Miracle-dz/UniFAR-FTUs huggingface dataset:
Sensitivity of Quantitative Signal Detection in Regards to Pharmacological Neuroenhancement

Pharmacological neuroenhancement (PNE) is a form of abuse and has not yet been addressed by methods of pharmacovigilance. In the present study, we tested if quantitative signal detection may be sensitive in regards to PNE. We evaluated the risk of drug abuse and dependence (DAAD) related to substances that are known to be used for PNE and divided this group into agents with (methylphenidate) and without a known abuse potential outside the field of PNE (atomoxetine, modafinil, acetylcholine esterase inhibitors, and memantine). Reporting odds ratios (RORs) were calculated using a case/non-case approach based on global and country-specific drug safety data from the Uppsala Monitoring Centre (UMC). Both control substances (diazepam and lorazepam) and methylphenidate were statistically associated with DAAD in all datasets (except methylphenidate in Italy). Modafinil was associated with DAAD in the total dataset (ROR, 2.7 (95% confidence interval (CI), 2.2–3.3)), Germany (ROR, 4.6 (95% CI, 1.8–11.5)), and the USA (ROR, 2.0 (95% CI, 1.6–2.5)). Atomoxetine was associated with DAAD in the total dataset (ROR, 1.3 (95% CI, 1.2–1.5)) and in the UK (ROR, 3.3 (95% CI, 1.8–6.1)). Apart from memantine, which was associated with DAAD in Germany (ROR, 1.8 (95% CI, 1.0–3.2)), no other antidementia drug was associated with DAAD. Quantitative signal detection is suitable to detect agents with a risk for DAAD. Its sensitivity regarding PNE is limited, although atomoxetine and modafinil, which do not have a known abuse potential outside PNE, and no antidementia drugs, whose use in PNE is presumably low, were associated with DAAD in our analysis.
Context-Oriented Programming for Adaptive Wireless Sensor Network Software

We present programming abstractions for implementing adaptive Wireless Sensor Network (WSN) software. The need for adaptability arises in WSNs because of unpredictable environment dynamics, changing requirements, and resource scarcity. However, after about a decade of research in WSN programming, developers are still left with no dedicated support. To address this issue, we bring concepts from Context-Oriented Programming (COP) down to WSN devices. Contexts model the situations that WSN software needs to adapt to. Using COP, programmers use a notion of layered function to implement context-dependent behavioral variations of WSN code. To this end, we provide language-independent design concepts to organize the context-dependent WSN operating modes, decoupling the abstractions from their concrete implementation in a programming language. Our own implementation, called CONESC, extends nesC with COP constructs. Based on three representative applications, we show that CONESC greatly simplifies the resulting code and yields increasingly decoupled implementations compared to nesC. For example, by model-checking every function in either implementations, we show a ~50% reduction in the number of program states that programmers need to deal with, indicating easier debugging. In our tests, this comes at the price of a maximum 2.5% (4.5%) overhead in program (data) memory.
Cardiopulmonary Arrest and Resuscitation Disrupts Cholinergic Anti-Inflammatory Processes: A Role for Cholinergic α7 Nicotinic Receptors

Cardiac arrest is a leading cause of death worldwide. While survival rates following sudden cardiac arrest remain relatively low, recent advancements in patient care have begun to increase the proportion of individuals who survive cardiac arrest. However, many of these individuals subsequently develop physiological and psychiatric conditions that likely result from ongoing neuroinflammation and neuronal death. The present study was conducted to better understand the pathophysiological effects of cardiac arrest on neuronal cell death and inflammation, and their modulation by the cholinergic system. Using a well validated model of cardiac arrest, here we show that global cerebral ischemia increases microglial activation, proinflammatory cytokine mRNA expression (interleukin-1β, interleukin-6, tumor necrosis factor-α), and neuronal damage. Cardiac arrest also induces alterations in numerous cellular components of central cholinergic signaling, including a reduction in choline acetyltransferase enzymatic activity and the number of choline acetyltransferase-positive neurons, as well as, reduced acetylcholinesterase and vesicular acetylcholine transporter mRNA. However, treatment with a selective agonist of the α7 nicotinic acetylcholine receptor, the primary receptor mediating the cholinergic anti-inflammatory pathway, significantly decreases the neuroinflammation and neuronal damage resulting from cardiac arrest. These data suggest that global cerebral ischemia results in significant declines in central cholinergic signaling, which may in turn diminish the capacity of the cholinergic anti-inflammatory pathway to control inflammation. Furthermore, we provide evidence that pharmacological activation of α7 nicotinic acetylcholine receptors provide significant protection against ischemia-related cell death and inflammation within a clinically relevant time frame.
Cost Prediction of Equipment System Using LS-SVM with PSO

Considering the shortcomings of conventional cost prediction methods, least squares support vector machine (LS-SVM) was adopted to establish the cost prediction model of equipment system, which could efficiently solve the problems on the determination of network structure and the phenomena of over-fitting in neural network methods. And due to the importance of parameters optimization in LS-SVM model, particle swarm optimization (PSO) was used to optimize the model parameters. The experiment results show that PSO can quickly obtain the optimal parameters satisfying the precision requirement with a simple calculation, which solves the problem of complex calculation and empiricism in conventional methods. The evaluation on the testing cases shows the LS-SVM model with PSO has a good generalization performance and can be popularized in cost prediction. At last, the experiment on an independent testing case shows the model optimized by PSO has a better prediction performance than by grid search.
Automated Regression Testing Using Constraint Programming

In software validation, regression testing aims to check the absence of regression faults in new releases of a software system. Typically, test cases used in regression testing are executed during a limited amount of time and are selected to check a given set of user requirements. When testing large systems, the number of regression tests grows quickly over the years, and yet the available time slot stays limited. In order to overcome this problem, an approach known as test suite reduction (TSR), has been developed in software engineering to select a smallest subset of test cases, so that each requirement remains covered at least once. However solving the TSR problem is difficult as the underlying optimization problem is NP-hard, but it is also crucial for vendors interested in reducing the time to market of new software releases. In this paper, we address regression testing and TSR with Constraint Programming (CP). More specifically, we propose new CP models to solve TSR that exploit global constraints, namely NVALUE and GCC. We reuse a set of preprocessing rules to reduce a priori each instance, and we introduce a structureaware search heuristic. We evaluated our CP models and proposed improvements against existing approaches, including a simple greedy approach and MINTS, the state-of-theart tool of the software engineering community. Our experiments show that CP outperforms both the greedy approach and MINTS when it is interfaced with MiniSAT, in terms of percentage of reduction and execution time. When MINTS is interfaced with CPLEX, we show that our CP model performs better only on percentage of reduction. Finally, by working closely with validation engineers from Cisco Systems, Norway, we integrated our CP model into an industrial regression testing process.
Local Tests of Global Entanglement and a Counterexample to the Generalized Area Law

We introduce a technique for applying quantum expanders in a distributed fashion, and use it to solve two basic questions: testing whether a bipartite quantum state shared by two parties is the maximally entangled state and disproving a generalized area law. In the process these two questions which appear completely unrelated turn out to be two sides of the same coin. Strikingly in both cases a constant amount of resources are used to verify a global property.
Autonomous Onboard Science Data Analysis for Comet Missions

Coming years will bring several comet rendezvous missions. The Rosetta spacecraft arrives at Comet 67P/Churyumov-Gerasimenko in 2014. Subsequent rendezvous might include a mission such as the proposed Comet Hopper with multiple surface landings, as well as Comet Nucleus Sample Return (CNSR) and Coma Rendezvous and Sample Return (CRSR). These encounters will begin to shed light on a population that, despite several previous flybys, remains mysterious and poorly understood. Scientists still have little direct knowledge of interactions between the nucleus and coma, their variation across different comets or their evolution over time. Activity may change on short timescales so it is challenging to characterize with scripted data acquisition. Here we investigate automatic onboard image analysis that could act faster than round-trip light time to capture unexpected outbursts and plume activity. We describe one edge-based method for detect comet nuclei and plumes, and test the approach on an existing catalog of comet images. Finally, we quantify benefits to specific measurement objectives by simulating a basic plume monitoring campaign.
Developing a Sentiment Polarity Visualization System for Local Event Information Analysis

It is important to analyze the reputation or demands for a local event, such as a school festival. The aim of this research is to develop a system that enables its users to collect and to analyze local event information from social media. In this paper, we have shown the over view and described the implementation of the proposed system. Our system extracts tweets by sentiment polarity classification and visualizes them. In this paper, we described the performance of the sentiment polarity classification and showed that the system is efficient for coordinating tweets. We collected tweets about a local event and classified them manually. The sentiment polarity classification of our system was evaluated by comparing it with the manually classified tweets. We also discussed an example of local event analysis using our system and showed the efficiency of our system.
Feature-based interpolation for the interactive editing of shading effects

In computer graphics, shading techniques play an important role when displaying realistic virtual objects. Although physically-based techniques can generate realistic results, it is often difficult to produce desired shading effects. Therefore, artists usually creates desired shading effects by manually placing many virtual light sources. However, this is not an easy task since it is difficult to predict the shading effects obtained by those virtual light sources. We propose an intuitive system that allows a user to produce the desired shading effects. In our system, the user can directly specify the desired intensities at arbitrary positions on the surfaces of objects. The desired shading effects are realized by our feature-based interpolation of the specified intensities.
The Measurement of Growth under Embodied Technical Change

New U.S. evidence from NIPA contradicts some of the well-known Kaldor stylized facts, and call for a reformulation of the modem theory of economic growth. Among these new facts, two must be stressed : A permanent decline in the relative price of durable goods, and a permanent increase in the real equipment to real GDP ratio. To be consistent with these new facts, growth models must include at least two sectors and address the problem of defining aggregate output. In this paper, the economic theory of index numbers is used to define the growth rate of real output in a growth model with embodied technical change. The main findings are : (i) NIPA's methodology measures growth in accordance with the economic theory on index numbers, and (ii) when the growth rate is measured as in NIPA, the contribution of embodied technical change to per capital GDP growth in the U.S. is 69%, which reinforce the claim that embodied technical change is important for growth.
A Phase Model of the Service Delivery Process for Bundle Services

Operational expenses represent a growing portion of costs for telecommunications services. Chronologically, the first service process is the Service Delivery, which starts after the Order Entry and leads to the entry into service. A model of the Service Delivery process is proposed that decomposes it into six phases. A statistical analysis is performed for the completion times of each phase, collected over a period of six months on an OLO (Other Licensed Operator). The Installation and the Number Portability phase appear as the most critical. The overall delivery time is shown to follow a lognormal distribution. An analysis of service delivery failure is also shown through the use of Pareto charts, where the most common cause of failures is the rejection by the incumbent operator of migration requests.
Effective Alternating Direction Optimization Methods for Sparsity-Constrained Blind Image Deblurring

Single-image blind deblurring for imaging sensors in the Internet of Things (IoT) is a challenging ill-conditioned inverse problem, which requires regularization techniques to stabilize the image restoration process. The purpose is to recover the underlying blur kernel and latent sharp image from only one blurred image. Under many degraded imaging conditions, the blur kernel could be considered not only spatially sparse, but also piecewise smooth with the support of a continuous curve. By taking advantage of the hybrid sparse properties of the blur kernel, a hybrid regularization method is proposed in this paper to robustly and accurately estimate the blur kernel. The effectiveness of the proposed blur kernel estimation method is enhanced by incorporating both the L1-norm of kernel intensity and the squared L2-norm of the intensity derivative. Once the accurate estimation of the blur kernel is obtained, the original blind deblurring can be simplified to the direct deconvolution of blurred images. To guarantee robust non-blind deconvolution, a variational image restoration model is presented based on the L1-norm data-fidelity term and the total generalized variation (TGV) regularizer of second-order. All non-smooth optimization problems related to blur kernel estimation and non-blind deconvolution are effectively handled by using the alternating direction method of multipliers (ADMM)-based numerical methods. Comprehensive experiments on both synthetic and realistic datasets have been implemented to compare the proposed method with several state-of-the-art methods. The experimental comparisons have illustrated the satisfactory imaging performance of the proposed method in terms of quantitative and qualitative evaluations.
Knowledge-based opportunistic forwarding in vehicular wireless ad hoc networks

When highly mobile nodes are interconnected via wireless links, the resulting network can be used as a transit network to connect other disjoint ad-hoc networks. In this paper, we compare five different opportunistic forwarding schemes, which vary in their overhead, their success rate, and the amount of knowledge about neighboring nodes that they require. In particular, we present the MoVe algorithm, which uses velocity information to make intelligent opportunistic forwarding decisions. Using auxiliary information to make forwarding decisions provides a reasonable trade-off between resource overhead and performance.
Improving HMM/DNN in ASR of under-resourced languages using probabilistic sampling

In HMM/DNN automatic speech recognition (ASR) systems, the DNNs model the posterior probabilities for triphone states. However, triphone states are unevenly distributed. In this situation, the training algorithm tends to converge to a local optimum more related to states with rich data than states with poor data. Thus, the imbalance of the training data decreases the ASR performances, especially for under-resourced languages. To deal with this issue, we explore a resampling technique, called “probabilistic sampling”, which can be seen as a linear smoothing between the original sampling and the uniform sampling. The effectiveness of the probabilistic sampling has been studied in two under-resourced ASR experiments. With the probabilistic sampling, the first experiment got a 6.3% relative phone error rate (PER) reduction compared to the conventional DNN baseline; the second experiment used shared-hidden-layer multilingual DNN as the baseline, and obtained a 4.9% relative PER reduction.
A calibration method for uncoupling projector and camera of a structured light system

This paper is focusing on the calibration of a 3D measurement system based on a structured light technique. Key to the proposed method for calibration of the system is to uncouple the complicated relationship between the projector and camera, and separate the calibration procedure into two parts. The first part is to calibrate the camera. After that, with the help of the parameters of the calibrated camera and the relation between the target feature points of camera calibration and projector calibration, the second part is to calibrate the projector. The proposed calibration method is fast, accurate and easy to implement. It significantly simplifies the calibration procedures of structured light systems. Both analytical and numerical solutions of the system calibration can be obtained. In this paper, the principle of the proposed method is described and some satisfactory experimental results are presented.
A Clinical Comparison of Variable-Damping and Mechanically Passive Prosthetic Knee Devices

Johansson JL, Sherrill DM, Riley PO, Bonato P, Herr H: A clinical comparison of variable-damping and mechanically passive prosthetic knee devices. Am J Phys Med Rehabil 2005;84:563–575. Objective:Although variable-damping knee prostheses offer some improvements over mechanically passive prostheses to transfemoral amputees, there is insufficient evidence that such prostheses provide advantages at self-selected walking speeds. In this investigation, we address this question by comparing two variable-damping knees, the hydraulic-based Otto Bock C-leg and the magnetorheological-based Össur Rheo, with the mechanically passive, hydraulic-based Mauch SNS. Design:For each prosthesis, metabolic data were collected on eight unilateral amputees walking at self-selected speeds across an indoor track. Furthermore, kinetic, kinematic, and electromyographic data were collected while walking at self-selected speeds across a 10-m walkway in a laboratory. Results:When using the Rheo, metabolic rate decreases by 5% compared with the Mauch and by 3% compared with the C-leg. Furthermore, for the C-leg and Rheo knee devices, we observe biomechanical advantages over the mechanically passive Mauch. These advantages include an enhanced smoothness of gait, a decrease in hip work production, a lower peak hip flexion moment at terminal stance, and a reduction in peak hip power generation at toe-off. Conclusion:The results of this study indicate that variable-damping knee prostheses offer advantages over mechanically passive designs for unilateral transfemoral amputees walking at self-selected ambulatory speeds, and the results further suggest that a magnetorheological-based system may have advantages over hydraulic-based designs.
10 Gbit / s Line Rate Packet Processing Using Commodity Hardware : Survey and new Proposals

The network stack of operating systems has been designed for general purpose communications. Network drivers are responsible for bridging network adapters with kernel pack et management facilities. While this approach is pretty flexibl e and general, it makes it unsuitable for high-speed network applications. This is because the journey of a packet betwee n the network adapter and the target application is pretty lon g, as packet have to cross several kernel layers that in specific cases could be circumvented. This paper presents a survey of techniques used to achieve fast packet capture and generation on commodity hardware. Then it shows in detail how two solutions developed by the authors can be used effectively to improve some previous proposals, not only reaching line rate but also exploiting a dvanced features of the NIC and limiting the number of cpu cycles spent for moving packets between the wire and the application. The validation on both FreeBSD and Linux operating systems has confirmed that legacy applications can significantl y improve their performance when using our solutions, and new applications can benefit from the exposure of hardware features usually hidden by standard APIs such as pcap.
Quantitative Methods for Design of Benchmark

In this paper we present black-box models of work-load diierence and use them to develop quantitative methods for the design of benchmark suites. The proposed design methods (the method of optimum subsets , and the method of optimum weights) combine a new concept of program space coverage functions, and the criteria of completeness, redundancy, density, and granularity of benchmark suites.
APHID: A Practical Architecture for High-Performance, Privacy-Preserving Data Mining

While the emerging field of privacy preserving data mining (PPDM) will enable many new data mining applications, it suffers from several practical difficulties. PPDM algorithms are difficult to develop and computationally intensive to execute. Developers need convenient abstractions to reduce the costs of engineering PPDM applications. The individual parties involved in the data mining process need a way to bring highperformance, parallel computers to bear on the computationally intensive parts of the PPDM tasks. This paper discusses APHID (Architecture for Private and High-performance Integrated Data mining), a practical software architecture for developing and executing large scale PPDM applications. At one tier, the system supports simplified use of cluster and grid resources, and at another tier, the system abstracts communication for easy PPDM algorithm development. This paper offers a detailed analysis of the challenges in developing PPDM algorithms with existing frameworks, and motivates the design of a new infrastructure based on these challenges.
Data allocation strategies for the management of Quality of Service in Virtualised Storage Systems

The amount of data managed by organisations continues to grow relentlessly. Driven by the high costs of maintaining multiple local storage systems, there is a well established trend towards storage consolidation using multi-tier Virtualised Storage Systems (VSSs). At the same time, storage infrastructures are increasingly subject to stringent Quality of Service (QoS) demands. Within a VSS, it is challenging to match desired QoS with delivered QoS, considering the latter can vary dramatically both across and within tiers. Manual efforts to achieve this match require extensive and ongoing human intervention.