Miracle-dz/UniFAR-FTUs huggingface dataset:
A semi-automatic method to guide the choice of ridge parameter in ridge regression

We consider the application of a popular penalised regression method, Ridge Regression, to data with very high dimensions and many more covariates than observations. Our motivation is the problem of out-of-sample prediction and the setting is high-density genotype data from a genome-wide association or resequencing study. Ridge regression has previously been shown to offer improved performance for prediction when compared with other penalised regression methods. One problem with ridge regression is the choice of an appropriate parameter for controlling the amount of shrinkage of the coefficient estimates. Here we propose a method for choosing the ridge parameter based on controlling the variance of the predicted observations in the model. Using simulated data, we demonstrate that our method outperforms subset selection based on univariate tests of association and another penalised regression method, HyperLasso regression, in terms of improved prediction error. We extend our approach to regression problems when the outcomes are binary (representing cases and controls, as is typically the setting for genome-wide association studies) and demonstrate the method on a real data example consisting of case-control and genotype data on Bipolar Disorder, taken from the Wellcome Trust Case Control Consortium and the Genetic Association Information Network.
Outlier-robust clustering using independent components

How can we efficiently find a clustering, i.e. a concise description of the cluster structure, of a given data set which contains an unknown number of clusters of different shape and distribution and is contaminated by noise? Most existing clustering methods are restricted to the Gaussian cluster model and are very sensitive to noise. If the cluster content follows a non-Gaussian distribution and/or the data set contains a few outliers belonging to no cluster, then the computed data distribution does not match well the true data distribution, or an unnaturally high number of clusters is required to represent the true data distribution of the data set. In this paper we propose OCI (Outlier-robust Clustering using Independent Components), a clustering method which overcomes these problems by (1) applying the exponential power distribution (EPD) as cluster model which is a generalization of Gaussian, uniform, Laplacian and many other distribution functions, (2) applying the Independent Component Analysis (ICA) for both determining the main directions inside a cluster as well as finding split planes in a top-down clustering approach, and (3) defining an efficient and effective filter for outliers, based on EPD and ICA. Our method is parameter-free and as a top-down clustering approach very efficient. An extensive experimental evaluation shows both the accuracy of the obtained clustering result as well as the efficiency of our method.
Eyes do not lie: spontaneous versus posed smiles

Automatic detection of spontaneous versus posed facial expressions received a lot of attention in recent years. However, almost all published work in this area use complex facial features or multiple modalities, such as head pose and body movements with facial features. Besides, the results of these studies are not given on public databases. In this paper, we focus on eyelid movements to classify spontaneous versus posed smiles and propose distance-based and angular features for eyelid movements. We assess the reliability of these features with continuous HMM, k-NN and naive Bayes classifiers on two different public datasets. Experimentation shows that our system provides classification rates up to 91 per cent for posed smiles and up to 80 per cent for spontaneous smiles by using only eyelid movements. We additionally compare the discrimination power of movement features from different facial regions for the same task.
Performance Evaluation of a Multi Antenna MC-CDMA System on Color Image Transmission under Implementation of Various Signal Detection Techniques

In this paper, we made a comprehensive study to evaluate the performance of a multi antenna supported multi carrier Code Division Multiple Access (MC-CDMA) system on color image transmission. The 2-by-2 spatially multiplexed ½-rated Convolutinally encoded MC-CDMA system under investigation implements three linear signal detection techniques (Equalizers) such as Minimum Mean Square Error (MMSE), Zero Forcing (ZF) and Sphere Decoding (SD) under BPSK, DPSK, QPSK and QAM digital modulations. The simulation results elucidate that a significant improvement of system performance is achieved in BPSK modulation with MMSE based signal detection scheme under AWGN and Rayleigh fading channels. The results are also indicative of noticeable reduction of BER performance with increase in order of digital modulation and noise power as compared to signal power.
Implications for Mouse Models of Human Disease Functional Rescue of Elastin Insufficiency in Mice by the Human Elastin Gene :

Diseases linked to the elastin gene arise from loss-of-function mutations leading to protein insufficiency (supravalvular aortic stenosis) or from missense mutations that alter the properties of the elastin protein (dominant cutis laxa). Modeling these diseases in mice is problematic because of structural differences between the human and mouse genes. To address this problem, we developed a humanized elastin mouse with elastin production being controlled by the human elastin gene in a bacterial artificial chromosome. The temporal and spatial expression pattern of the human transgene mirrors the endogenous murine gene, and the human gene accurately recapitulates the alternative-splicing pattern found in humans. Human elastin protein interacts with mouse elastin to form functional elastic fibers and when expressed in the elastin haploinsufficient background reverses the hypertension and cardiovascular changes associated with that phenotype. Elastin from the human transgene also rescues the perinatal lethality associated with the null phenotype. The results of this study confirm that reestablishing normal elastin levels is a logical objective for treating diseases of elastin insufficiency such as supravalvular aortic stenosis. This study also illustrates how differences in gene structure and alternative splicing present unique problems for modeling human diseases in mice. (Circ Res. 2007;101:523-531.)
Verboseness Fission for BM25 Document Length Normalization

BM25 is probably the most well known term weighting model in Information Retrieval. It has, depending on the formula variant at hand, 2 or 3 parameters (k1, b, and k3). This paper addresses b - the document length normalization parameter. Based on the observation that the two cases previously discussed for length normalization (multi-topicality and verboseness) are actually three: multi-topicality, verboseness with word repetition (repetitiveness) and verboseness with synonyms, we propose and test a new length normalization method that removes the need for a b parameter in BM25. Testing the new method on a set of purposefully varied test collections, we observe that we can obtain results statistically indistinguishable from the optimal results, therefore removing the need for ground-truth based optimization.
Detecting most central actors of an unknown network using friendship paradox

Most central people have more influence on business communication, knowledge diffusion, viral marketing and some other fields over other persons in a social network. Networks for which digital information is available such as email communication, phone call etc., one can easily find central actors using social network analysis tools. But for networks where no such information is available, for example farmers in a remote village, secret networks of a criminal organization etc., finding central actors is challenging. We call such network as unknown network. In this research, we have propose a method based on the idea of friendship paradox (FP) to find the most prominent actors from an unknown network. Extensive simulation results demonstrate that our method finds the most centrals by exploring only a small population of an unknown social network with a high accuracy.
Sliced Regression for Dimension Reduction

A new dimension-reduction method involving slicing the region of the response and applying local kernel regression to each slice is proposed. Compared with the traditional inverse regression methods [e.g., sliced inverse regression (SIR)], the new method is free of the linearity condition and has much better estimation accuracy. Compared with the direct estimation methods (e.g., MAVE), the new method is much more robust against extreme values and can capture the entire central subspace (CS) exhaustively. To determine the CS dimension, a consistent cross-validation criterion is developed. Extensive numerical studies, including a real example, confirm our theoretical findings.
Principle, Discretion, and Symbolic Power in Rousseau's Account of Judicial Virtue

Rousseau's understanding of legislation as the expression of the general will implies a constitutional principle of legislative supremacy. In turn, this should translate to a narrow, mechanical account of adjudication, lest creative judicial interpretation subvert the primacy of legislative power. Yet in his constitutional writings, Rousseau recommends open‐textured and vague legislative codes, which he openly admits will require judicial development. Thus he apparently trusts a great deal in judicial discretion. Ostensibly, then, he overlooks the problem of how legislative indeterminacy — and correspondingly, judicial discretion — may undermine the authority of the general will. However, I argue that Rousseau aims to check judicial subversion of legislative supremacy simply by extending his broader social politics — and specifically, his peculiar concept of republican virtue — to the domain of law. His main concern is that the law should not develop as a mystifying expert practice; therefore, he necessarily rejects any understanding of judicial virtue as lying in principled discourse. Instead, he envisages that judicial power will be checked by a more generic sense of republican virtue. In turn this echoes his apprehension of social differentiation and social complexity as sources of domination and hierarchy.
Caught between cultures: case study of an “out of school” ethnic minority student in Hong Kong

Abstract This paper reports a case study on Maneesha Rai, a Nepalese girl living in Hong Kong and an “out of school” student. Based on in-depth interviews, a case was constructed of her previous school days and current “out of school” days. These provided a vivid picture of her life and several themes were created using schema analysis that help explain the reasons for her “dropping out” of school after Form Five. It has been common to attribute school failure for ethnic minority students in Hong Kong to problems with Chinese language education. Yet Maneesha’s case study shows that her experience of failure in other subjects such as Mathematics and Science contributed to her lack of successful schooling. Maneesha’s school failure was more than simply a consequence of academic failure. Rather, there were many other interrelated factors such as peer and community factors, dropout history in the family, racism, differences in schooling culture found that contributed to her school failure. In addition, Maneesha, like many of her ethnic minority friends, enjoyed the freedom afforded her in Hong Kong, but it seemed such freedom also meant inadequate attention from her teachers.
When Annotation Schemes Change Rules Help: A Configurable Approach to Coreference Resolution beyond OntoNotes

This paper approaches the challenge of adapting coreference resolution to different coreference phenomena and mention-border definitions when there is no access to large training data in the desired target scheme. We take a configurable, rule-based approach centered on dependency syntax input, which we test by examining coreference types not covered in benchmark corpora such as OntoNotes. These include cataphora, compound modifier coreference, generic anaphors, predicate marka-bles, i-within-i, and metonymy. We test our system, called xrenner, using different configurations on two very different datasets: Wall Street Journal material from OntoNotes and four types Wiki data from the GUM corpus. Our system compares favorably with two leading rule based and stochastic approaches in handling the different annotation formats.
Selection of attributes for modeling Bach chorales by a genetic algorithm

A genetic algorithm selected combinations of attributes for a machine learning system. The algorithm used 90 Bach chorale melodies to train models and randomly selected sets of 10 chorales for evaluation. Compression of pitch was used as the fitness evaluation criterion. The best models were used to compress a different test set of chorales and their performance compared to human generated models. GA models outperformed the human models, improving compression by 10 percent.
Chronic Antidepressant Treatment in Normal Mice Induces Anxiety and Impairs Stress-coping Ability

Antidepressants are clinically used for patients with major depression. Antidepressant treatments in certain groups of patients are effective for relieving depression as well as anxiety disorder. However, it is not clearly known whether the use of current antidepressants in healthy persons is beneficial for upcoming depression- and anxiety-inducing life events. To address this question, normal mice were intraperitoneally administered with imipramine or fluoxetine for more than 2 weeks, and behaviors related to anxiety and depression were evaluated. Mice treated with imipramine or fluoxetine for more than 14 days exhibited significantly decreased immobility time in the forced swim test and tail suspension test, but these mice exhibited enhanced anxiety in several behavioral tests. Furthermore, chronic antidepressant treatments followed by sub-threshold level of stress in normal mice profoundly aggravated antidepressant-induced anxiety-like behaviors without further affecting depression-related behaviors. Chronic antidepressant treatments followed by sub-threshold level of stress produced swollen vesicles and ulcerations on the lips as well as a watery and inflammatory nose. Mice given chronic antidepressant treatments displayed intestinal abnormalities evidenced by a highly enlarged and inflamed small intestine full of defecation materials. These results suggest that chronic antidepressant treatment in normal mice provokes anxiety-like behaviors and impairs their stress-coping ability.
Pricing Hydroelectric Power Plants with / without Operational Restrictions : a Stochastic Control Approach 3 1 . 1 Previous work

Abstract. In this paper, we value hydroelectric power plant cash flows under a stochastic control framework, taking into consideration the implication of operational constraints such as ramping and minimum flow rate constraints for the purpose of environmental protection. The power plant valuation problem under a ramping constraint is characterized as a bounded stochastic control problem, resulting in a Hamilton-Jacobi-Bellman (HJB) partial integrodifferential equation (PIDE). The valuation problem without the ramping restriction is characterized as an unbounded stochastic control problem; we propose an impulse control formulation, resulting in an HJB variational inequality, for the valuation problem under this scenario. We develop a consistent numerical scheme for solving both the HJB PIDE for the bounded control problem and the HJB variational inequality for the unbounded control problem. We prove the convergence of the numerical scheme to the viscosity solution of each pricing equation, provided a strong comparison result holds. Numerical results indicate that failing to consider operational constraints may considerably overestimate the value of hydroelectric power plant cashflows.
H-Nuclear magnetic resonance-based metabolomic analysis of brain in mice with nicotine treatment

Background: Nicotine is rapidly absorbed from cigarette smoke and therefore induces a number of chronic illnesses with the widespread use of tobacco products. Studies have shown a few cerebral metabolites modified by nicotine; however, endogenous metabolic profiling in brain has not been well explored. Results: H NMR-based on metabonomics was applied to investigate the endogenous metabolic profiling of brain hippocampus, nucleus acumens (NAc), prefrontal cortex (PFC) and striatum. We found that nicotine significantly increased CPP in mice, and some specific cerebral metabolites differentially changed in nicotine-treated mice. These modified metabolites included glutamate, acetylcholine, tryptamine, glucose, lactate, creatine, 3-hydroxybutyrate and nicotinamide-adenine dinucleotide (NAD), which was closely associated with neurotransmitter and energy source. Additionally, glutathione and taurine in hippocampus and striatum, phosphocholine in PFC and glycerol in NAc were significantly modified by nicotine, implying the dysregulation of anti-oxidative stress response and membrane metabolism. Conclusions: Nicotine induces significant metabonomic alterations in brain, which are involved in neurotransmitter disturbance, energy metabolism dysregulation, anti-oxidation and membrane function disruptions, as well as amino acid metabolism imbalance. These findings provide a new insight into rewarding effects of nicotine and the underlying mechanism.
Does cancer affect the divorce rate

Discrete-time hazard regression models were employed to register and census data on 1.4 million Norwegian married couples from 1974-2001 to explore the probability of divorce following cancer illness. Divorce rates for around 215 000 persons diagnosed with cancer were compared to divorce rates for persons for whom all the other observed variables were the same. No overall harmful influence of a cancer diagnosis was observed. Most cancer forms resulted in small, immediate declines in divorce rates the first years following diagnosis. Exceptions were significant increases in the divorce rates for persons diagnosed with cervical and testicular cancer.
Market Risk, Credit Risk, and Futures Trading in Commodity Markets

One of the main purposes to use the futures products in commodity markets is to fill the hedging needs for relatively large market risk and counterparty risk in commodity spot markets. We believe that these two types of market and credit risk will be incorporated into the commodity futures prices. The paper proposes a price model for commodity futures using a new volatility based convenience yield fluctuation and an interest rate fluctuation, which represent the market and credit risk, respectively, in commodity futures markets at the first order approximation. We offer the additional explanation for commodity futures trades such that the futures trades may be conducted for the mitigation of the large spot price volatility in commodity markets characterized by its mean reversion. It is shown that the market risk model, i.e., the new volatility based convenience yield model, can incorporate the inverse hump shape, which is often observed in commodity markets, into the futures term structure and that the interest rate extended model of the market risk model can enhance the contango shape. Empirical studies are conducted using WTI crude oil, heating oil, and natural gas futures traded on the NYMEX. It is shown that the Samuelson effects for heating oil and natural gas futures are captured for both of the proposed models while the Samuelson effects for the WTI crude oil futures are not relevant. In addition, the market risk model using the estimated parameters can demonstrate the inverse hump shape in the crude oil futures term structure and the inclusion of the interest rate model can enhance the contango shape.
A method to calculate the statistic characters of the transient electromagnetic pulse coupling into stochastic transmission lines

In this paper, an improved method based on the Gauss-Legendre quadrature and Unscented Transform (UT) is proposed to estimate the statistical characters of the EMC/EMI problems with stochastic parameters. The UT method provides a fast method to propagate mean and covariance information through nonlinear transformations. The Gauss-Legendre quadrature as an efficient numerical integral method is used to select the sigma points and weights. The coupling problem of electromagnetic field and transmission line can be treated as a nonlinear maping. So, we calculate the statistical characters of coupling voltage on the load of the transmission line by the GLUT method. It only needs about five times of FDTD simulation to obtain the mean and standard deviation of the coupling voltage. The Monte Carlo (MC) method has been used to validate the GLUT method. The numerical simulation results indicate that the GL-UT method can reduce the number of FDTD simulation from more than thousands of times to several times.
Efficient failure handling in grid computing using failure prediction algorithm

Grid computing involves heterogeneous, geographically distributed resources that can work on a task together. Since the resource availability is dynamic, the grid infrastructure is prone to failure. So inorder to adapt to the failure, fault tolerant mechanism must be implemented. Commonly used techniques for fault tolerance are checkpointing and load balancing. To have an efficient fault tolerance mechanism this paper comes up with an optimal checkpointing algorithm that reduces overhead caused due to checkpointing. The proposed system uses Job replication to ensure completion of work and Dynamic Load balancing is used to avoid overload in any resources and to achieve maximum resource utilization and maximize throughput.
Alternating Current-Dielectrophoresis Collection and Chaining of Phytoplankton on Chip: Comparison of Individual Species and Artificial Communities

The capability of alternating current (AC) dielectrophoresis (DEP) for on-chip capture and chaining of the three species representative of freshwater phytoplankton was evaluated. The effects of the AC field intensity, frequency and duration on the chaining efficiency and chain lengths of green alga Chlamydomonas reinhardtii, cyanobacterium Synechocystis sp. and diatom Cyclotella meneghiniana were characterized systematically. C. reinhardtii showed an increase of the chaining efficiency from 100 Hz to 500 kHz at all field intensities; C. meneghiniana presented a decrease of chaining efficiency from 100 Hz to 1 kHz followed by a significant increase from 1 kHz to 500 kHz, while Synechocystis sp. exhibited low chaining tendency at all frequencies and all field intensities. The experimentally-determined DEP response and cell alignment of each microorganism were in agreement with their effective polarizability. Mixtures of cells in equal proportion or 10-times excess of Synechocystis sp. showed important differences in terms of chaining efficiency and length of the chains compared with the results obtained when the cells were alone in suspension. While a constant degree of chaining was observed with the mixture of C. reinhardtii and C. meneghiniana, the presence of Synechocystis sp. in each mixture suppressed the formation of chains for the two other phytoplankton species. All of these results prove the potential of DEP to discriminate different phytoplankton species depending on their effective polarizability and to enable their manipulation, such as specific collection or separation in freshwater.