Miracle-dz/UniFAR-FTUs huggingface dataset:
Stochastic model predicts evolving preferences in the Iowa gambling task

Learning under uncertainty is a common task that people face in their daily life. This process relies on the cognitive ability to adjust behavior to environmental demands. Although the biological underpinnings of those cognitive processes have been extensively studied, there has been little work in formal models seeking to capture the fundamental dynamic of learning under uncertainty. In the present work, we aimed to understand the basic cognitive mechanisms of outcome processing involved in decisions under uncertainty and to evaluate the relevance of previous experiences in enhancing learning processes within such uncertain context. We propose a formal model that emulates the behavior of people playing a well established paradigm (Iowa Gambling Task - IGT) and compare its outcome with a behavioral experiment. We further explored whether it was possible to emulate maladaptive behavior observed in clinical samples by modifying the model parameter which controls the update of expected outcomes distributions. Results showed that the performance of the model resembles the observed participant performance as well as IGT performance by healthy subjects described in the literature. Interestingly, the model converges faster than some subjects on the decks with higher net expected outcome. Furthermore, the modified version of the model replicated the trend observed in clinical samples performing the task. We argue that the basic cognitive component underlying learning under uncertainty can be represented as a differential equation that considers the outcomes of previous decisions for guiding the agent to an adaptive strategy.
An elderly assisted living system with wireless sensor networks

This paper proposes the new emerging technologies for the elderly assistive system. By combining Internet, embedded systems, and wireless sensor networks, the system can offer a great potential to assist the elderly people to live safely in their homes. The low cost tiny fall detection is also proposed in this paper. The final contribution of this work is to demonstrate our wireless sensor networks technology that applied in the elderly assistive living. This leads to give the promise of improving the healthcare in the developing countries.
Signaling b Smad-Dependent TGF-Antiapoptotic Functions of IL-2 by Uncoupling of Promitogenic and

TGF- (cid:1) opposes proliferative signaling by IL-2 through mechanisms that remain incompletely deﬁned. In a well-characterized CD8 (cid:2) T cell model using wild-type and mutated IL-2 receptors, we examined the effects of TGF- (cid:1) on distinct IL-2 signaling events in CD8 (cid:2) T cells. IL-2 induces c- myc , cyclin D2 , and cyclin E in a redundant manner through the Shc and STAT5 pathways. TGF- (cid:1) inhibited the ability of either the Shc or STAT5 pathway to induce these genes, as well as T cell proliferation. The inhibitory effects of TGF- (cid:1) were reversed by expression of a dominant-negative form of Smad3. TGF- (cid:1) did not impair proximal signaling by Shc or STAT5, and induction of some downstream genes, including cytokine-inducible Src homology-2-containing protein ( CIS ), bcl-x L , and bcl-2 , was spared. Experiments with c- fos , cyclin D2 , and CIS reporter genes revealed that promoter-proximal regulatory elements dictate the sensitivity of IL-2 target genes to inhibition by TGF- (cid:1) . By leaving the Shc and STAT5 pathways functional while inhibiting their target genes selectively, TGF- (cid:1) was found to uncouple the proliferative and antiapoptotic functions of IL-2. Thus, TGF- (cid:1) is not a simple antagonist of IL-2, but rather serves to qualitatively modify the IL-2 signal to create a unique pattern of gene expression that neither cytokine can induce independently. The Journal of Immunology, 2003, 170: 5563–5570.
A motivation based behavior in hybrid intelligent agents for intention reconsideration process in vessel berthing applications

Strategic planning and dynamism in decision making are essential factors in a vessel berthing application in any container port to assure faster turnaround time and high productivity. BDI agents have been used in many applications with limited capabilities. We propose a new hybrid BDI architecture with learning capacities overcoming some limitations exists in the generic BDI agent model. A new "knowledge acquisition model" (KAM) module is proposed with a supervised neural network and adaptive neuro fuzzy inference system (ANFIS) in the intention reconsideration process of the agent model. Commitment strategy of the new intention reconsideration process is based on the motivation of the state transitions and the effect of belief changes in the environment.
Circle Detection using the Image Ray Transform - A Novel Technique for using a Ray Analogy to Extract Circular Features

Physical analogies are an exciting paradigm for creating techniques for image feature extraction. A transform using an analogy to light rays has been developed for the detection of circular and tubular features. It uses a 2D ray tracing algorithm to follow rays through an image, interacting at a low level, to emphasise higher level features. It has been empirically tested as a pre-processor to aid circle detection with the Hough Transform and has been shown to provide a clear improvement over standard techniques. The transform was also used on natural images and we show its ability to highlight circles even in complex scenes. We also show the flexibility available to the technique through adjustment of parameters.
Formal Methods for Developing Software Specifications: Paths to Wider Usage

Although many formal methods have been proposed for improving the quality of software speciications, a number of barriers to widespread use of these methods remain. This paper describes four of these barriers|failure to scale, unnatural interfaces, limited analysis capabilities, and insuu-cient tool integration|and suggests some promising approaches for overcoming them. These approaches include automated abstraction, user interfaces designed for ease of use, and the application of powerful decision procedures. To illustrate the barriers and approaches to overcoming them, several examples are presented based on the SCR (Software Cost Reduction) requirements method.
A Quantitative Model of the Economic Contribution Due to Human Capital

This paper presents a way to build a conceptual framework for establishing the economic impact of human resources on projects in the product market. The starting point for this search is the rejection of the assumption of the substitutability of human capital, ingrained in nearly all discussions about the economic value of a project in the product market. The acting management team has already climbed a particular learning curve. This experience translates into a well-defined assembly of skills and know-how “honed in house” that are not marketable and that can be the basis for sustained competitive advantage. This specificity is encoded in the parameters of a function that represents the responses of the team to aspiration levels proposed by management vis-a-vis its perceptions of market conditions. How the net present value of the project depends on those parameters reflects the weight of the human resources on its value. This approach reveals that the contribution of human resources to the value of a project does not follow any simple additive rule, and that the notion of added value comes mainly from comparisons against the performance of competing management teams.
1 Towards HPC-ABDS : An Initial High-Performance Big Data Stack

Many scientific problems depend on the ability to analyze and compute on large amounts of data. This analysis often does not scale well, its effectiveness hampered by the increasing volume, variety and rate of change (velocity) of big data. There is a need to integrate features of traditional high-performance computing, such as scientific libraries, communication and resource management middleware, with the rich set of capabilities found in the commercial ecosystem, resulting in an integrated system generically called highperformance big data system (HPBDS). Our proposed preliminary implementation of the HPBDS – including many important software systems such as Hadoop available from the Apache open source community and thus referred to as High-Performance Computing-Big Data Stack (HPC–ABDS) – has two fundamental building blocks: (i) Middleware for Data-Intensive Analytics and Science (MIDAS) that will enable scalable applications with the performance of High Performance Computing (HPC) and the rich functionality of the commodity Apache big data Stack. (ii) The second building block will design and implement a set of crosscutting high-performance data-analysis libraries SPIDAL (Scalable Parallel Interoperable Data Analytics Library), which will support new programming and execution models for data-intensive analysis in a wide range of science and engineering applications. These libraries will be implemented to be scalable and interoperable across a range of computing systems including clouds, clusters and supercomputers. The project libraries will have the same beneficial impact on data analytics that scientific libraries such as PETSc, MPI and SCALAPACK have had for supercomputer simulations. In this paper, we study many big data applications from a variety of research and commercial areas and suggest a set of characteristic features and possible kernel benchmarks that stress those features for data analytics. We draw conclusions for the hardware and software architectures that are suggested by this analysis.
Optimization of an Efficient Protein Extraction Protocol Compatible with Two-Dimensional Electrophoresis and Mass Spectrometry from Recalcitrant Phenolic Rich Roots of Chickpea (Cicer arietinum L.)

Two-dimensional electrophoresis and mass spectrometry are undoubtedly two essential tools popularly used in proteomic analyses. Utilization of these techniques however largely depends on efficient and optimized sample preparation, regarded as one of the most crucial steps for recovering maximum amount of reliable information. The present study highlights the optimization of an effective and efficient protocol, capable of extraction of root proteins from recalcitrant phenolic rich tissues of chickpea. The widely applicable TCA-acetone and phenol-based methods have been comparatively evaluated, amongst which the latter appeared to be better suited for the sample. The phenol extraction-based method further complemented with sodium dodecyl sulphate (SDS) and pulsatory treatments proved to be the most suitable method represented by greatest spot number, good resolution, and spot intensities. All the randomly selected spots showed successful identification when subjected to further downstream MALDI-TOF and MS/MS analyses. Hence, the information obtained collectively proposes the present protein extraction protocol to be an effective one that could be applicable for recalcitrant leguminous root samples.
Optimization of Catheter Ablation of Atrial Fibrillation: Insights Gained from Clinically-Derived Computer Models

Atrial fibrillation (AF) is the most common heart rhythm disturbance, and its treatment is an increasing economic burden on the health care system. Despite recent intense clinical, experimental and basic research activity, the treatment of AF with current antiarrhythmic drugs and catheter/surgical therapies remains limited. Radiofrequency catheter ablation (RFCA) is widely used to treat patients with AF. Current clinical ablation strategies are largely based on atrial anatomy and/or substrate detected using different approaches, and they vary from one clinical center to another. The nature of clinical ablation leads to ambiguity regarding the optimal patient personalization of the therapy partly due to the fact that each empirical configuration of ablation lines made in a patient is irreversible during one ablation procedure. To investigate optimized ablation lesion line sets, in silico experimentation is an ideal solution. 3D computer models give us a unique advantage to plan and assess the effectiveness of different ablation strategies before and during RFCA. Reliability of in silico assessment is ensured by inclusion of accurate 3D atrial geometry, realistic fiber orientation, accurate fibrosis distribution and cellular kinetics; however, most of this detailed information in the current computer models is extrapolated from animal models and not from the human heart. The predictive power of computer models will increase as they are validated with human experimental and clinical data. To make the most from a computer model, one needs to develop 3D computer models based on the same functionally and structurally mapped intact human atria with high spatial resolution. The purpose of this review paper is to summarize recent developments in clinically-derived computer models and the clinical insights they provide for catheter ablation.
Improved Round Robin Scheduling for MIMO Cellular Systems

Packet scheduling is investigated for the downlink of a multiple-input multiple-output (MIMO) cel- lular system. The conventional round robin scheduling (RRS) scheme does not utilize the benefits of multiuser environments, although it provides fair channel access chance to users. We propose an antenna-assisted round robin scheduling (AA-RRS) scheme to improve the conventional RRS scheme. The AA-RRS scheme provides fair channel access chance to users as the RRS scheme, whereas it increases the system capacity through the ef- fective use of multiple antennas in achieving a diversity effect from multiple users. Computer simulations are conducted to compare the RRS and AA-RRS schemes in terms of the expected and outage capacities. It is shown that the AA-RRS scheme provides significant capacity gain over the RRS scheme, especially in terms of the out- age capacity. We also discuss the effects of power control, signal-to-noise ratio (SNR), and spatial correlation on the system capacities.
Towards More Practical Reinforcement Learning

Reinforcement Learning is beginning to be applied outside traditional domains such as robotics, and into human-centric domains such as healthcare and education. In these domains, two problems are critical to address: We must be able to evaluate algorithms with a collection of prior data if one is available, and we must devise algorithms that carefully trade off exploration and exploitation in such a way that they are guaranteed to converge to optimal behavior quickly, while retaining very good performance with limited data. In this thesis, I examine these two problems, with an eye towards applications to educational games.
Pricing path dependent options under variance gamma dynamics

The pricing of derivative securities in finance has been dominated by modelling the underlying security as an Ito diffusion process. A major problem with diffusion processes is that their sample paths are continuous almost surely and operate in a complete market model. Whilst local (and stochastic) volatility models in a diffusion setting do well in alleviating the volatility smile, they ignore one fundamental qualitative aspect of risky asset price dynamics: prices jump. Thus price dynamics driven by general Levy processes may provide a more realistic framework. This dissertation explores the variance gamma process, ,,;hich consists entirely of jumps, as a model for pricing derivatives. General properties are discussed including a method of calibrating the model to the implied risk-neutral distribution. The pricing of an up-snd-out call, a lookback put and a GouLle barrier option are discussed assuming Geometric Brownian motion dynamics, followed by variance gamma dynamics. An efficient Monte Carlo method known as the 'truncated difference-of-gammas sampling' is discussed and implemented. A comparison of pnces concludes the dissertation. Un ive rsi ty of Ca pe To wn
Climate Change and Trade: Taxing Carbon at the Border?

This study analyses the economic and political consequences of introducing a tax on the carbon content of imported goods at EU borders and whether such a tax would be compatible with WTO rules. The major findings are: 1. A CO2 border tax or import tariff would increase global welfare; 2. Such a carbon import tariff can be made to be compatible with WTO rules; 3. There are no insurmountable practical obstacles to introducing such a tariff; 4. The equity concerns of the UNFCCC could be taken into account by rebating the proceeds of the tariff to those countries manifestly unable to shoulder the burden themselves.
Quasiparticle picture of black holes and the entropy-area relation

We propose an effective description of 0-brane black holes, in which the black hole is modeled as a gas of noninteracting quasiparticles in dual quantum mechanics. This simple model is shown to account for many of the static thermodynamic properties of the black hole. It also accounts for dynamical properties, such as the rate at which energy gets thermalized by the black hole. We use the model to show that the entropy of quantum mechanics is proportional to the black hole horizon area in Planck units.
Motion Analysis in Video: Dolls, Dynamic Cues and Modern Art

This paper addresses the problem of synthesising animations from video clips; in particular emphasising the motion of tracked objects. We introduce idynamic cuesi as a class of motion emphasis cue, encompassing traditional animation techniques such as anticipation and exaggeration. We present methods for automatically synthesising such cues within video premised upon the recovery of articulated gures, and the subsequent manipulation of the recovered pose trajectories. Additionally, we apply our motion emphasis framework to emulate artwork in the Futurist style, popularised by Duchamp.
The NMDA receptor complex: a multifunctional machine at the glutamatergic synapse

The N-methyl-D-aspartate receptors (NMDARs) are part of a large multiprotein complex at the glutamatergic synapse. The assembly of NMDARs with synaptic proteins offers a means to regulate NMDAR channel properties and receptor trafficking, and couples NMDAR activation to distinct intracellular signaling pathways, thus contributing to the versatility of NMDAR functions. Receptor-protein interactions at the synapse provide a dynamic and powerful mechanism for regulating synaptic efficacy, but can also contribute to NMDAR overactivation-induced excitotoxicity and cellular damage under pathological conditions. An emerging concept is that by understanding the mechanisms and functions of disease-specific protein-protein interactions in the NMDAR complex, we may be able to develop novel therapies based on protein-NMDAR interactions for the treatment of brain diseases in which NMDAR dysfunction is at the root of their pathogenesis.
Isolate-Dependent Growth, Virulence, and Cell Wall Composition in the Human Pathogen Aspergillus fumigatus

The ubiquitous fungal pathogen Aspergillus fumigatus is a mediator of allergic sensitization and invasive disease in susceptible individuals. The significant genetic and phenotypic variability between and among clinical and environmental isolates are important considerations in host-pathogen studies of A. fumigatus-mediated disease. We observed decreased radial growth, rate of germination, and ability to establish colony growth in a single environmental isolate of A. fumigatus, Af5517, when compared to other clinical and environmental isolates. Af5517 also exhibited increased hyphal diameter and cell wall β-glucan and chitin content, with chitin most significantly increased. Morbidity, mortality, lung fungal burden, and tissue pathology were decreased in neutropenic Af5517-infected mice when compared to the clinical isolate Af293. Our results support previous findings that suggest a correlation between in vitro growth rates and in vivo virulence, and we propose that changes in cell wall composition may contribute to this phenotype.
Video inpainting model for camera motion based on improved background subtraction method

Motion object detection has been widely used in traffic monitoring and target tracking fields. In order to solve the difficulty of building a model of the background and improving the accuracy of the update rate in the background subtraction, a new detection moving object method combining the surf algorithm and the background subtraction is proposed in this paper. Firstly, according to the continuity of motion, motion regions are labeled and filled in; Secondly, the image mosaic by surf algorithm is conducted to obtain the image with the whole background, then the whole background image mosaic is used to obtain the frame image with the whole background; Finally, frame difference method is used to obtain the foreground object, and then a morphology is processed. The experiment has not only achieved higher accuracy which is improved by about 8%~10%, but also got lower error which is reduced by 3.5%~4.0%. In a word, the algorithm has better robustness.
Classification of Polarimetric SAR Images Based on Optimum SVMs Classifier Using Bees Algorithm

Because of Polarimetric Synthetic Aperture Radar (PolSAR) contains the different features which relate to the physical properties of the terrain in unique ways, polarimetric imagery provides an efficient tool for the classification of the complex geographical areas. Support Vector Machines (SVMs) are particularly attractive in the remote sensing field due to their ability to handle the nonlinear classifier problem in high dimensional feature space. However, they also suffer from optimum SVMs parameters assignment and optimum feature subset selection issues that can significantly affect on the obtained results. In optimization of SVMs parameters and feature space, traditional optimization algorithms usually trap in local optimum. Thus, it is inevitable to apply meta- heuristic optimization algorithms to obtain global optimum solution. As results, the superior performance of SVMs achieved by simultaneously optimization of SVMs parameters and input feature subset on Polarimetric imagery are demonstrated. This study, initially aims to optimize the accuracy of SVMs classifier by selecting the subset of best informative features and determining the best values for the SVMs parameters. Accordingly, we apply three proposed classification strategies by combining of SVMs classifier and Bees Algorithm. These strategies respectively are; classification based on SVMs parameter optimization, classification based on input feature optimization, and classification based on SVMs parameters and input features optimization. As an experimental result, the potential of three proposed optimization methods for determination of SVMs parameters and feature subset selection based on Bees Algorithm is evaluated and compared with Genetic Algorithm (GA).