Miracle-dz/UniFAR-FTUs huggingface dataset:
Applying GARCH for examining CAPM and APT across time

This paper examines the performance of APT and CAPM across time, considering the acquired betas from GARCH-type models. This paper also includes structural breaks for those factors that have undergone significant change in their mean or volatility. Correction for hetero-scedasticity in the estimation of beta in the first-stage of the twostage estimation procedure of CAPM gives an improvement over OLS. Relative to OLS, IGARCH gives smaller estimates of betas, higher risk premia, increased in log-likelihood and higher R2. However, adding the macro-economic factors to the model, the result for market portfolio is different from that of the CAPM. After adding the economics factors coefficient, the power of the market portfolio's beta has either diminished or reduced. Relative to OLS, applying IGARCH improved the performance of APT for the full timeperiod, however, over entire three samples, the correction for hetero-scedasticity by GARCH is not sufficient to lead to satisfactory ressult for APT. This paper similar to Soufian, Joseph and Ritche (2000) shows that different macro-economic factors capture the variation in average returns for different time periods.
Network infrastructure visualisation using high-dimensional node-attribute data

We present an extended version of targeted projection pursuit, a high dimensional data exploration tool adapted for producing graph layouts using node-attributes. Attributes are generated based on detected events in the intrusion detection system and firewall logs and how often they occur for each IP address. Edges are the directed links between source and destination IPs. The layout is interactive and users can manipulate the points in order to find interesting layouts and then further analyse how these layouts are related to the events in the logs. Thus, they first allow the user to detect anomalies and then gives them a platform to investigate why they occur.
Relaxed Significance Criteria for Linkage Analysis

Linkage analysis involves performing significance tests at many loci located throughout the genome. Traditional criteria for declaring a linkage statistically significant have been formulated with the goal of controlling the rate at which any single false positive occurs, called the genomewise error rate (GWER). As complex traits have become the focus of linkage analysis, it is increasingly common to expect that a number of loci are truly linked to the trait. This is especially true in mapping quantitative trait loci (QTL), where sometimes dozens of QTL may exist. Therefore, alternatives to the strict goal of preventing any single false positive have recently been explored, such as the false discovery rate (FDR) criterion. Here, we characterize some of the challenges that arise when defining relaxed significance criteria that allow for at least one false positive linkage to occur. In particular, we show that the FDR suffers from several problems when applied to linkage analysis of a single trait. We therefore conclude that the general applicability of FDR for declaring significant linkages in the analysis of a single trait is dubious. Instead, we propose a significance criterion that is more relaxed than the traditional GWER, but does not appear to suffer from the problems of the FDR. A generalized version of the GWER is proposed, called GWERk, that allows one to provide a more liberal balance between true positives and false positives at no additional cost in computation or assumptions.
Robust field-aligned global parametrization

We present a robust method for computing locally bijective global parametrizations aligned with a given cross-field. The singularities of the parametrization in general agree with singularities of the field, except in a small number of cases when several additional cones need to be added in a controlled way. Parametric lines can be constrained to follow an arbitrary set of feature lines on the surface. Our method is based on constructing an initial quad patch partition using robust cross-field integral line tracing. This process is followed by an algorithm modifying the quad layout structure to ensure that consistent parametric lengths can be assigned to the edges. For most meshes, the layout modification algorithm does not add new singularities; a small number of singularities may be added to resolve an explicitly described set of layouts. We demonstrate that our algorithm succeeds on a test data set of over a hundred meshes.
The Reduced Cochlear Output and the Failure to Adapt the Central Auditory Response Causes Tinnitus in Noise Exposed Rats

Tinnitus is proposed to be caused by decreased central input from the cochlea, followed by increased spontaneous and evoked subcortical activity that is interpreted as compensation for increased responsiveness of central auditory circuits. We compared equally noise exposed rats separated into groups with and without tinnitus for differences in brain responsiveness relative to the degree of deafferentation in the periphery. We analyzed (1) the number of CtBP2/RIBEYE-positive particles in ribbon synapses of the inner hair cell (IHC) as a measure for deafferentation; (2) the fine structure of the amplitudes of auditory brainstem responses (ABR) reflecting differences in sound responses following decreased auditory nerve activity and (3) the expression of the activity-regulated gene Arc in the auditory cortex (AC) to identify long-lasting central activity following sensory deprivation. Following moderate trauma, 30% of animals exhibited tinnitus, similar to the tinnitus prevalence among hearing impaired humans. Although both tinnitus and no-tinnitus animals exhibited a reduced ABR wave I amplitude (generated by primary auditory nerve fibers), IHCs ribbon loss and high-frequency hearing impairment was more severe in tinnitus animals, associated with significantly reduced amplitudes of the more centrally generated wave IV and V and less intense staining of Arc mRNA and protein in the AC. The observed severe IHCs ribbon loss, the minimal restoration of ABR wave size, and reduced cortical Arc expression suggest that tinnitus is linked to a failure to adapt central circuits to reduced cochlear input.
Association between Tumor Necrosis Factor-α rs1800629 Polymorphism and Risk of Asthma: A Meta-Analysis

Objective The purpose of this study was to explore the association between the TNF-α rs1800629 (also refers as -308G/A) polymorphism and asthma susceptibility. Methods We searched the Pubmed, Embase, Cochrane Central Register of Controlled Trials (CENTRAL) and Wanfang databases. Odds ratios (ORs) with 95% confidence intervals (CIs) were used to calculate the strength of association. Results A total of 34 studies involving 5477 asthma patients and 5962 controls were included in present study. The results indicated that TNF-α rs1800629 polymorphism was significantly associated with asthma risk in a recessive genetic model (OR = 1.46, 95% CI 1.21–1.76, P<0.0001). Subgroup analyses found that the TNF-α rs1800629 polymorphism was significantly associated with asthma risk in West Asians and South Asians (OR = 2.47, 95% CI = 1.48–4.12, P = 0.0005; OR = 1.83, 95% CI = 1.42–2.36, P<0.00001), but not East Asians and Caucasians. Furthermore, significant association also was observed in allergic asthma (OR = 1.51, 95% CI = 1.24–1.83, P<0.0001), adults and children (OR = 1.43, 95 CI% = 1.07–1.91, P = 0.02; OR = 1.57, 95% CI = 1.19–2.06, P = 0.001). Conclusions This meta-analysis suggested that the rs1800629 polymorphism in TNF-α was a risk factor for asthma.
A Closed-loop Background Subtraction Approach for Multiple Models based Multiple Objects Tracking

Normally visual surveillance systems are basedon background subtraction to detect foreground objects andthen conduct multiple objects tracking with data associationand tracking filters in an open-loop procedure. Differentfrom the state-of-the-art approaches, this paper discusses aclosed-loop object detection and tracking method. In ourproposed method, each pixel is first modeled with an adaptiveGaussian Mixture Models (GMMs). Second, foregroundmoving objects are tracked by Multiple Hypotheses Trackers(MHT) together with an adaptive Interacting MultipleModels (IMM) method. With the IMM approach, object’sdynamic properties can be better modeled to get more accuratedynamic tracking results. Third, our proposed closedloopapproach uses the object tracking results to adjust theGMMs’ parameters to extract foreground object pixels moreaccurately. With this closed-loop approach, the accuracy ofboth object detection and tracking are improved withoutincreasing computational costs. The proposed new algorithmis tested with extensive experimental videos collected fromdifferent scenarios such as urban streets, intersections, andhighways. Experimental results demonstrated the efficiencyand robustness of our proposed algorithm in handling objectdetection and tracking in real-time.
Progress update: Pharmacological treatment of Alzheimer’s disease

A number of drugs have been approved for the treatment of Alzheimer’s disease (AD) and a larger number are being studied as possible therapies. The current mainstays of the pharmacotherapy of AD are the cholinesterase inhibitors (donepezil, galantamine, rivastigmine) and memantine. They collectively have acceptable tolerability and proven but modest efficacy. The agents being studied include dietary supplements (eg, vitamin E), herbal preparations (eg, Ginkgo biloba), medications approved for other indications (eg, HMG-CoA reductase enzyme inhibitors) and research drugs. In this review we discuss in detail the approved agents and review a number of the unapproved therapies that are currently available to the practitioner. While our era offers much more in the way of therapeutics for AD, it is clear that more work still needs to be done.
A Wide-Tunable LC-Based Voltage-Controlled Oscillator Using a Divide-by-N Injection-Locked Frequency Divider

This paper proposes a novel wideband voltage-controlled oscillator (VCO) for multi-band transceivers. The proposed oscillator has a core VCO and a tuning-range extension circuit, which consists of an injection-locked frequency divider (ILFD) and flip flop dividers. The two-stage differential ILFD generates quadrature outputs and realizes two, three, four, and six of divide ratio with very wide output frequency range. The proposed circuit is implemented by using a 90 nm CMOS process, and the chip area is 250 x 200 μm 2 . The measured result achieves continuous frequency tuning range of 9.3 MHz-to-5.7 GHz (199%) with ―210 dBc/Hz of figure-of-merit (FoM T ).
Using Cognitive Semantics to Integrate Perception and Motion in a Behavior-Based Robot

The ADAPT project is a collaboration of three universities building a unified architecture for mobile robots. The goal of this project is to endow robots with the full range of cognitive abilities, including perception, use of natural language, learning and the ability to solve complex problems. The perspective of this work is that such an architecture should be based on language and visualization. ADAPT is based on an the structure and semantics of language, and more specifically on algebraic linguistics and visualization of semantics. ADAPT organizes its knowledge using linguistic robot schemas, which implement linguistic units within a concurrent, distributed programming language. Each schema is associated with one or more 3D visualizations that provide its semantics. These visualizations are dynamic, and are composed within a virtual world to create ADAPTpsilas representation of itself and its environment.
Model-Based Validation of Industrial Control Systems

Current industrial applications demand the design of more and more complex, safe and trustworthy control systems exhibiting a high degree of flexibility and reutilization. To achieve this, the engineering process should be improved by making the engineering tools involved in the development process to collaborate during the design. This paper presents a model-based approach for designing complex automation applications. The core of the approach is constituted by a set of domain specific models that depend on the application field and whose elements, syntax and semantics are defined from the point of view of the experts that participate in the design of the system. The domain models are defined using engineering tools as the design progresses and they can be used to achieve tool integration through model collaboration. This can be achieved following the Model Driven Engineering approach by means of model transformations. This paper specifically focuses on the first step of this paradigm: the definition of domain languages, in this case for industrial control systems, as well as validation mechanisms of application designs coming from different domain tools. Three well known and widely used industrial standards have been used: Computer Aided Engineering eXchange (CAEX), PLCopen (a representation format for the IEC 61131-3 standard) and MathML (a language for defining mathematical constraints). Using model checking it is possible to assure the correctness of the control system specification and using model transformation it is possible to detect design errors in early stages of the design.
Lao-Thai machine translation using statistical model

This paper aims to present the Lao-Thai machine translation (MT). In order to develop the system based on statistical model, the language model was constructed. Due to Lao is a non-segmented language, the input text in Lao language was segmented into sequence of words using syllable longest matching. These segmented words were used to look for Thai corresponding words from the Lao-Thai dictionary and generate all possible translation sentences. Finally, the system selected the most probability of translation using the language model. The translation accuracy of the proposed system was 88.64%. The result shown that, this system outperforms the translation by Google Translate.
Assay of Serum FructosamineThat MinimizesStandardizationand Matrix Problems : Use to Assess Componentsof BiologicalVariation

Methodological problems with the assay of fructosamine in serum-standardization, matrix effects, and dependence on buffer pH-have been minimized with a method involving calorimetric assay of each specimen and subsequent reassay after standard addition of 1-deoxy-1 -morpholinofructose. Absorbance at optimum wavelength of 540 nm vanes linearly with fructosamine concentration to at least 5.5 mmoi/L, and between-run precision is about 6% for both patients’ specimens and quality control materials. Correction of fructosamine to serum albumin of 40 g/L minimizes the effect of albumin while maintaining transferability of data and reference values. From data on biological variation, the analytical goal for precision (CV) is 2.6%. The square root of the ratio of intrato intenndividual variance is low, indicating that fructosamine concentrations have a high index of individuality; thus conventional population-based reference values are of limited use. Although this assay may be useful in monitoring disease, we doubt that it provides a valid screening test for diabetes.
Variational bayes learning of time-varying graphical models

We propose a tuning-free Bayesian approach to learn a set of sparse graphical models, in which adjacent graphs share similar structures. This model can be applied to estimating dynamic networks that evolve smoothly with regard to a covariate (e.g., time). Specifically, a novel structured spike and slab prior is constructed. This prior allows time-varying sparsity pattern by smoothing the spike probabilities across time using a Gauss-Markov chain. An efficient variational Bayes (VB) algorithm is then derived to learn the model, and is compared to related frequentist methods in the literature. We further extend the proposed mechanism to learn graphical models for multivariate time series in frequency domain. As an example, we analyze scalp electroencephalograms (EEG) recordings of patients at early stages of Alzheimer disease (AD), and quantify the loss of synchrony in comparison with control subjects.
Feature selection with conditional mutual information maximin in text categorization

Feature selection is an important component of text categorization. This technique can both increase a classifier's computation speed, and reduce the overfitting problem. Several feature selection methods, such as information gain and mutual information, have been widely used. Although they greatly improve the classifier's performance, they have a common drawback, which is that they do not consider the mutual relationships among the features. In this situation, where one feature's predictive power is weakened by others, and where the selected features tend to bias towards major categories, such selection methods are not very effective. In this paper, we propose a novel feature selection method for text categorization called <i>conditional mutual information maximin</i> (CMIM). It can select a set of individually discriminating and weakly dependent features. The experimental results show that CMIM can perform much better than traditional feature selection methods.
Genetic Weighted K-means for Large-Scale Clustering Problems

This paper proposes a genetic weighted K-means algorithm called GWKMA, which is a hybridization of a genetic algorithm (GA) and a weighted K-means algorithm (WKMA). GWKMA encodes each individual by a partitioning table which uniquely determines a clustering, and employs three genetic operators (selection, crossover, mutation) and a WKMA operator. The superiority of the GWKMA over the WKMA and other GA-clustering algorithms without the WKMA operator is demonstrated.
Analysis of sentiments using unsupervised learning techniques

Sentimental analysis, a sub discipline within data mining and computational linguistics, refers to the computational methodology for mining, understanding and assessing the opinions expressed in many opinion rich resources like blogs, discussion forums etc. The goal of sentiment analysis is to identify emotional states in online text. Most of the time classifiers trained in one domain do not perform well in another domain. Also the problem in existing approaches is not to detect sentiment and topics. Sentiments may vary with topics. This paper proposes a new model called Combined Sentiment Topic (CST) model to detect sentiments and topics simultaneously from text. This model is based on Gibbs sampling algorithm. Besides, unlike supervised approaches to opinion mining which often fail to produce good performance when shifting to other domains, the unsupervised nature of CST makes it highly portable to other domains. CST model performs better compared to existing semi- supervised approaches.
Laser surface alloying of ZM5 Mg alloy with Al powder

The aim of this work was to improve the corrosion resistance of the cast ZM5 Mg alloy by laser surface alloying with Al powder. The laser alloying with Al on ZM5 Mg alloy was carried out by a pulsed Nd: YAG laser. The microhardness of the surface alloyed layer was measured by a microhardness Vickers tester. The microstructure of the alloyed layer is network. Microhardness of the laser surface alloyed layer was enhanced to 125–143Hv as compared to 72.7–92.6Hv for the untreated substrate. The corrosion resistance of the surface alloyed layer was improved as compared to alloy without laser treatment.
A survey of outlier detection algorithms for data streams

Data mining is characterized as the process of examining hidden patterns and outlining into some useful information. It is an exciting field of research for researchers. Data streams are continuous instance of records and mining interesting knowledge from this instance is known as data stream mining. Outlier detection is currently an important research problem in many fields and is also involved in many of the applications. Outlier detection in streaming data is a challenging task as only one scan is possible and they need huge amount of storage which is practically infeasible. There are many existing methods for outlier detection based on distance measure but are not efficient for data stream as they are dynamic in nature. This paper discusses on various algorithms for outlier detection on data streams.
MPEG Video deployment in interactive multimedia systems : HEVC vs . AVC codec performance study

The coding efficiency of three generations of video coding standards is compared by means of PSNR and subjective testing in interactive video applications, such as video chat, video conferencing and telepresence systems. An unified approach is applied to the analysis of designs of MPEG-H HEVC/H.265, MPEG-4 AVC/H.264, and H.263 reference software implementation. The results of performance tests for selected HD720 video sequences indicate that HEVC encoders achieve equivalent objective video quality as encoders that conform to AVC when using approximately 60% less bit rate on average. Bitrate reduction BRr and coding gain CG based on PSNR measure and complexity based on encoding/decoding time of HEVC MP vs. AVC HP vs. H.263 CHC are tested at bitrates of 0.256, 0.384, 0.512, 0.850, and 1.500 Mbps using the lowdelay encoding constraints typical for real-time conversational applications. Low-delay coding are considered by selecting appropriate prediction structures and coding options in software configuration of encoders. Realtime decoding complexity where studied on personal Ultrabook x86 computer in conversational application. Key-Words: operational configuration, coding gain, subjective quality, codec complexity
Optimalwaveform design for MIMO radarwith low probability of interception

In this paper, we consider the waveform design problem of multiple input multiple output (MIMO) radar in the presence of an interceptor. By the joint considerations of high estimation accuracy and low probability of interception, we formulate the problem as maximizing the mutual information with a constraint on the Kullback-Liebler (KL) divergence. Then it is converted effectively to a convex optimization, which can be solved efficiently to give the optimal waveform. Simulation results show that there exits the optimal tradeoff between the estimation accuracy and the probability of interception.
Performance Evaluation of Space-Time Block Coding on MIMO Communication System Using Spatial Diversity

Wireless technology makes our life easy and comfortable. And also the demand on bandwidth and spectral availability are endless. However, the designers have got difficult task of limited availability of radio spectrum, fading, multi-path, interference, to meet the demand for high data rate [1]. The 2G and 3G standards are not good enough to satisfy the demand of high capacity. Therefore, the new standard 4G which is the successor of 2G and 3G and Broadband Wireless Access (BWA) such as WiMAX have emerged as a promising solution for providing fully broadband internet for mobile and stationary users and they use the new technology called MIMO (Multiple Input Multiple Output). In this paper analysis of 16QAM modulation scheme in AWGN and Rayleigh channels were performed using different number of transmit and receive antennas to provide spatial diversity. The single-input single-output(SISO) system was compared with the various MIMO systems using Signal-to Noise Ratio (SNR) vs Bit Error Rate (BER). Significant SNR improvement and diversity gains were achieved when the MIMO system employs the same number of antenna both at the transmitter and receiver and especially if this number is greater or equal to 2. Significant SNR improvement can also be achieved when more antennas are used at the receiver than at the transmitter to provide diversity gain, SNR improvement and higher data rate.