Miracle-dz/UniFAR-FTUs huggingface dataset:
Logistic Regression for Extremely Rare Events: The Case of School Shootings

School shootings are often used in public policy debate as a justification for increased regulation, based on qualitative arguments. However, to date, no effort has been made to find valid quantitative evidence for the claims bolstering the regulation recommendations. In defense of this absence of evidence, it is usually argued that the rarity of such events does not allow the employment of quantitative methods. This paper, using a simulation study, shows that, based on the number of shool shootings in the United States and Germany combined, the well-known method of logistic regression can be applied to a case-control study, making it possible to at least test for an association between hypothesized influential variables and the occurrences. Moderate relative risks, explained by an observed variable, would lead to a high power of the appropriate test. A moderate numbers of cases generated by such a variable would suffice to show a significant association.
Policies for Habitat Fragmentation: Combining Econometrics with GIS-Based Landscape Simulations

Habitat fragmentation is widely considered a primary threat to biodiversity. In this paper, we analyze incentive-based policies designed to reduce forest fragmentation in the coastal plain region of South Carolina. Our approach integrates an econometric model of land use with simulations that predict the spatial pattern of land-use change. We analyze how subsidies for afforestation affect distributions defined over fragmentation metrics and derive the marginal costs of altering landscape patterns. We find the costs of reducing fragmentation vary greatly with initial landscape conditions and that a simple uniform subsidy performs well relative to a more complicated spatially targeted policy. (JEL R14, R52)
Cell Therapy for Diabetes Mellitus

The number of diabetic patients in the world is increasing in recent years and the prevention of diabetes mellitus is therefore one of the urgent medical issues. Exogenous insulin is used for the control of blood glucose in diabetic patients; however, hypoglycemic episodes are unavoidable. Over the last several decades, islet transplantation has been developed as a promising method to achieve strict control of blood glucose and a potential cure for type 1 diabetes. However, due to the shortage of donor pancreata, alternative sources of islets have been sought through the generation of beta cells from stem cells, use of porcine islets, and beta cell expansion with growth factors. However, differentiation and expansion of embryonic and pancreatic stem cells and expansion of differentiated beta cells in vitro is limited. Expansion of primary beta cells by growth factors is also hampered by the senescence of the cells. Thus, we focused on establishing a human pancreatic beta cell line that is functionally equivalent to primary beta cells and can yield large amounts of cells for transplantation. Using Cre/loxP-based reversible immortalization, we constructed a reversibly immortalized pancreatic beta cell clone (NAKT-15). The cells may overcome the limitation of primary pancreatic beta cells for transplantation to control type 1 diabetes. In order to avoid the use of immunosuppressive agents, we are currently engaged in developing an implantable bag-type bioartificial pancreas. In this article, I discuss the hurdles of the current therapy for diabetes and introduce the possible future treatment of diabetes.
Protective effect of Spirulina against doxorubicin‐induced cardiotoxicity

The generation of reactive oxygen species and mitochondrial dysfunction has been implicated in doxorubicin (DOX)‐induced cardiotoxicity. The aim of the present study was to determine whether Spirulina, a blue‐green algae, could serve as a cardioprotective agent during DOX treatment in a mouse model. Mice were treated with DOX (4 mg/kg bw, intraperitoneally), weekly, for 4 weeks. Spirulina was administered orally for 3 days twice daily, then for 7 weeks along with the four equal injections of DOX. Cardiotoxicity was assessed, at 3 weeks after the end of the DOX‐treatment period, by mortality, volume of ascites, liver congestion, oxidative stress and ultrastructural changes of heart tissue. The DOX‐treated animals showed higher mortality (53%) and more ascites. Myocardial damage, as assessed by ultrastructural changes, showed loss of myofibrils, cytoplasmic vacuolization and mitochondrial swelling. Myocardial superoxide dismutase and glutathione peroxidase activities were decreased and lipid peroxidation was increased. Pretreatment with Spirulina significantly protected the mice from DOX‐induced cardiotoxic effects as evidenced from lower mortality (26%), less ascites, lower levels of lipid peroxidation, normalization of antioxidant enzymes and ultrastructural studies showing minimal damage to the heart. In vitro cytotoxic studies using ovarian cancer cells demonstrated that Spirulina did not compromise the anti‐tumor activity of doxorubicin. These results suggest that Spirulina has a protective effect against cardiotoxicity induced by DOX and it may, therefore, improve the therapeutic index of DOX. Copyright © 2005 John Wiley & Sons, Ltd.
k-Means Has Polynomial Smoothed Complexity

The k-means method is one of the most widely used clustering algorithms, drawing its popularity from its speed in practice. Recently, however, it was shown to have exponential worst-case running time. In order to close the gap between practical performance and theoretical analysis, the k-means method has been studied in the model of smoothed analysis. But even the smoothed analyses so far are unsatisfactory as the bounds are still super-polynomial in the number n of data points. In this paper, we settle the smoothed running time of the k-means method. We show that the smoothed number of iterations is bounded by a polynomial in n and 1/sigma, where sigma is the standard deviation of the Gaussian perturbations. This means that if an arbitrary input data set is randomly perturbed, then the k-means method will run in expected polynomial time on that input set.
What Sparse Light Field Coding Reveals about Scene Structure

In this paper, we propose a novel method for depth estimation in light fields which employs a specifically designed sparse decomposition to leverage the depth-orientation relationship on its epipolar plane images. The proposed method learns the structure of the central view and uses this information to construct a light field dictionary for which groups of atoms correspond to unique disparities. This dictionary is then used to code a sparse representation of the light field. Analyzing the coefficients of this representation with respect to the disparities of their corresponding atoms yields an accurate and robust estimate of depth. In addition, if the light field has multiple depth layers, such as for reflective or transparent surfaces, statistical analysis of the coefficients can be employed to infer the respective depth of the superimposed layers.
Feature subspaces selection via one-class SVM: Application to textured image segmentation

This paper presents a feature subspaces selection method which uses an ensemble of one-class SVMs. The objective is to improve or preserve the performance of a decision system in the presence of noise, loss of information or feature non-stationarity. The proposed method consists in first generating an ensemble of feature subspaces from the initial full-dimensional space, and then making the decision by using only the subspaces which are supposed to be immune to the non-stationary disturbance. One particularity of this method is that we use the one-class SVM ensemble to carry out the feature selection and the classification tasks at the same time. Textured image segmentation constitutes an appropriate application for the evaluation of the proposed approach. The experimental results demonstrate the effectiveness of the decision system that we have developed.
Effects of perspective taking on memory for self and other

Recent functional neuroimaging evidence suggests that recalling autobiographical memories, imagining fictitious autobiographical episodes, and taking the perspective of another person activate a similar network of brain regions. Results from the two studies presented here provide further evidence of this common neural network. Previous evidence also suggests that recalling autobiographical memories from a first person or from a third person perspective can influence the way in which those memories are experienced as well as the brain regions that are engaged; however, the effect of perspective on imagining autobiographical events remains unclear. Results from Study 1 indicated that brain regions implicated in both remembering and imagining were differentially engaged during these tasks depending on whether a first person or a third person perspective was taken. In addition, while recalling autobiographical memories from a third person perspective can result in the feeling that a past self is more like another person, imagining oneself in the position of another person can result in the feeling that that person is more similar to oneself; this suggests a possible link between perspective in memory and social perspective taking. In Study 2, we identified several brain regions exhibiting a pattern of increasing or decreasing activation as a function of whether socially interactive events were recalled from a first person perspective, by imagining oneself as one’s partner, or from a third person perspective (i.e., as a function of distance from one’s own perspective). Together, our findings suggest that perspective plays an important role in the way in which brain regions that are part of this common neural network are engaged during memory, imagination, and socially interactive tasks.
Land cover mapping in support of LAI and FPAR retrievals from EOS-MODIS and MISR: Classification methods and sensitivities to errors

Land cover maps are used widely to parameterize the biophysical properties of plant canopies in models that describe terrestrial biogeochemical processes. In this paper, we describe the use of supervised classification algorithms to generate land cover maps that characterize the vegetation types required for Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) retrievals from MODIS and MISR. As part of this analysis, we examine the sensitivity of remote sensing-based retrievals of LAI and FPAR to land cover information used to parameterize vegetation canopy radiative transfer models. Specifically, a decision tree classification algorithm is used to generate a land cover map of North America from Advanced Very High Resolution Radiometer (AVHRR) data with 1 km spatial resolution using a six-biome classification scheme. To do this, a time series of normalized difference vegetation index data from the AVHRR is used in association with extensive site-based training data compiled using Landsat Thematic Mapper (TM) and ancillary map sources. Accuracy assessment of the map produced via decision tree classification yields a cross-validated map accuracy of 73%. Results comparing LAI and FPAR retrievals using maps from different sources show that disagreement in land cover labels generally do not translate into strong disagreement in LAI and FPAR maps. Further, the main source of disagreement in LAI and FPAR maps can be attributed to specific biome classes that are characterized by a continuum of fractional cover and canopy structure.
Rule based Part of speech Tagger for Homoeopathy Clinical realm

A tagger is a mandatory segment of most text scrutiny systems, as it consigned a s yntax class (e.g., noun, verb, adjective, and adverb) to every word in a sentence. In this paper, we present a simple part of speech tagger for homoeopathy clinical language. This paper reports about the anticipated part of speech tagger for homoeopathy clinical language. It exploit standard pattern for evaluating sentences, untagged clinical corpus of 20085 words is used, from which we had selected 125 sentences (2322 tokens). The problem of tagging in natural language processing is to find a way to tag every word in a text as a meticulous part of speech. The basic idea is to apply a set of rules on clinical sentences and on each word, Accuracy is the leading factor in evaluating any POS tagger so the accuracy of proposed tagger is also conversed.
In vivo somatic delivery of plasmid DNA and retrograde transport to obtain cell‐specific gene expression in the central nervous system

We utilised the retrograde transport machinery of neurones to deliver naked plasmid DNA into the central nervous system. A 5.4‐kb fragment of the glycine receptor (GlyR) α1 subunit gene was cloned and used to drive the expression of a construct encoding for the enhanced green fluorescent protein (EGFP). Injections of the plasmid DNA in the tongue of mice resulted in the expression of the marker protein in hypoglossal motor neurones, showing that the GlyRα1 promoter sequence is sufficient to drive expression of the transgene. In order to determine the specificity of expression of the 5.4‐kb fragment of the GlyR α1 subunit gene promoter, we subsequently injected the plasmid DNA into the mouse central nucleus of the amygdala. This nucleus receives projections from the parabrachial nucleus, a brainstem area that has a high density of GlyRs, and from the insular cortex, a forebrain structure devoid of GlyRs. We observed EGFP‐labelled neurones in the parabrachial nucleus, but not in the insular cortex, indicating that the 5.4‐kb GlyR α1 subunit gene promoter confers specificity of expression. This approach provides a simple and rapid way to identify, in vivo, promoter elements that mediate neurone‐specific gene expression.
A novel partial order for the information sets of polar codes over B-DMCs

We study partial orders on the information sets of polar codes designed for binary discrete memoryless channels. We show that the polar transform defined by Arikan preserves `symmetric convex/concave orders'. While for symmetric channels this ordering turns out to be equivalent to the stochastic degradation ordering already known to order the information sets of polar codes, we show that a strictly weaker partial order is obtained when at least one of the channels is asymmetric. We also discuss two tools which can be useful for verifying this ordering: a criterion known as the cut criterion and channel symmetrization.
Eigengaze - covert behavioral biometric exploiting visual attention characteristics

It is possible for the visual attention characteristics of a person to be exploited as a biometric for authentication or identification of individual viewers. The visual attention characteristics of a person can be easily monitored by tracking the gaze of a viewer during the presentation of a known or unknown visual scene. The positions and sequences of gaze locations during viewing may be determined by overt (conscious) or covert (sub-conscious) viewing behaviour. This paper presents a method to authenticate individuals using their covert viewing behaviour, thus yielding a unique behavioural biometric. A method to quantify the spatial and temporal patterns established by the viewer for their covert behaviour is proposed utilsing a principal component analysis technique called ‘eigenGaze’. Experimental results suggest that it is possible to capture the unique visual attention characteristics of a person to provide a simple behavioural biometric.
Cluster Integrated Updation Strategies for ACO Algorithms

Ant Colony Optimization (ACO) algorithm has evolved as the most popular way to attack the combinatorial problems. The ACO algorithm employs multi agents called ants that are capable of finding optimal solution for a given problem instances. These ants at each step of the computation make probabilistic choices to include good solution component in partially constructed solution, so that better solution can be obtained in the search process. The ant algorithms are typically characterized by co-operation among the ants, greedy, heuristics and feedback approaches that helps them to achieve their goals. In this paper, we propose new updation mechanism based on clustering techniques, which is aimed at exploring the nearby solutions region. We also report in detail the impact on performance due to integration of cluster and ACO.
One-tag checker: Message-locked integrity auditing on encrypted cloud deduplication storage

In this paper, we investigate the problem of integrity auditing for cloud deduplication storage. Specifically, in addition to the outsourced data confidentiality, we also aim to ensure the integrity of the deduplicated cloud storage. With the existing works based on Provable Data Possession (PDP)/Proof of Retrievability (PoR), we are either required to rely on a fully trusted proxy server or inevitably sacrifice the privacy and efficiency. In contrast, we present a novel message-locked integrity auditing scheme without an additional proxy server, which is applicable to both file-level and chunk-level deduplication systems. In particular, our scheme is storage efficient in the sense that apart from eliminating the ciphertext redundancy, we also enable the integrity tag deduplication by a message-derived signing key, which merely incurs minimal client-side computation overhead. Besides, we can still publicly perform the integrity check over any client's cloud storage by incorporating the proxy re-signature technique. We show that the proposed scheme will not disclose the data ownership information and is provably secure under the Computational Diffie-Hellman (CDH) assumption in the random oracle model. Finally, the performance evaluation demonstrates its effectiveness and efficiency.
Characterization of lipid metabolism in a novel immortalized human hepatocyte cell line

The development of hepatocyte cell models that represent fatty acid partitioning within the human liver would be beneficial for the study of the development and progression of nonalcoholic fatty liver disease (NAFLD). We sought to develop and characterize a novel human liver cell line (LIV0APOLY) to establish a model of lipid accumulation using a physiological mixture of fatty acids under low- and high-glucose conditions. LIV0APOLY cells were compared with a well-established cell line (HepG2) and, where possible, primary human hepatocytes. LIV0APOLY cells were found to proliferate and express some mature liver markers and were wild type for the PNPLA3 (rs738409) gene, whereas HepG2 cells carried the Ile148Met variant that is positively associated with liver fat content. Intracellular triglyceride content was higher in HepG2 than in LIV0APOLY cells; exposure to high glucose and/or exogenous fatty acids increased intracellular triglyceride in both cell lines. Triglyceride concentrations in media were higher from LIV0APOLY compared with HepG2 cells. Culturing LIV0APOLY cells in high glucose increased a marker of endoplasmic reticulum stress and attenuated insulin-stimulated Akt phosphorylation whereas low glucose and exogenous fatty acids increased AMPK phosphorylation. Although LIV0APOLY cells and primary hepatocytes stored similar amounts of exogenous fatty acids as triglyceride, more exogenous fatty acids were partitioned toward oxidation in the LIV0APOLY cells than in primary hepatocytes. LIV0APOLY cells offer the potential to be a renewable cellular model for studying the effects of exogenous metabolic substrates on fatty acid partitioning; however, their usefulness as a model of lipoprotein metabolism needs to be further explored.
A Grammar-driven Convolution Tree Kernel for Semantic Role Classification

Convolution tree kernel has shown promising results in semantic role classification. However, it only carries out hard matching, which may lead to over-fitting and less accurate similarity measure. To remove the constraint, this paper proposes a grammardriven convolution tree kernel for semantic role classification by introducing more linguistic knowledge into the standard tree kernel. The proposed grammar-driven tree kernel displays two advantages over the previous one: 1) grammar-driven approximate substructure matching and 2) grammardriven approximate tree node matching. The two improvements enable the grammardriven tree kernel explore more linguistically motivated structure features than the previous one. Experiments on the CoNLL-2005 SRL shared task show that the grammardriven tree kernel significantly outperforms the previous non-grammar-driven one in SRL. Moreover, we present a composite kernel to integrate feature-based and tree kernel-based methods. Experimental results show that the composite kernel outperforms the previously best-reported methods.
Clustering of Count Data Using Generalized Dirichlet Multinomial Distributions

In this paper, we examine the problem of count data clustering. We analyze this problem using finite mixtures of distributions. The multinomial distribution and the multinomial Dirichlet distribution (MDD) are widely accepted to model count data. We show that these two distributions cannot be the best choice in all the applications, and we propose another model called the multinomial generalized Dirichlet distribution (MGDD) that is the composition of the generalized Dirichlet distribution and the multinomial, in the same way that the MDD is the composition of the Dirichlet and the multinomial. The estimation of the parameters and the determination of the number of components in our model are based on the deterministic annealing expectation-maximization (DAEM) approach and the minimum description length (MDL) criterion, respectively. We compare our method to standard approaches such as multinomial and multinomial Dirichlet mixtures to show its merits. The comparison involves different applications such as spatial color image databases indexing, handwritten digit recognition, and text document clustering.
Kmeans-ICA based automatic method for ocular artifacts removal in a motorimagery classification

Electroencephalogram (EEG) recordings aroused as inputs of a motor imagery based BCI system. Eye blinks contaminate the spectral frequency of the EEG signals. Independent Component Analysis (ICA) has been already proved for removing these artifacts whose frequency band overlap with the EEG of interest. However, already ICA developed methods, use a reference lead such as the ElectroOculoGram (EOG) to identify the ocular artifact components. In this study, artifactual components were identified using an adaptive thresholding by means of Kmeans clustering. The denoised EEG signals have been fed into a feature extraction algorithm extracting the band power, the coherence and the phase locking value and inserted into a linear discriminant analysis classifier for a motor imagery classification.
N-cadherin antagonists as oncology therapeutics

The cell adhesion molecule (CAM), N-cadherin, has emerged as an important oncology therapeutic target. N-cadherin is a transmembrane glycoprotein mediating the formation and structural integrity of blood vessels. Its expression has also been documented in numerous types of poorly differentiated tumours. This CAM is involved in regulating the proliferation, survival, invasiveness and metastasis of cancer cells. Disruption of N-cadherin homophilic intercellular interactions using peptide or small molecule antagonists is a promising novel strategy for anti-cancer therapies. This review discusses: the discovery of N-cadherin, the mechanism by which N-cadherin promotes cell adhesion, the role of N-cadherin in blood vessel formation and maintenance, participation of N-cadherin in cancer progression, the different types of N-cadherin antagonists and the use of N-cadherin antagonists as anti-cancer drugs.