Miracle-dz/UniFAR-FTUs huggingface dataset:
Risk bounds of learning processes for Lévy processes

Leevy processes refer to a class of stochastic processes, for example, Poisson processes and Brownian motions, and play an important role in stochastic processes and machine learning. Therefore, it is essential to study risk bounds of the learning process for time-dependent samples drawn from a Leevy process (or briefly called learning process for Leevy process). It is noteworthy that samples in this learning process are not independently and identically distributed (i.i.d.). Therefore, results in traditional statistical learning theory are not applicable (or at least cannot be applied directly), because they are obtained under the sample-i.i. d. assumption. In this paper, we study risk bounds of the learning process for time-dependent samples drawn from a Leevy process, and then analyze the asymptotical behavior of the learning process. In particular, we first develop the deviation inequalities and the symmetrization inequality for the learning process. By using the resultant inequalities, we then obtain the risk bounds based on the covering number. Finally, based on the resulting risk bounds, we study the asymptotic convergence and the rate of convergence of the learning process for Leevy process. Meanwhile, we also give a comparison to the related results under the sample-i.i. d. assumption.
An effectual facial expression recognition using HMM

In general human face has similar and different characteristics which play a very important role in recognizing facial expression. In this work, a new method presents to recognize different facial expressions from time sequential facial expression images. The performance of an automatic facial expression recognition system can be significantly improved by modeling the accuracy of various streams of facial expression information utilizing multi stream hidden Markov models (HMMs). Active Appearance Model (AAM) landmarks are measured for each frame of the videos. The AAMs were used to identify the face and extract its graphic features. HMM is act as a classifier for the expression prediction. The k-NN used for classification or regression because of its lazy learning also able to identify the person in the image. The experiment explored that the proposed approach has show encouraging accuracy in realizing all expression.
Solvent coarse-graining and the string method applied to the hydrophobic collapse of a hydrated chain

With computer simulations of >100,000 atoms, the mechanism for the hydrophobic collapse of an idealized hydrated chain was obtained by tiling space with (0.2 nm)3 cubes and projecting the atomistic water molecule positions onto this grid. With the coarse-grained field thus defined, the string method in collective variables was used to compute a minimum free-energy pathway (MFEP) for the collapsing chain. These calculations provide a proof of principle for a coarse-grained description of water solvent. Furthermore, the calculated MFEP characterizes the mechanism for the collapse of the hydrated chain by providing a path of maximum likelihood for dynamical trajectories. The reliability of the calculated MFEP was confirmed with the use of conventional molecular dynamics trajectories. Analysis of the MFEP provides atomistic confirmation for the mechanism of hydrophobic collapse proposed by ten Wolde and Chandler. In particular, we show that length-scale-dependent hydrophobic dewetting is the rate-limiting step in the hydrophobic collapse of the considered chain.
Agricultural Applications of Value-at-Risk Analysis: A Perspective

Value-at-Risk (VaR) determines the probability of a portfolio of assets losing a certain amount in a given time period due to adverse market conditions with a particular level of confidence. Value-at-Risk has received considerable attention from financial economists and financial practitioners for its use in risk reporting, in particular the risks of derivatives. This paper provides a "state-of-the-art" review of VaR estimation techniques and empirical findings found in the finance literature. The ability of VaR estimates to represent large losses associated with tail events varies among procedure, confidence level, and data used. To date, there is no consensus to the most appropriate estimation technique. Potential applications of Value-at-Risk are suggested in the context of agricultural risk management. In the wake of the Hedge-to-Arrive crisis, the lifting of agricultural trade options by the CFTC, and the decreased government participation, VaR seems to have a place in the agricultural risk manager's toolkit.
MIND: A Scalable Classi er in Relational Databases

Classi cation is a key function of many \business intelligence" toolkits and a fundamental building block in data mining. The best known results indicate that immense data may be needed to train a classi er for good accuracy. The state-of-art classi ers [21, 25] need an in-memory data structure of size O(N), where N is the size of the training data, to achieve e ciency. For large data sets, such a data structure will not t in the internal memory. The best previously known classi er does a quadratic number of I/Os for large N . In this paper, we propose a novel classi cation algorithm (classi er) called MIND (MINing in Databases). MIND can be phrased in such a way that its implementation is very easy using the extended relational calculus SQL, and this in turn allows the classi er to be built into a relational database system directly. MIND is truly scalable with respect to I/O e ciency, which is important since scalability is a key requirement for any data mining algorithm. We built a prototype of MIND in the relational database manager DB2 and benchmarked its performance. We describe the working prototype and report the measured performance with respect to the previous method of choice. MIND scales not only with the size of the datasets but also with the number of processors on an IBM SP2 computer system. Even on uniprocessors, MIND scales well beyond the dataset sizes previously published for classi ers. We also give some insights that may have an impact on the evolution of the extended relational calculus SQL.
A Two Levels Architecture For Bufferless WDM Optical Packet Switches

The objective of this study is to propose a new bufferless optical packet switching architecture in which thew converters, needed to solve output packet contentions, are used in two levels. The architecture is equipped with limited-range wavelength converters (LRWCs) at the first level and with shared and full-range wavelength converters (FRWCs) at the second level. In a WDM all optical network, in the absence of wavelength conversion ability, the signal is required to be on the same wavelength from hop to hop, which is referred to as the wavelength continuity constraint Studies show that network performance is greatly improved by using wavelength converters [1]. If a wavelength converter can convert a wavelength to any other wavelength in the optical system, it is called full range wavelength converter. However, this type of wavelength conversion is quite difficult and expensive to implement due to technological limitations [2]. In proposed architecture, limited numbers of FRWCs are shared at the output of optical packet switch, to make that more cost effective. Although a few number of FRWCs are used, it causes reduction of packet loss probability about 40%.
Unsupervised Knowledge Extraction for Taxonomies of Concepts from Wikipedia

A novel method for unsupervised acquisition of knowledge for taxonomies of concepts from raw Wikipedia text is presented. We assume that the concepts classified under the same node in a taxonomy are described in a comparable way in Wikipedia. The concepts in 6 taxonomies extracted from WordNet are mapped onto Wikipedia pages and the lexico-syntactic patterns describing semantic structures expressing relevant knowledge for the concepts are automatically learnt.
Fusing Aligned and Non-aligned Face Information for Automatic Affect Recognition in the Wild: A Deep Learning Approach

Face alignment can fail in real-world conditions, negatively impacting the performance of automatic facial expression recognition (FER) systems. In this study, we assume a realistic situation including non-alignable faces due to failures in facial landmark detection. Our proposed approach fuses information about non-aligned and aligned facial states, in order to boost FER accuracy and efficiency. Six experimental scenarios using discriminative deep convolutional neural networks (DCNs) are compared, and causes for performance differences are identified. To handle non-alignable faces better, we further introduce DCNs that learn a mapping from non-aligned facial states to aligned ones, alignment-mapping networks (AMNs). We show that AMNs represent geometric transformations of face alignment, providing features beneficial for FER. Our automatic system based on ensembles of the discriminative DCNs and the AMNs achieves impressive results on a challenging database for FER in the wild.
Online Mining of Recent Music Query Streams

Mining multimedia data is one of the most important issues in data mining. In this paper, we propose an online one-pass algorithm to mine the set of frequent temporal patterns in online music query streams with a sliding window. An effective bit-sequence representation is used to reduce the processing time and memory needed to slide the windows. Experiments show that the proposed algorithm only needs a half of memory requirement of original music query data, and just scans the data once
Spatial Analysis for Colon Biopsy Classification from Hyperspectral Imagery

Automatic classification of histology images, the objective of our research, is aimed at supporting the pathologists in their diagnosis. In this paper, we present a comparative study between 3D spectral/spatial analysis (SSA) and 2D spatial analysis (SA) for the classification of colon biopsy samples from their hyperspectral images. Classification results suggest that textural analysis of 2D bands can achieve comparable performance to 3D spectral/spatial analysis.
Use of G-Protein-Coupled and -Uncoupled CCR5 Receptors by CCR5 Inhibitor-Resistant and -Sensitive Human Immunodeficiency Virus Type 1 Variants

ABSTRACT Small-molecule CCR5 inhibitors such as vicriviroc (VVC) and maraviroc (MVC) are allosteric modulators that impair HIV-1 entry by stabilizing a CCR5 conformation that the virus recognizes inefficiently. Viruses resistant to these compounds are able to bind the inhibitor-CCR5 complex while also interacting with the free coreceptor. CCR5 also interacts intracellularly with G proteins, as part of its signal transduction functions, and this process alters its conformation. Here we investigated whether the action of VVC against inhibitor-sensitive and -resistant viruses is affected by whether or not CCR5 is coupled to G proteins such as Gαi. Treating CD4+ T cells with pertussis toxin to uncouple the Gαi subunit from CCR5 increased the potency of VVC against the sensitive viruses and revealed that VVC-resistant viruses use the inhibitor-bound form of Gαi-coupled CCR5 more efficiently than they use uncoupled CCR5. Supportive evidence was obtained by expressing a signaling-deficient CCR5 mutant with an impaired ability to bind to G proteins, as well as two constitutively active mutants that activate G proteins in the absence of external stimuli. The implication of these various studies is that the association of intracellular domains of CCR5 with the signaling machinery affects the conformation of the external and transmembrane domains and how they interact with small-molecule inhibitors of HIV-1 entry.
Ontology-Based Fast Devices Configuration in 3D Configuration Software

Using 3D user interface in the configuration software, the user interface can become realistic and friendly. And it can reflect the devices running status in the industrial field so realistically. Moreover, it can avoid the Window-Effect effectively due to the screen size influence. The window information content has the enormous enhancement. But it brings some other problems such as configuration difficultly. This paper presents a ontology-based fast configuration method by attaching semantic information to the device model. The computer can understand the user intention and assist him do the configuration work. Using this method, the devices configuration becomes so simple and convenient. The users that are not 3D professionals can also carry on the devices configuration relaxed.
Candida albicans Is Resistant to Polyglutamine Aggregation and Toxicity

Disruption of protein quality control can be detrimental, having toxic effects on single cell organisms and contributing to neurodegenerative diseases such as Alzheimer’s, Parkinson’s and Huntington’s in humans. Here, we examined the effects of polyglutamine (polyQ) aggregation in a major fungal pathogen of humans, Candida albicans, with the goal of identifying new approaches to disable this fungus. However, we discovered that expression of polyQ stretches up to 230Q had no effect on C. albicans ability to grow and withstand proteotoxic stress. Bioinformatics analysis demonstrates that C. albicans has a similarly glutamine-rich proteome to the unicellular fungus Saccharomyces cerevisiae, which exhibits polyQ toxicity with as few as 72Q. Surprisingly, global transcriptional profiles indicated no significant change upon induction of up to 230Q. Proteomic analysis highlighted two key interactors of 230Q, Sis1 and Sgt2; however, loss of either protein had no additional effect on C. albicans toxicity. Our data suggest that C. albicans has evolved powerful mechanisms to overcome the toxicity associated with aggregation-prone proteins, providing a unique model for studying polyQ-associated diseases.
Role of the Adiponectin Binding Protein, T-Cadherin (cdh13), in Pulmonary Responses to Subacute Ozone

Adiponectin, an adipose derived hormone with pleiotropic functions, binds to several proteins, including T-cadherin. We have previously reported that adiponectin deficient (Adipo−/−) mice have increased IL-17A-dependent neutrophil accumulation in their lungs after subacute exposure to ozone (0.3 ppm for 72 hrs). The purpose of this study was to determine whether this anti-inflammatory effect of adiponectin required adiponectin binding to T-cadherin. Wildtype, Adipo−/−, T-cadherin deficient (T-cad−/−), and bideficient (Adipo−/−/T-cad−/−) mice were exposed to subacute ozone or air. Compared to wildtype mice, ozone-induced increases in pulmonary IL-17A mRNA expression were augmented in T-cad−/− and Adipo−/− mice. Compared to T-cad−/− mice, there was no further increase in IL-17A in Adipo−/−/T-cad−/− mice, indicating that adiponectin binding to T-cadherin is required for suppression of ozone-induced IL-17A expression. Similar results were obtained for pulmonary mRNA expression of saa3, an acute phase protein capable of inducing IL-17A expression. Comparison of lung histological sections across genotypes also indicated that adiponectin attenuation of ozone-induced inflammatory lesions at bronchiolar branch points required T-cadherin. BAL neutrophils and G-CSF were augmented in T-cad−/− mice and further augmented in Adipo−/−/T-cad−/− mice. Taken together with previous observations indicating that augmentation of these moieties in ozone exposed Adipo−/− mice is partially IL-17A dependent, the results indicate that effects of T-cadherin deficiency on BAL neutrophils and G-CSF are likely secondary to changes in IL-17A, but that adiponectin also acts via T-cadherin independent pathways. Our results indicate that T-cadherin is required for the ability of adiponectin to suppress some but not all aspects of ozone-induced pulmonary inflammation.
An extended TAM for analyzing adoption behavior of mobile coupon

This study based on the technology acceptance model (TAM) and presents an extended TAM that integrates social influence, compatibility, perceived credibility and coupon proneness construct into the TAM to analyzing adoption behavior of mobile coupons (MC). The proposed model was empirically tested using data collected from a survey of 824 consumers. The structural equation modeling technique was used to evaluate the causal model and confirmatory factor analysis was performed to examine the reliability and validity of the measurement model. Social influence and compatibility plays a major role in influencing the intention to use mobile coupons. The results of these studies confirm that in the mobile technology context, traditional adoption models such as TAM could be applied but they need to be modified and extended in order to increase their prediction and explanation power.
Gender and Assimilation Among Mexican Americans

Using 1994-2003 CPS data, we study gender and assimilation of Mexican Americans. Source country patterns, particularly the more traditional gender division of labor in the family in Mexico, strongly influence the outcomes and behavior of Mexican immigrants. On arrival in the United States, immigrant women have a higher incidence of marriage (spouse present), higher fertility, and much lower labor supply than comparable white natives; wage differences are smaller than labor supply differences, and smaller than comparable wage gaps for men. Immigrant women's labor supply assimilates dramatically: the ceteris paribus immigrant shortfall is virtually eliminated after twenty years. While men experience moderate wage assimilation, evidence is mixed for women. Rising education in the second generation considerably reduces raw labor supply (especially for women) and wage gaps with nonhispanic whites. Female immigrants' high marriage rates assimilate towards comparable natives', but immigrant women and men remain more likely to be married even after long residence. The remaining ceteris paribus marriage gap is eliminated in the second generation. Immigrants' higher fertility does not assimilate toward the native level, and, while the size of the Mexican American- white native fertility differential declines across generations, it is not eliminated.
Design Issue and Proposed Implementation of Communication Aid for Deaf & Dumb People

— Deaf and dumb people communicate among themselves using sign languages, but they find it difficult to communicate with the outside world. This paper proposes a full proof system to aid communication of deaf and dumb people communicationg using Indian sign language (ISL) with normal people where hand gestures will be converted into appropriate text message. The hand gestures corresponding to ISL English alphabets, numbers and words will be captured through a webcam. Main objective is to design an algorithm to convert dynamic gesture to text at real time. Finally after testing is done the system will be implemented on android platform and will be available as an application for smart phone and tablet pc.
Optimal distributed flow control over multiple parallel wireless channels

This paper addresses a distributed flow and channel allocation method for Point-to-MultiPoint (PMP) networks operating over multiple parallel channels, where a central node shares its rate capacity with multiple subscribers. The problem formulation takes into account fairness and Quality of Service (QoS) considerations by means of generalized utility functions. Assuming that a local area network (LAN), wired or wireless, is attached to each of the subscribers, a centralized allocation scheme becomes prohibitive in terms of required network signalling when the number of connections within the LANs become high. In order to overcome this and to keep practicality in terms of iterations needed to converge, we apply a combination of a Mean Value Cross (MVC) decomposition strategy with the novel coupled-decompositions method that achieves the optimal solution with a reduced number of iterations when compared to classical approaches.
An Analysis of the Effects of Maquiladora Production on Performance

This study analyzes financial performance in firms that employ maquiladora production, a manufacturing arrangement utilizing Mexican labor. We argue that although accountants can readily identify and value the obvious benefits of labor savings, they have more difficulty identifying and valuing incremental costs from any frictions encountered. The result is that this offshore labor strategy may not be as cost-effective as it would initially appear. We cannot explicitly measure the costs associated with these frictions; we therefore compare performance of a sample of firms that implemented maquiladora production (treatment firms) with performance of similar firms (control firms), examining median return on assets (ROAs) during periods before and after implementation. Results for the median difference tests suggest that although treatment firms benefit from lower committed costs and lower labor costs, there is no corresponding increase in ROA. Results from the regression models are similar and indicate no differences in ROA after implementation, and no differences in committed costs and labor costs. To identify those firms that could have more difficulty estimating costs and benefits, we partition the maquiladora firms on variance of profits, assuming that high-variance firms may be more cautious in their decision making. We find that compared with the low-variance sample firms, high-variance firms experience increases in ROA after implementing maquiladora production. The production process is another firm characteristic that could influence maquiladora success. As expected, when we partition the maquiladora firms on production technology, we find that maquiladora implementation leads to higher ROAs for low-production technology firms.