Miracle-dz/UniFAR-FTUs huggingface dataset:
Sequential logic optimization by redundancy addition and removal

This paper presents a method of multi-level logic optimization for combinational and synchronous sequential logic. The circuits are optimized through iterative addition and removal of redundancies. Among the large number of possible connections that can be added, the proposed method can efficiently identify those connections that would create more redundancies and, thus, would result in a smaller network. This is done with the use of combinational and sequential ATPG techniques based up the concept of mandatory assignments. Experiments on ISCAS-85 combinational benchmark circuits show that best results are obtained for most of them. For sequential circuits, experimental results on MCNC FSM benchmarks and ISCAS-89 sequential benchmark circuits show that a significant amount of area reduction can be achieved beyond combinational optimization and sequential redundancy removal.
A Three-Pathway Psychobiological Framework of Salient Object Detection Using Stereoscopic Technology

Saliency detection, finding the most important parts of an image, has become increasingly popular in computer vision. Existing proposal methods are mostly based on color information, which may not be effective for cluttered backgrounds. We propose a new algorithm leveraging stereopsis to generate optical flow which can obtain addition cue (depth cue) to get the final saliency map. The proposed framework consists of three pathways. The first pathway eliminates the background based on cellular automata. The second pathway gets the optical flow and color flow saliency map. The third pathway calculates a coarse saliency map. Finally, we fuse these three pathways to generate the final saliency map. Besides, we construct a new high-quality dataset with the complex scene to make computer challenge human vision. Experimental results on our dataset and another three popular datasets demonstrate that our method is superior to the existing methods in terms of robustness.
Actor-Critic Reinforcement Learning with Energy-Based Policies

We consider reinforcement learning in Markov decision processes with high dimensional state and action spaces. We parametrize policies using energy-based models (particularly restricted Boltzmann machines), and train them using policy gradient learning. Our approach builds upon Sallans and Hinton (2004), who parameterized value functions using energy-based models, trained using a non-linear variant of temporal-di! erence (TD) learning. Unfortunately, non-linear TD is known to diverge in theory and practice. We introduce the first sound and e"cient algorithm for training energy-based policies, based on an actorcritic architecture. Our algorithm is computationally e"cient, converges close to a local optimum, and outperforms Sallans and Hinton (2004) in several high dimensional domains.
A General Framework for Weighted Gene Co-Expression Network Analysis

Gene co-expression networks are increasingly used to explore the system-level functionality of genes. The network construction is conceptually straightforward: nodes represent genes and nodes are connected if the corresponding genes are significantly co-expressed across appropriately chosen tissue samples. In reality, it is tricky to define the connections between the nodes in such networks. An important question is whether it is biologically meaningful to encode gene co-expression using binary information (connected=1, unconnected=0). We describe a general framework for `soft' thresholding that assigns a connection weight to each gene pair. This leads us to define the notion of a weighted gene co-expression network. For soft thresholding we propose several adjacency functions that convert the co-expression measure to a connection weight. For determining the parameters of the adjacency function, we propose a biologically motivated criterion (referred to as the scale-free topology criterion). We generalize the following important network concepts to the case of weighted networks. First, we introduce several node connectivity measures and provide empirical evidence that they can be important for predicting the biological significance of a gene. Second, we provide theoretical and empirical evidence that the `weighted' topological overlap measure (used to define gene modules) leads to more cohesive modules than its `unweighted' counterpart. Third, we generalize the clustering coefficient to weighted networks. Unlike the unweighted clustering coefficient, the weighted clustering coefficient is not inversely related to the connectivity. We provide a model that shows how an inverse relationship between clustering coefficient and connectivity arises from hard thresholding.We apply our methods to simulated data, a cancer microarray data set, and a yeast microarray data set.
A Power Model Combined of Architectural Level and Gate Level for Multicore Processors

Low power consumption is becoming a critical factor for multicore processors. As the multicore processor design complexity increases, power estimation for multicore processors has gained more importance. This paper presents a new power model combined of architectural level and gate level for multicore processors. The model maps the multicore processors to a combination of building blocks, and estimates the gate-level power of these blocks using parameterized RTL. Then, the power numbers are made in the form of look-up tables, and integrated in architecture simulators. The experiments show that for peak power estimation, an excellent accuracy has been reached and simulation performance is greatly improved compared to the gate level.
Identification of the Human Receptor Activity-modifying Protein 1 Domains Responsible for Agonist Binding Specificity*

When co-expressed with receptor activity-modifying protein (RAMP) 1, calcitonin receptor-like receptor (CRLR) can function as a receptor for both calcitonin gene-related peptide (CGRP) and adrenomedullin (AM). To investigate the structural determinants of ligand binding specificity, we examined the extracellular domain of human (h) RAMP1 using various deletion mutants. Co-expression of the hRAMP1 mutants with hCRLR in HEK-293 cells revealed that deletion of residues 91–94, 96–100, or 101–103 blocked [125I]CGRP binding and completely abolished intracellular cAMP accumulation normally elicited by CGRP or AM. On the other hand, the deletion of residues 78–80 or 88–90 significantly attenuated only AM-evoked responses. In all of these cases, the receptor heterodimers were fully expressed at the cell surface. Substituting alanine for residues 91–103 one at a time had little effect on CGRP-induced responses, indicating that although this segment is essential for high affinity agonist binding to the receptors, none of the residues directly interacts with either CGRP or AM. This finding suggests that RAMPs probably determine ligand specificity by contributing to the structure of the ligand-binding pocket or by allosteric modulation of the conformation of the receptor. Interestingly, the L94A mutant up-regulated surface expression of the receptor heterodimer to a greater degree than wild-type hRAMP1, thereby increasing CGRP binding and signaling. L94A also significantly increased cell surface expression of the hRAMP1 deletion mutant D101–103 when co-transfected with hCRLR, and expression of a L94A/D101–103 double mutant markedly attenuated the activity of endogenous RAMP1 in HEK-293T cells.
HUMAN DETECTION IN HOURS OF DARKNESS USING GAUSSIAN MIXTURE MODEL ALGORITHM

Video surveillance systems are very important in oureveryday life. Video surveillance applications are used in airports, banks, offices and even our homes toremain us secure. Night vision is the ability to see in low light conditions. Surveillance video may not be seen clearly. Especially under the weakillumination conditions. The details of the image are very poor at night. Most of the previous work has focused on daytime Surveillance. This paper proposes a method of Human detection in hours of darkn ess (night time) using Gaussian Mixture Model(GMM) in real night environment employing an infrared radiation camera. Infrared video is taken as Input to perform Human detection at night.Then the video was processed using Gaussian mixture model algorithm, it was confirmed thatby using this methodaccurate detection of human at night is possible.
Detecting Switches Against Non-Stationary Opponents ( JAAMAS Extended

Interactions in multiagent systems are generally more complicated than single agent ones. Game theory provides solutions on how to act in multiple agent scenarios; however, it assumes that all agents will act rationally. Moreover, some works also assume the opponent will use a stationary strategy. These assumptions usually do not hold in real world scenarios where agents have limited capacities and may deviate from a perfect rational response. Our goal is still to act optimally in this cases by learning the appropriate response and without any prior policies on how to act. Thus, we focus on the problem when another agent in the environment uses different stationary strategies over time. This paper introduces DriftER, an algorithm that 1) learns a model of the opponent, 2) uses that to obtain an optimal policy and then 3) determines when it must re-learn due to an opponent strategy change. We provide theoretical results showing that DriftER guarantees to detect switches with high probability. Also, we provide empirical results in normal form games and then in a more realistic scenario, the Power TAC simulator.
Variable Step-Size NLMS Algorithm for Under-Modeling Acoustic Echo Cancellation

In acoustic echo cancellation (AEC) applications, where the acoustic echo paths are extremely long, the adaptive filter works most likely in an under-modeling situation. Most of the adaptive algorithms for AEC were derived assuming an exact modeling scenario, so that they do not take into account the under-modeling noise. In this letter, a variable step-size normalized least-mean-square (VSS-NLMS) algorithm suitable for the under-modeling case is proposed. This algorithm does not require any a priori information about the acoustic environment; as a result, it is very robust and easy to control in practice. The simulation results indicate the good performance of the proposed algorithm.
Top-kriging - geostatistics on stream networks

Abstract. We present Top-kriging, or topological kriging, as a method for estimating streamflow-related variables in ungauged catchments. It takes both the area and the nested nature of catchments into account. The main appeal of the method is that it is a best linear unbiased estimator (BLUE) adapted for the case of stream networks without any additional assumptions. The concept is built on the work of Sauquet et al. (2000) and extends it in a number of ways. We test the method for the case of the specific 100-year flood for two Austrian regions. The method provides more plausible and, indeed, more accurate estimates than Ordinary Kriging. For the variable of interest, Top-kriging also provides estimates of the uncertainty. On the main stream the estimated uncertainties are smallest and they gradually increase as one moves towards the headwaters. The method as presented here is able to exploit the information contained in short records by accounting for the uncertainty of each gauge. We suggest that Top-kriging can be used for spatially interpolating a range of streamflow-related variables including mean annual discharge, flood characteristics, low flow characteristics, concentrations, turbidity and stream temperature.
Financial Development and the Patt erns of International Capital Flows

We develop a tractable two-country overlapping-generations model and show that cross-country differences in financial development can explain three recent empirical patterns of international capital flows: Financial capital flows from relatively poor to relatively rich countries while foreign direct investment flows in the opposite direction; net capital flows go from poor to rich countries; despite of its negative net international investment positions, the United States receives a positive net investment income. We also explore the welfare and distributional effects of international capital flows and show that the patterns of capital flows may reverse along the convergence process of a developing country.
Minorities' perceptions of minority-white biracials: The role of identification for cognitive, affective, and behavioral responses

MINORITIES’ PERCEPTIONS OF MINORITY-WHITE BIRACIALS: THE ROLE OF IDENTIFICATION FOR COGNITIVE, AFFECTIVE, AND BEHAVIORAL RESPONSES by Sabrica Barnett Advisors: Professors Daryl Wout and William Cross, Jr. Research on intergroup relations has a rich history in social psychology, with scholars devoting a considerable effort investigating factors that influence stereotyping, prejudice and discriminatory behavior. The results of these studies suggest that individuals’ cognitions, affect, and behaviors are affected by their own group memberships as well as the groups to which others belong. People generally view the groups that they belong to (their ingroup) positively, and view the groups that others belong to (outgroups) stereotypically (Tajfel & Turner, 1986). However, much of the research on social identification and subsequent perceptions has focused on socially distinct groups rather than groups that blur categorical boundaries. As such, there is a dearth of research on how individuals identify with and perceive people who belong to multiple racial groups. To address this gap in the literature, I investigated minorities’ identification with minority-White biracials, as well as the downstream cognitive (warmth and competence stereotypes), affective (pride, shame), and behavioral (facilitation, distancing) consequences of identification across three studies. Results demonstrated that Black (Study 1) and Hispanic (Study 2) participants were equally identified with biracials and other ingroup members (Blacks, Hispanics), and were less identified with outgroup members (Whites). In contrast, White
Blood BDNF Level Is Gender Specific in Severe Depression

Though the role of brain derived neurotrophic factor (BDNF) as a marker for major depressive disorder (MDD) and antidepressant efficacy has been widely studied, the role of BDNF in distinct groups of patients remains unclear. We evaluated the diagnostic value of BDNF as a marker of disease severity measured by HAM-D scores and antidepressants efficacy among MDD patients. Fifty-one patients who met DSM-IV criteria for MDD and were prescribed antidepressants and 38 controls participated in this study. BDNF in serum was measured at baseline, 1st, 2nd and 8th treatment weeks. Depression severity was evaluated using the Hamilton Rating Scale for Depression (HAM-D). BDNF polymorphism rs6265 (val66met) was genotyped. We found a positive correlation between blood BDNF levels and severity of depression only among untreated women with severe MDD (HAM-D>24). Serum BDNF levels were lower in untreated MDD patients compared to control group. Antidepressants increased serum BDNF levels and reduced between-group differences after two weeks of treatment. No correlations were observed between BDNF polymorphism, depression severity, duration of illness, age and BDNF serum levels. Further supporting the role of BDNF in the pathology and treatment of MDD, we suggest that it should not be used as a universal biomarker for diagnosis of MDD in the general population. However, it has diagnostic value for the assessment of disease progression and treatment efficacy in individual patients.
On the Rate Loss of Multiple Sources Codes in the Wyner–Ziv Setting

In this paper, we consider the rate loss and achievable region of multiple sources codes (also named multiterminal source codes) in the Wyner-Ziv setting (WZ-MSC), in which two stationary memoryless sources (X, Y) are compressed separately from each other at respective rates R<sub>1</sub> and R<sub>2</sub>, and the joint decoder, having access to the side information S which is correlated with sources (X, Y), reconstructs X and Y with distortion D<sub>1</sub> and D<sub>2</sub>, respectively. We define the rate loss L<sub>1</sub> = R<sub>1</sub> R<sub>X|Y,S</sub>(D<sub>1</sub>), L<sub>2</sub> = R<sub>2</sub> R<sub>Y|X,S</sub>(D<sub>2</sub>), and L<sub>0</sub> = R<sub>1</sub> - R<sub>2</sub> - R<sub>X,Y|S</sub>(D<sub>1</sub>,D<sub>2</sub>). For general real-valued stationary memoryless sources and mean squared error distortion measure, we show that the rate loss can be bounded by L<sub>i</sub> ≤ K<sub>i</sub> + 1,i = 1,2, and L<sub>0</sub> ≤ 1, where K<sub>1</sub> = R<sub>X,Y|S</sub>(D<sub>1</sub>, D<sub>2</sub>) - R<sub>Y|S</sub>(D<sub>2</sub>) R<sub>X|Y,S</sub>(D<sub>1</sub>) and K<sub>2</sub> = R<sub>X,Y|S</sub>(D<sub>1</sub>,D<sub>2</sub>) - R<sub>X|S</sub>(D<sub>1</sub>) R<sub>Y|X,S</sub>(D<sub>2</sub>). When the sources and side information have a jointly Gaussian distribution, we give upper bounds of K<sub>1</sub> and K<sub>2</sub> by a function of conditional correlation coefficient ρ<sub>XY|S</sub>. For a special case when there is no side information, our result tightens Feng's rate loss bounds. Using our upper bounds of the rate loss, we give good approximations of the achievable region.
Long-Term Occupational Exposure to Organic Solvents Affects Color Vision, Contrast Sensitivity and Visual Fields

The purpose of this study was to evaluate the visual outcome of chronic occupational exposure to a mixture of organic solvents by measuring color discrimination, achromatic contrast sensitivity and visual fields in a group of gas station workers. We tested 25 workers (20 males) and 25 controls with no history of chronic exposure to solvents (10 males). All participants had normal ophthalmologic exams. Subjects had worked in gas stations on an average of 9.6±6.2 years. Color vision was evaluated with the Lanthony D15d and Cambridge Colour Test (CCT). Visual field assessment consisted of white-on-white 24–2 automatic perimetry (Humphrey II-750i). Contrast sensitivity was measured for sinusoidal gratings of 0.2, 0.5, 1.0, 2.0, 5.0, 10.0 and 20.0 cycles per degree (cpd). Results from both groups were compared using the Mann–Whitney U test. The number of errors in the D15d was higher for workers relative to controls (p<0.01). Their CCT color discrimination thresholds were elevated compared to the control group along the protan, deutan and tritan confusion axes (p<0.01), and their ellipse area and ellipticity were higher (p<0.01). Genetic analysis of subjects with very elevated color discrimination thresholds excluded congenital causes for the visual losses. Automated perimetry thresholds showed elevation in the 9°, 15° and 21° of eccentricity (p<0.01) and in MD and PSD indexes (p<0.01). Contrast sensitivity losses were found for all spatial frequencies measured (p<0.01) except for 0.5 cpd. Significant correlation was found between previous working years and deutan axis thresholds (rho = 0.59; p<0.05), indexes of the Lanthony D15d (rho = 0.52; p<0.05), perimetry results in the fovea (rho = −0.51; p<0.05) and at 3, 9 and 15 degrees of eccentricity (rho = −0.46; p<0.05). Extensive and diffuse visual changes were found, suggesting that specific occupational limits should be created.
Performance of alternative strategies for primary cervical cancer screening in sub-Saharan Africa: systematic review and meta-analysis of diagnostic test accuracy studies

Objective To assess and compare the accuracy of visual inspection with acetic acid (VIA), visual inspection with Lugol’s iodine (VILI), and human papillomavirus (HPV) testing as alternative standalone methods for primary cervical cancer screening in sub-Saharan Africa. Design Systematic review and meta-analysis of diagnostic test accuracy studies. Data sources Systematic searches of multiple databases including Medline, Embase, and Scopus for studies published between January 1994 and June 2014. Review methods Inclusion criteria for studies were: alternative methods to cytology used as a standalone test for primary screening; study population not at particular risk of cervical cancer (excluding studies focusing on HIV positive women or women with gynaecological symptoms); women screened by nurses; reference test (colposcopy and directed biopsies) performed at least in women with positive screening results. Two reviewers independently screened studies for eligibility and extracted data for inclusion, and evaluated study quality using the quality assessment of diagnostic accuracy studies 2 (QUADAS-2) checklist. Primary outcomes were absolute accuracy measures (sensitivity and specificity) of screening tests to detect cervical intraepithelial neoplasia grade 2 or worse (CIN2+). Results 15 studies of moderate quality were included (n=61 381 for VIA, n=46 435 for VILI, n=11 322 for HPV testing). Prevalence of CIN2+ did not vary by screening test and ranged from 2.3% (95% confidence interval 1.5% to 3.3%) in VILI studies to 4.9% (2.7% to 7.8%) in HPV testing studies. Positivity rates of VILI, VIA, and HPV testing were 16.5% (9.8% to 24.7%), 16.8% (11.0% to 23.6%), and 25.8% (17.4% to 35.3%), respectively. Pooled sensitivity was higher for VILI (95.1%; 90.1% to 97.7%) than VIA (82.4%; 76.3% to 87.3%) in studies where the reference test was performed in all women (P<0.001). Pooled specificity of VILI and VIA were similar (87.2% (78.1% to 92.8%) v 87.4% (77.1% to 93.4%); P=0.85). Pooled sensitivity and specificity were similar for HPV testing versus VIA (both P≥0.23) and versus VILI (both P≥0.16). Accuracy of VIA and VILI increased with sample size and time period. Conclusions For primary screening of cervical cancer in sub-Saharan Africa, VILI is a simple and affordable alternative to cytology that demonstrates higher sensitivity than VIA. Implementation studies are needed to assess the effect of these screening strategies on the incidence and outcomes of cervical cancer in the region.
Proteasome Inhibitors Block DNA Repair and Radiosensitize Non-Small Cell Lung Cancer

Despite optimal radiation therapy (RT), chemotherapy and/or surgery, a majority of patients with locally advanced non-small cell lung cancer (NSCLC) fail treatment. To identify novel gene targets for improved tumor control, we performed whole genome RNAi screens to identify knockdowns that most reproducibly increase NSCLC cytotoxicity. These screens identified several proteasome subunits among top hits, including the topmost hit PSMA1, a component of the core 20 S proteasome. Radiation and proteasome inhibition showed synergistic effects. Proteasome inhibition resulted in an 80–90% decrease in homologous recombination (HR), a 50% decrease in expression of NF-κB-inducible HR genes BRCA1 and FANCD2, and a reduction of BRCA1, FANCD2 and RAD51 ionizing radiation-induced foci. IκBα RNAi knockdown rescued NSCLC radioresistance. Irradiation of mice with NCI-H460 xenografts after inducible PSMA1 shRNA knockdown markedly increased murine survival compared to either treatment alone. Proteasome inhibition is a promising strategy for NSCLC radiosensitization via inhibition of NF-κB-mediated expression of Fanconi Anemia/HR DNA repair genes.