Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards a Discourse Model for Knowledge Elicitation

Knowledge acquisition has been and still remains a hard problem. When it comes to eliciting knowledge from human subjects, an artificial interviewer can be of tremendous benefit. In this paper we present a discourse model for representing the explicit propositional content of a text along with question raising mechanism based on it. This feature is perfectly aligned with the purpose of acquiring more knowledge from the human respondent and acting as a self-extending knowledge base.
Performance of approximate algorithm for p-persistent slotted ALOHA

The distributed network of dynamic spectrum sharing communication system used p-persist slotted ALOHA as medium access control protocol, but MAC layer usually does not support floating-point operations. In this paper, we analyze the principle of dynamic adaptive p-persistent algorithm (DA-pPA), and work toward the computational issue of the current MAC algorithms. The proposed algorithms incluse the binary shifted approximate algorithm (BSAA) and Thaler series approximate algorithm (TSAA). The system performance of the approximation algorithms are evaluated via numerical calculation and simulation. The results shows that the approximation algorithm is able to simplify the computational complexity, and consequently approach the theoretical performance of the p-persistent algorithm.
Interrogating protein interaction networks through structural biology

Protein–protein interactions are central to most biological processes. Although much recent effort has been put into methods to identify interacting partners, there has been a limited focus on how these interactions compare with those known from three-dimensional (3D) structures. Because comparison of protein interactions often involves considering homologous, but not identical, proteins, a key issue is whether proteins that are homologous to an interacting pair will interact in the same way, or interact at all. Accordingly, we describe a method to test putative interactions on complexes of known 3D structure. Given a 3D complex and alignments of homologues of the interacting proteins, we assess the fit of any possible interacting pair on the complex by using empirical potentials. For studies of interacting protein families that show different specificities, the method provides a ranking of interacting pairs useful for prioritizing experiments. We evaluate the method on interacting families of proteins with multiple complex structures. We then consider the fibroblast growth factor/receptor system and explore the intersection between complexes of known structure and interactions proposed between yeast proteins by methods such as two-hybrids. We provide confirmation for several interactions, in addition to suggesting molecular details of how they occur.
A study on visual attention modeling — A linear regression method based on EEG

In an increasingly knowledge based world, people are confronted with an explosion of information from the environment which must be viewed in restricted attention spans. Hence there is a need to investigate how best to model our Visual Attention (VA) with a view to allocate our attention efficiently. We use the color-word Stroop task combined with electroencephalogram (EEG) to model VA: subjects undertake the Stroop task and their EEG is recorded. This is in contrast to other studies that use techniques such as Event Related Potentials (ERP), Contextual Modeling Frameworks, eye movements and facial recognition. The paper presents a simple and useful model to recognize VA dynamically. We use the linear EEG features of different cortical fields as the main inference factors, and take the response time (RT) of the Stroop task as a metric to quantify subject performance. First, we obtain the most relevant EEG feature vectors from the recording, using a correlation analysis. Second, we use experimental data for training the VA model, using a regression method. Last, we then apply further experimental data to test the proposed model. The results from the tests conducted demonstrate that our model maps visual attention very closely.
Tissue Specific Induction of p62/Sqstm1 by Farnesoid X Receptor

Background Farnesoid X Receptor (FXR) is a member of the nuclear receptor superfamily and is a ligand-activated transcription factor essential for maintaining liver and intestinal homeostasis. FXR is protective against carcinogenesis and inflammation in liver and intestine as demonstrated by the development of inflammation and tumors in the liver and intestine of FXR knock-out mice. However, mechanisms for the protective effects of FXR are not completely understood. This study reports a novel role of FXR in regulating expression of Sqstm1, which encodes for p62 protein. p62 plays an important role in maintaining cellular homeostasis through selective autophagy and activating signal transduction pathways, such as NF-κB to support cell survival and caspase-8 to initiate apoptosis. FXR regulation of Sqstm1 may serve as a protective mechanism. Methods and Results This study showed that FXR bound to the Sqstm1 gene in both mouse livers and ileums as determined by chromatin immunoprecipitation. In addition, FXR activation enhanced transcriptional activation of Sqstm1 in vitro. However, wild-type mice treated with GW4064, a synthetic FXR ligand, showed that FXR activation induced mRNA and protein expression of Sqstm1/p62 in ileum, but not in liver. Interestingly, FXR-transgenic mice showed induced mRNA expression of Sqstm1 in both liver and ileum compared to wild-type mice. Conclusions Our current study has identified a novel role of FXR in regulating the expression of p62, a key factor in protein degradation and cell signaling. Regulation of p62 by FXR indicates tissue-specific and gene-dosage effects. Furthermore, FXR-mediated induction of p62 may implicate a protective mechanism of FXR.
An Evaluation of Commercial Fluorescent Bead-Based Luminex Cytokine Assays

The recent introduction of fluorescent bead-based technology, allowing the measurement of multiples analytes in a single 25–50 µl sample has revolutionized the study of cytokine responses. However, such multiplex approaches may compromise the ability of these assays to accurately measure actual cytokine levels. This study evaluates the performance of three commercially available multiplex cytokine fluorescent bead-based immunoassays (Bio-Rad's Cytokine 17-plex kit; LINCO Inc's 29-plex kit; and RnD System's Fluorokine-Multi Analyte Profiling (MAP) base kit A and B). The LINCO Inc kit was found to be the most sensitive assay for measuring concentrations of multiple recombinant cytokines in samples that had been spiked with serial dilutions of the standard provided by the manufacturer, followed respectively by the RnD Fluorokine-(MAP) and Bio-Rad 17-plex kits. A positive correlation was found in the levels of IFN-γ measured in antigen stimulated whole blood culture supernatants by the LINCO Inc 29-plex, RnD Fluorokine-(MAP) and RnD system IFN-γ Quantikine ELISA kits across a panel of controls and stimulated samples. Researchers should take the limitation of such multiplexed assays into account when planning experiments and the most appropriate use for these tests may currently be as screening tools for the selection of promising markers for analysis by more sensitive techniques.
VLSI Hardware Design for Genetic Algorithms and Its Parallel and Distributed Extensions

The advance in VLSI technologies has enabled us to implement genetic algorithms (GA) in VLSI hardware with a view to drastically improving performance. This paper presents a VLSI hardware design for GA, which we name GAP (Genetic Algorithm Processor), and its extensions for parallel and distributed GA. The basic architecture of GAP employs the steadystate GA, and introduces the simplified tournament selection scheme. This architecture enabled us to achieve two-level parallelization of parallel GA (fine-grained parallelism) and distributed GA (coarse-grained parallelism). We implemented the design of GAP, and evaluated it by logic simulation and logic synthesis. Our examples for evaluation include a datapartitioning problem which is one of the most complex problems ever addressed by GA-VLSIs. Our prototype implementations and experiments prove that the basic architecture of GAP facilitates two-level parallelization of parallel GA and distributed GA, which is effective in improving performance and convergence. Norihiko Yoshida 1) Tomohiro Yasuoka 2) Toshiomi Moriki 3) Toshihiko Shimokawa 4) 1) Department of Computer and Information Sciences, Nagasaki University, Nagasaki 852-8521, Japan 2) Algorithm Research Center, Sony Corporation, Shinagawa, Tokyo 141-0001, Japan 3) Central Research Laboratory, Hitachi, Ltd., Kokubunji, Tokyo 185-8601, Japan 4) Graduate School of Information Science and Electrical Engineering, Kyushu University, Fukuoka 812-8581, Japan
Adaptive template adjustment for personalized gesture recognition based on a finger-worn device

Wearable device based gesture recognition has become a hot topic in healthcare research fields. Effective gesture recognition is helpful to not only provide services for health monitoring, but also develop various applications like appliance control and emergency call. However, significantly individual difference of gesture performance brings challenge for accurate gesture recognition. In this paper, a personalized method of gesture recognition is proposed, which can analyze personal gesture features and automatically adjust gesture templates to improve recognition accuracy. The method was evaluated on a finger-worn device named Magic Ring that collected eight gestures from three subjects for one week testing. Results show the effectiveness of the method that average improvement of 16% in recognition accuracy has been achieved.
A Noninvasive Threat Analyzer for Advanced Metering Infrastructure in Smart Grid

Advanced Metering Infrastructure (AMI) is the core component in a smart grid that exhibits a highly complex network configuration. AMI comprises heterogeneous cyber-physical components, which are interconnected through different communication media, protocols, and security measures. They are operated using different data delivery modes and security policies. The inherent complexity and heterogeneity in AMI significantly increases the potential of security threats due to misconfiguration or absence of defense, which may cause devastating damage to AMI. Therefore, there is a need for creating a formal model that can represent the global behavior of AMI configuration in order to verify the potential threats. In this paper, we present SmartAnalyzer, a security analysis tool, which offers manifold contributions: (i) formal modeling of AMI configuration that includes device configurations, topology, communication properties, interactions among the devices, data flows, and security properties; (ii) formal modeling of AMI invariants and user-driven constraints based on the interdependencies among AMI device configurations, security properties, and security control guidelines; (iii) verifying the AMI configuration's compliance with security constraints using a Satisfiability Modulo Theory (SMT) solver; (iv) reporting of potential security threats based on constraint violations, (v) analyzing the impact of potential threats on the system; and (vi) systematic diagnosing of SMT unsatisfiable traces and providing necessary remediation plans. The accuracy and scalability of the tool are evaluated on an AMI testbed and various synthetic test networks.
A neutronic evaluation of VHTR and LS-VHTR

VHTR (Very High Temperature Reactor) and LS-VHTR (Liquid Salted - Very High Temperature Reactor) reactors were analyzed and compared from the viewpoint of the neutronic parameters. The multiplication coefficient (kinf), the fuel and the moderator temperature coefficients (αTF and αTM, respectively) and the hardening spectrum (øF/øT, fast flux/total flux) parameters were analyzed focusing to understanding better the VHTR and LS-VHTR neutronic behaviors. The experience obtained performing this work will be used in the future to study and to simulate the possibility of such systems for fuel reprocessing and recycling always analyzing how the process can affect the hydrogen production efficiency. The preliminary analyses have demonstrated that the VHTR reactor has a safer behavior in comparison with to the LS-VHTR one considering the evolution of the safety parameters along the burnup using WIMSD simulations.
Effects of progesterone on hippocampal ultrastructure and expression of inflammatory mediators in neonatal rats with hypoxic-ischemic brain injury

Progesterone (PROG) has been shown to exhibit a protective function against hypoxic-ischemic brain damage. The aim of the present study was to study the effects of PROG in a neonatal rat model of hypoxic-ischemic brain injury. A total of 30 Wistar rats, aged 7 days, were randomly divided into three groups: Sham, model and PROG. The rats in the model and PROG groups underwent a left common carotid artery ligation and were placed in a sealed container at 37°C with 8% O2 and 92% N2 gas mixtures for 2.5 h to establish animal models of hypoxic-ischemic encephalopathy. The rats in the PROG group were intraperitoneally treated with 8 mg/kg PROG solution 30 min prior to the induction of hypoxia-ischemia. All animals were sacrificed after 24 h and neuronal changes were observed with electron microscopy to investigate the hypoxic-ischemic brain damage. The protein and mRNA expression levels of tumor necrosis factor-α (TNF-α) and nuclear factor-κB (NF-κB) in the hippocampus were detected by immunohistochemistry and quantitative polymerase chain reaction, respectively. The results revealed that the neuronal structures in the sham group were normal. The neuronal structures in the model group exhibited cavitation changes, but these were reduced following PROG administration. The protein and mRNA expression levels of TNF-α and NF-κB in the hippocampal neurons were increased in the model group, and pretreatment with 8 mg/kg PROG was shown to reduce the expression levels of these inflammatory mediators. Therefore, PROG was shown to exert an important protective function in hypoxic-ischemic brain injury by inhibiting the cascade of inflammatory injury induced by TNF-α and NF-κB.
Gossip-Based Information Spreading in Mobile Networks

In this paper, we analyze the effect of mobility on information spreading in geometric networks through natural random walks. Specifically, our focus is on epidemic propagation via mobile gossip, a variation from its static counterpart. Our contributions are twofold. Firstly, we propose a new performance metric, mobile conductance, which allows us to separate the details of mobility models from the study of mobile spreading time. Secondly, we utilize geometrical properties to explore this metric for several popular mobility models, and offer insights on the corresponding results. Large scale network simulation is conducted to verify our analysis.
Do Parties Matter for Fiscal Policy Choices

In this paper I test whether there are strategic considerations in the making of fiscal policy. The general idea is that if a government anticipates the possibility of defeat in the next election it will try to use the debt strategically in order to influence the policy of its successor. Previous empirical studies have either rejected the strategic explanation of debt or have not been able to isolate this effect. I argue that these findings might be due to potential difficulties with the data sets used. To come to grips with these problems I make use of a data set from Swedish local governments. The main advantages of this panel data set are the institutional and constitutional homogeneity of the sample and the large number of observations from elections, nearly 2000. The main findings of this paper strongly suggest that there are strategic considerations in the making of fiscal policy. A right-wing government accumulates more debt during its term of office if it thinks that it will be defeated as compared to when it expects to remain in office. On the other hand, a left-wing government decreases the level of debt the higher the possibility of its defeat. Moreover, the larger the inherited debt the more a newly elected government has to reduce spending. These results are consistent with the predictions from a model developed by Persson and Svensson (1989).
Flexible and Accessible Workflows for Improved Proteogenomic Analysis Using the Galaxy Framework

Proteogenomics combines large-scale genomic and transcriptomic data with mass-spectrometry-based proteomic data to discover novel protein sequence variants and improve genome annotation. In contrast with conventional proteomic applications, proteogenomic analysis requires a number of additional data processing steps. Ideally, these required steps would be integrated and automated via a single software platform offering accessibility for wet-bench researchers as well as flexibility for user-specific customization and integration of new software tools as they emerge. Toward this end, we have extended the Galaxy bioinformatics framework to facilitate proteogenomic analysis. Using analysis of whole human saliva as an example, we demonstrate Galaxy’s flexibility through the creation of a modular workflow incorporating both established and customized software tools that improve depth and quality of proteogenomic results. Our customized Galaxy-based software includes automated, batch-mode BLASTP searching and a Peptide Sequence Match Evaluator tool, both useful for evaluating the veracity of putative novel peptide identifications. Our complex workflow (approximately 140 steps) can be easily shared using built-in Galaxy functions, enabling their use and customization by others. Our results provide a blueprint for the establishment of the Galaxy framework as an ideal solution for the emerging field of proteogenomics.
Central, But Not Basolateral, Amygdala Is Critical for Control of Feeding by Aversive Learned Cues

Environmental factors contribute to the motivation to eat and can override homeostatic signals to stimulate eating in sated states, or inhibit eating in states of hunger. In particular, stress, fear, and anxiety have been linked to suppression of eating and anorexia nervosa. Here, we use a rodent model of an aversive cue-induced cessation of feeding. In this setting, food-deprived rats suppress eating when presented with a tone [conditioned stimulus (CS)] that was previously paired with footshocks [unconditioned stimulus (US)]. To begin to delineate the underlying neural circuitry we examined the two regions of the amygdala with well known roles in associative learning—the central nucleus (CEA) and the basolateral area (BLA; includes the basolateral, basomedial, and lateral nuclei). We produced selective, bilateral, neurotoxic lesions of the CEA or BLA, and then trained these rats together with sham-lesioned controls in a behavioral protocol that allowed a test for food consumption in the presence of an aversive CS. Both sham- and BLA-lesioned rats showed inhibition of eating when presented with the CS. In contrast, bilateral, neurotoxic lesions of the CEA abolished this effect. These results demonstrate that the CEA, but not BLA, is critical for control of feeding by an aversive CS. Previously we demonstrated that enhancement of eating by an appetitive CS is dependent on the integrity of BLA, but not CEA. Those findings together with the current results show a double dissociation between amygdalar subsystems that control food consumption by appetitive and aversive learned cues.
Separating C Stars from Dust in the Central Region of the Seyfert 2 Galaxy NGC 1241

The Seyfert 2 galaxy NGC 1241 presents a 1.5 kpc large circumnuclear ring (CNR) of star formation embracing a small bar plus leading arms. Those structures are Paα emitters but barely seen in Hα. It also presents stellar trailing arms inside the CNR. Gemini and HST imagery allow the construction of high-resolution (V - H) and (J - Ks) color maps, as well as a (J - Ks) versus K color-magnitude diagram of this complex region. The CNR is heavily obscured in V, but a fairly transparent window appears in the direction of the nucleus. Nonetheless, the nucleus presents a (J - Ks) color that is redder than the CNR. The CNR is composed of extremely young H II regions still enshrouded in their dust cocoons. However, the nuclear (J - Ks) color cannot be explained in this manner. Therefore, we propose the contribution of C stars as the most feasible mechanism for explaining the colors. If the nuclear stellar population is comparable to that of the Large Magellanic Cloud bar, 500 C stars and 25,000 asymptotic giant branch O-rich stars inside 50 pc may reproduce the observed colors. C stars release enriched material to the nuclear environment, probably fueling the central engine of this Seyfert 2 galaxy during the lifetime of stars with masses in the range 2 M☉ < MC star < 6 M☉ (C-star phase). The ejected material that remains trapped in the central potential might also explain the systematically observed increased strength of the optical CN bands in Seyfert 2 galaxies and is consistent with the significant contribution of intermediate age stars to the optical continuum of low-luminosity active galactic nuclei.
Compact Representation of planar curves based on wavelet shape descriptors for multimedia applicatio

We present an analysis of a 2-D planar shape descriptor based on the Discrete Wavelet Transform (DWT), as an approach for compact object representation in multimedia applications. To generate the wavelet shape descriptor (WD), a partial 1-D DWT is applied on the vertical and horizontal components of a parameterized closed curve that represents the contour of an object of interest. With this shape representation tool and based on a multiresolution analysis, the curve can be reconstructed on a scale-by-scale basis to the desired degree of approximation. We show that contours of typical multimedia video objects are well represented with a small number of the largest magnitude wavelet coeÆcients, allowing for an eÆcient object shape representation. Some issues on the selection of the coefcients are studied. A comparison between the performance of the WD using di erent wavelet lters and a Fourier shape descriptor is presented, for synthetic and natural contours. The potential use of this descriptor in a multimedia application framework is discussed.
Shot-boundary detection: unraveled and resolved?

Partitioning a video sequence into shots is the first step toward video-content analysis and content-based video browsing and retrieval. A video shot is defined as a series of interrelated consecutive frames taken contiguously by a single camera and representing a continuous action in time and space. As such, shots are considered to be the primitives for higher level content analysis, indexing, and classification. The objective of this paper is twofold. First, we analyze the shot-boundary detection problem in detail and identify major issues that need to be considered in order to solve this problem successfully. Then, we present a conceptual solution to the shot-boundary detection problem in which all issues identified in the previous step are considered. This solution is provided in the form of a statistical detector that is based on minimization of the average detection-error probability. We model the required statistical functions using a robust metric for visual content discontinuities (based on motion compensation) and take into account all (a priori) knowledge that we found relevant to shot-boundary detection. This knowledge includes the shot-length distribution, visual discontinuity patterns at shot boundaries, and characteristic temporal changes of visual features around a boundary. Major advantages of the proposed detector are its robust and sequence-independent performance, while there is also the possibility to detect different types of shot boundaries simultaneously. We demonstrate the performance of our detector regarding two most widely used types of shot boundaries: hard cuts and dissolves.