nils-herrmann/clean_scirepeval huggingface dataset:
Learning to recommend with explicit and implicit social relations

Recommender systems have been well studied and developed, both in academia and in industry recently. However, traditional recommender systems assume that all the users are independent and identically distributed; this assumption ignores the connections among users, which is not consistent with the real-world observations where we always turn to our trusted friends for recommendations. Aiming at modeling recommender systems more accurately and realistically, we propose a novel probabilistic factor analysis framework which naturally fuses the users' tastes and their trusted friends' favors together. The proposed framework is quite general, and it can also be applied to pure user-item rating matrix even if we do not have explicit social trust information among users. In this framework, we coin the term social trust ensemble to represent the formulation of the social trust restrictions on the recommender systems. The complexity analysis indicates that our approach can be applied to very large datasets since it scales linearly with the number of observations, while the experimental results show that our method outperforms state-of-the-art approaches.
Synchronous neural oscillations and cognitive processes

The central problem for cognitive neuroscience is to describe how cognitive processes arise from brain processes. This review summarizes the recent evidence that synchronous neural oscillations reveal much about the origin and nature of cognitive processes such as memory, attention and consciousness. Memory processes are most closely related to theta and gamma rhythms, whereas attention seems closely associated with alpha and gamma rhythms. Conscious awareness may arise from synchronous neural oscillations occurring globally throughout the brain rather than from the locally synchronous oscillations that occur when a sensory area encodes a stimulus. These associations between the dynamics of the brain and cognitive processes indicate progress towards a unified theory of brain and cognition.
A general algorithm for exploration with Gaussian processes in complex, unknown environments

We propose a novel algorithm for efficient exploration with a single agent in unknown environments, populated with static obstacles. Every next position is computed by employing environment inference on top of path planning. The path planning process that ensures obstacle avoidance uses a modified version of the A* algorithm  and the inference is performed by direct sensing and by predicting values at yet-not-visited positions using Gaussian processes. We have validated our algorithm with densely measured data of an indoor magnetic field with significant spatial variations in different obstacle setups. Additionally, it is shown that it outperforms a previously developed algorithm , for obstacle-free scenarios, and the random movement of the agent.
Multi-Human Parsing Machines

Human parsing is an important task in human-centric analysis. Despite the remarkable progress in single-human parsing, the more realistic case of multi-human parsing remains challenging in terms of the data and the model. Compared with the considerable number of available single-human parsing datasets, the datasets for multi-human parsing are very limited in number mainly due to the huge annotation effort required. Besides the data challenge to multi-human parsing, the persons in real-world scenarios are often entangled with each other due to close interaction and body occlusion, making it difficult to distinguish body parts from different person instances. In this paper we propose the Multi-Human Parsing Machines (MHPM) system, which contains an MHP Montage model and an MHP Solver, to address both challenges in multi-human parsing. Specifically, the MHP Montage model in MHPM generates realistic images with multiple persons together with the parsing labels. It intelligently composes single persons onto background scene images while maintaining the structural information between persons and the scene. The generated images can be used to train better multi-human parsing algorithms. On the other hand, the MHP Solver in MHPM solves the bottleneck of distinguishing multiple entangled persons with close interaction. It employs a Group-Individual Push and Pull (GIPP) loss function, which can effectively separate persons with close interaction. We experimentally show that the proposed MHPM can achieve state-of-the-art performance on the multi-human parsing benchmark and the person individualization benchmark, which distinguishes closely entangled person instances.
Control structure design for complete chemical plants

Control structure design deals with the structural decisions of the control system, including what to control and how to pair the variables to form control loops. Although these are very important issues, these decisions are in most cases made in an ad hoc fashion, based on experience and engineering insight, without considering the details of each problem. In the paper, a systematic procedure for control structure design for complete chemical plants (plantwide control) is presented. It starts with carefully defining the operational and economic objectives, and the degrees of freedom available to fulfill them. Other issues, discussed in the paper, include inventory and production rate control, decentralized versus multivariable control, loss in performance by bottom-up design, and a definition of a the ‘‘complexity number’’ for the control system.
Optimal Rectangle Packing: An Absolute Placement Approach

We consider the problem of finding all enclosing rectangles of minimum area that can contain a given set of rectangles without overlap. Our rectangle packer chooses the xcoordinates of all the rectangles before any of the y-coordinates. We then transform the problem into a perfect-packing problem with no empty space by adding additional rectangles. To determine the y-coordinates, we branch on the different rectangles that can be placed in each empty position. Our packer allows us to extend the known solutions for a consecutive-square benchmark from 27 to 32 squares. We also introduce three new benchmarks, avoiding properties that make a benchmark easy, such as rectangles with shared dimensions. Our third benchmark consists of rectangles of increasingly high precision. To pack them efficiently, we limit the rectangles’ coordinates and the bounding box dimensions to the set of subset sums of the rectangles’ dimensions. Overall, our algorithms represent the current state-of-the-art for this problem, outperforming other algorithms by orders of magnitude, depending on the benchmark.
Bayesian Neural Word Embedding

Recently, several works in the domain of natural language processing presented successful methods for word embedding. Among them, the Skip-Gram with negative sampling, known also as word2vec, advanced the state-of-the-art of various linguistics tasks. In this paper, we propose a scalable Bayesian neural word embedding algorithm. The algorithm relies on a Variational Bayes solution for the SkipGram objective and a detailed step by step description is provided. We present experimental results that demonstrate the performance of the proposed algorithm for word analogy and similarity tasks on six different datasets and show it is competitive with the original Skip-Gram method.
Accurate Whole Human Genome Sequencing using Reversible Terminator Chemistry

DNA sequence information underpins genetic research, enabling discoveries of important biological or medical benefit. Sequencing projects have traditionally used long (400–800 base pair) reads, but the existence of reference sequences for the human and many other genomes makes it possible to develop new, fast approaches to re-sequencing, whereby shorter reads are compared to a reference to identify intraspecies genetic variation. Here we report an approach that generates several billion bases of accurate nucleotide sequence per experiment at low cost. Single molecules of DNA are attached to a flat surface, amplified in situ and used as templates for synthetic sequencing with fluorescent reversible terminator deoxyribonucleotides. Images of the surface are analysed to generate high-quality sequence. We demonstrate application of this approach to human genome sequencing on flow-sorted X chromosomes and then scale the approach to determine the genome sequence of a male Yoruba from Ibadan, Nigeria. We build an accurate consensus sequence from >30× average depth of paired 35-base reads. We characterize four million single-nucleotide polymorphisms and four hundred thousand structural variants, many of which were previously unknown. Our approach is effective for accurate, rapid and economical whole-genome re-sequencing and many other biomedical applications.
YAGO2: exploring and querying world knowledge in time, space, context, and many languages

We present YAGO2, an extension of the YAGO knowledge base with focus on temporal and spatial knowledge. It is automatically built from Wikipedia, GeoNames, and WordNet, and contains nearly 10 million entities and events, as well as 80 million facts representing general world knowledge. An enhanced data representation introduces time and location as first-class citizens. The wealth of spatio-temporal information in YAGO can be explored either graphically or through a special time- and space-aware query language.
Alibi Routing

There are several mechanisms by which users can gain insight into where their packets have gone, but no mechanisms allow users undeniable proof that their packets did not traverse certain parts of the world while on their way to or from another host. This paper introduces the problem of finding "proofs of avoidance": evidence that the paths taken by a packet and its response avoided a user-specified set of "forbidden" geographic regions. Proving that something did not happen is often intractable, but we demonstrate a low-overhead proof structure built around the idea of what we call "alibis": relays with particular timing constraints that, when upheld, would make it impossible to traverse both the relay and the forbidden regions.
 We present Alibi Routing, a peer-to-peer overlay routing system for finding alibis securely and efficiently. One of the primary distinguishing characteristics of Alibi Routing is that it does not require knowledge of--or modifications to--the Internet's routing hardware or policies. Rather, Alibi Routing is able to derive its proofs of avoidance from user-provided GPS coordinates and speed of light propagation delays. Using a PlanetLab deployment and larger-scale simulations, we evaluate Alibi Routing to demonstrate that many source-destination pairs can avoid countries of their choosing with little latency inflation. We also identify when Alibi Routing does not work: it has difficulty avoiding regions that users are very close to (or, of course, inside of).
Measuring Invariances in Deep Networks

For many pattern recognition tasks, the ideal input feature would be invariant to multiple confounding properties (such as illumination and viewing angle, in computer vision applications). Recently, deep architectures trained in an unsupervised manner have been proposed as an automatic method for extracting useful features. However, it is difficult to evaluate the learned features by any means other than using them in a classifier. In this paper, we propose a number of empirical tests that directly measure the degree to which these learned features are invariant to different input transformations. We find that stacked autoencoders learn modestly increasingly invariant features with depth when trained on natural images. We find that convolutional deep belief networks learn substantially more invariant features in each layer. These results further justify the use of “deep” vs. “shallower” representations, but suggest that mechanisms beyond merely stacking one autoencoder on top of another may be important for achieving invariance. Our evaluation metrics can also be used to evaluate future work in deep learning, and thus help the development of future algorithms.
Inferring the impacts of social media on crowdfunding

Crowdfunding -- in which people can raise funds through collaborative contributions of general public (i.e., crowd) -- has emerged as a billion dollars business for supporting more than one million ventures. However, very few research works have examined the process of crowdfunding. In particular, none has studied how social networks help crowdfunding projects to succeed. To gain insights into the effects of social networks in crowdfunding, we analyze the hidden connections between the fundraising results of projects on crowdfunding websites and the corresponding promotion campaigns in social media. Our analysis considers the dynamics of crowdfunding from two aspects: how fundraising activities and promotional activities on social media simultaneously evolve over time, and how the promotion campaigns influence the final outcomes. From our investigation, we identify a number of important principles that provide a useful guide for devising effective campaigns. For example, we observe temporal distribution of customer interest, strong correlations between a crowdfunding project's early promotional activities and the final outcomes, and the importance of concurrent promotion from multiple sources. We then show that these discoveries can help predict several important quantities, including overall popularity and the success rate of the project. Finally, we show how to use these discoveries to help design crowdfunding sites.
Topic-Relevance Map: Visualization for Improving Search Result Comprehension

We introduce topic-relevance map, an interactive search result visualization that assists rapid information comprehension across a large ranked set of results. The topic-relevance map visualizes a topical overview of the search result space as keywords with respect to two essential information retrieval measures: relevance and topical similarity. Non-linear dimensionality reduction is used to embed high-dimensional keyword representations of search result data into angles on a radial layout. Relevance of keywords is estimated by a ranking method and visualized as radiuses on the radial layout. As a result, similar keywords are modeled by nearby points, dissimilar keywords are modeled by distant points, more relevant keywords are closer to the center of the radial display, and less relevant keywords are distant from the center of the radial display. We evaluated the effect of the topic-relevance map in a search result comprehension task where 24 participants were summarizing search results and produced a conceptualization of the result space. The results show that topic-relevance map significantly improves participants' comprehension capability compared to a conventional ranked list presentation.
Ultraflat Interleaved Triangular Current Mode (TCM) Single-Phase PFC Rectifier

This paper presents the analysis and realization of a topology suitable to realize a power factor correction (PFC) rectifier with a thickness of only a few millimeters. The low height of the converter requires all components to be integrated into the printed circuit board (PCB). Still reasonable dimensions of the converter PCB are feasible (221 mm × 157 mm for a 200 W PFC rectifier), since PCB-integrated inductors and capacitors allow for high energy densities due to their large surface area which facilitates a low thermal resistance to ambient. A multicell totem-pole PFC rectifier employing a soft-switching modulation scheme over the complete mains period is identified as an adequate topology. The mode of operation is entitled triangular current mode (TCM) due to the triangular-shaped inductor currents. The modulation technique requires a reliable description of the switching transition of a half-bridge in order to provide accurate timing parameters. For this purpose, a simplified model of the nonlinear MOSFETs' output capacitances facilitates closed-form analytical expressions for duty cycle and switching frequency. Furthermore, this paper details the control of three interleaved converter cells which yields a reduction of the input current ripple. A 200 W TCM PFC rectifier with a low height of 5 mm has been realized and measurement results are provided in order to validate the theoretical considerations. The presented TCM PFC rectifier achieves an efficiency of 94.6% and a power factor of 99.3% at nominal power.
Customer Knowledge Contribution Behavior in Social Shopping Communities

Social shopping communities, a special form of social media, have offered fertile ground for customers to communicate their opinions and exchange product information. Although social shopping communities have the potential to transform the way online customers acquire knowledge in everyday life, research in information systems has paid little attention to this emerging type of social media. Thus, the goal of this paper is to enhance our understanding of user behavior in this new form of community. We propose and empirically test an integrative theoretical model of customer knowledge contribution based on social capital theory. By analyzing panel data collected over two weeks from 2,251 customers in a social shopping community, we found that reputation, reciprocity, network centrality, as well as customer expertise have significant impact on customer knowledge contribution. These results contribute significantly to the literature and provide important implications for future research and practice.
A Log-Logistic Model-Based Interpretation of TF Normalization of BM25

The effectiveness of BM25 retrieval function is mainly due to its sub-linear term frequency (TF) normalization component, which is controlled by a parameter k1. Although BM25 was derived based on the classic probabilistic retrieval model, it has been so far unclear how to interpret its parameter k1 probabilistically, making it hard to optimize the setting of this parameter. In this paper, we provide a novel probabilistic interpretation of the BM25 TF normalization and its parameter k1 based on a log-logistic model for the probability of seeing a document in the collection with a given level of TF. The proposed interpretation allows us to derive different approaches to estimation of parameter k1 based solely on the current collection without requiring any training data, thus effectively eliminating one free parameter from BM25. Our experiment results show that the proposed approaches can accurately predict the optimal k1 without requiring training data and achieve better or comparable retrieval performance to a well-tuned BM25 where k1 is optimized based on training data.
A weight initialization method for improving training speed in feedforward neural network

An algorithm for determining the optimal initial weights of feedforward neural networks based on the Cauchy's inequality and a linear algebraic method is developed. The algorithm is computational e$cient. The proposed method ensures that the outputs of neurons are in the active region and increases the rate of convergence. With the optimal initial weights determined, the initial error is substantially smaller and the number of iterations required to achieve the error criterion is signi"cantly reduced. Extensive tests were performed to compare the proposed algorithm with other algorithms. In the case of the sunspots prediction, the number of iterations required for the network initialized with the proposed method was only 3.03% of those started with the next best weight initialization algorithm.
A 240 G-ops/s Mobile Coprocessor for Deep Neural Networks

Deep networks are state-of-the-art models used for understanding the content of images, videos, audio and raw input data. Current computing systems are not able to run deep network models in real-time with low power consumption. In this paper we present nn-X: a scalable, low-power coprocessor for enabling real-time execution of deep neural networks. nn-X is implemented on programmable logic devices and comprises an array of configurable processing elements called collections. These collections perform the most common operations in deep networks: convolution, subsampling and non-linear functions. The nn-X system includes 4 high-speed direct memory access interfaces to DDR3 memory and two ARM Cortex-A9 processors. Each port is capable of a sustained throughput of 950 MB/s in full duplex. nn-X is able to achieve a peak performance of 227 G-ops/s, a measured performance in deep learning applications of up to 200 G-ops/s while consuming less than 4 watts of power. This translates to a performance per power improvement of 10 to 100 times that of conventional mobile and desktop processors.
Case series of selenium toxicity from a nutritional supplement.

Selenium is an essential trace element, but can be toxic in excess. In May 2008, US FDA reported 201 individuals with adverse reactions to liquid nutritional supplements containing excess selenium and chromium resulting in the largest epidemic of selenosis in the history of the United States.



To describe the clinical features, biomonitoring data of selenium levels, and the estimated total dose of selenium ingestions of nine patients with selenium toxicity who presented after use of a liquid dietary supplement with a formulation error.



A retrospective observational case series was performed on nine patients presenting to our medical toxicology clinic between March 2008 and May 2008 with symptoms of selenosis after consuming a nutritional supplement. Institutional IRB approval was obtained for this case series.



Supplement testing revealed almost 200 times the reported amount of selenium. There were 5 males and 4 females and their ages ranged from 15 to 57 years (median 46 years, mean 44.2 years). The mean estimated cumulative dose of selenium ingested in our patients was 1.3 gram over a mean period of 37.5 days (10-60 days). In each case, the symptoms of selenium toxicity manifested within 1 week from the start of ingestion. Initial symptoms included alopecia, dystrophic fingernail changes, GI symptoms, and memory difficulties. The initial whole blood selenium concentrations ranged from 150 to 732 mcg/L (reference mean range 123-193 mcg/L) at an average of 27 days post cessation of the formula. The urinary selenium concentrations ranged from 41 to 220 mcg/g Creat (reference < 25 mcg/g Creatinine). None of the patients required more than supportive care for symptoms and none required hospitalization.



Selenium is an essential element, which can result in significant toxicity if ingested in large amounts.
Evaluating question answering over linked data

The availability of large amounts of open, distributed and structured semantic data on the web has no precedent in the history of computer science. In recent years, there have been important advances in semantic search and question answering over RDF data. In particular, natural language interfaces to online semantic data have the advantage that they can exploit the expressive power of Semantic Web data models and query languages, while at the same time hiding their complexity from the user. However, despite the increasing interest in this area, there are no evaluations so far that systematically evaluate this kind of systems, in contrast to traditional question answering and search interfaces to document spaces. To address this gap, we have set up a series of evaluation challenges for question answering over linked data. The main goal of the challenge was to get insight into the strengths, capabilities and current shortcomings of question answering systems as interfaces to query linked data sources, as well as benchmarking how these interaction paradigms can deal with the fact that the amount of RDF data available on the web is very large and heterogeneous with respect to the vocabularies and schemas used. Here we report on the results from the first and second of such evaluation campaigns. We also discuss how the second evaluation addressed some of the issues and limitations which arose from the first one, as well as the open issues to be addressed in future competitions.