Miracle-dz/UniFAR-FTUs huggingface dataset:
Implementation of a Biologically Realistic Parallel Neocortical-Neural Network Simulator

The primary goal of this simulator is to create a novel classi er based on a biologically realistic neocortical-neural network. Parallel processing of this very large-scale, object-oriented simulator is key for approaching real-time simulation of synaptic and neocortical network dynamics. Clustering algorithms applied to the dense cellconnection matrix enable load-balancing and data parallelism by organizing highly connected groups of cells onto a particular node, thus reducing the performance cost of inter-nodal communication. The simulation is accomplished by modeling a whole community of cells within a brain structure and observing the emergent behavior of this system. This modeling is done using ne grain parallelization, as opposed to very small scale cellular networks and coarse grain parallelism, such as that found in NEURON or GENESIS[7]. The structure of the rest of this paper is as follows: in Section 2 we begin with an overview of the connectivity within the brain and its relevance to our simulation. In Section 3 we present the Object-Oriented design of this simulation. Section 4
Title: Moments, Sums of Squares and Semidefinite Programming

We introduce the generalized problem of moments (GPM) which has developments and impact in various area of Mathematics like algebra, Fourier analysis, functional analysis, operator theory, probability and statistics, to cite a few. In addition, and despite its rather simple and short formulation, the GPM has a large number of important applications in various fields like optimization, probability, mathematical finance, control, signal processing, chemistry, cristallography, tomography, etc. If in full generality the GPM is unsolvable numerically, the situation is much nicer for the GPM with polynomial data. Indeed, one may then define a hierarchy of semidefinite relaxations that provide lower bounds of increasing quality on its optimal value. Even more, thanks to powerful results of real algebraic geometry, one may prove convergence to the optimal value. We will describe various applications that can be formulated as GPM instances in different areas and introduce a hierarchy of semidefinite relaxations for the general GPM which illustrates the duality between moments and sums of squares. Finally, we show how to exploit powerful results from real agebraic geometry to prove convergence to the optimal value of the GPM.
A competency framework for domestic violence counselling

ABSTRACT Domestic violence (DV) affects around 20% of the population globally and is linked with complex mental health conditions and difficulties in client therapeutic engagement. Developing therapeutic competency to meet the standards for independent, professional practice is a significant part of counsellor training. A competency framework for DV is a useful addition to the literature. This framework has been systematically developed from a client-informed DV counselling model (grounded theory) and integrates well with existing international publications. It highlights high level and wide-ranging counselling skills, specialist knowledge and specific personal characteristics for working with this client group, beyond the level required for independent practice. This framework could be used as a basis for pre- and post-qualification counsellor training, recruitment and assessment.
RLS based MIMO channel identification for FEXT compensation in vectored xDSL system

Far-end Crosstalk (FEXT) is the major limiting factor in further increase of data rate in xDSL systems. Having the knowledge of the MIMO channel transmittance one can design appropriate precoder, which by the introduction of appropriate pre-distortion into transmitted signals results in FEXT compensation. As is shown in the paper identification of MIMO downstream channel in xDSL systems can be performed in frequency domain with a recursive least-squares method (RLS). Such an approach decreases computational complexity as well as memory requirement and enables tracking of individual subchannels variation.
Reconstruction of sparse connectivity in neural networks from spike train covariances

The inference of causation from correlation is in general highly problematic. Correspondingly, it is difficult to infer the existence of physical synaptic connections between neurons from correlations in their activity. Covariances in neural spike trains and their relation to network structure have been the subject of intense research, both experimentally and theoretically. The influence of recurrent connections on covariances can be characterized directly in linear models, where connectivity in the network is described by a matrix of linear coupling kernels. However, as indirect connections also give rise to covariances, the inverse problem of inferring network structure from covariances can generally not be solved unambiguously. Here we study to what degree this ambiguity can be resolved if the sparseness of neural networks is taken into account. To reconstruct a sparse network, we determine the minimal set of linear couplings consistent with the measured covariances by minimizing the L1 norm of the coupling matrix under appropriate constraints. Contrary to intuition, after stochastic optimization of the coupling matrix, the resulting estimate of the underlying network is directed, despite the fact that a symmetric matrix of count covariances is used for inference. The performance of the new method is best if connections are neither exceedingly sparse, nor too dense, and it is easily applicable for networks of a few hundred nodes. Full coupling kernels can be obtained from the matrix of full covariance functions. We apply our method to networks of leaky integrate-and-fire neurons in an asynchronous–irregular state, where spike train covariances are well described by a linear model.
Fusing Deep Convolutional Networks for Large Scale Visual Concept Classification

Deep learning architectures are showing great promise in various computer vision domains including image classification, object detection, event detection and action recognition. In this study, we investigate various aspects of convolutional neural networks (CNNs) from the big data perspective. We analyze recent studies and different network architectures both in terms of running time and accuracy. We present extensive empirical information along with best practices for big data practitioners. Using these best practices we propose efficient fusion mechanisms both for single and multiple network models. We present state-of-the art results on benchmark datasets while keeping computational costs at a lower level. Another contribution of our paper is that these state-of-the-art results can be reached without using extensive data augmentation techniques.
A Review of the Application of Optical and Radar Remote Sensing Data Fusion to Land Use Mapping and Monitoring

The wealth of complementary data available from remote sensing missions can hugely aid efforts towards accurately determining land use and quantifying subtle changes in land use management or intensity. This study reviewed 112 studies on fusing optical and radar data, which offer unique spectral and structural information, for land cover and use assessments. Contrary to our expectations, only 50 studies specifically addressed land use, and five assessed land use changes, while the majority addressed land cover. The advantages of fusion for land use analysis were assessed in 32 studies, and a large majority (28 studies) concluded that fusion improved results compared to using single data sources. Study sites were small, frequently 300–3000 km 2 or individual plots, with a lack of comparison of results and accuracies across sites. Although a variety of fusion techniques were used, pre-classification fusion followed by pixel-level inputs in traditional classification algorithms (e.g., Gaussian maximum likelihood classification) was common, but often without a concrete rationale on the applicability of the method to the land use theme being studied. Progress in this field of research requires the development of robust techniques of fusion to map the intricacies of land uses and changes therein and systematic procedures to assess the benefits of fusion over larger spatial scales.
Likelihood Modelling of the Space Geodesy Facility Laser Ranging Sensor for Bayesian Filtering

This work analyzes the data output of laser ranging data collected from the SGF (Herstmonceux, UK), and proposes a bespoke likelihood function for its processing in the context of Bayesian filtering. It is then illustrated in a single-target Bayesian filter, performing successfully on simulated and real data, under a variety of noise profiles encountered in typical outputs of the sensor.
Multiproduct Monopolist and Full-line Forcing: The Efficiency Argument Revisited

Shaffer (1991) shows that a multiproduct monopolist selling differentiated products through a unique retailer cannot earn monopoly profit using brand specific two-part tariffs and that full-line forcing restores monopoly power. We extend this analysis to more general contracts and shows that full-line forcing is efficient as it increases both industry profits and consumers' surplus.
A progressive region-merging algorithm for stereo matching

A progressive region-merging algorithm for stereo matching is put forward in this paper. Firstly, the reference image is segmented by mean shift algorithm and the initial disparity maps are calculated using loopy belief propagation method. Secondly, we derive the reliable pixels through left-right consistent constraint. In addition, we present a new approach to exclude mismatch pixels in textureless regions, which will also reduce errors in the following region merging process. Finally, a progressive region-merging algorithm based on hierarchical clustering algorithm is put forward to merge neighboring regions and refine the plane parameters. Angle and distance are used together to measure whether the two planes are the same or not, which will reduce the calculation complexity. Experiments validate the proposed algorithm.
RELATIONSHIP LENDING WITHIN A BANK-BASED SYSTEM: EVIDENCE FROM EUROPEAN SMALL BUSINESS DATA

This paper adds to the relationship lending debate by investigating detailed contract information obtained from examining nearly eighteen thousand bank loans. The beneficiaries all were very small firms that operate within the continental European bank-based system. That is, with data gathered for Belgium, we investigate price and non-price terms of the loan contract. We test for the possibility of intertemporal rent shifting by banks. The empirical evidence shows two opposing effects. On the one hand, the length of a bank-firm relationship increases the loan rate. On the other hand, widening the relationship by buying other information sensitive products from a bank decreases the loan rate. Thus the effect on the price operates more through the dimension of the relationship than through the length of the relationship. We also find that the length of the financial relationship slightly negatively influences the probability of pledging collateral.
Sleep Problems in Cancer: Effective Psychological Interventions

Introduction: Sleep problems are one of the most prevalent complications cancer patients experience. These have been shown to produce harmful effects such as reduced immunity and mood disturbances. While pharmacological agents for sleep problems may be used, psychological therapies should be considered where possible. Aim: The aim of this review is to evaluate the effectiveness of different psychological interventions for sleep problems in cancer patients. Methods: A systematic review was conducted. Databases searched included PsychINFO, Embase, MEDLINE, and Web of Science for papers published during the period from January 2003 to December 2013. Combinations of the following terms were used: “sleep disturbance”, “sleep problems”, “insomnia”, “cognitive behavioural therapy”, “CBT”, “non-pharmacological”, “sleep hygiene”, “psychological intervention”, “psychological treatment”, and “cancer”. Results: 22 papers were selected for analysis. The main finding from this systematic review was that cognitive behavioural therapy (CBT) and mindfulness-based stress reduction (MBSR) is the most promising psychological intervention for the treatment of insomnia in cancer patients. Other psychological treatments such as sleep education and multimodal programmes also demonstrate good potential for use in oncology. Conclusions: Further randomized controlled trials comparing the effectiveness of CBT and MBSR in different types of cancer are required to determine their relative benefits and costs.
Lyapunov filtering of objectivity for Spanish Sentiment Model

Objective sentences lack sentiments and, hence, can reduce the accuracy of a sentiment classifier. Traditional methods prior to 2001 used hand-crafted templates to identify subjectivity and did not generalize well for resource-deficient languages such as Spanish. Later works published between 2002 and 2009 proposed the use of deep neural networks to automatically learn a dictionary of features (in the form of convolution kernels) that is portable to new languages. Recently, recurrent neural networks are being used to model alternating subjective and objective sentences within a single review. Such networks are difficult to train for a large vocabulary of words due to the problem of vanishing gradients. Hence, in this paper we consider use of a Lyapunov linear matrix inequality to classify Spanish text as subjective or objective by combining Spanish features and features obtained from the corresponding translated English text. The aligned features for each sentence are next evolved using multiple kernel learning. The proposed Lyapunov deep neural network outperforms baselines by over 10% and the features learned in the hidden layers improve our understanding subjective sentences in Spanish.
A Direct Distance to the Large Magellanic Cloud Cepheid HV 12198 from the Infrared Surface Brightness Technique

We report on the first application of the infrared surface brightness technique on a Cepheid in the Large Magellanic Cloud, the variable HV 12198 in the young globular cluster NGC 1866. From this one star, we determine a distance modulus of 18.42 ± 0.10 (random and systematic uncertainty) to the cluster. When the results on further member Cepheids in NGC 1866 become available, we expect to derive the distance to the LMC with a ±3%-4% accuracy, including systematic errors, from this technique.
#ADA25 Campaign: Using Social Media to Promote Participation, Social Inclusion, and Civic Engagement of People With Intellectual and Developmental Disabilities

Social media is used for civic engagement and building social inclusion. Twitter is one social media tool that facilitates conversations on important societal events. A digital divide between the general population and people with intellectual and developmental disabilities (IDD) remains. Given the divide, we sought to understand the inclusion of the community of people with IDD in the conversations around the 25th anniversary of the Americans with Disabilities Act of 1990 (ADA). We conducted a content analysis of tweets associated with the anniversary and few tweets referenced the community of people with IDD. Our findings suggest that people with IDD are not included in larger civic conversations. Implications include targeted social media training for people with IDD and their support networks.
Towards domain independent speaker clustering

Speaker clustering is a key component in many speech processing applications. We focus on Broadcast News meta data annotation and speaker adaptation. In this setting, speaker clustering consists of identifying who spoke, and when they spoke in a long news broadcast. Speaker clustering is given a set of short audio segments. Ideally, it will discover how many people are speaking in the broadcast, and when they are speaking. The same problem can be transposed to a different domain. In this paper, we present two techniques that do not require a priori training. The speaker clustering is based on information collected solely on encountered test data. They aim at being portable across domains. The first method is based on a Bayesian information criterion (BIC), with single full-covariance Gaussians. It is fairly primitive but effective. The second method, called speaker triangulation, constructs a coordinate system based on conditional likelihoods of the audio segments. Clusters are located in this coordinate system. We are able to achieve state-of-the-art performance on NIST evaluations across different data sets.
Integration of Host Position Prediction into Hierarchical Aggregation

The current RTP/RTCP [1] standard is not optimal for deployment in large-scale sessions with a single source. As the IPTV market is expected to be growing in the coming years, many universities and research laboratories are involved in optimizing for this couple of protocols. One of the most promising technologies for such purposes seems to be the Hierarchical Aggregation (HA). It optimizes transmitting of RTCP back channel and this leads to lower bandwidth requirements. This paper describes hierarchical aggregation and proposes how host position prediction can be integrated. Furthermore, this paper proposes how the host prediction can be improved using additional feedback targets and how it can be used for statistical purposes.
A Story of Suo Motos, Judicial Activism, and Article 184 (3)

The synergy between Big Data and Open Data has the potential to revolutionize information access in the developing world. Following this mantra, we present the analysis of more than a decade worth of open judgements and orders from the Supreme Court of Pakistan. Our overarching goal is to discern the presence of judicial activism in the country in the wake of the Lawyers' Movement. Using Apache Spark as the processing engine we analyze hundreds of unstructured PDF documents to sketch the evolution and various organs of judicial activism in Pakistan since 2009. Our results show that the judiciary has indeed been pursuing matters of public interest, especially those that pertain to the fundamental rights of the citizens. Furthermore, we show how the size of the presiding bench in a case and citations of Articles from the Constitution and prior judgements can aid in classifying legal judgements. Throughout the analysis we also highlight the challenges that anyone who aims to apply Big Data techniques to Open Data will face. We hope that this work will be one in a series of community efforts to use Open Data as a lens to analyze real-world events and phenomena in the developing world.
On the challenge of assessing overlay topology adaptation mechanisms

This thesis is that a peer-to-peer network's overlay topology should adapt to match the demand graph of the peer-to-peer network. In order to assess the effectiveness of various adaptation mechanisms, a comparison with an optimal topology for a given demand graph would be helpful. However, several related optimization/decision problems have been shown to be NP-hard. The contributions of this paper are threefold: i) NP-hardness results related to assessing overlay adaptation strategies were briefly surveyed, ii) a specific optimization problem and metric was presented, and iii) experimental results indicating the potential of optimizing the overlay topology were provided. Finally, in the spirit of a 'challenge paper' various open issues were stated and discussed.
Origin of springtime ozone enhancements in the lower troposphere over Beijing: in situ measurements and model analysis

Abstract. Ozone (O3) concentrations in the lower troposphere (LT) over Beijing have significantly increased over the past 2 decades as a result of rapid industrialization in China, with important implications for regional air quality and the photochemistry of the background troposphere. We characterize the vertical distribution of lower-tropospheric (0–6 km) O3 over Beijing using observations from 16 ozonesonde soundings during a field campaign in April–May 2005 and MOZAIC (Measurement of Ozone and Water Vapor by Airbus In-Service Aircraft) over 13 days in the same period. We focus on the origin of LT O3 enhancements observed over Beijing, particularly in May. We use a global 3-D chemistry and transport model (GEOS-Chem CTM; GEOS – Goddard Earth Observing System) driven by assimilated meteorological fields to examine the transport pathways for O3 pollution and to quantify the sources contributing to O3 and its enhancements in the springtime LT over Beijing. Output from the Global Modeling Initiative (GMI) CTM is also used. High O3 concentrations (up to 94.7 ppbv) were frequently observed at the altitude of ~ 1.5–2 km. The CTMs captured the timing of the occurrences but significantly underestimated their magnitude. GEOS-Chem simulations and a case study showed that O3 produced in the Asian troposphere (especially from Asian anthropogenic pollution) made major contributions to the observed O3 enhancements. Contributions from anthropogenic pollution in the European and North American troposphere were reduced during these events, in contrast with days without O3 enhancements when contributions from Europe and North America were substantial. The O3 enhancements typically occurred under southerly wind and warmer conditions. It is suggested that an earlier onset of the Asian summer monsoon would cause more O3 enhancement events in the LT over the North China Plain in late spring and early summer.
Region 2 Response to CST AG Recommendations on the Lower Passaic River Project Early Action

The Lower Passaic River Restoration Project team briefed the Contaminated Sediments Technical Advisory Group (CST AG) on the Lower Passaic River Early Action evaluation on February 12, 2008. The briefmg was based on a draft Focused Feasibility Study (FFS) completed in June 2007. On April 1, 2008, CSTAG provided recommendations to the Region. Before responding to each comment individually, a general discussion ofCSTAG's key concerns and the approach developed to address those issues going forward is provided below.
Symbiogenesis in Learning Classifier Systems

Symbiosis is the phenomenon in which organisms of different species live together in close association, resulting in a raised level of fitness for one or more of the organisms. Symbiogenesis is the name given to the process by which symbiotic partners combine and unify, that is, become genetically linked, giving rise to new morphologies and physiologies evolutionarily more advanced than their constituents. The importance of this process in the evolution of complexity is now well established. Learning classifier systems are a machine learning technique that uses both evolutionary computing techniques and reinforcement learning to develop a population of cooperative rules to solve a given task. In this article we examine the use of symbiogenesis within the classifier system rule base to improve their performance. Results show that incorporating simple rule linkage does not give any benefits. The concept of (temporal) encapsulation is then added to the symbiotic rules and shown to improve performance in ambiguous/non-Markov environments.
A Control and Decision System for Smart Buildings

The employment of a control and decision process supported by wireless sensor and actuator networks (WSANs) is a promising way to improve energy efficiency of Smart Buildings. We present and evaluate CONDE, a decentralized system for decision and control for Smart Building applications based on WSANs. Performed experiments shown that since data is processed within the network instead of transmitted to a central location, there is a gain in terms of the system response time and resource consumption. Therefore, CONDE improves the energy efficiency of both the monitored building and the WSAN infrastructure, when compared to centralized approaches. Moreover, it exploits integration of applications at the decision level to further improve the system efficiency.