nils-herrmann/clean_scirepeval huggingface dataset:
DISEASES: text mining and data integration of disease-gene associations.

Text mining is a flexible technology that can be applied to numerous different tasks in biology and medicine. We present a system for extracting disease-gene associations from biomedical abstracts. The system consists of a highly efficient dictionary-based tagger for named entity recognition of human genes and diseases, which we combine with a scoring scheme that takes into account co-occurrences both within and between sentences. We show that this approach is able to extract half of all manually curated associations with a false positive rate of only 0.16%. Nonetheless, text mining should not stand alone, but be combined with other types of evidence. For this reason, we have developed the DISEASES resource, which integrates the results from text mining with manually curated disease-gene associations, cancer mutation data, and genome-wide association studies from existing databases. The DISEASES resource is accessible through a web interface at http://diseases.jensenlab.org/, where the text-mining software and all associations are also freely available for download.
Euthanasia-related strain and coping strategies in animal shelter employees.

To identify and evaluate coping strategies advocated by experienced animal shelter workers who directly engaged in euthanizing animals.



Cross-sectional study.



Animal shelters across the United States in which euthanasia was conducted (5 to 100 employees/shelter).


PROCEDURES
With the assistance of experts associated with the Humane Society of the United States, the authors identified 88 animal shelters throughout the United States in which animal euthanasia was actively conducted and for which contact information regarding the shelter director was available. Staff at 62 animal shelters agreed to participate in the survey. Survey packets were mailed to the 62 shelter directors, who then distributed them to employees. The survey included questions regarding respondent age, level of education, and role and asked those directly involved in the euthanasia of animals to provide advice on strategies for new euthanasia technicians to deal with the related stress. Employees completed the survey and returned it by mail. Content analysis techniques were used to summarize survey responses.



Coping strategies suggested by 242 euthanasia technicians were summarized into 26 distinct coping recommendations in 8 categories: competence or skills strategies, euthanasia behavioral strategies, cognitive or self-talk strategies, emotional regulation strategies, separation strategies, get-help strategies, seek long-term solution strategies, and withdrawal strategies.



Euthanizing animals is a major stressor for many animal shelter workers. Information regarding the coping strategies identified in this study may be useful for training new euthanasia technicians.
Abfraction, abrasion, biocorrosion, and the enigma of noncarious cervical lesions: a 20-year perspective.

Hitherto, noncarious cervical lesions (NCCLs) of teeth have been generally ascribed to either toothbrush-dentifrice abrasion or acid "erosion." The last two decades have provided a plethora of new studies concerning such lesions. The most significant studies are reviewed and integrated into a practical approach to the understanding and designation of these lesions. A paradigm shift is suggested regarding use of the term "biocorrosion" to supplant "erosion" as it continues to be misused in the United States and many other countries of the world. Biocorrosion embraces the chemical, biochemical, and electrochemical degradation of tooth substance caused by endogenous and exogenous acids, proteolytic agents, as well as the piezoelectric effects only on dentin. Abfraction, representing the microstructural loss of tooth substance in areas of stress concentration, should not be used to designate all NCCLs because these lesions are commonly multifactorial in origin. Appropriate designation of a particular NCCL depends upon the interplay of the specific combination of three major mechanisms: stress, friction, and biocorrosion, unique to that individual case. Modifying factors, such as saliva, tongue action, and tooth form, composition, microstructure, mobility, and positional prominence are elucidated.



By performing a comprehensive medical and dental history, using precise terms and concepts, and utilizing the Revised Schema of Pathodynamic Mechanisms, the dentist may successfully identify and treat the etiology of root surface lesions. Preventive measures may be instituted if the causative factors are detected and their modifying factors are considered.
Magnetic Treatment of Irrigation Water and its Effect on Water Salinity

The influence of magnetic field on the structure of water and aqueous solutions are similar and can alter the physical and chemical properties of water-dispersed systems. With the application of magnetic field, hydration of salt ions and other impurities slides down and improve the possible technological characteristics of the water. Magnetic field can enhance the characteristic of water i.e. better salt solubility, kinetic changes in salt crystallization, accelerated coagulation, etc. Gulf countries are facing critical problem due to depletion of water resources and increasing food demands to cover the human needs; therefore water shortage is being increasingly accepted as a major limitation for increased agricultural production and food security. In arid and semiarid regions sustainable agricultural development is influenced to a great extent by water quality that might be used economically and effectively in developing agriculture programs. In the present study, the possibility of using magnetized water to desalinate the soil is accounted for the enhanced dissolving capacity of the magnetized water. Magnetic field has been applied to treat brackish water. The study showed that the impact of magnetic field on saline water is sustained up to three hours (with and without shaking). These results suggest that even low magnetic field can decrease the electrical conductivity and total dissolved solids which are good for the removal of salinity from the irrigated land by using magnetized water.
An investigation of Bluetooth security vulnerabilities

As Bluetooth technology has evolved and improved over the years, it has gained widespread acceptance and is increasingly found in many aspects of everyday life. It's convenient and easy to use, but it also has security flaws which make it vulnerable to attacks. In this paper, we discuss and demonstrate some of the tools and techniques that are currently available to attackers to exploit the vulnerabilities in Bluetooth. We also discuss some of the techniques to mitigate those risks to protect data and devices. Yet despite its world-wide acceptance and continued proliferation, as we have learned through our research and analysis, security vulnerabilities in Bluetooth still persist.
Inducing Grammars with and for Neural Machine Translation

Machine translation systems require semantic knowledge and grammatical understanding. Neural machine translation (NMT) systems often assume this information is captured by an attention mechanism and a decoder that ensures fluency. Recent work has shown that incorporating explicit syntax alleviates the burden of modeling both types of knowledge. However, requiring parses is expensive and does not explore the question of what syntax a model needs during translation. To address both of these issues we introduce a model that simultaneously translates while inducing dependency trees. In this way, we leverage the benefits of structure while investigating what syntax NMT must induce to maximize performance. We show that our dependency trees are 1. language pair dependent and 2. improve translation quality.
Modeling and Solving the Tactical Berth Allocation Problem

In this paper we integrate at the tactical level two decision problems arising in container terminals: the berth allocation problem, which consists of assigning and scheduling incoming ships to berthing positions, and the quay crane assignment problem, which assigns to incoming ships a certain QC profile (i.e. number of quay cranes per working shift). We present two formulations: a mixed integer quadratic program and a linearization which reduces to a mixed integer linear program. The objective function aims, on the one hand, to maximize the total value of chosen QC profiles and, on the other hand, to minimize the housekeeping costs generated by transshipment flows between ships. To solve the problem we developed a heuristic algorithm which combines tabu search methods and mathematical programming techniques. Computational results on instances based on real data are presented and compared to those obtained through a commercial solver.
Joint Feature Selection and Structure Preservation for Domain Adaptation

The essence of domain adaptation is to explore common latent factors shared by the involved domains. These factors can be specific features or geometric structures. Most of previous methods exploit either the shared features or the shared geometric structures separately. However, the two strategies are complementary with each other and jointly exploring them is more optimal. This paper proposes a novel approach, named joint Feature Selection and Structure Preservation (FSSP), for unsupervised domain adaptation. FSSP smoothly integrates structure preservation and feature selection into a unified optimization problem. Intensive experiments on text categorization, image classification and video event recognition demonstrate that our method performs better, even with up to 30% improvement in average, compared with the stateof-the-art methods.
Readiness for discharge in parents of hospitalized children.

Parental preparation for a child's discharge from the hospital sets the stage for successful transitioning to care and recovery at home. In this study of 135 parents of hospitalized children, the quality of discharge teaching, particularly the nurses' skills in "delivery" of parent teaching, was associated with increased parental readiness for discharge, which was associated with less coping difficulty during the first 3 weeks postdischarge. Parental coping difficulty was predictive of greater utilization of posthospitalization health services. These results validate the role of the skilled nurse as a teacher in promoting positive outcomes at discharge and beyond the hospitalization.
Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping

Instrumenting and collecting annotated visual grasping datasets to train modern machine learning algorithms can be extremely time-consuming and expensive. An appealing alternative is to use off-the-shelf simulators to render synthetic data for which ground-truth annotations are generated automatically. Unfortunately, models trained purely on simulated data often fail to generalize to the real world. We study how randomized simulated environments and domain adaptation methods can be extended to train a grasping system to grasp novel objects from raw monocular RGB images. We extensively evaluate our approaches with a total of more than 25,000 physical test grasps, studying a range of simulation conditions and domain adaptation methods, including a novel extension of pixel-level domain adaptation that we term the GraspGAN. We show that, by using synthetic data and domain adaptation, we are able to reduce the number of real-world samples needed to achieve a given level of performance by up to 50 times, using only randomly generated simulated objects. We also show that by using only unlabeled real-world data and our GraspGAN methodology, we obtain real-world grasping performance without any real-world labels that is similar to that achieved with 939,777 labeled real-world samples.
Working Paper No . 346 Network models and financial stability

Systemic risk is a key concern for central banks charged with safeguarding overall financial stability. In this paper we investigate how systemic risk is affected by the structure of the financial system. We construct banking systems that are composed of a number of banks that are connected by interbank linkages. We then vary the key parameters that define the structure of the financial system — including its level of capitalisation, the degree to which banks are connected, the size of interbank exposures and the degree of concentration of the system — and analyse the influence of these parameters on the likelihood of contagious (knock-on) defaults. First, we find that the better capitalised banks are, the more resilient is the banking system against contagious defaults and this effect is non-linear. Second, the effect of the degree of connectivity is non-monotonic, that is, initially a small increase in connectivity increases the contagion effect; but after a certain threshold value, connectivity improves the ability of a banking system to absorb shocks. Third, the size of interbank liabilities tends to increase the risk of knock-on default, even if banks hold capital against such exposures. Fourth, more concentrated banking systems are shown to be prone to larger systemic risk, all else equal. In an extension to the main analysis we study how liquidity effects interact with banking structure to produce a greater chance of systemic breakdown. We finally consider how the risk of contagion might depend on the degree of asymmetry (tiering) inherent in the structure of the banking system. A number of our results have important implications for public policy, which this paper also draws out.
Implementing a vulnerability management process GIAC ( GSEC ) Gold Certification

This paper looks at how a vulnerability management (VM) process could be designed & implemented within an organization. Articles and studies about VM usually focus mainly on the technology aspects of vulnerability scanning. The goal of this study is to call attention to something that is often overlooked: a basic VM process which could be easily adapted and implemented in any part of the organization. Implementing a vulnerability management process 2 Tom Palmaers
Automatic Goal Generation for Reinforcement Learning Agents

In addition to training our policy on the goals that were generated in the current iteration, we also save a list (“regularized replay buffer”) of goals that were generated during previous iterations (update replay). These goals are also used to train our policy, so that our policy does not forget how to achieve goals that it has previously learned. When we generate goals for our policy to train on, we sample two thirds of the goals from the Goal GAN and we sample the one third of the goals uniformly from the replay buffer. To prevent the replay buffer from concentrating in a small portion of goal space, we only insert new goals that are further away than from the goals already in the buffer, where we chose the goal-space metric and to be the same as the ones introduced in Section 3.1.
Music and emotion: electrophysiological correlates of the processing of pleasant and unpleasant music.

Human emotion and its electrophysiological correlates are still poorly understood. The present study examined whether the valence of perceived emotions would differentially influence EEG power spectra and heart rate (HR). Pleasant and unpleasant emotions were induced by consonant and dissonant music. Unpleasant (compared to pleasant) music evoked a significant decrease of HR, replicating the pattern of HR responses previously described for the processing of emotional pictures, sounds, and films. In the EEG, pleasant (contrasted to unpleasant) music was associated with an increase of frontal midline (Fm) theta power. This effect is taken to reflect emotional processing in close interaction with attentional functions. These findings show that Fm theta is modulated by emotion more strongly than previously believed.
Variational Bayesian Reinforcement Learning with Regret Bounds

We consider the exploration-exploitation trade-off in reinforcement learning and we show that an agent imbued with a risk-seeking utility function is able to explore efficiently, as measured by regret. The parameter that controls how risk-seeking the agent is can be optimized exactly, or annealed according to a schedule. We call the resulting algorithm K-learning and show that the corresponding K-values are optimistic for the expected Q-values at each state-action pair. The K-values induce a natural Boltzmann exploration policy for which the ‘temperature’ parameter is equal to the risk-seeking parameter. This policy achieves an expected regret bound of Õ(L √ SAT ), where L is the time horizon, S is the number of states, A is the number of actions, and T is the total number of elapsed time-steps. This bound is only a factor of L larger than the established lower bound. K-learning can be interpreted as mirror descent in the policy space, and it is similar to other well-known methods in the literature, including Q-learning, soft-Q-learning, and maximum entropy policy gradient, and is closely related to optimism and count based exploration methods. K-learning is simple to implement, as it only requires adding a bonus to the reward at each state-action and then solving a Bellman equation. We conclude with a numerical example demonstrating that K-learning is competitive with other state-of-the-art algorithms in practice. 1 ar X iv :1 80 7. 09 64 7v 1 [ cs .L G ] 2 5 Ju l 2 01 8
Self-Healing in Cementitious Materials—A Review

Concrete is very sensitive to crack formation. As wide cracks endanger the durability, repair may be required. However, these repair works raise the life-cycle cost of concrete as they are labor intensive and because the structure becomes in disuse during repair. In 1994, C. Dry was the first who proposed the intentional introduction of self-healing properties in concrete. In the following years, several researchers started to investigate this topic. The goal of this review is to provide an in-depth comparison of the different self-healing approaches which are available today. Among these approaches, some are aimed at improving the natural mechanism of autogenous crack healing, while others are aimed at modifying concrete by embedding capsules with suitable healing agents so that cracks heal in a completely autonomous way after they appear. In this review, special attention is paid to the types of healing agents and capsules used. In addition, the various methodologies have been evaluated based on the trigger mechanism used and attention has been paid to the properties regained due to self-healing.
Trust in Close Relationships

A theoretical model describing interpersonal trust in close relationships is presented. Three dimensions of trust are identified, based on the type of attributions drawn about a partner's motives. These dimensions are also characterized by a developmental progression in the relationship. The validity of this theoretical perspective was examined through evidence obtained from a survey of a heterogeneous sample of established couples. An analysis of the Trust Scale in this sample was consistent with the notion that the predictability, dependability, and faith components represent distinct and coherent dimensions. A scale to measure interpersonal motives was also developed. The perception of intrinsic motives in a partner emerged as a dimension, as did instrumental and extrinsic motives. As expected, love and happiness were closely tied to feelings of faith and the attribution of intrinsic motivation to both self and partner. Women appeared to have more integrated, complex views of their relationships than men: All three forms of trust were strongly related and attributions of instrumental motives in their partners seemed to be self-affirming. Finally, there was a tendency for people to view their own motives as less self-centered and more exclusively intrinsic in flavor than their partner's motives.
CoolStreaming/DONet: a data-driven overlay network for peer-to-peer live media streaming

This paper presents DONet, a data-driven overlay network for live media streaming. The core operations in DONet are very simple: every node periodically exchanges data availability information with a set of partners, and retrieves unavailable data from one or more partners, or supplies available data to partners. We emphasize three salient features of this data-driven design: 1) easy to implement, as it does not have to construct and maintain a complex global structure; 2) efficient, as data forwarding is dynamically determined according to data availability while not restricted by specific directions; and 3) robust and resilient, as the partnerships enable adaptive and quick switching among multi-suppliers. We show through analysis that DONet is scalable with bounded delay. We also address a set of practical challenges for realizing DONet, and propose an efficient member and partnership management algorithm, together with an intelligent scheduling algorithm that achieves real-time and continuous distribution of streaming contents. We have extensively evaluated the performance of DONet over the PlanetLab. Our experiments, involving almost all the active PlanetLab nodes, demonstrate that DONet achieves quite good streaming quality even under formidable network conditions. Moreover, its control overhead and transmission delay are both kept at low levels. An Internet-based DONet implementation, called CoolStreaming v.0.9, was released on May 30, 2004, which has attracted over 30000 distinct users with more than 4000 simultaneously being online at some peak times. We discuss the key issues toward designing CoolStreaming in this paper, and present several interesting observations from these large-scale tests; in particular, the larger the overlay size, the better the streaming quality it can deliver.
Cloud Computing : Issues and Challenges

Network bandwidth and hardware technology are developing rapidly, resulting in the vigorous development of the Internet. A new concept, cloud computing, uses low-power hosts to achieve high reliability. The cloud computing, an Internet-based development in which dynamicallyscalable and often virtualized resources are provided as a service over the Internet has become a significant issues. In this paper, we aim to pinpoint the challenges and issues of Cloud computing. We first discuss two related computing paradigms Service-Oriented Computing and Grid computing, and their relationships with Cloud computing. We then identify several challenges from the Cloud computing adoption perspective. Last, we will highlight the Cloud interoperability issue that deserves substantial further research and development. __________________________________________________*****_________________________________________________
Constructing Datasets for Multi-hop Reading Comprehension Across Documents

Most Reading Comprehension methods limit themselves to queries which can be answered using a single sentence, paragraph, or document. Enabling models to combine disjoint pieces of textual evidence would extend the scope of machine comprehension methods, but currently no resources exist to train and test this capability. We propose a novel task to encourage the development of models for text understanding across multiple documents and to investigate the limits of existing methods. In our task, a model learns to seek and combine evidence — effectively performing multihop, alias multi-step, inference. We devise a methodology to produce datasets for this task, given a collection of query-answer pairs and thematically linked documents. Two datasets from different domains are induced, and we identify potential pitfalls and devise circumvention strategies. We evaluate two previously proposed competitive models and find that one can integrate information across documents. However, both models struggle to select relevant information; and providing documents guaranteed to be relevant greatly improves their performance. While the models outperform several strong baselines, their best accuracy reaches 54.5% on an annotated test set, compared to human performance at 85.0%, leaving ample room for improvement.