Miracle-dz/UniFAR-FTUs huggingface dataset:
The psychological correlates of bullying among secondary school students in Dublin

The aim of this research was to investigate the experience that students have of bullying in secondary schools in Dublin, and to identify if there are relationships between bullying behaviours and psychological issues. A sample of 138 students across 3 secondary schools in south Dublin completed a questionnaire comprised; The Olweus Questionnaire on Bullying for Students, (Olweus, 2006), The Rosenberg Self-esteem Scale (Rosenberg, 1989), The Perceived Stress Scale (Cohen, Kamarck & Mermelstein, 1983) and The Satisfaction with Life Scale (Diener, Emmons, Larsen & Griffin, 1985). Results indicate that levels of a minority of students are affected by bullying, primarily name calling. Victims are more affected by psychological issues than bullies and bystanders, finally males have significantly higher bullying behaviour scores than females.
DNA preservation: A microsatellite‐DNA study on ancient skeletal remains

To determine the effect of environmental factors on the preservation of DNA, archeological teeth of approximately similar age but greatly differing site milieu were examined for DNA content. The complex relational system of locational milieu of the samples was reduced to its essential and, at the same time, easily measurable factors. These are temperature, humidity, pH value, the geochemical properties of the soil, the amount of postmortal organic substances and the general degree of microbial infestation in the respective soil. The relative DNA content in the samples was established by determining the rate of successful polymerase chain reaction (PCR) amplifications. Differences in quantity and quality of the results are attributed to the respective prevailing environmental factor or to the respective storage conditions. Dryness, low temperature and absence of microorganisms favors the preservation of DNA. The bioapatite of bones and teeth, like the DNA, are preserved under neutral or slightly alkaline conditions. Brief storage at room temperature does not affect the amount of amplifiable DNA but does affect the reproducibility of the results. Long storage outside a lab freezer reduces the amount and the reproducibility of DNA amplifications in ancient specimens.
Epstein-Barr virus: general factors, virus-related diseases and measurement of viral load after transplant

The Epstein-Barr virus is responsible for infectious mononucleosis syndrome and is also closely associated to several types of cancer. The main complication involving Epstein-Barr virus infection, both in recipients of hematopoietic stem cells and solid organs, is post-transplant lymphoproliferative disease. The importance of this disease has increased interest in the development of laboratory tools to improve post-transplant monitoring and to detect the disease before clinical evolution. Viral load analysis for Epstein-Barr virus through real-time polymerase chain reaction is, at present, the best tool to measure viral load. However, there is not a consensus on which sample type is the best for the test and what is its predictive value for therapeutic interventions.
Functional changes of the reward system underlie blunted response to social gaze in cocaine users

Significance Social interaction deficits in drug users likely impede treatment, increase the burden of the affected families, and consequently contribute to the high costs for society associated with addiction. However, the neural origin contributing to altered social interaction in drug users is unknown thus far. The present study illuminates the nature of basic social interaction deficits as exemplified by social gaze behavior in cocaine users by applying behavioral, psychophysiological, and functional brain-imaging methods. The results suggest that basal social interaction impairments probably arise from blunted social reward processing that was again related to impaired real-life social behavior in cocaine users. These results point to the importance of reinstatement of social reward in the treatment of stimulant addiction. Social interaction deficits in drug users likely impede treatment, increase the burden of the affected families, and consequently contribute to the high costs for society associated with addiction. Despite its significance, the neural basis of altered social interaction in drug users is currently unknown. Therefore, we investigated basal social gaze behavior in cocaine users by applying behavioral, psychophysiological, and functional brain-imaging methods. In study I, 80 regular cocaine users and 63 healthy controls completed an interactive paradigm in which the participants’ gaze was recorded by an eye-tracking device that controlled the gaze of an anthropomorphic virtual character. Valence ratings of different eye-contact conditions revealed that cocaine users show diminished emotional engagement in social interaction, which was also supported by reduced pupil responses. Study II investigated the neural underpinnings of changes in social reward processing observed in study I. Sixteen cocaine users and 16 controls completed a similar interaction paradigm as used in study I while undergoing functional magnetic resonance imaging. In response to social interaction, cocaine users displayed decreased activation of the medial orbitofrontal cortex, a key region of reward processing. Moreover, blunted activation of the medial orbitofrontal cortex was significantly correlated with a decreased social network size, reflecting problems in real-life social behavior because of reduced social reward. In conclusion, basic social interaction deficits in cocaine users as observed here may arise from altered social reward processing. Consequently, these results point to the importance of reinstatement of social reward in the treatment of stimulant addiction.
Deep Denoising Auto-encoder for Statistical Speech Synthesis

This paper proposes a deep denoising auto-encoder technique to extract better acoustic features for speech synthesis. The technique allows us to automatically extract low-dimensional features from high dimensional spectral features in a non-linear, data-driven, unsupervised way. We compared the new stochastic feature extractor with conventional mel-cepstral analysis in analysis-by-synthesis and text-to-speech experiments. Our results confirm that the proposed method increases the quality of synthetic speech in both experiments.
An implementation of a coordinate based location system

This paper explains the co-ordinate based location system built for XenoSearch, a resource discovery system in the XenoServer Open Platform. The system is builds on the work of GNP, Lighthouse and many more recent schemes. We also present results from various combinations of algorithms to perform the actual co-ordinate calculation based on GNP, Lighthouse and spring based systems and show our implementations of the various algorithms give similar prediction errors.
Automatic Identification of Salient Acoustic Instances in Couples' Behavioral Interactions Using Diverse Density Support Vector Machines

Behavioral coding focuses on deriving higher-level behavioral annotations using observational data of human interactions. Automatically identifying salient events in the observed signal data could lead to a deeper understanding of how specific events in an interaction correspond to the perceived high-level behaviors of the subjects. In this paper, we analyze a corpus of married couples’ interactions, in which a number of relevant behaviors, e.g., level of acceptance, were manually coded at the sessionlevel. We propose a multiple instance learning approach called Diverse Density Support Vector Machines, trained with acoustic features, to classify extreme cases of these behaviors, e.g., low acceptance vs. high acceptance. This method has the benefit of identifying salient behavioral events within the interactions, which is demonstrated by comparable classification performance to traditional SVMs while using only a subset of the events from the interactions for classification. Index Terms: behavioral signal processing, multiple instance learning, diverse density, support vector machines
Revisiting Browser Security in the Modern Era: New Data-Only Attacks and Defenses

The continuous discovery of exploitable vulnerabilitiesin popular applications (e.g., web browsers and documentviewers), along with their heightening protections againstcontrol flow hijacking, has opened the door to an oftenneglected attack strategy—namely, data-only attacks. In thispaper, we demonstrate the practicality of the threat posedby data-only attacks that harness the power of memorydisclosure vulnerabilities. To do so, we introduce memorycartography, a technique that simplifies the construction ofdata-only attacks in a reliable manner. Specifically, we showhow an adversary can use a provided memory mapping primitive to navigate through process memory at runtime, andsafely reach security-critical data that can then be modifiedat will. We demonstrate this capability by using our cross-platform memory cartography framework implementation toconstruct data-only exploits against Internet Explorer andChrome. The outcome of these exploits ranges from simpleHTTP cookie leakage, to the alteration of the same originpolicy for targeted domains, which enables the cross-originexecution of arbitrary script code. The ease with which we can undermine the security ofmodern browsers stems from the fact that although isolationpolicies (such as the same origin policy) are enforced atthe script level, these policies are not well reflected in theunderlying sandbox process models used for compartmentalization. This gap exists because the complex demands oftoday's web functionality make the goal of enforcing thesame origin policy through process isolation a difficult oneto realize in practice, especially when backward compatibility is a priority (e.g., for support of cross-origin IFRAMEs). While fixing the underlying problems likely requires a majorrefactoring of the security architecture of modern browsers(in the long term), we explore several defenses, includingglobal variable randomization, that can limit the power ofthe attacks presented herein.
High SNR Consistent Thresholding for Variable Selection

This work states and proves necessary and sufficient condition for a threshold based estimate of set of active regression coefficients to be high SNR consistent. It is further shown that popular thresholding schemes like universal threshold, Bonferroni correction etc fails to meet the necessary condition and hence are inconsistent at high SNR. The sufficient conditions provides a very rich class of threshold based estimators with varying rate of convergence to consistency. Simulation results demonstrates the superior performance of the proposed threshold based estimator over Lasso, Dantzig selector and Orthogonal Matching Pursuit.
Modelling Interaction with Experts: The Role of a Shared Task Model

The role of an agreed, shared task model as an intermediate representation of a design/decision task upon which (1) negotiation between an expert user and a knowledge engineer, and (2) interaction between an expert user and an intelligent design/decision support system are based, is the focus of the research presented. A declarative compositional approach to user-centered system design (within the DESIRE framework) is presented and illustrated on the basis of a shared task model for the design of environmental policy.
Genetic Manipulation of NK Cells for Cancer Immunotherapy: Techniques and Clinical Implications

Given their rapid and efficient capacity to recognize and kill tumor cells, natural killer (NK) cells represent a unique immune cell to genetically reprogram in an effort to improve the outcome of cell-based cancer immunotherapy. However, technical and biological challenges associated with gene delivery into NK cells have significantly tempered this approach. Recent advances in viral transduction and electroporation have now allowed detailed characterization of genetically modified NK cells and provided a better understanding for how these cells can be utilized in the clinic to optimize their capacity to induce tumor regression in vivo. Improving NK cell persistence in vivo via autocrine IL-2 and IL-15 stimulation, enhancing tumor targeting by silencing inhibitory NK cell receptors such as NKG2A, and redirecting tumor killing via chimeric antigen receptors, all represent approaches that hold promise in preclinical studies. This review focuses on available methods for genetic reprograming of NK cells and the advantages and challenges associated with each method. It also gives an overview of strategies for genetic reprograming of NK cells that have been evaluated to date and an outlook on how these strategies may be best utilized in clinical protocols. With the recent advances in our understanding of the complex biological networks that regulate the ability of NK cells to target and kill tumors in vivo, we foresee genetic engineering as an obligatory pathway required to exploit the full potential of NK-cell based immunotherapy in the clinic.
Motion and Structure from Optical Flow

We show that a moving observer can (in principle) compute the relative depth map of the environment given only the instantaneous positional velocity field on his "retina". The depth map (and surface normals) are computed by recovering the egomotion parameters which in turn determine the ratio of depth associated with any two neighbouring "retinal" points. The surface normal map may be computed to a degree of accuracy determined by the resolution of the "retina", and by the accuracy of approximation obtainable in the solution of three non-linear equations of the third degree in three unknowns.
Spotting using Durational Entropy

This paper deals with the task of detection of a given keyword in continuous speech. We build upon a previously proposed algorithm where a modified Viterbi search algorithm is used to detect keywords, without requiring any explicit garbage or filler models. In this work, the concept of durational entropy is used to further discard a large fraction of false alarm errors. Durational entropy is defined as the entropy of the distribution of state occupancies. A method to recursively compute it for all Viterbi paths is also presented in this paper. Experimental results on one hour of broadcast news data suggest that durational entropy constraints can indeed be used to avoid a large number of false alarms errors at a minimal cost of degradation in keyword detection accuracy.
Effects of exercise intensity on 24-h energy expenditure and substrate oxidation.

The purpose of this study was to determine: 1) the reliability of 24-h respiratory calorimetry measurements, and 2) the effects of low- versus high-intensity exercise on energy expenditure (EE) and substrate oxidation over a 24-h period. Eight women (age 28 +/- 4.3 yr) were measured for body composition, maximal oxygen consumption while cycling, and EE in three, 24-h calorimeter tests, with identical work output but differing intensity during a 60-min exercise session. Low-intensity (LI) exercise involved continuous cycling at 50% VO2max; whereas high-intensity (HI) exercise involved interval cycling (2 min exercise/recovery) at 100% VO2max. Subjects were randomly assigned to the first two tests at LI or HI, with the third test at the alternate intensity. No differences in EE or respiratory quotient (RQ) during rest, sleep, exercise, or over the 24 h were found between the first two tests (C.V. = 6.0%), demonstrating the reliability of the measurements. The HI protocol elicited significantly higher EE than LI during rest, exercise, and over 24 h, whereas sleeping EE approached significance. No significant differences in RQ during rest, sleep, or over 24 h were found, but 24-h lipid and carbohydrate oxidation were similar in the two protocols. The HI exercise RQ was significantly higher than LI. These findings demonstrated higher 24-h EE in the HI than LI protocol, but similar 24-h substrate oxidation rates.
Fast self-recovering controllers

A fast fault-tolerant controller structure is presented which is capable of recovering from transient faults by performing a rollback operation in hardware. The proposed fault-tolerant controller structure utilizes the rollback hardware also for system mode and this way achieves performance improvements of more than 50% compared to controller structures made fault tolerant by conventional techniques, while the hardware overhead is often negligible. The proposed approach is compatible with state-of-the-art methods for FSM decomposition, state encoding and logic synthesis.
An Improved Variable Step PO and Global Scanning MPPT Method for PV Systems under Partial Shading Condition

In the case of partially shading PV array would appear multi-peak characteristics of the P-U curve, and it is difficult to track the maximum power point with the conventional method. Therefore, this paper proposes an improved variable step PO (perturb and observe) and Global scanning method (VSPO&GS) to achieve the maximum power point tracking. This algorithm is guaranteed not to fall into local optimum, and to make the power oscillation less. This scheme is divided into two steps: 1) The improved variable step size PO is used to track the local optimum, 2) Then the global scanning is used to obtain the global optimum. Finally, the proposed algorithm is compared with a conventional fixed step size PO algorithm and validated for sudden changes in the irradiance. The simulation results show the proposed algorithm can accurately track the maximum power point (MPP) to avoid the power loss and improve the efficiency of PV array under partial shaded conditions.
Unapparent Information Revelation for Counterterrorism: Visualizing Associations using a Hybrid Graph-based Approach

Unapparent Information Revelation refers to the task in the text mining of a document collection of revealing interesting information other than that which is explicitly stated. It focuses on detecting possible links between concepts across multiple text documents by generating a graph that matches the evidence trail found in the documents. A Concept Chain Graph is a statistical technique to find links in snippets of information where singularly each small piece appears to be unconnected. In relation to algorithm performance, Latent Semantic Indexing and the Contextual Network Graph are found to be comparable to the Concept Chain Graph. These aspects are explored and discussed. In this paper, a review is performed on these three similarly grounded approaches. The Concept Chain Graph is proposed as being suited to extracting interesting relations among concepts that co-occur within text collections due to its prominent ability to construct a directed graph, representing the evidence trail. It is the baseline study for our hybrid Concept Chain Graph approach.
Memory-efficient logic layer communication platform for 3D-stacked memory-on-processor architectures

Three Dimensional (3D) chip stacking technology is emerging as a viable candidate to address the memory bandwidth problem by stacking multiple DRAM layers on top of a multiprocessor layer (logic layer) to reduce wire delay and energy consumption between them. In addition, combining the benefits of 3D memory-on-processor stacking architecture and on-chip networks provides a significant performance gain. To fully exploit the benefits of the 3D stacked memory-on-processor architectures, an efficient on-chip communication platform is required to be integrated in the logic layer. In this paper, we present an on-chip communication platform for the logic layer to exploit the potential bandwidth of stacked memory-on-processor architectures. This platform guarantees low-latency access to the stacked DRAM layers by employing an adaptive memory controller. Experimental results demonstrate that the proposed platform mitigates the average memory access latency (34%) and average memory utilization (31%) considerably and the overall performance gain is about 20%.
A Data analytical Framework for Improving Real-Time, Decision Support Systems in Healthcare

Title of dissertation: A DATA ANALYTICAL FRAMEWORK FOR IMPROVING REAL-TIME, DECISION SUPPORT SYSTEMS IN HEALTHCARE Inbal Yahav, PhD Candidate, 2010 Dissertation directed by: Associate Professor Galit Shmueli Department of Decisions, Operations and Information Technologies In this dissertation we develop a framework that combines data mining, statistics and operations research methods for improving real-time decision support systems in healthcare. Our approach consists of three main concepts: data gathering and preprocessing, modeling, and deployment. We introduce the notion of offline and semi-offline modeling to differentiate between models that are based on known baseline behavior and those based on a baseline with missing information. We apply and illustrate the framework in the context of two important healthcare contexts: biosurveillance and kidney allocation. In the biosurveillance context, we address the problem of early detection of disease outbreaks. We discuss integer programming-based univariate monitoring and statistical and operations research-based multivariate monitoring approaches. We assess method performance on authentic biosurveillance data. In the kidney allocation context, we present a two-phase model that combines an integer programming-based learning phase and a dataanalytical based real-time phase. We examine and evaluate our method on the current Organ Procurement and Transplantation Network (OPTN) waiting list. In both contexts, we show that our framework produces significant improvements over existing methods. A DATA ANALYTICAL FRAMEWORK FOR IMPROVING REAL-TIME, DECISION SUPPORT SYSTEMS IN HEALTHCARE
Efficiently enabling conventional block sizes for very large die-stacked DRAM caches

Die-stacking technology enables multiple layers of DRAM to be integrated with multicore processors. A promising use of stacked DRAM is as a cache, since its capacity is insufficient to be all of main memory (for all but some embedded systems). However, a 1GB DRAM cache with 64-byte blocks requires 96MB of tag storage. Placing these tags on-chip is impractical (larger than on-chip L3s) while putting them in DRAM is slow (two full DRAM accesses for tag and data). Larger blocks and sub-blocking are possible, but less robust due to fragmentation.
Evolving stochastic processes using feature tests and genetic programming

The synthesis of stochastic processes using genetic programming is investigated. Stochastic process behaviours take the form of time series data, in which quantities of interest vary over time in a probabilistic, and often noisy, manner. A suite of statistical feature tests are performed on time series plots from example processes, and the resulting feature values are used as targets during evolutionary search. A process algebra, the stochastic π-calculus, is used to denote processes. Investigations consider variations of GP representations for a subset of the stochastic π-calculus, for example, the use of channel unification, and various grammatical constraints. Target processes of varying complexity are studied. Results show that the use of grammatical GP with statistical feature tests can successfully synthesize stochastic processes. Success depends upon a selection of appropriate feature tests for characterizing the target behaviour, and the complexity of the target process.