Miracle-dz/UniFAR-FTUs huggingface dataset:
rlk/TXK Encodes Two Forms of a Novel Cysteine String Tyrosine Kinase Activated by Src Family Kinases

ABSTRACT Rlk/Txk is a member of the BTK/Tec family of tyrosine kinases and is primarily expressed in T lymphocytes. Unlike other members of this kinase family, Rlk lacks a pleckstrin homology (PH) domain near the amino terminus and instead contains a distinctive cysteine string motif. We demonstrate here that Rlk protein consists of two isoforms that arise by alternative initiation of translation from the same cDNA. The shorter, internally initiated protein species lacks the cysteine string motif and is located in the nucleus when expressed in the absence of the larger form. In contrast, the larger form is cytoplasmic. We show that the larger form is palmitoylated and that mutation of its cysteine string motif both abolishes palmitoylation and allows the protein to migrate to the nucleus. The cysteine string, therefore, is a critical determinant of both fatty acid modification and protein localization for the larger isoform of Rlk, suggesting that Rlk regulation is distinct from the other Btk family kinases. We further show that Rlk is phosphorylated and changes localization in response to T-cell-receptor (TCR) activation and, like the other Btk family kinases, can be phosphorylated and activated by Src family kinases. However, unlike the other Btk family members, Rlk is activated independently of the activity of phosphatidylinositol 3-kinase, consistent with its lack of a PH domain. Thus, Rlk has two distinct isoforms, each of which may have unique properties in signaling downstream from the TCR.
Bayesian Optimization for Black-Box Evasion of Machine Learning Systems

Machine learning and big data algorithms have had widespread adoption in recent times, with extensive use in big industries such as advertising, e-commerce, finance, and healthcare. Despite the increased reliance on machine learning algorithms, general understanding of its vulnerabilities are still in the early stages. Because of this, there has to be a better grasp of its security implications to prevent attacks that could undermine the integrity of machine learning systems. Currently, attackers can use adversarial samples to fool even the state-of-the-art machine learning algorithms. Adversarial samples are legitimate inputs altered by adding a specially crafted perturbation, these samples retain their true class while forcing the target algorithm to misclassify them. However, current attack methods require knowledge of the target’s underlying model or training data. To add to this, there is a lack of a general technique that can detect adversarial samples. I introduce a novel black-box attack against machine learning classifiers that uses Bayesian optimization. This technique is data-efficient and model-independent, two properties that existing attacks do not have. Its model-independence allows it to find adversarial regions regardless of the underlying algorithm, and its data-efficiency makes it suitable for attacking machine learning systems undetected. I demonstrate its effective against three different types of classifiers and show that it outperforms a white-box attack, fast gradient sign method (FGSM), when against a convolutional neural network (CNN). Bayesian optimization achieves an evasion rate of 99% while FGSM achieves 62% for the same perturbation setting. This implies that Bayesian optimization can be used to attack high-dimensional problems and supports the hypothesis that machine learning algorithm have low effective dimensionality. The results also show that a black-box attack through Bayesian optimization can potentially unmask adversarial samples that are not even found by the strongest white-box attackers. It has the potential to become a general framework for probing any machine learning algorithm or defense for adversarial samples.
Education for Rural Development: Embedding Rural Dimensions in Initial Teacher Preparation.

In South Africa, rural education and development are issues of social justice, especially in places that were previously established as homelands. This article presents some of the tensions that are inherent in the conceptions of rurality, rural education and the possibility of sustainable rural education and development. We propose the notion of education for rural development as a useful concept for its potential for transforming and sustaining rural education. We describe the implications of education for rural development on teacher education change by focusing on the teaching experience (TE) programme.
A boundary element and level set based bi-directional evolutionary structural optimisation with a volume constraint

A new topology optimisation algorithm is implemented and presented for compliance minimisation of continuum structures using a volume preserving mechanism which effectively handles a volume constraint. The volume preserving mechanism is based on a unique combination of the level set method and a boundary element based bi-directional evolutionary structural optimisation approach using a bisectioning algorithm. The evolving structural geometry is implicitly represented with the level sets, efficiently handling complex topological shape changes, including holes merging with each other and with the boundary. Numerical results for two-dimensional linear elasticity problems suggest that the proposed adaptation provides smooth convergence of the objective function and a more robust, smoother geometrical description of the optimal design. Moreover, this new implementation allows for efficient material re-distribution within the design domain such that the ∗Corresponding author Email address: baseerullah@gmail.com (BaseerUllah) objective function is minimised at constant volume. The proposed volume preserving mechanism can be easily extended to three-dimensional space.
ZHAISHAN TUNNEL 3 D DIGITIZED AND DISPLAYED IN KINMEN

There were some battles for defending Taiwan straits safety that is Kinmen’s past experience, for example, Guningtou battle, 93-cannon battle, 823-cannon battle. Therefore some unable be wiped out historical vestige and battle historical remains are remained. Else historic spots, ancestral hall and special folk custom are historical culture worthy, the Central tunnel shelter of Taiwu-mountain, Qingtian-hall, Qionglin’s tunnel shelter, Mason broadcast station, Zhaishan and Jiugong tunnel shelter that are special battle culture in Kinmen. That important value is connoted with special geographical features and deep historical memory. This plan has spirit in accordance with digital book reservation is a responsibility of information society and global conservative development. To choose tunnel shelter that has wartime and historical value to be digital book reservation target. Develop correlative digital reservation areas with network and compile web contents with existence results, base information, science papers and research reports. Book reservation contents include precision space information of 3D digital cloud points of architecture and virtual guiding web. Get the purpose of the optimum digital reservation. The plan result can be increased and showed by other Kinmen’s culture and has continuity reservation operating environment.
Optique – Zooming In on Big Data Access

Despite the dramatic growth of data accumulated by enterprises, obtaining value out of it is extremely challenging. In particular, the data access bottleneck prevents domain experts from getting the right piece of data within a constrained time frame. The Optique Platform unlocks the access to Big Data by providing end users support for directly formulating their information needs through an intuitive visual query interface. The submitted query is then transformed into highly optimized queries over the data sources, which may include streaming data, and exploiting massive parallelism in the backend whenever possible. The Optique Platform thus responds to one major challenge posed by Big Data in data-intensive industrial settings. Keywords—8.II.VIII.VI Knowledge management applications, 8.II. IV.IV Distributed databases, 8.V. II.VI Graphical user interfaces, 8.II. IV.VIII Query processing, 8.II. IV.XIII Temporal databases
Number : D 202 _ 02 Ultra Large-Scale Power System Control Architecture

Electric power grids are becoming stressed by integration of intermittent renewable resources and significant adoption of distributed energy resources. The complexity of the grid is growing rapidly as we attempt to support technical, business, and societal goals for which power grids were not originally designed. Today, we largely take stability of the grid for granted. However, stability could collapse because of new dynamics introduced to the grid, and because the extreme complexity makes traditional control analysis intractable, so that grid behaviour is more unpredictable. To ensure grid stability and have the agility to remain reliable under highly dynamic destabilizing conditions requires that grid control systems also evolve in ways that address these new changes and the resultant operational problems. Ultra-large power system control architecture macro architecture for grid control that can solve the problems inherent in the present power grid evolutionary path is needed and has not been addressed in present smart grid architecture efforts.
Real-Time Traffic Sign Detection and Recognition Using GPU

This paper presents a Graphic Processing Unit (GPU) implementation of real-time traffic sign detection and recognition which can classify 48 classes of traffic signs in the library. The GPU-based system has three stages: pre-processing, feature extraction and classification. The proposed design is to optimize the tradeoff between accuracy and computing time which has not been well studied previously. The GPU-based system can achieve the frame rate of 21.3 frames per second (fps) for the image resolution of 1,238 by 1,628. The experimental results show the detection rate of 91.69% and the recognition rate of 93.77%.
Gene expression FastMap : Fast eQTL mapping in homozygous populations

Motivation: Gene expression Quantitative Trait Locus (eQTL) mapping measures the association between transcript expression and genotype in order to find genomic locations likely to regulate transcript expression. The availability of both gene expression and high-density genotype data has improved our ability to perform eQTL mapping in inbred mouse and other homozygous populations. However, existing eQTL mapping software does not scale well when the number of transcripts and markers are on the order of 105 and 105–106, respectively. Results: We propose a new method, FastMap, for fast and efficient eQTL mapping in homozygous inbred populations with binary allele calls. FastMap exploits the discrete nature and structure of the measured single nucleotide polymorphisms (SNPs). In particular, SNPs are organized into a Hamming distance-based tree that minimizes the number of arithmetic operations required to calculate the association of a SNP by making use of the association of its parent SNP in the tree. FastMap’s tree can be used to perform both single marker mapping and haplotype association mapping over an m-SNP window. These performance enhancements also permit permutation-based significance testing. Availability: The FastMap program and source code are available at the website: http://cebc.unc.edu/fastmap86.html Contact: iir@unc.edu; nobel@email.unc.edu Supplementary information: Supplementary data are available at Bioinformatics online.
S Interactive and Dynamic Multi-Objective Software Refactoring Recommendations

Successful software products evolve through a process of continual change. However, this process may weaken the design of the software and make it unnecessarily complex, leading to significantly reduced productivity and increased fault-proneness. Refactoring improves the software design while preserving overall functionality and behavior, and is an important technique in managing the growing complexity of software systems. Most of the existing work on software refactoring uses either an entirely manual or a fully automated approach. Manual refactoring is time-consuming, error-prone and unsuitable for large-scale, radical refactoring. On the other hand, fully automated refactoring yields a static list of refactorings which, when applied, leads to a new and often hard to comprehend design. Furthermore, it is difficult to merge these refactorings with other changes performed in parallel by developers. In this paper, we propose a refactoring recommendation approach that dynamically adapts and interactively suggests refactorings to developers and takes their feedback into consideration. Our approach uses NSGA-II to find a set of good refactoring solutions that improve software quality while minimizing the deviation from the initial design. These refactoring solutions are then analyzed to extract interesting common features between them such as the frequently occurring refactorings in the best non-dominated solutions. Based on this analysis, the refactorings are ranked and suggested to the developer in an interactive fashion as a sequence of transformations. The developer can approve, modify or reject each of the recommended refactorings, and this feedback is then used to update the proposed rankings of recommended refactorings. After a number of introduced code changes and interactions with the developer, the interactive NSGA-II algorithm is executed again on the new modified system to repair the set of refactoring solutions based on the new changes and the feedback received from the developer. We evaluated our approach on a set of eight open source systems and two industrial projects provided by an industrial partner. Statistical analysis of our experiments shows that our dynamic interactive refactoring approach performed significantly better than four existing search-based refactoring techniques and one fully-automated refactoring tool not based on heuristic search.
Developing a Multilateral Telerehabilitation System for Patient with Diminished Hand Function

Telerehabilitation or E-rehabilitation is the delivery of rehabilitation services over telecom networks. According to the World Health Organization (WHO), there are about 650 million people with disabilities worldwide; these statistics drive the need for a more applicable Telehealthcare Systems. This paper aims to provide a design description of a novel multilateral telerehabilitation system for Patient with diminished hand function which needs minimum interaction between patient and therapist. The proposed configuration consists of the Central Intelligent System (CIS) including multi-function modules in a homepage for patient in physiotherapy office, and a Hand Rehabilitation System including a Robot, Motion Analysis Module as well as Wireless Sensor Network (WSN) in patient’s home. This system works by a diagnostic feedback from a Hand Rehabilitation System to the Central Intelligent System (CIS). Patient fixes sensors on his hand and doing daily activity. Data transmitted by WSN will be analyzed by the Motion Analysis Module in order to diagnose the latest degree of recovery. Information is feed backed automatically to the CIS through network. An appropriate program corresponding to the feed-backed information will be prepared by CIS and transmitted into the robot. After doing exercise by the system, joint angles are measured and send to the CIS again to diagnose the level of patient recovery. Cost of care by this configuration will be reduced significantly. This paper presents a prototype that successfully operates as a multilateral telerehabilitation system.
Multicore programming techniques for high-performance ATE

As CPU clock rates approach their theoretical physical limits, technology is moving toward new processors with multiple processing cores. Industry experts recognize that taking advantage of multicore processors for programming applications is challenging. This paper examines the various multicore programming techniques that programmers can use to overcome these challenges for both existing and future programs and discusses the following two key focus areas of multicore technology adoption - reducing test time & optimizing complex ATE analysis.
Development of Noiseless Steganography Method in Role-Playing Game Resa

Noiseless Steganography (Nostega) is a variant of steganography in which the method does not use or produce noise that can expose the message’s existence in the media. A potential media for Nostega is role-playing game as it has an abundance of uncertain elements that can be used for embedding messages. Using the Nostega architecture, parameters, message encoder, and communications protocol and covert channels are defined and implemented in the game Empyrean Campaign as a case study. The method is then verified to be noiseless based on a survey result that respondents does not realize that a hidden message exists in the game. Keywords—steganography, noiseless steganography, roleplaying game
Comparison of clinical outcome of induction immunosuppressive therapy with thymoglobulin and standard therapy in kidney transplantation; a randomized double-blind clinical trial

Introduction: Thymoglobulin is a lymphocyte-depleting polyclonal antibody, administered for induction therapy at the time of kidney transplantation to reduce the risk of acute allograft rejection. The appropriate dosage and duration of therapy is controversial. The higher dosages are associated with infection and malignancy. Objectives: In this study efficacy and safety of lower dosage (in comparison with previous studies) of thymoglobulin in kidney transplant recipients was evaluated. Patients and Methods: In this clinical trial, 106 adult kidney transplant recipients, were randomized before transplantation in two groups (case and control). The case group (53 patients) were received induction therapy with thymoglobulin (1.5 mg/kg/d for 3 days) and the control group (53 patients) were received non-induction regiment. Delayed graft function (DGF), glomerular filtration rate (GFR), acute allograft rejection and thymoglobulin complications were evaluated during the first post-transplantation year. Results: Around 106 kidney transplant recipients were enrolled (71 or 66.98% deceased donor) to the study. No significant statistical differences were found in GFR at the time of discharge from hospital (P=0.399) and at 1 year (P=0.851) and acute allograft rejection (P= 0.304) between two groups. Graft survival (73.5% in case group versus 81.1% in control group, P=0.392) at month 12th was similar among groups. Additionally, no significant differences of acute allograft rejection in recipient from deceased or living donor between two groups were detected. There was a higher incidence of DGF in the control group (26.4%) than the thymoglobulin group (5.8%) and the difference was statistically significant (P= 0.004). Thrombocytopenia (17% versus 49.1%, P<0.001) and leukopenia (11.3% versus 50.9%, P<0.001) were also significantly higher in the case group. Conclusion: While the incidence of DGF was reduced in thymoglobulin group, the short-term acute allograft rejection rate was not reduced compared to the control group. However, our results require further consideration with larger samples
Somatic gene transfer of NGF to the aged brain: behavioral and morphological amelioration

Primary fibroblasts modified to secrete nerve growth factor (NGF) were implanted into the nucleus basalis magnocellularis (NBM) of aged memory impaired rats. The NGF-producing fibroblasts survived for 6 weeks following transplantation and continued expressing NGF mRNA through the duration of the experiment. A significant amelioration of the memory impairment and a significant increase in size and number of low- affinity NGF receptor (p75)-positive neurons in the basal forebrain were observed. Implantation of NGF-producing cells into normal young adult rats resulted in a transient but significant memory impairment and hypertrophy of low-affinity NGF receptor-positive neurons. These results show that naturally occurring age-related memory loss can be reversed by grafting cells engineered to secrete NGF directly to the NBM, and that either cholinergic hyper- or hypofunction may lead to cognitive impairments.
Neural Network Subtyping of Depression

Objective: To examine the applicability of a neural network classification strategy to examine the independent contribution of psychomotor disturbance (PMD) and endogeneity symptoms to the DSM-III-R definition of melancholia. Method: We studied 407 depressed patients with the clinical dataset comprising 17 endogeneity symptoms and the 18-item CORE measure of behaviourally rated PMD. A multilayer perceptron neural network was used to fit non-linear models of varying complexity. A linear discriminant function analysis was also used to generate a model for comparison with the non-linear models. Results: Models (linear and non-linear) using PMD items only and endogeneity symptoms only had similar rates of successful classification, while non-linear models combining both PMD and symptom scores achieved the best classifications. Conclusions: Our current non-linear model was superior to a linear analysis, a finding which may have wider application to psychiatric classification. Our non-linear analysis of depressive subtypes supports the binary view that melancholic and non-melancholic depression are separate clinical disorders rather than different forms of the same entity. This study illustrates how non-linear modelling with neural networks is a potentially fruitful approach to the study of the diagnostic taxonomy of psychiatric disorders and to clinical decision-making.
Hidden Markov models based on multi-space probability distribution for pitch pattern modeling

This paper discusses a hidden Markov model (HMM) based on multi-space probability distribution (MSD). The HMMs are widely-used statistical models to characterize the sequence of speech spectra and have successfully been applied to speech recognition systems. From these facts, it is considered that the HMM is useful for modeling pitch patterns of speech. However, we cannot apply the conventional discrete or continuous HMMs to pitch pattern modeling since the observation sequence of the pitch pattern is composed of one-dimensional continuous values and a discrete symbol which represents "unvoiced". MSD-HMM includes discrete HMMs and continuous mixture HMMs as special cases, and further can model the sequence of observation vectors with variable dimension including zero-dimensional observations, i.e., discrete symbols. As a result, MSD-HMMs can model pitch patterns without heuristic assumption. We derive a reestimation algorithm for the extended HMM and show that it can find a critical point of the likelihood function.
A PROBABILISTIC MODEL TO PREDICT BUILDIN G OCCUPANTS ’ DIVERSITY TOWARDS THEIR INTERACTIONS WITH THE BUILDING ENVELOPE

Based on observations conducted in an office building, we apply advanced statistical analysis methods, leading to the formulation of stochastic models for the prediction of buildings occupants’ actions on window openings and shading devices. The statistical analysis method – based on generalised linear mixed models – enables a correct treatment of the longitudinal nature of the datasets, an accurate estimation of the calibration parameters’ uncertainty and a detailed study of the differences between the occupants surveyed. This analysis results in the formulation of stochastic models for the prediction of occupants’ interactions with the key elements of the building envelope, which include explicit in-built probabilistic terms to account for occupants’ diversity if required. Furthermore, we show that the properties of these probabilistic terms can be used to infer a statistical distribution of the model’s calibration parameters, which comprehensively represent the diversity of observed behaviours between building occupants, and can be applied to simulate their behavioural properties.