Miracle-dz/UniFAR-FTUs huggingface dataset:
Cytokine-mediated protection of human dendritic cells from prostate cancer-induced apoptosis is regulated by the Bcl-2 family of proteins

Prostate cancer is the most common cancer in men in the United States, and second in cancer-induced mortality. It is likely that tumour-induced immunosuppression is one of the reasons for low treatment efficacy in patients with advanced prostate cancer. It has been recently demonstrated that prostate cancer tissue is almost devoid of dendritic cells (DC), the major antigen-presenting cells responsible for the induction of specific antitumour immune responses. In this study, we have tested the hypothesis that prostate cancer induces progressive suppression of the DC system. We found that co-incubation of human DC with three prostate cancer cell lines led to the high levels of premature apoptosis of DC, which were significantly higher than in DC cultures co-incubated with normal prostate cells or blood leucocytes. Stimulation of DC for 24 hours with CD40 ligand (CD154), IL-12 or IL-15 prior to their co-incubation with prostate cancer cells resulted in a significant increase in DC survival in the tumour microenvironment. Furthermore, activation of DC with these cytokines was also accompanied by increased expression of the anti-apoptotic protein Bcl-xLin DC, suggesting a possible mechanism involved in DC protection from apoptotic death. In summary, our data demonstrate that prostate cancer induces active elimination of DC in the tumour microenvironment. Stimulation of DC by CD154, IL-12 or IL-15 leads to an increased expression of the anti-apoptotic protein Bcl-xLand increased resistance of DC to prostate cancer-induced apoptosis. These results suggest a new mechanism of tumour escape from immune recognition and demonstrate the cytokine-based approaches which might significantly increase the efficacy of DC-based therapies for cancer. © 2000 Cancer Research Campaign
Haptic simulator of abnormal biomechanics during patient-clinician interaction

— We present the development of a haptic simulator of abnormal limb biomechanics. The system can be used as a training device for clinicians to understand the causality of the haptic sensation associated with neurological impairments and abnormal biomechanics during manipulation of a patients' limb. Other uses include the testing of operational research algorithms to maximize the exchange of haptic information; reproducing conditions of human-human interaction; and a test bed for developing novel assessment techniques and targeted interventions. Experimental results support the hypothesis that the best assessment of abnormalities can be obtained via specific manipulation strategies.
Using natural language parsers in plagiarism detection

The problem of plagiarism detection system design is a subject of numerous works of the last decades. Various advanced file-file comparison techniques were developed. However, most existing systems, aimed at natural language texts, do not perform any significant preprocessing of the input documents. So in many cases it is possible to hide the presence of plagiarism by utilizing some simple techniques. In this work we show how a natural language parser can be used to fight against basic plagiarism hiding methods.
Dynamic cerebral autoregulation during passive heat stress in humans.

This study tested the hypothesis that passive heating impairs cerebral autoregulation. Transfer function analyses of resting arterial blood pressure and middle cerebral artery blood velocity (MCA V(mean)), as well as MCA V(mean) and blood pressure responses to rapid deflation of previously inflated thigh cuffs, were examined in nine healthy subjects under normothermic and passive heat stress (increase core temperature 1.1 +/- 0.2 degrees C, P < 0.001) conditions. Passive heating reduced MCA V(mean) [change (Delta) of 8 +/- 8 cm/s, P = 0.01], while blood pressure was maintained (Delta -1 +/- 4 mmHg, P = 0.36). Coherence was decreased in the very-low-frequency range during heat stress (0.57 +/- 0.13 to 0.26 +/- 0.10, P = 0.001), but was >0.5 and similar between normothermia and heat stress in the low- (0.07-0.20 Hz, P = 0.40) and high-frequency (0.20-0.35 Hz, P = 0.12) ranges. Transfer gain was reduced during heat stress in the very-low-frequency (0.88 +/- 0.38 to 0.59 +/- 0.19 cm.s(-1). mmHg(-1), P = 0.02) range, but was unaffected in the low- and high-frequency ranges. The magnitude of the decrease in blood pressure (normothermia: 20 +/- 4 mmHg, heat stress: 19 +/- 6 mmHg, P = 0.88) and MCA V(mean) (13 +/- 4 to 12 +/- 6 cm/s, P = 0.59) in response to cuff deflation was not affected by the thermal condition. Similarly, the rate of regulation of cerebrovascular conductance (CBVC) after cuff release (0.44 +/- 0.22 to 0.38 +/- 0.13 DeltaCBVC units/s, P = 0.16) and the time for MCA V(mean) to recover to precuff deflation baseline (10.0 +/- 7.9 to 8.7 +/- 4.9 s, P = 0.77) were not affected by heat stress. Counter to the proposed hypothesis, similar rate of regulation responses suggests that heat stress does not impair the ability to control cerebral perfusion after a rapid reduction in perfusion pressure, while reduced transfer function gain and coherence in the very-low-frequency range during heat stress suggest that dynamic cerebral autoregulation is improved during spontaneous oscillations in blood pressure within this frequency range.
Open Middleware Architecture for the LBS Domain

The combination of position fixing mechanisms with location-dependent, geographical information can offer truly customized personal communication services through the mobile phone or other types of devices. Prompted by the avalanche of technology advances in the aforementioned areas, in this article we present a generic framework for delivering location-based services (LBS). The framework is capable of providing the full functionality required for delivering LBS, starting from the specification of the service, and covering issues like the deployment and maintenance of services, the service invocation and the final delivery of the produced results to the calling user. The main focus of the article is on the technical specification, the design, and the functionality of the framework. However, with the purpose of assessing the proposed architecture, a prototype implementation based on the discussed specifications was built and its performance was evaluated using a series of pilot services.
Relations between Brain Pathology and Temporal Lobe Epilepsy

Temporal lobe epilepsy, the most common type of epilepsy in adult humans, is characterized clinically by the progressive development of spontaneous recurrent seizures of temporal lobe origin and pathologically by hippocampal neuronal loss and mossy fiber sprouting. In this study, we sought to test the prominent hypothesis that neuronal loss and mossy fiber sprouting play a critical role in the genesis and progression of temporal lobe epilepsy. Rats receiving a single kainic acid injection experienced a single sustained episode of epileptic status with massive neuronal loss and mossy fiber sprouting, whereas rats receiving triple kainic acid injections experienced two priming episodes and one sustained episode of epileptic status with no detectable neuronal loss and mossy fiber sprouting. Early in the process of chronic seizure development, primed rats that failed to show detectable neuronal loss and mossy fiber sprouting exhibited a starting date and a frequency of spontaneous recurrent seizures similar to those of nonprimed rats that showed massive neuronal loss and mossy fiber sprouting. However, nonprimed rats displayed significantly prolonged episodes of spontaneous recurrent seizures over the whole process of chronic seizure development and more frequent severe seizures later in the process. Similar results were observed in both Fischer-344 and Wistar rats as well as in the rat pilocarpine preparation of temporal lobe epilepsy. These results fail to reveal a relation between neuronal loss–mossy fiber sprouting and the genesis of temporal lobe epilepsy but suggest that neuronal loss, mossy fiber sprouting, or both contribute to the intensification of chronic seizures.
ROBUST ESTIMATON OF ILLUMINATION COLOUR FROM DICHROMATIC PATCHES

In this paper we present a new robust approach to the problem of illumination colour estimation. We assume the dichromatic reflection model, a linear camera and interface reflection of dielectrics to be an almost constant function in the visible spectrum. Under such assumptions, observed colours corresponding to a surface of a single dielectric material should lie in a plane passing through the origin of the measurement space and including the illumination colour. Since colour measurements can arbitrarily change due to geometry, apperture, sensor gain etc., the analysis is performed in the chromatic plane. The mean and the covariance matrix of chormaticities taken from a small patch obtained by region-growing from the image are computed. A line passing through the mean with the direction of the first eigenvector of the covariance matrix is taken as the local estimate of possible illumination chromaticity. The estimates are combined by a Hough Transform-like procedure that accumulates votes in a discretised chromatic space. The framework places no constraints on the illumination spectral power distribution, surface reflectances, sensors (e.g. that they occupy some low dimensional subspace) or geometry of the scene (e.g. Mondriaan world asumption). Moreover, colour of multiple light sources may be recovered from the structure of the accumulator space. Experiments on images from a publicly available database with known illuminants show that the method is accurate and outperforms simple standard methods.
Bootstrap confidence intervals: when, which, what? A practical guide for medical statisticians.

Since the early 1980s, a bewildering array of methods for constructing bootstrap confidence intervals have been proposed. In this article, we address the following questions. First, when should bootstrap confidence intervals be used. Secondly, which method should be chosen, and thirdly, how should it be implemented. In order to do this, we review the common algorithms for resampling and methods for constructing bootstrap confidence intervals, together with some less well known ones, highlighting their strengths and weaknesses. We then present a simulation study, a flow chart for choosing an appropriate method and a survival analysis example.
Towards Optimal Execution of Density-based Clustering on Heterogeneous Hardware

Data Clustering is an important and highly utilized data mining technique in various application domains. With ever increasing data volumes in the era of big data, the efficient execution of clustering algorithms is a fundamental prerequisite to gain understanding and acquire novel, previously unknown knowledge from data. To establish an efficient execution, the clustering algorithms have to be re-engineered to fully exploit the provided hardware capabilities. Shared-memory multiprocessor systems like graphics processing units (GPUs) provide extremely high parallelism combined with a high bandwidth transfer at low cost. The availability of such computing units increases with upcoming processors, where a common CPU and various computing units, like GPU, are tightly coupled using a unified shared memory hierarchy. In this paper, we consider density-based clustering for such heterogeneous systems. In particular, we optimize the configuration of CUDA-DClust - a density-based clustering algorithm for GPUs - and show that our configuration approach enables an efficient and deterministic execution. Our configuration approach is based on data as well as hardware properties, so that we are able to adjust the algorithm execution in both directions. In our evaluation, we show the applicability of our approach and present open challenges which have to be solved next.
Improving the Intrusion Detection using Discriminative Machine Learning Approach and Improve the Time Complexity by Data Mining Feature Selection Methods

As the dependence of daily life is increasing on Internet technology, the attacks on the systems, servers are also rapidly increasing. The motives of attacks are to steal the confidential data from the systems or making the system unavailable to the authorised users. An effective approach is required to detect the intrusions to provide the defence to the Networks. First we applied the feature selection to reduce the dimensions of NSL-KDD data set. By feature reduction and machine learning approach we able to build Intrusion detection model to find attacks on system and improve the intrusion detection using the captured data. The intrusion detection accuracy of learning algorithms is also performed on the data set, without the level 21 attacks which is most easy to identify attacks, using learning algorithms and the success rate of proposed model is calculated over the attacks which are hard to detect.
Predicting field problems using metrics based models : a survey of current research

Methods that can lower the cost of software field problems (e.g. faults, errors, failures, bugs, and defects) need field problem predictions. Models that predict field problems generally fall into two classes: time based models and metrics based models. In this paper, we examine metrics based models in detail. Metrics based models are better suited to predict field problems when an operational profile is not available, when the software and hardware configurations in use are unknown, and when the deployment and usage patterns are unknown. We present important concepts and the current state of research in inputs, output, and modeling methods. Note to 654/754 students You are only required to read sections 1-4. Also, this is a draft paper. So please excuse any mistakes (e.g. spelling mistakes and grammar mistakes), and let me know if you spot a mistake or feel uncomfortable about anything. Feedback is welcome and appreciated: Paul.Li@cs.cmu. edu.
IGGSA-STEPS: Shared Task on Source and Target Extraction from Political Speeches

Accurate opinion mining requires the exact identification of the source and target of an opinion. To evaluate diverse tools, the research community relies on the existence of a gold standard corpus covering this need. Since such a corpus is currently not available for German, the Interest Group on German Sentiment Analysis decided to create such a resource and make it available to the research community in the context of a shared task. In this paper, we describe the selection of textual sources, development of annotation guidelines, and first evaluation results in the creation of a gold standard corpus for the German language.
Continuum surface energy from a lattice model

We investigate connections between the continuum and atomistic descriptions of deformable crystals, using certain interesting results from number theory. The energy of a deformed crystal is calculated in the context of a lattice model with general binary interactions in two dimensions. A new bond counting approach is used, which reduces the problem to the lattice point problem of number theory. The main contribution is an explicit formula for the surface energy density as a function of the deformation gradient and boundary normal. The result is valid for a large class of domains, including faceted (polygonal) shapes and regions with piecewise smooth boundaries.
The influence of leadership on the strategies and tactics of Islamic State and its predecessors

ABSTRACT Using data from the Consortium for the Study of Terrorism and Responses to Terrorism's Global Terrorism Database, this paper analyses terrorist incidents perpetrated by the Islamic State and its antecedent groups under three different modes of leadership. We investigated whether changes in leadership are followed by changes in the targeting strategies and attacking styles of the group. We compared the frequency and the lethality of attacks, fatalities per day and the rates of suicide attacks, and investigated the differences in target selection and attack modalities using cross tabulation with chi-square analysis. The results indicate that the group followed different trajectories under the three different leadership styles examined in terms of target selection and the tactics employed to carry out the attacks. The analysis also shows that in the case of the Islamic State and its predecessors, the decapitation of terrorist leaders did not reduce the frequency of attacks, and fatalities per day. Further research is necessary to test to what extent changes in the strategies and tactics of terrorist organisations are caused by changes in leadership.
Two-Stage Filter-Bank System for Improved Single-Channel Noise Reduction in Hearing Aids

The filter-bank system implemented in hearing aids has to fulfill various constraints such as low latency and high stop-band attenuation, usually at the cost of low frequency resolution. In the context of frequency-domain noise-reduction algorithms, insufficient frequency resolution may lead to annoying residual noise artifacts since the spectral harmonics of the speech cannot properly be resolved. Especially in case of female speech signals, the noise between the spectral harmonics causes a distinct roughness of the processed signals. Therefore, this work proposes a two-stage filter-bank system, such that the frequency resolution can be improved for the purpose of noise reduction, while the original first-stage hearing-aid filter-bank system can still be used for compression and amplification. We also propose methods to implement the second filter-bank stage with little additional algorithmic delay. Furthermore, the computational complexity is an important design criterion. This finally leads to an application of the second filter-bank stage to lower frequency bands only, resulting in the ability to resolve the harmonics of speech. The paper presents a systematic description of the second filter-bank stage, discusses its influence on the processed signals in detail and further presents the results of a listening test which indicates the improved performance compared to the original single-stage filter-bank system.
SimFusion+: extending simfusion towards efficient estimation on large and dynamic networks

SimFusion has become a captivating measure of similarity between objects in a web graph. It is iteratively distilled from the notion that "the similarity between two objects is reinforced by the similarity of their related objects". The existing SimFusion model usually exploits the Unified Relationship Matrix (URM) to represent latent relationships among heterogeneous data, and adopts an iterative paradigm for SimFusion computation. However, due to the row normalization of URM, the traditional SimFusion model may produce the trivial solution; worse still, the iterative computation of SimFusion may not ensure the global convergence of the solution. This paper studies the revision of this model, providing a full treatment from complexity to algorithms. (1) We propose SimFusion+ based on a notion of the Unified Adjacency Matrix (UAM), a modification of the URM, to prevent the trivial solution and the divergence issue of SimFusion. (2) We show that for any vertex-pair, SimFusion+ can be performed in O(1) time and O(n) space with an O(km)-time precomputation done only once, as opposed to the O(kn3) time and O(n2) space of its traditional counterpart, where n, m, and k denote the number of vertices, edges, and iterations respectively. (3) We also devise an incremental algorithm for further improving the computation of SimFusion+ when networks are dynamically updated, with performance guarantees for similarity estimation. We experimentally verify that these algorithms scale well, and the revised notion of SimFusion is able to converge to a non-trivial solution, and allows us to identify more sensible structure information in large real-world networks.
Towards an ontological concepts for domain-driven software design

Software design process has been followed and widely used to describe logical structure of software using different types of design model, to ensure consistency among the multiple views of a design. This paper describes an effort to identify the major concepts in software design that are required to understand system functionality from various perspectives. It also presents systematic framework ontology for software design lifecycle context, ontology development, and ontology representation. The aim is to resolve misunderstanding or misinterpretation especially with ambiguous terms for designers and users who have different backgrounds and knowledge of the software for functional-oriented, object-oriented, component-oriented, agent-oriented designs, and represent the reasons that led the designers to choose one design among the other alternatives.
Novel Naive Bayes based on Attribute Weighting in Kernel Density Estimation

Naive Bayes (NB) learning is more popular, faster and effective supervised learning method to handle the labeled datasets especially in which have some noises, NB learning also has well performance. However, the conditional independent assumption of NB learning imposes some restriction on the property of handling data of real world. Some researchers proposed lots of methods to relax NB assumption, those methods also include attribute weighting, kernel density estimating. In this paper, we propose a novel approach called NB Based on Attribute Weighting in Kernel Density Estimation (NBAWKDE) to improve the NB learning classification ability via combining kernel density estimation and attribute weighting based on conditional mutual information. Our method makes the weights embedded in kernel have the relatively interpretable meaning, it is flexible that we also can choice different metrics and methods to measure the weights based on our attribute weighting in kernel density estimation framework.
Motor Learning of Novel Dynamics Is Not Represented in a Single Global Coordinate System: Evaluation of 1 Mixed Coordinate Representations and Local Learning 2 3 Abbreviated Title: Evidence for Mixed Coordinate Representations and Local Learning 14

Motor learning of novel dynamics is not represented in a single global coordinate system: evaluation of 1 mixed coordinate representations and local learning 2 3 Max Berniker , David W. Franklin , J. Randall Flanagan , Daniel M. Wolpert , Konrad Kording 1‡ 4 5 1 Rehabilitation Institute of Chicago, Northwestern University, Chicago, IL 60611, USA 6 2 Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, 7 Cambridge, CB2 1PZ, United Kingdom 8 3 Departments of Psychology and Centre for Neuroscience Studies, Queen’s University, Kingston, Ontario, 9 Canada 10 11 † ‡ equal contributions 12 13 Abbreviated title: Evidence for mixed coordinate representations and local learning 14 15 Correspondence: 16 Max Berniker 17 Rehabilitation Institute of Chicago, 18 Northwestern University, Chicago, IL 60611, USA 19 mbernike@northwestern.edu 20 21 Articles in PresS. J Neurophysiol (December 18, 2013). doi:10.1152/jn.00493. 2013
ESTIMATING SCALING EXPONENTS IN AUDITORY

Fractal stochastic point processes FSPPs provide good mathematical models for long term correlations present in auditory nerve bers in a number of species Sim ulations and analytical results for FSPPs concur with experimental data over long times Refractoriness modi ed Poisson point processes in contrast model only the short term characteristics of these data FSPPs incorporating refractoriness yield superior results over all time scales and analytical results for the statistics of this model agree closely with both computer simulations and auditory data Furthermore these processes provide a superior method for estimating the fractal exponent which parametrizes the long term correlation found in these record ings