Miracle-dz/UniFAR-FTUs huggingface dataset:
An Introduction to the DSm Theory for the Combination of Paradoxical, Uncertain, and Imprecise Sources of Information

The management and combination of uncertain, imprecise, fu zzy and even paradoxical or high conflicting sources of information has always been, and still remains today, of pri mal importance for the development of reliable modern infor mation systems involving artificial reasoning. In this introduction, we pr esent a survey of our recent theory of plausible and paradoxi cal reasoning, known as Dezert-Smarandache Theory (DSmT) in the literatur e, developed for dealing with imprecise, uncertain and para doxical sources of information. We focus our presentation here rath er on the foundations of DSmT, and on the two important new rul es of combination, than on browsing specific applications of DSmTavailable in literature. Several simple examples are giventhroughout the presentation to show the efficiency and the generality of thi s new approach.
Exploration of the additivity approximation for spectral magnitudes

The separation of acoustic signals is often accomplished through subtractive decompositions of frequency-domain representations. This is typically enabled by the zero phase approximation or the un-correlated signals approximation but both of these are very coarse approximations in the mathematical sense. We investigate this disconnect between what works in practice and what is mathematically correct. We conduct a broad search for a domain where the addi-tivity of spectral magnitudes is best satisfied. We apply objective estimators to time-domain reconstructions to characterize the true auditory impact of the magnitude additivity approximation. Our results show the auditory impacts of additivity approximations and allow comparison with the impact of using mixture phase and exact magnitudes in the time-domain reconstruction.
Supraspinal Brain-Derived Neurotrophic Factor Signaling: A Novel Mechanism for Descending Pain Facilitation

In the adult mammalian brain, brain-derived neurotrophic factor (BDNF) is critically involved in long-term synaptic plasticity. Here, we show that supraspinal BDNF-tyrosine kinase receptor B (TrkB) signaling contributes to pain facilitation. We show that BDNF-containing neurons in the periaqueductal gray (PAG), the central structure for pain modulation, project to and release BDNF in the rostral ventromedial medulla (RVM), a relay between the PAG and spinal cord. BDNF in PAG and TrkB phosphorylation in RVM neurons are upregulated after inflammation. Intra-RVM sequestration of BDNF and knockdown of TrkB by RNA interference attenuate inflammatory pain. Microinjection of BDNF (10–100 fmol) into the RVM facilitates nociception, which is dependent on NMDA receptors (NMDARs). In vitro studies with RVM slices show that BDNF induces tyrosine phosphorylation of the NMDAR NR2A subunit in RVM via a signal transduction cascade involving IP3, PKC, and Src. The supraspinal BDNF-TrkB signaling represents a previously unknown mechanism underlying the development of persistent pain. Our findings also caution that application of BDNF for recovery from CNS disorders could lead to undesirable central pain.
Kathaa : NLP Systems as Edge-Labeled Directed Acyclic MultiGraphs

We present Kathaa, an Open Source web-based Visual Programming Framework for Natural Language Processing (NLP) Systems. Kathaa supports the design, execution and analysis of complex NLP systems by visually connecting NLP components from an easily extensible Module Library. It models NLP systems an edge-labeled Directed Acyclic MultiGraph, and lets the user use publicly co-created modules in their own NLP applications irrespective of their technical proficiency in Natural Language Processing. Kathaa exposes an intuitive web based Interface for the users to interact with and modify complex NLP Systems; and a precise Module definition API to allow easy integration of new state of the art NLP components. Kathaa enables researchers to publish their services in a standardized format to enable the masses to use their services out of the box. The vision of this work is to pave the way for a system like Kathaa, to be the Lego blocks of NLP Research and Applications. As a practical use case we use Kathaa to visually implement the Sampark Hindi-Panjabi Machine Translation Pipeline and the Sampark Hindi-Urdu Machine Translation Pipeline, to demonstrate the fact that Kathaa can handle really complex NLP systems while still being intuitive for the end user.
Blood Perfusion Models for Infrared Face Recognition

Infrared (IR) technology has traditionally been applied to military use and remote sensing. During the last two decades, the cost of IR cameras (especially uncooled imagers) has been significantly reduced with the development of CCD technology, and therefore civil applications have increased constantly due to its unique features. One of such applications is IR face recognition (Prokoski et al., 1992, Prokoski, 2000, Kong et al., 2005). The fundamentals behind it are, as indicated by Kong et al (Kong et al., 2005) that IR images are independent of external illumination. While visible images represent the reflectance information of the face surface, IR face images contain more fundamental information about faces themselves, such as anatomical information (Prokoski, et al., 1992, Prokoski, 2000); the thermal characteristics of faces with variations in facial expression and make-up remain nearly invariant (Socolinsky & Selinger, 2002) and the tasks of face detection, localization, and segmentation are relatively easier and more reliable than those in visible images (Kong et al., 2005). It has been pointed by Prokoski et al. (Prokoski et al., 1992) that humans are homoiotherm and hence capable of maintaining constant temperature under different surroundings. The thermal images collected over 20 years have demonstrated that the thermal measurements of individuals are highly repeatable under the same conditions. Furthermore, as discussed by Prokoski (Prokoski, 2000), a facial thermal pattern is determined by the vascular structure of each face, which is irreproducible and unique. Based on the assumption that facial thermal patterns are determined by blood vessels transporting warm blood, Prokoski tried to extract the blood vessel minutiae (Prokoski, 2001) or vascular network (Buddharaju et al., 2004, Buddharaju et al., 2005) as the facial features for recognition. The basic idea is to extract such features using image segmentation. It has been indicated by Guyton & Hall (Guyton & Hall, 1996) that the average diameter of blood vessels is around 10~15μm, which is too small to be detected by current IR cameras (limited by the spatial resolution); the skin directly above a blood vessel is on average 0.1°C warmer than the adjacent skin, which is beyond the thermal accuracy of current IR cameras. The methods using image segmentation in (Prokoski, 2001, Buddharaju et al., 2004, Buddharaju et al., 2005) are heuristic, and it still remains a big challenge to capture the pattern of blood vessels on each face. On the other hand, the phenomenon of “homoiotherm” due to human temperature regulation has led to the direct use of thermograms for recognition (Wilder et al., 1996, O pe n A cc es s D at ab as e w w w . in te ch w eb . o rg
Extinction Training in Conjunction with a Partial Agonist of the Glycine Site on the NMDA Receptor Erases Memory Trace

Much evidence indicates that extinction training does not erase memory traces but instead forms inhibitory learning that prevents the expression of original memory. Fear conditioning induces long-term potentiation and drives synaptic insertion of AMPA receptors into the amygdala. Here we show that extinction training applied 1 h after training reversed the conditioning-induced increase in surface glutamate receptor subunit 1 (GluR1) in parallel with the inhibition of startle potentiation. However, if applied 24 h after training, extinction training reduced startle potentiation without influencing the GluR1 increase. We infused d-cycloserine (DCS), a partial agonist of the glycine site on the NMDA receptor, bilaterally into the amygdala 30 min before extinction training. This augmented the extinction training-elicited reduction in startle and reversed the conditioning-induced increase in GluR1. Delivery of five sets of tetanic stimulation (TS) to the external capsule produced a robust enhancement of synaptic responses in the lateral amygdala neurons that persisted for >2 h. Low-frequency stimulation applied 1 h after TS had no long-lasting effect on synaptic responses. The same treatments, however, induced depotentiation in the presence of DCS and reversed TS-induced increase in surface GluR1. Together, this study has two important findings: (1) whether a memory trace remains intact or is erased depends on the interval between conditioning and extinction training and (2) DCS facilitates the reversal of memory trace. DCS-induced augmentation of extinction and reversal of GluR1 surface expression are likely mediated by DCS-facilitated endocytosis of AMPA receptors.
Network Coding Based CQI Reporting for Two-Way Multi-Relay Networks

This paper considers channel quality indicator (CQI) reporting for data exchange in a two-way multi-relay network. We first propose an efficient CQI reporting scheme based on network coding, where two terminals are allowed to simultaneously estimate the CQI of the distant terminal-relay link without suffering from additional overhead. In addition, the transmission time for CQI feedback at the relays is reduced by half while the increase in complexity and the loss of performance are negligible. This results in a significant improvement of system throughput especially when the number of relays is large. Upper and lower bounds of the mean square error (MSE) of the estimated CQI are derived to study performance behaviour of our proposed scheme. It is found that the MSE of the estimated CQI increases proportionally with the square of the cardinality of CQI level sets though an increased number of CQI levels would eventually lead to a higher data-rate transmission. Based on the derived bounds, a low-complexity relay selection (RS) scheme is then proposed. Simulation results show that, in comparison with optimal methods, our suboptimal bound-based RS scheme achieves satisfactory performance while reducing the complexity at least three times in case of large number of relays. Quoc-Tuan Vien is with School of Engineering and Built Environment, Glasgow Caledonian University, Cowcaddens Road, Glasgow G4 0BA, United Kingdom. Email: quoctuan.vien@gcu.ac. uk. Huan X. Nguyen is with School of Engineering and Information Sciences, Middlesex University, The Burroughs, London NW4 4BT, United Kingdom. Email: h.nguyen@mdx.ac. uk.
On stepwise regression

Given data $y$ and $k$ covariates $x$ one problem in linear regression is to decide which in any of the covariates to include when regressing $y$ on the $x$. If $k$ is small it is possible to evaluate each subset of the $x$. If however $k$ is large then some other procedure must be use. Stepwise regression and the lasso are two such procedures but they both assume a linear model with error term. A different approach is taken here which does not assume a model. A covariate is included if it is better than random noise. This defines a procedure which is simple both conceptually and algorithmically
A Hidden Markov Model for Alphabet-Soup Word Recognition

Recent work on the “alphabet soup” paradigm has demonstrated effective segmentation-free character-based recognition of cursive handwritten historical text documents. The approach first uses a joint boosting technique to detect potential characters the alphabet soup. A second stage uses a dynamic programming algorithm to recover the correct sequence of characters. Despite experimental success, the ad hoc dynamic programming method previously lacked theoretical justification. This paper puts the method on a sounder footing by recasting the dynamic programming as inference on an ensemble of hidden Markov models (HMMs). Although some work has questioned the use of score outputs from classifiers like boosting and support vector machines for probability estimates, experiments in this case show good results from treating shifted boosting scores as log probabilities.
Recovery Rates of Bank Loans: Empirical Evidence for Germany

The first aim of this empirical study is to gain insights into the level and distribution of recovery rates of defaulted borrowers at a large German bank, because very little research actually exists for continental Europe. Subsequently, potential influencing factors on the recovery rate, both discussed in the literature and self-defined, are examined and compared to empirical findings in the literature for US banks to illustrate possible country differences due to factors like the insolvency laws, the financing structure of the companies and the composition of the industry classification. The remainder of this paper is organized in the following manner. Section 2 reviews the literature regarding recovery rates of loans. Based on these studies, hypotheses concerning potential influencing factors about the recovery rate are defined in section 3. Subsequently, the description of the data set takes place in section 4. In section 5 information about the level and distribution of recovery rates are described and the postulated hypotheses are tested. Section 6 concludes the paper and suggests avenues for further research.
Automated extraction of DNA and RNA from a single formalin-fixed paraffin-embedded tissue section for analysis of both single-nucleotide polymorphisms and mRNA expression.

BACKGROUND There is an increasing need for the identification of both DNA and RNA biomarkers from pathodiagnostic formalin-fixed paraffin-embedded (FFPE) tissue samples for the exploration of individualized therapy strategies in cancer. We investigated a fully automated, xylene-free nucleic acid extraction method for the simultaneous analysis of RNA and DNA biomarkers related to breast cancer. METHODS We copurified both RNA and DNA from a single 10-μm section of 210 paired samples of FFPE tumor and adjacent normal tissues (1-25 years of archival time) using a fully automated extraction method. Half of the eluate was DNase I digested for mRNA expression analysis performed by using reverse-transcription quantitative PCR for the genes estrogen receptor 1 (ESR1), progesterone receptor (PGR), v-erb-b2 erythroblastic leukemia viral oncogene homolog 2, neuro/glioblastoma derived oncogene homolog (avian) (ERBB2), epoxide hydrolase 1 (EPHX1), baculoviral IAP repeat-containing 5 (BIRC5), matrix metallopeptidase 7 (MMP7), vascular endothelial growth factor A (VEGFA), and topoisomerase (DNA) II alpha 170kDa (TOP2A). The remaining undigested aliquot was used for the analysis of 7 single-nucleotide polymorphisms (SNPs) by MALDI-TOF mass spectrometry. RESULTS In 208 of 210 samples (99.0%) the protocol yielded robust quantification-cycle values for both RNA and DNA normalization. Expression of the 8 breast cancer genes was detected in 81%-100% of tumor tissues and 21%-100% of normal tissues. The 7 SNPs were successfully genotyped in 91%-97% of tumor and 94%-97% of normal tissues. Allele concordance between tumor and normal tissue was 98.9%-99.5%. CONCLUSIONS This fully automated process allowed an efficient simultaneous extraction of both RNA and DNA from a single FFPE section and subsequent dual analysis of selected genes. High gene expression and genotyping detection rates demonstrate the feasibility of molecular profiling from limited archival patient samples.
The Invisibility of Mild Traumatic Brain Injury: Impaired Cognitive Performance as a Silent Symptom.

The present study was designed to tackle two notorious features of mild traumatic brain injury (mTBI)-heterogeneity and invisibility-by characterizing the full scope of mTBI symptoms. Mice were exposed to brain injuries of different intensities utilizing a weight-drop model (10, 30, 50, and 70 g) and subsequently subjected to a comprehensive battery of behavioral tests at different time points and immunohistochemical examination of cortical slices. Whereas the physiological, neurological, emotional, and motor function of mTBI mice (i.e., their well-being) remained largely intact, cognitive deficits were identified by the y-maze and novel object recognition. Results from these two cognitive tests were combined and a dose-response relationship was established between injury intensity and cognitive impairment, ranging from an 85% decline after a 70-g impact (p < 0.001) to a 20% decline after a 10-g impact (essentially no effect). In addition, higher intensities of injury were accompanied by decreased expression of axonal and synaptic markers. Thus, our mTBI mice showed a clear discrepancy between performance (poor cognitive function) and appearance (healthy demeanor). This is of major concern given that diagnosis of mTBI is established on the presence of clinical symptoms and emphasizes the need for an alternative diagnostic modality.
Developing a toolkit to support collaboration for new product development

Identifying needs and aspirations of potential users for future products becomes ever more challenging as technology advances to fulfill new functions in all areas of people’s activities. Within the domain of industrial design, we have developed a number of techniques that capture a variety of requirements. These techniques were developed to enable users to express their needs in more effective and efficient ways. They were combined into a toolkit to support the design process by enabling collaboration between designers, design researchers, and users. Through these, designers can gain access to essential information about users through a number of communication channels – through group discussion, creative user activities, questionnaires, use of images, and observation of user activities. These techniques were specifically chosen to support designers’ creative processes directly, and enable their direct involvement into user research activities.
Computational Approach to Arabic Broken Derived Nouns Morphology

Arabic morphology is generally considered to be of non-concatenative type which depends on manipulating root letters by means which are called patterns. In addition, the affixation, syllabifications and derivational processes may cause stems to undergo redundant modifications. The basic problem is the large number of redundant variants that must be captured and implemented in a prefect irredundant manner. In this paper, we seek to reduce the redundancy of Arabic broken noun morphology using root-and-pattern morphology theory to represent linguistic resources and Xerox finite state machine as a computational tool to implement them. We show that the space of the meta-data network morphology can be kept small if we consider pattern-sharing technique above pattern-non-sharing. The reduction in the number of states and arcs keeps the system small. By this, it is saving time, memory and processing cost. We primarily focus on broken noun patterns because of their complexity and diversity.
Advanced Multi-level Successive Elimination Algorithms Using the Locality of Pixels in Block

In this paper, we present fast algorithms to reduce the computations of block matching algorithms for motion estimation in video coding. Advanced Multi-level Successive Elimination Algorithms are based on the Multi-level Successive Elimination Algorithm. Advanced Multi-level Successive Elimination Algorithms consist of two algorithms. The first algorithm is that when we calculate the sum of absolute difference between the sum norms of sub-blocks in Multi-level Successive Elimination Algorithm, we use the locality of pixels in block. By using the first algorithm, we can get the greater values of the sum of absolute difference between the sum norms of sub-blocks early. The second algorithm is that when we calculate the sum of absolute difference between the sum norms of sub-blocks, we use the partial distortion elimination scheme. By using the second algorithm in the process of the first algorithm, we can eliminate the blocks which can not become the best matching block efficiently. So, the computations of Multi-level Successive Elimination Algorithm can be reduced further. We proved the effectiveness of the proposed algorithms by experimental results.
Using Linkage Analysis to Detect Gene-Gene Interaction by Stratifying Family Data on Known Disease, or Disease-Associated, Alleles

Detecting gene-gene interaction in complex diseases is a major challenge for common disease genetics. Most interaction detection approaches use disease-marker associations and such methods have low power and unknown reliability in real data. We developed and tested a powerful linkage-analysis-based gene-gene interaction detection strategy based on conditioning the family data on a known disease-causing allele or disease-associated marker allele. We computer-generated multipoint linkage data for a disease caused by two epistatically interacting loci (A and B). We examined several two-locus epistatic inheritance models: dominant-dominant, dominant-recessive, recessive-dominant, recessive-recessive. At one of the loci (A), there was a known disease-related allele. We stratified the family data on the presence of this allele, eliminating family members who were without it. This elimination step has the effect of raising the “penetrance” at the second locus (B). We then calculated the lod score at the second locus (B) and compared the pre- and post-stratification lod scores at B. A positive difference indicated interaction. We also examined if it was possible to detect interaction with locus B based on a disease-marker association (instead of an identified disease allele) at locus A. We also tested whether the presence of genetic heterogeneity would generate false positive evidence of interaction. The power to detect interaction for a known disease allele was 60–90%. The probability of false positives, based on heterogeneity, was low. Decreasing linkage disequilibrium between the disease and marker at locus A decreased the likelihood of detecting interaction. The allele frequency of the associated marker made little difference to the power.
The influence of sitting time and physical activity on health outcomes in public housing residents.

OBJECTIVES Examine differences between levels of physical activity and sitting time for residents of public housing developments located in high vs low income neighborhoods, and whether physical activity or sitting time had a greater influence on health outcomes. DESIGN Secondary data analysis from the Healthful Options Using Streets and Transportation in Our Neighborhoods (HOUSTON) project. SETTING Public housing developments located in Houston, TX. PARTICIPANTS African American, adult males and females. MAIN OUTCOME MEASURES Self-reported PA and time spent sitting on weekdays were measured using the International Physical Activity Questionnaire (IPAQ) short form. Participants completed measures of BMI (kg/m2), % body fat (%BF) and resting blood pressure to assess health outcomes. Neighborhood income was defined as the median household income at the census block group level, obtained from the 2006-2010 American Community Survey. RESULTS All participants (N = 216) had an annual household income of < or = $19,350, and neighborhood income ranged from $9,226 to $57,618. Participants reported an average of 4342.2 +/- 4828.3 MET-min/wk of physical activity, and 4.5 +/- 3.2 hours of sitting per weekday. Time spent sitting was associated with BMI (beta = .50, t = 2.4, P = .018), %BF (beta = .87, t = 3.6, P =.000), and diastolic blood pressure (beta = .62, t = 2.1, P = .041). Physical activity was not significantly associated with any health outcomes. CONCLUSION Our findings indicate that public housing residents' health statuses are vulnerable to sedentary behaviors regardless of the affluence of the neighborhood surrounding the housing development.