nils-herrmann/clean_scirepeval huggingface dataset:
Memes Online: Extracted, Subtracted, Injected, and Recollected

Social media is playing an increasingly vital role in information dissemination. But with dissemination being more distributed, content often makes multiple hops, and consequently has opportunity to change. In this paper we focus on content that should be changing the least, namely quoted text. We find changes to be frequent, with their likelihood depending on the authority of the copied source and the type of site that is copying. We uncover patterns in the rate of appearance of new variants, their length, and popularity, and develop a simple model that is able to capture them. These patterns are distinct from ones produced when all copies are made from the same source, suggesting that information is evolving as it is being processed collectively in online social media.
Clinical Applications of Resting State Functional Connectivity

During resting conditions the brain remains functionally and metabolically active. One manifestation of this activity that has become an important research tool is spontaneous fluctuations in the blood oxygen level-dependent (BOLD) signal of functional magnetic resonance imaging (fMRI). The identification of correlation patterns in these spontaneous fluctuations has been termed resting state functional connectivity (fcMRI) and has the potential to greatly increase the translation of fMRI into clinical care. In this article we review the advantages of the resting state signal for clinical applications including detailed discussion of signal to noise considerations. We include guidelines for performing resting state research on clinical populations, outline the different areas for clinical application, and identify important barriers to be addressed to facilitate the translation of resting state fcMRI into the clinical realm.
Comparing Person- and Process-centric Strategies for Obtaining Quality Data on Amazon Mechanical Turk

In the past half-decade, Amazon Mechanical Turk has radically changed the way many scholars do research. The availability of a massive, distributed, anonymous crowd of individuals willing to perform general human-intelligence micro-tasks for micro-payments is a valuable resource for researchers and practitioners. This paper addresses the challenges of obtaining quality annotations for subjective judgment oriented tasks of varying difficulty. We design and conduct a large, controlled experiment (N=68,000) to measure the efficacy of selected strategies for obtaining high quality data annotations from non-experts. Our results point to the advantages of person-oriented strategies over process-oriented strategies. Specifically, we find that screening workers for requisite cognitive aptitudes and providing training in qualitative coding techniques is quite effective, significantly outperforming control and baseline conditions. Interestingly, such strategies can improve coder annotation accuracy above and beyond common benchmark strategies such as Bayesian Truth Serum (BTS).
Innovating Transport with QUIC: Design Approaches and Research Challenges

In today's Internet, there are many challenges such as low-latency support for interactive communication, security and privacy of user data, as well as development and deployment of new transport mechanisms. Quick UDP Internet Connections (QUIC) is a new transport protocol that addresses these challenges, focusing on HTTP/2 transmission as a first use case. The first QUIC working group meeting took place at IETF-97 in November 2016, and it has begun the standardization process. This article introduces the key features of QUIC and discusses the potential challenges that require further consideration.
Local , Distortional , and Euler Buckling of Thin-Walled Columns

Open cross-section, thin-walled, cold-formed steel columns have at least three competing buckling modes: local, dis and Euler~i.e., flexural or flexural-torsional ! buckling. Closed-form prediction of the buckling stress in the local mode, includ interaction of the connected elements, and the distortional mode, including consideration of the elastic and geometric stiffne web/flange juncture, are provided and shown to agree well with numerical methods. Numerical analyses and experiments postbuckling capacity in the distortional mode is lower than in the local mode. Current North American design specificati cold-formed steel columns ignore local buckling interaction and do not provide an explicit check for distortional buckling. E experiments on cold-formed channel, zed, and rack columns indicate inconsistency and systematic error in current design me provide validation for alternative methods. A new method is proposed for design that explicitly incorporates local, distortional an buckling, does not require calculations of effective width and/or effective properties, gives reliable predictions devoid of systema and provides a means to introduce rational analysis for elastic buckling prediction into the design of thin-walled columns.  Thin-wall structures; Columns; Buckling; Cold-formed steel.
Computational methods in drug discovery

The process for drug discovery and development is challenging, time consuming and expensive. Computer-aided drug discovery (CADD) tools can act as a virtual shortcut, assisting in the expedition of this long process and potentially reducing the cost of research and development. Today CADD has become an effective and indispensable tool in therapeutic development. The human genome project has made available a substantial amount of sequence data that can be used in various drug discovery projects. Additionally, increasing knowledge of biological structures, as well as increasing computer power have made it possible to use computational methods effectively in various phases of the drug discovery and development pipeline. The importance of in silico tools is greater than ever before and has advanced pharmaceutical research. Here we present an overview of computational methods used in different facets of drug discovery and highlight some of the recent successes. In this review, both structure-based and ligand-based drug discovery methods are discussed. Advances in virtual high-throughput screening, protein structure prediction methods, protein-ligand docking, pharmacophore modeling and QSAR techniques are reviewed.
BOLD fMRI and psychophysical measurements of contrast response to broadband images

We have measured the relationship between image contrast, perceived contrast, and BOLD fMRI activity in human early visual areas, for natural, whitened, pink noise, and white noise images. As root-mean-square contrast increases, BOLD response to natural images is stronger and saturates more rapidly than response to the whitened images. Perceived contrast and BOLD fMRI responses are higher for pink noise than for white noise patterns, by the same ratio as between natural and whitened images. Spatial phase structure has no measurable effect on perceived contrast or BOLD fMRI response. The fMRI and perceived contrast response results can be described by models of spatial frequency response in V1, that match the contrast sensitivity function at low contrasts, and have more uniform spatial frequency response at high contrasts.
Traffic accident detection through a hydrodynamic lens

In this paper we present a novel method for automatic traffic accident detection, based on Smoothed Particles Hydrodynamics (SPH). In our method, a motion flow field is obtained from the video through dense optical flow extraction. Then a thermal diffusion process (TDP) is exploited to turn the motion flow field into a coherent motion field. Approximating the moving particles to individuals, their interaction forces, represented as endothermic reactions, are computed using the enthalpy measure, thus obtaining the potential particles of interest. Furthermore, we exploit SPH that accumulates the contribution of each particle in a weighted form, based on a kernel function. The experimental evaluation is conducted on a set of video sequences collected from Youtube, and the obtained results are compared against a state of the art technique.
TM dual-mode pseudoelliptic filters using nonresonating modes

This paper presents a new class of very compact pseudoelliptic dual-mode filters. The basic idea consists of using a pair of resonant TMmn0 modes, while nonresonating modes are exploited so as to produce a direct coupling between the input and the output of the cavity. In this way, a single cavity can provide two reflection and two transmission zeros, thus leading to Nth order dual-mode filters having N transmission zeros. In addition, the proposed configuration yields very compact and geometrically simple structures that can be properly analyzed by a fullwave mode-matching technique, thus dramatically easing the design procedure. The analysis technique has been validated by comparison with commercial EM tools and by the measurement of a manufactured prototype. Several filter examples are proposed showing the design flexibility of the proposed filter class.
Design Considerations for Small Signal Modeling of DC-DC Converters Using Inductor DCR Current Sensing Under Time Constants Mismatch Conditions

This paper compares advantages and disadvantages of several commonly used current sensing methods such as dedicated sense resistor sensing, MOSFET Rds(on) current sensing, and inductor DC resistance (DCR) current sensing. Among these current sense methods, inductor DCR current sense that shows more advantages over other current sensing methods is chosen for analysis. The time constants mismatch issue between the time constant made by the current sensing RC network and the one formed with output inductor and its DC resistance is addressed in this paper. And an unified small signal modeling of a buck converter using inductor DCR current sensing with matched and mismatched time constants is presented, and the modeling has been verified experimentally.
Defect Detection in Solar Modules Using ICA Basis Images

Solar power has become an attractive alternative of electricity energy. Solar cells that form the basis of a solar power system are mainly based on multicrystalline silicon. A set of solar cells are assembled and interconnected into a large solar module to offer a large amount of electricity power for commercial applications. Many defects in a solar module cannot be visually observed with the conventional CCD imaging system. This paper aims at defect inspection of solar modules in electroluminescence (EL) images. The solar module charged with electrical current will emit infrared light whose intensity will be darker for intrinsic crystal grain boundaries and extrinsic defects including micro-cracks, breaks and finger interruptions. The EL image can distinctly highlight the invisible defects but also create a random inhomogeneous background, which makes the inspection task extremely difficult. The proposed method is based on independent component analysis (ICA), and involves a learning and a detection stage. The large solar module image is first divided into small solar cell subimages. In the training stage, a set of defect-free solar cell subimages are used to find a set of independent basis images using ICA. In the inspection stage, each solar cell subimage under inspection is reconstructed as a linear combination of the learned basis images. The coefficients of the linear combination are used as the feature vector for classification. Also, the reconstruction error between the test image and its reconstructed image from the ICA basis images is also evaluated for detecting the presence of defects. Experimental results have shown that the image reconstruction with basis images distinctly outperforms the ICA feature extraction approach. It can achieve a mean recognition rate of 93.4% for a set of 80 test samples.
A Wideband Circularly Polarized Magnetoelectric Dipole Antenna

This letter proposes a novel wideband circularly polarized magnetoelectric dipole antenna. In the proposed antenna, a pair of rotationally symmetric horizontal patches functions as an electric dipole, and two vertical patches with the ground act as an equivalent magnetic dipole. A Γ-shaped probe is used to excite the antenna, and a metallic cavity with two gaps is designed for wideband and good performance in radiation. A prototype was fabricated and measured. The experimental results show that the proposed antenna has an impedance bandwidth of 65% for SWR≤2 from 1.76 to 3.46 GHz, a 3-dB axial-ratio bandwidth of 71.5% from 1.68 to 3.55 GHz, and a stable gain of 8 ± 1 dBi. Good unidirectional radiation characteristic and low back-lobe level are achieved over the whole operating frequency band.
Sentiment Analysis : A Multi-Faceted Problem

Sentiment analysis or opinion mining is the computational study of people’s opinions, appraisals, and emotions toward entities, events and their attributes. In the past few years, it attracted a great deal of attentions from both academia and industry due to many challenging research problems and a wide range of applications . Opinions are important because whenever we need to make a decision we want to hear others’ opinions. This is not only true for individuals but also true for organizations. However, there was almost no computational study on opinions before the Web because there was little opinionated text available. In the past, when an individual needed to make a decision, he/she typically asked for opinions from friends and families. When an organization wanted to find opinions of the general public about its products and services, it conducted surveys and focus groups. However, with the explosive growth of the social media content on the Web in the past few years, the world has been transformed. People can now post reviews of products at merchant sites and express their views on almost anything in discussion forums and blogs, and at social network sites. Now if one wants to buy a product, one is no longer limited to asking one’s friends and families because there are many user reviews on the Web. For a company, it may no longer need to conduct surveys or focus groups in order to gather consumer opinions about its products and those of its competitors because there is a plenty of such information publicly available.
Efficient approaches for escaping higher order saddle points in non-convex optimization

Local search heuristics for non-convex optimizations are popular in applied machine learning. However, in general it is hard to guarantee that such algorithms even converge to a local minimum, due to the existence of complicated saddle point structures in high dimensions. Many functions have degenerate saddle points such that the first and second order derivatives cannot distinguish them with local optima. In this paper we use higher order derivatives to escape these saddle points: we design the first efficient algorithm guaranteed to converge to a third order local optimum (while existing techniques are at most second order). We also show that it is NP-hard to extend this further to finding fourth order local optima.
Crime scene investigation (as seen on TV).

A mysterious green ooze is injected into a brightly illuminated and humming machine; 10s later, a printout containing a complete biography of the substance is at the fingertips of an attractive young investigator who exclaims "we found it!" We have all seen this event occur countless times on any and all of the three CSI dramas, Cold Cases, Crossing Jordans, and many more. With this new style of "infotainment" (Surette, 2007), comes an increasingly blurred line between the hard facts of reality and the soft, quick solutions of entertainment. With these advances in technology, how can crime rates be anything but plummeting as would-be criminals cringe at the idea of leaving the smallest speck of themselves at a crime scene? Surely there are very few serious crimes that go unpunished in today's world of high-tech, fast-paced gadgetry. Science and technology have come a great distance since Sir Arthur Conan Doyle first described the first famous forensic scientist (Sherlock Holmes), but still have light-years to go.
Hippocampal-prefrontal input supports spatial encoding in working memory

Spatial working memory, the caching of behaviourally relevant spatial cues on a timescale of seconds, is a fundamental constituent of cognition. Although the prefrontal cortex and hippocampus are known to contribute jointly to successful spatial working memory, the anatomical pathway and temporal window for the interaction of these structures critical to spatial working memory has not yet been established. Here we find that direct hippocampal–prefrontal afferents are critical for encoding, but not for maintenance or retrieval, of spatial cues in mice. These cues are represented by the activity of individual prefrontal units in a manner that is dependent on hippocampal input only during the cue-encoding phase of a spatial working memory task. Successful encoding of these cues appears to be mediated by gamma-frequency synchrony between the two structures. These findings indicate a critical role for the direct hippocampal–prefrontal afferent pathway in the continuous updating of task-related spatial information during spatial working memory.
A Flexible, Corpus-Driven Model of Regular and Inverse Selectional Preferences

We present a vector space–based model for selectional preferences that predicts plausibility scores for argument headwords. It does not require any lexical resources (such as WordNet). It can be trained either on one corpus with syntactic annotation, or on a combination of a small semantically annotated primary corpus and a large, syntactically analyzed generalization corpus. Our model is able to predict inverse selectional preferences, that is, plausibility scores for predicates given argument heads. We evaluate our model on one NLP task (pseudo-disambiguation) and one cognitive task (prediction of human plausibility judgments), gauging the influence of different parameters and comparing our model against other model classes. We obtain consistent benefits from using the disambiguation and semantic role information provided by a semantically tagged primary corpus. As for parameters, we identify settings that yield good performance across a range of experimental conditions. However, frequency remains a major influence of prediction quality, and we also identify more robust parameter settings suitable for applications with many infrequent items.
Semantics of programming languages

class Expression { abstract Expression smallStep(State state) throws CanNotReduce; abstract Type typeCheck(Environment env) throws TypeError; } abstract class Value extends Expression { final Expression smallStep(State state) throws CanNotReduce{ throw new CanNotReduce("I’m a value"); } }class Value extends Expression { final Expression smallStep(State state) throws CanNotReduce{ throw new CanNotReduce("I’m a value"); } } class CanNotReduce extends Exception{ CanNotReduce(String reason) {super(reason);} } class TypeError extends Exception { TypeError(String reason) {super(reason);}} class Bool extends Value { boolean value; Bool(boolean b) { value = b; } public String toString() TRUE" : "FALSE"; } Type typeCheck(Environment env) throws TypeError { return Type.BOOL; } } class Int extends Value { int value; Int(int i) { value = i; } public String toString(){return ""+ value;} Type typeCheck(Environment env) throws TypeError { return Type.INT; } } class Skip extends Value { public String toString(){return "SKIP";} Type typeCheck(Environment env) throws TypeError { return Type.UNIT; } }
Multimodal Sentiment Analysis of Social Media

This paper describes the approach we take to the analysis of social media, combining opinion mining from text and multimedia (images, videos, etc), and centred on entity and event recognition. We examine a particular use case, which is to help archivists select material for inclusion in an archive of social media for preserving community memories, moving towards structured preservation around semantic categories. The textual approach we take is rule-based and builds on a number of sub-components, taking into account issues inherent in social media such as noisy ungrammatical text, use of swear words, sarcasm etc. The analysis of multimedia content complements this work in order to help resolve ambiguity and to provide further contextual information. We provide two main innovations in this work: first, the novel combination of text and multimedia opinion mining tools; and second, the adaptation of NLP tools for opinion mining specific to the problems of social media.
Hardware Support for Non-photorealistic Rendering

Special features such as ridges, valleys and silhouettes, of a polygonal scene are usually displayed by explicitly identifying and then rendering `edges' for the corresponding geometry. The candidate edges are identified using the connectivity information, which requires preprocessing of the data. We present a non-obvious but surprisingly simple to implement technique to render such features without connectivity information or preprocessing. At the hardware level, based only on the vertices of a given flat polygon, we introduce new polygons, with appropriate color, shape and orientation, so that they eventually appear as special features.
Enabling 5G mobile wireless technologies

Research on 5G mobile wireless technologies has been very active in both academia and industry in the past few years. While there has been certain consensus on the overall requirements of 5G wireless systems (e.g., in data rate, network capacity, delay), various enabling wireless technologies have been considered and studied to achieve these performance targets. It has been quite clear, however, that there would be no single enabling technology that can achieve all diverse and even conflicting 5G requirements. In general, many fundamental changes and innovations to re-engineer the overall network architecture and algorithms in different layers and to exploit new system degrees of freedom would be needed for the future 5G wireless system. In particular, we may need to consider other potential waveform candidates that can overcome limitations of the orthogonal frequency multiple access (OFDM) waveform employed in the current 4G system, develop disruptive technologies to fulfill 5G rate and capacity requirements including network densification, employment of large-scale (massive) multiple input multiple output (MIMO), and exploitation of the millimeter wave (mmWave) spectrum to attain Gigabit communications. In addition, design tools from the computer networking domain including software defined networking, virtualization, and cloud computing are expected to play important roles in defining the more flexible, intelligent, and efficient 5G network architecture. This paper aims at describing key 5G enabling wireless mobile technologies and discussing their potentials and open research challenges. We also present how papers published in our special issue contribute to the developments of these disruptive 5G technologies.
Selective Supervision: Guiding Supervised Learning with Decision-Theoretic Active Learning

An inescapable bottleneck with learning from large data sets is the high cost of labeling training data. Unsupervised learning methods have promised to lower the cost of tagging by leveraging notions of similarity among data points to assign tags. However, unsupervised and semi-supervised learning techniques often provide poor results due to errors in estimation. We look at methods that guide the allocation of human effort for labeling data so as to get the greatest boosts in discriminatory power with increasing amounts of work. We focus on the application of value of information to Gaussian Process classifiers and explore the effectiveness of the method on the task of classifying voice messages.