nils-herrmann/clean_scirepeval huggingface dataset:
Effect of a novel NOP receptor agonist (SCH 225288) on guinea pig irritant-evoked, feline mechanically induced and canine infectious tracheobronchitis cough.

Previous studies have demonstrated that nociceptin/orphanin FQ (N/OFQ), the endogenous peptide ligand for the G-protein-coupled NOP receptor, inhibits cough in experimental models. SCH 225288 is a nonpeptide, orally active NOP agonist that may provide the foundation for the development of novel treatments for cough.



First we characterized the selectivity of SCH 225288 in human receptor binding assays. Afterwards, the antitussive activity of SCH 225288 was studied in three mechanistically distinct cough models. Specifically, we observed the cough-suppressant effect of SCH 225288 in a guinea pig capsaicin irritant-evoked cough model, a feline mechanically induced cough model and finally in a canine Bordetella bronchiseptica disease model.



SCH 225288 selectively binds human NOP receptor (K(i) = 0.38 +/- 0.02 nmol/l) over classical opioid receptors (COR). In a guinea pig capsaicin cough model, SCH 225288 (0.1-1 mg/kg) suppressed cough at 2, 4, and 6 h after oral administration. The antitussive effect of SCH 225288 (3.0 mg/kg, p.o.) was blocked by the NOP antagonist J113397 (12 mg/kg, i.p.) but not by the classical opioid receptor (COR) antagonist, naltrexone (3.0 mg/kg, i.p.). In the anesthetized cat, we evaluated the effects of SCH 225288 given either intravenously or via the intravertebral artery against the increases in cough number and respiratory expiratory and inspiratory muscle (rectus abdominis and parasternal) electromyographic (EMG) activities due to perturbations of the intrathoracic trachea. SCH 225288 (0.03-3.0 mg/kg, i.v.) inhibited both cough number and abdominal EMG amplitudes. Similarly, SCH 225288 (0.001-0.3 mg/kg) administered intra-arterially also diminished cough number and abdominal EMG amplitudes. No significant effect of the drug was noted on parasternal EMG activity. Finally, we studied the antitussive actions of SCH 225288 (1.0 mg/kg) in a canine B. bronchiseptica disease model. In this model, dogs were challenged intranasally with B. bronchiseptica. Comparisons were made between a vehicle group, an SCH 225288 (1.0 mg/kg, p.o., q.d.) and a butorphanol (0.6 mg/kg, p.o., b.i.d.) group on the mean change in cough scores from baseline values and days 6-9 after B. bronchiseptica challenge. SCH 225288 (1.0 mg/kg, p.o., q.d.) displayed a positive antitussive tendency (p = 0.06) to inhibit B. bronchiseptica cough whereas butorphanol (0.6 mg/kg, p.o., b.i.d.) was devoid of antitussive activity.



Taken together, the present data show that SCH 225288 is a potent and effective antitussive agent in animal models of cough. Furthermore, these findings indicate that NOP agonists represent a promising new therapeutic approach for the treatment of cough without the side effect liabilities associated with opioid antitussives.
Waveform Modeling Using Stacked Dilated Convolutional Neural Networks for Speech Bandwidth Extension

This paper presents a waveform modeling and generation method for speech bandwidth extension (BWE) using stacked dilated convolutional neural networks (CNNs) with causal or non-causal convolutional layers. Such dilated CNNs describe the predictive distribution for each wideband or high-frequency speech sample conditioned on the input narrowband speech samples. Distinguished from conventional frame-based BWE approaches, the proposed methods can model the speech waveforms directly and therefore avert the spectral conversion and phase estimation problems. Experimental results prove that the BWE methods proposed in this paper can achieve better performance than the state-of-the-art frame-based approach utilizing recurrent neural networks (RNNs) incorporating long shortterm memory (LSTM) cells in subjective preference tests.
CIM and IEC 61850 integration issues: Application to power systems

Common Information Model (CIM) is emerging as a standard for information modelling for power control centers. While, IEC 61850 by International Electrotechnical Commission (IEC) is emerging as a standard for achieving interoperability and automation at the substation level. In future, once these two standards are well adopted, the issue of integration of these standards becomes imminent. Some efforts reported towards the integration of these standards have been surveyed. This paper describes a possible approach for the integration of IEC 61850 and CIM standards based on mapping between the representation of elements of these two standards. This enables seamless data transfer from one standard to the other. Mapping between the objects of IEC 61850 and CIM standards both in the static and dynamic models is discussed. A CIM based topology processing application is used to demonstrate the design of the data transfer between the standards. The scope and status of implementation of CIM in the Indian power sector is briefed.
Climate Change during and after the Roman Empire : Reconstructing the Past from Scienti a c and Historical Evidence

Climate Change during and after the Roman Empire: Reconstructing the Past from Scientiac and Historical Evidence When this journal pioneered the study of history and climate in 1979, the questions quickly outstripped contemporary science and history. Today climate science uses a formidable and expanding array of new methods to measure pre-modern environments, and to open the way to exploring how Journal of Interdisciplinary History, xliii:2 (Autumn, 2012), 169–220.
ACTIVE SELF INTERFERENCE CANCELLATION TECHNIQUES IN FULL DUPLEX COMMUNICATION SYSTEMS-A SURVEY

In this paper we present a survey based on vast res ea ch done in the area of various important factors relating to full duplex communication revealing several facts. In fact thes e revelation has served to be vital in the future d esign of any full duplex communication system . The major problem or challen ge faced in such a system was the interference caus ed from the transmitting antenna on the receiver antenna placed at the same nod . The interference was termed as ' self interfe r nce'. Wireless transceivers nodes can select between transmit and receive funct ions on the same frequency band at different time ( Time Division Duplex-TDD) or they can transmit at the same time at different frequencies ( Frequency Division Duplex-FDD). Recen tly researchers have put forth a new radio system, that can operate full duplex ov er a single channel , that is transmitting and rec eiving at the same time on the same frequency band. This kind of system is overwhe lmed by interference. Interference need to be redu c to the point where the receiver can detect even the weakest signal of inte rest. Several active interference cancellation tech niques that can help in self interference reduction have been proposed. The in d epth survey was helpful enough to come out with a n ovel solution to an antenna design based on passive suppression techniques usin g polarization and other diversity techniques so a to reduce the self interference
Designing Puzzles for Collaborative Gaming Experience - CASE: eScape

This paper examines the issues of puzzle design in the context of collaborative gaming. The qualitative research approach involves both the conceptual analysis of key terminology and a case study of a collaborative game called eScape. The case study is a design experiment, involving both the process of designing a game environment and an empirical study, where data is collected using multiple methods. The findings and conclusions emerging from the analysis provide insight into the area of multiplayer puzzle design. The analysis and reflections answer questions on how to create meaningful puzzles requiring collaboration and how far game developers can go with collaboration design. The multiplayer puzzle design introduces a new challenge for game designers. Group dynamics, social roles and an increased level of interaction require changes in the traditional conceptual understanding of a single-player puzzle.
Dynamic super-efficiency interval data envelopment analysis

Conventional DEA models assume deterministic and precise data for input and output observations in a static situation, and their DMUs are often ranked incompletely. To work with interval data, DMUS' complete ranking as well as dynamic assessment issues synchronously, we put forward a hybrid model for evaluating the relative efficiencies of a set of DMUs over an observed time period with consideration of interval DEA, super-efficiency DEA and dynamic DEA. However, few researchers, if any, considered this issue within the combination of these three models. The hybrid model proposed in this paper enables us to (i) take interval data in input and output into account, (ii) rank DEA efficient DMUs completely, (iii) obtain the overall dynamic efficiency of DMUs over the entire observed period. We finally illustrate the calculation procedure of the proposed approach by a numerical example.
A Practical Template Attack on MICKEY-128 2.0 Using PSO Generated IVs and LS-SVM

The reported power analysis attacks on hardware implementations of the MICKEY family of streams ciphers require a large number of power traces. The primary motivation of our work is to break an implementation of the cipher when only a limited number of power traces can be acquired by an adversary. In this paper, we propose a novel approach to mount a Template attack (TA) on MICKEY-128 2.0 stream cipher using Particle Swarm Optimization (PSO) generated initialization vectors (IVs). In addition, we report the results of power analysis against a MICKEY-128 2.0 implementation on a SASEBO-GII board to demonstrate our proposed attack strategy. The captured power traces were analyzed using Least Squares Support Vector Machine (LS-SVM) learning algorithm based binary classifiers to segregate the power traces into the respective Hamming distance (HD) classes. The outcomes of the experiments reveal that our proposed power analysis attack strategy requires a much lesser number of IVs compared to a standard Correlation Power Analysis (CPA) attack on MICKEY-128 2.0 during the key loading phase of the cipher.
AdSplit: Separating Smartphone Advertising from Applications

A wide variety of smartphone applications today rely on third-party advertising services, which provide libraries that are linked into the hosting application. This situation is undesirable for both the application author and the advertiser. Advertising libraries require their own permissions, resulting in additional permission requests to users. Likewise, a malicious application could simulate the behavior of the advertising library, forging the user’s interaction and stealing money from the advertiser. This paper describes AdSplit, where we extended Android to allow an application and its advertising to run as separate processes, under separate user-ids, eliminating the need for applications to request permissions on behalf of their advertising libraries, and providing services to validate the legitimacy of clicks, locally and remotely. AdSplit automatically recompiles apps to extract their ad services, and we measure minimal runtime overhead. AdSplit also supports a system resource that allows advertisements to display their content in an embedded HTML widget, without requiring any native code.
Decentralized and centralized model predictive control to reduce the bullwhip effect in supply chain management

Mitigating the bullwhip effect is one of crucial problems in supply chain management. In this research, centralized and decentralized model predictive control strategies are applied to control inventory positions and to reduce the bullwhip effect in a benchmark four-echelon supply chain. The supply chain under consideration is described by discrete dynamic models characterized by balance equations on product and information flows with an ordering policy serving as the control schemes. In the decentralized control strategy, a MPC-EPSAC (Extended Prediction Self-Adaptive Control) approach is used to predict the changes in the inventory position levels. A closed-form solution of an optimal ordering decision for each echelon is obtained by locally minimizing a cost function, which consists of the errors between predicted inventory position levels and their setpoints, and a weighting function that penalizes orders. The single model predictive controller used in centralized control strategy optimizes globally and finds an optimal ordering policy for each echelon. The controller relies on a linear discrete-time state-space model to predict system outputs. But the predictions are approached by either of two multi-step predictors depending on whether the states of the controller model are directly observed or not. The objective function takes a quadratic form and thus the resulting optimization problem can be solved via standard quadratic programming method. The comparisons on performances of the two MPC strategies are illustrated with a numerical example.
An Assessment of Middleware Platforms for Accessing Remote Services

Due to the shift from software-as-a-product (SaaP) to software-as-a-service (SaaS), software components that were developed to run in a single address space must increasingly be accessed remotely across the network. Distribution middleware is frequently used to facilitate this transition. Yet a range of middleware platforms exist, and there are few existing guidelines to help the programmer choose an appropriate middleware platform to achieve desired goals for performance, expressiveness, and reliability. To address this limitation, in this paper we describe a case study of transitioning an Open Service Gateway Initiative (OSGi) service from local to remote access. Our case study compares five remote versions of this service, constructed using different distribution middleware platforms. These platforms are implemented by widely-used commercial technologies or have been proposed as improvements on the state of the art. In particular, we implemented a service-oriented version of our own Remote Batch Invocation abstraction. We compare and contrast these implementations in terms of their respective performance, expressiveness, and reliability. Our results can help remote service programmers make informed decisions when choosing middleware platforms for their applications.
Answer Set Programming Modulo 'Space-Time'

We present ASP Modulo ‘Space-Time’, a declarative representational and computational framework to perform commonsense reasoning about regions with both spatial and temporal components. Supported are capabilities for mixed qualitative-quantitative reasoning, consistency checking, and inferring compositions of space-time relations; these capabilities combine and synergise for applications in a range of AI application areas where the processing and interpretation of spatio-temporal data is crucial. The framework and resulting system is the only general KR-based method for declaratively reasoning about the dynamics of ‘space-time’ regions as first-class objects. We present an empirical evaluation (with scalability and robustness results), and include diverse application examples involving interpretation and control tasks.
Modeling and control for longitudinal attitude of a twin-rotor tail-sitter unmanned aerial vehicle

In this paper, the modeling and model reference adaptive control (MRAC) for longitudinal attitude of a twin-rotor tail-sitter unmanned aerial vehicle (UAV), which is highly unstable during flight, are presented. First, the attitude dynamic models are established. Linearized model for longitudinal attitude in vertical flight mode is given that is used in later derivation of controller as well as for testing the algorithms in simulation. Then, a control law based on the MRAC technique is utilized to stabilize the longitudinal attitude control system with uncertainty. Simulation results show that the MRAC of pitch angle has good trajectory tracking and the designed control law has strong adaptive ability and anti-jamming ability.
Parallel plate Transmission Line Transformer

A Transmission Line Transformer (TLT) can be used to transform high-voltage nanosecond pulses. These transformers rely on the fact that the length of the pulse is shorter than the transmission lines used. This allows connecting the transmission lines in parallel at the input and in series at the output. In the ideal case such structures achieve a voltage gain which equals the number of transmission lines used. To achieve maximum efficiency, mismatch and secondary modes must be suppressed. Here we describe a TLT based on parallel plate transmission lines. The chosen geometry results in a high efficiency, due to good matching and minimized secondary modes. A second advantage of this design is that the electric field strength between the conductors is the same throughout the entire TLT. This makes the design suitable for high voltage applications. To investigate the concept of this TLT design, measurements are done on two different TLT designs. One TLT consists of 4 transmission lines, while the other one has 8 lines. Both designs are constructed of DiBond™. This material consists of a flat polyethylene inner core with an aluminum sheet on both sides. Both TLT's have an input impedance of 3.125 Ω. Their output impedances are 50 and 200 Ω, respectively. The measurements show that, on a matched load, this structure achieves a voltage gain factor of 3.9 when using 4 transmission lines and 7.9 when using 8 lines.
A survey on context-aware recommender systems based on computational intelligence techniques

The demand for ubiquitous information processing over the Web has called for the development of context-aware recommender systems capable of dealing with the problems of information overload and information filtering. Contemporary recommender systems harness context-awareness with the personalization to offer the most accurate recommendations about different products, services, and resources. However, such systems come across the issues, such as sparsity, cold start, and scalability that lead to imprecise recommendations. Computational Intelligence (CI) techniques not only improve recommendation accuracy but also substantially mitigate the aforementioned issues. Large numbers of context-aware recommender systems are based on the CI techniques, such as: (a) fuzzy sets, (b) artificial neural networks, (c) evolutionary computing, (d) swarm intelligence, and (e) artificial immune systems. This survey aims to encompass the state-of-the-art context-aware recommender systems based on the CI techniques. Taxonomy of the CI techniques is presented and challenges particular to the context-aware recommender systems are also discussed. Moreover, the ability of each of the CI techniques to deal with the aforesaid challenges is also highlighted. Furthermore, the strengths and weaknesses of each of the CI techniques used in context-aware recommender systems are discussed and a comparison of the techniques is also presented.
A Short-Form Functional Capacity Evaluation Predicts Time to Recovery but Not Sustained Return-to-Work

To evaluate the ability of a short-form FCE to predict future timely and sustained return-to-work.  A prospective cohort study was conducted using data collected during a cluster RCT. Subject performance on the items in the short-form FCE was compared to administrative recovery outcomes from a workers’ compensation database. Outcomes included days to claim closure, days to time loss benefit suspension and future recurrence (defined as re-opening a closed claim, restarting benefits, or filing a new claim for injury to the same body region). Analysis included multivariable Cox and logistic regression using a risk factor modeling strategy. Potential confounders included age, sex, injury duration, and job attachment status, among others.  The sample included 147 compensation claimants with a variety of musculoskeletal injuries. Subjects who demonstrated job demand levels on all FCE items were more likely to have their claims closed (adjusted Hazard Ratio 5.52 (95% Confidence Interval 3.42–8.89), and benefits suspended (adjusted Hazard Ratio 5.45 (95% Confidence Interval 2.73–10.85) over the follow-up year. The proportion of variance explained by the FCE ranged from 18 to 27%. FCE performance was not significantly associated with future recurrence.  A short-form FCE appears to provide useful information for predicting time to recovery as measured through administrative outcomes, but not injury recurrence. The short-form FCE may be an efficient option for clinicians using FCE in the management of injured workers.
Baduk (the Game of Go) Improved Cognitive Function and Brain Activity in Children with Attention Deficit Hyperactivity Disorder

Attention deficit hyperactivity disorder (ADHD) symptoms are associated with the deficit in executive functions. Playing Go involves many aspect of cognitive function and we hypothesized that it would be effective for children with ADHD.



Seventeen drug naïve children with ADHD and seventeen age and sex matched comparison subjects were participated. Participants played Go under the instructor's education for 2 hours/day, 5 days/week. Before and at the end of Go period, clinical symptoms, cognitive functions, and brain EEG were assessed with Dupaul's ADHD scale (ARS), Child depression inventory (CDI), digit span, the Children's Color Trails Test (CCTT), and 8-channel QEEG system (LXE3208, Laxtha Inc., Daejeon, Korea).



There were significant improvements of ARS total score (z=2.93, p<0.01) and inattentive score (z=2.94, p<0.01) in children with ADHD. However, there was no significant change in hyperactivity score (z=1.33, p=0.18). There were improvement of digit total score (z=2.60, p<0.01; z=2.06, p=0.03), digit forward score (z=2.21, p=0.02; z=2.02, p=0.04) in both ADHD and healthy comparisons. In addition, ADHD children showed decreased time of CCTT-2 (z=2.21, p=0.03). The change of theta/beta right of prefrontal cortex during 16 weeks was greater in children with ADHD than in healthy comparisons (F=4.45, p=0.04). The change of right theta/beta in prefrontal cortex has a positive correlation with ARS-inattention score in children with ADHD (r=0.44, p=0.03).



We suggest that playing Go would be effective for children with ADHD by activating hypoarousal prefrontal function and enhancing executive function.
Amanuensis: The Programmer's Apprentice

This document provides an overview of the material covered in a course taught at Stanford in the spring quarter of 2018. The course draws upon insight from cognitive and systems neuroscience to implement hybrid connectionist and symbolic reasoning systems that leverage and extend the state of the art in machine learning by integrating human and machine intelligence. As a concrete example we focus on digital assistants that learn from continuous dialog with an expert software engineer while providing initial value as powerful analytical, computational and mathematical savants. Over time these savants learn cognitive strategies (domain-relevant problem solving skills) and develop intuitions (heuristics and the experience necessary for applying them) by learning from their expert associates. By doing so these savants elevate their innate analytical skills allowing them to partner on an equal footing as versatile collaborators — effectively serving as cognitive extensions and digital prostheses, thereby amplifying and emulating their human partner’s conceptually-flexible thinking patterns and enabling improved access to and control over powerful computing resources.  8 N ov 2 01 8