforcement learning (QRL) in solving sequential decision tasks. The framework incorporates prioritized experience replay, asynchronous training and novel matrix loss into the training algorithm to reduce the high sampling complexities. Numerical simulations demonstrate that QDQN-DPER outperforms the baseline dist ...

H. -M. Heyn; E. Knauss

Artificial Intelligence (AI), and especially machine learning can be used to find statistical patterns in datasets with thousands of variables with ease. But an understanding of causality is difficult to learn for a machine. For humans however, realising causal relations is often not a difficult process, as we can refer to experience or scientific knowledge. Here we propose the use of structural c ...

Karabchuk, Tatiana and Shomotova, Aizhan and Chmel, Kirill

This paper investigates the paradox of research productivity of higher‐education institutions in the Arab Gulf Countries. Exploring the case of the United Arab Emirates (UAE) the article fills the gap in the literature on the Gulf higher education research efficiency. Despite the considerable investment into higher education, UAE universities showed rather slow growth in research output. This puzz ...

J. Vargas-Calixto; Y. Wu; M. Kuzniewicz; M. -C. Cornet; H. Forquer; L. Gerstley; E. Hamilton; P. A. Warrick; R. E. Kearney

Visual assessment of the evolution of fetal heart rate (FHR) and uterine pressure (UP) patterns is the standard of care in the intrapartum period. Unfortunately, this assessment has high levels of intra- and inter-observer variability. This study processed and analyzed FHR and UP patterns using computerized pattern recognition tools. The goal was to evaluate differences in FHR and UP patterns betw ...

J. H. Husen; H. Washizaki; H. T. Tun; N. Yoshioka; Y. Fukazawa; H. Takeuchi

Machine learning-based system requires specific attention towards their safety characteristics while considering the higher-level requirements. This study describes our approach for analyzing machine learning safety requirements top-down from higher-level business requirements, functional requirements, and risks to be mitigated. Our approach utilizes six different modeling techniques: AI Project C ...

Havey, Nicholas and Chang, Mitchell J.

Using five years of publishing data from the Journal of Higher Education, we describe the publication pipeline at the journal, explore trends with respect to topic, the geographic distribution of authors, and each paper's methodological approach. Following the presentation of these trends, we discuss implications for the field of higher education and those publishing within it. ...

L. Pham; D. Ngo; K. Tran; T. Hoang; A. Schindler; I. McLoughlin

This paper evaluates a range of deep learning frameworks for detecting respiratory anomalies from input audio. Audio recordings of respiratory cycles collected from patients are transformed into time-frequency spectrograms to serve as front-end two-dimensional features. Cropped spectrogram segments are then used to train a range of back-end deep learning networks to classify respiratory cycles int ...

A. Khanna; V. Schaffer; G. Gürsoy; M. Gerstein

Machine learning is playing an increasingly critical role in health science with its capability of inferring valuable information from high-dimensional data. More training data provides greater statistical power to generate better models that can help decision-making in healthcare. However, this often requires combining research and patient data across institutions and hospitals, which is not alwa ...

R. Dedhiya; S. T. Kakileti; G. Deepu; K. Gopinath; N. Opoku; C. King; G. Manjunath

Onchocerciasis is causing blindness in over half a million people in the world today. Drug development for the disease is crippled as there is no way of measuring effectiveness of the drug without an invasive procedure. Drug efficacy measurement through assessment of viability of onchocerca worms requires the patients to undergo nodulectomy which is invasive, expensive, time-consuming, skill-depen ...

M. Etter; F. Mehta

Software projects change hands frequently. Oftentimes, developers are interested in the quality of the code before taking over responsibility on a project. This quality is commonly assessed using various code metrics, reducing the code into a handful of numbers. While useful, these numerical reductions quickly become detached from the real code. CodePanorama uses an alternative approach to summari ...

B. Johnson; Y. Brun

Advances in how we build and use software, specifically the integration of machine learning for decision making, have led to widespread concern around model and software fairness. We present fairkit-learn, an interactive Python toolkit designed to support data scientists’ ability to reason about and understand model fairness. We outline how fairkit-learn can support model training, evaluation, and ...

Grant, Julie and Tomaselli, Keyan G. and Dyll, Lauren

Post-doctoral research fellowships (PDRF) in South Africa often require the fellow to produce a particular number of publications each year. Many of the PDRFs, however, find publishing a challenge. In response to this, a project was established to support, encourage and enable PDRFs based in South African universities to publish in this special issue of Critical Arts. During the course of the proj ...

A. Tazarv; S. Labbaf; S. M. Reich; N. Dutt; A. M. Rahmani; M. Levorato

Since stress contributes to a broad range of mental and physical health problems, the objective assessment of stress is essential for behavioral and physiological studies. Although several studies have evaluated stress levels in controlled settings, objective stress assessment in everyday settings is still largely under-explored due to challenges arising from confounding contextual factors and lim ...

Z. Liu; Y. Lin; Y. Cao; H. Hu; Y. Wei; Z. Zhang; S. Lin; B. Guo

This paper presents a new vision Transformer, called Swin Transformer, that capably serves as a general-purpose backbone for computer vision. Challenges in adapting Transformer from language to vision arise from differences between the two domains, such as large variations in the scale of visual entities and the high resolution of pixels in images compared to words in text. To address these differ ...

D. C. Wunsch III; D. B. Hier

When features in a high dimension dataset are organized hierarchically, there is an inherent opportunity to reduce dimensionality. Since more specific concepts are subsumed by more general concepts, subsumption can be applied successively to reduce dimensionality. We tested whether sub-sumption could reduce the dimensionality of a disease dataset without impairing classification accuracy. We start ...

Y. -D. Chih; P. -H. Lee; H. Fujiwara; Y. -C. Shih; C. -F. Lee; R. Naous; Y. -L. Chen; C. -P. Lo; C. -H. Lu; H. Mori; W. -C. Zhao; D. Sun; M. E. Sinangil; Y. -H. Chen; T. -L. Chou; K. Akarvardar; H. -J. Liao; Y. Wang; M. -F. Chang; T. -Y. J. Chang

From the cloud to edge devices, artificial intelligence (AI) and machine learning (ML) are widely used in many cognitive tasks, such as image classification and speech recognition. In recent years, research on hardware accelerators for AI edge devices has received more attention, mainly due to the advantages of AI at the edge: including privacy, low latency, and more reliable and effective use of ...

E. Lendaro; E. Balouji; K. Baca; A. S. Muhammad; M. Ortiz-Catalan

Phantom Limb Pain (PLP) is a chronic condition frequent among individuals with acquired amputation. PLP has been often investigated with the use of functional MRI focusing on the changes that take place in the sensorimotor cortex after amputation. In the present study, we investigated whether a different type of data, namely electroencephalographic (EEG) recordings, can be used to study the condit ...

L. Applis; A. Panichella; A. van Deursen

Metamorphic testing is a well-established testing technique that has been successfully applied in various domains, including testing deep learning models to assess their robustness against data noise or malicious input. Currently, metamorphic testing approaches for machine learning (ML) models focused on image processing and object recognition tasks. Hence, these approaches cannot be applied to ML ...

R. Casas; K. Martin; M. Sandison; P. S. Lum

In previous work, we developed an exoskeleton (HandSOME II) that allows movement at 15 hand degrees of freedom (DOF) and is intended for take-home use. An activity tracking device was developed in order to track index finger movement with a pair of magnetometers and magnet. The goal was to detect grip attempts by the individual. Machine learning was utilized to estimate angles for metacarpophalang ...

J. E. Geelen; M. P. Branco; N. F. Ramsey; F. C. T. van der Helm; W. Mugge; A. C. Schouten

Motion capture systems are extensively used to track human movement to study healthy and pathological movements, allowing for objective diagnosis and effective therapy of conditions that affect our motor system. Current motion capture systems typically require marker placements which is cumbersome and can lead to contrived movements.Here, we describe and evaluate our developed markerless and modul ...

R. R. Fletcher; G. Schneider; L. Bikorimana; G. Rukundo; A. Niyigena; E. Miranda; R. Riviello; F. Kateera; B. Hedt-Gauthier

The ability to detect surgical site infections (SSI) is a critical need for healthcare worldwide, but is especially important in low-income countries, where there is limited access to health facilities and trained clinical staff. In this paper, we present a new method of predicting SSI using a thermal image collected with a smart phone. Machine learning algorithms were developed using images colle ...

C. Zheng; X. Chen; B. T. Nguyen; P. Sanpitak; J. Vu; U. Bagci; L. Golestanirad

The number of patients with active implantable medical devices continues to rise in the United States and around the world. It is estimated that 50-75% of patients with conductive implants will need magnetic resonance imaging (MRI) in their lifetime. A major risk of performing MRI in patients with elongated conductive implants is the radiofrequency (RF) heating of the tissue surrounding the implan ...

A. Ghrissi; D. Almonfrey; R. C. de Almeida; F. Squara; J. Montagnat; V. Zarzoso

Catheter ablation is increasingly used to treat atrial fibrillation (AF), the most common sustained cardiac arrhythmia encountered in clinical practice. A recent breakthrough finding in AF ablation consists in identifying ablation sites based on their spatiotemporal dispersion (STD). STD stands for a delay of the cardiac activation observed in intracardiac electrograms (EGMs) across contiguous lea ...

D. Khanna; A. Prasad

The Covid-19 pandemic brought extraordinary disruption to the education landscape with the campuses closing everywhere almost overnight. Schools and colleges in India have a different pattern of education where one cannot physically attend classes but has to attend classes virtually, a new problem occurs. here we will analyze some issues like internet connectivity problems and basic understanding ...

A. Das; M. Wasif Ansari; R. Basak

COVID-19 pandemic has rapidly affected our day-to-day life disrupting the world trade and movements. Wearing a protective face mask has become a new normal. In the near future, many public service providers will ask the customers to wear masks correctly to avail of their services. Therefore, face mask detection has become a crucial task to help global society. This paper presents a simplified appr ...

Y. T. Prasetyo; D. G. D. D. Fuente

With the rise of online shopping during COVID-19 pandemic in the Philippines, it is important to understand the buying decision among Filipinos. The purpose of this study was to determine the factors affecting customer satisfaction in online shopping. A valid response of 251 Filipinos participated in the empirical study. Structural Equation Modeling (SEM) was used to validate the measurement model ...

B. Galhotra; A. Dewan

Digital platforms are considered to be the tools for performing a huge number of tasks. It mainly enables a data-driven world rather than process-driven. Digital platforms are used in huge sectors mainly in market places, media platforms, knowledge platforms, etc. Over the years, digital platforms have been observing a massive increase as people tend to buy online and also use other services which ...

M. N. Asiedu; E. Skerrett; G. Sapiro; N. Ramanujam

We apply feature-extraction and machine learning methods to multiple sources of contrast (acetic acid, Lugol's iodine and green light) from the white Pocket Colposcope, a low-cost point of care colposcope for cervical cancer screening. We combine features from the sources of contrast and analyze diagnostic improvements with addition of each contrast. We find that overall AUC increases with additio ...

B. Wang; Y. Yao; S. Shan; H. Li; B. Viswanath; H. Zheng; B. Y. Zhao

Lack of transparency in deep neural networks (DNNs) make them susceptible to backdoor attacks, where hidden associations or triggers override normal classification to produce unexpected results. For example, a model with a backdoor always identifies a face as Bill Gates if a specific symbol is present in the input. Backdoors can stay hidden indefinitely until activated by an input, and present a s ...

D. Varshni; K. Thakral; L. Agarwal; R. Nijhawan; A. Mittal

Pneumonia is a life-threatening infectious disease affecting one or both lungs in humans commonly caused by bacteria called Streptococcus pneumoniae. One in three deaths in India is caused due to pneumonia as reported by World Health Organization (WHO). Chest X-Rays which are used to diagnose pneumonia need expert radiotherapists for evaluation. Thus, developing an automatic system for detecting p ...

P. Kocher; J. Horn; A. Fogh; D. Genkin; D. Gruss; W. Haas; M. Hamburg; M. Lipp; S. Mangard; T. Prescher; M. Schwarz; Y. Yarom

Modern processors use branch prediction and speculative execution to maximize performance. For example, if the destination of a branch depends on a memory value that is in the process of being read, CPUs will try to guess the destination and attempt to execute ahead. When the memory value finally arrives, the CPU either discards or commits the speculative computation. Speculative logic is unfaithf ...

A. D. Ames; S. Coogan; M. Egerstedt; G. Notomista; K. Sreenath; P. Tabuada

This paper provides an introduction and overview of recent work on control barrier functions and their use to verify and enforce safety properties in the context of (optimization based) safety-critical controllers. We survey the main technical results and discuss applications to several domains including robotic systems. ...

G. Baykal; U. Demir; I. Shyti; G. Ünal

While the technology improves rapidly in today's world, the fact that visually impaired people still face complications about monetary situations in their social life reveals technology is needed to propose a solution. In this study, a system to classify Turkish Lira banknotes is implemented with convolutional neural networks and the results of different architectures are compared. A new and uniqu ...

S. A. K. Tareen; Z. Saleem

Image registration is the process of matching, aligning and overlaying two or more images of a scene, which are captured from different viewpoints. It is extensively used in numerous vision based applications. Image registration has five main stages: Feature Detection and Description; Feature Matching; Outlier Rejection; Derivation of Transformation Function; and Image Reconstruction. Timing and a ...

P. P. Shinde; S. Shah

Machine learning is one of the fields in the modern computing world. A plenty of research has been undertaken to make machines intelligent. Learning is a natural human behavior which has been made an essential aspect of the machines as well. There are various techniques devised for the same. Traditional machine learning algorithms have been applied in many application areas. Researchers have put m ...

J. Bos; L. Ducas; E. Kiltz; T. Lepoint; V. Lyubashevsky; J. M. Schanck; P. Schwabe; G. Seiler; D. Stehle

Rapid advances in quantum computing, together with the announcement by the National Institute of Standards and Technology (NIST) to define new standards for digitalsignature, encryption, and key-establishment protocols, have created significant interest in post-quantum cryptographic schemes. This paper introduces Kyber (part of CRYSTALS - Cryptographic Suite for Algebraic Lattices - a package subm ...

J. Hu; L. Shen; G. Sun

Convolutional neural networks are built upon the convolution operation, which extracts informative features by fusing spatial and channel-wise information together within local receptive fields. In order to boost the representational power of a network, several recent approaches have shown the benefit of enhancing spatial encoding. In this work, we focus on the channel relationship and propose a n ...

T. Shan; B. Englot

We propose a lightweight and ground-optimized lidar odometry and mapping method, LeGO-LOAM, for realtime six degree-of-freedom pose estimation with ground vehicles. LeGO-LOAM is lightweight, as it can achieve realtime pose estimation on a low-power embedded system. LeGO-LOAM is ground-optimized, as it leverages the presence of a ground plane in its segmentation and optimization steps. We first app ...

P. Mohassel; Y. Zhang

Machine learning is widely used in practice to produce predictive models for applications such as image processing, speech and text recognition. These models are more accurate when trained on large amount of data collected from different sources. However, the massive data collection raises privacy concerns. In this paper, we present new and efficient protocols for privacy preserving machine learni ...

S. Albawi; T. A. Mohammed; S. Al-Zawi

The term Deep Learning or Deep Neural Network refers to Artificial Neural Networks (ANN) with multi layers. Over the last few decades, it has been considered to be one of the most powerful tools, and has become very popular in the literature as it is able to handle a huge amount of data. The interest in having deeper hidden layers has recently begun to surpass classical methods performance in diff ...

Z. Zheng; S. Xie; H. Dai; X. Chen; H. Wang

Blockchain, the foundation of Bitcoin, has received extensive attentions recently. Blockchain serves as an immutable ledger which allows transactions take place in a decentralized manner. Blockchain-based applications are springing up, covering numerous fields including financial services, reputation system and Internet of Things (IoT), and so on. However, there are still many challenges of blockc ...

R. Shokri; M. Stronati; C. Song; V. Shmatikov

We quantitatively investigate how machine learning models leak information about the individual data records on which they were trained. We focus on the basic membership inference attack: given a data record and black-box access to a model, determine if the record was in the model's training dataset. To perform membership inference against a target model, we make adversarial use of machine learnin ...

N. Carlini; D. Wagner

Neural networks provide state-of-the-art results for most machine learning tasks. Unfortunately, neural networks are vulnerable to adversarial examples: given an input x and any target classification t, it is possible to find a new input x' that is similar to x but classified as t. This makes it difficult to apply neural networks in security-critical areas. Defensive distillation is a recently pro ...

K. He; G. Gkioxari; P. Dollár; R. Girshick

We present a conceptually simple, flexible, and general framework for object instance segmentation. Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance. The method, called Mask R-CNN, extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognit ...

N. Loubet; T. Hook; P. Montanini; C. . -W. Yeung; S. Kanakasabapathy; M. Guillom; T. Yamashita; J. Zhang; X. Miao; J. Wang; A. Young; R. Chao; M. Kang; Z. Liu; S. Fan; B. Hamieh; S. Sieg; Y. Mignot; W. Xu; S. . -C. Seo; J. Yoo; S. Mochizuki; M. Sankarapandian; O. Kwon; A. Carr; A. Greene; Y. Park; J. Frougier; R. Galatage; R. Bao; J. Shearer; R. Conti; H. Song; D. Lee; D. Kong; Y. Xu; A. Arceo; Z. Bi; P. Xu; R. Muthinti; J. Li; R. Wong; D. Brown; P. Oldiges; R. Robison; J. Arnold; N. Felix; S. Skordas; J. Gaudiello; T. Standaert; H. Jagannathan; D. Corliss; M. . -H. Na; A. Knorr; T. Wu; D. Gupta; S. Lian; R. Divakaruni; T. Gow; C. Labelle; S. Lee; V. Paruchuri; H. Bu; M. Khare

In this paper, for the first time we demonstrate that horizontally stacked gate-all-around (GAA) Nanosheet structure is a good candidate for the replacement of FinFET at the 5nm technology node and beyond. It offers increased Weff per active footprint and better performance compared to FinFET, and with a less complex patterning strategy, leveraging EUV lithography. Good electrostatics are reported ...

G. Huang; Z. Liu; L. Van Der Maaten; K. Q. Weinberger

Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output. In this paper, we embrace this observation and introduce the Dense Convolutional Network (DenseNet), which connects each layer to every other layer in a feed-forward fashion. Whereas ...

R. Hadani; S. Rakib; M. Tsatsanis; A. Monk; A. J. Goldsmith; A. F. Molisch; R. Calderbank

A new two-dimensional modulation technique called Orthogonal Time Frequency Space (OTFS) modulation designed in the delay-Doppler domain is introduced. Through this design, which exploits full diversity over time and frequency, OTFS coupled with equalization converts the fading, time-varying wireless channel experienced by modulated signals such as OFDM into a time-independent channel with a compl ...

A. Dorri; S. S. Kanhere; R. Jurdak; P. Gauravaram

Internet of Things (IoT) security and privacy remain a major challenge, mainly due to the massive scale and distributed nature of IoT networks. Blockchain-based approaches provide decentralized security and privacy, yet they involve significant energy, delay, and computational overhead that is not suitable for most resource-constrained IoT devices. In our previous work, we presented a lightweight ...

D. A. Al-Sabti; A. V. Singh; S. K. Jha

Social media is a technology that can be developed in a fast and flexible manner, where internet connections are transformed into interactive platforms. Social networking programs are increasingly spreading around the world. Facebook users reached 1.44 billion per month in 2015, which means that most of the people of the world have a stamp on social networking platforms. Social media platforms hav ...

R. R. Selvaraju; M. Cogswell; A. Das; R. Vedantam; D. Parikh; D. Batra

We propose a technique for producing `visual explanations' for decisions from a large class of Convolutional Neural Network (CNN)-based models, making them more transparent. Our approach - Gradient-weighted Class Activation Mapping (Grad-CAM), uses the gradients of any target concept (say logits for `dog' or even a caption), flowing into the final convolutional layer to produce a coarse localizati ...

T. -Y. Lin; P. Dollár; R. Girshick; K. He; B. Hariharan; S. Belongie

Feature pyramids are a basic component in recognition systems for detecting objects at different scales. But pyramid representations have been avoided in recent object detectors that are based on deep convolutional networks, partially because they are slow to compute and memory intensive. In this paper, we exploit the inherent multi-scale, pyramidal hierarchy of deep convolutional networks to cons ...

J. -Y. Zhu; T. Park; P. Isola; A. A. Efros

Image-to-image translation is a class of vision and graphics problems where the goal is to learn the mapping between an input image and an output image using a training set of aligned image pairs. However, for many tasks, paired training data will not be available. We present an approach for learning to translate an image from a source domain X to a target domain Y in the absence of paired example ...

J. Redmon; A. Farhadi

We introduce YOLO9000, a state-of-the-art, real-time object detection system that can detect over 9000 object categories. First we propose various improvements to the YOLO detection method, both novel and drawn from prior work. The improved model, YOLOv2, is state-of-the-art on standard detection tasks like PASCAL VOC and COCO. Using a novel, multi-scale training method the same YOLOv2 model can r ...

R. Q. Charles; H. Su; M. Kaichun; L. J. Guibas

Point cloud is an important type of geometric data structure. Due to its irregular format, most researchers transform such data to regular 3D voxel grids or collections of images. This, however, renders data unnecessarily voluminous and causes issues. In this paper, we design a novel type of neural network that directly consumes point clouds, which well respects the permutation invariance of point ...

P. Isola; J. -Y. Zhu; T. Zhou; A. A. Efros

We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible to apply the same generic approach to problems that traditionally would require very different loss formulations. We demonstrate ...

N. P. Jouppi; C. Young; N. Patil; D. Patterson; G. Agrawal; R. Bajwa; S. Bates; S. Bhatia; N. Boden; A. Borchers; R. Boyle; P. -l. Cantin; C. Chao; C. Clark; J. Coriell; M. Daley; M. Dau; J. Dean; B. Gelb; T. V. Ghaemmaghami; R. Gottipati; W. Gulland; R. Hagmann; C. R. Ho; D. Hogberg; J. Hu; R. Hundt; D. Hurt; J. Ibarz; A. Jaffey; A. Jaworski; A. Kaplan; H. Khaitan; D. Killebrew; A. Koch; N. Kumar; S. Lacy; J. Laudon; J. Law; D. Le; C. Leary; Z. Liu; K. Lucke; A. Lundin; G. MacKean; A. Maggiore; M. Mahony; K. Miller; R. Nagarajan; R. Narayanaswami; R. Ni; K. Nix; T. Norrie; M. Omernick; N. Penukonda; A. Phelps; J. Ross; M. Ross; A. Salek; E. Samadiani; C. Severn; G. Sizikov; M. Snelham; J. Souter; D. Steinberg; A. Swing; M. Tan; G. Thorson; B. Tian; H. Toma; E. Tuttle; V. Vasudevan; R. Walter; W. Wang; E. Wilcox; D. H. Yoon

Many architects believe that major improvements in cost-energy-performance must now come from domain-specific hardware. This paper evaluates a custom ASIC-called a Tensor Processing Unit (TPU)-deployed in datacenters since 2015 that accelerates the inference phase of neural networks (NN). The heart of the TPU is a 65,536 8-bit MAC matrix multiply unit that offers a peak throughput of 92 TeraOps/se ...

R. Akita; A. Yoshihara; T. Matsubara; K. Uehara

This paper proposes a novel application of deep learning models, Paragraph Vector, and Long Short-Term Memory (LSTM), to financial time series forecasting. Investors make decisions according to various factors, including consumer price index, price-earnings ratio, and miscellaneous events reported in newspapers. In order to assist their decisions in a timely manner, many automatic ways to analyze ...

Y. -H. Chen; J. Emer; V. Sze

Deep convolutional neural networks (CNNs) are widely used in modern AI systems for their superior accuracy but at the cost of high computational complexity. The complexity comes from the need to simultaneously process hundreds of filters and channels in the high-dimensional convolutions, which involve a significant amount of data movement. Although highly-parallel compute paradigms, such as SIMD/S ...

M. Hermann; T. Pentek; B. Otto

The increasing integration of the Internet of Everything into the industrial value chain has built the foundation for the next industrial revolution called Industrie 4.0. Although Industrie 4.0 is currently a top priority for many companies, research centers, and universities, a generally accepted understanding of the term does not exist. As a result, discussing the topic on an academic level is d ...

A. Singh; N. Thakur; A. Sharma

Supervised machine learning is the construction of algorithms that are able to produce general patterns and hypotheses by using externally supplied instances to predict the fate of future instances. Supervised machine learning classification algorithms aim at categorizing data from prior information. Classification is carried out very frequently in data science problems. Various successful techniq ...

A. Azaria; A. Ekblaw; T. Vieira; A. Lippman

Years of heavy regulation and bureaucratic inefficiency have slowed innovation for electronic medical records (EMRs). We now face a critical need for such innovation, as personalization and data science prompt patients to engage in the details of their healthcare and restore agency over their medical data. In this paper, we propose MedRec: a novel, decentralized record management system to handle ...

F. Milletari; N. Navab; S. -A. Ahmadi

Convolutional Neural Networks (CNNs) have been recently employed to solve problems from both the computer vision and medical image analysis fields. Despite their popularity, most approaches are only able to process 2D images while most medical data used in clinical practice consists of 3D volumes. In this work we propose an approach to 3D image segmentation based on a volumetric, fully convolution ...

K. He; X. Zhang; S. Ren; J. Sun

Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these resid ...

W. Hess; D. Kohler; H. Rapp; D. Andor

Portable laser range-finders, further referred to as LIDAR, and simultaneous localization and mapping (SLAM) are an efficient method of acquiring as-built floor plans. Generating and visualizing floor plans in real-time helps the operator assess the quality and coverage of capture data. Building a portable capture platform necessitates operating under limited computational resources. We present th ...

G. Williams; P. Drews; B. Goldfain; J. M. Rehg; E. A. Theodorou

In this paper we present a model predictive control algorithm designed for optimizing non-linear systems subject to complex cost criteria. The algorithm is based on a stochastic optimal control framework using a fundamental relationship between the information theoretic notions of free energy and relative entropy. The optimal controls in this setting take the form of a path integral, which we appr ...

J. Redmon; S. Divvala; R. Girshick; A. Farhadi

We present YOLO, a new approach to object detection. Prior work on object detection repurposes classifiers to perform detection. Instead, we frame object detection as a regression problem to spatially separated bounding boxes and associated class probabilities. A single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation. Since the whole detec ...

A. Kosba; A. Miller; E. Shi; Z. Wen; C. Papamanthou

Emerging smart contract systems over decentralized cryptocurrencies allow mutually distrustful parties to transact safely without trusted third parties. In the event of contractual breaches or aborts, the decentralized blockchain ensures that honest parties obtain commensurate compensation. Existing systems, however, lack transactional privacy. All transactions, including flow of money between pse ...

G. Zyskind; O. Nathan; A. '. Pentland

The recent increase in reported incidents of surveillance and security breaches compromising users' privacy call into question the current model, in which third-parties collect and control massive amounts of personal data. Bit coin has demonstrated in the financial space that trusted, auditable computing is possible using a decentralized network of peers accompanied by a public ledger. In this pap ...

J. Long; E. Shelhamer; T. Darrell

Convolutional networks are powerful visual models that yield hierarchies of features. We show that convolutional networks by themselves, trained end-to-end, pixels-to-pixels, exceed the state-of-the-art in semantic segmentation. Our key insight is to build “fully convolutional” networks that take input of arbitrary size and produce correspondingly-sized output with efficient inference and learning ...

J. Bonneau; A. Miller; J. Clark; A. Narayanan; J. A. Kroll; E. W. Felten

Bit coin has emerged as the most successful cryptographic currency in history. Within two years of its quiet launch in 2009, Bit coin grew to comprise billions of dollars of economic value despite only cursory analysis of the system's design. Since then a growing literature has identified hidden-but-important properties of the system, discovered attacks, proposed promising alternatives, and single ...

N. Moustafa; J. Slay

One of the major research challenges in this field is the unavailability of a comprehensive network based data set which can reflect modern network traffic scenarios, vast varieties of low footprint intrusions and depth structured information about the network traffic. Evaluating network intrusion detection systems research efforts, KDD98, KDDCUP99 and NSLKDD benchmark data sets were generated a d ...

C. Szegedy; Wei Liu; Yangqing Jia; P. Sermanet; S. Reed; D. Anguelov; D. Erhan; V. Vanhoucke; A. Rabinovich

We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14). The main hallmark of this architecture is the improved utilization of the computing resources inside the network. By a carefully crafted design, we increased the depth and wi ...

R. Girshick

This paper proposes a Fast Region-based Convolutional Network method (Fast R-CNN) for object detection. Fast R-CNN builds on previous work to efficiently classify object proposals using deep convolutional networks. Compared to previous work, Fast R-CNN employs several innovations to improve training and testing speed while also increasing detection accuracy. Fast R-CNN trains the very deep VGG16 n ...

K. Dahiya; S. Bhatia

With the rapid development of telecommunication industry, the service providers are inclined more towards expansion of the subscriber base. To meet the need of surviving in the competitive environment, the retention of existing customers has become a huge challenge. In the survey done in the Telecom industry, it is stated that the cost of acquiring a new customer is far more that retaining the exi ...

F. Yamaguchi; N. Golde; D. Arp; K. Rieck

The vast majority of security breaches encountered today are a direct result of insecure code. Consequently, the protection of computer systems critically depends on the rigorous identification of vulnerabilities in software, a tedious and error-prone process requiring significant expertise. Unfortunately, a single flaw suffices to undermine the security of a system and thus the sheer amount of co ...

E. Ben Sasson; A. Chiesa; C. Garman; M. Green; I. Miers; E. Tromer; M. Virza

Bit coin is the first digital currency to see widespread adoption. While payments are conducted between pseudonyms, Bit coin cannot offer strong privacy guarantees: payment transactions are recorded in a public decentralized ledger, from which much information can be deduced. Zero coin (Miers et al., IEEE S&P 2013) tackles some of these privacy issues by unlinking transactions from the payment's o ...

Z. -K. Zhang; M. C. Y. Cho; C. -W. Wang; C. -W. Hsu; C. -K. Chen; S. Shieh

The Internet of Things (IoT) opens opportunities for wearable devices, home appliances, and software to share and communicate information on the Internet. Given that the shared data contains a large amount of private information, preserving information security on the shared data is an important issue that cannot be neglected. In this paper, we begin with general information security background of ...

M. Horowitz

Our challenge is clear: The drive for performance and the end of voltage scaling have made power, and not the number of transistors, the principal factor limiting further improvements in computing performance. Continuing to scale compute performance will require the creation and effective use of new specialized compute engines, and will require the participation of application experts to be succes ...

R. Girshick; J. Donahue; T. Darrell; J. Malik

Object detection performance, as measured on the canonical PASCAL VOC dataset, has plateaued in the last few years. The best-performing methods are complex ensemble systems that typically combine multiple low-level image features with high-level context. In this paper, we propose a simple and scalable detection algorithm that improves mean average precision (mAP) by more than 30% relative to the p ...

J. Hamari; J. Koivisto; H. Sarsa

This paper reviews peer-reviewed empirical studies on gamification. We create a framework for examining the effects of gamification by drawing from the definitions of gamification and the discussion on motivational affordances. The literature review covers results, independent variables (examined motivational affordances), dependent variables (examined psychological/behavioral outcomes from gamifi ...

Y. Saito; Y. Kishiyama; A. Benjebbour; T. Nakamura; A. Li; K. Higuchi

This paper presents a non-orthogonal multiple access (NOMA) concept for cellular future radio access (FRA) towards the 2020s information society. Different from the current LTE radio access scheme (until Release 11), NOMA superposes multiple users in the power domain although its basic signal waveform could be based on the orthogonal frequency division multiple access (OFDMA) or the discrete Fouri ...

S. Sagiroglu; D. Sinanc

Big data is a term for massive data sets having large, more varied and complex structure with the difficulties of storing, analyzing and visualizing for further processes or results. The process of research into massive amounts of data to reveal hidden patterns and secret correlations named as big data analytics. These useful informations for companies or organizations with the help of gaining ric ...

L. Szekeres; M. Payer; T. Wei; D. Song

Memory corruption bugs in software written in low-level languages like C or C++ are one of the oldest problems in computer security. The lack of safety in these languages allows attackers to alter the program's behavior or take full control over it by hijacking its control flow. This problem has existed for more than 30 years and a vast number of potential solutions have been proposed, yet memory ...

A. Graves; A. -r. Mohamed; G. Hinton

Recurrent neural networks (RNNs) are a powerful model for sequential data. End-to-end training methods such as Connectionist Temporal Classification make it possible to train RNNs for sequence labelling problems where the input-output alignment is unknown. The combination of these methods with the Long Short-term Memory RNN architecture has proved particularly fruitful, delivering state-of-the-art ...

M. Smith; C. Szongott; B. Henne; G. von Voigt

Big Data is a new label given to a diverse field of data intensive informatics in which the datasets are so large that they become hard to work with effectively. The term has been mainly used in two contexts, firstly as a technological challenge when dealing with dataintensive domains such as high energy physics, astronomy or internet search, and secondly as a sociological problem when data about ...

J. Bonneau; C. Herley; P. C. v. Oorschot; F. Stajano

We evaluate two decades of proposals to replace text passwords for general-purpose user authentication on the web using a broad set of twenty-five usability, deployability and security benefits that an ideal scheme might provide. The scope of proposals we survey is also extensive, including password management software, federated login protocols, graphical password schemes, cognitive authenticatio ...

Y. Zhou; X. Jiang

The popularity and adoption of smart phones has greatly stimulated the spread of mobile malware, especially on the popular platforms such as Android. In light of their rapid growth, there is a pressing need to develop effective solutions. However, our defense capability is largely constrained by the limited understanding of these emerging mobile malware and the lack of timely access to related sam ...

J. R. Mayer; J. C. Mitchell

In the early days of the web, content was designed and hosted by a single person, group, or organization. No longer. Webpages are increasingly composed of content from myriad unrelated "third-party" websites in the business of advertising, analytics, social networking, and more. Third-party services have tremendous value: they support free content and facilitate web innovation. But third-party ser ...

H. Chourabi; T. Nam; S. Walker; J. R. Gil-Garcia; S. Mellouli; K. Nahon; T. A. Pardo; H. J. Scholl

Making a city "smart" is emerging as a strategy to mitigate the problems generated by the urban population growth and rapid urbanization. Yet little academic research has sparingly discussed the phenomenon. To close the gap in the literature about smart cities and in response to the increasing use of the concept, this paper proposes a framework to understand the concept of smart cities. Based on t ...

A. Geiger; P. Lenz; R. Urtasun

Today, visual recognition systems are still rarely employed in robotics applications. Perhaps one of the main reasons for this is the lack of demanding benchmarks that mimic such scenarios. In this paper, we take advantage of our autonomous driving platform to develop novel challenging benchmarks for the tasks of stereo, optical flow, visual odometry/SLAM and 3D object detection. Our recording pla ...

D. Chen; H. Zhao

It is well-known that cloud computing has many potential advantages and many enterprise applications and data are migrating to public or hybrid cloud. But regarding some business-critical applications, the organizations, especially large enterprises, still wouldn't move them to cloud. The market size the cloud computing shared is still far behind the one expected. From the consumers' perspective, ...

S. O. H. Madgwick; A. J. L. Harrison; R. Vaidyanathan

This paper presents a novel orientation algorithm designed to support a computationally efficient, wearable inertial human motion tracking system for rehabilitation applications. It is applicable to inertial measurement units (IMUs) consisting of tri-axis gyroscopes and accelerometers, and magnetic angular rate and gravity (MARG) sensor arrays that also include tri-axis magnetometers. The MARG imp ...

E. Rublee; V. Rabaud; K. Konolige; G. Bradski

Feature matching is at the base of many computer vision problems, such as object recognition or structure from motion. Current methods rely on costly descriptors for detection and matching. In this paper, we propose a very fast binary descriptor based on BRIEF, called ORB, which is rotation invariant and resistant to noise. We demonstrate through experiments how ORB is at two orders of magnitude f ...

R. A. Newcombe; S. Izadi; O. Hilliges; D. Molyneaux; D. Kim; A. J. Davison; P. Kohi; J. Shotton; S. Hodges; A. Fitzgibbon

We present a system for accurate real-time mapping of complex and arbitrary indoor scenes in variable lighting conditions, using only a moving low-cost depth camera and commodity graphics hardware. We fuse all of the depth data streamed from a Kinect sensor into a single global implicit surface model of the observed scene in real-time. The current sensor pose is simultaneously obtained by tracking ...

J. Levinson; J. Askeland; J. Becker; J. Dolson; D. Held; S. Kammel; J. Z. Kolter; D. Langer; O. Pink; V. Pratt; M. Sokolsky; G. Stanek; D. Stavens; A. Teichman; M. Werling; S. Thrun

In order to achieve autonomous operation of a vehicle in urban situations with unpredictable traffic, several realtime systems must interoperate, including environment perception, localization, planning, and control. In addition, a robust vehicle platform with appropriate sensors, computational hardware, networking, and software infrastructure is essential. We previously published an overview of J ...

R. B. Rusu; S. Cousins

With the advent of new, low-cost 3D sensing hardware such as the Kinect, and continued efforts in advanced point cloud processing, 3D perception gains more and more importance in robotics, as well as other fields. In this paper we present one of our most recent initiatives in the areas of point cloud perception: PCL (Point Cloud Library - http://pointclouds.org). PCL presents an advanced and exten ...

D. Mellinger; V. Kumar

We address the controller design and the trajectory generation for a quadrotor maneuvering in three dimensions in a tightly constrained setting typical of indoor environments. In such settings, it is necessary to allow for significant excursions of the attitude from the hover state and small angle approximations cannot be justified for the roll and pitch. We develop an algorithm that enables the r ...

Farhan Bashir Shaikh; S. Haider

Cloud computing is set of resources and services offered through the Internet. Cloud services are delivered from data centers located throughout the world. Cloud computing facilitates its consumers by providing virtual resources via internet. General example of cloud services is Google apps, provided by Google and Microsoft SharePoint. The rapid growth in field of “cloud computing” also increases ...

K. Shvachko; H. Kuang; S. Radia; R. Chansler

The Hadoop Distributed File System (HDFS) is designed to store very large data sets reliably, and to stream those data sets at high bandwidth to user applications. In a large cluster, thousands of servers both host directly attached storage and execute user application tasks. By distributing storage and computation across many servers, the resource can grow with demand while remaining economical a ...

T. Lee; M. Leok; N. H. McClamroch

This paper provides new results for the tracking control of a quadrotor unmanned aerial vehicle (UAV). The UAV has four input degrees of freedom, namely the magnitudes of the four rotor thrusts, that are used to control the six translational and rotational degrees of freedom, and to achieve asymptotic tracking of four outputs, namely, three position variables for the vehicle center of mass and the ...

S. Asur; B. A. Huberman

In recent years, social media has become ubiquitous and important for social networking and content sharing. And yet, the content that is generated from these websites remains largely untapped. In this paper, we demonstrate how social media content can be used to predict real-world outcomes. In particular, we use the chatter from Twitter.com to forecast box-office revenues for movies. We show that ...

T. Dillon; C. Wu; E. Chang

Many believe that Cloud will reshape the entire ICT industry as a revolution. In this paper, we aim to pinpoint the challenges and issues of Cloud computing. We first discuss two related computing paradigms - Service-Oriented Computing and Grid computing, and their relationships with Cloud computing. We then identify several challenges from the Cloud computing adoption perspective. Last, we will h ...

A. Horé; D. Ziou

In this paper, we analyse two well-known objective image quality metrics, the peak-signal-to-noise ratio (PSNR) as well as the structural similarity index measure (SSIM), and we derive a simple mathematical relationship between them which works for various kinds of image degradations such as Gaussian blur, additive Gaussian white noise, jpeg and jpeg2000 compression. A series of tests realized on ...

MacEachren, Alan M. and Stryker, Michael S. and Turton, Ian J. and Pezanowski, Scott

Background: The volume of health science publications is escalating rapidly. Thus, keeping up with developments is becoming harder as is the task of finding important cross-domain connections. When geographic location is a relevant component of research reported in publications, these tasks are more difficult because standard search and indexing facilities have limited or no ability to identify ge ...

Chenguang Pan; Wenxin Li

With the collaborative filtering techniques becoming more and more mature, recommender systems are widely used nowadays, especially in electronic commerce and social networks. However, the utilization of recommender system in academic research itself has not received enough attention. A research paper recommender system would greatly help researchers to find the most desirable papers in their fiel ...

Lu Tan; Neng Wang

Nowadays, the main communication form on the Internet is human-human. But it is foreseeable that in a near soon that any object will have a unique way of identification and can be addressed so that every object can be connected. The Internet will become to the Internet of Things. The communicate forms will expand from human-human to human-human, human-thing and thing-thing (also called M2M).This w ...

R. B. Rusu; N. Blodow; M. Beetz

In our recent work [1], [2], we proposed Point Feature Histograms (PFH) as robust multi-dimensional features which describe the local geometry around a point p for 3D point cloud datasets. In this paper, we modify their mathematical expressions and perform a rigorous analysis on their robustness and complexity for the problem of 3D registration for overlapping point cloud views. More concretely, w ...

J. Deng; W. Dong; R. Socher; L. -J. Li; Kai Li; Li Fei-Fei

The explosion of image data on the Internet has the potential to foster more sophisticated and robust models and algorithms to index, retrieve, organize and interact with images and multimedia data. But exactly how such data can be harnessed and organized remains a critical problem. We introduce here a new database called “ImageNet”, a large-scale ontology of images built upon the backbone of the ...

E. A. Lee

Cyber-Physical Systems (CPS) are integrations of computation and physical processes. Embedded computers and networks monitor and control the physical processes, usually with feedback loops where physical processes affect computations and vice versa. The economic and societal potential of such systems is vastly greater than what has been realized, and major investments are being made worldwide to d ...

Masaya Miyahara; Yusuke Asada; Daehwa Paik; Akira Matsuzawa

This paper presents a low offset voltage, low noise dynamic latched comparator using a self-calibrating technique. The new calibration technique does not require any amplifiers for the offset voltage cancellation and quiescent current. It achieves low offset voltage of 1.69 mV at 1 sigma in low power consumption, while 13.7 mV is measured without calibration. Furthermore the proposed comparator re ...

F. T. Liu; K. M. Ting; Z. -H. Zhou

Most existing model-based approaches to anomaly detection construct a profile of normal instances, then identify instances that do not conform to the normal profile as anomalies. This paper proposes a fundamentally different model-based method that explicitly isolates anomalies instead of profiles normal points. To our best knowledge, the concept of isolation has not been explored in current liter ...

O. Tremblay; L. -A. Dessaint; A. -I. Dekkiche

This paper presents an easy-to-use battery model applied to dynamic simulation software. The simulation model uses only the battery State-Of-Charge (SOC) as a state variable in order to avoid the algebraic loop problem. It is shown that this model, composed of a controlled voltage source in series with a resistance, can accurately represent four types of battery chemistries. The model's parameters ...

D. Schinkel; E. Mensink; E. Klumperink; E. van Tuijl; B. Nauta

A latch-type voltage sense amplifier in 90nm CMOS is designed with a separated input and cross-coupled stage. This separation enables fast operation over a wide common-mode and supply voltage range. With a 1-sigma offset of 8mV, the circuit consumes 92fJ/decision with a 1.2V supply. It has an input equivalent noise of 1.5mV and requires 18ps setup-plus-hold time ...

K. -Y. Hwa; W. M. Lin; Y. -I. Hou; T. -M. Yeh

Molecular mimicry defined as similar structures shared by molecules from dissimilar genes or by their protein products, is a general strategy for pathogens to infect host cells. Therefore, identification of the molecular mimic regions of a pathogen may be helpful to understand the disease. Severe acute respiratory syndrome (SARS) is a new human respiratory infectious disease caused by SARS coronav ...

A. Buades; B. Coll; J. . -M. Morel

We propose a new measure, the method noise, to evaluate and compare the performance of digital image denoising methods. We first compute and analyze this method noise for a wide class of denoising algorithms, namely the local smoothing filters. Second, we propose a new algorithm, the nonlocal means (NL-means), based on a nonlocal averaging of all pixels in the image. Finally, we present some exper ...

N. Dalal; B. Triggs

We study the question of feature sets for robust visual object recognition; adopting linear SVM based human detection as a test case. After reviewing existing edge and gradient based descriptors, we show experimentally that grids of histograms of oriented gradient (HOG) descriptors significantly outperform existing feature sets for human detection. We study the influence of each stage of the compu ...

J. Lofberg

The MATLAB toolbox YALMIP is introduced. It is described how YALMIP can be used to model and solve optimization problems typically occurring in systems and control theory. In this paper, free MATLAB toolbox YALMIP, developed initially to model SDPs and solve these by interfacing eternal solvers. The toolbox makes development of optimization problems in general, and control oriented SDP problems in ...

Z. Wang; E. P. Simoncelli; A. C. Bovik

The structural similarity image quality paradigm is based on the assumption that the human visual system is highly adapted for extracting structural information from the scene, and therefore a measure of structural similarity can provide a good approximation to perceived image quality. This paper proposes a multiscale structural similarity method, which supplies more flexibility than previous sing ...

A. Lesnicar; R. Marquardt

This paper presents a new multilevel converter topology suitable for very high voltage applications, especially network interties in power generation and transmission. The fundamental concept and the applied control scheme is introduced. Simulation results of a 36 MW-network intertie illustrate the efficient operating characteristics. A suitable structure of the converter-control is proposed. ...

P. Viola; M. Jones

This paper describes a machine learning approach for visual object detection which is capable of processing images extremely rapidly and achieving high detection rates. This work is distinguished by three key contributions. The first is the introduction of a new image representation called the "integral image" which allows the features used by our detector to be computed very quickly. The second i ...

D. G. Lowe

An object recognition system has been developed that uses a new class of local image features. The features are invariant to image scaling, translation, and rotation, and partially invariant to illumination changes and affine or 3D projection. These features share similar properties with neurons in inferior temporal cortex that are used for object recognition in primate vision. Features are effici ...

J. Kennedy; R. Eberhart

A concept for the optimization of nonlinear functions using particle swarm methodology is introduced. The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed. Benchmark testing of the paradigm is described, and applications, including nonlinear function optimization and neural network training, are proposed. The relationships between particle swar ...