nils-herrmann/clean_scirepeval huggingface dataset:
Reinforcement learning models and their neural correlates: An activation likelihood estimation meta-analysis.

Reinforcement learning describes motivated behavior in terms of two abstract signals. The representation of discrepancies between expected and actual rewards/punishments-prediction error-is thought to update the expected value of actions and predictive stimuli. Electrophysiological and lesion studies have suggested that mesostriatal prediction error signals control behavior through synaptic modification of cortico-striato-thalamic networks. Signals in the ventromedial prefrontal and orbitofrontal cortex are implicated in representing expected value. To obtain unbiased maps of these representations in the human brain, we performed a meta-analysis of functional magnetic resonance imaging studies that had employed algorithmic reinforcement learning models across a variety of experimental paradigms. We found that the ventral striatum (medial and lateral) and midbrain/thalamus represented reward prediction errors, consistent with animal studies. Prediction error signals were also seen in the frontal operculum/insula, particularly for social rewards. In Pavlovian studies, striatal prediction error signals extended into the amygdala, whereas instrumental tasks engaged the caudate. Prediction error maps were sensitive to the model-fitting procedure (fixed or individually estimated) and to the extent of spatial smoothing. A correlate of expected value was found in a posterior region of the ventromedial prefrontal cortex, caudal and medial to the orbitofrontal regions identified in animal studies. These findings highlight a reproducible motif of reinforcement learning in the cortico-striatal loops and identify methodological dimensions that may influence the reproducibility of activation patterns across studies.
Scalable Attestation Resilient to Physical Attacks for Embedded Devices in Mesh Networks

Interconnected embedded devices are increasingly used in various scenarios, including industrial control, building automation, or emergency communication. As these systems commonly process sensitive information or perform safety critical tasks, they become appealing targets for cyber attacks. A promising technique to remotely verify the safe and secure operation of networked embedded devices is remote attestation. However, existing attestation protocols only protect against software attacks or show very limited scalability. In this paper, we present the first scalable attestation protocol for interconnected embedded devices that is resilient to physical attacks. Based on the assumption that physical attacks require an adversary to capture and disable devices for some time, our protocol identifies devices with compromised hardware and software. Compared to existing solutions, our protocol reduces communication complexity and runtimes by orders of magnitude, precisely identifies compromised devices, supports highly dynamic and partitioned network topologies, and is robust against failures. We show the security of our protocol and evaluate it in static as well as dynamic network topologies. Our results demonstrate that our protocol is highly efficient in well-connected networks and robust to network disruptions.
How to conduct an effective literature search.

The ability to describe and analyse published literature on a topic and develop discussion and argument is central to evidence-based patient care. A literature review is an assessment procedure that is commonly applied in nursing settings. Effective literature searching is a crucial stage in the process of writing a literature review, the significance of which is often overlooked. Although many current textbooks refer to the subject, information is often of insufficient depth to guide an effective search. This article outlines important considerations in the search strategy and recommends practical advice for students to ensure best use of their valuable time. It is suggested that a systematic, organised search of the literature, that uses available resources effectively, is more likely to produce quality work.
Hand gesture recognition using MYO armband

Surface electromyography (sEMG) is widely used in clinical diagnosis, rehabilitation engineering and humancomputer interaction and other fields. In this paper, we use Myo armband to collect sEMG signals. Myo armband can be worn above any elbow of any arm and it can capture the bioelectric signal generated when the arm muscles move. MYO can pass of signals through its low-power Blue-tooth, and its interference is small, which makes the signal quality really good. By collecting the sEMG signals of the upper limb forearm, we extract five eigenvalues in the time domain, and use the BP neural network classification algorithm to realize the recognition of six gestures in this paper. Experimental results show that the use of MYO for gesture recognition can get a very good recognition results, it can accurately identify the six hand movements with the average recognition rate of 93%.
Testing Exchangeability On-Line

The majority of theoretical work in machine learning is done under the assumption of exchangeability: essentially, it is assumed that the examples are generated from the same probability distribution independently. This paper is concerned with the problem of testing the exchangeability assumption in the on-line mode: examples are observed one by one and the goal is to monitor on-line the strength of evidence against the hypothesis of exchangeability. We introduce the notion of exchangeability martingales, which are on-line procedures for detecting deviations from exchangeability; in essence, they are betting schemes that never risk bankruptcy and are fair under the hypothesis of exchangeability. Some specific exchangeability martingales are constructed using Transductive Confidence Machine. We report experimental results showing their performance on the USPS benchmark data set of hand-written digits (known to be somewhat heterogeneous); one of them multiplies the initial capital by more than 10; this means that the hypothesis of exchangeability is rejected at the significance level 10−18.
Natural Actor-Critic

This paper investigates a novel model-free reinforcement learning architecture, the Natural Actor-Critic. The actor updates are based on stochastic policy gradients employing Amari’s natural gradient approach, while the critic obtains both the natural policy gradient and additional parameters of a value function simultaneously by linear regression. We show that actor improvements with natural policy gradients are particularly appealing as these are independent of coordinate frame of the chosen policy representation, and can be estimated more efficiently than regular policy gradients. The critic makes use of a special basis function parameterization motivated by the policy-gradient compatible function approximation. We show that several well-known reinforcement learning methods such as the original Actor-Critic and Bradtke’s Linear Quadratic Q-Learning are in fact Natural Actor-Critic algorithms. Empirical evaluations illustrate the effectiveness of our techniques in comparison to previous methods, and also demonstrate their applicability for learning control on an anthropomorphic robot arm.
Handwritten Digit Recognition Using Multiple Feature Extraction Techniques and Classifier Ensemble

It is herein proposed a handwritten digit recognition system which uses multiple feature extraction methods and classifier ensemble. The combination of the feature extraction methods is motivated by the observation that different feature extraction algorithms have a better discriminative power for some types of digits. Six features sets were extracted, two proposed by the authors and four published in previous works. It is shown that combining these feature sets is sufficient to achieve high recognition rates. Several combination schemes were tested, showing good results. A scheme using neural networks as a combiner achieved a recognition rate of 99.68%, the highest one on the MNIST database. Handwritten recognition, Feature Extraction, Classifier Ensemble, Neural Networks.
Meticillin-resistant Staphylococcus pseudintermedius: clinical challenge and treatment options.

Meticillin-resistant Staphylococcus pseudintermedius (MRSP) has emerged as a major therapeutic challenge for small animal veterinarians over the past 10 years and continues to spread worryingly in many countries. This review focuses on the clinical aspects of MRSP infections seen in patients with skin disease and on currently available treatment options. In addition, it discusses the implications for in-contact people, other animals and the environment, because infection control strategies are likely to have a significant impact on treatment success and prevention of spread. There is currently no indication that MRSP is more virulent than meticillin-susceptible S. pseudintermedius, and reported infections have mostly been treated successfully, although possibly with a longer time to resolution than infections with more susceptible S. pseudintermedius. However, in vitro testing of MRSP isolates indicates resistance to most or all antibacterial agents licensed for use in pets. Based on susceptibility results, the most useful systemic antimicrobials may include chloramphenicol, rifampicin, amikacin, clindamycin and/or minocycline. Adverse effects of some of these medications may limit their usefulness. While in vitro susceptibility to vancomycin and linezolid is reported by some laboratories, use of these drugs in animals is strongly discouraged because of ethical considerations. Aggressive topical therapy has been effective as the only treatment in certain cases. Awareness, continued research and comprehensive management of infections are required by veterinary practitioners not only to help treat infected animals but also to limit the spread and prevent the establishment of this highly drug-resistant and zoonotic pathogen in veterinary facilities and in the community.
Model-Free reinforcement learning with continuous action in practice

Reinforcement learning methods are often considered as a potential solution to enable a robot to adapt to changes in real time to an unpredictable environment. However, with continuous action, only a few existing algorithms are practical for real-time learning. In such a setting, most effective methods have used a parameterized policy structure, often with a separate parameterized value function. The goal of this paper is to assess such actor-critic methods to form a fully specified practical algorithm. Our specific contributions include 1) developing the extension of existing incremental policy-gradient algorithms to use eligibility traces, 2) an empirical comparison of the resulting algorithms using continuous actions, 3) the evaluation of a gradient-scaling technique that can significantly improve performance. Finally, we apply our actor-critic algorithm to learn on a robotic platform with a fast sensorimotor cycle (10ms). Overall, these results constitute an important step towards practical real-time learning control with continuous action.
HomeCloud: An edge cloud framework and testbed for new application delivery

Conventional centralized cloud computing is a success for benefits such as on-demand, elasticity, and high colocation of data and computation. However, the paradigm shift towards “Internet of things” (IoT) will pose some unavoidable challenges: (1) massive data volume impossible for centralized datacenters to handle; (2) high latency between edge “things” and centralized datacenters; (3) monopoly, inhibition of innovations, and non-portable applications due to the proprietary application delivery in centralized cloud. The emergence of edge cloud gives hope to address these challenges. In this paper, we propose a new framework called “HomeCloud” focusing on an open and efficient new application delivery in edge cloud integrating two complementary technologies: Network Function Virtualization (NFV) and Software-Defined Networking (SDN). We also present a preliminary proof-of-concept testbed demonstrating the whole process of delivering a simple multi-party chatting application in the edge cloud. In the future, the HomeCloud framework can be further extended to support other use cases that demand portability, cost-efficiency, scalability, flexibility, and manageability. To the best of our knowledge, this framework is the first effort aiming at facilitating new application delivery in such a new edge cloud context.
Parameterless Stochastic Natural Gradient Method for Discrete Optimization and its Application to Hyper-Parameter Optimization for Neural Network

Black box discrete optimization (BBDO) appears in wide range of engineering tasks. Evolutionary or other BBDO approaches have been applied, aiming at automating necessary tuning of system parameters, such as hyper parameter tuning of machine learning based systems when being installed for a specific task. However, automation is often jeopardized by the need of strategy parameter tuning for BBDO algorithms. An expert with the domain knowledge must undergo timeconsuming strategy parameter tuning. This paper proposes a parameterless BBDO algorithm based on information geometric optimization, a recent framework for black box optimization using stochastic natural gradient. Inspired by some theoretical implications, we develop an adaptation mechanism for strategy parameters of the stochastic natural gradient method for discrete search domains. The proposed algorithm is evaluated on commonly used test problems. It is further extended to two examples of simultaneous optimization of the hyper parameters and the connection weights of deep learning models, leading to a faster optimization than the existing approaches without any effort of parameter tuning.
Bounded Geometries, Fractals, and Low-Distortion Embeddings

The doubling constant of a metric space(X; d) is the smallest value such that every ball inX can be covered by balls of half the radius. Thedoubling dimensionof X is then defined as dim(X) = log2 . A metric (or sequence of metrics) is calleddoublingprecisely when its doubling dimension is bounded. This is a robust class of metric spaces which contains many families of metrics that occur in applied settings. We give tight bounds for embedding doubling metrics into (low-dimensional) normed spaces. We consider both general doubling metrics, as well as more restricted families such as those arising from trees, from graphs excluding a fixed minor, and from snowflaked metrics. Our techniques include decomposition theorems for doubling metrics, and an analysis of a fractal in the plane due to Laakso [21]. Finally, we discuss some applications and point out a central open question regarding dimensionality reduction in L2.
A trio-interaction theory for Madden – Julian oscillation

The Madden–Julian oscillation (MJO) is the dominant mode of tropical atmospheric intraseasonal variability and a primary source of predictability for global sub-seasonal prediction. Understanding the origin and perpetuation of the MJO has eluded scientists for decades. The present paper starts with a brief review of progresses in theoretical studies of the MJO and a discussion of the essential MJO characteristics that a theory should explain. A general theoretical model framework is then described in an attempt to integrate the major existing theoretical models: the frictionally coupled Kelvin–Rossby wave, the moisture mode, the frictionally coupled dynamic moisture mode, the MJO skeleton, and the gravity wave interference, which are shown to be special cases of the general MJO model. The last part of the present paper focuses on a special form of trio-interaction theory in terms of the general model with a simplified Betts–Miller (B-M) cumulus parameterization scheme. This trio-interaction theory extends the Matsuno–Gill theory by incorporating a trio-interaction among convection, moisture, and wave-boundary layer (BL) dynamics. The model is shown to produce robust large-scale characteristics of the observed MJO, including the coupled Kelvin–Rossby wave structure, slow eastward propagation (~5 m/s) over warm pool, the planetary (zonal) scale circulation, the BL low-pressure and moisture convergence preceding major convection, and amplification/decay over warm/cold sea surface temperature (SST) regions. The BL moisture convergence feedback plays a central role in coupling equatorial Kelvin and Rossby waves with convective heating, selecting a preferred eastward propagation, and generating instability. The moisture feedback can enhance Rossby wave component, thereby substantially slowing down eastward propagation. With the trio-interaction theory, a number of fundamental issues of MJO dynamics are addressed: why the MJO possesses a mixed Kelvin–Rossby wave structure and how the Kelvin and Rossby waves, which propagate in opposite directions, could couple together with convection and select eastward propagation; what makes the MJO move eastward slowly in the eastern hemisphere, resulting in the 30–60-day periodicity; why MJO amplifies over the warm pool ocean and decays rapidly across the dateline. Limitation and ramifications of the model results to general circulation modeling of MJO are discussed.
PedVis: A Structured, Space-Efficient Technique for Pedigree Visualization

Public genealogical databases are becoming increasingly populated with historical data and records of the current population's ancestors. As this increasing amount of available information is used to link individuals to their ancestors, the resulting trees become deeper and more dense, which justifies the need for using organized, space-efficient layouts to display the data. Existing layouts are often only able to show a small subset of the data at a time. As a result, it is easy to become lost when navigating through the data or to lose sight of the overall tree structure. On the contrary, leaving space for unknown ancestors allows one to better understand the tree's structure, but leaving this space becomes expensive and allows fewer generations to be displayed at a time. In this work, we propose that the H-tree based layout be used in genealogical software to display ancestral trees. We will show that this layout presents an increase in the number of displayable generations, provides a nicely arranged, symmetrical, intuitive and organized fractal structure, increases the user's ability to understand and navigate through the data, and accounts for the visualization requirements necessary for displaying such trees. Finally, user-study results indicate potential for user acceptance of the new layout.
Changes in Muscle Stiffness of the Trapezius Muscle After Application of Ischemic Compression into Myofascial Trigger Points in Professional Basketball Players

The study aimed to assess the effects of compression trigger point therapy on the stiffness of the trapezius muscle in professional basketball players (Part A), and the reliability of the MyotonPRO device in clinical evaluation of athletes (Part B). Twelve professional basketball players participated in Part A of the study (mean age: 19.8 ± 2.4 years, body height 197 ± 8.2 cm, body mass: 91.8 ± 11.8 kg), with unilateral neck or shoulder pain at the dominant side. Part B tested twelve right-handed male athletes (mean ± SD; age: 20.4 ± 1.2 years; body height: 178.6 ± 7.7 cm; body mass: 73.2 ± 12.6 kg). Stiffness measurements were obtained directly before and after a single session trigger point compression therapy. Measurements were performed bilaterally over 5 points covering the trapezius muscle. The effects were evaluated using a full-factorial repeated measure ANOVA and the Bonferroni post-hoc test for equal variance. A p-value < .05 was considered significant. The RM ANOVA revealed a significant decrease in muscle stiffness for the upper trapezius muscle. Specifically, muscle stiffness decreased from 243.7 ± 30.5 to 215.0 ± 48.5 N/m (11.8%), (p = .008) (Part A). The test-retest relative reliability of trapezius muscle stiffness was found to be high (ICC from 0.821 to 0.913 for measurement points). The average SEM was 23.59 N/m and the MDC 65.34 N/m, respectively (Part B). The present study showed that a single session of compression trigger point therapy can be used to significantly decrease the stiffness of the upper trapezius among professional basketball players.
Evolving memory-augmented neural architecture for deep memory problems

In this paper, we present a new memory-augmented neural network called Gated Recurrent Unit with Memory Block (GRU-MB). Our architecture builds on the gated neural architecture of a Gated Recurrent Unit (GRU) and integrates an external memory block, similar to a Neural Turing Machine (NTM). GRU-MB interacts with the memory block using independent read and write gates that serve to decouple the memory from the central feedforward operation. This allows for regimented memory access and update, administering our network the ability to choose when to read from memory, update it, or simply ignore it. This capacity to act in detachment allows the network to shield the memory from noise and other distractions, while simultaneously using it to effectively retain and propagate information over an extended period of time. We evolve GRU-MB using neuroevolution and perform experiments on two different deep memory tasks. Results demonstrate that GRU-MB performs significantly faster and more accurately than traditional memory-based methods, and is robust to dramatic increases in the depth of these tasks.
Whither Radioimmunotherapy: To Be or Not To Be?

Therapy of cancer with radiolabeled monoclonal antibodies has produced impressive results in preclinical experiments and in clinical trials conducted in radiosensitive malignancies, particularly B-cell lymphomas. Two "first-generation," directly radiolabeled anti-CD20 antibodies, 131iodine-tositumomab and 90yttrium-ibritumomab tiuxetan, were FDA-approved more than a decade ago but have been little utilized because of a variety of medical, financial, and logistic obstacles. Newer technologies employing multistep "pretargeting" methods, particularly those utilizing bispecific antibodies, have greatly enhanced the therapeutic efficacy of radioimmunotherapy and diminished its toxicities. The dramatically improved therapeutic index of bispecific antibody pretargeting appears to be sufficiently compelling to justify human clinical trials and reinvigorate enthusiasm for radioimmunotherapy in the treatment of malignancies, particularly lymphomas. Cancer Res; 77(9); 2191-6.
Falcon: fault localization in concurrent programs

Concurrency fault are difficult to find because they usually occur under specific thread interleavings. Fault-detection tools in this area find data-access patterns among thread interleavings, but they report benign patterns as well as actual faulty patterns. Traditional fault-localization techniques have been successful in identifying faults in sequential, deterministic programs, but they cannot detect faulty data-access patterns among threads. This paper presents a new dynamic fault-localization technique that can pinpoint faulty data-access patterns in multi-threaded concurrent programs. The technique monitors memory-access sequences among threads, detects data-access patterns associated with a program's pass/fail results, and reports dataaccess patterns with suspiciousness scores. The paper also presents the description of a prototype implementation of the technique in Java, and the results of an empirical study we performed with the prototype on several Java benchmarks. The empirical study shows that the technique can effectively and efficiently localize the faults for our subjects.