nils-herrmann/clean_scirepeval huggingface dataset:
Reinforcement learning in robotics: A survey

Reinforcement learning offers to robotics a framework and set of tools for the design of sophisticated and hard-to-engineer behaviors. Conversely, the challenges of robotic problems provide both inspiration, impact, and validation for developments in reinforcement learning. The relationship between disciplines has sufficient promise to be likened to that between physics and mathematics. In this article, we attempt to strengthen the links between the two research communities by providing a survey of work in reinforcement learning for behavior generation in robots. We highlight both key challenges in robot reinforcement learning as well as notable successes. We discuss how contributions tamed the complexity of the domain and study the role of algorithms, representations, and prior knowledge in achieving these successes. As a result, a particular focus of our paper lies on the choice between model-based and model-free as well as between value function-based and policy search methods. By analyzing a simple problem in some detail we demonstrate how reinforcement learning approaches may be profitably applied, and we note throughout open questions and the tremendous potential for future research.  reinforcement learning, learning control, robot, survey
Wide-Coverage Semantic Representations from a CCG Parser

This paper shows how to construct semantic representations from the derivations produced by a wide-coverage CCG parser. Unlike the dependency structures returned by the parser itself, these can be used directly for semantic interpretation. We demonstrate that well-formed semantic representations can be produced for over 97% of the sentences in unseen WSJ text. We believe this is a major step towards widecoverage semantic interpretation, one of the key objectives of the field of NLP.
Logistics and Supply Chain Management : An Area with a Strategic Service Perspective

Logistics and supply chain management is an area that evolved deeply in the past years, integrating developments of other areas of knowledge, both entrepreneurial and general. In this paper, a perspective of the evolution of logistics and supply chain management is somehow designed. Traditionally, one may find logistics and supply chain management in friction with marketing and claiming for its own space. Nowadays, it seems difficult to see internal (logistics) versus external (marketing) wars and different orientations between marketing and logistics because they are both service and relations oriented. Simple transactions have been substituted, long time ago, for sustainable relations in the area of logistics and supply chain management. Finally, a more service oriented logic has been the footprint of logistics and supply chain management in current days and not, as pretended for some current rows of investigation, a simple transaction approach under a goods dominant logic. Logistics and supply chain management is nowadays in parallel with an S-D logic (service dominant logic) because it is an area where relations matter, where sustainable links between networks of companies are crucial and where service is key in order to accommodate the contemporary thoughts and practices in the area. The main purpose of the paper is to stress the point that logistics and supply chain management is an area of service and value creation (or co-creation) and not a simple area of goods exchange and simple transactions.
MaltParser: A Data-Driven Parser-Generator for Dependency Parsing

We introduce MaltParser, a data-driven parser generator fo r dependency parsing. Given a treebank in dependency format , MaltParser can be used to induce a parser for the language of the treebank . M ltParser supports several parsing algorithms and learn ing algorithms, and allows user-defined feature models, consisting of arbit r y combinations of lexical features, part-of-speech fea tures and dependency features. MaltParser is freely available for research and e ducational purposes and has been evaluated empirically on S wedish, English, Czech, Danish and Bulgarian.
Japanese-to-English Machine Translation Using Recurrent Neural Networks

Neural network machine translation systems have recently demonstrated encouraging results. We examine the performance of a recently proposed recurrent neural network model for machine translation on the task of Japanese-to-English translation. We observe that with relatively little training the model performs very well on a small hand-designed parallel corpus, and adapts to grammatical complexity with ease, given a small vocabulary. The success of this model on a small corpus warrants more investigation of its performance on a larger corpus.
Changing Ethical Attitudes : The Case of the Enron and ImClone Scandals n

We analyze the process of changing ethical attitudes over time by focusing on a specific set of ‘‘natural experiments’’ that occurred over an 18-month period, namely, the accounting scandals that occurred involving Enron/Arthur Andersen and insider-trader allegations related to ImClone.  Given the amount of media attention devoted to these ethical scandals, we test whether respondents in a cross-sectional sample taken over 18 months become less accepting of ethically charged vignettes dealing with ‘‘accounting tricks’’ and ‘‘insider trading’’ over time.  We find a significant and gradual decline in the acceptance of the vignettes over the 18-month period.  Findings presented here may provide valuable insight into potential triggers of changing ethical attitudes. An intriguing implication of these results is that recent highly publicized ethical breaches may not be only a symptom, but also a cause of changing attitudes.
Regulation of Muscle Fiber Type and Running Endurance by PPARδ

Endurance exercise training can promote an adaptive muscle fiber transformation and an increase of mitochondrial biogenesis by triggering scripted changes in gene expression. However, no transcription factor has yet been identified that can direct this process. We describe the engineering of a mouse capable of continuous running of up to twice the distance of a wild-type littermate. This was achieved by targeted expression of an activated form of peroxisome proliferator-activated receptor delta (PPARdelta) in skeletal muscle, which induces a switch to form increased numbers of type I muscle fibers. Treatment of wild-type mice with PPARdelta agonist elicits a similar type I fiber gene expression profile in muscle. Moreover, these genetically generated fibers confer resistance to obesity with improved metabolic profiles, even in the absence of exercise. These results demonstrate that complex physiologic properties such as fatigue, endurance, and running capacity can be molecularly analyzed and manipulated.
Joint Layout Estimation and Global Multi-view Registration for Indoor Reconstruction

In this paper, we propose a novel method to jointly solve scene layout estimation and global registration problems for accurate indoor 3D reconstruction. Given a sequence of range data, we first build a set of scene fragments using KinectFusion and register them through pose graph optimization. Afterwards, we alternate between layout estimation and layout-based global registration processes in iterative fashion to complement each other. We extract the scene layout through hierarchical agglomerative clustering and energy-based multi-model fitting in consideration of noisy measurements. Having the estimated scene layout in one hand, we register all the range data through the global iterative closest point algorithm where the positions of 3D points that belong to the layout such as walls and a ceiling are constrained to be close to the layout. We experimentally verify the proposed method with the publicly available synthetic and real-world datasets in both quantitative and qualitative ways.
RELATIONAL NEURAL EXPECTATION MAXIMIZATION

Common-sense physical reasoning is an essential ingredient for any intelligent agent operating in the real-world. For example, it can be used to simulate the environment, or to infer the state of parts of the world that are currently unobserved. In order to match real-world conditions this causal knowledge must be learned without access to supervised data. To solve this problem, we present a novel method that incorporates prior knowledge about the compositional nature of human perception to factor interactions between object-pairs and to learn them efficiently. It learns to discover objects and to model physical interactions between them from raw visual images in a purely unsupervised fashion. On videos of bouncing balls we show the superior modelling capabilities of our method compared to other unsupervised neural approaches, that do not incorporate such prior knowledge. We show its ability to handle occlusion and that it can extrapolate learned knowledge to environments with different numbers of objects.
A Practical Guide to Wavelet Analysis

A practical step-by-step guide to wavelet analysis is given, with examples taken from time series of the El Niño– Southern Oscillation (ENSO). The guide includes a comparison to the windowed Fourier transform, the choice of an appropriate wavelet basis function, edge effects due to finite-length time series, and the relationship between wavelet scale and Fourier frequency. New statistical significance tests for wavelet power spectra are developed by deriving theoretical wavelet spectra for white and red noise processes and using these to establish significance levels and confidence intervals. It is shown that smoothing in time or scale can be used to increase the confidence of the wavelet spectrum. Empirical formulas are given for the effect of smoothing on significance levels and confidence intervals. Extensions to wavelet analysis such as filtering, the power Hovmöller, cross-wavelet spectra, and coherence are described. The statistical significance tests are used to give a quantitative measure of changes in ENSO variance on interdecadal timescales. Using new datasets that extend back to 1871, the Niño3 sea surface temperature and the Southern Oscillation index show significantly higher power during 1880–1920 and 1960–90, and lower power during 1920–60, as well as a possible 15-yr modulation of variance. The power Hovmöller of sea level pressure shows significant variations in 2–8-yr wavelet power in both longitude and time. Corresponding author address: Dr. Christopher Torrence, Advanced Study Program, National Center for Atmospheric Research, P.O. Box 3000, Boulder, CO 80307-3000.  torrence@ucar.edu In final form 20 October 1997.
Automated Road Traffic Congestion Detection and Alarm Systems: Incorporating V2I communications into ATCSs

In this position paper, we address the problems of automated road congestion detection and alerting systems and their security properties. We review different theoretical adaptive road traffic control approaches, and three widely deployed adaptive traffic control systems (ATCSs), namely, SCATS, SCOOT and InSync. We then discuss some related research questions, and the corresponding possible approaches, as well as the adversary model and potential attack scenarios. Two theoretical concepts of automated road congestion alarm systems (including system architecture, communication protocol, and algorithms) are proposed on top of ATCSs, such as SCATS, SCOOT and InSync, by incorporating secure wireless vehicle-to-infrastructure (V2I) communications. Finally, the security properties of the proposed system have been discussed and analysed using the ProVerif protocol verification tool.
Extension of simplified internal model control for systems with double integrators

Disturbance compensation is one of the major problems in control system design. Due to external disturbance or model uncertainty that can be treated as disturbance, all control systems are subject to disturbances. When it comes to networked control systems, not only disturbances but also time delay is inevitable where controllers are remotely connected to plants through communication network. Hence, simultaneous compensation for disturbance and time delay is important. Prior work includes a various combinations of smith predictor, internal model control, and disturbance observer tailored to simultaneous compensation of both time delay and disturbance. In particular, simplified internal model control simultaneously compensates for time delay and disturbances. But simplified internal model control is not applicable to the plants that have two poles at the origin. We propose a modified simplified internal model control augmented with disturbance observer which simultaneously compensates time delay and disturbances for the plants with two poles at the origin. Simulation results are provided.
Kafka versus RabbitMQ

Publish/subscribe is a distributed interaction paradigm well adapted to the deployment of scalable and loosely coupled systems. Apache Kaa and RabbitMQ are two popular open-source and commercially-supported pub/sub systems that have been around for almost a decade and have seen wide adoption. Given the popularity of these two systems and the fact that both are branded as pub/sub systems, two frequently asked questions in the relevant online forums are: how do they compare against each other and which one to use? In this paper, we frame the arguments in a holistic approach by establishing a common comparison framework based on the core functionalities of pub/sub systems. Using this framework, we then venture into a qualitative and quantitative (i.e. empirical) comparison of the common features of the two systems. Additionally, we also highlight the distinct features that each of these systems has. Aer enumerating a set of use cases that are best suited for RabbitMQ or Kaa, we try to guide the reader through a determination table to choose the best architecture given his/her particular set of requirements.
Muscle injuries: optimising recovery.

Muscle injuries are one of the most common traumas occurring in sports. Despite their clinical importance, there are only a few clinical studies on the treatment of muscle injuries. Lack of clinical studies is most probably attributable to the fact that there is not only a high heterogeneity in the severity of injuries, but also the injuries take place in different muscles, making it very demanding to carry out clinical trials. Accordingly, the current treatment principles of muscle injuries have either been derived from experimental studies or been tested empirically only. Clinically, first aid for muscle injuries follows the RICE (Rest, Ice, Compression and Elevation) principle. The objective of RICE is to stop the injury-induced bleeding into the muscle tissue and thereby minimise the extent of the injury. Clinical examination should be carried out immediately after the injury and 5-7 days after the initial trauma, at which point the severity of the injury can be assessed more reliably. At that time, a more detailed characterisation of the injury can be made using imaging diagnostic modalities (ultrasound or MRI) if desired. The treatment of injured skeletal muscle should be carried out by immediate immobilisation of the injured muscle (clinically, relative immobility/avoidance of muscle contractions). However, the duration of immobilisation should be limited to a period sufficient to produce a scar of sufficient strength to bear the forces induced by remobilisation without re-rupture and the return to activity (mobilisation) should then be started gradually within the limits of pain. Early return to activity is needed to optimise the regeneration of healing muscle and recovery of the flexibility and strength of the injured skeletal muscle to pre-injury levels. The rehabilitation programme should be built around progressive agility and trunk stabilisation exercises, as these exercises seem to yield better outcome for injured skeletal muscle than programmes based exclusively on stretching and strengthening of the injured muscle.
Gated Softmax Classification

We describe a ”log-bilinear” model that computes class probabilities by combining an input vector multiplicatively with a vector of binary latent variables. Even though the latent variables can take on exponentially many possible combinations of values, we can efficiently compute the exact probability of each class by marginalizing over the latent variables. This makes it possible to get the exact gradient of the log likelihood. The bilinear score-functions are defined using a three-dimensional weight tensor, and we show that factorizing this tensor allows the model to encode invariances inherent in a task by learning a dictionary of invariant basis functions. Experiments on a set of benchmark problems show that this fully probabilistic model can achieve classification performance that is competitive with (kernel) SVMs, backpropagation, and deep belief nets.
Circuit Breakers, Discovery, and API Gateways in Microservices

We review some of the most widely used patterns for the programming of microservices: circuit breaker, service discovery, and API gateway. By systematically analysing different deployment strategies for these patterns, we reach new insight especially for the application of circuit breakers. We also evaluate the applicability of Jolie, a language for the programming of microservices, for these patterns and report on other standard frameworks offering similar solutions. Finally, considerations for future developments are presented.
Personality: the universal and the culturally specific.

There appears to be a universal desire to understand individual differences. This common desire exhibits both universal and culturally specific features. Motivations to view oneself positively differ substantially across cultural contexts, as do a number of other variables that covary with this motivation (i.e., approach-avoidance motivations, internal-external frames of reference, independent-interdependent views of self, incremental-entity theories of abilities, dialectical self-views, and relational mobility). The structure of personality traits, particularly the five-factor model of personality, emerges quite consistently across cultures, with some key variations noted when the structure is drawn from indigenous traits in other languages. The extent to which each of the Big 5 traits is endorsed in each culture varies considerably, although we note some methodological challenges with comparing personality traits across cultures. Finally, although people everywhere can conceive of each other in terms of personality traits, people in collectivistic cultures appear to rely on traits to a lesser degree when understanding themselves and others, compared with those from individualistic cultures.
Gender differences in brain networks supporting empathy

Females frequently score higher on standard tests of empathy, social sensitivity, and emotion recognition than do males. It remains to be clarified, however, whether these gender differences are associated with gender specific neural mechanisms of emotional social cognition. We investigated gender differences in an emotion attribution task using functional magnetic resonance imaging. Subjects either focused on their own emotional response to emotion expressing faces (SELF-task) or evaluated the emotional state expressed by the faces (OTHER-task). Behaviorally, females rated SELF-related emotions significantly stronger than males. Across the sexes, SELF- and OTHER-related processing of facial expressions activated a network of medial and lateral prefrontal, temporal, and parietal brain regions involved in emotional perspective taking. During SELF-related processing, females recruited the right inferior frontal cortex and superior temporal sulcus stronger than males. In contrast, there was increased neural activity in the left temporoparietal junction in males (relative to females). When performing the OTHER-task, females showed increased activation of the right inferior frontal cortex while there were no differential activations in males. The data suggest that females recruit areas containing mirror neurons to a higher degree than males during both SELF- and OTHER-related processing in empathic face-to-face interactions. This may underlie facilitated emotional "contagion" in females. Together with the observation that males differentially rely on the left temporoparietal junction (an area mediating the distinction between the SELF and OTHERS) the data suggest that females and males rely on different strategies when assessing their own emotions in response to other people.
A Comparative Study of Anomaly Detection Schemes in Network Intrusion Detection

Intrusion detection corresponds to a suite of techniques that are used to identify attacks against computers and network infrastructures. Anomaly detection is a key element of intrusion detection in which perturbations of normal behavior suggest the presence of intentionally or unintentionally induced attacks, faults, defects, etc. This paper focuses on a detailed comparative study of several anomaly detection schemes for identifying different network intrusions. Several existing supervised and unsupervised anomaly detection schemes and their variations are evaluated on the DARPA 1998 data set of network connections [9] as well as on real network data using existing standard evaluation techniques as well as using several specific metrics that are appropriate when detecting attacks that involve a large number of connections. Our experimental results indicate that some anomaly detection schemes appear very promising when detecting novel intrusions in both DARPA’98 data and real network data.
Double Q-learning

In some stochastic environments the well-known reinforcement learning algorithm Q-learning performs very poorly. This poor performance is caused by large overestimations of action values. These overestimations result from a positive bias that is introduced because Q-learning uses the maximum action value as an approximation for the maximum expected action value. We introduce an alternative way to approximate the maximum expected value for any set of random variables. The obtained double estimator method is shown to sometimes underestimate rather than overestimate the maximum expected value. We apply the double estimator to Q-learning to construct Double Q-learning, a new off-policy reinforcement learning algorithm. We show the new algorithm converges to the optimal policy and that it performs well in some settings in which Q-learning performs poorly due to its overestimation.
Machine Learning for Survival Analysis: A Survey

Survival analysis is a subfield of statistics where the goal is to analyze and model data where the outcome is the time until an event of interest occurs. One of the main challenges in this context is the presence of instances whose event outcomes become unobservable after a certain time point or when some instances do not experience any event during the monitoring period. This so-called censoring can be handled most effectively using survival analysis techniques. Traditionally, statistical approaches have been widely developed in the literature to overcome the issue of censoring. In addition, many machine learning algorithms have been adapted to deal with such censored data and tackle other challenging problems that arise in real-world data. In this survey, we provide a comprehensive and structured review of the statistical methods typically used and the machine learning techniques developed for survival analysis, along with a detailed taxonomy of the existing methods. We also discuss several topics that are closely related to survival analysis and describe several successful applications in a variety of real-world application domains. We hope that this article will give readers a more comprehensive understanding of recent advances in survival analysis and offer some guidelines for applying these approaches to solve new problems arising in applications involving censored data.