Miracle-dz/UniFAR-FTUs huggingface dataset:
Role of Lactobacillus pentosus Strain b240 and the Toll-Like Receptor 2 Axis in Peyer's Patch Dendritic Cell-Mediated Immunoglobulin A Enhancement

Lactic acid bacteria are well known to possess immune-modulating effects, but the mechanisms underlying their modulation of the gut immune system are not fully understood. Here, we examined the localization of heat-killed Lactobacillus pentosus strain b240 (b240) in intestinal tissues and the effect of b240 on adaptive immune cascades in the gut. Histological analysis showed that b240 co-localized with dendritic cells (DCs) in the subepithelial dome region of Peyer's patches (PPs). In a PP cell culture system, b240 promoted the production of immunoglobulin A (IgA), interleukin (IL)-6, IL-10, interferon (IFN)-γ, and tumor necrosis factor, but not IL-4, IL-5, B-cell activating factors, IFN-α, IFN-β, and transforming growth factor-β1. The enhanced IgA production by b240 was attenuated by neutralizing IL-6, a potent IgA-enhancing cytokine. b240 stimulated DCs to produce an elevated amount of IL-6 in a Toll-like receptor (TLR) 2-, but not TLR4- or TLR9-dependent manner. Finally, we demonstrated that TLR2-mediated IL-6 production from PP DCs in response to b240 activated B cells to produce a large amount of IgA in a DC-B cell co-culture system. Our findings open up the possibility that the heat-killed form of Lactobacillus pentosus strain b240 can be used as a TLR2-mediated DC-activating biologic for enhancing IgA production in the intestine.
Reverse Engineering of Dynamic Parallel Program Behavior from Execution Traces

Trace-driven simulation has been used widely for architectural exploitation in electronic system-level (ESL) designs for complex SoCs (system-on-chips). Most often the trace files are provided by third parties without source code. This makes it difficult to process and manipulate the trace files, e.g. to identify the region of interest (ROI), for efficient and more focused simulations. It is thus necessary to deduce high-level structures and patterns in the trace to facilitate such manipulations. This can be viewed as a reverse engineering process to derive the dynamic control flow of the original program from its execution trace. Furthermore, after the high-level structure is manipulated, it is also important to generate a new trace from the resultant structure that preserves the characteristics of the original trace for effective trace-driven simulations. The problem becomes even more difficult if the trace was generated from a parallel program because of the complex interactions between the multiple threads of executions. In this paper, a novel scheme to reverse-engineering the program execution trace is proposed, which can obtain the high-level dynamic control structure of the original parallel program as well as regenerate a similar trace from the derived structure, all without referencing the source code. The effectiveness of the proposed scheme is evaluated and verified with extensive experiments.
Dutch Dependency Parser Performance Across Domains

In the past decade several natural language parsing systems have emerged, which use different methods and formalisms. For instance, systems that employ a hand-crafted grammar with a statistical disambiguation component versus purely statistical data-driven systems. What they have in common is the lack of portability to new domains: their performance might decrease substantially as the distance between test and training domain increases. Yet, to which degree do they suffer from this problem, i.e. which kind of parsing system is more affected by domain shifts? To address this question, we evaluate the performance variation of two kinds of dependency parsing systems for Dutch (grammar-driven versus data-driven) across several domains. We examine (1) how parser performance correlates to simple statistical properties of the text and (2) how sensitive a given system is to the text domain. This will give us an estimate of which kind of system is more affected by domain shifts, and thus more in need for domain adaptation techniques. To this end, we extend the statistical measures used by Zhang and Wang (2009a) for English and propose a new simple measure to quantify domain sensitivity.
Control with Pole Placement Constraints for Flexible Structures Vibration Reduction

The vibration control of a flexible structure presents some difficulties for the controller design. The flexible structure is continuous and presents infinite vibration modes. The mathematical model, obtained by finite elements or experimentally identified, is truncated generating model uncertainties. These uncertainties are reflected at the positions of the closed loop poles provoking the spillover phenomenon and instability. Besides, the controllers are in general non-collocated which characterize a more difficult control problem. These aspects require the use of a design technique that considers this kind of difficulty. The H∞ technique aims the reduction in the highest peak of the respective transfer function. This ensures some robustness to the project. But the H∞ controller based on a reduced model can not be enough to control the real structure, which motivates the use of additional constraints related to the closed loop pole placement. The active vibration control problem can be formulated using the LMI (linear matrix inequalities) approach which consists of minimizing a linear objective subjected to LMI constraints. The H∞ control using output feedback with regional pole placement constraints in terms of LMIs is investigated in this work for a flexible beam. Some numerical results are presented and discussed in the paper.
Depth Perception in Interactive Virtual Acoustic Environments using Higher Order Ambisonic Soundfields

In this paper, we present an investigation into the perception of source depth in interactive virtual auditory environments in the context of Higher Order Ambisonic (HOA) reproduction. In particular, we investigate the accuracy of soundﬁeld reproduction over virtual loudspeakers (headphone reproduction) with increasing Ambisonic order. Performance of 1 st , 2 nd and 3 rd order Ambisonics in representing distance cues is assessed in subjective audio perception tests. Results demonstrate that ﬁrst order soundﬁelds are sufﬁcient in representing distance cues for virtual loudspeaker reproduction.
Active Inference and Learning for Classifying Streams

In this position paper we introduce Active Inference, a paradigm for intelligently requesting human labels for inference and learning in situations with a nite budget for applying human resources for labeling cases. Many machine learning systems are applied to a stream of instances that can repeat, such as queries entered in a search engine or web pages for potential ad impressions. When a particular instance x can be subject to classication more than once, we have an additional complication to the budgeted learning setting. In such applications, frequently the distributions will be non-uniform; for instance, in the above applications the distributionsp(x) over examples are highly skewed and thus a few x’s result in a large percentage of the actual cases for prediction. In such settings, it may be benecial to allocate a human \labeling" budget selectively perform direct inference, requesting human labels on a selected subset of the instances to be provided to an end system in an eort to reduce misclassication cost on the x’s with the highest expected utility. In estimating the utility of labeling a particular x, one must consider three factors: misclassication cost, the probability of encountering x, p(x), and the valuex and its associated label may bring for (active) learning. We will discuss the illustrative application of machine learning for safe advertising, where there is a limited budget for acquiring ground-truth labels for labeling web-pages.
Criteria for variable selection with dependence

We propose a new data-driven procedure of model selection based loss estimation and valid for the whole family of spherically symmetric distributions, allowing for dependence between noise components. We give explicit formulas for the Firm Shrinkage estimator, and we compare by simulation our results in terms of selection with the classical AIC, BIC and leave-one-out cross-validation.
Brain Volume of the Newly-Discovered Species Rhynchocyon udzungwensis (Mammalia: Afrotheria: Macroscelidea): Implications for Encephalization in Sengis

The Gray-faced Sengi (Rhynchocyon udzungwensis) is a newly-discovered species of sengi (elephant-shrew) and is the largest known extant representative of the order Macroscelidea. The discovery of R. udzungwensis provides an opportunity to investigate the scaling relationship between brain size and body size within Macroscelidea, and to compare this allometry among insectivorous species of Afrotheria and other eutherian insectivores. We performed a spin-echo magnetic resonance imaging (MRI) scan on a preserved adult specimen of R. udzungwensis using a 7-Tesla high-field MR imaging system. The brain was manually segmented and its volume was compiled into a dataset containing previously-published allometric data on 56 other species of insectivore-grade mammals including representatives of Afrotheria, Soricomorpha and Erinaceomorpha. Results of log-linear regression indicate that R. udzungwensis exhibits a brain size that is consistent with the allometric trend described by other members of its order. Inter-specific comparisons indicate that macroscelideans as a group have relatively large brains when compared with similarly-sized terrestrial mammals that also share a similar diet. This high degree of encephalization within sengis remains robust whether sengis are compared with closely-related insectivorous afrotheres, or with more-distantly-related insectivorous laurasiatheres.
An incremental associative classification algorithm used for malware detection

Associative classification(AC) is a promising approach used for auto malware detection. However, when data operation occurs (training data added over time), traditional AC algorithms have to re-learn repetitive which is expensive or even become invalidly because of massive data and limited computing resource. To resolve the challenges above, an efficient incremental associative classification algorithm (EIAC) is proposed which can keep the last mining results and learn from the new data set. First, EIAC learns new potential ruleitems from the new data set; and then updates the frequent count of original and potential ruleitems by constructing and searching two trees based on FP-Tree respectively; at last, updates the classification association rules with the frequent information of updated ruleitems. The promising studies on real daily data collection and prediction illustrate that: compared with the traditional AC and other classification methods, EIAC can maintain the classification association rules effectively and ensure a higher predictability of the classification model. So it can be well used for malware detection.
Bayesian shape recognition using Principle Component Analysis and Modified Chain Codes

This paper represents a shape recognition method using PCA (principal component analysis) and Bayesian probability with MCC (modified chain code). MCC is a shape descriptor which is invariant in 2D shapepsilas scale, translation and rotation. Shape prior information is analyzed by PCA using shape database. We recognized shapes by Bayesian probability with shape prior information. In this paper we describe traditional chain code to describe object features and using its modification version, PCA and Bayesian probabilistic analysis and classification are represented.
Sentimentos de familiares de pacientes internados na emergência psiquiátrica: um olhar sobre a família/ Feelings of relatives of patients hospitalized in the psychiatric emergency care unit: a look on the family

O presente estudo teve como objetivo compreender os sentimentos dos familiares que chegam a emergencia psiquiatrica com um ente em agudizacao dos sintomas do transtorno mental. Trata-se de uma pesquisa descritiva-exploratoria com abordagem qualitativa. Participaram 20 familiares de pessoas com transtorno mental atendidos na Emergencia Psiquiatrica de um Hospital da Rede Publica do Interior do Estado de Sao Paulo. Para coleta de dados,utilizou-se um roteiro de entrevista individual. As entrevistas foram audio-gravadas e tratadas conforme analise de conteudo tematica de Bardin. Os dados resultaram em duas categorias: A internacao no servico de emergencia psiquiatrica como alivio da sobrecarga familiar; e A agudizacao do transtorno mental como gerador de angustia e tristeza para a familia. Desta forma, compreender os sentimentos predominantes nos familiares dos pacientes no ato da internacao no Servico de Emergencia contribui para a elaboracao de estrategias para a construcao de uma atuacao profissional humanistica visando a inclusao da unidade familiar em todas as etapas do cuidado.
Design and Implementation Issues of Flexible Network Architecture

Present Internet protocol was developed long way back with the intention to provide simple end-to-end network functionalities for network applications of 1980's. Since then many times network and its related applications were reformed over technological advancement. To support new applications more and more protocols were created as patches which resulted in IP bloat. As a result of this Internet community researcher worries about upcoming days of failure of current Internet. This paper emphasis on design and implementation issues of flexible network using with Service Oriented Architecture methodology. And also highlights a framework for Flexible Network Architecture which is one of the way to address current as well as future network application problem.
Inner Supervision in Multi-Sensor Data Fusion Using the Concepts of Stackelberg Games

As a new means for data acquisition and fusion, mobile ad hoc sensor networks has been the focus of many researchers during the previous years. To enhance both the quality of service (QoS) and the network's robustness, the concept of mobility were introduced which gives the designer the capability of using various topologies to perform a certain task. This paper proposes a novel management idea for positioning of the sensor agents based on the concepts of Stackelberg games. It is explained that the new method lowers the cost of internal communication while eases the network description as well
Bayesian Estimation of a DSGE Model for the Portuguese Economy

In this paper, a New-Keynesian DSGE model for a small open economy integrated in a monetary union is developed and estimated for the Portuguese economy, using a Bayesian approach. Estimates for some key structural parameters are obtained and a set of exercises exploring the model's empirical properties and the results' robustness are performed. A survey on the main events and literature associated with DSGE models is also provided, as well as a comprehensive discussion of Bayesian estimation and model comparison techniques. The model features five types of economic agents namely households, firms, aggregators, the rest of the world and the government, and includes a number of shocks and frictions, which enable a closer matching of the short-run properties of the data and a more realistic short- term adjustment to shocks. It is assumed from the outset that monetary policy is defined by the union's central bank and that the domestic economy's size is negligible, relative to the union's one, and therefore its specific economic fluctuations have no influence on the union's macroeconomic aggregates and monetary policy. A risk-premium is considered, to allow for deviations of the domestic economy's interest rate from the union's one. Furthermore it is assumed that all trade and financial flows are performed with countries belonging to the union, which implies that the nominal exchange rate is irrevocably set to unity.
On Rényi Divergence Measures for Continuous Alphabet Sources

The idea of 'probabilistic distances' (also called divergences), which in some sense assess how 'close' two probability distributions are from one another, has been widely employed in probability, statistics, information theory, and related fields. Of particular importance due to their generality and applicability are the Rényi divergence measures. While the closely related concept of Rényi entropy of a probability distribution has been studied extensively, and closed-form expressions for the most common univariate and multivariate continuous distributions have been obtained and compiled [57, 45, 62], the literature currently lacks the corresponding compilation for continuous Rényi divergences. The present thesis addresses this issue for the analytically tractable cases. Closed-form expressions for Kullback-Leibler divergences are also derived and compiled , as they can be seen as an extension by continuity of the Rényi divergences. Additionally , we establish a connection between Rényi divergence and the variance of the log-likelihood ratio of two distributions, which extends the work of Song [57] on the relation between Rényi entropy and the log-likelihood function, and which becomes practically useful in light of the Rényi divergence expressions we have derived. Lastly, we consider the Rényi divergence rate between two stationary Gaussian processes.
SigGraph: Brute Force Scanning of Kernel Data Structure Instances Using Graph-based Signatures

Brute force scanning of kernel memory images for finding kernel data structure instances is an important function in many computer security and forensics applications. Brute force scanning requires effective, robust signatures of kernel data structures. Existing approaches often use the value invariants of certain fields as data structure signatures. However, they do not fully exploit the rich pointsto relations between kernel data structures. In this paper, we show that such points-to relations can be leveraged to generate graph-based structural invariant signatures. More specifically, we develop SigGraph, a framework that systematically generates non-isomorphic signatures for data structures in an OS kernel. Each signature is a graph rooted at a subject data structure with its edges reflecting the points-to relations with other data structures. Our experiments with a range of Linux kernels show that SigGraph-based signatures achieve high accuracy in recognizing kernel data structure instances via brute force scanning. We further show that SigGraph achieves better robustness against pointer value anomalies and corruptions, without requiring global memory mapping and object reachability. We demonstrate that SigGraph can be applied to kernel memory forensics, kernel rootkit detection, and kernel version inference.
Modulation of the Pharmacological Activities of Secretory Phospholipase A2 from Crotalus durissus cascavella Induced by Naringin

In this work we have characterized the action of the naringin, a flavonoid found in grapefruit and known for its various pharmacological effects, which include antioxidant, blood lipid lowering and anticancer activity, on the structure and biochemical activities of a secretory phospholipase A (sPLA2) from Crotalus durissus cascavella, an important protein involved in the releasinge of arachidonic acid in phospholipid membranes. sPLA2 was incubated with naringin (mol:mol) at 37 °C and a discrete reduction in the UV scanning signal and a modification of the circular dichroism spectra were observed after treatment with naringin, suggesting modifications of the secondary structure of the protein. This flavonoid was able to decrease enzymatic activity and some pharmacological effects, such as myonecrosis, platelet aggregation, and neurotoxic activity caused by sPLA2, however, the inflammatory effect was not affected by naringin. In addition, small angle X-ray scattering (SAXS) data were collected for sPLA2 and naringin-treated sPLA2 to evaluate possible modifications of the protein structure. These structural investigations have shown that sPLA2 is an elongated dimer in solution and after treatment with naringin a conformational change in the dimeric configuration was observed. Our results suggest that structural modification may be correlated with the loss of enzymatic activity and alterations in pharmacological properties.
Teaching Cognitive Modeling Using PDP++

PDP++ is a freely available, open source software package designed to support the development, simulation, and analysis of research-grade connectionist models of cognitive processes. It supports most popular parallel distributed processing paradigms and artificial neural network architectures, and it also provides an implementation of the LEABRA computational cognitive neuroscience framework. Models are typically constructed and examined using the PDP++ graphical user interface, but the system may also be extended through the incorporation of user-written C++ code. This article briefly reviews the features of PDP++, focusing on its utility for teaching cognitive modeling concepts and skills to university undergraduate and graduate students. An informal evaluation of the software as a pedagogical tool is provided, based on the author’s classroom experiences at three research universities and several conference-hosted tutorials. both as a printable and a collection of linked pages. This paper provides a brief description of the most salient features of PDP++, focusing on the role that it can play in teaching connectionist modeling concepts and skills. The use of PDP++ in classroom settings, involving either undergraduate or graduate students, is of primary interest here, though many of the issues raised are equally relevant to the education of established researchers or to self-learning situations. After providing an overview of the software, an informal evaluation of the pedagogical strengths and weaknesses of PDP++ is offered. This evaluation is based on the author’s experience using PDP++ when teaching courses on parallel distributed processing, artificial neural networks, and computational cognitive neuroscience at Carnegie Mellon University (1999), Vanderbilt University (2002–2006), and the University of California, Merced (2007). This evaluation also draws on experience using PDP++ in tutorial settings, including its use at an International Brain Research Organization (IBRO) summer school (2004), and during single day presentations at the International Conference on Cognitive Modeling (ICCM 2007) and the Cognitive Science Society Conference (CSS 2007). Importantly, this report does not provide instruction in the use of PDP++ for research purposes. It offers only an overview and informal assessment of the role that this free software package can play in the education of future cognitive scientists.
Chip-independent Error Correction in main memories

Main memory reliability is an important concern in today's computer systems. Error Correction Codes (ECCs) improve memory reliability but have high area and energy overheads. Furthermore, ECCs cannot be easily applied to memories with wide chips such as stacked memories. In this paper, we introduce a new low-overhead error correction scheme, which can easily be applied to DRAM memories with wide devices. The scheme is called Chip-Independent Error Correction (CIEC) because it is independent of the memory chip width. Our simulation results in the context of transient faults show that CIEC has only 4.5% energy overhead, 0.5% performance overhead, and 0.7% area overhead on the processor chip as compared to a non-ECC DIMM while its reliability is much higher than the reliability of non-ECC DIMMs.