Miracle-dz/UniFAR-FTUs huggingface dataset:
GenNet: A Platform for Hybrid Network Experiments

We describe General Network (GenNet), a software plugin for the real time experimental interface (RTXI) dynamic clamp system that allows for straightforward and flexible implementation of hybrid network experiments. This extension to RTXI allows for hybrid networks that contain an arbitrary number of simulated and real neurons, significantly improving upon previous solutions that were limited, particularly by the number of cells supported. The benefits of this system include the ability to rapidly and easily set up and perform scalable experiments with hybrid networks and the ability to scan through ranges of parameters. We present instructions for installing, running and using GenNet for hybrid network experiments and provide several example uses of the system.
SPAR: A system to detect spam in Arabic opinions

The evaluation of the public opinion through websites, social networks, news feedback, etc. is currently getting an extensive research to discover public opinion regarding the current social and political changes in the Middle Eastern countries. However, the level of trust or confidentiality of such public opinion evaluations may have the risk of being spammed. This study aims to detect the spam opinions in the Yahoo! -Maktoob social network. The proposed system reads the opinions and classifies them into one of the following two classes: spam and non-spam opinions, based on a number of features. Each spam opinion categorizes into; high levels spam and low level spam, based on special metrics. While each non-spam opinion is labeled as; positive, negative, or neutral based on the language polarity dictionaries. Those dictionaries include words that can be classified as: positive, negative or neutral. The proposed system adopts machine learning classification technique to perform classification and prediction.
Inhibition of bacterial translocation by chronic ethanol consumption in the rat

Chronic ethanol ingestion has been associated with small intestine morphological changes, disrupted host mucosal defenses and bacterial overgrowth. Since bacterial translocation (BT) may result from such alterations, we have investigated the potential effect of chronic ethanol consumption on BT. For this purpose, male Wistar rats were fed a liquid diet containing 5% v/v ethanol for 4 weeks (EG, n=16), and a pair‐fed group received equal daily amounts of calories in a similar diet without ethanol (PFG, n=16). On experimental day 29, distal ileum ligature and small intestine inoculation of a tetracycline‐resistant E. coli strain (Tc®E. coli R6) followed by duodenal ligature was performed. After 1 or 5 h post inoculation, mesenteric lymph nodes, liver, spleen and kidney were excised. Unexpectedly, rats of the EG presented markedly less BT to the mesenteric lymph nodes (p<0.001) and to the other organs examined compared to rats of the PFG. This BT inhibition was observed at 1 and 5 h after bacterial inoculation, and may be attributed exclusively to chronic ethanol ingestion. Since alcoholism is well known to decrease host immunity, these results suggest that other factors, independent of the immune function, may be involved in the BT inhibition observed in this study.
Automatic clustering of collocation for detecting practical sense boundary

This paper talks about the deciding practical sense boundary of homonymous words. The important problem in dictionaries or thesauri is the confusion of the sense boundary by each resource. This also becomes a bottleneck in the practical language processing systems. This paper proposes the method about discovering sense boundary using the collocation from the large corpora and the clustering methods. In the experiments, the proposed methods show the similar results with the sense boundary from a corpus-based dictionary and sense-tagged corpus.
Successive Nonnegative Projection Algorithm for Robust Nonnegative Blind Source Separation

In this paper, we propose a new fast and robust recursive algorithm for near-separable nonnegative matrix factorization, a particular nonnegative blind source separation problem. This algorithm, which we refer to as the successive nonnegative projection algorithm (SNPA), is closely related to the popular successive projection algorithm (SPA) but takes advantage of the nonnegativity constraint in the decomposition. We prove that SNPA is more robust than SPA and can be applied to a broader class of nonnegative matrices. This is illustrated on some synthetic data sets and on a real-world hyperspectral image.
Fitting ideals of ℓ-adic realizations of Picard 1-motives and class groups of global function fields

Abstract In previous work, we proved a result on the equivariant Fitting ideal of the ℓ-adic realization of the Picard 1-motive attached to an abelian covering of curves defined over a finite field. In this paper, we build upon this work to deduce results on the equivariant Fitting ideal of the Tate modules of the Jacobian of the top curve, and on the equivariant Fitting ideal of the (dual of the) degree zero class group of the top curve. At the end, we discuss refinements of Brumer's conjecture and consider examples.
Static timing analysis taking crosstalk into account

Capacitance coupling can have a significant impact on gate delay in today's deep submicron circuits. In this paper we present a static timing analysis tool that calculates the longest path of synchronous circuits taking the impact of crosstalk on gate delays into account. We show that passive modeling of the coupling capacitance can significantly underestimate the delay and that an assumption of permanent worst-case coupling unnecessarily overestimates it. Our method is validated by comparison to Spice simulations.
A METHODOLOGY FOR AUTOMATED TRAJECTORY PREDICTION ANALYSIS

A number of air traffic management decision support tools (DST) are being developed to help air traffic managers and controllers improve capacity, efficiency, and safety in the National Airspace System. Although DST functionality may vary widely, trajectory prediction algorithms can be found at the core of most DST. A methodology is presented for the automated statistical analysis of trajectory prediction accuracy as a function of phase of flight (level-flight, climb, descent) and look-ahead time. The methodology is focused on improving trajectory prediction algorithm performance for DST applications such as conflict detection and arrival metering. The methodology has been implemented in software and tested with air traffic data. Aggregate trajectory prediction accuracy statistics are computed and displayed in histogram format based on 2,774 large commercial jet flights from five different days of Fort Worth Center air traffic data. The results show that trajectory prediction anomalies can be detected by examining error distributions for large numbers of trajectory predictions. The ability of the trajectory analysis methodology to detect the effects of subsequent changes to the trajectory prediction algorithm and to aircraft performance model parameters was also demonstrated.
Temporal Second Difference Traces

Q-learning is a reliable but inefficient off-policy temporal-difference method, backing up reward only one step at a time. Replacing traces, using a recency heuristic, are more efficient but less reliable. In this work, we introduce model-free, off-policy temporal difference methods that make better use of experience than Watkins' Q(\lambda). We introduce both Optimistic Q(\lambda) and the temporal second difference trace (TSDT). TSDT is particularly powerful in deterministic domains. TSDT uses neither recency nor frequency heuristics, storing (s,a,r,s',\delta) so that off-policy updates can be performed after apparently suboptimal actions have been taken. There are additional advantages when using state abstraction, as in MAXQ. We demonstrate that TSDT does significantly better than both Q-learning and Watkins' Q(\lambda) in a deterministic cliff-walking domain. Results in a noisy cliff-walking domain are less advantageous for TSDT, but demonstrate the efficacy of Optimistic Q(\lambda), a replacing trace with some of the advantages of TSDT.
Transcriptome-wide Changes in Coral Gene Expression at Noon and Midnight Under Field Conditions

Reef-building corals experience high daily variation in their environment, food availability, and physiological activities such as calcification and photosynthesis by endosymbionts. On Ofu Island, American Samoa, we investigated day-night differences in gene expression under field conditions of changing pH, temperature, light, and oxygen. Using RNASeq techniques, we compared two replicate transcriptomes from a single coral colony of Acropora hyacinthus over six noons and five midnights. We identified 344 contigs with significant expression differences across 16,800 contigs in the transcriptome, most with small fold-changes. However, there were 21 contigs with fold-changes ranging from 10 to 141. The largest changes were in a set of transcription factors strongly associated with day-night gene regulation in other animals, including cryptochromes, thyrotroph embryonic factor, and D site-binding protein. We also found large daytime increases in a set of genes involved in glucose transport and glycogen storage. We found small expression differences in genes associated with aerobic ATP production and hypoxia response, along with slightly higher expression of most calcification genes at noon. Although >40-fold-changes in expression occur in important transcription factors, downstream gene regulation seems very stable in corals from day to night compared to other animals studied.
Glycogen synthase kinase‐3 (GSK‐3) regulates TGF‐β1‐induced differentiation of pulmonary fibroblasts

Chronic lung diseases such as asthma, COPD and pulmonary fibrosis are characterized by abnormal extracellular matrix (ECM) turnover. TGF‐β is a key mediator stimulating ECM production by recruiting and activating lung fibroblasts and initiating their differentiation process into more active myofibroblasts. Glycogen synthase kinase‐3 (GSK‐3) regulates various intracellular signalling pathways; its role in TGF‐β1‐induced myofibroblast differentiation is currently largely unknown.
Modeling Network Intrusion Detection System Using Feature Selection and Parameters Optimization

Previous approaches for modeling Intrusion Detection System (IDS) have been on twofold: improving detection model(s) in terms of (i) feature selection of audit data through wrapper and filter methods and (ii) parameters optimization of detection model design, based on classification, clustering algorithms, etc. In this paper, we present three approaches to model IDS in the context of feature selection and parameters optimization: First, we present Fusion of Genetic Algorithm (GA) and Support Vector Machines (SVM) (FuGAS), which employs combinations of GA and SVM through genetic operation and it is capable of building an optimal detection model with only selected important features and optimal parameters value. Second, we present Correlation-based Hybrid Feature Selection (CoHyFS), which utilizes a filter method in conjunction of GA for feature selection in order to reduce long training time. Third, we present Simultaneous Intrinsic Model Identification (SIMI), which adopts Random Forest (RF) and shows better intrusion detection rates and feature selection results, along with no additional computational overheads. We show the experimental results and analysis of three approaches on KDD 1999 intrusion detection datasets.
Educating women about HIV/AIDS: some international comparisons

This paper describes current trends in the HIV/AIDS epidemic. It looks at issues and strategies involved in educating women about HIV/AIDS in the context of the global pandemic, focusing particularly on Canada and Vietnam. These strategies are essential steps in preventing the spread of HIV and in caring for those who have already developed AIDS. The strategies identified include the formulation of public policy within Canada, Vietnam, and the UK which would strive to eliminate inequality and discrimination, while at the same time positioning HIV/AIDS education within a gender‐sensitive perspective. Other educational strategies include teaching women how to practise negotiated safety while understanding the power dynamics within which safer sex must occur, being sensitive to local cultures and moving away from individualistic programmes towards programmes that focus on structural inequalities.
Drosophila SOCS36E negatively regulates JAK/STAT pathway signaling via two separable mechanisms

The low-complexity Drosophila genome includes previously uncharacterized SOCS36E, an ancestral SOCS4/5 homologue. It is shown that SOCS36E suppresses JAK/STAT signaling through two separate mechanisms: via receptor stability, mediated by the conserved SOCS-box domain, and via suppression of receptor phosphorylation that requires the N-terminal domain.
Relevant magnetic and soil parameters as potential indicators of soil conservation status of Mediterranean agroecosystems

SUMMARY The main sources of magnetic minerals in soils unaffected by anthropogenic pollution are iron oxides and hydroxides derived from parent materials through soil formation processes. Soil magnetic minerals can be used as indicators of environmental factors including soil forming processes, degree of pedogenesis, weathering processes and biological activities. In this study measurements of magnetic susceptibility are used to detect the presence and the concentration of soil magnetic minerals in topsoil and bulk samples in a small cultivated field, which forms a hydrological unit that can be considered to be representative of the rainfed agroecosystems of Mediterranean mountain environments. Additionalmagneticstudiessuchasisothermalremanentmagnetization(IRM),anhysteretic remanent magnetization (ARM) and thermomagnetic measurements are used to identify and characterize the magnetic mineralogy of soil minerals. The objectives were to analyse the spatial variability of the magnetic parameters to assess whether topographic factors, soil redistribution processes, and soil properties such as soil texture, organic matter and carbonate contents analysed in this study, are related to the spatial distribution pattern of magnetic properties. The medians of mass specific magnetic susceptibility at low frequency (χlf )w ere 36.0 and 31.1 × 10 −8 m 3 kg −1 in bulk and topsoil samples respectively. High correlation coefficients were found between the χlf in topsoil and bulk core samples (r = 0.951, p < 0.01). Inaddition,volumetricmagneticsusceptibilitywasmeasuredinsituinthefield(κis)andvalues varied from 13.3 to 64.0 × 10 −5 SI. High correlation coefficients were found between χlf in topsoil measured in the laboratory and volumetric magnetic susceptibility field measurements (r = 0.894, p < 0.01). The results obtained from magnetic studies such as IRM, ARM and thermomagnetic measurements show the presence of magnetite, which is the predominant magnetic carrier, and hematite. The predominance of superparamagnetic minerals in upper soil layers suggests enrichment in pedogenic minerals. The finer soil particles, the organic matter content and the magnetic susceptibility values are statistically correlated and their spatial variability is related to similar physical processes. Runoff redistributes soil components including magnetic minerals and exports fine particles out the field. This research contributed tofurtherknowledgeontheapplicationofsoilmagneticpropertiestoderiveusefulinformation on soil processes in Mediterranean cultivated soils.
Mindtagger: A Demonstration of Data Labeling in Knowledge Base Construction

End-to-end knowledge base construction systems using statistical inference are enabling more people to automatically extract high-quality domain-specific information from unstructured data. As a result of deploying DeepDive framework across several domains, we found new challenges in debugging and improving such end-to-end systems to construct high-quality knowledge bases. DeepDive has an iterative development cycle in which users improve the data. To help our users, we needed to develop principles for analyzing the system's error as well as provide tooling for inspecting and labeling various data products of the system. We created guidelines for error analysis modeled after our colleagues' best practices, in which data labeling plays a critical role in every step of the analysis. To enable more productive and systematic data labeling, we created Mindtagger, a versatile tool that can be configured to support a wide range of tasks. In this demonstration, we show in detail what data labeling tasks are modeled in our error analysis guidelines and how each of them is performed using Mindtagger.
Incorporating Aggregate Diversity in Recommender Systems Using Scalable Optimization Approaches

The success of a recommender system is generally evaluated with respect to the accuracy of recommendations. However, recently diversity of recommendations has also become an important aspect in evaluating recommender systems. One dimension of diversity is called aggregate diversity, which refers to the diversity of items in the recommendation lists of all users and can be defined with different metrics. The maximization of both accuracy and the aggregate diversity simultaneously renders a multiobjective optimization problem that can be handled by different approaches. In this paper, after providing a thorough analysis of the multiobjective optimization approaches for this problem, we propose a new model that takes into account both accuracy and aggregate diversity. Different from previous works, our model is specifically designed to incorporate distributional diversity metrics, which measure how evenly the items are distributed in the recommendation lists of users. To solve the large-scale instances, we propose a column generation algorithm and a Lagrangian relaxation approach based on the decomposition of the model. We present the results of the mathematical models and the performance of the proposed methodology that are obtained by computational experiments on real-world data sets. These results reveal that our model successfully captures the trade-off between the objectives and reaches very high levels of distributional diversity.
Interpersonal rigidity, hostility, and complementarity in musical bands.

In this test of interpersonal theory (R. C. Carson, 1969; D. J. Kiesler, 1983; T. Leary, 1957; J. S. Wiggins, 1982), complementarity and interpersonal rigidity were examined in relation to positive regard for others and group integration among 206 members of 54 musical bands. Complementarity was associated with more positive regard and more group integration in bands consisting of musicians who were high in interpersonal rigidity (as indexed by vector length on the interpersonal circle), whereas the effect was less strong in bands consisting of musicians who were lower in rigidity. The meager support for personality compatibility in groups and dyads observed in previous research could thus be due to a neglect of interpersonal rigidity-flexibility. There was also evidence that hostile complementarity was beneficial to the relationships of hostile individuals.
Design and development of daughter board for USB-UART communication between Raspberry Pi and PC

Development of real time applications using platforms such as Raspberry Pi often requires interfacing of different sensors and communication modules. They can be easily interfaced using RS232 port but now a days it is replaced by USB. Interfacing sensors and modules having only USB ports, some sort of port conversion is required that can emulate a serial port providing a virtual COM through UART interface. This paper propose design & development of daughter board for USB to UART Communication to Raspberry Pi that provides communication with different embedded devices by the use of USB to UART Convertor Boards. With the help of these convertors, bidirectional data transmission is achieved. FT232RL & CP2102 IC's are used in the proposed daughter boards. A comparative analysis is presented between these boards in terms of cost, performance, reliability, packaging, required baud rate.
Mutator/Hypermutable Fetal/Juvenile Metakaryotic Stem Cells and Human Colorectal Carcinogenesis

Adult age-specific colorectal cancer incidence rates increase exponentially from maturity, reach a maximum, then decline in extreme old age. Armitage and Doll (1) postulated that the exponential increase resulted from “n” mutations occurring throughout adult life in normal “cells at risk” that initiated the growth of a preneoplastic colony in which subsequent “m” mutations promoted one of the preneoplastic “cells at risk” to form a lethal neoplasia. We have reported cytologic evidence that these “cells at risk” are fetal/juvenile organogenic, then preneoplastic metakaryotic stem cells. Metakaryotic cells display stem-like behaviors of both symmetric and asymmetric nuclear divisions and peculiarities such as bell shaped nuclei and amitotic nuclear fission that distinguish them from embryonic, eukaryotic stem cells. Analyses of mutant colony sizes and numbers in adult lung epithelia supported the inferences that the metakaryotic organogenic stem cells are constitutively mutator/hypermutable and that their contributions to cancer initiation are limited to the fetal/juvenile period. We have amended the two-stage model of Armitage and Doll and incorporated these several inferences in a computer program CancerFit v.5. 0. We compared the expectations of the amended model to adult (15–104 years) age-specific colon cancer rates for European-American males born 1890–99 and observed remarkable concordance. When estimates of normal colonic fetal/juvenile APC and OAT gene mutation rates (∼2–5 × 10−5 per stem cell doubling) and preneoplastic colonic gene loss rates (∼8 × 10−3) were applied, the model was in accordance only for the values of n = 2 and m = 4 or 5.
Active interoceptive inference and the emotional brain

We review a recent shift in conceptions of interoception and its relationship to hierarchical inference in the brain. The notion of interoceptive inference means that bodily states are regulated by autonomic reflexes that are enslaved by descending predictions from deep generative models of our internal and external milieu. This re-conceptualization illuminates several issues in cognitive and clinical neuroscience with implications for experiences of selfhood and emotion. We first contextualize interoception in terms of active (Bayesian) inference in the brain, highlighting its enactivist (embodied) aspects. We then consider the key role of uncertainty or precision and how this might translate into neuromodulation. We next examine the implications for understanding the functional anatomy of the emotional brain, surveying recent observations on agranular cortex. Finally, we turn to theoretical issues, namely, the role of interoception in shaping a sense of embodied self and feelings. We will draw links between physiological homoeostasis and allostasis, early cybernetic ideas of predictive control and hierarchical generative models in predictive processing. The explanatory scope of interoceptive inference ranges from explanations for autism and depression, through to consciousness. We offer a brief survey of these exciting developments. This article is part of the themed issue ‘Interoception beyond homeostasis: affect, cognition and mental health’.