nils-herrmann/clean_scirepeval huggingface dataset:
Bias, optimal linear estimation, and the differences between open-loop simulation and closed-loop performance of spiking-based brain-computer interface algorithms

The activity of dozens of simultaneously recorded neurons can be used to control the movement of a robotic arm or a cursor on a computer screen. This motor neural prosthetic technology has spurred an increased interest in the algorithms by which motor intention can be inferred. The simplest of these algorithms is the population vector algorithm (PVA), where the activity of each cell is used to weight a vector pointing in that neuron's preferred direction. Off-line, it is possible to show that more complicated algorithms, such as the optimal linear estimator (OLE), can yield substantial improvements in the accuracy of reconstructed hand movements over the PVA. We call this open-loop performance. In contrast, this performance difference may not be present in closed-loop, on-line control. The obvious difference between open and closed-loop control is the ability to adapt to the specifics of the decoder in use at the time. In order to predict performance gains that an algorithm may yield in closed-loop control, it is necessary to build a model that captures aspects of this adaptation process. Here we present a framework for modeling the closed-loop performance of the PVA and the OLE. Using both simulations and experiments, we show that (1) the performance gain with certain decoders can be far less extreme than predicted by off-line results, (2) that subjects are able to compensate for certain types of bias in decoders, and (3) that care must be taken to ensure that estimation error does not degrade the performance of theoretically optimal decoders.
Analysis of a Nature Inspired Firefly Algorithm based Back-propagation Neural Network Training

Optimization algorithms are normally influenced by meta-heuristic approach. In recent years several hybrid methods for optimization are developed to find out a better solution. The proposed work using meta-heuristic Nature Inspired algorithm is applied with back-propagation method to train a feed-forward neural network. Firefly algorithm is a nature inspired meta-heuristic algorithm, and it is incorporated into back-propagation algorithm to achieve fast and improved convergence rate in training feed-forward neural network. The proposed technique is tested over some standard data set. It is found that proposed method produces an improved convergence within very few iteration. This performance is also analyzed and compared to genetic algorithm based back-propagation. It is observed that proposed method consumes less time to converge and providing improved convergence rate with minimum feed-forward neural network design
EAGr: supporting continuous ego-centric aggregate queries over large dynamic graphs

In this paper, we present EAGr, a system for supporting large numbers of continuous neighborhood-based ("ego-centric") aggregate queries over large, highly dynamic, rapidly evolving graphs. Examples of such queries include computation of personalized, tailored trends in social networks, anomaly or event detection in communication or financial transaction networks, local search and alerts in spatio-temporal networks, to name a few. Key challenges in supporting such continuous queries include very high update rates typically seen in these situations, large numbers of queries that need to be executed simultaneously, stringent low latency requirements. In this paper, we propose a flexible, general, extensible in-memory framework for executing different types of ego-centric aggregate queries over large dynamic graphs with low latencies. Our framework is built around the notion of an aggregation overlay graph, a pre-compiled data structure that encodes the computations to be performed when an update or a query is received. The overlay graph enables sharing of partial aggregates across different ego centric queries (corresponding to different nodes in the graph), also allows partial pre-computation of the aggregates to minimize the query latencies. We present several highly scalable techniques for constructing an overlay graph given an aggregation function, also design incremental algorithms for handling changes to the structure of the underlying graph itself, that may result in significant changes to the neighborhoods on which queries are posed. We also present an optimal, polynomial-time algorithm for making the pre-computation decisions given an overlay graph. Although our approach is naturally parallelizable, we focus on a single-machine deployment in this paper and show that our techniques can easily handle graphs of size up to 320 million nodes and edges, achieve update and query throughputs of over 500,000/s using a single, powerful machine.
Effects of Forward Head and Rounded Shoulder Posture on Scapular Kinematics , Muscle Activity , and Shoulder Coordination

CHARLES A. THIGPEN: Effects Of Forward Head And Rounded Shoulder Posture On Scapular Kinematics, Muscle Activity, And Shoulder Coordination (Under the direction of Dr. Darin A. Padua) Forward head and rounded shoulder posture (FHRSP) has been identified as a potential risk factor for the development of shoulder pain. The mechanism through which forward head and rounded shoulder can facilitate shoulder injury is not well understood. Altered scapular kinematics, muscle activity, and shoulder joint coordination due to FHRSP may lead to the development of shoulder pain. However, there is little evidence to support the influence of FHRSP on scapular kinematics, muscle activity, and shoulder joint coordination. Therefore, the purpose of this study was to compare scapular kinematics, muscle activity, and shoulder joint coordination in individuals with and without FHRSP. Eighty volunteers without shoulder pain were classified as having FHRSP or ideal posture. An electromagnetic tracking system together with hard-wired surface electromyography was used to collect three-dimensional scapular kinematics concurrently with muscle activity of the upper and lower trapezius as well as the serratus anterior during
Gaussian Processes for Data-Efficient Learning in Robotics and Control

Autonomous learning has been a promising direction in control and robotics for more than a decade since data-driven learning allows to reduce the amount of engineering knowledge, which is otherwise required. However, autonomous reinforcement learning (RL) approaches typically require many interactions with the system to learn controllers, which is a practical limitation in real systems, such as robots, where many interactions can be impractical and time consuming. To address this problem, current learning approaches typically require task-specific knowledge in form of expert demonstrations, realistic simulators, pre-shaped policies, or specific knowledge about the underlying dynamics. In this paper, we follow a different approach and speed up learning by extracting more information from data. In particular, we learn a probabilistic, non-parametric Gaussian process transition model of the system. By explicitly incorporating model uncertainty into long-term planning and controller learning our approach reduces the effects of model errors, a key problem in model-based learning. Compared to state-of-the art RL our model-based policy search method achieves an unprecedented speed of learning. We demonstrate its applicability to autonomous learning in real robot and control tasks.
Recent change of vegetation growth trend in China

Using satellite-derived normalized difference vegetation index (NDVI) data, several previous studies have indicated that vegetation growth significantly increased in most areas of China during the period 1982–99. In this letter, we extended the study period to 2010. We found that at the national scale the growing season (April–October) NDVI significantly increased by 0.0007 yr−1 from 1982 to 2010, but the increasing trend in NDVI over the last decade decreased in comparison to that of the 1982–99 period. The trends in NDVI show significant seasonal and spatial variances. The increasing trend in April and May (AM) NDVI (0.0013 yr−1) is larger than those in June, July and August (JJA) (0.0003 yr−1) and September and October (SO) (0.0008 yr−1). This relatively small increasing trend of JJA NDVI during 1982–2010 compared with that during 1982–99 (0.0012 yr−1) (Piao et al 2003  implies a change in the JJA vegetation growth trend, which significantly turned from increasing (0.0039 yr−1) to slightly decreasing (−0.0002 yr−1) in 1988. Regarding the spatial pattern of changes in NDVI, the growing season NDVI increased (over 0.0020 yr−1) from 1982 to 2010 in southern China, while its change was close to zero in northern China, as a result of a significant changing trend reversal that occurred in the 1990s and early 2000s. In northern China, the growing season NDVI significantly increased before the 1990s as a result of warming and enhanced precipitation, but decreased after the 1990s due to drought stress strengthened by warming and reduced precipitation. Our results also show that the responses of vegetation growth to climate change vary across different seasons and ecosystems.
Risk factors for antenatal depression, postnatal depression and parenting stress

Given that the prevalence of antenatal and postnatal depression is high, with estimates around 13%, and the consequences serious, efforts have been made to identify risk factors to assist in prevention, identification and treatment. Most risk factors associated with postnatal depression have been well researched, whereas predictors of antenatal depression have been less researched. Risk factors associated with early parenting stress have not been widely researched, despite the strong link with depression. The aim of this study was to further elucidate which of some previously identified risk factors are most predictive of three outcome measures: antenatal depression, postnatal depression and parenting stress and to examine the relationship between them.



Primipara and multiparae women were recruited antenatally from two major hoitals as part of the beyondblue National Postnatal Depression Program 1. In this subsidiary study, 367 women completed an additional large battery of validated questionnaires to identify risk factors in the antenatal period at 26-32 weeks gestation. A subsample of these women (N = 161) also completed questionnaires at 10-12 weeks postnatally. Depression level was measured by the Beck Depression Inventory (BDI).



Regression analyses identified significant risk factors for the three outcome measures. (1). Significant predictors for antenatal depression: low self-esteem, antenatal anxiety, low social support, negative cognitive style, major life events, low income and history of abuse. (2). Significant predictors for postnatal depression: antenatal depression and a history of depression while also controlling for concurrent parenting stress, which was a significant variable. Antenatal depression was identified as a mediator between seven of the risk factors and postnatal depression. (3). Postnatal depression was the only significant predictor for parenting stress and also acted as a mediator for other risk factors.



Risk factor profiles for antenatal depression, postnatal depression and parenting stress differ but are interrelated. Antenatal depression was the strongest predictor of postnatal depression, and in turn postnatal depression was the strongest predictor for parenting stress. These results provide clinical direction suggesting that early identification and treatment of perinatal depression is important.
Anomaly Detection in Concurrent Software by Static Data Flow Analysis

Algorithms are presented for detecting errors and anomalies in programs which use synchronization constructs to implement concurrency. The algorithms employ data flow analysis techniques. First used in compiler object code optimization, the techniques have more recently been used in the detection of variable usage errors in dngle process programs. By adapting these existing algorithms, the sane classes of variable usage errors can be detected in concurrent process programs. Important classes of errors unique to concurrent process programs are also described, and algorithms for their detection are presented.
Endogenous and exogenous attention shifts are mediated by the same large-scale neural network

Event-related fMRI was used to examine the neural basis of endogenous (top-down) and exogenous (bottom-up) spatial orienting. Shifts of attention were induced by central (endogenous) or peripheral (exogenous) cues. Reaction times on subsequently presented targets showed the expected pattern of facilitation and inhibition in both conditions. No difference in brain activity was observed when the two orienting conditions were contrasted with a liberal threshold, showing that both forms of orienting were mediated by the same neural network. Compared to within-block control trials, both endogenous and exogenous orienting activated a fronto-parietal network consisting of premotor cortex, posterior parietal cortex, medial frontal cortex and right inferior frontal cortex. Within these regions, equally strong activation was observed for both orienting conditions. It is concluded that endogenous and exogenous orienting are mediated by the same large-scale network of frontal and parietal brain areas.
Design and Analysis of a Totally Decoupled Flexure-Based XY Parallel Micromanipulator

In this paper, a concept of totally decoupling is proposed for the design of a flexure parallel micromanipulator with both input and output decoupling. Based on flexure hinges, the design procedure for an XY totally decoupled parallel stage (TDPS) is presented, which is featured with decoupled actuation and decoupled output motion as well. By employing (double) compound parallelogram flexures and a compact displacement amplifier, a class of novel XY TDPS with simple and symmetric structures are enumerated, and one example is chosen for further analysis. The kinematic and dynamic modeling of the manipulator are conducted by resorting to compliance and stiffness analysis based on the matrix method, which are validated by finite-element analysis (FEA). In view of predefined performance constraints, the dimension optimization is carried out by means of particle swarm optimization, and a prototype of the optimized stage is fabricated for performance tests. Both FEA and experimental studies well validate the decoupling property of the XY stage that is expected to be adopted into micro-/nanoscale manipulations.
Evaluation of Power System Transient Stability and Definition of the Basic Criterion

Power system stability is defined as an ability of the power system to reestablish the initial steady state or come into the new steady state after any variation of the system's operation value or after system´s breakdown. The stability and reliability of the electric power system is highly actual topic nowadays, especially in the light of recent accidents like splitting of UCTE system and north-American blackouts. This paper deals with the potential of the evaluation in term of transient stability of the electric power system within the defense plan and the definition of the basic criterion for the transient stability – Critical Clearing Time (CCT).
Finding trendsetters in information networks

Influential people have an important role in the process of information diffusion. However, there are several ways to be influential, for example, to be the most popular or the first that adopts a new idea. In this paper we present a methodology to find trendsetters in information networks according to a specific topic of interest. Trendsetters are people that adopt and spread new ideas influencing other people before these ideas become popular. At the same time, not all early adopters are trendsetters because only few of them have the ability of propagating their ideas by their social contacts through word-of-mouth. Differently from other influence measures, a trendsetter is not necessarily popular or famous, but the one whose ideas spread over the graph successfully. Other metrics such as node in-degree or even standard Pagerank focus only in the static topology of the network. We propose a ranking strategy that focuses on the ability of some users to push new ideas that will be successful in the future. To that end, we combine temporal attributes of nodes and edges of the network with a Pagerank based algorithm to find the trendsetters for a given topic. To test our algorithm we conduct innovative experiments over a large Twitter dataset. We show that nodes with high in-degree tend to arrive late for new trends, while users in the top of our ranking tend to be early adopters that also influence their social contacts to adopt the new trend.
Decoding fast-paced error-related potentials in monitoring protocols

Error-related EEG potentials (ErrP) can be used for brain-machine interfacing (BMI). Decoding of these signals, indicating subject's perception of erroneous system decisions or actions can be used to correct these actions or to improve the overall interfacing system. Multiple studies have shown the feasibility of decoding these potentials in single-trial using different types of experimental protocols and feedback modalities. However, previously reported approaches are limited by the use of long inter-stimulus intervals (ISI > 2 s). In this work we assess if it is possible to overcome this limitation. Our results show that it is possible to decode error-related potentials elicited by stimuli presented with ISIs lower than 1 s without decrease in performance. Furthermore, the increase in the presentation rate did not increase the subject workload. This suggests that the presentation rate for ErrP-based BMI protocols using serial monitoring paradigms can be substantially increased with respect to previous works.
Near-Eye Display Gaze Tracking via Convolutional Neural Networks

Virtual reality and augmented reality systems are currently entering the market and attempt to mimic as many naturally occurring stimuli in order to give a sense of immersion. Many aspects such as orientation and positional tracking have already been reliably implemented, but an important missing piece is eye tracking. VR and AR systems equipped with eye tracking could provide a more natural interface with the virtual environment, as well as opening the possibility for foveated rendering and gaze-contingent focus. In this work, we approach an eye tracking solution specifically designed for near-eye displays via convolutional neural networks, that is robust against lighting changes and occlusions that might be introduced when placing a camera inside of a near-eye display. We create a new dense eye tracking dataset specifically intended for neural networks to train on. We present the dataset as well as report initial results using this method.
An ontology-driven framework towards building enterprise semantic information layer

Enterprises Information Systems (EIS) have been applied for decades in Computer-Aided Engineering (CAE) and Computer-Aided Design (CAD), where huge amount of increasing data is stored in the heterogeneous and distributed systems. As systems evaluating, system redesign and reengineering are demanded. A facing challenge is how to interoperate among different systems by overcoming the gap of conceptual heterogeneity. In this article, an enlarged data representation called Semantic Information Layer (SIL) is described for facilitating heterogeneous systems interoperable. SIL plays a role as mediation media and knowledge representation among various systems. The SIL building process is based on ontology engineering, including ontology extraction from relational database (RDB), ontology enrichment and ontology alignment. Mapping path will maintain the links between SIL and data source, and query implementation and user interface are applied to retrieve data and interact with end users. We described fully a practical ontology-driven framework for building SIL and introduced extensively relevant standards and techniques for implementing the framework. In the core part of ontology development, a dynamic multistrategies ontology alignment with automatic matcher selection and dynamic similarity aggregation is proposed. A demonstration case study in the scenario of mobile phone industry is used to illustrate the proposed framework.
Baseline: A Library for Rapid Modeling, Experimentation and Development of Deep Learning Algorithms targeting NLP

We introduce Baseline: a library for reproducible deep learning research and fast model development for NLP. The library provides easily extensible abstractions and implementations for data loading, model development, training and export of deep learning architectures. It also provides implementations for simple, high-performance, deep learning models for various NLP tasks, against which newly developed models can be compared. Deep learning experiments are hard to reproduce, Baseline provides functionalities to track them. The goal is to allow a researcher to focus on model development, delegating the repetitive tasks to the library.
SNOOZE: Toward a Stateful NetwOrk prOtocol fuzZEr

Fuzzing is a well-known black-box approach to the security testing of applications. Fuzzing has many advantages in terms of simplicity and effectiveness over more complex, expensive testing approaches. Unfortunately, current fuzzing tools suffer from a number of limitations, and, in particular, they provide little support for the fuzzing of stateful protocols. In this paper, we present SNOOZE, a tool for building flexible, securityoriented, network protocol fuzzers. SNOOZE implements a stateful fuzzing approach that can be used to effectively identify security flaws in network protocol implementations. SNOOZE allows a tester to describe the stateful operation of a protocol and the messages that need to be generated in each state. In addition, SNOOZEprovides attack-specific fuzzing primitives that allow a tester to focus on specific vulnerability classes. We used an initial prototype of the SNOOZE tool to test programs that implement the SIP protocol, with promising results. SNOOZE supported the creation of sophisticated fuzzing scenarios that were able to expose realworld bugs in the programs analyzed.
On spiking neural P systems

This work deals with several aspects concerning the formal verification of SN P systems and the computing power of some variants. A methodology based on the information given by the transition diagram associated with an SN P system is presented. The analysis of the diagram cycles codifies invariants formulae which enable us to establish the soundness and completeness of the system with respect to the problem it tries to resolve. We also study the universality of asynchronous and sequential SN P systems and the capability these models have to generate certain classes of languages. Further, by making a slight modification to the standard SN P systems, we introduce a new variant of SN P systems with a special I/O mode, called SN P modules, and study their computing power. It is demonstrated that, as string language acceptors and transducers, SN P modules can simulate several types of computing devices such as finite automata, a-finite transducers, and systolic trellis automata.