nils-herrmann/clean_scirepeval huggingface dataset:
The Dynamic Interplay between HIV-1, SAMHD1, and the Innate Antiviral Response

The innate immune response constitutes the first cellular line of defense against initial HIV-1 infection. Immune cells sense invading virus and trigger signaling cascades that induce antiviral defenses to control or eliminate infection. Professional antigen-presenting cells located in mucosal tissues, including dendritic cells and macrophages, are critical for recognizing HIV-1 at the site of initial exposure. These cells are less permissive to HIV-1 infection compared to activated CD4+ T-cells, which is mainly due to host restriction factors that serve an immediate role in controlling the establishment or spread of viral infection. However, HIV-1 can exploit innate immune cells and their cellular factors to avoid detection and clearance by the host immune system. Sterile alpha motif and HD-domain containing protein 1 (SAMHD1) is the mammalian deoxynucleoside triphosphate triphosphohydrolase responsible for regulating intracellular dNTP pools and restricting the replication of HIV-1 in non-dividing myeloid cells and quiescent CD4+ T-cells. Here, we review and analyze the latest literature on the antiviral function of SAMHD1, including the mechanism of HIV-1 restriction and the ability of SAMHD1 to regulate the innate immune response to viral infection. We also provide an overview of the dynamic interplay between HIV-1, SAMHD1, and the cell-intrinsic antiviral response to elucidate how SAMHD1 modulates HIV-1 infection in non-dividing immune cells. A more complete understanding of SAMHD1's role in the innate immune response to HIV-1 infection may help develop stratagems to enhance its antiviral effects and to more efficiently block HIV-1 replication and avoid the pathogenic result of viral infection.
Addiction, a disease of compulsion and drive: involvement of the orbitofrontal cortex.

Understanding the changes in the brain which occur in the transition from normal to addictive behavior has major implications in public health. Here we postulate that while reward circuits (nucleus accumbens, amygdala), which have been central to theories of drug addiction, may be crucial to initiate drug self-administration, the addictive state also involves disruption of circuits involved with compulsive behaviors and with drive. We postulate that intermittent dopaminergic activation of reward circuits secondary to drug self-administration leads to dysfunction of the orbitofrontal cortex via the striato-thalamo-orbitofrontal circuit. This is supported by imaging studies showing that in drug abusers studied during protracted withdrawal, the orbitofrontal cortex is hypoactive in proportion to the levels of dopamine D2 receptors in the striatum. In contrast, when drug abusers are tested shortly after last cocaine use or during drug-induced craving, the orbitofrontal cortex is hypermetabolic in proportion to the intensity of the craving. Because the orbitofrontal cortex is involved with drive and with compulsive repetitive behaviors, its abnormal activation in the addicted subject could explain why compulsive drug self-administration occurs even with tolerance to the pleasurable drug effects and in the presence of adverse reactions. This model implies that pleasure per se is not enough to maintain compulsive drug administration in the drugaddicted subject and that drugs that could interfere with the activation of the striato-thalamo-orbitofrontal circuit could be beneficial in the treatment of drug addiction.
Stereo camera upgraded to equal baseline multiple camera set (EBMCS)

The paper presents the results of using a set of five cameras called Equal Baseline Multiple Camera Set (EBMCS) for making 3D images, disparity maps and depth maps. Cameras in the set are located in the vicinity of each other and therefore the set can be used for the purpose of stereoscopy similarly as a stereo camera. EBMCS provides disparity maps and depth maps which have a better quality than these maps obtained with the use of a stereo camera. Moreover, EBMCS has many advantages over other kinds of equipment for making 3D images such as a Time-of-flight camera (TOF), Light Detection and Ranging (LIDAR), a structured-light 3D scanner, a camera array and a camera matrix. These advantages are described in the paper. The paper also compares the performance of EBMCS to the performance of stereo cameras.
Depth Estimation using Light-Field Cameras

Plenoptic cameras or light field cameras are a recent type of imaging devices that are starting to regain some popularity. These cameras are able to acquire the plenoptic function (4D light field) and, consequently, able to output the depth of a scene, by making use of the redundancy created by the multi-view geometry, where a single 3D point is imaged several times. Despite the attention given in the literature to standard plenoptic cameras, like Lytro, due to their simplicity and lower price, we did our work based on results obtained from a multi-focus plenoptic camera (Raytrix, in our case), due to their quality and higher resolution images. In this master thesis, we present an automatic method to estimate the virtual depth of a scene. Since the capture is done using a multi-focus plenoptic camera, we are working with multi-view geometry and lens with different focal lengths, and we can use that to back trace the rays in order to obtain the depth. We start by finding salient points and their respective correspondences using a scaled SAD (sum of absolute differences) method. In order to perform the referred back trace, obtaining robust results, we developed a RANSAC-like method, which we call COMSAC (Complete Sample Consensus). It is an iterative method that back trace the ligth rays in order to estimate the depth, eliminating the outliers. Finally, and since the depth map obtained is sparse, we developed a way to make it dense, by random growing. Since we used a publicly available dataset from Raytrix, comparisons between our results and the manufacturers’ ones are also presented. A paper was also submitted to 3DV 2014 (International Conference on 3D Vision), a conference on three-dimensional vision.
A review of sex estimation techniques during examination of skeletal remains in forensic anthropology casework.

Sex estimation is considered as one of the essential parameters in forensic anthropology casework, and requires foremost consideration in the examination of skeletal remains. Forensic anthropologists frequently employ morphologic and metric methods for sex estimation of human remains. These methods are still very imperative in identification process in spite of the advent and accomplishment of molecular techniques. A constant boost in the use of imaging techniques in forensic anthropology research has facilitated to derive as well as revise the available population data. These methods however, are less reliable owing to high variance and indistinct landmark details. The present review discusses the reliability and reproducibility of various analytical approaches; morphological, metric, molecular and radiographic methods in sex estimation of skeletal remains. Numerous studies have shown a higher reliability and reproducibility of measurements taken directly on the bones and hence, such direct methods of sex estimation are considered to be more reliable than the other methods. Geometric morphometric (GM) method and Diagnose Sexuelle Probabiliste (DSP) method are emerging as valid methods and widely used techniques in forensic anthropology in terms of accuracy and reliability. Besides, the newer 3D methods are shown to exhibit specific sexual dimorphism patterns not readily revealed by traditional methods. Development of newer and better methodologies for sex estimation as well as re-evaluation of the existing ones will continue in the endeavour of forensic researchers for more accurate results.
TecDEM: A MATLAB based toolbox for tectonic geomorphology, Part 2: Surface dynamics and basin analysis

We present the analytical capability of TecDEM, a MATLAB toolbox used in conjunction with Global DEMs for the extraction of tectonic geomorphologic information. TecDEM includes a suite of algorithms to analyze topography, extracted drainage networks and sub-basins. The aim of part 2 of this paper series is the generation of morphometric maps for surface dynamics and basin analysis. TecDEM therefore allows the extraction of parameters such as isobase, incision, drainage density and surface roughness maps. We also provide tools for basin asymmetry and hypsometric analysis. These are efficient graphical user interfaces (GUIs) for mapping drainage deviation from basin mid-line and basin hypsometry. A morphotectonic interpretation of the Kaghan Valley (Northern Pakistan) is performed with TecDEM and the findings indicate a high correlation between surface dynamics and basin analysis parameters with neotectonic features in the study area. & 2010 Elsevier Ltd. All rights reserved.
Internet of Things for Smart Healthcare: Technologies, Challenges, and Opportunities

Internet of Things (IoT) technology has attracted much attention in recent years for its potential to alleviate the strain on healthcare systems caused by an aging population and a rise in chronic illness. Standardization is a key issue limiting progress in this area, and thus this paper proposes a standard model for application in future IoT healthcare systems. This survey paper then presents the state-of-the-art research relating to each area of the model, evaluating their strengths, weaknesses, and overall suitability for a wearable IoT healthcare system. Challenges that healthcare IoT faces including security, privacy, wearability, and low-power operation are presented, and recommendations are made for future research directions.
Feature Selection: A literature Review

Relevant feature identification has become an essential task to apply data mining algorithms effectively in real-world scenarios. Therefore, many feature selection methods have been proposed to obtain the relevant feature or feature subsets in the literature to achieve their objectives of classification and clustering. This paper introduces the concepts of feature relevance, general procedures, evaluation criteria, and the characteristics of feature selection. A comprehensive overview, categorization, and comparison of existing feature selection methods are also done, and the guidelines are also provided for user to select a feature selection algorithm without knowing the information of each algorithm. We conclude this work with real world applications, challenges, and future research directions of feature selection.
Factors Influencing the Adoption of Internet Banking in Tunisia

The purpose of this paper is to determine those factors that influence the adoption of internet banking services in Tunisia. A theoretical model is provided that conceptualizes and links different factors influencing the adoption of internet banking. A total of 253 respondents in Tunisia were sampled for responding: 95 were internet bank users, 158 were internet bank non users. Factor analyses and regression technique are employed to study the relationship. The results of the model tested clearly that use of internet banking in Tunisia is influenced most strongly by convenience, risk, security and prior internet knowledge. Only information on online banking did not affect intention to use internet banking service in Tunisia. The results also propose that demographic factors impact significantly internet banking behaviour, specifically, occupation and instruction. Finally, this paper suggests that an understanding the factors affecting intention to use internet banking is very important to the practitioners who plan and promote new forms of banking in the current competitive market.
Auditable Zerocoin

Bitcoin is the first widely adopted decentralized digitale-cash system. All Bitcoin transactions that include addresses of senders and receivers are stored in the public blockchain which could cause privacy problems. The Zerocoin protocol hides the link between individual Bitcoin transactions without adding trusted third parties. However such an untraceable remittance system could cause illegal transfers such as money laundering. In this paper we address this problem and propose an auditable decentralized e-cash scheme based on the Zerocoin protocol. Our scheme allows designated auditors to extract link information from Zerocoin transactions while preventing other users including miners from obtaining it. Respecting the mind of the decentralized system, the auditor doesn't have other authorities such as stopping transfers, confiscating funds, and deactivating accounts. A technical contribution of our scheme is that a coin sender embeds audit information with a non-interactive zeroknowledge proof of knowledge (NIZKP). This zero-knowledge prevents malicious senders from embedding indiscriminate audit information, and we construct it simply using only the standard Schnorr protocol for discrete logarithm without zk-SNARKs or other recent techniques for zero-knowledge proof.
Honeypots: Catching the Insider Threat

In the past several years there has been extensive research into honeypot technologies, primarily for detection and information gathering against external threats. However, little research has been done for one of the most dangerous threats, the advance insider, the trusted individual who knows your internal organization. These individuals are not after your systems, they are after your information. This presentation discusses how honeypot technologies can be used to detect, identify, and gather information on these specific threats.
Sequence to Sequence Model for Video Captioning

Automatically generating video captions with natural language remains a challenge for both the field of nature language processing and computer vision. Recurrent Neural Networks (RNNs), which models sequence dynamics, has proved to be effective in visual interpretation. Based on a recent sequence to sequence model for video captioning, which is designed to learn the temporal structure of the sequence of frames and the sequence model of the generated sentences with RNNs, we investigate how pretrained language model and attentional mechanism can aid the generation of natural language descriptions of videos. We evaluate our improvements on the Microsoft Video Description Corpus (MSVD) dataset, which is a standard dataset for this task. The results demonstrate that our approach outperforms original sequence to sequence model and achieves state-of-art baselines. We further run our model one a much harder Montreal Video Annotation Dataset (M-VAD), where the model also shows promising results.
Learning Role-based Graph Embeddings

Random walks are at the heart of many existing network embedding methods. However, such algorithms have many limitations that arise from the use of random walks, e.g., the features resulting from these methods are unable to transfer to new nodes and graphs as they are tied to vertex identity. In this work, we introduce the Role2Vec framework which uses the flexible notion of attributed random walks, and serves as a basis for generalizing existing methods such as DeepWalk, node2vec, and many others that leverage random walks. Our proposed framework enables these methods to be more widely applicable for both transductive and inductive learning as well as for use on graphs with attributes (if available). This is achieved by learning functions that generalize to new nodes and graphs. We show that our proposed framework is effective with an average AUC improvement of 16.55% while requiring on average 853x less space than existing methods on a variety of graphs.
l / VARIATION IN AMERICAN ENGLISH : A CORPUS

We investigated the variation of /l/ in a large speech corpus through forced alignment. The results demonstrated that there is a categorical distinction between dark and light /l/ in American English. /l/ in syllable onset is light, and /l/ in syllable coda is dark. Intervocalic /l/ can be either light or dark, depending on the stress of the vowels. There is a correlation between duration (the rime duration and the duration of /l/) and /l/-darkness for dark /l/, but not for light /l/. Intervocalic dark /l/ is less dark than canonical syllable-coda /l/, but it is always dark, even in very short rimes. Intervocalic light /l/ is less light than canonical syllable-onset /l/, but it is always light. We argue that there are two levels of contrast in /l/ variation. The first level is determined by its affiliation to a single position in the syllable structure, and the second level is determined by its phonetic context.
Toward A Service Platform for Developing Smart Contracts on Blockchain in BDD and TDD Styles

In recent years, Blockchain technology has been highly valued, and the related applications have begun to be developed in large numbers. A smart contract is a software component encompass business logics and transactions that run on a blockchain. Thus, verifying whether the contract logics fully reflect the business requirements are one of the most important software engineering issues in blockchain application development. Currently, developing smart contracts is still a challenging task even for experienced programmers due to the lacking of an integrated tool for developing and testing. In response to this challenge, this paper presents a service platform that supports BDD-style (Behavior-Driven Development) smart contract development, testing, and deployment for the Ethereum-based blockchains. This platform focuses on providing and resolving the cross-cutting concerns across the life-cycle of smart contract development. The feasibility of this platform is shown by demonstrating how an application scenario, namely, loyalty points exchange, can be implemented using the proposed platform. Our experiences indicate that the burdens of developers when developing smart contracts can be effectively reduced and thus increases the quality of contracts.
Understanding and Comparing Deep Neural Networks for Age and Gender Classification

Recently, deep neural networks have demonstrated excellent performances in recognizing the age and gender on human face images. However, these models were applied in a black-box manner with no information provided about which facial features are actually used for prediction and how these features depend on image preprocessing, model initialization and architecture choice. We present a study investigating these different effects. In detail, our work compares four popular neural network architectures, studies the effect of pretraining, evaluates the robustness of the considered alignment preprocessings via cross-method test set swapping and intuitively visualizes the model's prediction strategies in given preprocessing conditions using the recent Layer-wise Relevance Propagation (LRP) algorithm. Our evaluations on the challenging Adience benchmark show that suitable parameter initialization leads to a holistic perception of the input, compensating artefactual data representations. With a combination of simple preprocessing steps, we reach state of the art performance in gender recognition.
Exploring heterogeneous algorithms for accelerating deep convolutional neural networks on FPGAs

Convolutional neural network (CNN) finds applications in a variety of computer vision applications ranging from object recognition and detection to scene understanding owing to its exceptional accuracy. There exist different algorithms for CNNs computation. In this paper, we explore conventional convolution algorithm with a faster algorithm using Winograd's minimal filtering theory for efficient FPGA implementation. Distinct from the conventional convolution algorithm, Winograd algorithm uses less computing resources but puts more pressure on the memory bandwidth. We first propose a fusion architecture that can fuse multiple layers naturally in CNNs, reusing the intermediate data. Based on this fusion architecture, we explore heterogeneous algorithms to maximize the throughput of a CNN. We design an optimal algorithm to determine the fusion and algorithm strategy for each layer. We also develop an automated toolchain to ease the mapping from Caffe model to FPGA bitstream using Vivado HLS. Experiments using widely used VGG and AlexNet demonstrate that our design achieves up to 1.99X performance speedup compared to the prior fusion-based FPGA accelerator for CNNs.
Interventional versus conservative treatment for patients with unstable angina or non-ST-elevation myocardial infarction: the British Heart Foundation RITA 3 randomised trial

Current guidelines suggest that, for patients at moderate risk of death from unstable coronary-artery disease, either an interventional strategy (angiography followed by revascularisation) or a conservative strategy (ischaemia-driven or symptom-driven angiography) is appropriate. We aimed to test the hypothesis that an interventional strategy is better than a conservative strategy in such patients.



We did a randomised multicentre trial of 1810 patients with non-ST-elevation acute coronary syndromes (mean age 62 years, 38% women). Patients were assigned an early intervention or conservative strategy. The antithrombin agent in both groups was enoxaparin. The co-primary endpoints were a combined rate of death, non-fatal myocardial infarction, or refractory angina at 4 months; and a combined rate of death or non-fatal myocardial infarction at 1 year. Analysis was by intention to treat.



At 4 months, 86 (9.6%) of 895 patients in the intervention group had died or had a myocardial infarction or refractory angina, compared with 133 (14.5%) of 915 patients in the conservative group (risk ratio 0.66, 95% CI 0.51-0.85, p=0.001). This difference was mainly due to a halving of refractory angina in the intervention group. Death or myocardial infarction was similar in both treatment groups at 1 year (68 [7.6%] vs 76 [8.3%], respectively; risk ratio 0.91, 95% CI 0.67-1.25, p=0.58). Symptoms of angina were improved and use of antianginal medications significantly reduced with the interventional strategy (p<0.0001).



In patients presenting with unstable coronary-artery disease, an interventional strategy is preferable to a conservative strategy, mainly because of the halving of refractory or severe angina, and with no increased risk of death or myocardial infarction.
Visualizing an Olfactory Sensory Map

We have developed a genetic approach to visualize axons from olfactory sensory neurons expressing a given odorant receptor, as they project to the olfactory bulb. Neurons expressing a specific receptor project to only two topographically fixed loci among the 1800 glomeruli in the mouse olfactory bulb. Our data provide direct support for a model in which a topographic map of receptor activation encodes odor quality in the olfactory bulb. Receptor swap experiments suggest that the olfactory receptor plays an instructive role in the guidance process but cannot be the sole determinant in the establishment of this map. This genetic approach may be more broadly applied to visualize the development and plasticity of projections in the mammalian nervous system.
Pedestrian Reactive Navigation for Crowd Simulation: a Predictive Approach

This paper addresses the problem of virtual pedestrian autonomous navigation for crowd simulation. It describes a method for solving interactions between pedestrians and avoiding inter-collisions. Our approach is agent-based and predictive: each agent perceives surrounding agents and extrapolates their trajectory in order to react to potential collisions. We aim at obtaining realistic results, thus the proposed model is calibrated from experimental motion capture data. Our method is shown to be valid and solves major drawbacks compared to previous approaches such as oscillations due to a lack of anticipation. We first describe the mathematical representation used in our model, we then detail its implementation, and finally, its calibration and validation from real data.