Miracle-dz/UniFAR-FTUs huggingface dataset:
Input-parallel output-parallel DC-DC converter with MPPT technique for grid connection of multiple distributed generators

Novel input-parallel output-parallel (IPOP) DC-DC converter with maximum power point tracking (MPPT) technique is presented in this paper, which consists of two cascaded stages including buck-boost and isolated boost, so as to achieve high voltage gain and galvanic isolation at proper duty ratio and low leakage energy. Using proposed DC-DC converter, multiple distributed generators (DGs) can be connected to DC grid or subsequent power management unit (PMU) through IPOP method, which is conducive to hot plug in and out of DGs in real applications. Open loop MPPT technique is combined with pulse width modulation (PWM) to control the main switch of the DC-DC converter. Simulation results show that for two identical DG inputs of ~24V, voltage gain reaches 12.6, and total output current is the sum of single unit. For two different DG inputs of ~24V and ~36V, output voltage is boosted by an average factor of 15.3. In both conditions, stable output with small ripple are obtained, proving the suitability of the proposed DC-DC converter and grid connection method in DG applications.
Abusive Supervision and Job Dissatisfaction: The Moderating Effects of Feedback Avoidance and Critical Thinking

Although research on the antecedents of job dissatisfaction has been developed greatly, we know little about the role of abusive supervision in generating job dissatisfaction. The contingencies under which abusive supervision relates to employees’ job dissatisfaction are still unknown. The present study aimed to fill this research gap by empirically exploring the abusive supervision-job dissatisfaction relationship as well as examining the moderating roles of feedback avoidance and critical thinking on this relationship. We tested the hypotheses with data from a sample of 248 employees from a high-tech communications company in northern China and found that: (a) abusive supervision was positively related to job dissatisfaction; (b) the positive relationship was moderated by both employees’ feedback avoidance and critical thinking. We conclude by extracting the theoretical as well as practical contributions, along with a discussion of the promising directions for future research.
A Meta-Analysis Investigating the Correlation Between Self-Esteem and Academic Achievement in African Americans: Merging Social Psychology and a Jesuit Educataion

Name: Pearl Lackner Major: Psychology & Neuroscience TITLE MERGINIG SOCIAL PSYCHOLOGY & A JESUIT EDUCATION Advisor’s Name: Dr. Brian Drwecki Reader’s Name: Dr. Rebecca Betjemann This meta-analysis examines the correlation between self-esteem and academic achievement in African Americans across 27 separate studies (resulting in 41 measures of effect size) that included 9872 individuals in total. Statistical analysis indicates that overall, self-esteem accounted for 4% of the variation in academic achievement (�� = .21, n = 9872, Bayesian 95% CI = 0.14-0.27, p < .05). In line with the common research practice utilized in examining the relationship between self-esteem and academic achievement, the effects of global self-esteem measures and academic-specific self-esteem measures were examined separately (Bachman & O’Malley, 1986; Bouchey & Harter, 2005; Dusek, 2000; Fuligni, 1997; Harter, Waters, Whitesell, & Kastelic, 1998; Mann, Hosman, Schaalma, & de Vries, 2004; Scheier, Botvin, & Griffin, 2000; Tafarodi & Miline, 2002; Wigfield, Eccles, & Schiefele, 2007), and individual effect sizes were calculated for both global measures and academic specific measures of selfesteem. These analyses indicate that global measures of self-esteem account for less than 1% of the variation in academic achievement (�� = .1, n = 5808, k = 23, Bayesian 95% CI = 0.06-0.14, p < .05), whereas academic specific self-esteem accounts for almost 13% of the variation in academic achievement (�� = .36, n = 4046, k = 18, Bayesian 95% CI = 0.34-0.37, p < .05). These results are discussed in the context of reducing the academic achievement gap.
An improved P_SKIP mode decision algorithm for H.264/AVC based on observers' low sensitivity to boundary parts of video images

H.264/AVC can achieve much higher compression efficiency, compared with previous video compression standards. However, the lower encoding efficiency is an influential factor for popularization of H.264. In this paper, an improved P_SKIP mode decision algorithm for H.264 encoder is proposed: As observers are less sensitive to boundary parts of video images, a certain degree of information loss for boundary parts would not affect the feeling of observers for video images; thus, larger difference between the efficiency of P_SKIP mode and P_16×16 mode is allowed for boundary macroblocks. Experimental results demonstrate that the algorithm can achieve encoding speed-up ratio of 1.18∼2.11 and bitrates reduction of 9.9% to 42.2%, the sequence with less activity can achieve higher speed-up ratio and higher bitrates reduction.
Distributed Detection in the Presence of Byzantine Attacks

Distributed detection in the presence of cooperative (Byzantine) attack is considered. It is assumed that a fraction of the monitoring sensors are compromised by an adversary, and these compromised (Byzantine) sensors are reprogrammed to transmit fictitious observations aimed at confusing the decision maker at the fusion center. For detection under binary hypotheses with quantized sensor observations, the optimal attacking distributions for Byzantine sensors that minimize the detection error exponent are obtained using a ldquowater-fillingrdquo procedure. The smallest error exponent, as a function of the Byzantine sensor population, characterizes the power of attack. Also obtained is the minimum fraction of Byzantine sensors that destroys the consistency of detection at the fusion center. The case when multiple measurements are made at the remote nodes is also considered, and it is shown that the detection performance scales with the number of sensors differently from the number of observations at each sensor.
ElastMan: autonomic elasticity manager for cloud-based key-value stores

The increasing spread of elastic Cloud services, together with the pay-as-you-go pricing model of Cloud computing, has led to the need of an elasticity controller. The controller automatically resizes an elastic service in response to changes in workload, in order to meet Service Level Objectives (SLOs) at a reduced cost. However, variable performance of Cloud virtual machines and nonlinearities in Cloud services complicates the controller design. We present the design and evaluation of ElastMan, an elasticity controller for Cloud-based elastic key-value stores. ElastMan combines feedforward and feedback control. Feedforward control is used to respond to spikes in the workload by quickly resizing the service to meet SLOs at a minimal cost. Feedback control is used to correct modeling errors and to handle diurnal workload. We have implemented and evaluated ElastMan using the Voldemort key-value store running in a Cloud environment based on OpenStack. Our evaluation shows the feasibility and effectiveness of our approach to automation of Cloud service elasticity.
Why phonological constraints are so coarse-grained

Current models of speech perception are divided with regard to the status of ‘‘phonology’’, or general implicit knowledge of the sound patterns of a language. In the TRACE model (McClelland & Elman, 1986) the phonotactic and prosodic constraints of phonology are treated as epiphenomenal from regularities in the lexicon. In contrast, Norris (1994), Vitevich and Luce (1998) and Merge (Norris, McQueen, & Cutler, 2000) respond to a growing body of experimental literature indicating that low-level encoding of the speech signal (the level whose result is passed up to the lexicon for potential word matches) is sensitive to phonotactic and prosodic constraints. Here, I will explore the consequences of the assumption that the architecture of the speech perception system includes a fast phonological preprocessor (hereafter, an FPP) which uses languagespeci! c, but still general, prosodic and phonotactic patterns to chunk the speech stream on its way up to the lexical network. By integrating such information, the FPP imputes possible word boundaries to particular temporal locations in the speech signal. An important question is what types of phonological patterns are candidates for being encoded in the FPP: Can the processing system exploit any statistical regularities whatsoever in the shape of words? Does absolutely any structural description which is logically possible in phonology provide a usable constraint? I will explore the causes and consequences of one observation about this issue, namely that viable constraints are coarse-grained. Although the logical apparatus of phonological theory would make it possible to state extremely ! ne-grained constraints (e.g., constraints containing complex and detailed combinations of features and structural positions), the constraints for which we now have linguistic or psycholinguistic evidence are considerably simpler.
Heal-T: An efficient PPG-based heart-rate and IBI estimation method during physical exercise

Photoplethysmography (PPG) is a simple, unobtrusive and low-cost technique for measuring blood volume pulse (BVP) used in heart-rate (HR) estimation. However, PPG based heart-rate monitoring devices are often affected by motion artifacts in on-the-go scenarios, and can yield a noisy BVP signal reporting erroneous HR values. Recent studies have proposed spectral decomposition techniques (e.g. M-FOCUSS, Joint-Sparse-Spectrum) to reduce motion artifacts and increase HR estimation accuracy, but at the cost of high computational load. The singular-value-decomposition and recursive calculations present in these approaches are not feasible for the implementation in real-time continuous-monitoring scenarios. In this paper, we propose an efficient HR estimation method based on a combination of fast-ICA, RLS and BHW filter stages that avoids sparse signal reconstruction, while maintaining a high HR estimation accuracy. The proposed method outperforms the state-of-the-art systems on the publicly available TROIKA data set both in terms of HR estimation accuracy (absolute error of 2.25 ± 1.93 bpm) and computational load.
Scale, Congestion and Growth

This paper explores the relationship between aggregate and relative congestion, returns to scale and economic growth. Aggregate congestion reduces the effective productivity of capital; relative congestion reduces the effective productivity of labour. Both forms of congestion adversely affect the equilibrium growth rate, although their relative effects depend upon aggregate returns to scale. The two forms of congestion have contrasting effects on the transitional dynamics. Relative congestion retards the rate of adjustment; aggregate congestion accelerates it. The externalities generated by congestion and non-optimal expenditure can be fully corrected, both during the transition and in steady state, by a time-invariant income tax.
A comparative study of k-NN and hazard-based models for incident duration prediction

The motivation behind this paper is to enhance the reliability of in-vehicle navigation systems by predicting the duration of incidents that cause congestion. The main objective of this paper is to develop a methodology for predicting incident duration using broadcast incident data and evaluate the performance of k-NN and hazard-based duration models for predicting incident duration; both of the models are presented in this paper. An incident dataset from the BBC for the Greater London area is used to evaluate the accuracy of both models so that the results give a direct comparison between the models. The strengths and weaknesses of the models are discussed in the paper based on this analysis. Results show that both k-NN and hazard based models have the potential to provide accurate incident duration prediction. While k-NN based models provided marginally more accurate prediction than hazard-based models, the hazard-based duration models can provide additional information such as delay probabilities that can be used by advanced routing and navigation algorithms. Results also show that traffic information incident feeds, such as the tpegML feed from the BBC or TMC information, can be used as a potential data source for incident duration prediction in vehicle navigation systems.
NoN-Greedy Routing Algorithms Are Optimal or Know thy Neighbor’s Neighbor

Greedy routing is a common approach when a graph has some underlying metric. We present an approach for designing routing algorithms called NoN-Greedy (Neighbor-of-Neighbor). We show that in two settings it reduces significantly the number of hops taken before the destination is reached and can yield degree optimal routing (in a network of degree O(log n) this means O(log / log logn) hops). In particular we consider the skip-graph peer-to-peer network and the small world settings and show that both models indeed permit degree optimal routing. The theoretic results are backed by simulations which show a significant improvement. While not being Greedy per-se, the NoN-Greedy algorithm preserves some good properties of greedy algorithms. Furthermore we show a lower bound proving that in order to reduce the number of hops, information regarding neighbors only is insufficient, thus the NoN approach is essential.
Genetic variants in telomere‐maintenance genes are associated with ovarian cancer risk and outcome

Most ovarian cancer patients present at an advanced stage with poor prognosis. Telomeres play a critical role in protecting chromosomes stability. The associations of genetic variants in telomere maintenance genes and ovarian cancer risk and outcome are unclear. We genotyped 137 single nucleotide polymorphisms (SNPs) in telomere‐maintenance genes in 417 ovarian cancer cases and 417 matched healthy controls to evaluate their associations with cancer risk, survival and therapeutic response. False discovery rate Q‐value was calculated to account for multiple testing. Eleven SNPs from two genes showed nominally significant associations with the risks of ovarian cancer. The most significant SNP was TEP1: rs2228026 with participants carrying at least one variant allele exhibiting a 3.28‐fold (95% CI: 1.72‐6.29; P < 0.001, Q = 0.028) increased ovarian cancer risk, which remained significant after multiple testing adjusting. There was also suggested evidence for the associations of SNPs with outcome, although none of the associations had a Q < 0.05. Seven SNPs from two genes showed associations with ovarian cancer survival (P < 0.05). The strongest association was found in TNKS gene (rs10093972, hazard ratio = 1.88; 95% CI: 1.20‐2.92; P = 0.006, Q = 0.076). Five SNPs from four genes showed suggestive associations with therapeutic response (P < 0.05). In a survival tree analysis, TEP1:rs10143407 was the primary factor contributing to overall survival. Unfavourable genotype analysis showed a cumulative effect of significant SNPs on ovarian cancer risk, survival and therapeutic response. Genetic variations in telomere‐maintenance genes may be associated with ovarian cancer risk and outcome.
Practical WiFi Localization for Autonomous Industrial Vehicles

Localization for industrial vehicles is a key issue in the development and use of autonomous systems on industrial worksites. The WiFi technology being widely used in such environments can be used as a base for an easy to set up system requiring little hardware. In this paper we propose a system estimating the location of industrial vehicles, based only on WiFi signals, that can operate both in and out of buildings and sheds, and that can deal with the kidnapped robot problem. It is based on the use of a signal strength intensity map and a particle filter, and has been demonstrated in a typical industrial worksite.
Spying Out Real User Preferences for Metasearch Engine Personalization.

Most current metasearch engines provide uniform service to users but do not cater for the specific needs of individual users. To address this problem, research has been done on personalizing a metasearch engine. An interesting and practical approach is to optimize its ranking function using clickthrough data. However, it is still challenging to infer accurate user preferences from the clickthrough data. In this paper, we propose a novel learning technique called “Spy Naive Bayes” (SpyNB) to identify the user preference pairs generated from clickthrough data. We then employ ranking SVM to build a metasearch engine optimizer. To evaluate the effectiveness of SpyNB on ranking quality, we develop a metasearch engine prototype that comprises three underlying search engines: MSNSearch, WiseNut and Overture to conduct experimental evaluation. The empirical results show that, compared with the original ranking, SpyNB can significantly improve the average ranks of users’ click by 20%, while the performance of the existing methods are not satisfactory.
Towards a Knowledge Level Analysis of Forgetting

Forgetting has been addressed in various areas in KR, including classical logic, logic programming, modal logic, and description logics. Here, we view forgetting as an abstract operator, independent of the underlying logic. We argue that forgetting amounts to a reduction in the signature of a language of a logic, and that the result of forgetting elements of a signature in a theory is the set of logical consequences over the reduced language. This definition offers several advantages. It provides a uniform approach to forgetting, applicable to any logic with a well-defined consequence relation. Obtained results are thus applicable to all subsumed formal systems, and typically are obtained much more straightforwardly. The approach also leads to insights with respect to specific logics: forgetting in first-order logic is somewhat different from the accepted approach; and the definition applied to logic programs yields a new syntax-independent notion of forgetting.
Artificial Cognition

The purpose of natural cognitive systems appears to be to allow the organism to respond more rapidly to it’s environment and thus survive. This is achieved in two ways. First there are reflexes, the built-in actions an organism will take to stimulus. The second is the prediction of what the future state of the immediate environment will be and thus avoid harmful situations. The greater the intelligence of a NC system, the further into the future it predicts. Humans often plan many years into the future.
Characterization of a Spontaneous Novel Mutation in the NPC2 Gene in a Cat Affected by Niemann Pick Type C Disease

Niemann-Pick C disease (NPC) is an autosomal recessive lysosomal storage disorder characterized by accumulation of unesterified cholesterol and other lipids within the lysosomes due to mutation in NPC1 or NPC2 genes. A feline model of NPC carrying a mutation in NPC1 gene has been previously described. We have identified two kittens affected by NPC disease due to a mutation in NPC2 gene. They manifested with tremors at the age of 3 months, which progressed to dystonia and severe ataxia. At 6 months of age cat 2 was unable to stand without assistance and had bilaterally reduced menace response. It died at the age of 10 months. Post-mortem histological analysis of the brain showed the presence of neurons with cytoplasmic swelling and vacuoles, gliosis of the substantia nigra and degeneration of the white matter. Spheroids with accumulation of ubiquitinated aggregates were prominent in the cerebellar cortex. Purkinje cells were markedly reduced in number and they showed prominent intracytoplasmic storage. Scattered perivascular aggregates of lymphocytes and microglial cells proliferation were present in the thalamus and midbrain. Proliferation of Bergmann glia was also observed. In the liver, hepatocytes were swollen because of accumulation of small vacuoles and foamy Kupffer cells were also detected. Foamy macrophages were observed within the pulmonary interstitium and alveoli as well. At 9 months cat 1 was unable to walk, developed seizures and it was euthanized at 21 months. Filipin staining of cultured fibroblasts showed massive storage of unesterified cholesterol. Molecular analysis of NPC1 and NPC2 genes showed the presence of a homozygous intronic mutation (c.82+5G>A) in the NPC2 gene. The subsequent analysis of the mRNA showed that the mutation causes the retention of 105 bp in the mature mRNA, which leads to the in frame insertion of 35 amino acids between residues 28 and 29 of NPC2 protein (p.G28_S29ins35).
Markov connected component fields

A new class of Gibbsian models with potentials associated with the connected components or homogeneous parts of images is introduced. The relationship with Markov random fields and marked point processes is explored and spatial Markov properties are established. Further, extensions to infinite lattices are studied. Statistical inference problems including geostatistical applications and statistical image analysis are also discussed. Finally, simulation studies are presented which show that the models may be appropriate for a variety of interesting patterns.
Identity management-based social trust model for mediating information sharing and privacy enhancement

As interest in social network studies has grown bigger along with the development of the Web, social network trust management and applications have come into the spotlight. The increasing interest in social network services that are open systems has motivated the need for a reliable trust model that enables practical information sharing and information protection. In this paper, we propose an identity management-based social trust model for solving a sparsity problem and an information leakage. The proposed trust model contributes to increasing the opportunities for information sharing. In addition, the creation and use of identity groups with a clustering approach and partial identities in the proposed approach effectively address security and privacy risks in social networks. In experiments, the performance of the proposed approach is evaluated using precision–recall and F-measures. Copyright © 2011 John Wiley & Sons, Ltd.
Neural Substrates for Reversing Stimulus–Outcome and Stimulus–Response Associations

Adaptive goal-directed actions require the ability to quickly relearn behaviors in a changing environment, yet how the brain supports this ability is barely understood. Using functional magnetic resonance imaging and a novel reversal learning paradigm, the present study examined the neural mechanisms associated with reversal learning for outcomes versus motor responses. Participants were extensively trained to classify novel visual symbols (Japanese Hiraganas) into two arbitrary classes (“male” or “female”), in which subjects could acquire both stimulus–outcome associations and stimulus–response associations. They were then required to relearn either the outcome or the motor response associated with the symbols, or both. The results revealed that during reversal learning, a network including anterior cingulate, posterior inferior frontal, and parietal regions showed extended activation for all types of reversal trials, whereas their activation decreased quickly for trials not involving reversal, suggesting their role in domain–general interference resolution. The later increase of right ventral lateral prefrontal cortex and caudate for reversal of stimulus–outcome associations suggests their importance in outcome reversal learning in the face of interference.