nils-herrmann/clean_scirepeval huggingface dataset:
Empath: A framework for evaluating entity-level sentiment analysis

Sentiment analysis is the fundamental component in text-driven monitoring or forecasting systems, where the general sentiment towards real-world entities (e.g., people, products, organizations) are analyzed based on the sentiment signals embedded in a myriad of web text available today. Building such systems involves several practically important problems, from data cleansing (e.g., boilerplate removal, web-spam detection), and sentiment analysis at individual mention-level (e.g., phrase, sentence-, document-level) to the aggregation of sentiment for each entity-level (e.g., person, company) analysis. Most previous research in sentiment analysis however, has focused only on individual mention-level analysis, and there has been relatively less work that copes with other practically important problems for enabling a large-scale sentiment monitoring system. In this paper, we propose Empath, a new framework for evaluating entity-level sentiment analysis. Empath leverages objective measurements of entities in various domains such as people, companies, countries, movies, and sports, to facilitate entity-level sentiment analysis and tracking. We demonstrate the utility of Empath for the evaluation of a large-scale sentiment system by applying it to various lexicons using Lydia, our own large scale text-analytics tool, over a corpus consisting of more than a terabyte of newspaper data. We expect that Empath will encourage research that encompasses end-to-end pipelines to enable a large-scale text-driven monitoring and forecasting systems.
UTD: Classifying Semantic Relations by Combining Lexical and Semantic Resources

This paper describes our system for SemEval-2010 Task 8 on multi-way classification of semantic relations between nominals. First, the type of semantic relation is classified. Then a relation typespecific classifier determines the relation direction. Classification is performed using SVM classifiers and a number of features that capture the context, semantic role affiliation, and possible pre-existing relations of the nominals. This approach achieved an F1 score of 82.19% and an accuracy of 77.92%.
Series-connected GaN transistors for ultra-fast high-voltage switch (>1kV)

The feasibility of a 1.2kV GaN switch based on two series-connected 650V GaN transistors is demonstrated in this paper. Aside to achieve ultra-fast transitions and reduced switching energy loss, stacking GaN transistors enables compatibility with high-voltage GaN-on-Silicon technologies. A proof-of-concept is provided by electrical characterization and hard-switching operation of a GaN Super-Cascode built with discrete components. Further investigations to enhance stability with auxiliary components are carried out by simulations and co-integrated prototypes are proven at wafer level.
An IGBT Gate Driver for Feed-Forward Control of Turn-on Losses and Reverse Recovery Current

This paper addresses the problem of turn on performances of an insulated gate bipolar transistor (IGBT) that works in hard switching conditions. The IGBT turn on dynamics with an inductive load is described, and corresponding IGBT turn on losses and reverse recovery current of the associated freewheeling diode are analysed. A new IGBT gate driver based on feed-forward control of the gate emitter voltage is presented in the paper. In contrast to the widely used conventional gate drivers, which have no capability for switching dynamics optimisation, the proposed gate driver provides robust and simple control and optimization of the reverse recovery current and turn on losses. The collector current slope and reverse recovery current are controlled by means of the gate emitter voltage control in feed-forward manner. In addition the collector emitter voltage slope is controlled during the voltage falling phase by means of inherent increase of the gate current. Therefore, the collector emitter voltage tail and the total turn on losses are significantly reduced. The proposed gate driver was experimentally verified and compared to a conventional gate driver, and the results are presented and discussed in the paper.
Decomposing Consumer Health Questions

This paper presents a method for decomposing long, complex consumer health questions. Our approach largely decomposes questions using their syntactic structure, recognizing independent questions embedded in clauses, as well as coordinations and exemplifying phrases. Additionally, we identify elements specific to disease-related consumer health questions, such as the focus disease and background information. To achieve this, our approach combines rank-and-filter machine learning methods with rule-based methods. Our results demonstrate significant improvements over the heuristic methods typically employed for question decomposition that rely only on the syntactic parse tree.
Indirect compensation techniques for three-stage CMOS op-amps

As CMOS technology continues to evolve, the supply voltages are decreasing while at the same time the transistor threshold voltages are remaining relatively constant. Making matters worse, the inherent gain available from the nano-CMOS transistors is dropping. Traditional techniques for achieving high-gain by vertically stacking (i.e. cascoding) transistors becomes less useful in nano-scale CMOS processes. Horizontal cascading (multi-stage) must be used in order to realize high-gain op-amps in low supply voltage processes. This paper discusses new design techniques for the realization of three-stage op-amps. The proposed and experimentally verified op-amps, fabricated in 500 nm CMOS, typically exhibit 30 MHz unity-gain frequency, near 100ns transient settling and 72° phase-margin for 500pF load. This results in significantly higher op-amp performance metrics over the traditional op-amp designs while at the same time having smaller layout area.
Adaptive Stochastic Gradient Descent Optimisation for Image Registration

We present a stochastic gradient descent optimisation method for image registration with adaptive step size prediction. The method is based on the theoretical work by Plakhov and Cruz ( 2004). Our main methodological contribution is the derivation of an image-driven mechanism to select proper values for the most important free parameters of the method. The selection mechanism employs general characteristics of the cost functions that commonly occur in intensity-based image registration. Also, the theoretical convergence conditions of the optimisation method are taken into account. The proposed adaptive stochastic gradient descent (ASGD) method is compared to a standard, non-adaptive Robbins-Monro (RM) algorithm. Both ASGD and RM employ a stochastic subsampling technique to accelerate the optimisation process. Registration experiments were performed on 3D CT and MR data of the head, lungs, and prostate, using various similarity measures and transformation models. The results indicate that ASGD is robust to these variations in the registration framework and is less sensitive to the settings of the user-defined parameters than RM. The main disadvantage of RM is the need for a predetermined step size function. The ASGD method provides a solution for that issue.
SceneNet RGB-D: Can 5M Synthetic Images Beat Generic ImageNet Pre-training on Indoor Segmentation?

We introduce SceneNet RGB-D, a dataset providing pixel-perfect ground truth for scene understanding problems such as semantic segmentation, instance segmentation, and object detection. It also provides perfect camera poses and depth data, allowing investigation into geometric computer vision problems such as optical flow, camera pose estimation, and 3D scene labelling tasks. Random sampling permits virtually unlimited scene configurations, and here we provide 5M rendered RGB-D images from 16K randomly generated 3D trajectories in synthetic layouts, with random but physically simulated object configurations. We compare the semantic segmentation performance of network weights produced from pretraining on RGB images from our dataset against generic VGG-16 ImageNet weights. After fine-tuning on the SUN RGB-D and NYUv2 real-world datasets we find in both cases that the synthetically pre-trained network outperforms the VGG-16 weights. When synthetic pre-training includes a depth channel (something ImageNet cannot natively provide) the performance is greater still. This suggests that large-scale high-quality synthetic RGB datasets with task-specific labels can be more useful for pretraining than real-world generic pre-training such as ImageNet. We host the dataset at http://robotvault. bitbucket.io/scenenet-rgbd.html.
Identifying Expectation Errors in Value / Glamour Strategies : A Fundamental Analysis Approach 2 Identifying Expectation Errors in Value / Glamour Strategies : A Fundamental Analysis Approach

It is well established that value stocks outperform glamour stocks, yet considerable debate exists about whether the return differential reflects compensation for risk or mispricing. Under mispricing explanations, prices of glamour (value) firms reflect systematically optimistic (pessimistic) expectations; thus, the value/glamour effect should be concentrated (absent) among firms with (without) ex ante identifiable expectation errors. Classifying firms based upon whether expectations implied by current pricing multiples are congruent with the strength of their fundamentals, we document that value/glamour returns and ex post revisions to market expectations are predictably concentrated (absent) among firms with ex ante biased (unbiased) market expectations.
Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing Their Input Gradients

Deep neural networks have proven remarkably effective at solving many classification problems, but have been criticized recently for two major weaknesses: the reasons behind their predictions are uninterpretable, and the predictions themselves can often be fooled by small adversarial perturbations. These problems pose major obstacles for the adoption of neural networks in domains that require security or transparency. In this work, we evaluate the effectiveness of defenses that differentiably penalize the degree to which small changes in inputs can alter model predictions. Across multiple attacks, architectures, defenses, and datasets, we find that neural networks trained with this input gradient regularization exhibit robustness to transferred adversarial examples generated to fool all of the other models. We also find that adversarial examples generated to fool gradient-regularized models fool all other models equally well, and actually lead to more “legitimate,” interpretable misclassifications as rated by people (which we confirm in a human subject experiment). Finally, we demonstrate that regularizing input gradients makes them more naturally interpretable as rationales for model predictions. We conclude by discussing this relationship between interpretability and robustness in deep neural
Mobile augmented reality survey: a bottom-up approach

Augmented Reality (AR) is becoming mobile. Mobile devices have many constraints but also rich new features that traditional desktop computers do not have. There are several survey papers on AR, but none is dedicated to Mobile Augmented Reality (MAR). Our work serves the purpose of closing this gap. The contents are organized with a bottom-up approach. We first present the state-of-the-art in system components including hardware platforms, software frameworks and display devices, follows with enabling technologies such as tracking and data management. We then survey the latest technologies and methods to improve run-time performance and energy efficiency for practical implementation. On top of these, we further introduce the application fields and several typical MAR applications. Finally we conclude the survey with several challenge problems, which are under exploration and require great research efforts in the future.
Nyami: a synthesizable GPU architectural model for general-purpose and graphics-specific workloads

Graphics processing units (GPUs) continue to grow in popularity for general-purpose, highly parallel, high-throughput systems. This has forced GPU vendors to increase their focus on general purpose workloads, sometimes at the expense of the graphics-specific workloads. Using GPUs for general-purpose computation is a departure from the driving forces behind programmable GPUs that were focused on a narrow subset of graphics rendering operations. Rather than focus on purely graphics-related or general-purpose use, we have designed and modeled an architecture that optimizes for both simultaneously to efficiently handle all GPU workloads. In this paper, we present Nyami, a co-optimized GPU architecture and simulation model with an open-source implementation written in Verilog. This approach allows us to more easily explore the GPU design space in a synthesizable, cycle-precise, modular environment. An instruction-precise functional simulator is provided for co-simulation and verification. Overall, we assume a GPU may be used as a general-purpose GPU (GPGPU) or a graphics engine and account for this in the architecture's construction and in the options and modules selectable for synthesis and simulation. To demonstrate Nyami's viability as a GPU research platform, we exploit its flexibility and modularity to explore the impact of a set of architectural decisions. These include sensitivity to cache size and associativity, barrel and switch-on-stall multithreaded instruction scheduling, and software vs. hardware implementations of rasterization. Through these experiments, we gain insight into commonly accepted GPU architecture decisions, adapt the architecture accordingly, and give examples of the intended use as a GPU research tool.
An Experimental Comparison of Partitioning Strategies in Distributed Graph Processing

In this paper, we study the problem of choosing among partitioning strategies in distributed graph processing systems. To this end, we evaluate and characterize both the performance and resource usage of different partitioning strategies under various popular distributed graph processing systems, applications, input graphs, and execution environments. Through our experiments, we found that no single partitioning strategy is the best fit for all situations, and that the choice of partitioning strategy has a significant effect on resource usage and application run-time. Our experiments demonstrate that the choice of partitioning strategy depends on (1) the degree distribution of input graph, (2) the type and duration of the application, and (3) the cluster size. Based on our results, we present rules of thumb to help users pick the best partitioning strategy for their particular use cases. We present results from each system, as well as from all partitioning strategies implemented in one common system (PowerLyra).
Band Selection for Hyperspectral Image Classification Using Mutual Information

Spectral band selection is a fundamental problem in hyperspectral data processing. In this letter, a new band-selection method based on mutual information (MI) is proposed. MI measures the statistical dependence between two random variables and can therefore be used to evaluate the relative utility of each band to classification. A new strategy is described to estimate the MI using a priori knowledge of the scene, reducing reliance on a "ground truth" reference map, by retaining bands with high associated MI values (subject to the so-called "complementary" conditions). Simulations of classification performance on 16 classes of vegetation from the AVIRIS 92AV3C data set show the effectiveness of the method, which outperforms an MI-based method using the associated reference map, an entropy-based method, and a correlation-based method. It is also competitive with the steepest ascent algorithm at much lower computational cost
Change of Heart: Emotion Tracking to Promote Behavior Change

Preventable behaviors contribute to many life threatening health problems. Behavior-change technologies have been deployed to modify these, but such systems typically draw on traditional behavioral theories that overlook affect. We examine the importance of emotion tracking for behavior change. First, we conducted interviews to explore how emotions influence unwanted behaviors. Next, we deployed a system intervention, in which 35 participants logged information for a self-selected, unwanted behavior (e.g., smoking or overeating) over 21 days. 16 participants engaged in standard behavior tracking using a Fact-Focused system to record objective information about goals. 19 participants used an Emotion-Focused system to record emotional consequences of behaviors. Emotion-Focused logging promoted more successful behavior change and analysis of logfiles revealed mechanisms for success: greater engagement of negative affect for unsuccessful days and increased insight were key to motivating change. We present design implications to improve behavior-change technologies with emotion tracking.
Real-time News Recommendations using Apache Spark

Recommending news articles is a challenging task due to the continuous changes in the set of available news articles and the contextdependent preferences of users. Traditional recommender approaches are optimized for analyzing static data sets. In news recommendation scenarios, characterized by continuous changes, high volume of messages, and tight time constraints, alternative approaches are needed. In this work we present a highly scalable recommender system optimized for the processing of streams. We evaluate the system in the CLEF NewsREEL challenge. Our system is built on Apache Spark enabling the distributed processing of recommendation requests ensuring the scalability of our approach. The evaluation of the implemented system shows that our approach is suitable for the news recommenation scenario and provides high-quality results while satisfying the tight time constraints.
A Novel Variable Reluctance Resolver with Nonoverlapping Tooth–Coil Windings

A novel variable reluctance (VR) resolver with nonoverlapping tooth-coil windings is proposed in this paper. It significantly simplifies the manufacturing process of multilayer windings in conventional products. Finite element (FE) analysis is used to illustrate the basic operating principle, followed by analytical derivation of main parameters and optimization of major dimensions, including air-gap length and slot opening width. Based on winding distributions and FE results, it is shown that identical stator and winding can be employed for a resolver with three different numbers of rotor poles. Further, other stator slot/rotor pole combinations based on the nonoverlapping tooth-coil windings are generalized. In addition, the influence of eccentricity and end-winding leakage on the proposed topology is investigated. Finally, a prototype is fabricated and tested to verify the analysis, including main parameters and electrical angle error.
Control of parallel connected inverters in stand-alone AC supply systems

A scheme for controlling parallel connected inverters in a stand-alone AC supply system is presented. A key feature of this scheme is that it uses only those variables which can be measured locally at the inverter, and does not need communication of control signals between the inverters. This feature is important in high reliability uninterruptible power supply (UPS) systems, and in large DC power sources connected to an AC distribution system. Real and reactive power sharing between inverters can be achieved by controlling two independent quantities at the inverter: the power angle and the fundamental inverter voltage magnitude.<<ETX>>
Deep Structured Learning for Facial Expression Intensity Estimation

We consider the task of automated estimation of facial expression intensity. This involves estimation of multiple output variables (facial action units — AUs) that are structurally dependent. Their structure arises from statistically induced co-occurrence patterns of AU intensity levels. Modeling this structure is critical for improving the estimation performance; however, this performance is bounded by the quality of the input features extracted from face images. The goal of this paper is to model these structures and estimate complex feature representations simultaneously by combining conditional random field (CRF) encoded AU dependencies with deep learning. To this end, we propose a novel Copula CNN deep learning approach for modeling multivariate ordinal variables. Our model accounts for ordinal structure in output variables and their non-linear dependencies via copula functions modeled as cliques of a CRF. These are jointly optimized with deep CNN feature encoding layers using a newly introduced balanced batch iterative training algorithm. We demonstrate the effectiveness of our approach on the task of AU intensity estimation on two benchmark datasets. We show that joint learning of the deep features and the target output structure results in significant performance gains compared to existing deep structured models for analysis of facial expressions.
A Feasibility Study of Tissue Characterization Using LC Sensors

Bio-implantable sensors using radio-frequency telemetry links that enable the continuous monitoring and recording of physiological data are receiving a great deal of attention. The objective of this paper is to study the feasibility of an implantable sensor for tissue characterization. This has been done by querying an LC sensor surrounded by dispersive tissues by an external antenna. The resonant frequency of the sensor is monitored by measuring the input impedance of the antenna, and correlated to the desired quantities. Using an equivalent circuit model of the sensor that accounts for the properties of the encapsulating tissue, analytical expressions have been developed for the extraction of the tissue permittivity and conductivity. Finally, experimental validation has been performed with a telemetry link that consists of a loop antenna and a fabricated LC sensor immersed in single and multiple dispersive phantom materials.
Emotion Distribution Learning from Texts

The advent of social media and its prosperity enable users to share their opinions and views. Understanding users’ emotional states might provide the potential to create new business opportunities. Automatically identifying users’ emotional states from their texts and classifying emotions into finite categories such as joy, anger, disgust, etc., can be considered as a text classification problem. However, it introduces a challenging learning scenario where multiple emotions with different intensities are often found in a single sentence. Moreover, some emotions co-occur more often while other emotions rarely coexist. In this paper, we propose a novel approach based on emotion distribution learning in order to address the aforementioned issues. The key idea is to learn a mapping function from sentences to their emotion distributions describing multiple emotions and their respective intensities. Moreover, the relations of emotions are captured based on the Plutchik’s wheel of emotions and are subsequently incorporated into the learning algorithm in order to improve the accuracy of emotion detection. Experimental results show that the proposed approach can effectively deal with the emotion distribution detection problem and perform remarkably better than both the state-of-theart emotion detection method and multi-label learning methods.
Merging SenticNet and WordNet-Affect emotion lists for sentiment analysis

SenticNet is currently one of the most comprehensive freely available semantic resources for opinion mining. However, it only provides numerical polarity scores, while more detailed sentiment-related information for its concepts is often desirable. Another important resource for opinion mining and sentiment analysis is WordNet-Affect, which in turn lacks quantitative information. We report a work on automatically merging these two resources by assigning emotion labels to more than 2700 concepts.