Miracle-dz/UniFAR-FTUs huggingface dataset:
Construction of Gene Networks With Hybrid Approach From Expression Profile and Gene Ontology

Gene regulatory networks have been long studied in model organisms as a means of identifying functional relationships among genes or their corresponding products. Despite many existing methods for genome-wide construction of such networks, solutions to the gene regulatory networks problem are however not trivial. Here, we present, a hybrid approach with gene expression profiles and gene ontology (HAEO). HAEO makes use of multimethods (overlapping clustering and reverse engineering methods) to effectively and efficiently construct gene regulatory networks from multisources (gene expression profiles and gene ontology). Application to yeast cell cycle dataset demonstrates HAEO's ability to construct validated gene regulatory networks, such as some potential gene regulatory pairs, which cannot be discovered by general inferring methods and identifying cycles (i.e., feedback loops) between genes. We also experimentally study the efficiency of building networks and show that the proposed method, HAEO is much faster than Bayesian networks method.
Quantitative Analysis of Synaptic Phosphorylation and Protein Expression*S

The postsynaptic density (PSD) signaling machinery contains proteins with diverse functions. Brain region-specific variations in PSD components mediate distinct physiological responses to synaptic activation. We have developed mass spectrometry-based methods to comprehensively compare both relative protein expression and phosphorylation status from proteins present in biochemical preparations of postsynaptic density. Using these methods, we determined the relative expression of 2159 proteins and 1564 phosphorylation sites in PSD preparations from murine cortex, midbrain, cerebellum, and hippocampus. These experiments were conducted twice using independent biological replicates, which allowed us to assess the experimental and biological variability in this system. Concerning protein expression, cluster analysis revealed that known functionally associated proteins display coordinated synaptic expression. Therefore, proteins identified as co-clustering with known protein complexes are prime candidates for assignment as previously unrecognized components. Concerning degree of phosphorylation, we observed more extensive phosphorylation sites on N-methyl-d-aspartate (NMDA) receptors than α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptors, consistent with the central role of N-methyl-d-aspartate receptors in processing synaptic transmission patterns. Average kinase and phosphatase levels were highest in the hippocampus, correlating with a higher overall phosphopeptide abundance present in this brain region. These findings suggest that the hippocampus utilizes reversible protein phosphorylation to a greater extent than other brain regions when modifying synaptic strength.
A Mathematical Programming Model for Scheduling Pharmaceutical Sales Representatives

To increase revenues, pharmaceutical companies rely on their sales forces to promote new and existing drugs to physicians. Compensation for sales representatives is largely commission based. However, individual effort may not be the driving force behind compensation. Sales representative might be unfairly rewarded only because they are assigned to physicians with high sales potential. Given a set of physicians and representatives, a mathematical program is developed to maximize profit for the pharmaceutical company, while balancing both workload and sales opportunities for representatives. The model is valuable for daily operational decisions. The methodology applies to any multi-product sales-force based industry.
Costi e distorsioni della transizione previdenziale ed effetti correttivi di alcune ipotesi di riforma

This paper focuses on alternative money's worth measures of the Italian (public) pension system for representative cohorts, considering both the present transition and the future steady state envisaged by recent reforms. Micro-based calculations of the aggregate budget effects induced by further policy changes are also presented. The main results of the simulation exercise are: i) young and future generations face a steady and strong reduction of their social security's worth mainly due to the discontinuities introduced by the 1992 and 1995 reforms; ii) during most of the transition period, the increase in benefits for an additional year of work, after reaching the seniority pension requirements, does not offset the financial costs generated by the additional contributions and the shorter expected retirement. The implied deadweight loss still represents a strong incentive to early retirement; iii) the extension, from year 2000, of the "pro rata" mechanism to all new pensioners, would generate a non negligible smoothing effect on microeconomic distortions but a comparatively small reduction in pension expenditure; iv) a much larger reduction can be obtained if seniority pensions are determined according to actuarial fairness, i.e. by taking into account the expected life at retirement. The paper finally highlights aspects of the social security problem that should be addressed in a more complete analysis, such as risk adjustments, welfare implications and general equilibrium feedback effects. Even without these extensions, we think our conclusions are quite robust, and may help policy discussion, particularly in avoiding ideological drifts.
Deficiency of the Stress Kinase P38α Results in Embryonic Lethality

The mitogen-activated protein (MAP) kinase p38 is a key component of stress response pathways and the target of cytokine-suppressing antiinflammatory drugs (CSAIDs). A genetic approach was employed to inactivate the gene encoding one p38 isoform, p38α. Mice null for the p38α allele die during embryonic development. p38α1/− embryonic stem (ES) cells grown in the presence of high neomycin concentrations demonstrated conversion of the wild-type allele to a targeted allele. p38α−/− ES cells lacked p38α protein and failed to activate MAP kinase–activated protein (MAPKAP) kinase 2 in response to chemical stress inducers. In contrast, p38α1/+ ES cells and primary embryonic fibroblasts responded to stress stimuli and phosphorylated p38α, and activated MAPKAP kinase 2. After in vitro differentiation, both wild-type and p38α−/− ES cells yielded cells that expressed the interleukin 1 receptor (IL-1R). p38α1/+ but not p38α−/− IL-1R–positive cells responded to IL-1 activation to produce IL-6. Comparison of chemical-induced apoptosis processes revealed no significant difference between the p38α1/+ and p38α−/− ES cells. Therefore, these studies demonstrate that p38α is a major upstream activator of MAPKAP kinase 2 and a key component of the IL-1 signaling pathway. However, p38α does not serve an indispensable role in apoptosis.
The hammond postulate and the principle of maximum hardness in some intramolecular rearrangement reactions

In this paper we use the energy, the electronic chemical potential, and the molecular hardness together with a similarity index and a thermodynamic index to rationalize the behavior of various intramolecular rearrangement reactions in terms of the Hammond postulate (HP) and the principle of maximum hardness (PMH). The following results have been obtained: (a) in general, Hammond and anti-Hammond reactions satisfy the PMH in the sense that they present an opposite behavior for the energy and hardness profile along the reaction coordinate; (b) in Hammond reactions the hardest species among reactants and products corresponds to the most stable one; and finally, (c) in anti-Hammond reactions the less stable species among reactants and products is the hardest one.
Location Preference for Risk-Averse Dutch Dairy Farmers Immigrating to the United States

Increased environmental regulations and a milk quota that restricts growth have increased the interest in immigration to the United States by Dutch dairy farmers. A risk-based economic analysis of 23 representative U.S. dairy farms versus a representative Dutch farm shows that risk-averse Dutch dairy farmers would prefer to liquidate their dairy farms and invest in a large dairy in Idaho or north Texas. The risk ranking suggested that continuing to farm in the Netherlands rather than immigrating to the United States is preferred over only two of the 23 U.S. representative farms analyzed.
Automatic Generation of Parallel Treebanks: An Efficient Unsupervised System

The need for syntactically annotated data for use in natural language processing has increased dramatically in recent years. This is true especially for parallel treebanks, of which very few exist. The ones that exist are mainly hand-crafted and too small for reliable use in data-oriented applications. In this work I introduce a novel open-source platform for the fast and robust automatic generation of parallel treebanks through sub-tree alignment, using a limited amount of external resources. The intrinsic and extrinsic evaluations that I undertook demonstrate that my system is a feasible alternative to the manual annotation of parallel treebanks. Therefore, I expect the presented platform to help boost research in the field of syntaxaugmented machine translation and lead to advancements in other fields where parallel treebanks can be employed.
Short Sale Constraints: Effects on Crashes, Price Discovery, and Market Volatility

The recent SEC ban on short selling has presented an unrivaled opportunity to explore the effects of short selling constraints on crashes, market efficiency, and volatility. In this paper I carry out two groups of empirical tests on the individual banned stocks and a series of portfolios created from them: the first tests the hypothesis that short sale constraints increase the frequency and magnitude of crashes, by testing Hong & Stein’s (2003) model of market crashes. The second tests the hypothesis that short sale constraints reduce market efficiency, by testing Miller’s (1977) model in which stocks that are hard (or impossible) to short tend to exhibit overpricing. In regards to the first group of tests, the results are ambiguous: the frequency and magnitude of crashes increased during the ban period, while the skewness of the returns distribution of the portfolios became more negative, as expected, but these changes hold for the market as a whole, as well. On the other hand, the skewness of the returns distribution of the individual banned stocks became more positive. The second group of tests provides ample support for Miller’s model, as the results coincide with the models predictions: banning short selling leads to positive abnormal returns (overpricing) in the affected stocks. The ban is also related with a decrease in volatility relative to the market, an important result from a policy perspective.
Analyzing grey matter diffusivity of autism in the context of white matter connectivity

Studies have revealed volumetric and connectivity abnormalities in brains of children with autism spectrum disorders (ASD). However, due to the complexity of ASD findings from these studies have been inconsistent. A possible way to improve the robustness of neuroimaging quantification is to introduce biological regularization. Motivated by previous studies on gray matter (GM) diffusivity, which can be used as surrogate biomarker for cortical volume deficits, we proposed a new network-regularized GM diffusivity analysis method to detect the GM diffusivity in the context of white matter (WM) connectivity. Our rationale is that GM diffusivity of anatomically connected regions has similar contributions to group differences. Experiments on simulated and clinical datasets showed better performance of the proposed method both on identification of atypical GM regions and classification of ASD and controls, compared with other methods without network regularization.
Hold-time violation analysis and fixing in near-threshold region

Operating in the Near Threshold Voltage (NTV) region improves the energy-efficiency of CMOS circuits by an order of magnitude. However, the number of hold-time violations significantly grows by scaling the supply voltage to the NTV region due to the increased delay variations. Furthermore, the conventional hold-time fixing approaches based on corner analysis are not applicable to the NTV region. In this paper, we propose a new iterative hold-time fixing flow for the NTV region based on Statistical Static Timing Analysis. The experimental results show 43.3% (35.4%) less energy (area) overhead compared to the conventional approach.
An Apriori-based Approach for First-Order Temporal Pattern

Previous studies on mining sequential patterns have focused on temporal patterns specified by some form of propositional temporal logic. However, there are some interesting sequential patterns whose specification needs a more expressive formalism, the first-order temporal logic. In this article, we focus on the problem of mining multi-sequential patterns which are first-order temporal patterns (not expressible in propositional temporal logic). We propose two Apriori-based algorithms to perform this mining task. The first one, the PM (Projection Miner) Algorithm adapts the key idea of the classical GSP algorithm for propositional sequential pattern mining by projecting the first-order pattern in two propositional components during the candidate generation and pruning phases. The second algorithm, the SM (Simultaneous Miner) Algorithm, executes the candidate generation and pruning phases without decomposing the pattern, that is, the mining process, in some extent, does not reduce itself to its propositional counterpart. Our extensive experiments shows that SM scales up far better than PM.
Design the Time-Interval Based Fairness Partitioning Method in DVE

MMORPGs may involve a great number of concurrent players, and those servers usually have to manage hundred, or even thousands of avatars coexisting in the same virtual world. So if failing to send a command or an event message, or sending it too late may cause damages to the avatar evolution(death, injury, loss of resources), and may result in unjustified penalties for the player. Many policies could be defined to realize a ranking evaluation of available servers. Unfortunately, due to the highly dynamic characteristics of server loads and network performances, any optimal allocation would soon become sub-optimal. In order to solve those problems we propose the "time-interval based fairness partitioning method" (TIP). TIP will distribute the avatar to the game server equally in order to avoid the problems form the unfairness of game servers load.
An interactive scene annotation tool for video surveillance

An interactive scene annotation tool for video surveillance is presented in this paper. The annotation process is divided into three stages. (1) camera rough calibration;(2) calibration refinement; (3) major surfaces annotation. Inputs are then rendered in a 3D environment, which again help users check calibration accuracy and annotation correctness. Experiments show that this tool is easy to use and attains acceptable annotation accuracy. The interactive procedure helps users without knowledge in computer vision to complete camera calibration as well as surface annotation.
Understanding Software Platforms for In-Memory Scientific Data Analysis: A Case Study of the Spark System

Over the last five years, Apache Spark has become a major software platform for in-memory data analysis. Acknowledging its widespread use, we present a comprehensive study of system characteristics of Spark with a focus on scientific data analytics performing large-scale matrix operations. We compare its performance to SciDB, a disk-based platform for array data analysis. A benchmark, ArrayBench, is developed to evaluate the performance of matrix processing for scientific data analytics. ArrayBench is applied to data from a real biological workflow whose data inputs are in matrix form. Herein, we report the findings, which shed light on the improvement of Spark and SciDB and future development of large-scale scientific data analytics.
You can have your cake and eat it too: Introducing Dynamical Systems to Cognitive Science

Timothy Van Gelder and Robert Port (PVG) have thrown down the gauntlet to Cognittive Science. Flaunting a young but dynamic tradition of looking to the computer as a possible model of what the brain is, PVG offer to replace this tradition by a new one based on dynamics. The tenets of this shift include a new emphasis on continuous state and time. Through their 500 page edited collection, PVG hope to convince the reader and the field that dynamics is a better paradigm for Cognitive Science.
DNA lesion identity drives choice of damage tolerance pathway in murine cell chromosomes

DNA-damage tolerance (DDT) via translesion DNA synthesis (TLS) or homology-dependent repair (HDR) functions to bypass DNA lesions encountered during replication, and is critical for maintaining genome stability. Here, we present piggyBlock, a new chromosomal assay that, using piggyBac transposition of DNA containing a known lesion, measures the division of labor between the two DDT pathways. We show that in the absence of DNA damage response, tolerance of the most common sunlight-induced DNA lesion, TT-CPD, is achieved by TLS in mouse embryo fibroblasts. Meanwhile, BP-G, a major smoke-induced DNA lesion, is bypassed primarily by HDR, providing the first evidence for this mechanism being the main tolerance pathway for a biologically important lesion in a mammalian genome. We also show that, far from being a last-resort strategy as it is sometimes portrayed, TLS operates alongside nucleotide excision repair, handling 40% of TT-CPDs in repair-proficient cells. Finally, DDT acts in mouse embryonic stem cells, exhibiting the same pattern—mutagenic TLS included—despite the risk of propagating mutations along all cell lineages. The new method highlights the importance of HDR, and provides an effective tool for studying DDT in mammalian cells.
Dimensionality, Discriminability, Density and Distance Distributions

For many large-scale applications in data mining, machine learning, and multimedia, fundamental operations such as similarity search, retrieval, classification, clustering, and anomaly detection generally suffer from an effect known as the `curse of dimensionality'. As the dimensionality of the data increases, distance values tend to become less discriminative, due to their increasing relative concentration about the mean of their distribution. For this reason, researchers have considered the analysis of structures and methods in terms of measures of the intrinsic dimensionality of the data sets. This paper is concerned with a generalization of a discrete measure of intrinsic dimensionality, the expansion dimension, to the case of continuous distance distributions. This notion of the intrinsic dimensionality of a distribution is shown to precisely coincide with a natural notion of the indiscriminability of distances and features. Furthermore, for any distance distribution with differentiable cumulative density function, a fundamental relationship is shown to exist between probability density, the cumulative density (cumulative probability divided by distance), intrinsic dimensionality, and discriminability.
Lazy tree mapping: generalizing and scaling deterministic parallelism

Many parallel programs are intended to yield deterministic results, but unpredictable thread or process interleavings can lead to subtle bugs and nondeterminism. We are exploring a producer-consumer memory model---SPMC---for efficient system-enforced deterministic parallelism. However, the previous eager page mapping wastes physical memory, and cannot support large-size and real applications. This paper presents a novel lazy tree mapping approach to the model, which introduces "shadow page table" for allocating pages "on demand", and extends an SPMC region by a tree of lazily generated pages, representing an infinite stream on reusing a finite-size of virtual addresses. We build Dlinux to emulate the SPMC model entirely in Linux user space to make the SPMC more powerful. Dlinux uses virtual memory to emulate physical pages, and sets up page tables at user-level to emulate lazy tree mapping. Atop the SPMC, DetMP and DetMPI are explored and integrated into Dlinux, offering both thread- and process-level deterministic message passing programming. Experimental evaluations suggest lazy tree mapping improves memory use and address reuse. Dlinux scales close to ideal with 2048*2048 matrices for matmult, and better than MPICH2 for some workloads with larger input datasets.
Disease and Development Revisited: A Reevaluation of the Eect of Life Expectancy on Economic Growth

In an important recent paper Acemoglu and Johnson (2007) con- clude that increased life expectancy causes decreases in income per capita rather than increases, both in the short and the long run. This is at odds with conventional wisdom, microestimates of the eect of life expectancy on income per capita and with the results of recent simulations of a neoclassical growth model by Ashraf et al. (2008). We present a possible solution to this puzzle. We …rst rederive the theoretical relationship between life expectancy and income per capita implied by the model of Acemoglu and Johnson. We then redo their empirical analysis of the eect of changes in life expectancy on income per capita. We obtain results that indicate that the impact of changes in life expectancy on income per capita are very much in line with what standard neoclassical growth theory predicts. In particular, we obtain estimates of the short run eect that are very similar to Acemoglu and Johnson but we also obtain results of the dynamic eects that are very dierent from the conclusions in Acemoglu and Johnson. Instead, our estimates of the dynamic eects are very much in line with the simulation results of Ashraf et al.
Exemplar-Based Word-Space Model for Compositionality Detection: Shared Task System Description

In this paper, we highlight the problems of polysemy in word space models of compositionality detection. Most models represent each word as a single prototype-based vector without addressing polysemy. We propose an exemplar-based model which is designed to handle polysemy. This model is tested for compositionality detection and it is found to outperform existing prototype-based models. We have participated in the shared task (Biemann and Giesbrecht, 2011) and our best performing exemplar-model is ranked first in two types of evaluations and second in two other evaluations.
Wearable Sensor-Based Behavioral Anomaly Detection in Smart Assisted Living Systems

Detecting behavioral anomalies in human daily life is important to developing smart assisted-living systems for elderly care. Based on data collected from wearable motion sensors and the associated locational context, this paper presents a coherent anomaly detection framework to effectively detect different behavioral anomalies in human daily life. Four types of anomalies, including spatial anomaly, timing anomaly, duration anomaly, and sequence anomaly, are detected using a probabilistic theoretical framework. This framework is based on complex activity recognition using dynamic Bayesian network modeling. The maximum-likelihood estimation algorithm and Laplace smoothing are used in learning the parameters in the anomaly detection model. We conducted experimental evaluation in a mock apartment environment, and the results verified the effectiveness of the proposed framework. We expect that this behavioral anomaly detection system can be integrated into future smart homes for elderly care.