nils-herrmann/clean_scirepeval huggingface dataset:
Break it Down for Me: A Study in Automated Lyric Annotation

Comprehending lyrics, as found in songs and poems, can pose a challenge to human and machine readers alike. This motivates the need for systems that can understand the ambiguity and jargon found in such creative texts, and provide commentary to aid readers in reaching the correct interpretation. We introduce the task of automated lyric annotation (ALA). Like text simplification, a goal of ALA is to rephrase the original text in a more easily understandable manner. However, in ALA the system must often include additional information to clarify niche terminology and abstract concepts. To stimulate research on this task, we release a large collection of crowdsourced annotations for song lyrics. We analyze the performance of translation and retrieval models on this task, measuring performance with both automated and human evaluation. We find that each model captures a unique type of information important to the task.
Learning and Transferring Multi-task Deep Representation for Face Alignment

Facial landmark detection of face alignment has long been impeded by the problems of occlusion and pose variation. Instead of treating the detection task as a single and independent problem, we investigate the possibility of improving detection robustness through multitask learning. Specifically, we wish to optimize facial landmark detection together with heterogeneous but subtly correlated tasks, e.g. head pose estimation and facial attribute inference. This is non-trivial since different tasks have different learning difficulties and convergence rates. To address this problem, we formulate a novel tasks-constrained deep model, with task-wise early stopping to facilitate learning convergence. Extensive evaluations show that the proposed task-constrained learning (i) outperforms existing methods, especially in dealing with faces with severe occlusion and pose variation, and (ii) reduces model complexity drastically compared to the state-of-the-art method based on cascaded deep model [29]. In this technical report, we extend the method presented in our ECCV 2014  paper to handle more landmark points (68 points instead of 5 major facial points) without either redesigning the deep model or involving significant increase in run time cost. This is made possible by transferring the learned 5-point model to the desired facial landmark configuration, through model fine-tuning with dense landmark annotations. Our new model achieves the state-of-the-art result on the 300-W benchmark dataset (mean error of 9.15% on the challenging IBUG subset).
Effects of β-alanine supplementation and high-intensity interval training on endurance performance and body composition in men; a double-blind trial

Intermittent bouts of high-intensity exercise result in diminished stores of energy substrates, followed by an accumulation of metabolites, promoting chronic physiological adaptations. In addition, beta-alanine has been accepted has an effective physiological hydrogen ion (H+) buffer. Concurrent high-intensity interval training (HIIT) and beta-alanine supplementation may result in greater adaptations than HIIT alone. The purpose of the current study was to evaluate the effects of combining beta-alanine supplementation with high-intensity interval training (HIIT) on endurance performance and aerobic metabolism in recreationally active college-aged men.



Forty-six men (Age: 22.2 +/- 2.7 yrs; Ht: 178.1 +/- 7.4 cm; Wt: 78.7 +/- 11.9; VO2peak: 3.3 +/- 0.59 l.min-1) were assessed for peak O2 utilization (VO2peak), time to fatigue (VO2TTE), ventilatory threshold (VT), and total work done at 110% of pre-training VO2peak (TWD). In a double-blind fashion, all subjects were randomly assigned into one either a placebo (PL - 16.5 g dextrose powder per packet; n = 18) or beta-alanine (BA - 1.5 g beta-alanine plus 15 g dextrose powder per packet; n = 18) group. All subjects supplemented four times per day (total of 6 g/day) for the first 21-days, followed by two times per day (3 g/day) for the subsequent 21 days, and engaged in a total of six weeks of HIIT training consisting of 5-6 bouts of a 2:1 minute cycling work to rest ratio.



Significant improvements in VO2peak, VO2TTE, and TWD after three weeks of training were displayed (p < 0.05). Increases in VO2peak, VO2TTE, TWD and lean body mass were only significant for the BA group after the second three weeks of training.



The use of HIIT to induce significant aerobic improvements is effective and efficient. Chronic BA supplementation may further enhance HIIT, improving endurance performance and lean body mass.
Artificial Intelligence and Economic Growth

This paper examines the potential impact of artificial intelligence (A.I.) on economic growth. We model A.I. as the latest form of automation, a broader process dating back more than 200 years. Electricity, internal combustion engines, and semiconductors facilitated automation in the last century, but A.I. now seems poised to automate many tasks once thought to be out of reach, from driving cars to making medical recommendations and beyond. How will this affect economic growth and the division of income between labor and capital? What about the potential emergence of “singularities” and “superintelligence,” concepts that animate many discussions in the machine intelligence community? How will the linkages between A.I. and growth be mediated by firm-level considerations, including organization and market structure? The goal throughout is to refine a set of critical questions about A.I. and economic growth and to contribute to shaping an agenda for the field. One theme that emerges is based on Baumol’s “cost disease” insight: growth may be constrained not by what we are good at but rather by what is essential and yet hard to improve. ∗We are grateful to Ajay Agrawal, Mohammad Ahmadpoor, Adrien Auclert, Sebastian Di Tella, Patrick Francois, Joshua Gans, Avi Goldfarb, Pete Klenow, Hannes Mahlmberg, Pascual Restrepo, Chris Tonetti, Michael Webb, and participants at the NBER Conference on Artificial Intelligence for helpful discussion and comments. 2 P. AGHION, B. JONES, AND C.
Consumers' purchase intention of organic food in China.

The global market for organic food has developed significantly in the past decade. The organic food industry in China is export oriented, with production growing rapidly, although the domestic market remains relatively small. This paper surveys 432 consumers from three cities in China, consequently establishing a logit model to analyse the main factors affecting consumers' choice for organic food.



The result indicates that Chinese consumers' intent to purchase organic food is strongly affected by factors such as income, degree of trust in organic food, degree of acceptance of organic food price, and consumers' concern on self-health. This intent is only slightly affected by factors such as consumers' age, education level and concern about environmental protection.



Based on the results, the following measures are recommended: reduce the cost of organic food through multiple channels to cut down the market price; establish and perfect the supervision system of organic food; and promote organic food through various channels.
Data mining with big data

Big Data concern large-volume, complex, growing data sets with multiple, autonomous sources. With the fast development of networking, data storage, and the data collection capacity, Big Data are now rapidly expanding in all science and engineering domains, including physical, biological and biomedical sciences. This paper presents a HACE theorem that characterizes the features of the Big Data revolution, and proposes a Big Data processing model, from the data mining perspective. This data-driven model involves demand-driven aggregation of information sources, mining and analysis, user interest modeling, and security and privacy considerations. We analyze the challenging issues in the data-driven model and also in the Big Data revolution.
A Berkeley View of Systems Challenges for AI

With the increasing commoditization of computer vision, speech recognition and machine translation systems and the widespread deployment of learning-based back-end technologies such as digital advertising and intelligent infrastructures, AI (Articial Intelligence) has moved from research labs to production. ese changes have been made possible by unprecedented levels of data and computation, by methodological advances in machine learning, by innovations in systems soware and architectures, and by the broad accessibility of these technologies. e next generation of AI systems promises to accelerate these developments and increasingly impact our lives via frequent interactions and making (oen mission-critical) decisions on our behalf, oen in highly personalized contexts. Realizing this promise, however, raises daunting challenges. In particular, we need AI systems that make timely and safe decisions in unpredictable environments, that are robust against sophisticated adversaries, and that can process ever increasing amounts of data across organizations and individuals without compromising condentiality. ese challenges will be exacerbated by the end of the Moore’s Law, which will constrain the amount of data these technologies can store and process. In this paper, we propose several open research directions in systems, architectures, and security that can address these challenges and help unlock AI’s potential to improve lives and society.
The impact of physical activity and fitness on academic achievement and cognitive performance in children

The potential for physical activity and fitness to improve cognitive function, learning and academic achievement in children has received attention by researchers and policy makers. This paper reports a systematic approach to identification, analysis and review of published studies up to early 2009. A threestep search method was adopted to identify studies that used measures of physical activity or fitness to assess either degree of association with or effect on a) academic achievement and b) cognitive performance. A total of 18 studies including one randomised control trial, six quasi-experimental and 11 correlational studies were included for data extraction. No studies meeting criteria that examined the links between physical activity and cognitive function were found. Weak positive associations were found between both physical activity and fitness and academic achievement and fitness and elements of cognitive function, but this was not supported by intervention studies. There is insufficient evidence to conclude that additional physical education time increases academic achievement; however there is no evidence that it is detrimental. The quality and depth of the evidence base is limited. Further research with rigour beyond correlational studies is essential.
Seeing 3D Chairs: Exemplar Part-Based 2D-3D Alignment Using a Large Dataset of CAD Models

This paper poses object category detection in images as a type of 2D-to-3D alignment problem, utilizing the large quantities of 3D CAD models that have been made publicly available online. Using the "chair" class as a running example, we propose an exemplar-based 3D category representation, which can explicitly model chairs of different styles as well as the large variation in viewpoint. We develop an approach to establish part-based correspondences between 3D CAD models and real photographs. This is achieved by (i) representing each 3D model using a set of view-dependent mid-level visual elements learned from synthesized views in a discriminative fashion, (ii) carefully calibrating the individual element detectors on a common dataset of negative images, and (iii) matching visual elements to the test image allowing for small mutual deformations but preserving the viewpoint and style constraints. We demonstrate the ability of our system to align 3D models with 2D objects in the challenging PASCAL VOC images, which depict a wide variety of chairs in complex scenes.
Mind-altering microorganisms: the impact of the gut microbiota on brain and behaviour

Recent years have witnessed the rise of the gut microbiota as a major topic of research interest in biology. Studies are revealing how variations and changes in the composition of the gut microbiota influence normal physiology and contribute to diseases ranging from inflammation to obesity. Accumulating data now indicate that the gut microbiota also communicates with the CNS — possibly through neural, endocrine and immune pathways — and thereby influences brain function and behaviour. Studies in germ-free animals and in animals exposed to pathogenic bacterial infections, probiotic bacteria or antibiotic drugs suggest a role for the gut microbiota in the regulation of anxiety, mood, cognition and pain. Thus, the emerging concept of a microbiota–gut–brain axis suggests that modulation of the gut microbiota may be a tractable strategy for developing novel therapeutics for complex CNS disorders.
Introduction and overview of therapeutic apheresis.

Chidi Okafor, David M. Ward, Michele H. Mokrzycki, Robert Weinstein, Pamela Clark, and Rasheed A. Balogun* Department of Medicine, Division of Nephrology, University of Virginia Health System, Charlottesville, Virginia Department of Medicine, University of California, San Diego, California Department of Medicine, Albert Einstein College of Medicine, Bronx, New York Departments of Medicine and Pathology, University of Massachusetts, Amherst, Massachusetts Department of Pathology, University of Virginia, Charlottesville, Virginia
A 5 G Cognitive System for Healthcare

Developments and new advances in medical technology and the improvement of people’s living standards have helped to make many people healthier. However, there are still large design deficiencies due to the imbalanced distribution of medical resources, especially in developing countries. To address this issue, a video conference-based telemedicine system is deployed to break the limitations of medical resources in terms of time and space. By outsourcing medical resources from big hospitals to rural and remote ones, centralized and high quality medical resources can be shared to achieve a higher salvage rate while improving the utilization of medical resources. Though effective, existing telemedicine systems only treat patients’ physiological diseases, leaving another challenging problem unsolved: How to remotely detect patients’ emotional state to diagnose psychological diseases. In this paper, we propose a novel healthcare system based on a 5G Cognitive System (5G-Csys). The 5G-Csys consists of a resource cognitive engine and a data cognitive engine. Resource cognitive intelligence, based on the learning of network contexts, aims at ultra-low latency and ultra-high reliability for cognitive applications. Data cognitive intelligence, based on the analysis of healthcare big data, is used to handle a patient’s health status physiologically and psychologically. In this paper, the architecture of 5G-Csys is first presented, and then the key technologies and application scenarios are discussed. To verify our proposal, we develop a prototype platform of 5G-Csys, incorporating speech emotion recognition. We present our experimental results to demonstrate the effectiveness of the proposed system. We hope this paper will attract further research in the field of healthcare based on 5G cognitive systems.
Stochastic Training of Neural Networks via Successive Convex Approximations

This paper proposes a new family of algorithms for training neural networks (NNs). These are based on recent developments in the field of nonconvex optimization, going under the general name of successive convex approximation techniques. The basic idea is to iteratively replace the original (nonconvex, highly dimensional) learning problem with a sequence of (strongly convex) approximations, which are both accurate and simple to optimize. Different from similar ideas (e.g., quasi-Newton algorithms), the approximations can be constructed using only first-order information of the NN function, in a stochastic fashion, while exploiting the overall structure of the learning problem for a faster convergence. We discuss several use cases, based on different choices for the loss function (e.g., squared loss and cross-entropy loss), and for the regularization of the NN’s weights. We experiment on several medium-sized benchmark problems and on a large-scale data set involving simulated physical data. The results show how the algorithm outperforms the state-of-the-art techniques, providing faster convergence to a better minimum. Additionally, we show how the algorithm can be easily parallelized over multiple computational units without hindering its performance. In particular, each computational unit can optimize a tailored surrogate function defined on a randomly assigned subset of the input variables, whose dimension can be selected depending entirely on the available computational power.
Peer-to-peer Crowdfunding: Information and the Potential for Disruption in Consumer Lending

Can peer-to-peer lending (P2P) crowdfunding disintermediate and mitigate information frictions in lending such that choices and outcomes for at least some borrowers and investors are improved? I offer a framing of issues and survey the nascent literature on P2P. On the investor side, P2P disintermediates an asset class of consumer loans, and investors seem to capture some rents associated with the removal of the cost of that financial intermediation. Risk and portfolio choice questions linger prior to any inference. On the borrower side, evidence suggests that proximate knowledge (direct or inferred) unearths soft information, and by implication, P2P should be able to offer pricing and/or access benefits to potential borrowers. However, social connections require costly certification (skin in the game) to inform credit risk. Early research suggests an ever-increasing scope for use of Big Data and incentivized re-intermediation of underwriting. I ask many more questions than current research can answer, hoping to motivate future research.
Mimicking Word Embeddings using Subword RNNs

Word embeddings improve generalization over lexical features by placing each word in a lower-dimensional space, using distributional information obtained from unlabeled data. However, the effectiveness of word embeddings for downstream NLP tasks is limited by out-of-vocabulary (OOV) words, for which embeddings do not exist. In this paper, we present MIMICK, an approach to generating OOV word embeddings compositionally, by learning a function from spellings to distributional embeddings. Unlike prior work, MIMICK does not require re-training on the original word embedding corpus; instead, learning is performed at the type level. Intrinsic and extrinsic evaluations demonstrate the power of this simple approach. On 23 languages, MIMICK improves performance over a word-based baseline for tagging part-of-speech and morphosyntactic attributes. It is competitive with (and complementary to) a supervised characterbased model in low-resource settings.
The Ideological Effects of Framing Threat on Immigration and Civil Liberties

Assuming that migration threat is multi-dimensional, this article seeks to investigate how various types of threats associated with immigration affect attitudes towards immigration and civil liberties. Through experimentation, the study unpacks the ‘securitization of migration’ discourse by disaggregating the nature of immigration threat, and its impact on policy positions and ideological patterns at the individual level. Based on framing and attitudinal analysis, we argue that physical security in distinction from cultural insecurity is enough to generate important ideological variations stemming from strategic input (such as framing and issue-linkage). We expect then that as immigration shifts from a cultural to a physical threat, immigration issues may become more politically salient but less politicized and subject to consensus. Interestingly, however, the findings reveal that the effects of threat framing are not ubiquitous, and may be conditional upon ideology. Liberals were much more susceptible to the frames than were conservatives. Potential explanations for the ideological effects of framing, as well as their implications, are explored.
Machine Learning and Deep Learning Methods for Cybersecurity

With the development of the Internet, cyber-attacks are changing rapidly and the cyber security situation is not optimistic. This survey report describes key literature surveys on machine learning (ML) and deep learning (DL) methods for network analysis of intrusion detection and provides a brief tutorial description of each ML/DL method. Papers representing each method were indexed, read, and summarized based on their temporal or thermal correlations. Because data are so important in ML/DL methods, we describe some of the commonly used network datasets used in ML/DL, discuss the challenges of using ML/DL for cybersecurity and provide suggestions for research directions.
The essence of entanglement

Entanglement, according to Erwin Schrödinger the essence of quantum mechanics, is at the heart of the Einstein-Podolsky-Rosen paradox and of the so called quantum-nonlocality – the fact that a local realistic explanation of quantum mechanics is not possible as quantitatively expressed by violation of Bell’s inequalities. Even as entanglement gains increasing importance in most quantum information processing protocols, its conceptual foundation is still widely debated. Among the open questions are: What is the conceptual meaning of quantum entanglement? What are the most general constraints imposed by local realism? Which general quantum states violate these constraints? Developing Schrödinger’s ideas in an information-theoretic context we suggest that a natural understanding of quantum entanglement results when one accepts (1) that the amount of information per elementary system is finite and (2) that the information in a composite system resides more in the correlations than in properties of individuals. The quantitative formulation of these ideas leads to a rather natural criterion of quantum entanglement. Independently, extending Bell’s original ideas, we obtain a single general Bell inequality that summarizes all possible constraints imposed by local realism on the correlations for a multi-particle system. Violation of the general Bell inequality results in an independent general criterion for quantum entanglement. Most importantly, the two criteria agree in essence, though the two approaches are conceptually very different. This concurrence strongly supports the information-theoretic interpretation of quantum entanglement and of quantum physics in general.
Chained Predictions Using Convolutional Neural Networks

In this work, we present an adaptation of the sequence-tosequence model for structured vision tasks. In this model, the output variables for a given input are predicted sequentially using neural networks. The prediction for each output variable depends not only on the input but also on the previously predicted output variables. The model is applied to spatial localization tasks and uses convolutional neural networks (CNNs) for processing input images and a multi-scale deconvolutional architecture for making spatial predictions at each step. We explore the impact of weight sharing with a recurrent connection matrix between consecutive predictions, and compare it to a formulation where these weights are not tied. Untied weights are particularly suited for problems with a fixed sized structure, where different classes of output are predicted at different steps. We show that chain models achieve top performing results on human pose estimation from images and videos.