Miracle-dz/UniFAR-FTUs huggingface dataset:
Practical compressive sensing with Toeplitz and circulant matrices

Compressive sensing encodes a signal into a relatively small number of incoherent linear measurements. In theory, the optimal incoherence is achieved by completely random measurement matrices. However, such matrices are often difficult and costly to implement in hardware realizations. Random Toeplitz and circulant matrices can be easily (or even naturally) realized in various applications. This paper introduces fast algorithms for reconstructing signals from incomplete Toeplitz and circulant measurements. Computational results are presented to show that Toeplitz and circulant matrices are not only as effective as random matrices for signal encoding, but also permit much faster decoding.
Multiple-Time-Series Clinical Data Processing for Classification With Merging Algorithm and Statistical Measures

A description of patient conditions should consist of the changes in and combination of clinical measures. Traditional data-processing method and classification algorithms might cause clinical information to disappear and reduce prediction performance. To improve the accuracy of clinical-outcome prediction by using multiple measurements, a new multiple-time-series data-processing algorithm with period merging is proposed. Clinical data from 83 hepatocellular carcinoma (HCC) patients were used in this research. Their clinical reports from a defined period were merged using the proposed merging algorithm, and statistical measures were also calculated. After data processing, multiple measurements support vector machine (MMSVM) with radial basis function (RBF) kernels was used as a classification method to predict HCC recurrence. A multiple measurements random forest regression (MMRF) was also used as an additional evaluation/classification method. To evaluate the data-merging algorithm, the performance of prediction using processed multiple measurements was compared to prediction using single measurements. The results of recurrence prediction by MMSVM with RBF using multiple measurements and a period of 120 days (accuracy 0.771, balanced accuracy 0.603) were optimal, and their superiority to the results obtained using single measurements was statistically significant (accuracy 0.626, balanced accuracy 0.459, P <; 0.01). In the cases of MMRF, the prediction results obtained after applying the proposed merging algorithm were also better than single-measurement results (P <; 0.05). The results show that the performance of HCC-recurrence prediction was significantly improved when the proposed data-processing algorithm was used, and that multiple measurements could be of greater value than single
An introduction to tag sequence grammars and the RASP system parser

This report describes the tag sequence grammars released as part of the Robust Accurate Statistical Parsing (RASP) system. It is intended to help users of RASP understand the linguistic and engineering rationale behind the grammars and prepare them to customize the system for their application. It also contains a fairly exhaustive list of references to extant work utilizing the RASP parser.
Therapy to target renal cell carcinoma using 131I-labeled B7-H3 monoclonal antibody

B7-H3 is a tumor-associated antigen that plays a critical role in potential tumor-targeted therapy. In this study, we aimed to assess the radiobiological effect of 131I-labeled B7-H3 monoclonal antibody (131I-4H7) in nude mice with human renal cell carcinoma (RCC) and evaluate the effect of 131I-4H7 on RCC treatment. The radiobiological activity and tumor uptake of 131I-4H7, and its effect on tumor growth were measured. 131I-4H7 was absorbed by the tumor and reached its maximal uptake rate (3.32% injected dose [ID]/g) at 24 h, at which point the drug concentration in the tumor was 7.36-, 2.06-, 1.80-, and 2.78-fold higher than that in muscle, kidneys, liver, and heart, respectively. Measurements and positron emission tomography–computed tomography imaging showed that tumor development was significantly inhibited by 131I-4H7. HE staining revealed that 131I-4H7 significantly injures tumor cells. Our results suggest that 131I-4H7 is markedly absorbed by the tumor and did suppress the development of RCC xenografted tumors in nude mice, which might provide a new candidate for antibody-mediated targeted radiotherapy in human RCC.
Continuously Evolving Programs in Genetic Programming Using Gradient Descent

This paper describes an approach to the use of gradient descent search in genetic programming for continuously evolving genetic programs for object classication problems. An inclusion factor is introduced to each node in a genetic program and gradient descent search is applied to the inclusion factors. Three new on-zero operators and two new continuous genetic operators are developed for evolution. This approach is examined and compared with a basic GP approach on three object classication problems of varying difculty . The results suggest that the new approach can evolve genetic programs continuously. The new method which uses the standard genetic operators and gradient descent applied to the inclusion factors substantially outperforms the basic GP approach which uses the standard genetic operators but does not use the gradient descent and inclusion factors. However, the new method with the continuous operators and the gradient descent on inclusion factors decreases the performance on all the problems.
Impact of Linguistic Analysis on the Semantic Graph Coverage and Learning of Document Extracts

Automatic document summarization is a problem of creating a document surrogate that adequately represents the full document content. We aim at a summarization system that can replicate the quality of summaries created by humans. In this paper we investigate the machine learning method for extracting full sentences from documents based on the document semantic graph structure. In particular, we explore how the Support Vector Machines (SVM) learning method is affected by the quality of linguistic analyses and the corresponding semantic graph representations. We apply two types of linguistic analysis: (1) a simple part-of-speech tagging of noun phrases and verbs and (2) full logical form analysis which identifies Subject-Predicate-Object triples, and then build the semantic graphs. We train the SVM classifier to identify summary nodes and use these nodes to extract sentences. Experiments with the DUC 2002 and CAST datasets show that the SVM based extraction of sentences does not differ significantly for the simple and the sophisticated syntactic analysis. In both cases the graph attributes used in learning are essential for the classifier performance and the quality of extracted summaries.
On maintaining multiple versions in STM

An effective way to reduce the number of aborts in software transactional memory (STM) is to keep multiple versions of transactional objects. In this paper, we study inherent properties of STMs that use multiple versions to guarantee successful commits of all read-only transactions. We first show that these STMs cannot be disjoint-access parallel. We then consider the problem of garbage collecting old object versions, and show that no STM can be optimal in the number of previous versions kept. Moreover, we show that garbage collecting useless versions is impossible in STMs that implement invisible reads. Finally, we present an STM algorithm using visible reads that efficiently garbage collects useless object versions.
Fibroblastic Transformation of Corneal Keratocytes by Rac Inhibition is Modulated by Extracellular Matrix Structure and Stiffness

The goal of this study was to investigate how alterations in extracellular matrix (ECM) biophysical properties modulate corneal keratocyte phenotypes in response to specific wound healing cytokines and Rho GTPases. Rabbit corneal keratocytes were plated within standard collagen matrices (2.5 mg/mL) or compressed collagen matrices (~100 mg/mL) and cultured in serum-free media, PDGF BB, IGF, FGF2 or TGFβ1, with or without the Rac1 inhibitor NSC23766 and/or the Rho kinase inhibitor Y-27632. After 1 to 4 days, cells were labeled for F-actin and imaged using confocal microscopy. Keratocytes within standard collagen matrices (which are highly compliant) maintained a dendritic phenotype following culture in serum-free media, PDGF, IGF and FGF, but developed stress fibers in TGFβ1. Keratocytes within compressed collagen (which has high stiffness and low porosity) maintained a dendritic phenotype following culture in serum-free media, PDGF and IGF, but developed stress fibers in both FGF and TGFβ1. The Rac inhibitor had no significant impact on growth factor responses in compliant matrices. Within compressed collagen matrices however, the Rac inhibitor induced fibroblastic transformation in serum-free media, PDGF and IGF. Fibroblast and myofibroblast transformation was blocked by Rho kinase inhibition. Overall, keratocyte growth factor responses appear to be regulated by both the interplay between Rho and Rac signaling, and the structural and mechanical properties of the ECM.
Term variation and semantics for document classification and detection of obesity and its co-morbidities cases

Within the translational medicine area, researchers aim at connecting more closely medical knowledge and individual patient data, in order to improve patient care quality. The 2008 I2B2 Obesity NLP challenge is to promote such research and to evaluate such systems within the same framework. We participated at this challenge. Our approach bets on use of semantics and term variants. The results obtained by our system are superior to the mean challenge results and seem to be promising, although there is room for improvements which we present and discuss in this paper.
Product Recommendation Based on Search Keywords

Recommender systems have been widely deployed on E-commerce websites. The cold start problem of making effective recommendations to new users without any historical data on the website is still challenging. These new users often have some available information, such as search keywords, before visiting the website. It is natural to use the information to predict users' preference, such that an immediate recommendation is possible. In this paper, we propose a new product recommendation approach for new users based on the implicit relationships between search keywords and products. The relationships between keywords and products are represented in a graph and relevance of keywords to products is derived from attributes of the graph. The relevance information will be utilized to predict preferences of new users. A preliminary experiment is conducted and shows that our approach outperforms the traditional approach (Recommending Most Popular Products).
On the Simulation of Postmodernism and Mental Debility using Recursive Transition Networks

Recursive transition networks are an abstraction related to context free grammars and nite state automata It is possible to generate random meaningless and yet realistic looking text in genres de ned using recursive transition networks often with quite amusing results One genre in which this has been accomplished is that of academic papers on postmodernism In his book G odel Escher Bach An Eternal Golden Braid Douglas Hofstadter demonstrated a method of generating meaningless but grammati cally correct English language text Hofstadter illustrated this method with an example a selection of fragments of text ten of which were generated using a computer program and three which were taken from a journal titled Art Language and a challenge to the reader to identify which sentences were generated arti cially The challenge is made interesting by the fact that the lan guage of the excerpts from the journal is so dense and jargon laden that to the casual observer it does not make any more sense than the computer generated fragments It was this that inspired me initially to write a program to generate arti cial travesties of such writing Recursive Transition Networks There are several approaches to generating travesties of natural language text by computer Perhaps the two most common approaches areMarkov models and recursive transition networks The approach of using Markov models consists of analysing sample text in a genre by breaking it up into units typically words or characters and building up tables of the probabilities of units following other units To generate text one performs a random walk through these tables starting at a unit selecting the following unit randomly with respect to the probabilities discovered doing so again and so on The advantages of this model are that it is automatic the models can be built algorithmically from input and can have delity proportional to the number of prior units considered the degree of the model However this is a dumb model it does not model sentence preposition adjective
Multi-relational Sequence Pattern Mining Method Based on Improved Prefix Tree in the Star Model

With the development of information technology and the increasing amount of data, the way of storing data in single table can not meet the actual needs, it will highlight the importance of the research on multi-relational sequence mining. This paper presents a multi-relational sequence pattern mining algorithm using the variant prefix tree, and the frequent sequence pattern is obtained by connecting all the tables in the improved star model. Using discretization method, combined with users' specified information, as well as the improved structure and the chi-square test of the prefix tree pruning strategy, the sequence patterns can reflect different relationships between entities, providing the effective solution to cross-links between tables in mining issues that the single-table mining failed. The experiments show that the proposed algorithm can efficiently mining the multi-relational sequence patterns with a good performance.
Fast Color-Texture Discrimination: Application to Car Tracking

Visual tracking methods have been intensively contributed in the past decade. Promising results have been brought, leading to partial solution of the problem. However it is still utmost difficult to maintain track of an object for a long time, because some events can strongly disrupt the tracking procedures. Such events are occlusions, clutters, illumination changes, particular movements or pose changes. To overcome these challenging events and produce more reliable tracking algorithms, image data must be exploited through several aspects, that is to say through several cues : texture, color, shape or movement. But before being able to use these sources, one must make sure that each of these sources is reliable and non-redundant. In this article, we reckon that texture and color must be jointly processed, and we propose a new color-texture feature called weighted cooccurrence matrices. Using this feature within a particle filter, successful car tracking examples are proposed.
Pop-ups, Ephemerality, and Consumer Experience: The Centrality of Buzz

Pop-up stores are retail solutions that suddenly appear and then disappear—whether days, weeks, or months later. Multiple brands have created pop-ups across a range of product categories in major international markets. They are characterized by their ephemerality but also by the experience realized during the consumer’s visit to the store. Our thesis is that positive response to pop-ups depends on two major factors: (1) the benefits received by consumers who visit pop-ups and (2) the buzz that is generated, especially through social media, where consumers share their pop-up visit experiences. Although little research has investigated pop-ups, we build on the literature from different fields, especially consumer behavior, sociology, marketing, psychology, modeling of social media, and economics to develop theoretical propositions. The ephemerality of pop-ups and the emotional responses they provoke are critical explanations in our conceptualization of consumers’ response.
Cooperative Gray Hole Attack Discovery and Elimination using Credit based Technique in MANET

Mobile Ad-hoc Network) is a network of mobile devices in a self-configuring and infrastructure less environment. The devices in MANET are linked by wireless medium. There are big chances of attacks in MANET due to its natural features including dynamically changing network topology, open medium, no centralized monitoring and management point. There is no assurance of attack free communication. The malicious node(s) causes dropping and forwarding only selective packets are called as gray hole. So, the security solution must be developed to address the protection of data and route. In this paper we attempted to mitigate the gray hole attack and proposes a credit based approach based on Ad-hoc On Demand Distance Vector (AODV) routing protocol. In the proposed and implemented method, we used credit value measurement for the detection of cooperative gray hole attack. This paper shows the technique which is capable of finding chains of cooperating gray hole nodes which drop a major part of communication. The paper shows the result based on varying density, pause time and mobility. KeywordsAd-hoc Network (MANET), Packet forwarding misbehavior, Cooperative gray hole attack, Security, AODV
Population genetic analysis of the DARC locus (Duffy) reveals adaptation from standing variation associated with malaria resistance in humans

The human DARC (Duffy antigen receptor for chemokines) gene encodes a membrane-bound chemokine receptor crucial for the infection of red blood cells by Plasmodium vivax, a major causative agent of malaria. Of the three major allelic classes segregating in human populations, the FY*O allele has been shown to protect against P. vivax infection and is near fixation in sub-Saharan Africa, while FY*B and FY*A are common in Europe and Asia, respectively. Due to the combination of its strong geographic differentiation and association with malaria resistance, DARC is considered a canonical example of a locus under positive selection in humans. Here, we use sequencing data from over 1,000 individuals in twenty-one human populations, as well as ancient human and great ape genomes, to analyze the fine scale population structure of DARC. We estimate the time to most recent common ancestor (TMRCA) of the FY*O mutation to be 42 kya (95% CI: 34–49 kya). We infer the FY*O null mutation swept to fixation in Africa from standing variation with very low initial frequency (0.1%) and a selection coefficient of 0.043 (95% CI:0.011–0.18), which is among the strongest estimated in the genome. We estimate the TMRCA of the FY*A mutation to be 57 kya (95% CI: 48–65 kya) and infer that, prior to the sweep of FY*O, all three alleles were segregating in Africa, as highly diverged populations from Asia and ≠Khomani San hunter-gatherers share the same FY*A haplotypes. We test multiple models of admixture that may account for this observation and reject recent Asian or European admixture as the cause. Author Summary Infectious diseases have undoubtedly played an important role in ancient and modern human history. Yet, there are relatively few regions of the genome involved in resistance to pathogens that have shown a strong selection signal. We revisit the evolutionary history of a gene associated with resistance to the most common malaria-causing parasite, Plasmodium vivax, and show that it is one of regions of the human genome that has been under strongest selective pressure in our evolutionary history (selection coefficient: 5%). Our results are consistent with a complex evolutionary history of the locus involving selection on a mutation that was at a very low frequency in the ancestral African population (standing variation) and a large differentiation between European, Asian and African populations.
The Effects of “ Right to Work ” Laws on Wages : Evidence from the Taft-Hartley Act of 1947 ∗

This paper uses the details of an historical setting in which the introduction of “right to work” laws was arguably exogenous the period following the passage of the Taft-Hartley Act in 1947 to produce credibly identified estimates of the effects of these laws on wages. The average effect of right to work laws on wages across all sectors of the economy is likely small and slightly negative. Some evidence indicates wage effects are more negative within the highly unionized sector. ∗I would like to thank Abigail Wozniak for her advice on this paper and Daniel Hungerman, James Sullivan, and seminar participants at the University of Notre Dame for helpful comments. All remaining errors are my own.
It's all about the trees towards a hybrid syntax-based MT system

The aim of this paper is to describe the first steps of research towards a hybridMT system that combines the strengths of rule-based syntactic transfer with recently developed syntax-based statistical translation methods within a unified framework. The similarities of both paradigms concerning the processing of syntactically parsed input trees serve as a basis for this reseach. We focus on the statistical part of the future system and present a syntax-based statistical machine translation system—BONSAI—for Polish-to-French translation. Although BONSAI is still under develepmont, it reaches a translation quality on par with that of a modern phrase-based system. We provide the theoretical background as well as some implementation details and preliminary evaluation results for BONSAI. At the end of this paper we shortly discuss the benefits of a combined approach.
Integrase Mediates Nuclear Localization of Ty3

ABSTRACT Retroviruses in nondividing cells and yeast retrotransposons must transit the nuclear membrane in order for integration to occur. Mutations in a bipartite basic motif in the carboxyl-terminal domain of the Ty3 integrase (IN) protein were previously shown to block transposition at a step subsequent to 3′-end processing of Ty3 extrachromosomal DNA. In this work, the Ty3 IN was shown to be sufficient to target green fluorescent protein to the nucleolus. Mutations in the bipartite basic motif abrogated this localization. The region containing the motif was shown to be sufficient for nuclear but not subnuclear localization of a heterologous protein. Viruslike particles (VLPs) from cells expressing a Ty3 element defective for nuclear localization were inactive in an in vitro integration assay, suggesting that nuclear entry is required to form active VLPs or that this motif is required for post-nuclear entry steps. Ty3 inserts at transcription initiation sites of genomic tRNA genes and plasmid-borne 5S and U6 RNA genes transcribed by RNA polymerase III. In situ hybridization with Ty3- and Ty3 long terminal repeat-specific probes showed that these elements which are associated with tRNA genes do not colocalize with the ribosomal DNA (rDNA). However, a PCR assay of cells undergoing transposition showed that Ty3 insertion does occur into the 5S genes, which, in yeast, are interspersed with the rDNA and therefore, like Ty3 IN, associated with the nucleolus.