Miracle-dz/UniFAR-FTUs huggingface dataset:
K-means implementation on FPGA for high-dimensional data using triangle inequality

One of the challenges to data mining raised by technology development is that both data size and dimensionality is growing rapidly. K-means, one of the most popular clustering algorithms in data mining, suffers in computational time when used for large data sets and data with high dimensionality. In this paper, we propose a hardware architecture for K-means with triangle inequality optimization on FPGA. An optimal 8-bit square calculator for 6-LUT architectures is described to minimize the hardware cost and an approximation solution is proposed to avoid square root calculation in the original triangle inequality optimization. Our software and hardware experiments are tested with the MNIST benchmark and uniform random numbers of various size. This approximation results in 2% more distance calculations for MNIST and 5% for uniform random numbers than the original optimization. Compared to the baseline hardware system without optimization, our approach achieves up to 77% improvement in processing time with about 10% logic overhead. We demonstrate that the hardware can achieve 55-fold speed up compared to software for the 1024 MNIST.
Marriage Market, Parents' Bargaining Powers, and Children's Education

Whether the family makes decisions as a unit or through a collective decision making process has been tested elsewhere by examining whether the income pooling hypothesis holds or by examining whether premarital assets have significant effects on household consumptions. The results, however, may be confounded by endogeneity of the income and asset variables. This paper suggests that the marriage market conditions, summarized by the sex ratio, can be used to test the household models and that the test may be combined with the income pooling test to produce more reliable inferences. Using data from Indonesia, this paper conducts the combined test of the household models by estimating the effects of the provincial sex ratio and the parents' nonlabor incomes/premarital assets on Indonesian household's investment in children's education. I find that in urban areas the sex ratio has a strong positive effect on education expenditures, but not in rural areas. Being consistent to the sex ratio effect estimates, the income pooling hypothesis is mostly rejected in urban areas, but not in rural areas. In addition, premarital assets are found to have significant effects on investment in children's education in urban areas, but not in rural areas. I find that the estimation results are robust against alternative definitions of the sex ratio and additional controls for women's fertility choices and community-level income/wealth differences
Nonlinear systems design by a novel fuzzy neural system via hybridization of em and PSO algorithms

In this paper, we propose a hybridization of electromagnetism-like mechanism (EM) and particle swarm optimization algorithm (PSO) algorithms to design the proposed functional-link based Petri recurrent fuzzy neural system (FLPRFNS) for application of nonlinear system control. The FLPRFNS has a TSK-type fuzzy consequent part which uses functional-link based orthogonal basis functions and a Petri layer is added to eliminate the redundant fuzzy rule for each input. In addition, the FLPRFNS is trained by a hybrid algorithmmodified EMPSO. The main modification is that the randomly neghiborhoodly local search is replaced by particle swarm optimization algorithm with an instant update particles velocity strategy. Each particle updates its velocity instantaneously one by one and every particle can get best information from system. The modified EMPSO combines the advantages of multipoint search, global optimization, and faster convergence. Simulation results show that the modified EMPSO has the ability of golbal optimization, advantages of faster convergence and FLPRFNS has effect of higher accuracy.
AGENT-MEDIATORS IN MEDIA-ON-DEMAND ELETRONIC COMMERCE

This paper explores the requirements and issues related to media-on-demand electronic commerce using agents as mediators in the customer-to-business and business-to-business relations and propose an architecture for multi-agent systems acting on a wide range of situations. Agent technology is suitable since media-ondemand is a personalised service by nature. The agents' roles are examined in the context of a Consumer Buying Behaviour (CBB) model.
Tumor necrosis factor: a mechanistic link between angiotensin-II-induced cardiac inflammation and fibrosis.

BACKGROUND Continuous angiotensin-II infusion induced the uptake of monocytic fibroblast precursors that initiated the development of cardiac fibrosis; these cells and concurrent fibrosis were absent in mice lacking tumor necrosis factor receptor 1 (TNFR1). We now investigated their cellular origin and temporal uptake and the involvement of TNFR1 in monocyte-to-fibroblast differentiation. METHODS AND RESULTS Within a day, angiotensin-II induced a proinflammatory environment characterized by production of inflammatory chemokines, cytokines, and TH1-interleukins and uptake of bone marrow-derived M1 cells. After a week, the cardiac environment changed to profibrotic with growth factor and TH2-interleukin synthesis, uptake of bone marrow-derived M2 cells, and the presence of M2-related fibroblasts. TNFR1 signaling was not necessary for early M1 uptake, but its absence diminished the amount of M2 cells. TNFR1-knockout hearts also showed reduced levels of cytokine expression, but not of TH-related lymphokines. Reconstitution of wild-type bone marrow into TNFR1-knockout mice was sufficient to restore M2 uptake, upregulation of proinflammatory and profibrotic genes, and development of fibrosis in response to angiotensin-II. We also developed an in vitro mouse monocyte-to-fibroblast maturation assay that confirmed the essential role of TNFR1 in the sequential progression of monocyte activation and fibroblast formation. CONCLUSIONS Development of cardiac fibrosis in response to angiotensin-II was mediated by myeloid precursors and consisted of 2 stages. A primary M1 inflammatory response was followed by a subsequent M2 fibrotic response. Although the first phase seemed to be independent of TNFR1 signaling, the later phase (and development of fibrosis) was abrogated by deletion of TNFR1.
Deep learning with consumer preferences for recommender system

With the arrival of big data era and the fast development of E-commerce, recommender systems(RSs) are used in more and more application domains to assist customers in the search for their favorite products. Collaborative filtering(CF) is one of the most successful and widely used recommendation approaches. Poor recommender quality is a major challenge in traditional CF. And one reason causing this circumstance is the sparsity of data. In this paper, we propose to a deep learning model to predict the values of null ratings. Moreover, we investigate personal recommendation based on customer preferences and search the neighbors through the customer preferences. Accordingly, it has effectively solved recommendation quality problems of traditional CF algorithm under the condition of data sparse and poor result quality.
Building Chinese Event Type Paradigm Based on Trigger Clustering

cn Abstract  Traditional Event Extraction mainly focuses on event type identification and event partici- pants extraction based on pre-specified event type annotations. However, different domains have different event type paradigms. When transferring to a new domain, we have to build a new event type paradigm. It is a costly task to discover and annotate event types manually. To address this problem, this paper proposes a novel approach of building an event type para- digm by clustering event triggers. Based on the trigger clusters, the event type paradigm can be built automatically. Experimental re- sults on three different corpora - ACE (small, homogeneous, open corpus), Financial News and Musical News (large scale, specific do- main, web corpus) indicate that our method can effectively build an event type paradigm and can be easily adapted to new domains.
An image-based control scheme for an active stereo vision system

An active vision system can control the gaze direction of mounted vision sensors and can overcome many shortcomings of other passive vision systems, which have fixed vision sensors. Furthermore, most visual servoing approaches are concerned with a vision sensor or a passive stereo vision system which has fixed relative coordination between two cameras. In this paper, the problem of controlling a motion by visual servoing with an active stereo vision system is addressed. The main goal is to track the object with our active vision system. Firstly, we discuss how to stack image Jacobians in an active stereo vision system and how to control the active stereo cameras based on images. Next, we introduce our active vision system, which has a fast and simple system architecture that uses a high-speed serial communication bus (IEEE 1394a). It is manipulated by a distributed and jerk-bounded control algorithm to ensure smooth system motion. Finally, simulation and experimental results show both the validity of the proposed definition of the stacked Jacobian to control the system and the feasibility of the proposed the control method.
Improving Large Scale Day-Ahead Security Constrained Unit Commitment Performance

As part of the day-ahead market clearing process, MISO solves one of the most challenging Security Constrained Unit Commitment (SCUC) models. With footprint expansions and increased activity from financial traders, the day-ahead market unit commitment encounters significant performance challenges from time to time. This paper reviews MISO's day-ahead market clearing process and identifies the factors contributing to these performance challenges. It then shares techniques that have been developed to overcome these challenges. Three alternative backup solving approaches were developed. An incremental solving algorithm is proposed to better handle the iterative market clearing process and to perform solution polishing and commitment reason identification. Numerical results based on MISO data are presented.
Biomarkers of Acute Kidney Injury

Acute kidney injury (AKI) is a common problem in both the inpatient and outpatient setting and often results from drug toxicities. Traditional methods of identifying AKI, through measurement of blood urea nitrogen and serum creatinine, are problematic in that they are slow to detect decreases in glomerular filtration rate (GFR) and are influenced by a variety of factors that are not related to GFR changes. The problems inherent in a creatinine-based diagnosis of AKI have impeded the development of proper therapeutics in AKI and posed problems in evaluating nephrotoxicity of drugs and other chemical exposures. In recent years, a number of new biomarkers of AKI with more favorable test characteristics than creatinine have been identified and studied in a variety of experimental and clinical settings. This review will consider the most well-established biomarkers and appraise the literature, with particular attention given to the use of biomarkers in identifying toxin-mediated AKI.
Load adaptive congestion control and rate readjustment for wireless mesh networks

Traffic in WMNs has a wide range of variations which contributes to channel saturation and may bring up congestion due to contention caused by concurrent transmission, buffer overflows and time varying wireless channel condition. In this paper, we propose a distributed congestion control scheme for wireless mesh networks to ensure higher network throughput while avoiding congestion and maintaining inter-flow fairness. Our proposed technique handles congestion by restricting the available transmission rate of downstream nodes among the upstream nodes according to their flow demands based on their: packet arrival rate, service rate and buffer occupancy. We also introduce prioritized queues in each node to treat real time and non-real time traffic differently. Therefore, congestion degree calculation, rate allocation and traffic prioritization ensures higher network throughput and guaranteed delivery of real time traffic. Experiments conducted on ns-2 simulations demonstrate that our proposed algorithm can achieve significant improvements in both overall network throughput and inter-flow fairness for both non real time and delay bound traffic.
Hansori 2001 - corpus-based implementation of the Korean hansori text-to-speech synthesizer

The improvement of Text-to-Speech (TTS) synthesizers’ speech quality and naturalness is a continuous concern of researchers worldwide. The present paper gives a brief introduction of several previous Hansori TTS systems and is introducing our approach on experimenting, adopting and implementing corpus-based techniques for the system. We are focusing on corpus selection, on the optimal unit search criteria, on the missing unit (triphone unit) replacement handling, and we present some useful development tool options included in the trial version of the system
Lexical Resource for Medical Events: A Polarity Based Approach

The continuous sophistication in clinical informationprocessing motivates the development of a dictionary likeWordNet for Medical Events in order to convey the valuableinformation (e.g., event definition, sense based contextualdescription, polarity etc.) to the experts (e.g. medicalpractitioners) and non-experts (e.g. patients) in their respective fields. The present paper reports the enrichment of medical terms such as identifying and describing events, times and the relations between them in clinical text by employing three different lexical resources namely seed list of medical events collected from SemEval 2015 Task-6, the WordNet and an English medical dictionary. In particular, we develop WordNet for Medical Events (WME) that uses contextual information for word sense disambiguation of medical terms and reduce the communication gap between doctors and patients. We have proposed two approaches (Sequential and Combined) for identifying the proper sense of a medical event based on each of the three types of texts. The polarity lexicons e.g., SentiWordNet, Affect Word List and Taboda's adjective list have been used for implementing the polarity based Word Sense Disambiguation of the medical events from their glosses as extracted from the lexicalresources. The proposed WME out-performed a previouslyproposed Lesk Word Sense Disambiguation in the range of 10-20%.
Resistance/Response Molecular Signature for Oral Tongue Squamous Cell Carcinoma

Worldwide, the incidence of oral tongue cancer is on the rise, adding to the existing burden due to prevailing low survival and high recurrence rates. This study uses high-throughput expression profiling to identify candidate markers of resistance/response in patients with oral tongue cancer. Analysis of primary and post-treatment samples (12 tumor and 8 normal) by the Affymetrix platform (HG U133 plus 2) identified 119 genes as differentially regulated in recurrent tumors. The study groups had distinct profiles, with induction of immune response and apoptotic pathways in the non-recurrent and metastatic/invasiveness pathways in the recurrent group. Validation was carried out in tissues by Quantitative Real-Time PCR (QPCR) (n=30) and immunohistochemistry (IHC) (n=35) and in saliva by QPCR (n=37). The markers, COL5A1, HBB, IGLA and CTSC individually and COL5A1 and HBB in combination had the best predictive power for treatment response in the patients. A subset of markers identified (COL5A1, ABCG1, MMP1, IL8, FN1) could be detected in the saliva of patients with oral cancers with their combined sensitivity and specificity being 0.65 and 0.87 respectively. The study thus emphasizes the extreme prognostic value of exploring markers of treatment resistance that are expressed in both tissue and saliva.
Tackling income inequality The role of taxes and transfers

Taxes and transfers reduce inequality in disposable income relative to market income. The effect varies, however, across OECD countries. The redistributive impact of taxes and transfers depends on the size, mix and the progressivity of each component. Some countries with a relatively small tax and welfare system (e.g. Australia) achieve the same redistributive impact as countries characterised by much higher taxes and transfers (e.g. Germany) because they rely more on income taxes, which are more progressive than other taxes, and on means-tested cash transfers. This article provides an assessment of the redistributive effect of the main taxes and cash transfers, based on various OECD data sources, a set of policy indicators and a literature review. Using cluster analysis, it also identifies empirically four groups of countries with tax and transfer systems that share broadly similar features.
How far we can improve micro features based face recognition systems?

This paper presents improvements for face recognition methods that use LBP descriptor as a main technique in encoding micro features of face images. Our improvements are focused on the feature extraction and dimension reduction steps. In feature extraction, we use a variant of Local Binary Pattern (LBP) so-called Elliptical Local Binary Pattern (ELBP), which is more efficient than LBP for extracting micro facial features of the human face. ELBP of one pixel is built by thresholding its gray value with its P neighboring pixels on a horizontal ellipse. ELBP operator is applied in Pattern of Oriented Edge Magnitudes (POEM) to build Elliptical POEM (EPOEM) descriptor. The dimension reduction step is conducted by using Singular Value Decomposition (SVD) based Whitened Principal Component Analysis (WPCA). For performance evaluation of our improvements, we compare them with LBP based, POEM based approaches and other popular face recognition systems. The experimental results on state-of-the-art FERET and AR face databases prove the advantages and effectiveness of our improvements.
Blind calibration of phased arrays using sparsity constraints on the signal model

Several blind calibration methods have been proposed in a compressive sensing framework to mitigate the detrimental effects of uncertainties in the measurement matrix due to sensor gain and phase errors. Most of these methods operate on the signal domain samples of the receiving elements. This becomes computationally intractable if a large number of time samples is required, for example in low-SNR applications. In this paper, we propose an iterative blind calibration method to estimate the receiver path gains and phases as well as the observed scene from the measured array covariance matrix under the assumption that the observed scene is sparse. We successfully demonstrate the effectiveness of our method using simulated data for a 20-element uniform linear array as well as actual data from a 48-element station (subarray) of the Low Frequency Array (LOFAR) radio astronomical phased array.
A Highly Temperature-sensitive Proton Current in Mouse Bone Marrow–derived Mast Cells

Proton (H+) conductive pathways are suggested to play roles in the regulation of intracellular pH. We characterized temperature-sensitive whole cell currents in mouse bone marrow–derived mast cells (BMMC), immature proliferating mast cells generated by in vitro culture. Heating from 24 to 36°C reversibly and repeatedly activated a voltage-dependent outward conductance with Q10 of 9.9 ± 3.1 (mean ± SD) (n = 6). Either a decrease in intracellular pH or an increase in extracellular pH enhanced the amplitude and shifted the activation voltage to more negative potentials. With acidic intracellular solutions (pH 5.5), the outward current was detected in some cells at 24°C and Q10 was 6.0 ± 2.6 (n = 9). The reversal potential was unaffected by changes in concentrations of major ionic constituents (K+, Cl−, and Na+), but depended on the pH gradient, suggesting that H+ (equivalents) is a major ion species carrying the current. The H+ current was featured by slow activation kinetics upon membrane depolarization, and the activation time course was accelerated by increases in depolarization, elevating temperature and extracellular alkalization. The current was recorded even when ATP was removed from the intracellular solution, but the mean amplitude was smaller than that in the presence of ATP. The H+ current was reversibly inhibited by Zn2+ but not by bafilomycin A1, an inhibitor for a vacuolar type H+-ATPase. Macroscopic measurements of pH using a fluorescent dye (BCECF) revealed that a rapid recovery of intracellular pH from acid-load was attenuated by lowering temperature, addition of Zn2+, and depletion of extracellular K+, but not by bafilomycin A1. These results suggest that the H+ conductive pathway contributes to intracellular pH homeostasis of BMMC and that the high activation energy may be involved in enhancement of the H+ conductance.
3D Texture Feature Extraction Based on a Cubic Uniformity Interpolation Method for Emphysema in Chest CT Images

The application of computer-aided diagnosis system for early diagnosis of lung diseases to achieve the purpose of early treatment is the best solution. In this paper, we propose a Cubic Uniformity Interpolation Method (CUIM) for three dimensional local brightness and structure to detect the pathological change in the chest CT images. we propose an extension of rotation invariant cubic local binary pattern (CLBP), extract texture features in three dimensional directions to represent the brightness uniformity pattern of the image.The 3D optimized texture parameter CLBP allows to discriminate among the three groups of tissue specimens(normal, emphysema ,panlobular emphysema) by quantitative assessment of the underlying texture properties.
Robust CCD and IR Image Registration Using Gradient-Based Statistical Information

This letter presents a robust similarity measure for registering charged-couple device (CCD) and infrared (IR) images. The measure is based on the entropy obtained from a 3-D joint histogram incorporating edginess and modified generalized gradient vector flow (GGVF). To make a reliable mapping relationship between the edge regions of two images, the concept of edginess was adopted so registration performance would be affected mainly by gradient existences rather than their magnitudes. In addition, by adopting the GGVF, we relaxed a narrow capture range problem in conventional gradient-based measures. Experimental results showed that the proposed measure performs more robustly than existing measures.
Semantic Network Analysis: Techniques for Extracting, Representing, and Querying Media Content

W oter an Att eeldt Wouter van Atteveldt http://vanatteveldt.com http://content-analysis.org Semantic Network Analysis is a technique in which the content of a message (such as a newspaper article) is extracted from text and represented as a network of semantic relations between actors and issues, which can be queried to look for specific patterns and answer various research questions. This technique yields data that are easier to compare, combine, and share than traditional content analysis techniques.