nils-herrmann/clean_scirepeval huggingface dataset:
FPGA-Based Accelerators of Deep Learning Networks for Learning and Classification: A Review

Due to recent advances in digital technologies, and availability of credible data, an area of artificial intelligence, deep learning, has emerged and has demonstrated its ability and effectiveness in solving complex learning problems not possible before. In particular, convolutional neural networks (CNNs) have demonstrated their effectiveness in the image detection and recognition applications. However, they require intensive CPU operations and memory bandwidth that make general CPUs fail to achieve the desired performance levels. Consequently, hardware accelerators that use application-specific integrated circuits, field-programmable gate arrays (FPGAs), and graphic processing units have been employed to improve the throughput of CNNs. More precisely, FPGAs have been recently adopted for accelerating the implementation of deep learning networks due to their ability to maximize parallelism and their energy efficiency. In this paper, we review the recent existing techniques for accelerating deep learning networks on FPGAs. We highlight the key features employed by the various techniques for improving the acceleration performance. In addition, we provide recommendations for enhancing the utilization of FPGAs for CNNs acceleration. The techniques investigated in this paper represent the recent trends in the FPGA-based accelerators of deep learning networks. Thus, this paper is expected to direct the future advances on efficient hardware accelerators and to be useful for deep learning researchers.
M2E-Try On Net: Fashion from Model to Everyone

Most existing virtual try-on applications require clean clothes images. Instead, we present a novel virtual Try-On network, M2E-Try On Net, which transfers the clothes from a model image to a person image without the need of any clean product images. To obtain a realistic image of person wearing the desired model clothes, we aim to solve the following challenges: 1) non-rigid nature of clothes we need to align poses between the model and the user; 2) richness in textures of fashion items preserving the fine details and characteristics of the clothes is critical for photo-realistic transfer; 3) variation of identity appearances it is required to fit the desired model clothes to the person identity seamlessly. To tackle these challenges, we introduce three key components, including the pose alignment network (PAN), the texture refinement network (TRN) and the fitting network (FTN). Since it is unlikely to gather image pairs of input person image and desired output image (i.e. person wearing the desired clothes), our framework is trained in a self-supervised manner to gradually transfer the poses and textures of the model’s clothes to the desired appearance. In the experiments, we verify on the Deep Fashion dataset and MVC dataset that our method can generate photo-realistic images for the person to try-on the model clothes. Furthermore, we explore the model capability for different fashion items, including both upper and lower garments.
Zero-Shot Transfer Learning for Event Extraction

Most previous supervised event extraction methods have relied on features derived from manual annotations, and thus cannot be applied to new event types without extra annotation effort. We take a fresh look at event extraction and model it as a generic grounding problem: mapping each event mention to a specific type in a target event ontology. We design a transferable architecture of structural and compositional neural networks to jointly represent and map event mentions and types into a shared semantic space. Based on this new framework, we can select, for each event mention, the event type which is semantically closest in this space as its type. By leveraging manual annotations available for a small set of existing event types, our framework can be applied to new unseen event types without additional manual annotations. When tested on 23 unseen event types, this zeroshot framework, without manual annotations, achieves performance comparable to a supervised model trained from 3,000 sentences annotated with 500 event
Semantic Specialization of Distributional Word Vector Spaces using Monolingual and Cross-Lingual Constraints

We present Attract-Repel, an algorithm for improving the semantic quality of word vectors by injecting constraints extracted from lexical resources. Attract-Repel facilitates the use of constraints from mono- and cross-lingual resources, yielding semantically specialized cross-lingual vector spaces. Our evaluation shows that the method can make use of existing cross-lingual lexicons to construct high-quality vector spaces for a plethora of different languages, facilitating semantic transfer from high- to lower-resource ones. The effectiveness of our approach is demonstrated with state-of-the-art results on semantic similarity datasets in six languages. We next show that Attract-Repel-specialized vectors boost performance in the downstream task of dialogue state tracking (DST) across multiple languages. Finally, we show that cross-lingual vector spaces produced by our algorithm facilitate the training of multilingual DST models, which brings further performance improvements.
Deep Fully-Connected Networks for Video Compressive Sensing

In this work we present a deep learning framework for video compressive sensing. The proposed formulation enables recovery of video frames in a few seconds at significantly improved reconstruction quality compared to previous approaches. Our investigation starts by learning a linear mapping between video sequences and corresponding measured frames which turns out to provide promising results. We then extend the linear formulation to deep fully-connected networks and explore the performance gains using deeper architectures. Our analysis is always driven by the applicability of the proposed framework on existing compressive video architectures. Extensive simulations on several video sequences document the superiority of our approach both quantitatively and qualitatively. Finally, our analysis offers insights into understanding how dataset sizes and number of layers affect reconstruction performance while raising a few points for future investigation.
A Comparison of Back Propagation Implementations

Back propagation training algorithms have been implemented by many researchers for their own purposes and provided publicly on the internet for others to use in veriication of published results and for reuse in unrelated research projects. Often, the source code of a package is used as the basis for a new package for demonstrating new algorithm variations, or some functionality is added speciically for analysis of results. However, there are rarely any guarantees that the original implementation is faithful to the algorithm it represents, or that its code is bug free or accurate. This report attempts to look at a few implementations and provide a test suite which shows deeciencies in some software available which the average researcher may not be aware of, and may not have the time to discover on their own. This test suite may then be used to test the correctness of new packages.
TutorialBank: A Manually-Collected Corpus for Prerequisite Chains, Survey Extraction and Resource Recommendation

The field of Natural Language Processing (NLP) is growing rapidly, with new research published daily along with an abundance of tutorials, codebases and other online resources. In order to learn this dynamic field or stay up-to-date on the latest research, students as well as educators and researchers must constantly sift through multiple sources to find valuable, relevant information. To address this situation, we introduce TutorialBank, a new, publicly available dataset which aims to facilitate NLP education and research. We have manually collected and categorized over 6,300 resources on NLP as well as the related fields of Artificial Intelligence (AI), Machine Learning (ML) and Information Retrieval (IR). Our dataset is notably the largest manually-picked corpus of resources intended for NLP education which does not include only academic papers. Additionally, we have created both a search engine 1 and a command-line tool for the resources and have annotated the corpus to include lists of research topics, relevant resources for each topic, prerequisite relations among topics, relevant subparts of individual resources, among other annotations. We are releasing the dataset and present several avenues for further research.
Traffic generation of IEC 61850 sampled values

The work presented in this paper is targeted at the first phase of the test and measurements product life cycle, namely standardisation. During this initial phase of any product, the emphasis is on the development of standards that support new technologies while leaving the scope of implementations as open as possible. To allow the engineer to freely create and invent tools that can quickly help him simulate or emulate his ideas are paramount. Within this scope, a traffic generation system has been developed for IEC 61850 Sampled Values which will help in the evaluation of the data models, data acquisition, data fusion, data integration and data distribution between the various devices and components that use this complex set of evolving standards in Smart Grid systems.
Information Fusion in Biometrics

User verification systems that use a single biometric indicator often have to contend with noisy sensor data, restricted degrees of freedom, non-universality of the biometric trait and unacceptable error rates. Attempting to improve the performance of individual matchers in such situations may not prove to be effective because of these inherent problems. Multibiometric systems seek to alleviate some of these drawbacks by providing multiple evidences of the same identity. These systems help achieve an increase in performance that may not be possible using a single biometric indicator. Further, multibiometric systems provide anti-spoofing measures by making it difficult for an intruder to spoof multiple biometric traits simultaneously. However, an effective fusion scheme is necessary to combine the information presented by multiple domain experts. This paper addresses the problem of information fusion in biometric verification systems by combining information at the matching score level. Experimental results on combining three biometric modalities (face, fingerprint and hand geometry) are presented.
Control Strategies for Synthetic Therapeutic Peptide APIs Part I : Analytical Considerations

IM A G E : LA G U N A D E S IG N /G E TT Y IM A G E S The United States Pharmacopeial Convention (USP) Therapeutic Peptides Expert Panel was formed in 2013 to evaluate quality attributes for synthetic peptides based on currently available regulatory guidance and expectations. Public quality standards for drug products and drug substances are developed by USP and enforceable by FDA. This series of three articles by the Panel explores the current manufacturing and regulatory landscape and provides a comprehensive overview of quality attributes to be considered for successful synthetic peptide active pharmaceutical ingredient (API) development from manufacturing to lot release. Specifically, the first article covers analytical characterization methods, lot release tests, and points to consider for synthetic peptide API manufacturers entering the market. The second article will focus on quality control of raw materials and impurities resulting from the starting materials used for peptide synthesis. The last article will be devoted to manufacturing processes and impurity control of synthetic peptide APIs. In 2012, the number of peptide drugs approved by FDA surpassed the number of approved monoclonal antibodies and enzymes (1). These approvals serve to highlight the recent revival of interest in peptides, which have generally been considered to be poor drug candidates due to their low oral bioavailability and propensity to be rapidly metabolized. However, new formulation and conjugation strategies for alternative routes of administration and overcoming short half-lives have emerged, resulting in a larger number of marketed peptide-based drugs, some of which have reached blockbuster status (2, 3). Despite these successes, some challenges still remain. Due to varying sizes and amino acid sequences, synthetic peptides are not easily classified into either small molecule or biologic categories. Therein lie the regulatory challenges with peptides, especially with respect to impurities and bioassay requirements. Understanding these challenges can help shape and create consistency among USP’s quality standards for this growing class of drugs. Control Strategies for Synthetic Therapeutic Peptide APIs
Efficient softmax approximation for GPUs

We propose an approximate strategy to efficiently train neural network based language models over very large vocabularies. Our approach, called adaptive softmax, circumvents the linear dependency on the vocabulary size by exploiting the unbalanced word distribution to form clusters that explicitly minimize the expectation of computation time. Our approach further reduces the computational time by exploiting the specificities of modern architectures and matrix-matrix vector operations, making it particularly suited for graphical processing units. Our experiments carried out on standard benchmarks, such as EuroParl and One Billion Word, show that our approach brings a large gain in efficiency over standard approximations while achieving an accuracy close to that of the full softmax. The code of our method is available at https://github.com/ facebookresearch/adaptive-softmax.
Airbnb in tourist cities: comparing spatial patterns of hotels and peer-to-peer accommodation

In recent years, what has become known as collaborative consumption has undergone rapid expansion through peer-to-peer (P2P) platforms. In the field of tourism, a particularly notable example is that of Airbnb, a service that puts travellers in contact with hosts for the purposes of renting accommodation, either rooms or entire homes/apartments. Although Airbnb may bring benefits to cities in that it increases tourist numbers, its concentration in certain areas of heritage cities can lead to serious conflict with the local population, as a result of rising rents and processes of gentrification. This article analyses the patterns of spatial distribution of Airbnb accommodation in Barcelona, one of Europe’s major tourist cities, and compares them with the accommodation offered by hotels and the places most visited by tourists. The study makes use of new sources of geolocated Big Data, such as Airbnb listings and geolocated photographs on Panoramio. Analysis of bivariate spatial autocorrelation reveals a close spatial relationship between the accommodation offered by Airbnb and the one offered by hotels, with a marked centre-periphery pattern, although Airbnb predominates over hotels around the city’s main hotel axis and hotels predominate over Airbnb in some peripheral areas of the city. Another interesting finding is that Airbnb capitalises more on the advantages of proximity to the city’s main tourist attractions than does the hotel sector. Finally, it was possible to detect those parts of the city that have seen the greatest increase in pressure from tourism related to Airbnb’s recent expansion.
NullHop: A Flexible Convolutional Neural Network Accelerator Based on Sparse Representations of Feature Maps

Convolutional neural networks (CNNs) have become the dominant neural network architecture for solving many state-of-the-art (SOA) visual processing tasks. Even though graphical processing units are most often used in training and deploying CNNs, their power efficiency is less than 10 GOp/s/W for single-frame runtime inference. We propose a flexible and efficient CNN accelerator architecture called NullHop that implements SOA CNNs useful for low-power and low-latency application scenarios. NullHop exploits the sparsity of neuron activations in CNNs to accelerate the computation and reduce memory requirements. The flexible architecture allows high utilization of available computing resources across kernel sizes ranging from  $1\times 1$  to  $7\times 7$ . NullHop can process up to 128 input and 128 output feature maps per layer in a single pass. We implemented the proposed architecture on a Xilinx Zynq field-programmable gate array (FPGA) platform and presented the results showing how our implementation reduces external memory transfers and compute time in five different CNNs ranging from small ones up to the widely known large VGG16 and VGG19 CNNs. Postsynthesis simulations using Mentor Modelsim in a 28-nm process with a clock frequency of 500 MHz show that the VGG19 network achieves over 450 GOp/s. By exploiting sparsity, NullHop achieves an efficiency of 368%, maintains over 98% utilization of the multiply–accumulate units, and achieves a power efficiency of over 3 TOp/s/W in a core area of 6.3 mm2. As further proof of NullHop’s usability, we interfaced its FPGA implementation with a neuromorphic event camera for real-time interactive demonstrations.
The Microsoft 2017 Conversational Speech Recognition System

We describe the latest version of Microsoft's conversational speech recognition system for the Switchboard and CallHome domains. The system adds a CNN-BLSTM acoustic model to the set of model architectures we combined previously, and includes character-based and dialog session aware LSTM language models in rescoring. For system combination we adopt a two-stage approach, whereby acoustic model posteriors are first combined at the senone/frame level, followed by a word-level voting via confusion networks. We also added another language model rescoring step following the confusion network combination. The resulting system yields a 5.1% word error rate on the NIST 2000 Switchboard test set, and 9.8% on the CallHome subset.
Avatars and Expectations: Influencing Perceptions of Trustworthiness in an Online Consumer Setting

This study investigates how individuals process an online product review when an avatar is included to represent the peer reviewer. The researchers predicted that both perceived avatar and textual credibility would have a positive influence on perceptions of source trustworthiness and the data supported this prediction. Expectancy violations theory also predicted that discrepancies between the perceived avatar and textual credibility would produce violations. Violations were statistically captured using a residual analysis. The results of this research ultimately demonstrated that discrepancies in perceived avatar and textual credibility can have a significant impact on perceptions of source trustworthiness. These findings suggest that predicting perceived source trustworthiness in an online consumer review setting goes beyond the linear effects of avatar and textual credibility.
No Metrics Are Perfect: Adversarial Reward Learning for Visual Storytelling

Though impressive results have been achieved in visual captioning, the task of generating abstract stories from photo streams is still a little-tapped problem. Different from captions, stories have more expressive language styles and contain many imaginary concepts that do not appear in the images. Thus it poses challenges to behavioral cloning algorithms. Furthermore, due to the limitations of automatic metrics on evaluating story quality, reinforcement learning methods with hand-crafted rewards also face difficulties in gaining an overall performance boost. Therefore, we propose an Adversarial REward Learning (AREL) framework to learn an implicit reward function from human demonstrations, and then optimize policy search with the learned reward function. Though automatic evaluation indicates slight performance boost over state-of-the-art (SOTA) methods in cloning expert behaviors, human evaluation shows that our approach achieves significant improvement in generating more human-like stories than SOTA systems. Code will be made available here1.
A Case Study for Blockchain in Healthcare : “ MedRec ” prototype for electronic health records and medical research data

A long-standing focus on compliance has traditionally constrained development of fundamental design changes for Electronic Health Records (EHRs). We now face a critical need for such innovation, as personalization and data science prompt patients to engage in the details of their healthcare and restore agency over their medical data. In this paper, we propose MedRec: a novel, decentralized record management system to handle EHRs, using blockchain technology. Our system gives patients a comprehensive, immutable log and easy access to their medical information across providers and treatment sites. Leveraging unique blockchain properties, MedRec manages authentication, confidentiality, accountability and data sharing—crucial considerations when handling sensitive information. A modular design integrates with providers' existing, local data storage solutions, facilitating interoperability and making our system convenient and adaptable. We incentivize medical stakeholders (researchers, public health authorities, etc.) to participate in the network as blockchain “miners”. This provides them with access to aggregate, anonymized data as mining rewards, in return for sustaining and securing the network via Proof of Work. MedRec thus enables the emergence of data economics, supplying big data to empower researchers while engaging patients and providers in the choice to release metadata. The purpose of this paper is to expose, in preparation for field tests, a working prototype through which we analyze and discuss our approach and the potential for blockchain in health IT and research.
On the Dynamics of Social Media Popularity: A YouTube Case Study

Understanding the factors that impact the popularity dynamics of social media can drive the design of effective information services, besides providing valuable insights to content generators and online advertisers. Taking YouTube as case study, we analyze how video popularity evolves since upload, extracting popularity trends that characterize groups of videos. We also analyze the referrers that lead users to videos, correlating them, features of the video and early popularity measures with the popularity trend and total observed popularity the video will experience. Our findings provide fundamental knowledge about popularity dynamics and its implications for services such as advertising and search.
Competitive Intelligence Analysis of Augmented Reality Technology Using Patent Information

Augmented reality has recently achieved a rapid growth through its applications in various industries, including education and entertainment. Despite the growing attraction of augmented reality, trend analyses in this emerging technology have relied on qualitative literature review, failing to provide comprehensive competitive intelligence analysis using objective data. Therefore, tracing industrial competition trends in augmented reality will provide technology experts with a better understanding of evolving competition trends and insights for further technology and sustainable business planning. In this paper, we apply a topic modeling approach to 3595 patents related to augmented reality technology to identify technology subjects and their knowledge stocks, thereby analyzing industrial competitive intelligence in light of technology subject and firm levels. As a result, we were able to obtain some findings from an inventional viewpoint: technological development of augmented reality will soon enter a mature stage, technologies of infrastructural requirements have been a focal subject since 2001, and several software firms and camera manufacturing firms have dominated the recent development of augmented reality.