Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Spatio-Temporal Signal Recovery from Noisy Counts in Social Media

Many real-world phenomena can be represented by a spatio-temporal signal: where, when, and how much. Social media is a tantalizing data source for those who wish to monitor such signals. Unlike most prior work, we assume that the target phenomenon is known and we are given a method to count its occurrences in social media. However, counting is plagued by sample bias, incomplete data, and, paradoxically, data scarcity -- issues inadequately addressed by prior work. We formulate signal recovery as a Poisson point process estimation problem. We explicitly incorporate human population bias, time delays and spatial distortions, and spatio-temporal regularization into the model to address the noisy count issues. We present an efficient optimization algorithm and discuss its theoretical properties. We show that our model is more accurate than commonly-used baselines. Finally, we present a case study on wildlife roadkill monitoring, where our model produces qualitatively convincing results.
Parallel ICA identifies sub-components of resting state networks that covary with behavioral indices

Parallel Independent Component Analysis (para-ICA) is a multivariate method that can identify complex relationships between different data modalities by simultaneously performing Independent Component Analysis on each data set while finding mutual information between the two data sets. We use para-ICA to test the hypothesis that spatial sub-components of common resting state networks (RSNs) covary with specific behavioral measures. Resting state scans and a battery of behavioral indices were collected from 24 younger adults. Group ICA was performed and common RSNs were identified by spatial correlation to publically available templates. Nine RSNs were identified and para-ICA was run on each network with a matrix of behavioral measures serving as the second data type. Five networks had spatial sub-components that significantly correlated with behavioral components. These included a sub-component of the temporo-parietal attention network that differentially covaried with different trial-types of a sustained attention task, sub-components of default mode networks that covaried with attention and working memory tasks, and a sub-component of the bilateral frontal network that split the left inferior frontal gyrus into three clusters according to its cytoarchitecture that differentially covaried with working memory performance. Additionally, we demonstrate the validity of para-ICA in cases with unbalanced dimensions using simulated data.
A COMPARATIVE ASSESSMENT OF THE PISARENKO HARMONIC DECOMPOSITION TYPE TONE DETECTOR

In this paper, we consider the performance of the Pisarenko harmonic decomposition method which is a special case of MUSIC algorithm for a single tone detection problem in additive white Gaussian noise environment. The detection process consists of comparing Pisarenko harmonic decomposition frequency estimate (PISFE) to the known tone frequency, and declare “tone present" if the two values are sufficiently close. We derive the conditional probability density function of the PISFE under both detection hypotheses and use them to evaluate the performance of the PISFE detector. Simulation and numerical results allow the choice of an optimum threshold for a frequency detection band setting given a false alarm rate PF. The receiver operating characteristics of the PISFE is obtained computing the corresponding detection probability PD . It is shown that the PISFE detector is a constant false alarm rate (CFAR) detector with respect to SNR for fixed tone frequency, but not a CFAR detector with respect to tone frequency for the given problem. The performance of the PISFE type detector is compared with both matched filter (MF) and autoregressive (AR) detectors and the analysis is supported by an extensive simulation work. Applications to FSK tone detection problem is discussed briefly.
Adaptive collision-limitation behavior for an assistive manipulator

An approach for adaptive shared control of an assistive manipulator is presented. A set of distributed collision and proximity sensors is used to aid in limiting collisions during direct control by the disabled user. Artificial neural networks adapt the use of the proximity sensors online, which limits movements in the direction of an obstacle before a collision occurs. The system learns by associating the different proximity sensors to the collision sensors where collisions are detected. This enables the user and the robot to adapt simultaneously and in real-time, with the objective of converging on a usage of the proximity sensors that increases performance for a given user, robot implementation and task-set. The system was tested in a controlled setting with a simulated 5 DOF assistive manipulator and showed promising reductions in the mean time on simplified manipulation tasks. It extends earlier work by showing that the approach can be applied to full multi-link manipulators.
A Survey on Knowledge Compilation

Knowledge compilation is an AI technique for addressing computationally demanding reasoning problems. In this paper we survey recent results in knowledge compilation of propositional knowledge bases. We first define and limit the scope of such a technique, then we survey exact and approximate knowledge compilation methods. We include a discussion of compilation for nondmonotonic knowledge bases.
Microglia, macrophages, perivascular macrophages, and pericytes: a review of function and identification

The phenotypic differentiation of systemic macrophages that have infiltrated the central nervous system, pericytes, perivascular macrophages, and the “real” resident microglial cells is a major immunocytochemical and immunohistochemical concern for all users of cultures of brain cells and brain sections. It is not only important in assessing the purity of cell cultures; it is also of fundamental importance in the assessment of the pathogenetic significance of perivascular inflammatory phenomena within the brain. The lack of a single membranous and/or biochemical marker allowing conclusive identification of these cells is still a major problem in neurobiology. This review briefly discusses the functions of these cells and catalogs a large number of membranous and biochemical markers, which can assist in the identification of these cells.
JavaScript-based device fingerprinting mitigation using personal HTTP proxy

In this paper, we designed a mechanism to mitigate the existence of device fingerprinting process by utilizing personal HTTP proxy. For the purpose of detection and prevention of device fingerprinting, the proxy utilizes a two-stage filtering process; header-based filtering and content-based filtering. We define 6 values of attribute among with their weight based on the value of the highest entropy. In addition, we also define the ConfLevel coefficient as the weight accumulation value of the existence of each attribute. There is also defined ThresholdLevel coefficient for the purpose of determining the limit condition if there is an indication of device fingerprinting. System testing was performed by accessing Alexa's Top 125 Indonesia Website. We obtained 15.5796 as the ThresholdLevel value. Among the 125 web pages tested, it was found that 38 web pages were indicated utilizing JavaScript as the media of device fingerprinting activity.
The Market Price of Risk in Interest Rate Swaps: The Roles of Default and Liquidity Risks

We study how the market prices the default and liquidity risks incorporated into interest rate swap spreads. We jointly model the Treasury, repo, and swap term structures using a five-factor affine framework and estimate the model by maximum likelihood. The credit spread is driven by a persistent liquidity process and a rapidly mean-reverting default intensity process. The credit premium for all but the shortest maturities is primarily compensation for liquidity risk. The term structure of liquidity premia increases steeply, while that of default premia is almost flat. Both liquidity and default premia vary significantly over time.
Use of Transformation-Based Learning in Annotation Pipeline of Igbo, an African Language

The accuracy of an annotated corpus can be increased through evaluation and re- vision of the annotation scheme, and through adjudication of the disagreements found. In this paper, we describe a novel process that has been applied to improve a part-of-speech (POS) tagged corpus for the African language Igbo. An inter-annotation agreement (IAA) exercise was undertaken to iteratively revise the tagset used in the creation of the initial tagged corpus, with the aim of refining the tagset and maximizing annotator performance. The tagset revisions and other corrections were efficiently propagated to the overall corpus in a semi-automated manner using transformation-based learning (TBL) to identify candidates for cor- rection and to propose possible tag corrections. The affected word-tag pairs in the corpus were inspected to ensure a high quality end-product with an accuracy that would not be achieved through a purely automated process. The results show that the tagging accuracy increases from 88% to 94%. The tagged corpus is potentially re-usable for other dialects of the language.
Consumption Equilibrium Asset Pricing in China Stock Market

While most of the empirical examinations of the CCAPM have been mainly confined to major developed financial markets, this study aims to expand the application of the CCAPM to China stock market by examining the consumption equilibrium asset pricing. The historical financial markets experience in China is characterized by high average equity returns and average risk-free returns. In addition, the market exhibits very high volatility in equity and excess returns. Working with historical data on stock price indices, cash dividends, and the risk-free rates, and using the Lucas (1978) model, we closely explore the connections between consumption fundamental and equity returns in the stock market. The remainder of the paper is organized as follows. Section 2 describes the consumption equilibrium asset pricing model, outlines the exact closed-form solution to the model, and derives the implied asset returns. Section 3 describes the stock market in China and discusses some stylized facts in the markets. Section 4 evaluates the performance of the Lucas (1978) model in explaining the observed asset returns in China. Section 5 concludes the paper. II. CONSUMPTION EQUILIBRIUM ASSET PRICING In a one-good, pure exchange Lucas (1978) model, assume that there is a single agent who maximizes the expected discounted stream of utility given by (1) below and a single asset that pays exogenous dividends of non-durable consumption goods. The representative agent faces the problem:
Toward a Robust Crowd-labeling Framework using Expert Evaluation and Pairwise Comparison

Crowd-labeling emerged from the need to label large-scale and complex data, a tedious, expensive, and time-consuming task. One of the main challenges in the crowd-labeling task is to control for or determine in advance the proportion of low-quality/malicious labelers. If that proportion grows too high, there is often a phase transition leading to a steep, non-linear drop in labeling accuracy as noted by Karger et al. [2014]. To address these challenges, we propose a new framework called Expert Label Injected Crowd Estimation (ELICE) and extend it to different versions and variants that delay phase transition leading to a better labeling accuracy. ELICE automatically combines and boosts bulk crowd labels supported by labels from experts for limited number of instances from the dataset. The expert-labels help to estimate the individual ability of crowd labelers and difficulty of each instance, both of which are used to aggregate the labels. Empirical evaluation shows the superiority of ELICE as compared to other state-of-the-art methods. We also derive a lower bound on the number of expert-labeled instances needed to estimate the crowd ability and dataset difficulty as well as to get better quality labels.
Current account balances and structural adjustment in the euro area

In the past decade, a set of euro area countries has accumulated large current account deficits. After a brief relaxation of the euro area internal imbalances in the wake of the financial crisis, it appears as if this pattern arises anew when times normalize again and Germany still sticks to export-led growth. This issue has been labelled one of the most challenging economic policy issues for Europe inter alia by the European Commission and some other players on the EU level. In this paper, we analyse the role of private restructuring and structural reforms for the urgently needed sustainable readjustment of intra-euro area current account balances. A panel regression reveals a significant impact of structural reforms on intra-euro area current account balances. This implies that in particular structural reforms and wage restraint in notorious current account and budget deficit countries such as Greece are highly suitable to support long-term economic stability in Europe.
Inhibition of Transforming Growth Factor-Activated Kinase 1 (TAK1) Blocks and Reverses Epithelial to Mesenchymal Transition of Mesothelial Cells

Peritoneal fibrosis is a frequent complication of peritoneal dialysis following repeated low grade inflammatory and pro-fibrotic insults. This pathological process may lead to ultrafiltration failure and eventually to the discontinuing of the therapy. Fibrosis is linked to epithelial to mesenchymal transition (EMT) of the peritoneal mesothelial cells, which acquire invasive and fibrogenic abilities. Here, we analyzed the role of the transforming growth factor-activated kinase-1 (TAK1) in the EMT of primary mesothelial cells from human peritoneum. The inhibition of TAK1 in mesenchymal-like mesothelial cells from the effluents of patients undergoing peritoneal dialysis led to the reacquisition of the apical to basolateral polarity, to increased expression of epithelial and to down-regulation of mesenchymal markers. TAK1 inhibition also resulted in decreased migratory/invasive abilities of effluent-derived mesothelial cells. Simultaneous inhibition of ERK1/2 and TAK1 pathways did not lead to an additive effect in the reacquisition of the epithelial phenotype. Inhibition of TAK1 also blocked EMT in vitro and reduced the levels of PAI-1, which is involved in fibrosis and invasion. Analysis of signalling pathways downstream of TAK1 involved in EMT induction, showed that TAK1 inhibition reduced the transcriptional activity of NF-κB and Smad3, as well as the phosphorylation of c-jun, while enhancing Smad1–5–8 activity. These results demonstrate that TAK1 is a cross-point in a network including different pro-EMT transcription factors, such as NF-κB, Snail, AP-1 and Smads. The identification of TAK1 as a main biochemical mediator of EMT and fibrosis in mesothelial cells from human peritoneum and the study of signalling pathways induced by its activity may be relevant in the design of new therapies aimed to counteract peritoneal fibrosis.
Identification of Novel Pathogenicity Loci in Clostridium perfringens Strains That Cause Avian Necrotic Enteritis

Type A Clostridium perfringens causes poultry necrotic enteritis (NE), an enteric disease of considerable economic importance, yet can also exist as a member of the normal intestinal microbiota. A recently discovered pore-forming toxin, NetB, is associated with pathogenesis in most, but not all, NE isolates. This finding suggested that NE-causing strains may possess other virulence gene(s) not present in commensal type A isolates. We used high-throughput sequencing (HTS) technologies to generate draft genome sequences of seven unrelated C. perfringens poultry NE isolates and one isolate from a healthy bird, and identified additional novel NE-associated genes by comparison with nine publicly available reference genomes. Thirty-one open reading frames (ORFs) were unique to all NE strains and formed the basis for three highly conserved NE-associated loci that we designated NELoc-1 (42 kb), NELoc-2 (11.2 kb) and NELoc-3 (5.6 kb). The largest locus, NELoc-1, consisted of netB and 36 additional genes, including those predicted to encode two leukocidins, an internalin-like protein and a ricin-domain protein. Pulsed-field gel electrophoresis (PFGE) and Southern blotting revealed that the NE strains each carried 2 to 5 large plasmids, and that NELoc-1 and -3 were localized on distinct plasmids of sizes ∼85 and ∼70 kb, respectively. Sequencing of the regions flanking these loci revealed similarity to previously characterized conjugative plasmids of C. perfringens. These results provide significant insight into the pathogenetic basis of poultry NE and are the first to demonstrate that netB resides in a large, plasmid-encoded locus. Our findings strongly suggest that poultry NE is caused by several novel virulence factors, whose genes are clustered on discrete pathogenicity loci, some of which are plasmid-borne.
Extended Design Patterns in New Object-Oriented Programming Languages (S)

Most of design patterns are implemented in major object-oriented programming languages such as C++ and Java. However, newer object-oriented programming languages than such languages has new language features which can improve implementations of design patterns. In this paper, we propose two extended design patterns called customizable state pattern and deeply immutable pattern. We compares implementations of our design patterns in Java, C++ and eight new object-oriented programming languages through our motivating example. As a result, we confirmed new languages, in particular Scala, improved implementations of our design patterns.
Effect of pattern and severity of respiratory muscle weakness on carbon monoxide gas transfer and lung volumes

In clinical practice, an elevated carbon monoxide (CO) transfer coefficient (KCO) and restrictive ventilatory defect are taken as features of respiratory muscle weakness (RMW). However, the authors hypothesised that both pattern and severity of RMW effect gas transfer and lung volumes. Measurements of CO transfer and lung volumes were performed in patients with isolated diaphragm weakness (n=10), inspiratory muscle weakness (n=12), combined inspiratory and expiratory muscle weakness (n=5) and healthy controls (n=6). Patients with diaphragm weakness and inspiratory muscle weakness had reduced total lung capacity (TLC) (83.6% predicted and 68.9% pred, respectively), functional residual capacity (FRC) (83.9% pred and 83.6% pred) and transfer factor of the lung for CO (TL,CO) (86.2% pred and 66.2% pred) with increased KCO (114.1% pred and 130.2% pred). Patients with combined inspiratory and expiratory muscle weakness had reduced TLC (80.9% pred) but increased FRC (109.9% pred) and RV (157.4% pred) with decreased TL,CO (58.0% pred) and KCO (85.5% pred). In patients with diaphragm weakness, the increase in carbon monoxide transfer coefficient was similar to that of normal subjects when alveolar volume was reduced. However, the increase in carbon monoxide transfer coefficient in inspiratory muscle weakness was often less than expected, while in combined inspiratory and expiratory muscle weakness, the carbon monoxide transfer coefficient was normal/reduced despite further reductions in alveolar volume, which may indicate subtle abnormalities of the lung parenchyma or pulmonary vasculature. Thus, this study demonstrates the limitations of using carbon monoxide transfer coefficient in the diagnosis of respiratory muscle weakness, particularly if no account is taken of the alveolar volume at which the carbon monoxide transfer coefficient is made.
Permutation Methods: A Basis for Exact Inference

The use of permutation methods for exact inference dates back to Fisher in 1935. Since then, the practicality of such methods has increased steadily with computing power. They can now easily be employed in many situations without concern for computing difficulties. We discuss the reasoning behind these methods and describe situations when they are exact and distribution-free. We illustrate their use in several examples.
TONIC: Target Oriented Network Intelligence Collection for the Social Web

In this paper we introduce the Target Oriented Network Intelligence Collection (TONIC) problem, which is the problem of finding profiles in a social network that contain information about a given target via automated crawling. We formalize TONIC as a search problem and a best-first approach is proposed for solving it.Several heuristics are presented to guide this search.These heuristics are based on the topology of the currently known part of the social network.The efficiency of the proposed heuristics and the effect of the graph topology on their performance is experimentally evaluated on the Google+ social network.
An Analogical Approach to Learning Constraints on Non-canonical Constructions

Non-canonical constructions differ from canonical structures in that they carry extra pragmatic meaning relating their arguments to the current discourse. One important question is how language users learn the pragmatic relationships that govern these variations. This paper proposes that these relationships can be learned through analogical generalization over first explicit and then inferentially related examples. We focus on the preposing construction and simulate this learning process for a proof of concept example using the SAGE model of analogical generalization.
DESIGN & IMPLEMENTATION OF FIXED WIDTH MODIFIED BOOTH MULTIPLIER

— Multiplication is the main operation in many signal processing algorithms. High accuracy and low power dissipation are the most important objectives in many multimedia and lossy applications such as filtering, convolution, Euclidean distance, fast Fourier transform (FFT). The fixed width multipliers are used to maintain a fixed format and allow a little accuracy loss of output data. In this paper for the reduction of truncation errors modifies the partial product matrix and derive an error compensation function. A simple compensation circuit mainly composed of the simplified sorting network is also proposed. The proposed error compensation circuits offer reduction in mean square error over the previous circuits.
Type-2 Fuzzy Logic Approach of a Maximum Power Point Tracking Employing SEPIC Converter for Photovoltaic System

This paper introduces a type-2 fuzzy logic controller (FLC) as a maximum power point tracker (MPPT), which can handle the uncertainties of the rules under high variations in weather conditions. The MPPT employed single-ended primary-inductor (SEPIC) converter. The new controller improves maximum power tracker search method by rules fuzzifying. An accurate and fast converging to maximum power point is offered by type-2 fuzzy tracker during both steady-state and varying weather conditions compared to conventional fuzzy MPPT methods. The performance of the proposed maximum power point tracker is demonstrated in MATLAB simulation at different operating conditions.
Influence of the characteristic length on the strength properties of a material model for spruce wood.

In this contribution an overview over a numerical scheme for the crack modelling of spruce wood under tensile loading is given. A material model for biaxially stressed spruce wood with consideration of the effect of knots on the strength properties has been developed. A necessary feature of this material model is its ability to treat cracks by means of the so-called smeared crack concept. For this reason the consideration of a so-called characteristic length in the corresponding evolution laws of the strength values is required. The successful implementation in the material model is shown by means of various numerical examples.
TweetSafa: Tweet Language Identification

This paper describes the methodology used for the SEPLN 14 shared task of tweet language identification (TweetLID), as explained on (Iñaki San Vicente, 2014). The system consists of 3 stages: pre-processing of tweets, creation of a dictionary of n-grams, and two algorithms ultimately used for language identification.