nils-herrmann/clean_scirepeval huggingface dataset:
Noisy Or-based model for Relation Extraction using Distant Supervision

Distant supervision, a paradigm of relation extraction where training data is created by aligning facts in a database with a large unannotated corpus, is an attractive approach for training relation extractors. Various models are proposed in recent literature to align the facts in the database to their mentions in the corpus. In this paper, we discuss and critically analyse a popular alignment strategy called the “at least one” heuristic. We provide a simple, yet effective relaxation to this strategy. We formulate the inference procedures in training as integer linear programming (ILP) problems and implement the relaxation to the “at least one ” heuristic via a soft constraint in this formulation. Empirically, we demonstrate that this simple strategy leads to a better performance under certain settings over the existing approaches.
A Semantical & Analytical Approach for Zero Shot Gesture Learning

Zero shot learning (ZSL) is about being able to recognize gesture classes that were never seen before. This type of recognition involves the understanding that the presented gesture is a new form of expression from those observed so far, and yet carries embedded information universal to all the other gestures (also referred as context). As part of the same problem, it is required to determine what action/command this new gesture conveys, in order to react to the command autonomously. Research in this area may shed light to areas where ZSL occurs, such as spontaneous gestures. People perform gestures that may be new to the observer. This occurs when the gesturer is learning, solving a problem or acquiring a new language. The ability of having a machine recognizing spontaneous gesturing, in the same manner as humans do, would enable more fluent human-machine interaction. In this paper, we describe a new paradigm for ZSL based on adaptive learning, where it is possible to determine the amount of transfer learning carried out by the algorithm and how much knowledge is acquired from a new gesture observation. Another contribution is a procedure to determine what are the best semantic descriptors for a given command and how to use those as part of the ZSL approach proposed.
Representation Learning for Visual-Relational Knowledge Graphs

A visual-relational knowledge graph (KG) is a multi-relational graph whose entities are associated with images. We introduce ImageGraph, a KG with 1,330 relation types, 14,870 entities, and 829,931 images. Visual-relational KGs lead to novel probabilistic query types where images are treated as first-class citizens. Both the prediction of relations between unseen images and multi-relational image retrieval can be formulated as query types in a visual-relational KG. We approach the problem of answering such queries with a novel combination of deep convolutional networks and models for learning knowledge graph embeddings. The resulting models can answer queries such as “How are these two unseen images related to each other?" We also explore a zero-shot learning scenario where an image of an entirely new entity is linked with multiple relations to entities of an existing KG. The multi-relational grounding of unseen entity images into a knowledge graph serves as the description of such an entity. We conduct experiments to demonstrate that the proposed deep architectures in combination with KG embedding objectives can answer the visual-relational queries efficiently and accurately.
Scaling up from gardens: biodiversity conservation in urban environments.

As urbanisation increases globally and the natural environment becomes increasingly fragmented, the importance of urban green spaces for biodiversity conservation grows. In many countries, private gardens are a major component of urban green space and can provide considerable biodiversity benefits. Gardens and adjacent habitats form interconnected networks and a landscape ecology framework is necessary to understand the relationship between the spatial configuration of garden patches and their constituent biodiversity. A scale-dependent tension is apparent in garden management, whereby the individual garden is much smaller than the unit of management needed to retain viable populations. To overcome this, here we suggest mechanisms for encouraging 'wildlife-friendly' management of collections of gardens across scales from the neighbourhood to the city.
Calculation method of three-phase induction machines equipped with combined star-delta windings

Combined star-delta windings in electrical machines result in a higher fundamental winding factor and cause a smaller spatial harmonic content. This leads to lower I2R losses in the stator and the rotor winding and thus to an increased efficiency. However, compared to an equivalent six-phase winding, additional spatial harmonics are generated due to the different magnetomotive force in the star and delta part of the winding. In this paper, a complete theory and analysis method for the analytical calculation of the efficiency of induction motors equipped with combined star-delta windings is developed. The method takes into account the additional harmonic content due to the different magnetomotive force in the star and delta part. To check the analysis' validity, an experimental test is reported both on a cage induction motor equipped with a combined star-delta winding in the stator and on a reference motor with the same core but with a classical three-phase winding.
Design and analysis of quad-band grid array microstrip antenna at UWB and ISM channel frequencies for WBAN operations

In this paper, we present design and simulation of a compact grid array microstrip patch antenna. In the design of antenna a RT/duroid 5880 substrate having relative permittivity, thickness and loss tangent of 2.2, 1.57 mm and 0.0009 respectively, has been used. The simulated antenna performance was obtained by Computer Simulation Technology Microwave Studio (CST MWS). The antenna performance was investigated by analyzing its return loss (S11), radiation pattern, voltage standing wave ratio (VSWR) parameters. The simulated S11 parameter has shown that antenna operates for Industrial, Scientific and Medical (ISM) band and Wireless Body Area Network (WBAN) applications at 2.45 GHZ ISM, 6.25 GHZ, 8.25 GHZ and 10.45 GHZ ultra-wideband (UWB) four resonance frequencies with bandwidth > 500MHz (S11 < −10dB). The antenna directivity increased towards higher frequencies. The VSWR of resonance frequency bands is also achieved succesfully less than 2. It has been observed that the simulation result values of the antenna are suitable for WBAN applications.
IoT-Aided Charity: An Excess Food Redistribution Framework

A trusted and active community aided and supported by the Internet of Things (IoT) is a key factor in food waste reduction and management. This paper proposes an IoT based context aware framework which can capture real-time dynamic requirements of both vendors and consumers and perform real-time match-making based on captured data. We describe our proposed reference framework and the notion of smart food sharing containers as enabling technology in our framework. A prototype system demonstrates the feasibility of a proposed approach using a smart container with embedded sensors.
Self-compassion and intuitive eating in college women: examining the contributions of distress tolerance and body image acceptance and action.

Self-compassion has been linked to higher levels of psychological well-being. The current study evaluated whether this effect also extends to a more adaptive food intake process. More specifically, this study investigated the relationship between self-compassion and intuitive eating among 322 college women. In order to further clarify the nature of this relationship this research additionally examined the indirect effects of self-compassion on intuitive eating through the pathways of distress tolerance and body image acceptance and action using both parametric and non-parametric bootstrap resampling analytic procedures. Results based on responses to the self-report measures of the constructs of interest indicated that individual differences in body image acceptance and action (β = .31, p < .001) but not distress tolerance (β = .00, p = .94) helped explain the relationship between self-compassion and intuitive eating. This effect was retained in a subsequent model adjusted for body mass index (BMI) and self-esteem (β = .19, p < .05).  provide preliminary support for a complementary perspective on the role of acceptance in the context of intuitive eating to that of existing theory and research. The present findings also suggest the need for additional research as it relates to the development and fostering of self-compassion as well as the potential clinical implications of using acceptance-based interventions for college-aged women currently engaging in or who are at risk for disordered eating patterns.
Analysis of the contribution of FTO, NPC1, ENPP1, NEGR1, GNPDA2 and MC4R genes to obesity in Mexican children

Recent genome wide association studies (GWAS) and previous positional linkage studies have identified more than 50 single nucleotide polymorphisms (SNPs) associated with obesity, mostly in Europeans. We aimed to assess the contribution of some of these SNPs to obesity risk and to the variation of related metabolic traits, in Mexican children. The association of six European obesity-related SNPs in or near FTO, NPC1, ENPP1, NEGR1, GNPDA2 and MC4R genes with risk of obesity was tested in 1,463 school-aged Mexican children (N cases  = 514; N controls  = 949). We also assessed effects of these SNPs on the variation of body mass index (BMI), fasting serum insulin levels, fasting plasma glucose levels, total cholesterol and triglyceride levels, in a subset of 1,171 nonobese Mexican children. We found a significant effect of GNPDA2 rs10938397 on risk of obesity (odds ratio [OR] = 1.30; P = 1.34 × 10-3). Furthermore, we found nominal associations between obesity risk or BMI variation and the following SNPs: ENPP1 rs7754561, MC4R rs17782313 and NEGR1 rs2815752. Importantly, the at-risk alleles of both MC4R rs17782313 and NPC1 rs1805081 showed significant effect on increased fasting glucose levels (β = 0.36 mmol/L; P = 1.47 × 10-3) and decreased fasting serum insulin levels (β = −0.10 μU/mL; P = 1.21 × 10-3), respectively. Our present results suggest that some obesity-associated SNPs previously reported in Europeans also associate with risk of obesity, or metabolic quantitative traits, in Mexican children. Importantly, we found new associations between MC4R and fasting glucose levels, and between NPC1 and fasting insulin levels.
Binarized Knowledge Graph Embeddings

Tensor factorization has become an increasingly popular approach to knowledge graph completion (KGC), which is the task of automatically predicting missing facts in a knowledge graph. However, even with a simple model like CANDECOMP/PARAFAC (CP) tensor decomposition, KGC on existing knowledge graphs is impractical in resourcelimited environments, as a large amount of memory is required to store parameters represented as 32-bit or 64-bit floating point numbers. This limitation is expected to become more stringent as existing knowledge graphs, which are already huge, keep steadily growing in scale. To reduce the memory requirement, we present a method for binarizing the parameters of the CP tensor decomposition by introducing a quantization function to the optimization problem. This method replaces floating point–valued parameters with binary ones after training, which drastically reduces the model size at run time. We investigate the trade-off between the quality and size of tensor factorization models for several KGC benchmark datasets. In our experiments, the proposed method successfully reduced the model size by more than an order of magnitude while maintaining the task performance. Moreover, a fast score computation technique can be developed with bitwise operations.
MyRIO based mobile robot for rescue competitions

Labview and National Instruments hardware is used to measure, analyze and solve multiple Industry problems, mostly in small mechatronics systems or fixed manipulators. myRIO have been used worldwide in the last few years to provide a reliable data acquisition. While in Industry and in Universities myRIO is vastly used, Arduino is still the most common tool for hobby or student based projects, therefore Mobile Robotics platforms integrate Arduino more often than myRIO. In this study, an overall hardware description will be presented, together with the software designed for autonomous and remote navigation in unknown scenarios. The designed robot was used in EuroSkills 2016 competition in Sweden.
Nonconscious processes and health.

Health behavior theories focus on the role of conscious, reflective factors (e.g., behavioral intentions, risk perceptions) in predicting and changing behavior. Dual-process models, on the other hand, propose that health actions are guided not only by a conscious, reflective, rule-based system but also by a nonconscious, impulsive, associative system. This article argues that research on health decisions, actions, and outcomes will be enriched by greater consideration of nonconscious processes.



A narrative review is presented that delineates research on implicit cognition, implicit affect, and implicit motivation. In each case, we describe the key ideas, how they have been taken up in health psychology, and the possibilities for behavior change interventions, before outlining directions that might profitably be taken in future research.



Correlational research on implicit cognitive and affective processes (attentional bias and implicit attitudes) has recently been supplemented by intervention studies using implementation intentions and practice-based training that show promising effects. Studies of implicit motivation (health goal priming) have also observed encouraging findings. There is considerable scope for further investigations of implicit affect control, unconscious thought, and the automatization of striving for health goals.



Research on nonconscious processes holds significant potential that can and should be developed by health psychologists. Consideration of impulsive as well as reflective processes will engender new targets for intervention and should ultimately enhance the effectiveness of behavior change efforts.
Logical Physical Clocks and Consistent Snapshots in Globally Distributed Databases

There is a gap between the theory and practice of distributed systems in terms of the use of time. The theory of distributed systems shunned the notion of time, and introduced “causality tracking” as a clean abstraction to reason about concurrency. The practical systems employed physical time (NTP) information but in a best effort manner due to the difficulty of achieving tight clock synchronization. In an effort to bridge this gap and reconcile the theory and practice of distributed systems on the topic of time, we propose a hybrid logical clock, HLC, that combines the best of logical clocks and physical clocks. HLC captures the causality relationship like logical clocks, and enables easy identification of consistent snapshots in distributed systems. Dually, HLC can be used in lieu of physical/NTP clocks since it maintains its logical clock to be always close to the NTP clock. Moreover HLC fits in to 64 bits NTP timestamp format, and is masking tolerant to NTP kinks and uncertainties. We show that HLC has many benefits for wait-free transaction ordering and performing snapshot reads in multiversion globally distributed databases.
Clustering Evolving Networks

Roughly speaking, clustering evolving networks aims at detecting structurally dense subgroups in networks that evolve over time. This implies that the subgroups we seek for also evolve, which results in many additional tasks compared to clustering static networks. We discuss these additional tasks and difficulties resulting thereof and present an overview on current approaches to solve these problems. We focus on clustering approaches in online scenarios, i.e., approaches that incrementally use structural information from previous time steps in order to incorporate temporal smoothness or to achieve low running time. Moreover, we describe a collection of real world networks and generators for synthetic data that are often used for evaluation.
Stacked-FET linear SOI CMOS SPDT antenna switch with input P1dB greater than 40dBm

The power handling capability is the most stringent specification for antenna switches, and this is dominated by a significant amount of leakage current of off-state FETs. For achieving maximum power handling capability of antenna switches, new DC I-V (FFI-V) characterization method to characterize RF P1dB point of off-state FETs is proposed and experimental study on optimum DC gate and body bias is performed based on proposed FFI-V method. Using Ron and Coff of minimum channel length MOSFETs at aforementioned optimum DC bias point, antenna switch design methodology for maximum power handling capability and minimum insertion loss is established. The designed SOI CMOS SPDT antenna switch integrated with switch controller shows insertion loss less than 0.5 dB and input P1dB greater than +40 dBm.
Forgery-Resistant Touch-based Authentication on Mobile Devices

Mobile devices store a diverse set of private user data and have gradually become a hub to control users' other personal Internet-of-Things devices. Access control on mobile devices is therefore highly important. The widely accepted solution is to protect access by asking for a password. However, password authentication is tedious, e.g., a user needs to input a password every time she wants to use the device. Moreover, existing biometrics such as face, fingerprint, and touch behaviors are vulnerable to forgery attacks. We propose a new touch-based biometric authentication system that is passive and secure against forgery attacks. In our touch-based authentication, a user's touch behaviors are a function of some random "secret". The user can subconsciously know the secret while touching the device's screen. However, an attacker cannot know the secret at the time of attack, which makes it challenging to perform forgery attacks even if the attacker has already obtained the user's touch behaviors. We evaluate our touch-based authentication system by collecting data from 25 subjects. Results are promising: the random secrets do not influence user experience and, for targeted forgery attacks, our system achieves 0.18 smaller Equal Error Rates (EERs) than previous touch-based authentication.
Structure and innervation of the labia minora: more than minor skin folds.

The objective of this study was to examine the histologic features of the labia minora, within the context of the female sexual response.



: Eight cadaver vulvectomy specimens were used for this study. All specimens were embedded in paraffin and were serially sectioned. Selected sections were stained with hematoxylin and eosin, elastic Masson trichrome, and S-100 antibody stains.



: The labia minora are thinly keratinized structures. The primary supporting tissue is collagen, with many vascular and neural elements structures throughout its core and elastin interspersed throughout.



: The labia minora are specialized, highly vascular folds of tissue with an abundance of neural elements. These features corroborate previous functional and observational data that the labia minora engorge with arousal and have a role in the female sexual response.
SBFT: a Scalable Decentralized Trust Infrastructure for Blockchains

We present SBFT: a scalable decentralized trust infrastructure for Blockchains. SBFT implements a new Byzantine fault tolerant algorithm that addresses the challenges of scalability and decentralization. Unlike many previous BFT systems that performed well only when centralized around less than 20 replicas, SBFT is optimized for decentralization and can easily handle more than 100 active replicas. SBFT provides a smart contract execution environment based on Ethereum’s EVM byte-code. We tested SBFT by running 1 million EVM smart contract transactions taken from a 4-month real-world Ethereum workload. In a geo-replicated deployment that has about 100 replicas and can withstand f = 32 Byzantine faults our system shows speedups both in throughput and in latency. SBFT completed this execution at a rate of 50 transactions per second. This is a 10× speedup compared to Ethereum current limit of 5 transactions per second. SBFT latency to commit a smart contract execution and make it final is sub-second, this is more than 10× speedup compared to Ethereum current > 15 second block generation for registering a smart contract execution and several orders of magnitude speedup relative to Proof-of-Work best-practice finality latency of one-hour.
A decision support system for stock investment recommendations using collective wisdom

a r t i c l e i n f o Previous research has shown that user-generated stock votes from online communities can be valuable for investment decisions. However, to support investors on a day-today basis, there is a need for an efficient support system to facilitate the use of the data and to transform crowd votes into actionable investment opportunities. We propose a decision support system (DSS) design that enables investors to include the crowd's recommendations in their investment decisions and use it to manage a portfolio. A prototype with two test scenarios shows the potential of the system as the portfolios recommended by the system clearly outperform the market benchmark and comparable public funds in the observation period in terms of absolute returns and with respect to the Reward-to-Variability-Ratio. The rise of user-generated content on the Internet enabled a wider public to participate in online content creation and publication without the need for deep technical expertise. The technical possibility to centrally aggregate the local contributions of a large crowd enables the creation of artifacts which are of equal or superior quality than those made by experts in the domain. Wikipedia, as an example, reaches a comparable quality to the renowned Britannica , solely depending on the contributions of a diverse anonymous crowd. This effect, coined as " wisdom of crowds " , is based on the diversity in information possession and processing of the individual members and is evident in a number of problem solving situations such as judging, estimating or decision making [18,28]. Estimation tasks are a problem class where group judgments prove to perform extraordinarily well. The reason behind is an effect called bracketing [39], which refers to the high likeliness that a part of the crowd will overestimate, while another part will underestimate the true value. Hence, averaging all judgments will lead to a more accurate judgment than that of the average judge [26]. Take an example: two people estimate the outside temperature for the next day as 60 °F and 80 °F, while the true temperature will be 73 °F. The estimates were wrong by 13 °F and 7 °F, or 10 °F on average. However, the mean of the two estimates, 60 °F and 80 °F, which is 70 °F, is off by only 3 °F. So using deviation as a measure, the average judgment outperforms the average judge [cf. 26]. One form …
Application of Artificial Neural Networks for Catalysis : A Review

Machine learning has proven to be a powerful technique during the past decades. Artificial neural network (ANN), as one of the most popular machine learning algorithms, has been widely applied to various areas. However, their applications for catalysis were not well-studied until recent decades. In this review, we aim to summarize the applications of ANNs for catalysis research reported in the literature. We show how this powerful technique helps people address the highly complicated problems and accelerate the progress of the catalysis community. From the perspectives of both experiment and theory, this review shows how ANNs can be effectively applied for catalysis prediction, the design of new catalysts, and the understanding of catalytic structures.