Miracle-dz/UniFAR-FTUs huggingface dataset:
Genotype Imputation with Thousands of Genomes

Genotype imputation is a statistical technique that is often used to increase the power and resolution of genetic association studies. Imputation methods work by using haplotype patterns in a reference panel to predict unobserved genotypes in a study dataset, and a number of approaches have been proposed for choosing subsets of reference haplotypes that will maximize accuracy in a given study population. These panel selection strategies become harder to apply and interpret as sequencing efforts like the 1000 Genomes Project produce larger and more diverse reference sets, which led us to develop an alternative framework. Our approach is built around a new approximation that uses local sequence similarity to choose a custom reference panel for each study haplotype in each region of the genome. This approximation makes it computationally efficient to use all available reference haplotypes, which allows us to bypass the panel selection step and to improve accuracy at low-frequency variants by capturing unexpected allele sharing among populations. Using data from HapMap 3, we show that our framework produces accurate results in a wide range of human populations. We also use data from the Malaria Genetic Epidemiology Network (MalariaGEN) to provide recommendations for imputation-based studies in Africa. We demonstrate that our approximation improves efficiency in large, sequence-based reference panels, and we discuss general computational strategies for modern reference datasets. Genome-wide association studies will soon be able to harness the power of thousands of reference genomes, and our work provides a practical way for investigators to use this rich information. New methodology from this study is implemented in the IMPUTE2 software package.
Characterizing the dynamic deformation of soft fingertips and analyzing its significance for multi-fingered operations

This paper characterizes the dynamic deformation of soft fingertips applied to multi-fingered object manipulations of humanoid robots and analyzes its significance for multi-fingered operations. To this end, we present a dynamic model for soft-fingered object manipulations. For analyzing the control performance of soft-fingered manipulating tasks, we present a dynamic manipulation control scheme that considers the deformation effects of soft fingertips. The simulation results validate the influence of the dynamic deformation of soft fingertips during manipulation in a two-fingered operation, and will enable us to recognize its significance for precise manipulating tasks.
Modelling of Drying of Wet Coarse Particles of Sludge in a Conical Spouted Bed Dryer

— In order to optimize the drying process at low temperature, the influence of geometric factors and operating conditions on drying time of wet coarse particle of sludge in a conical spouted bed dryer has been analyzed by means of a factorial experimental design. The effect of the geometric factors and operating conditions on drying time has been determined by a statistical analysis. Operation regimes of beds consisting of wet coarse particles in a conical spouted bed dryer have been determined by pressure drop fluctuations at inlet gas temperatures from room temperature up to 120 ºC. Results of drying modelling for beds of coarse particle wastes at different regimes have been verified through some experimental drying time of sludge.
NEU Systems in SIGHAN Bakeoff 2012

This paper describes the methods used for the parsing the Sinica Treebank for the bakeoff task of SigHan 2012. Based on the statistics of the training data and the experimental results, we show that the major difficulties in parsing the Sinica Treebank comes from both the data sparse problem caused by the fine-grained annotation and the tagging ambiguity.
Validation of Satellite-Derived Liquid Water Paths Using ARM SGP Microwave Radiometers

Satellites are useful for monitoring climatological parameters over large domains. They are especially useful for measuring various cloud microphysical and radiative parameters where ground-based instruments are not available. The geostationary operational environmental satellite (GOES) has been used to retrieve cloud and radiative properties over an extended domain centered on the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) Central Facility (CF). One of the microphysical parameters available from the GOES-8 dataset is cloud liquid water path (LWP), which is crucial for linking the atmospheric hydrological and radiative budgets. Preliminary validation of this parameter has been limited to a very few cases of thick stratus during March 2000. To better understand and validate the GOES-derived LWP more completely, this paper compares it with LWP retrievals based on ARM’s ground-based microwave radiometers (MWR) at the SGP central and boundary facilities. The comparisons utilize data taken in a variety of cloud conditions during March 2000 to examine the relationships between the GOES-8 and microwave LWP retrievals as related to cloud temperature, cloud type, and viewing angle.
Barter-based cooperation in delay-tolerant personal wireless networks

In this paper, we consider the application of delay-tolerant networks to personal wireless communications. In these networks, selfish nodes can exploit the services provided by other nodes by downloading messages that interest them, but refusing to store and distribute messages for the benefit of other nodes. We propose a mechanism to discourage selfish behavior based on the principles of barter. We develop a game-theoretic model in which we show that the proposed approach indeed stimulates cooperation of the nodes. In addition, the results show that the individually most beneficial behavior leads to the social optimum of the system.
A Mixed Finite Element Formulation for Solving Phase Change Problems with Convection

An enhanced mixed formulation, based on the general form of the enthalpy, is proposed for phase change problem with convection. This formulation presents the advantage to consider both equal and different liquid-solid physical properties. A fully implicit second order accurate temporal and spatial finite element discretizations is also employed. The numerical simulations for water solidification are presented and compared to the experimental data to illustrate the performance of the proposed numerical method.
Different Serum Free Fatty Acid Profiles in NAFLD Subjects and Healthy Controls after Oral Fat Load

Background: Free fatty acid (FFA) metabolism can impact on metabolic conditions, such as obesity and nonalcoholic fatty liver disease (NAFLD). This work studied the increase in total FFA shown in NAFLD subjects to possibly characterize which fatty acids significantly accounted for the whole increase. Methods: 21 patients with NAFLD were selected according to specified criteria. The control group consisted of nine healthy subjects. All subjects underwent an oral standard fat load. Triglycerides; cholesterol; FFA; glucose and insulin were measured every 2 h with the determination of fatty acid composition of FFA. Results: higher serum FFA levels in NAFLD subjects are mainly due to levels of oleic, palmitic and linoleic acids at different times. Significant increases were shown for docosahexaenoic acid, linolenic acid, eicosatrienoic acid, and arachidonic acid, although this was just on one occasion. In the postprandial phase, homeostatic model assessment HOMA index positively correlated with the ω3/ω6 ratio in NAFLD patients. Conclusions: the higher serum levels of FFA in NAFLD subjects are mainly due to levels of oleic and palmitic acids which are the most abundant circulating free fatty acids. This is almost exactly corresponded with significant increases in linoleic acid. An imbalance in the n-3/n-6 fatty acids ratio could modulate postprandial responses with more pronounced effects in insulin-resistant subjects, such as NAFLD patients.
Tissue distribution and subcellular localization of mammalian myosin I

Myosin I, a nonfilamentous single-headed actin-activated ATPase, has recently been purified from mammalian tissue (Barylko, B., M. C. Wagner, O. Reizes, and J. P. Albanesi. 1992. Proc. Natl. Acad. Sci. USA. 89:490-494). To investigate the distribution of this enzyme in cells and tissues mAbs were generated against myosin I purified from bovine adrenal gland. Eight antibodies were characterized, five of them (M4-M8) recognize epitope(s) on the catalytic "head" portion of myosin I while the other three (M1-M3) react with the "tail" domain. Immunoblot analysis using antiadrenal myosin I antibody M2 demonstrates the widespread distribution of the enzyme in mammalian tissues. Myosin I was immunolocalized in several cell types including bovine kidney (MDBK), rat kidney (NRK), rat brain, rat phaeochromocytoma (PC12), fibroblast (Swiss 3T3), and CHO cells. In all cases, myosin I was concentrated at the cell periphery. The most intense labeling was observed in regions of the cell usually associated with motile activity (i.e., filopodia, lamellipodia and growth cones). These results are consistent with earlier observations on protozoan myosin I that suggest a motile role for the enzyme at the plasma membrane.
A new hybrid approach for data clustering

Data clustering has been applied in multiple fields such as machine learning, data mining, wireless sensor networks and pattern recognition. One of the most famous clustering approaches is K-means which effectively has been used in many clustering problems, but this algorithm has some problems such as local optimal convergence and initial point sensitivity. Artificial fishes swarm algorithm (AFSA) is one of the swarm intelligent algorithms and its major application is in solving optimization problems. Of its characteristics, it can refer to high convergent rate and insensitivity to initial values. In this paper a hybrid clustering method based on artificial fishes swarm algorithm and K-means so called KAFSA is proposed. In the proposed algorithm, K-means algorithm is used as one of the behaviors of artificial fishes in AFSA. The proposed algorithm has been tested on five data sets and its efficiency was compared with particle swarm optimization (PSO), K-means and standard AFSA algorithms. Experimental results showed that proposed approach has suitable and acceptable efficacy in data clustering.
Sources of Test Anxiety: A Qualitative Approach

Test anxiety comes as an irrefutable fact of education. Testing and being tested, whatever the level of education, is a natural source of concern. The intention of this study is to obtain data that will be the starting point for future intervention programs for test-anxiety. For the research, 10 middle school students and 10 high school students and semi-structured interview forms were interviewed. It has been seen that middle school students describe test anxiety with emotional symptoms and high school students with cognitive symptoms. While the anxiety of the test anxiety affects the life of the students and the influence of the family relations in the high school students is in the preliminary stage, the middle school students show themselves as negative thoughts about the future and the negative affect appears as a common theme in both groups. As sources of test anxiety, we see parents’ and teachers’ higher success expectations in both age groups. Besides, the words that parents’ and teachers’ motivational words the students cause them to worry more about not being the anticipated effect in the students. These negative attitudes cause an sense of over responsibility in students. According to the findings of the study, it is revealed that it is a great necessity for the parents and teachers to be involved in the intervention programs for the students who are experiencing the test anxiety. Keywords: Text anxiety, high school students, secondary school students, source of text anxiety, qualitative research
INTERPERSONAL RELATIONS AND GROUP PROCESSES From Lynching to Gay Bashing : The Elusive Connection Between Economic Conditions and Hate Crime

Trends in bigoted violence are often explained by reference to frustrations arising from macroeconomic downturns. Historical and recent time-series studies have turned up significant links between economic conditions and lynchings of Blacks in the pre-Depression South (e.g., Hepworth & West, 1988; Hovland & Sears, 1940). However, replicating the time-series analyses of lynching, extending them through the Great Depression, and applying similar techniques to contemporary data fail to provide robust evidence of a link between economic performance and intolerant behavior directed against minorities. The authors speculate that the predictive force of macroeconomic fluctuation is undermined by the rapid rate of decay in the frustration-bred aggressive impulse and the absence of prominent political actors affixing economic blame on target groups.
Emotion mining research on micro-blog

In order to identify the emotion expressed in corpus and to estimate the feelings conveyed by micro-blog data, in this paper, we categorize emotional words, build attitudinal words weight dictionary(WD) which consists of 1342 words, and construct self-defined negative words dictionary(NWD), degree words dictionary(DWD) and interjection words dictionary(IWD). We then process classified statistics on 2213 micro-blog items, finding that items whose first sentence express the main idea account for 23.8% of all the complex sentences, and items whose last sentence express the main idea 51.3%, respectively. We first calculate the weights of each clause in a micro-blog item, then take special treatment to the first and last sentence, finally we add up all the weights to get the emotional index(EI) of the item. Test results from the micro-blog emotion weight calculator(MBEWC) developed in C# are cross-checked and reach an accuracy rate of 80.6%.
Applying appraisal theories of emotion to the concept of emotional labor

ii ACKNOWLEDGEMENTS I would like to thank all of those who helped make this project a success. I offer special thanks to my husband and to my parents for their patience and support. I am extremely grateful to Jim Diefendorff for agreeing to supervise the project from afar-a commitment that required a great deal of extra time and effort on his part. I also would like to thank my committee members: for the thoughtful, very helpful contributions that they made to the project. I'd also like to acknowledge and thank Yoshinori Kamo for agreeing to serve as Graduate School representative. Finally, I offer thanks to Glenn Gibeson, Kara Jeansonne, and Christine Meche for their help with participant recruitment and data collection.
Statistical analysis of BTI in the presence of process-induced voltage and temperature variations

In nano-scale regime, there are various sources of uncertainty and unpredictability of VLSI designs such as transistor aging mainly due to Bias Temperature Instability (BTI) as well as Process-Voltage-Temperature (PVT) variations. BTI exponentially varies by temperature and the actual supply voltage seen by the transistors within the chip which are functions of leakage power. Leakage power is strongly impacted by PVT and BTI which in turn results in thermal-voltage variations. Hence, neglecting one or some of these aspects can lead to a considerable inaccuracy in the estimated BTI-induced delay degradation. However, a holistic approach to tackle all these issues and their interdependence is missing. In this paper, we develop an analytical model to predict the probability density function and covariance of temperatures and voltage droops of a die in the presence of the BTI and process variation. Based on this model, we propose a statistical method that characterizes the life-time of the circuit affected by BTI in the presence of process-induced temperature-voltage variations. We observe that for benchmark circuits, treating each aspect independently and ignoring their intrinsic interactions results in 16% over-design, translating to unnecessary yield and performance loss.
DECK: Detecting Events from Web Click-Through Data

In the past few years, there has been increased research interest in detecting previously unidentified events from Web resources. Our focus in this paper is to detect events from the click-through data generated by Web search engines. Existing event detection algorithms, which mainly study the news archive data, cannot be employed directly because of the following two unique features of click-through data: 1) the information provided by click-through data is quite limited; 2) not every query issued to a Web search engine corresponds to an event in the real world. In this paper, we address this problem by proposing an effective algorithm which Detects Events from ClicK-through data DECK. We firstly transform click-through data to the 2D polar space by considering the semantic dimension and temporal dimension of queries. Robust subspace estimation is performed to detect subspaces such that each subspace consists of queries of similar semantics. Next, we prune uninteresting subspaces which do not contain queries corresponding to real events by simultaneously considering the respective distribution of queries along the semantic dimension and the temporal dimension in each subspace. Finally, events are detected from interesting subspaces using a nonparametric clustering technique. Compared with an existing approach, our experimental results based on real-life data have shown that the proposed approach is more accurate and effective in detecting real events from click-through data.
Modeling multiple autonomous robot behaviors and behavior switching with a single reservoir computing network

Reservoir computing (RC) uses a randomly created Recurrent Neural Network as a reservoir of rich dynamics which projects the input to a high dimensional space. These projections are mapped to the desired output using a linear output layer, which is the only part being trained by standard linear regression. In this work, RC is used for imitation learning of multiple behaviors which are generated by different controllers using an intelligent navigation system for mobile robots previously published in literature. Target seeking and exploration behaviors are conflicting behaviors which are modeled with a single RC network. The switching between the learned behaviors is implemented by an extra input which is able to change the dynamics of the reservoir, and in this way, change the behavior of the system. Experiments show the capabilities of Reservoir Computing for modeling multiple behaviors and behavior switching.
PTSD and PTSD-Linked Victimization Sensitivity as Risk Factors for Alcohol Use among Juvenile Offenders: A Group-Based Trajectory Modeling Approach

ABSTRACT Research has indicated that post-traumatic stress disorder is linked to heightened levels of alcohol use. Sensitivity to strain may increase alcohol use as a coping strategy among this population. Using the Pathways to Desistance dataset, this research examines these effects among juvenile offenders and the experience of victimization as strain. Results indicated a near tripling of the relative risk of assignment to the high alcohol use group if one had ever met clinical criteria for the disorder at baseline. There was a significant interaction between ever meeting clinical criteria for post-traumatic stress disorder and experiencing victimization during an observation period.
On the performance of vector quantizers empirically designed from dependent sources

Suppose we are given n real valued samples Z/sub 1/, Z/sub 2/, . , Z/sub n/ from a stationary source P. We consider the following question. For a compression scheme that uses blocks of length k, what is the minimal distortion (for encoding the true source P) induced by a vector quantizer of fixed rate R, designed from the training sequence. For a certain class of dependent sources, we derive conditions ensuring that the empirically designed quantizer performs as well (on the average) as the optimal quantizer, for almost every training sequence emitted by the source. In particular, we observe that for a code rate R, the optimal way to choose the dimension of the quantizer is k/sub n/=[(1-/spl delta/)R/sup -1/ log n]. The problem of empirical design of a vector quantizer of fixed dimension k based on a vector valued training sequence X/sub 1/, X/sub 2/, . , X/sub n/ is also considered. For a class of dependent sources, it is shown that the mean squared error (MSE) of the empirically designed quantizer w.r. t the true source distribution converges to the minimum possible MSE at a rate of O(/spl radic/(log n/n)), for almost every training sequence emitted by the source. In addition, the expected value of the distortion redundancy-the difference between the MSEs of the quantizers-converges to zero for a sequence of increasing block lengths k, if we have at our disposal corresponding training sequences whose length grows as n=2/sup (R+/spl delta/)k/. Some of the derivations extend results in empirical quantizer design using an i.i. d. Training sequence, obtained by Linder et al. (see IEEE Trans. on Info. Theory, vol.40, p.1728-40, 1994) and Merhav and Ziv (see IEEE Trans. on Info. Theory, vol.43, p.1112-23, 1997). Proof of the techniques rely on the results in the theory of empirical processes, indexed by VC function classes.
Mobility prediction based seamless RAN-cache handover in HetNet

The radio access network (RAN) cache is recently proposed to improve quality of experience perceived by mobile users on Internet content access and reduce intense pressure on the backhaul network. However, because RAN caches (e.g., associated with base stations) are located below mobility management entities in mobile core network, the continuity of content service for the handover users with unfinished RANcache content transmission is destroyed. Thus motivated, we design the seamless RAN-cache handover framework based on mobility prediction algorithm (MPA), where the smart terminal with unfinished content service during mobility predicts the break and the target handover access node (such as the BS or the WiFi access point), and pre-triggers the source RAN cache to return the content service to CDN servers outside the core network (CN) or to notify the RAN cache associated with the target handover access node to prepare for serving the handover user. The framework incurs no change to cellular infrastructure including RAN and CN. Different from prior arts of assumption that users' mobility is pre-known, we design the parallel autoregressive (AR) based received signal strength (RSS) prediction approach to achieve mobility prediction, and mathematically define its false probability during different tolerant intervals. The simulation scenario deployment engine and the user movement engine are designed, considering practical issues. Through simulations, the the false probability of seamless RAN-cache handover pre-trigger through MPA is less than 1.36%, and it can be guaranteed less than 1% as the maximal RAN-cache handover processing time is increased by 8%.
Separate estimation of azimuth and elevation DOA using microphones located at apices of regular tetrahedron

We propose a method for DOA (direction of arrival) estimation of speech signals in both azimuth and elevation directions. Our previous DOA estimation method achieves high precision and uniform spatial resolution by integrating the frequency array data (Hioka, Y. et al., J. Sig. Process., vol.7, no.1, p.105-9, 2003) generated from microphone pairs in an equilateral-triangular microphone array (Hioka and Hamada, N., IEICE Technical Report, EA2003-44, p.9-16, 2003). We now extend the method using four microphones located at the apices of a regular tetrahedron to enable the method to estimate the elevation angle from the array plane as well. Furthermore, we introduce an idea for separate estimation of azimuth and elevation to reduce the computational loads.