nils-herrmann/clean_scirepeval huggingface dataset:
A convolutional neural network approach for face verification

In this paper, we present a convolutional neural network (CNN) approach for the face verification task. We propose a “Siamese” architecture of two CNNs, with each CNN reduced to only four layers by fusing convolutional and subsampling layers. Network training is performed using the stochastic gradient descent algorithm with annealed global learning rate. Generalization ability of network is investigated via unique pairing of face images, and testing is done on AT&T face database. Experimental work shows that the proposed CNN system can classify a pair of 46×46 pixel face images in 0.6 milliseconds, which is significantly faster compared to equivalent network architecture with cascade of convolutional and subsampling layers. The verification accuracy achieved is 3.33% EER (equal error rate). Learning converges within 20 epochs, and the proposed technique can verify a test subject unseen in training. This work shows the viability of the “Siamese” CNN for face verification applications, and further improvements to the architecture are under construction to enhance its performance.
Microphone Array Processing for Distant Speech Recognition: From Close-Talking Microphones to Far-Field Sensors

Distant speech recognition (DSR) holds the promise of the most natural human computer interface because it enables man-machine interactions through speech, without the necessity of donning intrusive body- or head-mounted microphones. Recognizing distant speech robustly, however, remains a challenge. This contribution provides a tutorial overview of DSR systems based on microphone arrays. In particular, we present recent work on acoustic beam forming for DSR, along with experimental results verifying the effectiveness of the various algorithms described here; beginning from a word error rate (WER) of 14.3% with a single microphone of a linear array, our state-of-the-art DSR system achieved a WER of 5.3%, which was comparable to that of 4.2% obtained with a lapel microphone. Moreover, we present an emerging technology in the area of far-field audio and speech processing based on spherical microphone arrays. Performance comparisons of spherical and linear arrays reveal that a spherical array with a diameter of 8.4 cm can provide recognition accuracy comparable or better than that obtained with a large linear array with an aperture length of 126 cm.
Accelerating Comparative Genomics Workflows in a Distributed Environment with Optimized Data Partitioning

The advent of new sequencing technology has generated massive amounts of biological data at unprecedented rates. High-throughput bioinformatics tools are required to keep pace with this. Here, we implement a workflow-based model for parallelizing the data intensive task of genome alignment and variant calling with BWA and GATK's Haplotype Caller. We explore different approaches of partitioning data and how each affect the run time. We observe granularity-based partitioning for BWA and alignment-based partitioning for Halo type Caller to be the optimal choices for the pipeline. We identify the various challenges encountered while developing such an application and provide an insight into addressing them. We report significant performance improvements, from 12 days to 4 hours, while running the BWA-GATK pipeline using 100 nodes for analyzing high-coverage oak tree data.
Summarization from medical documents: a survey

The aim of this paper is to survey the recent work in medical documents summarization.



During the last decade, documents summarization got increasing attention by the AI research community. More recently it also attracted the interest of the medical research community as well, due to the enormous growth of information that is available to the physicians and researchers in medicine, through the large and growing number of published journals, conference proceedings, medical sites and portals on the World Wide Web, electronic medical records, etc.



This survey gives first a general background on documents summarization, presenting the factors that summarization depends upon, discussing evaluation issues and describing briefly the various types of summarization techniques. It then examines the characteristics of the medical domain through the different types of medical documents. Finally, it presents and discusses the summarization techniques used so far in the medical domain, referring to the corresponding systems and their characteristics.



The paper discusses thoroughly the promising paths for future research in medical documents summarization. It mainly focuses on the issue of scaling to large collections of documents in various languages and from different media, on personalization issues, on portability to new sub-domains, and on the integration of summarization technology in practical applications.
Characterizing Online Discussion Using Coarse Discourse Sequences

In this work, we present a novel method for classifying comments in online discussions into a set of coarse discourse acts towards the goal of better understanding discussions at scale. To facilitate this study, we devise a categorization of coarse discourse acts designed to encompass general online discussion and allow for easy annotation by crowd workers. We collect and release a corpus of over 9,000 threads comprising over 100,000 comments manually annotated via paid crowdsourcing with discourse acts and randomly sampled from the site Reddit. Using our corpus, we demonstrate how the analysis of discourse acts can characterize different types of discussions, including discourse sequences such as Q&A pairs and chains of disagreement, as well as different communities. Finally, we conduct experiments to predict discourse acts using our corpus, finding that structured prediction models such as conditional random fields can achieve an F1 score of 75%. We also demonstrate how the broadening of discourse acts from simply question and answer to a richer set of categories can improve the recall performance of Q&A extraction.
The business model concept: theoretical underpinnings and empirical illustrations

27 March 2002 : 26 July 2002 Accepted:15 October 2002  The business model concept is becoming increasingly popular within IS, management and strategy literature. It is used within many fields of research, including both traditional strategy theory and in the emergent body of literature on e-business. However, the concept is often used independently from theory, meaning model components and their interrelations are relatively obscure. Nonetheless, we believe that the business model concept is useful in explaining the relation between IS and strategy. This paper offers an outline for a conceptual business model, and proposes that it should include customers and competitors, the offering, activities and organisation, resources and factor market interactions. The causal inter-relations and the longitudinal processes by which business models evolve should also be included. The model criticises yet draws on traditional strategy theory and on the literature that addresses business models directly. The business model is illustrated by an ERP implementation in a European multi-national company. European Journal of Information Systems (2003) 12, 49–59. doi:10.1057/ palgrave.ejis.3000446
Comprehensive Study of Single-Phase AC-DC Power Factor Corrected Converters With High-Frequency Isolation

Solid-state switch mode AC-DC converters having high-frequency transformer isolation are developed in buck, boost, and buck-boost configurations with improved power quality in terms of reduced total harmonic distortion (THD) of input current, power-factor correction (PFC) at AC mains and precisely regulated and isolated DC output voltage feeding to loads from few Watts to several kW. This paper presents a comprehensive study on state of art of power factor corrected single-phase AC-DC converters configurations, control strategies, selection of components and design considerations, performance evaluation, power quality considerations, selection criteria and potential applications, latest trends, and future developments. Simulation results as well as comparative performance are presented and discussed for most of the proposed topologies.
Verifying Properties of Parallel Programs: An Axiomatic Approach

An axiomatic method for proving a number of properties of parallel programs is presented. Hoare has given a set of axioms for partial correctness, but they are not strong enough in most cases. This paper defines a more powerful deductive system which is in some sense complete for partial correctness. A crucial axiom provides for the use of auxiliary variables, which are added to a parallel program as an aid to proving it correct. The information in a partial correctness proof can be used to prove such properties as mutual exclusion, freedom from deadlock, and program termination. Techniques for verifying these properties are presented and illustrated by application to the dining philosophers problem.
Probabilistic Semi-Dense Mapping from Highly Accurate Feature-Based Monocular SLAM

In the last years several direct (i.e. featureless) monocular SLAM approaches have appeared showing impressive semi-dense or dense scene reconstructions. These works have questioned the need of features, in which consolidated SLAM techniques of the last decade were based. In this paper we present a novel feature-based monocular SLAM system that is more robust, gives more accurate camera poses, and obtains comparable or better semi-dense reconstructions than the current state of the art. Our semi-dense mapping operates over keyframes, optimized by local bundle adjustment, allowing to obtain accurate triangulations from wide baselines. Our novel method to search correspondences, the measurement fusion and the inter-keyframe depth consistency tests allow to obtain clean reconstructions with very few outliers. Against the current trend in direct SLAM, our experiments show that by decoupling the semi-dense reconstruction from the trajectory computation, the results obtained are better. This opens the discussion on the benefits of features even if a semi-dense reconstruction is desired.
SLA-Driven VM Scheduling in Mobile Edge Computing

Mobile-Edge Computing (MEC) is about offering application developers and service providers cloud-computing capabilities and an IT service environment at the edge of the mobile network. However, although cloud computing can be used to meet traditional challenges, like scalability concerns and provide for fast resource provisioning times, a multifaceted analysis is required when it comes in multi-operator environments with time-critical applications and services. In this work, we claim that the service importance must be at the epicenter when it comes to the scheduling and placement decision of whether to deploy the service at the edge network or not. Virtual machine (VM) scheduling decisions should avoid SLA violations for popular or time-critical services, and be fair between the service providers. A Lyapunov optimization framework is derived to solve this stochastic optimization problem that aims to maximize the revenue of the physical infrastructure owner in a multi-network operator-sharing environment with time-critical SLAs. A series of simulation experiments validate the high effectiveness of the proposed approach over benchmarking ones.
Neural encoding of the speech envelope by children with developmental dyslexia

Developmental dyslexia is consistently associated with difficulties in processing phonology (linguistic sound structure) across languages. One view is that dyslexia is characterised by a cognitive impairment in the "phonological representation" of word forms, which arises long before the child presents with a reading problem. Here we investigate a possible neural basis for developmental phonological impairments. We assess the neural quality of speech encoding in children with dyslexia by measuring the accuracy of low-frequency speech envelope encoding using EEG. We tested children with dyslexia and chronological age-matched (CA) and reading-level matched (RL) younger children. Participants listened to semantically-unpredictable sentences in a word report task. The sentences were noise-vocoded to increase reliance on envelope cues. Envelope reconstruction for envelopes between 0 and 10Hz showed that the children with dyslexia had significantly poorer speech encoding in the 0-2Hz band compared to both CA and RL controls. These data suggest that impaired neural encoding of low frequency speech envelopes, related to speech prosody, may underpin the phonological deficit that causes dyslexia across languages.
Realizing Ultra-Massive MIMO (1024×1024) communication in the (0.06-10) Terahertz band

The increasing demand for higher bandwidth and higher speed wireless communication motivates the exploration of higher frequency bands. The Terahertz (THz) band (0.06–10 THz) is envisioned as one of the key players to meet the demand for such higher bandwidth and data rates. However, the available bandwidth at THz frequencies comes with the cost of a much higher propagation loss. Due to the power limitations of compact solid-state THz transceivers, this results in very short communication distances of approximately one meter. In this paper, the concept of Ultra-Massive Multiple Input Multiple Output (UM MIMO) communication is introduced as a way to increase the communication distance and the achievable capacity of THz-band communication networks. The very small size of THz plasmonic nano-antennas, which leverage the properties of nanomaterials and metamaterials, enables the development of very large plasmonic arrays in very small footprints. For frequencies in the 0.06–1 THz range, metamaterials enable the design of plasmonic antenna arrays with hundreds of elements in a few square centimeters (e.g., 144 elements in 1 cm2 at 60 GHz). In the 1 to 10 THz band, graphene-based plasmonic nano-antenna arrays with thousands of elements can be embedded in a few square millimeters (e.g., 1024 elements in 1 mm2 at 1 THz). The resulting arrays can be utilized both in transmission and in reception (e.g., 1024x1024 UM MIMO at 1 THz) to support di↵erent modes, from razor-sharp UM beamforming to UM spatial multiplexing, as well as multi-band communication schemes. After introducing the main properties of plasmonic nano-antenna arrays, the working modes of UM MIMO are presented, and preliminary results are provided to highlight the potential of this paradigm. Finally, open challenges and potential solutions to enable UM MIMO communication are described.
Haswell: The Fourth-Generation Intel Core Processor

Haswell, Intel's fourth-generation core processor architecture, delivers a range of client parts, a converged core for the client and server, and technologies used across many products. It uses an optimized version of Intel 22-nm process technology. Haswell provides enhancements in power-performance efficiency, power management, form factor and cost, core and uncore microarchitecture, and the core's instruction set.
Towards enhanced hierarchical attention networks in ICD-9 tagging of clinical notes

Text is an important element in document classification in many natural language applications. Natural language processing (NLP) is today's computational advancement that provides many significant modern uses of text documents such as efficient information retrieval. In this paper, we describe the theoretical framework of predicting ICD-9 codes through tagging of clinical notes using our improved framework in deep learning called EnHANs. This proposed model improvement covers combination of word and topic embedding, as well as adding character-level representation of a document in a hierarchical attention neural networks. This paper also present the use of sigmoid activation function in the last layer of the enhanced neural network in order to arrive with a multi-label, multi-class prediction of clinical notes with ICD-9 codes.
Beyond the right hemisphere: brain mechanisms mediating vocal emotional processing

Vocal perception is particularly important for understanding a speaker's emotional state and intentions because, unlike facial perception, it is relatively independent of speaker distance and viewing conditions. The idea, derived from brain lesion studies, that vocal emotional comprehension is a special domain of the right hemisphere has failed to receive consistent support from neuroimaging. This conflict can be reconciled if vocal emotional comprehension is viewed as a multi-step process with individual neural representations. This view reveals a processing chain that proceeds from the ventral auditory pathway to brain structures implicated in cognition and emotion. Thus, vocal emotional comprehension appears to be mediated by bilateral mechanisms anchored within sensory, cognitive and emotional processing systems.
Cognitive behavioural therapy for major psychiatric disorder: does it really work? A meta-analytical review of well-controlled trials.

Although cognitive behavioural therapy (CBT) is claimed to be effective in schizophrenia, major depression and bipolar disorder, there have been negative findings in well-conducted studies and meta-analyses have not fully considered the potential influence of blindness or the use of control interventions.



We pooled data from published trials of CBT in schizophrenia, major depression and bipolar disorder that used controls for non-specific effects of intervention. Trials of effectiveness against relapse were also pooled, including those that compared CBT to treatment as usual (TAU). Blinding was examined as a moderating factor.



CBT was not effective in reducing symptoms in schizophrenia or in preventing relapse. CBT was effective in reducing symptoms in major depression, although the effect size was small, and in reducing relapse. CBT was ineffective in reducing relapse in bipolar disorder.



CBT is no better than non-specific control interventions in the treatment of schizophrenia and does not reduce relapse rates. It is effective in major depression but the size of the effect is small in treatment studies. On present evidence CBT is not an effective treatment strategy for prevention of relapse in bipolar disorder.
Executing SQL over encrypted data in the database-service-provider model

Rapid advances in networking and Internet technologies have fueled the emergence of the "software as a service" model for enterprise computing. Successful examples of commercially viable software services include rent-a-spreadsheet, electronic mail services, general storage services, disaster protection services. "Database as a Service" model provides users power to create, store, modify, and retrieve data from anywhere in the world, as long as they have access to the Internet. It introduces several challenges, an important issue being data privacy. It is in this context that we specifically address the issue of data privacy.There are two main privacy issues. First, the owner of the data needs to be assured that the data stored on the service-provider site is protected against data thefts from outsiders. Second, data needs to be protected even from the service providers, if the providers themselves cannot be trusted. In this paper, we focus on the second challenge. Specifically, we explore techniques to execute SQL queries over encrypted data. Our strategy is to process as much of the query as possible at the service providers' site, without having to decrypt the data. Decryption and the remainder of the query processing are performed at the client site. The paper explores an algebraic framework to split the query to minimize the computation at the client site. Results of experiments validating our approach are also presented.
Semantic Search of Unstructured Data using Contextual Network Graphs

The authors present a graph-based algorithm for searching potentially large collections of unstructured data, and discuss its implementation as a search engine designed to offer advanced relevance feedback features to users who may have limited familiarity with search tools. The technique, which closely resembles the spreading activation network model described by Scott Preece, uses a term-document matrix to generate a bipartite graph of term and document nodes representing the document collection. This graph can be searched by a simple recursive procedure that distributes energy from an initial query node. Nodes that acquire energy above a specified threshold comprise the result set. Initial results on live collections suggest that this technique may offer performance comparable to latent semantic indexing (LSI), while avoiding some of that technique’s computational pitfalls. Both the algorithm and its implementation in a production Web environment are discussed.
General Image-Quality Equation: GIQE.

A regression-based model was developed relating aerial image quality, expressed in terms of the National Imagery Interpretability Rating Scale (NIIRS), to fundamental image attributes. The General Image-Quality Equation (GIQE) treats three main attributes: scale, expressed as the ground-sampled distance; sharpness, measured from the system modulation transfer function; and the signal-to-noise ratio. The GIQE can be applied to any visible sensor and predicts NIIRS ratings with a standard error of 0.3 NIIRS. The image attributes treated by the GIQE are influenced by system design and operation parameters. The GIQE allows system designers and operators to perform trade-offs for the optimization of image quality.
Accelerating aggregation using intra-cycle parallelism

Modern CPUs have word width of 64 bits but real data values are usually represented using bits fewer than a CPU word. This underutilization of CPU at register level has motivated the recent development of bit-parallel algorithms that carry out data processing operations (e.g, filter scan) on CPU words packed with data values (e.g, 8 data values are packed into one 64-bit word). Bit-parallel algorithms fully unleash the intra-cycle parallelism of modern CPUs and they are especially attractive to main-memory column stores whose goal is to process data at the speed of the “bare metal”. Main-memory column stores generally focus on analytical queries, where aggregation is a common operation. Current bit-parallel algorithms, however, have not covered aggregation yet. In this paper, we present a suite of bit-parallel algorithms to accelerate all standard aggregation operations: SUM, MIN, MAX, AVG, MEDIAN, COUNT. The algorithms are designed to fully leverage the intra-cycle parallelism in CPU cores when aggregating words of packed values. Experimental evaluation shows that our bit-parallel aggregation algorithms exhibit significant performance benefits compared with non-bit-parallel methods.
Classification of document pages using structure-based features

Searching for documents by their type or genre is a natural way to enhance the effectiveness of document retrieval. The layout of a document contains a significant amount of information that can be used to classify it by type in the absence of domain-specific models. Our approach to classification is based on “visual similarity” of layout structure and is implemented by building a supervised classifier, given examples of each class. We use image features such as percentages of text and non-text (graphics, images, tables, and rulings) content regions, column structures, relative point sizes of fonts, density of content area, and statistics of features of connected components which can be derived without class knowledge. In order to obtain class labels for training samples, we conducted a study where subjects ranked document pages with respect to their resemblance to representative page images. Class labels can also be assigned based on known document types, or can be defined by the user. We implemented our classification scheme using decision tree classifiers and self-organizing maps.