nils-herrmann/clean_scirepeval huggingface dataset:
Image Retrieval: Current Techniques, Promising Directions, and Open Issues

This paper provides a comprehensive survey of the technical achievements in the research area of image retrieval, especially content-based image retrieval, an area that has been so active and prosperous in the past few years. The survey includes 100+ papers covering the research aspects of image feature representation and extraction, multidimensional indexing, and system design, three of the fundamental bases of content-based image retrieval. Furthermore, based on the state-of-the-art technology available now and the demand from real-world applications, open research issues are identified and future promising research directions are suggested.
Adversarial Learning for Semi-supervised Semantic Segmentation

We propose a method for semi-supervised semantic segmentation using an adversarial network. While most existing discriminators are trained to classify input images as real or fake on the image level, we design a discriminator in a fully convolutional manner to differentiate the predicted probability maps from the ground truth segmentation distribution with the consideration of the spatial resolution. We show that the proposed discriminator can be used to improve semantic segmentation accuracy by coupling the adversarial loss with the standard cross entropy loss of the proposed model. In addition, the fully convolutional discriminator enables semi-supervised learning through discovering the trustworthy regions in predicted results of unlabeled images, thereby providing additional supervisory signals. In contrast to existing methods that utilize weakly-labeled images, our method leverages unlabeled images to enhance the segmentation model. Experimental results on the PASCAL VOC 2012 and Cityscapes datasets demonstrate the effectiveness of the proposed algorithm.
An FPGA-Based Real-Time System for 3D Stereo Matching, Combining Absolute Differences and Census with Aggregation and Belief Propagation

The implementation of 3D stereo matching in real time is an important problem for many vision applications and algorithms. The current work, extending previous results by the same authors, presents in detail an architecture which combines the methods of Absolute Differences, Census, and Belief Propagation in an integrated architecture suitable for implementation with Field Programmable Gate Array (FPGA) logic. Emphasis on the present work is placed on the justification of dimensioning the system, as well as detailed design and testing information for a fully placed and routed design to process 87 frames per sec (fps) in 1920 × 1200 resolution, and a fully implemented design for 400 × 320 which runs up to 1570 fps.
FutureMapping: The Computational Structure of Spatial AI Systems

We discuss and predict the evolution of Simultaneous Localisation and Mapping (SLAM) into a general geometric and semantic ‘Spatial AI’ perception capability for intelligent embodied devices. A big gap remains between the visual perception performance that devices such as augmented reality eyewear or comsumer robots will require and what is possible within the constraints imposed by real products. Co-design of algorithms, processors and sensors will be needed. We explore the computational structure of current and future Spatial AI algorithms and consider this within the landscape of ongoing hardware developments.
Self-referential processing in our brain—A meta-analysis of imaging studies on the self

The question of the self has intrigued philosophers and psychologists for a long time. More recently, distinct concepts of self have also been suggested in neuroscience. However, the exact relationship between these concepts and neural processing across different brain regions remains unclear. This article reviews neuroimaging studies comparing neural correlates during processing of stimuli related to the self with those of non-self-referential stimuli. All studies revealed activation in the medial regions of our brains' cortex during self-related stimuli. The activation in these so-called cortical midline structures (CMS) occurred across all functional domains (e.g., verbal, spatial, emotional, and facial). Cluster and factor analyses indicate functional specialization into ventral, dorsal, and posterior CMS remaining independent of domains. Taken together, our results suggest that self-referential processing is mediated by cortical midline structures. Since the CMS are densely and reciprocally connected to subcortical midline regions, we advocate an integrated cortical-subcortical midline system underlying human self. We conclude that self-referential processing in CMS constitutes the core of our self and is critical for elaborating experiential feelings of self, uniting several distinct concepts evident in current neuroscience.
Learning Hierarchical Semantic Segmentations of LIDAR Data

This paper investigates a method for semantic segmentation of small objects in terrestrial LIDAR scans in urban environments. The core research contribution is a hierarchical segmentation algorithm where potential merges between segments are prioritized by a learned affinity function and constrained to occur only if they achieve a significantly high object classification probability. This approach provides a way to integrate a learned shape-prior (the object classifier) into a search for the best semantic segmentation in a fast and practical algorithm. Experiments with LIDAR scans collected by Google Street View cars throughout ~100 city blocks of New York City show that the algorithm provides better segmentations and classifications than simple alternatives for cars, vans, traffic lights, and street lights.
In the Eye of the Beholder: A Survey of Models for Eyes and Gaze

Despite active research and significant progress in the last 30 years, eye detection and tracking remains challenging due to the individuality of eyes, occlusion, variability in scale, location, and light conditions. Data on eye location and details of eye movements have numerous applications and are essential in face detection, biometric identification, and particular human-computer interaction tasks. This paper reviews current progress and state of the art in video-based eye detection and tracking in order to identify promising techniques as well as issues to be further addressed. We present a detailed review of recent eye models and techniques for eye detection and tracking. We also survey methods for gaze estimation and compare them based on their geometric properties and reported accuracies. This review shows that, despite their apparent simplicity, the development of a general eye detection technique involves addressing many challenges, requires further theoretical developments, and is consequently of interest to many other domains problems in computer vision and beyond.
Heat Transfer in Friction Stir Welding — Experimental and Numerical Studies

In the friction stir welding (FSW) process, heat is generated by friction between the and the workpiece. This heat flows into the workpiece as well as the tool. The amo heat conducted into the workpiece determines the quality of the weld, residual stres distortion of the workpiece. The amount of the heat that flows to the tool dictates th of the tool and the capability of the tool for the joining process. In this paper, we for late the heat transfer of the FSW process into two boundary value problems (BVP steady state BVP for the tool and a transient BVP for the workpiece. To quantify physical values of the process the temperatures in the workpiece and the tool are sured during FSW. Using the measured transient temperature fields finite element nu cal analyses were performed to determine the heat flux generated from the friction workpiece and the tool. Detailed temperature distributions in the workpiece and the are presented. Discussions relative to the FSW process are then given. In particula results show that (1) the majority of the heat generated from the friction, i.e., about 9 is transferred into the workpiece and only 5% flows into the tool and (2) the fractio the rate of plastic work dissipated as heat is about 80%.
Twitter as a Lifeline: Human-annotated Twitter Corpora for NLP of Crisis-related Messages

Microblogging platforms such as Twitter provide active communication channels during mass convergence and emergency events such as earthquakes, typhoons. During the sudden onset of a crisis situation, affected people post useful information on Twitter that can be used for situational awareness and other humanitarian disaster response efforts, if processed timely and effectively. Processing social media information pose multiple challenges such as parsing noisy, brief and informal messages, learning information categories from the incoming stream of messages and classifying them into different classes among others. One of the basic necessities of many of these tasks is the availability of data, in particular human-annotated data. In this paper, we present human-annotated Twitter corpora collected during 19 different crises that took place between 2013 and 2015. To demonstrate the utility of the annotations, we train machine learning classifiers. Moreover, we publish first largest word2vec word embeddings trained on 52 million crisis-related tweets. To deal with tweets language issues, we present human-annotated normalized lexical resources for different lexical variations.
Nonlinear modelling of cancer: bridging the gap between cells and tumours.

Despite major scientific, medical and technological advances over the last few decades, a cure for cancer remains elusive. The disease initiation is complex, and including initiation and avascular growth, onset of hypoxia and acidosis due to accumulation of cells beyond normal physiological conditions, inducement of angiogenesis from the surrounding vasculature, tumour vascularization and further growth, and invasion of surrounding tissue and metastasis. Although the focus historically has been to study these events through experimental and clinical observations, mathematical modelling and simulation that enable analysis at multiple time and spatial scales have also complemented these efforts. Here, we provide an overview of this multiscale modelling focusing on the growth phase of tumours and bypassing the initial stage of tumourigenesis. While we briefly review discrete modelling, our focus is on the continuum approach. We limit the scope further by considering models of tumour progression that do not distinguish tumour cells by their age. We also do not consider immune system interactions nor do we describe models of therapy. We do discuss hybrid-modelling frameworks, where the tumour tissue is modelled using both discrete (cell-scale) and continuum (tumour-scale) elements, thus connecting the micrometre to the centimetre tumour scale. We review recent examples that incorporate experimental data into model parameters. We show that recent mathematical modelling predicts that transport limitations of cell nutrients, oxygen and growth factors may result in cell death that leads to morphological instability, providing a mechanism for invasion via tumour fingering and fragmentation. These conditions induce selection pressure for cell survivability, and may lead to additional genetic mutations. Mathematical modelling further shows that parameters that control the tumour mass shape also control its ability to invade. Thus, tumour morphology may serve as a predictor of invasiveness and treatment prognosis.
Two-Dimensional Canonical Correlation Analysis

In this letter, we present a method of two-dimensional canonical correlation analysis (2D-CCA) where we extend the standard CCA in such a way that relations between two different sets of image data are directly sought without reshaping images into vectors. We stress that 2D-CCA dramatically reduces the computational complexity, compared to the standard CCA. We show the useful behavior of 2D-CCA through numerical examples of correspondence learning between face images in different poses and illumination conditions.
Cyberbullying: The hidden side of college students

The purpose of this study was to investigate how university students perceive their involvement in the cyberbullying phenomenon, and its impact on their well-being. Thus, this study presents a preliminary approach of how college students’ perceived involvement in acts of cyberbullying can be measured. Firstly, Exploratory Factor Analysis (N = 349) revealed a unidimensional structure of the four scales included in the Cyberbullying Inventory for College Students. Then, Item Response Theory (N = 170) was used to analyze the unidimensionality of each scale and the interactions between participants and items. Results revealed good item reliability and Cronbach’s a for each scale. Results also showed the potential of the instrument and how college students underrated their involvement in acts of cyberbullying. Additionally, aggression types, coping strategies and sources of help to deal with cyberbullying were identified and discussed. Lastly, age, gender and course-related issues were considered in the analysis. Implications for researchers and practitioners are discussed.
Supervised Open Information Extraction

We present data and methods that enable a supervised learning approach to Open Information Extraction (Open IE). Central to the approach is a novel formulation of Open IE as a sequence tagging problem, addressing challenges such as encoding multiple extractions for a predicate. We also develop a biLSTM transducer, extending recent deep Semantic Role Labeling models to extract Open IE tuples and provide confidence scores for tuning their precision-recall tradeoff. Furthermore, we show that the recently released Question-Answer Meaning Representation dataset can be automatically converted into an Open IE corpus which significantly increases the amount of available training data. Our supervised model, made publicly available,1 outperforms the state-of-the-art in Open IE on benchmark datasets.
ETH Zurich at TREC Clinical Decision Support 2016

This paper describes ETH Zurich’s submission to the TREC 2016 Clinical Decision Support (CDS) track. In three successive stages, we apply query expansion based on literal as well as semantic term matches, rank documents in a negation-aware manner and, finally, re-rank them based on clinical intent types as well as semantic and conceptual affinity to the medical case in question. Empirical results show that the proposed method can distill patient representations from raw clinical notes that result in a retrieval performance superior to that of manually constructed case descriptions.
Discovery of Topically Coherent Sentences for Extractive Summarization

Extractive methods for multi-document summarization are mainly governed by information overlap, coherence, and content constraints. We present an unsupervised probabilistic approach to model the hidden abstract concepts across documents as well as the correlation between these concepts, to generate topically coherent and non-redundant summaries. Based on human evaluations our models generate summaries with higher linguistic quality in terms of coherence, readability, and redundancy compared to benchmark systems. Although our system is unsupervised and optimized for topical coherence, we achieve a 44.1 ROUGE on the DUC-07 test set, roughly in the range of state-of-the-art supervised models.
Bicomp: A Bilayer Scalable Nakamoto Consensus Protocol

Blockchain has received great attention in recent years and motivated innovations in different scenarios. However, many vital issues which affect its performance are still open. For example, it is widely convinced that high level of security and scalability and full decentralization are still impossible to achieve simultaneously. In this paper, we propose Bicomp, a bilayer scalable Nakamoto consensus protocol, which is an approach based on high security and pure decentralized Nakamoto consensus, and with a significant improvement on scalability. In Bicomp, two kinds of blocks are generated, i.e., microblocks for concurrent transaction packaging in network, and macroblocks for leadership competition and chain formation. A leader is elected at beginning of each round by using a macroblock header from proof-of-work. An elected leader then receives and packages multiple microblocks mined by different nodes into one macroblock during its tenure, which results in a bilayer block structure. Such design limits a leader’s power and encourages as many nodes as possible to participate in the process of packaging transactions, which promotes the sharding nature of the system. Furthermore, several mechanisms are carefully designed to reduce transaction overlapping and further limit a leader’s power, among which a novel transaction diversity based metric is proposed as the second level criteria besides the longest-chain-first principle on selecting a legitimate chain when fork happens. Security issues and potential attacks to Bicomp are extensively discussed and experiments for evaluation are performed. From the experimental results based on 50 nodes all over the world, Bicomp achieves significant improvement on scalability than that of Bitcoin and Ethereum, while the security and decentralization merits are still preserved.
A 100 MHz 82.4% Efficiency Package-Bondwire Based Four-Phase Fully-Integrated Buck Converter With Flying Capacitor for Area Reduction

In today's fully-integrated converters, the integrated LC components dominate the chip-area and have become the major limitation of reducing the cost and increasing the current density. This paper presents a 100 MHz four-phase fully-integrated buck converter with standard package bondwire inductors and a flying capacitor (CFLY) topology for chip-area reduction, occupying 1.25 mm2 effective area in 0.13-μm CMOS technology. A four-phase operation is introduced for chip-area reduction with the cost penalty minimized by utilizing standard package bondwire inductance as power inductors. Meanwhile, an extra more than 40% chip-area saving is achieved by the simple but effective CFLY topology to take advantage of the parasitic bondwire inductance at the input for ripple attenuation. A maximum output current of 1.2 A is obtained by the four-phase operation, while only 3.73 nF overall integrated capacitors are required. Also, with the chip-area hungry integrated spiral metal inductors eliminated, the current density is significantly increased. 0.96 A/mm2 current density and 82.4% efficiency is obtained with 1.2 V to 0.9 V voltage conversion without using any off-chip inductors or advanced processes. The reliability is also verified by measurement with various bondwire inductances and configurations.
Topical Keyphrase Extraction from Twitter

Summarizing and analyzing Twitter content is an important and challenging task. In this paper, we propose to extract topical keyphrases as one way to summarize Twitter. We propose a context-sensitive topical PageRank method for keyword ranking and a probabilistic scoring function that considers both relevance and interestingness of keyphrases for keyphrase ranking. We evaluate our proposed methods on a large Twitter data set. Experiments show that these methods are very effective for topical keyphrase extraction.
Scaling Learning Algorithms towards AI

One long-term goal of machine learning research is to produce methods at are applicable to highly complex tasks, such as perception (vision, audition ), reasoning, intelligent control, and other artificially intelligent behaviors. We arg ue that in order to progress toward this goal, the Machine Learning community must endeavor to discover algorithms that can learn highly complex functions, with minimal need for prior knowledge, and with minimal human intervention. We pr esent mathematical and empirical evidence suggesting that many popular appr oaches to non-parametric learning, particularly kernel methods, are fundame ntally limited in their ability to learn complex high-dimensional functions. Our analysis focuses on two problems. First, kernel machines are shallow architectures , in which one large layer of simple template matchers i followed by a single layer of trainable coefficients. We argue that shallow architectures can be ver y in fficient in terms of required number of computational elements and example s. Second, we analyze a limitation of kernel machines with a local kernel, linked to th e curse of dimensionality, that applies to supervised, unsupervised (man ifold learning) and semi-supervised kernel machines. Using empirical results on invariant image recognition tasks, kernel methods are compared with deep architectures , in which lower-level features or concepts are progressively combined into more abstract and higher-level representations. We argue that deep architec tures have the potential to generalize in non-local ways, i.e., beyond immediate neighbo rs, and that this is crucial in order to make progress on the kind of complex tasks r equired for artificial intelligence.
Predicting Intent Using Activity Logs: How Goal Specificity and Temporal Range Affect User Behavior

People have different intents in using online platforms. They may be trying to accomplish specific, short-term goals, or less well-defined, longer-term goals. While understanding user intent is fundamental to the design and personalization of online platforms, little is known about how intent varies across individuals, or how it relates to their behavior. Here, we develop a framework for understanding intent in terms of goal specificity and temporal range. Our methodology combines survey-based methodology with an observational analysis of user activity. Applying this framework to Pinterest, we surveyed nearly 6000 users to quantify their intent, and then studied their subsequent behavior on the web site. We find that goal specificity is bimodal – users tend to be either strongly goal-specific or goalnonspecific. Goal-specific users search more and consume less content in greater detail than goal-nonspecific users: they spend more time using Pinterest, but are less likely to return in the near future. Users with short-term goals are also more focused and more likely to refer to past saved content than users with long-term goals, but less likely to save content for the future. Further, intent can vary by demographic, and with the topic of interest. Last, we show that user’s intent and activity are intimately related by building a model that can predict a user’s intent for using Pinterest after observing their activity for only two minutes. Altogether, this work shows how intent can be predicted from user behavior.
A Dual-polarized Planar Antenna Using Four Folded Dipoles and Its Array for Base Stations

A ±45° dual-polarized base station antenna with a planar structure is proposed for 2G/3G bands. The +45° antenna and the -45° antenna share the same radiator, which is composed of four folded dipoles and four coplanar striplines. The coplanar striplines work as λ/4 impedance transformers. Two Y-shaped feeding lines is designed to obtain a good impedance matching and a planar reflector is employed to achieve a stable radiation pattern. The antenna element has a wide impedance bandwidth of 27.8% with SWR <; 1.5 (1.7-2.25 GH), and a stable radiation pattern and a high cross polarization discrimination (XPD) are also obtained across its operating frequency band. Also, an antenna array that consists of ten elements is developed for base station applications. The array is proposed for electrical downtilt and the downtilt angle is designed from 0° to 8°. The measured results show that a wide bandwidth of 30% (1.7-2.3 GHz), a great port-toport isolation (>30 dB), a stable radiation pattern with 3-dB beamwidth 65° ± 5° in the horizontal plane, a high XPD and a high front-to-back ratio were achieved.