Miracle-dz/UniFAR-FTUs huggingface dataset:
Equal opportunity and affirmative action

The Regents of the University of Minnesota reaffirm that it is the policy of the Unive provide equal educational access and opportunity to persons of every race and ethn sex, religion and creed, as well as fairness to the individual in the competition for ed opportunity, and the appropriate administrative officers and faculty are directed to e continue any admission policies and guidelines necessary to implement this policy; f order to promote ethnic and cultural diversity which will enhance the campus enviro educational programs of the University; in order to increase the representation of m minority groups at the highest professional levels, providing encouragement to mem minority groups to seek to attain the highest achievements of which they are capab to alleviate the shortage of training professionals in diverse fields who will serve unm community needs; and, in order to compensate for the inequalities that have existed the Regents reaffirm and declare that it is the policy of the University of Minnesota t equal educational access and opportunity to members of minority groups and the ap administrative officers and faculty are directed to develop affirmative action admissi and to review admission policies to insure that the intent of this policy continues to implemented. The University of Minnesota is an equal opportunity educator and employer.
ON BEHAVIOR ANALYSIS IN VIDEO SURVEILLANCE

With the large number of surveillance cameras now in operation, both in public placesand in commercial centers, significant research efforts have been invested in attempts to automate surveillance video analysis. The goal of visual surveillance is not only to put cameras in the place of human eyes, but also to accomplish the entire surveillance task as automatically as possible. Recently, the problem of analyzing behavior in videos has been the focus of several researchers’ efforts. They concentrate on developing intelligent visual surveillance systems to replace traditional passive video surveillance systems which can only store surveillance videos but are not able to identify or describe interesting activities. In this paper, we give a survey of behavior analysis work in video surveillance and compare the performance of the state-of-the-art algorithms on different datasets.Furthermore, useful datasets are analyzed in order to provide help for initiating research projects.
Hybrid approach to extract adjectives for implicit aspect identification in opinion mining

Opinion Mining (or Sentiment analysis) is a rapidly growing research field that has attracted both academia and industry because of the challenging research problems it poses and the potential benefits it can provide in many real life applications. Aspect-based sentiment analysis, in particular, is one of the fundamental challenges within this research field. In this paper, we propose an approach that combines a corpus based and WordNet (WN) dictionary based models for Implicit Aspect Terms (IAT) extraction. Then, it uses its hybrid model to support Naïve Bayes Training for identifying Implicit Aspects. Our schema is structured in three major phases: In Implicit Aspect Representation stage, our approach aims at representing implicit aspects implied by corpus adjectives combined with their WN related words. In Post training stage, the learnt model (on implicit aspect identification) is improved by removing the noisy WN related words. In Implicit Aspect Identification step, we use NB classifier which identifies the final implicit aspects. We conducted experiments using NB classifier on electronic products and restaurant datasets. The results show that : (1) NB performs better using our hybrid model than using corpus based model alone. (2) For more efficiency, our approach needs to adjust the learnt NB model by removing noisy WN terms which negatively bias NB training.
THE NONISOTHERMAL STAGE OF MAGNETIC STAR FORMATION. I. FORMULATION OF THE PROBLEM AND METHOD OF SOLUTION

We formulate the problem of the formation and subsequent evolution of fragments (or cores) in magnetically supported, self-gravitating molecular clouds in two spatial dimensions. The six-fluid (neutrals, electrons, molecular and atomic ions, positively charged, negatively charged, and neutral grains) physical system is governed by the radiation, nonideal magnetohydrodynamic equations. The magnetic flux is not assumed to be frozen in any of the charged species. Its evolution is determined by a newly derived generalized Ohm's law, which accounts for the contributions of both elastic and inelastic collisions to ambipolar diffusion and Ohmic dissipation. The species abundances are calculated using an extensive chemical-equilibrium network. Both MRN and uniform grain size distributions are considered. The thermal evolution of the protostellar core and its effect on the dynamics are followed by employing the gray flux-limited diffusion approximation. Realistic temperature-dependent grain opacities are used that account for a variety of grain compositions. We have augmented the publicly available Zeus-MP code to take into consideration all these effects and have modified several of its algorithms to improve convergence, accuracy, and efficiency. Results of magnetic star formation simulations that accurately track the evolution of a protostellar fragment from a density ≃103 cm−3 to a density ≃1015 cm−3, while rigorously accounting for both nonideal MHD processes and radiative transfer, are presented in a separate paper.
Pretreatment with paricalcitol attenuates inflammation in ischemia-reperfusion injury via the up-regulation of cyclooxygenase-2 and prostaglandin E2.

BACKGROUND The effect of paricalcitol on renal ischemia-reperfusion injury (IRI) has not been investigated. We examined whether paricalcitol is effective in preventing inflammation in a mouse model of IRI, and evaluated the cyclooxygenase-2 (COX-2) and prostaglandin E2 (PGE2) pathways as a protective mechanism of paricalcitol. METHODS Paricalcitol (0.3 μg/kg) was administered to male C57BL/6 mice 24 h before IRI. Bilateral kidneys were subjected to 23 min of ischemia, and mice were killed 72 h after IRI. The effects of paricalcitol on renal IRI were evaluated in terms of renal function, tubular necrosis, apoptotic cell death, inflammatory cell infiltration and inflammatory cytokines. The effects of paricalcitol on COX-2, PGE2 and its receptors were investigated. RESULTS Paricalcitol pretreatment improved renal function (decreased blood urea nitrogen and serum creatinine levels), tubular necrosis and apoptotic cell death in IRI-mice kidneys. The infiltration of inflammatory cells (T cells and macrophages), and the production of proinflammatory cytokines (RANTES, tumor necrosis factor-α, interleukin-1β and interferon-γ) were reduced in paricalcitol-treated mice with IRI. Paricalcitol up-regulated COX-2 expression, PGE2 synthesis and mRNA expression of receptor subtype EP4 in post-ischemic renal tissue. The cotreatment of a selective COX-2 inhibitor with paricalcitol restored functional injury and tubular necrosis in paricalcitol-treated mice with IRI. CONCLUSIONS Our study demonstrates that paricalcitol pretreatment prevents renal IRI via the inhibition of renal inflammation, and the up-regulation of COX-2 and PGE2 is one of the protective mechanisms of paricalcitol in renal IRI.
Genetic Epidemiology Research Article a Bivariate Mann-whitney Approach for Unraveling Genetic Variants and Interactions Contributing to Comorbidity

Although comorbidity among complex diseases (e.g., drug dependence syndromes) is well documented, genetic variants contributing to the comorbidity are still largely unknown. The discovery of genetic variants and their interactions contributing to comorbidity will likely shed light on underlying pathophysiological and etiological processes, and promote effective treatments for comorbid conditions. For this reason, studies to discover genetic variants that foster the development of comorbidity represent high-priority research projects, as manifested in the behavioral genetics studies now underway. The yield from these studies can be enhanced by adopting novel statistical approaches, with the capacity of considering multiple genetic variants and possible interactions. For this purpose, we propose a bivariate Mann-Whitney (BMW) approach to unravel genetic variants and interactions contributing to comorbidity, as well as those unique to each comorbid condition. Through simulations, we found BMW outperformed two commonly adopted approaches in a variety of underlying disease and comorbidity models. We further applied BMW to datasets from the Study of Addiction: Genetics and Environment, investigating the contribution of 184 known nicotine dependence (ND) and alcohol dependence (AD) single nucleotide polymorphisms (SNPs) to the comorbidity of ND and AD. The analysis revealed a candidate SNP from CHRNA5, rs16969968, associated with both ND and AD, and replicated the findings in an independent dataset with a P-value of 1.06 × 10 –03 .
Optimizing Routes of Public Transportation Systems by Analyzing the Data of Taxi Rides

Public transportation systems are flexible and affordable for the passengers. In contrast, the operation and construction of the necessary infrastructure is cost-intensive and requires extensive planning. Decisions about the scheduling, capacities and the location of stations are dependent on various economic, social, and environmental factors and have a major impact on the structure of a city. In this context, information about the starting points and destinations of potential passengers is highly relevant for operators. Unfortunately, the collection of this data is not trivial and often based on time intensive and expensive studies. In this paper we present a novel approach to gain knowledge for transportation system optimization based on the data of taxi rides, which have been recorded for documentation purposes. This data can be analyzed and offers an insight into the fine-grained travel intentions of millions of people. We introduce an interactive web application, which enables the analysis of about 700 millions taxi rides in New York City. Additionally to the exploration of the most frequent travel routes, the application can automatically suggest useful extensions of the exciting transportation system or suggest an optimized route map, which can be used to evaluate the existing one. With this functionality, the presented software effectively supports the decision processes of operators and enables the continuous evaluation of the existing systems.
Gut dysbiosis is associated with metabolism and systemic inflammation in patients with ischemic stroke

The role of metabolic diseases in ischemic stroke has become a primary concern in both research and clinical practice. Increasing evidence suggests that dysbiosis is associated with metabolic diseases. The aim of this study was to investigate whether the gut microbiota, as well as concentrations of organic acids, the major products of dietary fiber fermentation by the gut microbiota, are altered in patients with ischemic stroke, and to examine the association between these changes and host metabolism and inflammation. We analyzed the composition of the fecal gut microbiota and the concentrations of fecal organic acids in 41 ischemic stroke patients and 40 control subjects via 16S and 23S rRNA-targeted quantitative reverse transcription (qRT)-PCR and high-performance liquid chromatography analyses, respectively. Multivariable linear regression analysis was subsequently performed to evaluate the relationships between ischemic stroke and bacterial counts and organic acid concentrations. Correlations between bioclinical markers and bacterial counts and organic acids concentrations were also evaluated. Although only the bacterial counts of Lactobacillus ruminis were significantly higher in stroke patients compared to controls, multivariable analysis showed that ischemic stroke was independently associated with increased bacterial counts of Atopobium cluster and Lactobacillus ruminis, and decreased numbers of Lactobacillus sakei subgroup, independent of age, hypertension, and type 2 diabetes. Changes in the prevalence of Lactobacillus ruminis were positively correlated with serum interleukin-6 levels. In addition, ischemic stroke was associated with decreased and increased concentrations of acetic acid and valeric acid, respectively. Meanwhile, changes in acetic acid concentrations were negatively correlated with the levels of glycated hemoglobin and low-density lipoprotein cholesterol, whereas changes in valeric acid concentrations were positively correlated with the level of high sensitivity C-reactive protein and with white blood cell counts. Together, our findings suggest that gut dysbiosis in patients with ischemic stroke is associated with host metabolism and inflammation.
An Efficient Framework for Pixel-wise Building Segmentation from Aerial Images

Detection of buildings in aerial images is an important and challenging task in computer vision and aerial image interpretation. This paper presents an efficient approach that combines Random forest (RF) and a fully connected conditional random field (CRF) on various features for the detection and segmentation of buildings at pixel level. RF allows one to learn extremely fast on big aerial image data. The unary potentials given by RF are then combined in a fully connected conditional random field model for pixel-wise classification. The use of high dimensional Gaussian filter for pairwise potentials makes the inference tractable while obtaining high classification accuracy. Experiments have been conducted on a challenging aerial image dataset from a recent ISPRS Semantic Labeling Contest [9]. We obtained state-of-the-art accuracy with a reasonable computation time.
Nonlinear switching in plasmonic directional couplers

We present a numerical study of all-optical switching in Kerr-nonlinear plasmonic directional couplers. We consider planar couplers that consist of two identical plasmonic slot waveguides and demonstrate the influence of geometrical and optical parameters on switching characteristics. Underlying physical mechanisms that affect the coupler performance are briefly discussed, too.
Afatinib: emerging next-generation tyrosine kinase inhibitor for NSCLC

The discovery of epidermal growth-factor receptor (EGFR)-activating mutations and the introduction of oral EGFR tyrosine kinase inhibitors (EGFR-TKIs) have expanded the treatment options for patients with non-small cell lung cancer. The first two reversible EGFR-TKIs, erlotinib and gefitinib, are approved for use in the first-line setting in patients with known EGFR-activating mutations and in the second- and third-line settings for all NSCLC patients. These first-generation EGFR-TKIs improve progression-free survival when compared to chemotherapy in patients with EGFR-activating mutations in the first-line setting. However, nearly all patients develop resistance to EGFR-directed agents. There is a need for further therapy options for patients with disease progression after treatment with reversible EGFR-TKIs. Afatinib is an irreversible ErbB family blocker that inhibits EGFR, HER2, and HER4. In vitro and in vivo, afatinib have shown increased inhibition of the common EGFR-activating mutations as well as the T790M resistance mutation when compared to erlotinib and gefitinib. Clinically, afatinib has been evaluated in the LUX-Lung series of trials, with improvement in progression-free survival reported in patients with EGFR-activating mutations in both first- and second-/third-line settings when compared to chemotherapy. Further investigation is needed to determine the precise role that afatinib will play in the treatment of patients with non-small cell lung cancer and EGFR-activating mutations.
A Distributed Bayesian Algorithm for data fault detection in wireless sensor networks

This paper proposes a new Distributed Bayesian Algorithm (DBA) for data fault detection in order to address problems of data fault in wireless sensor networks. Although many methods for data fault detection have been proposed, the diagnosis accuracy is still very low especially in the situation of the densely deployed WSN with a large number of faulty nodes. In DBA, the Bayesian network is introduced to calculate the fault probability of sensor nodes, and the fault probability will be adjusted by exploiting the border nodes to improve the accuracy of fault probability and avoid the negative impact of the large number of faulty nodes. A comprehensive simulation has been made to compare the performance of DBA and Distributed Fault Detection (DFD). The simulation results show DBA can improve the fault detection accuracy significantly even in the situation with a large number of faulty nodes.
Feedback Control of a Patient-Mounted Telerobot using Electro-magnetic Tracking of Needle Angle

The Robopsy device is a lightweight, largely plastic telerobot (shown below in Fig. 1A) that affixes directly to a patient’s chest to pivot the needle in two degrees of freedom about a skin insertion point [1]. This is accomplished via two motor-actuated concentric, crossed and partially nested hoops. A medical image-based interface converts doctor clicks on the DICOM images to motor commands that align the needle along the desired trajectory and subsequently insert it. [2]. The kinematic equations to determine the motor commands to align the current needle trajectory with its desired trajectory are outlined.
Improving light propagation Monte Carlo simulations with accurate 3D modeling of skin tissue

In this paper, we present a 3D light propagation model to simulate multispectral reflectance images of large skin surface areas. In particular, we aim to simulate more accurately the effects of various physiological properties of the skin in the case of subcutaneous vein imaging compared to existing models. Our method combines a Monte Carlo light propagation model, a realistic three-dimensional model of the skin using parametric surfaces and a vision system for data acquisition. We describe our model in detail, present results from the Monte Carlo modeling and compare our results with those obtained with a well established Monte Carlo model and with real skin reflectance images.
Inferences from DNA data: population histories, evolutionary processes and forensic match probabilities

Summary. We develop a flexible class of Metropolis–Hastings algorithms for drawing inferences about population histories and mutation rates from deoxyribonucleic acid (DNA) sequence data. Match probabilities for use in forensic identification are also obtained, which is particularly useful for mitochondrial DNA profiles. Our data augmentation approach, in which the ancestral DNA data are inferred at each node of the genealogical tree, simplifies likelihood calculations and permits a wide class of mutation models to be employed, so that many different types of DNA sequence data can be analysed within our framework. Moreover, simpler likelihood calculations imply greater freedom for generating tree proposals, so that algorithms with good mixing properties can be implemented. We incorporate the effects of demography by means of simple mechanisms for changes in population size and structure, and we estimate the corresponding demographic parameters, but we do not here allow for the effects of either recombination or selection. We illustrate our methods by application to four human DNA data sets, consisting of DNA sequences, short tandem repeat loci, single‐nucleotide polymorphism sites and insertion sites. Two of the data sets are drawn from the male‐specific Y‐chromosome, one from maternally inherited mitochondrial DNA and one from the β‐globin locus on chromosome 11.
Herd behavior and volatility in financial markets

Relaxing an unrealistic assumption of a representative percolation model, this paper demonstrates that herd behavior leads to a high increase in volatility but not trading volume, in contrast with information flows that give rise to increases in both volatility and trading volume. Although detecting herd behavior has posed a great challenge due to its empirical difficulty, this paper proposes a new methodology for detecting trading days with herding. Furthermore, this paper suggests a herd-behavior-stochastic-volatility model, which accounts for herding in financial markets. Strong evidence in favor of the model specification over the standard stochastic volatility model is based on empirical application with high frequency data in the Korean equity market, strongly supporting the intuition that herd behavior causes excess volatility. In addition, this research indicates that strong persistence in volatility, which is a prevalent feature in financial markets, is likely attributed to herd behavior rather than news.
Stem Cells in the Adult Zebrafish Cerebellum: Initiation and Maintenance of a Novel Stem Cell Niche

In the adult CNS, neurogenesis takes place in special niches. It is not understood how these niches are formed during development and how they are maintained. In contrast to mammals, stem cell niches are abundant in zebrafish and also found in other parts of the brain than telencephalon. To understand common characteristics of neural stem cell niches in vertebrates, we studied the origin and architecture of a previously unknown stem cell niche using transgenic lines, in vivo imaging, and marker analysis. We show that bipotent stem cells are maintained in a distinct niche in the adult zebrafish cerebellum. Remarkably, the stem cells are not typical glia but instead retain neuroepithelial characteristics. The cerebellar stem cell niche is generated by the coordinated displacement of ventricle and rhombic lip progenitors in a two-step process involving morphogenetic movements and tissue growth. Importantly, the niche and its stem cells still remain in ventricular contact through a previously unknown derivative of the ventricle. Factors propagated in the ventricle are thought to be important regulators of stem cell activity. To test the requirements of one family of important factors, Fibroblast growth factors, we used zebrafish with an inducible dominant-negative Fgf receptor. Inhibition of Fgf signaling leads to significant reduction of stem cell activity. In contrast to the predominant view, adult neural stem cells in nonmammalian vertebrates show more neuroepithelial than glial characteristics. Nevertheless, retained epithelial properties such as distinct polarization and ventricular contact are critical common determinants to maintain neural stem cell activity in vertebrates.