Miracle-dz/UniFAR-FTUs huggingface dataset:
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Phase change memory (PCM) has been considered an attractive alternative to flash memory and DRAM. It has promising features, including non-volatile storage, byte addressability, fast read and write operations, and supports random accesses. However, there are challenges in designing algorithms for PCM-based memory systems, such as longer write latency and higher energy consumption compared to DRAM. In this paper, we propose a new predictive B+-tree index, called the Bp-tree, which is tailored for database systems that make use of PCM. Our Bp-tree reduces data movements caused by tree node splits and merges that arise from insertions and deletions. This is achieved by pre-allocating space on PCM for near future data. To ensure the space are allocated where they are needed, we propose a novel predictive model to ascertain future data distribution based on the current data. In addition, as in [4], when keys are inserted into a leaf node, they are packed but need not be in sorted order. We have implemented the Bp-tree in PostgreSQL and evaluated it in an emulated environment. Our experimental results show that the Bp-tree significantly reduces the number of writes, therefore making it write and energy efficient and suitable for a PCM-like hardware environment.
Classification and Intellectual Disabilities: An Investigation of the Factors That Predict the Performance of Athletes With Intellectual Disability

It has been shown that having intellectual disabilities impacts to reduce performance compared to athletes without this impairment. However, it has also been demonstrated that there is a not a direct link between intelligence and athletic performance. To advance elite ID sport more needs to be understood about the relationship between this impairment and sporting performance. This is vital if competition classification systems are to be based on theory and evidence. This study used the International Classification of Functioning, Disability and Health (ICF) as an approach to classification and examined the impact of multiple health problems on athletic performance. A health survey was administered to two groups of athletes with ID: elite and regional level athletes. Athletes with Down Syndrome were also identified. Overall disability scores predicted sporting performance, but not IQ or Down Syndrome. The implications of these findings are discussed with reference to the ICF framework and classification.
Scans as Primitive Parallel Operations

A study of the effects of adding two scan primitives as unit-time primitives to PRAM (parallel random access machine) models is presented. It is shown that the primitives improve the asymptotic running time of many algorithms by an O(log n) factor, greatly simplifying the description of many algorithms, and are significantly easier to implement than memory references. It is argued that the algorithm designer should feel free to use these operations as if they were as cheap as a memory reference. The author describes five algorithms that clearly illustrate how the scan primitives can be used in algorithm design: a radix-sort algorithm, a quicksort algorithm, a minimum-spanning-tree algorithm, a line-drawing algorithm, and a merging algorithm. These all run on an EREW (exclusive read, exclusive write) PRAM with the addition of two scan primitives and are either simpler or more efficient than their pure PRAM counterparts. The scan primitives have been implemented in microcode on the Connection Machine system, are available in PARIS (the parallel instruction set of the machine). >
Assessing the Effects of Second Language as a Medium of Instruction in Teaching Preschool Children

Abstract The issue of appropriate and useful medium of instruction in teaching learners remains a very contentious issue in education. As such, preschool teachers are pressurised by parents and guardians who expect their children to be fluent in English by the time they enter Grade 1. The purpose of the study was to investigate the effects of second language (English) as a medium of instruction in preschools. This was a qualitative study which was underpinned by interpretivist paradigm. The study adopted a qualitative case study design. A purposive sample of eight preschool teachers participated in the study. Data was collected through interviews, observation and document analysis. Semi-structured interviews were used as a method of generating data from the, teachers and parents. Four preschools were selected in this study. Data was analysed through content analysis as the responses from the participants were categorised and then coded according to the emerging themes. The findings of the study showed that maintaining the first language did not interfere with the learning of the second language. When preschoolers continued to develop their abilities in the languages throughout their preschool years, they gained a deeper understanding of language. It is recommended that preschool teachers be well equipped so that they are equal to the challenges. The study have significant implications for the teachers and other stakeholders who are directly and indirectly involved in early childhood education.
Using the Existing XFree86/X.Org Loadable Driver Framework to Achieve a Composited X Desktop

In this paper, we make the case for using the existing XFree86/X.Org DDX loadable driver framework to achieve a production-quality composited X desktop, as opposed to the X-on-OpenGL model. While the X-on-OpenGL model demonstrates what the graphics hardware is capable of, everything that the X-on-OpenGL model can achieve is equally possible with the current framework. Furthermore, the current framework offers flexibility to driver developers to expose vendor-specific features that may not be possible through the X-on-OpenGL model. There is increasing interest in delivering a composited X desktop, and debate about what driver model can best accelerate the compositing operations. Two candidate models are: 1) the existing XFree86/X.Org DDX loadable driver framework, and 2) a replacement DDX that uses OpenGL for its rendering and modesetting. Candidate 1, the XFree86/X.Org DDX loadable driver framework, has been in use since the release of XFree86 4.0 in March 2000, and is what both the XFree86 and X.Org X servers use in shipping operating systems. In this framework, a hardware-specific driver is loaded and performs the tasks of rendering and modesetting. The framework does not dictate how the loaded driver performs these tasks. Candidate 2, the X-on-OpenGL model, eliminates hardware-specific X drivers, and instead uses OpenGL to perform all X rendering. The X server, as proposed in this model, also uses the OpenGL driver to perform modesetting, through the EGL_MESA_screen_surface [1] extension. In this paper we provide background on the technology that enables a composited X desktop, and identify goals related to a composited desktop for the X windows community. We then compare the two driver models, and make the case that the existing loadable driver model is better suited for achieving our goals. Finally, we end with a roadmap for how to achieve a production-quality composited X desktop.
The underlying event and the total cross section from Tevatron to the LHC

Multiple partonic interactions are widely used to simulate the hadronic final state in high energy hadronic collisions, and successfully describe many features of the data. It is important to make maximum use of the available physical constraints on such models, particularly given the large extrapolation from current high energy data to LHC energies. In eikonal models, the rate of multiparton interactions is coupled to the energy dependence of the total cross section. Using a Monte Carlo implementation of such a model, we study the connection between the total cross section, the jet cross section, and the underlying event. By imposing internal consistency on the model and comparing to current data we constrain the allowed range of its parameters. We show that measurements of the total proton-proton cross-section at the LHC are likely to break this internal consistency, and thus to require an extension of the model. Likely such extensions are that hard scatters probe a denser matter distribution inside the proton in impact parameter space than soft scatters, a conclusion also supported by Tevatron data on double-parton scattering, and/or that the basic parameters of the model are energy dependent.
Volumetric 3D Tracking by Detection

In this paper, we propose a new framework for 3D tracking by detection based on fully volumetric representations. On one hand, 3D tracking by detection has shown robust use in the context of interaction (Kinect) and surface tracking. On the other hand, volumetric representations have recently been proven efficient both for building 3D features and for addressing the 3D tracking problem. We leverage these benefits by unifying both families of approaches into a single, fully volumetric tracking-by-detection framework. We use a centroidal Voronoi tessellation (CVT) representation to compactly tessellate shapes with optimal discretization, construct a feature space, and perform the tracking according to the correspondences provided by trained random forests. Our results show improved tracking and training computational efficiency and improved memory performance. This in turn enables the use of larger training databases than state of the art approaches, which we leverage by proposing a cross-tracking subject training scheme to benefit from all subject sequences for all tracking situations, thus yielding better detection and less overfitting.
Applications of kernel machines to structured data

In this thesis we are concerned with the application of supervised learning methods to two problems of rather different nature – one originating from computational neuro-science, the other one from computer vision. The kernel algorithms that will be used allow classification of complex objects that need not to be elements of a Euclidean vector space. For example in the applications presented below these objects are time series of neural activity and images described by a collection of local descriptors. The flexibility of kernel algorithms is achieved through the use of a kernel function that specifies similarity of the objects as a numerical value. To make an application successful, one has to find appropriate kernel functions that adequately describe similarity and at the same time must fulfil certain mathematical requirements. The focus of our work is the development and adaptation of kernel functions for decoding of neural activity and for image categorisation. Each topic is treated separately in one of the two parts of this thesis. In part I the application of kernel algorithms for decoding of neural activity is explored. Sequences of action potentials that were measured as response to a visual stimulus are used to reconstruct characteristic attributes of the stimulus. Most of the current methods in neuroscience consider only the number of action potentials in a certain time interval and neglect the temporal distribution of these events. With the kernel functions for neural activity that are proposed in this thesis an extended analysis of spike trains is possible. The similarity of two sequences is not only determined by the frequency of spikes but also takes potential temporal patterns into account. An evaluation of the kernels is performed on artificially generated data as well as on real recordings from a neurophysiological experiment. Experiments on this second type of data allow some conclusions about the actual importance of temporal patterns for the encoding of stimulus attributes in the organism under consideration. In a second set of experiments the simultaneously recorded activity of multiple neurons is taken as a basis of reconstruction. Here the results show that the tested kernel algorithms can perform reconstruction in most cases with a significantly higher precision than current methods of computational neuroscience. The second part of this thesis presents an application of support vector machines as one prominent example of kernel algorithms to the task of object categorisation. Computer vision research has found that it …
The Trw Type IV Secretion System of Bartonella Mediates Host-Specific Adhesion to Erythrocytes

Bacterial pathogens typically infect only a limited range of hosts; however, the genetic mechanisms governing host-specificity are poorly understood. The α-proteobacterial genus Bartonella comprises 21 species that cause host-specific intraerythrocytic bacteremia as hallmark of infection in their respective mammalian reservoirs, including the human-specific pathogens Bartonella quintana and Bartonella bacilliformis that cause trench fever and Oroya fever, respectively. Here, we have identified bacterial factors that mediate host-specific erythrocyte colonization in the mammalian reservoirs. Using mouse-specific Bartonella birtlesii, human-specific Bartonella quintana, cat-specific Bartonella henselae and rat-specific Bartonella tribocorum, we established in vitro adhesion and invasion assays with isolated erythrocytes that fully reproduce the host-specificity of erythrocyte infection as observed in vivo. By signature-tagged mutagenesis of B. birtlesii and mutant selection in a mouse infection model we identified mutants impaired in establishing intraerythrocytic bacteremia. Among 45 abacteremic mutants, five failed to adhere to and invade mouse erythrocytes in vitro. The corresponding genes encode components of the type IV secretion system (T4SS) Trw, demonstrating that this virulence factor laterally acquired by the Bartonella lineage is directly involved in adherence to erythrocytes. Strikingly, ectopic expression of Trw of rat-specific B. tribocorum in cat-specific B. henselae or human-specific B. quintana expanded their host range for erythrocyte infection to rat, demonstrating that Trw mediates host-specific erythrocyte infection. A molecular evolutionary analysis of the trw locus further indicated that the variable, surface-located TrwL and TrwJ might represent the T4SS components that determine host-specificity of erythrocyte parasitism. In conclusion, we show that the laterally acquired Trw T4SS diversified in the Bartonella lineage to facilitate host-restricted adhesion to erythrocytes in a wide range of mammals.
Baryons from quarks in curved space and deconfinement

Detailed account is given of the fact that the Cornell potential predicted by Lattice QCD and its exactly solvable trigonometric extension recently reported by us can be viewed as the respective approximate and exact counterparts on a curved space to an 1/r flat space potential. The "curved" potential describes a confinement phenomenon because it is of infinite depth and has only bound states. It furthermore has the remarkable property of preserving both the SO(4) and SO(2,1) symmetries of the ordinary 1/r potential. We first make the case that this particular geometric version on confinement provides a remarkably adequate description of both nucleon and Delta spectra and the proton mean square charge radius as well, and suggests an intriguing venue toward quark deconfinement as shut-down of the curvature considered as temperature dependent. Next we observe that the SO(2,1) symmetry of the "curved" potential allows to place it within the context of AdS_5/CFT correspondence and to establish in this manner the algbraic link of the the latter to QCD potentiology.
Study of the Z Boson at LEP

The Z line shape is measured at LEP with an accuracy at the per mille level. Usually it is described in the Standard Model of electroweak interactions with account of quantum corrections. Alternatively, one may attempt different model-independent approaches in order to extract quantities like mass and width of the Z boson. If a fit deviates from that in the standard approach, this may give hints for New Physics contributions. I describe two model-independent approaches and compare their applications to LEP data with the Standard Model approach.
Against the Mainstream in Bug Prediction

Bug prediction is a technique used to estimate the most bug-prone entities in software systems. Bug prediction approaches vary in many design options, such as dependent variables, independent variables, and machine learning models. Choosing the right combination of design options to build an effective bug predictor is hard. Previous studies do not consider this complexity and draw conclusions based on fewer-than-necessary experiments. We argue that each software project is unique from the perspective of its development process. Consequently, metrics and AI models perform differently on different projects, in the context of bug prediction. We confirm our hypothesis empirically by running different bug pre- dictors on different systems. We show that no single bug prediction configuration works globally on all projects and, thus, previous bug prediction findings cannot generalize.
Could grid facilitate demanding watermarking media applications

Copyright protection is becoming an important issue for organisations that distribute digital content through e-learning applications. One of the most promising methods for copyright protection is digital watermarking, a highly demanding application in terms of CPU load. In this communication, some specific cases are presented where GRID computing could be used as an enabling/assisting technology for watermarking. Specific cases of heavy load mathematical transformations for large media objects are discussed. A proposal for incorporating the watermarking procedure in the Grid architecture is also briefly presented.
Integrating tissue specific mechanisms into GWAS summary results

To understand the biological mechanisms underlying thousands of genetic variants robustly associated with complex traits, scalable methods that integrate GWAS and functional data generated by largescale efforts are needed. Here we propose a method termed MetaXcan that addresses this need by inferring the downstream consequences of genetically regulated components of molecular traits on complex phenotypes using summary data only. MetaXcan allows multiple causal variants and flexible multivariate models enabling the testing of a variety of complex processes under different contexts. As an example application, we trained prediction models of gene expression levels in 44 human tissues and inferred the consequences of their regulation in 40 complex phenotypes. Our examination of this broad set of human tissues revealed many novel genes and re-identified known ones with patterns of regulation in expected as well as unexpected tissues.
Large-scale MIMO is capable of eliminating power-thirsty channel coding for wireless transmission of HEVC/H.265 video

A wireless video transmission architecture relying on the emerging LS-MIMO technique is proposed. Upon using the most advanced high efficiency video coding (also known as H.265), we demonstrate that the proposed architecture invoking the low-complexity linear zero-forcing detector and dispensing with any channel coding is capable of significantly outperforming the conventional small-scale MIMO-based architecture, even if the latter employs the high-complexity optimal maximum likelihood detector and a rate- 1/3 recursive systematic convolutional channel codec. Specifically, compared to the conventional small-scale MIMO system, the effective system throughput of the proposed LS-MIMO-based scheme is increased by a factor of up to three, and the quality of reconstructed video quantified in terms of the PSNR is improved by about 22.5 dB at a channel SNR of Eb/N0 ~ 6 dB for delay-tolerant video file delivery applications, and about 20 dB for lip-synchronized real-time interactive video applications. Alternatively, viewing the attainable improvement from a power-saving perspective, a channel SNR gain as high as δEb/N0 ~ 5 dB is observed at a PSNR of 36 dB for the scenario of delay-tolerant video applications, and again, an even higher channel SNR gain is achieved in the real-time video application scenario. Therefore, we envisage that LS-MIMO-aided wireless multimedia communications is capable of dispensing with the power-thirsty channel codec altogether!
Methodology to characterize the performance of IEEE 802.11 nodes to be deployed in multi-hop environments

This paper proposes an experimental methodology to characterize the performance of an IEEE 802.11 WLAN node that is going to be used in a wireless multi-hop network. The methodology parameterizes the (in)accuracy of the node to conduct carrier sensing measurements and its capacity to handle incoming and outgoing traffic concurrently. These two parameters characterize sources of unexpected information losses, instability, and unfairness. In this way, they help to design and analyze multi-hop deployments. Finally, the methodology is used in an experimental setup to characterize an actual wireless node.
Cloud Scheduling Optimization: A Reactive Model to Enable Dynamic Deployment of Virtual Machines Instantiations

This study proposes a model for supporting the decision making process of the cloud policy for the deployment of virtual machines in cloud environments. We explore two configurations, the static case in which virtual machines are generated according to the cloud orchestration, and the dynamic case in which virtual machines are reactively adapted according to the job submissions, using migration, for optimizing performance time metrics. We integrate both solutions in the same simulator for measuring the performance of various combinations of virtual machines, jobs and hosts in terms of the average execution and total simulation time. We conclude that the dynamic configuration is prosperus as it offers optimized job execution performance.
Adjuvants for Leishmania vaccines: from models to clinical application

Two million new cases of leishmaniasis occur every year, with the cutaneous leishmaniasis (CL) presentation accounting for approximately two-thirds of all cases. Despite the high incidence rates and geographic expansion of the disease, CL remains a neglected tropical disease without effective intervention strategies. Efforts to address this deficit have given rise to the experimental murine model of CL. By virtue of its simplicity and pliability, the CL model has been used to provide substantial information regarding cellular immunity, as well as in the discovery and evaluation of various vaccine adjuvants. The CL model has facilitated in vivo studies of the mechanism of action of many adjuvants, including the TLR4 agonist monophosphoryl lipid A, the TLR7/8 agonist imiquimod, the TLR9 agonist CpG, adenoviral vectors, and the immunostimulatory complexes. Together, these studies have helped to unveil the requirement for certain types of immune responses at specific stages of CL disease and provide a basis to aid the design of effective second-generation vaccines for human CL. This review focuses on adjuvants that have been tested in experimental CL, outlining how they have helped advance our understanding of the disease and ultimately, how they have performed when applied within clinical trials against human CL.