Miracle-dz/UniFAR-FTUs huggingface dataset:
Wave scattering by small bodies of arbitrary shapes

In this paper we review the results of the author on the theory of scalar and vector wave scattering by small bodies of an arbitrary shape with the emphasis on practical applicability of the formulas obtained and on the mathematical rigor of the theory. For the scalar wave scattering by a single body, our main results can be described as follows: (1) Analytical formulas for the scattering amplitude for a small body of an arbitrary shape are obtained; dependence of the scattering amplitude on the boundary conditions is described. (2) An analytical formula for the scattering matrix for electromagnetic wave scattering by a small body of an arbitrary shape is given. Applications of these results are outlined (calculation of the properties of a rarefied medium; inverse radio measurement problem; formulas for the polarization tensors and capacitance). (3) The multi-particle scattering problem is analyzed and interaction of the scattered waves is taken into account. For the self-consistent field in a medium consisting of many particles ~ 10^{23}, integral-differential equations are found. The equations depend on the boundary conditions on the particle surfaces. These equations offer a possibility of solving the inverse problem of finding the medium properties from the scattering data. For about 5 to 10 bodies the fundamental integral equations of the theory can be solved numerically to study the interaction between the bodies.
A zooming Web browser

We are developing a prototype zooming World-Wide Web browser within Pad++, a multiscale graphical environment. Instead of having a single page visible at a time, multiple pages and the links between them are depicted on a large zoomable information surface. Pages are scaled so that the page in focus is clearly readable with connected pages shown at smaller scales to provide context. We quantitatively compared performance with the Pad++ Web browser and Netscape in several different scenarios. We examined how quickly users could answer questions about a specific Web site designed for this test. Initially we found that subjects answered questions slightly slower with Pad++ than with Netscape. After analyzing the results of this study, we implemented several changes to the Pad++ Web browser, and repeated one Pad++ condition. After improvements were made to the Pad++ browser, subjects using Pad++ answered questions 23% faster than those using Netscape.
Association of GABAA receptors and alcohol dependence and the effects of genetic imprinting

GABA receptor genes have been postulated as candidates affecting the risk for alcoholism. The potential association between genes encoding five subunits of the GABAA receptors and alcoholism (alcohol dependence) was analyzed in the multiplex alcoholic pedigrees collected by the Collaborative Study on the Genetics of Alcoholism (COGA) using family‐based association tests. We found consistent, although weak, linkage disequilibrium between GABRB1 (located on chromosome 4) and alcoholism (P < 0.03). Genes encoding GABRA1 and GABRA6, on chromosome 5, did not provide evidence for association with alcoholism. GABRA5 and GABRB3, on chromosome 15, were reported to be expressed uniparentally from the paternal chromosome. Analyses of paternal transmission of alleles of GABRA5 provided evidence for association with alcoholism, particularly in the Caucasian population and with the stricter ICD‐10 definition of alcoholism (P < 0.004). Evidence of association was also observed during paternal transmission with GABRB3 in the Caucasian population (P < 0.007). Maternal transmissions provided no evidence for association. These data are consistent with an association between the expressed alleles in the GABAA‐gene cluster on chromosome 15 and alcoholism that is modulated by genetic imprinting. © 2003 Wiley‐Liss, Inc.
Dosimetry in Japanese male and female models for a low-frequency electric field

The present study quantified induced current in anatomically based Japanese male and female models for exposure to low-frequency electric fields. A quasi-static finite-difference time-domain (FDTD) method was applied to analyze this problem. For our computational results, the difference of the induced current density averaged over an area of 1 cm2 between Japanese male and female models was less than 30% for each nerve tissue. The difference of induced current density between the present study and earlier works was less than 50% for the same conductivities, despite the different morphology. Particularly, maximum current density in central nerve tissues appeared in the retina of Japanese models, the same as in the earlier works.
A language learner’s target language-mediated socializing in an affinity space in the host country: An autoethnography

The number of international students keeps increasing worldwide. This is partly attributed to their expectation of improving their language skills by actually using the target language (TL) in the host countries. However, past studies show that such opportunities are not automatically given to them. Perceiving himself as one English learner, the author conducted an autoethnography to explore the processes of availing of opportunities to use the TL in Hawaii during his sabbatical. By recording observations and informal interviews in a diary of his own TL-mediated socialization, the author found that he could engage himself as an active social agent within a type of social space called affinity space which greatly promoted his situated TL-learning in naturalistic contexts. The author discusses how his own case can be applied to other learners studying abroad and presents some educational implications.
Stance Detection and Analysis in Social Media

Computational approaches to opinion mining have mostly focused on polarity detection of product reviews by classifying the given text as positive, negative or neutral. While, there is less effort in the direction of socio-political opinion mining to determine favorability towards given targets of interest, particularly for social media data like news comments and tweets. In this research, we explore the task of automatically determining from the text whether the author of the text is in favor of, against, or neutral towards a proposition or target. The target may be a person, an organization, a government policy, a movement, a product, etc. Moreover, we are interested in detecting the reasons behind authors’ positions. This thesis is organized into three main parts: the first part on Twitter stance detection and interaction of stance and sentiment labels, the second part on detecting stance and the reasons behind it in online news comments, and the third part on multi-target stance classification. One may express favor (or disfavor) towards a target by using positive or negative language. Here, for the first time, we present a dataset of tweets annotated for whether the tweeter is in favor of or against pre-chosen targets, as well as for sentiment. These targets may or may not be referred to in the tweets, and they may or may not be the target of opinion in the tweets. We develop a simple stance detection system that outperforms all 19 teams that participated in a recent shared task competition on the same dataset (SemEval-2016 Task #6). Additionally, access to both stance and sentiment annotations allows us to conduct several experiments to tease out their interactions. Next, we proposed a novel framework for joint learning of stance and reasons behind it. This framework relies on topic modeling. Unlike other machine learning approaches for argument tagging which often require a large set of labeled data, our approach is minimally supervised. The extracted arguments are subsequently employed for stance classification. Furthermore, we create and make available the first dataset of online news comments manually annotated for stance and arguments. Experiments on this dataset demonstrate the benefits of using topic modeling, particularly Non-Negative Matrix Factorization, for argument detection. Previous models for stance classification often treat each target independently, ignoring the potential (sometimes very strong) dependency that could exist among targets. However, in many applications, there exist natural dependencies among targets. In this research, we relieve such independence assumptions in order to jointly model the stance expressed towards multiple targets. We present a new dataset that we built for this task and make it publicly available. Next, we show that an attention-based encoder-decoder framework is very effective for this problem, outperforming several alternatives that jointly learn dependent subjectivity through cascading classification or multi-task learning.
Cuisine Classification and Recipe Generation

Classification of recipes into respective cuisines based on ingredients has been done by [1] using techniques like associative classification and support vector machines. A graph-like model is proposed by [2] to model recipes and tries to match substructures among the graphs to find similar recipes. On the other hand, [3] tries to model recipes using a variety of features like taste, smell, texture, nutritive value etc. For our project, we have performed a variety of classification techniques and analyzed how they perform in comparison.
Router and cell library co-development for improving redundant via insertion at pins

In this paper we propose a synergetic approach that integrates router design and cell library engineering for improving post-routing via1 (via between M1 and M2) doubling rate at pins. We develop a double-via (DV) aware multilevel router to exploit the via1 doubling possibilities provided to the cells in a conventional as well as a DV-driven cell library. Compared to a non-DV-aware router using a conventional cell library, our approach using a DV-driven library can on average raise via1 doubling rate by 34%, raise total via doubling rate by 11%, reduce the total number of vias by 3%, and reduce the total number of via1s by 8%. All this can be achieved without incurring any performance and area penalties.
Implications for Structural Health Monitoring and Load Rating Resulting from the Performance of Damage Integrated Bridges

According to the ASCE’s Report Card for America’s Infrastructure, in-service bridges have received scores as low as C+, highlighting an overall poor condition state. This grade reflects the need for strategies and solutions to improve the overall performance. Various studies have been conducted in attempts to resolve or ease this concern, but few have successfully integrated structural health monitoring (SHM) as a standard practice. SHM has the capability of providing accurate, realtime structural data, which can in turn be used to model the response of a structure under loading. The basic premise of SHM provides a rational framework to characterize this performance, but most studies to date have focused primarily on the measurement of response, with fewer efforts focused on the implications of this response. What is currently lacking within the structural health monitoring community is a fundamental understanding of what the measured response means for the overall health of the structure. This investigation presents the results of an ongoing investigation on the characterization and behaviour of damaged in-service bridges. The study includes the numerical simulation of system level behaviour and characterization of two common bridge types, composite steel girder and adjacent prestressed box girders, under the presence of common damage mechanisms. The numerical models were developed using actual in-service bridges and validated against load testing results. In addition, simulation results illustrated the influence of damage on functionality, capacity and ductility, all of which have implications for performance-based decision-making practices such as load rating, maintenance, and preservation. The described framework provides a foundation that can be coupled with traditional measurements to provide credibility and coherence with existing SHM strategies. This coupling would allow for performance-based decisions such as load rating and preservation solutions to be developed based on the scientific merit of behaviour and system response.
Multiple Health Characteristics on Cognitive Function in Older Adults

Individual health characteristics, namely, physical activity, smoking, and weight status, have a positive effect on cognitive function, especially during the aging process. However, fewer studies have evaluated the combined associations of these health characteristics on cognitive function among older adults, which was this study’s purpose. Data from the 1999–2002 National Health and Nutrition Examination Survey were used ( N = 2,370 adults 60+ years). Clinical measures were assessed using the Digit Symbol Substitution Test (DSST), self-reported physical activity, and smoking status. Weight status was evaluated based on measured body mass index. Being a non-smoker [β = 2.44; 95% confidence interval (CI): −0.03–4.92; P = 0.05] and meeting the physical activity guidelines (β = 5.73; 95% CI: 4.34–7.12; P < 0.001) were independently associated with DSST. There was also an evidence of an additive association of these three health characteristics on cognitive function. The highest area under the receiver operating characteristic curve [area under the curve (AUC)] was observed among those who met physical activity guidelines (AUC = 0.59; 95% CI: 0.57–0.61) compared to smoking status (receiver operator characteristic = 0.51; 95% CI: 0.49–0.52) or weight status (AUC = 0.49; 95% CI: 0.47–0.51). Weight status and smoking are independently associated with cognitive function, and individuals with all three evaluated health characteristics (non-smoking, normal weight, and active) had the highest cognitive function.
Mapping Migratory Bird Prevalence Using Remote Sensing Data Fusion

Background Improved maps of species distributions are important for effective management of wildlife under increasing anthropogenic pressures. Recent advances in lidar and radar remote sensing have shown considerable potential for mapping forest structure and habitat characteristics across landscapes. However, their relative efficacies and integrated use in habitat mapping remain largely unexplored. We evaluated the use of lidar, radar and multispectral remote sensing data in predicting multi-year bird detections or prevalence for 8 migratory songbird species in the unfragmented temperate deciduous forests of New Hampshire, USA. Methodology and Principal Findings A set of 104 predictor variables describing vegetation vertical structure and variability from lidar, phenology from multispectral data and backscatter properties from radar data were derived. We tested the accuracies of these variables in predicting prevalence using Random Forests regression models. All data sets showed more than 30% predictive power with radar models having the lowest and multi-sensor synergy (“fusion”) models having highest accuracies. Fusion explained between 54% and 75% variance in prevalence for all the birds considered. Stem density from discrete return lidar and phenology from multispectral data were among the best predictors. Further analysis revealed different relationships between the remote sensing metrics and bird prevalence. Spatial maps of prevalence were consistent with known habitat preferences for the bird species. Conclusion and Significance Our results highlight the potential of integrating multiple remote sensing data sets using machine-learning methods to improve habitat mapping. Multi-dimensional habitat structure maps such as those generated from this study can significantly advance forest management and ecological research by facilitating fine-scale studies at both stand and landscape level.
Prolonged survival of B-lineage acute lymphoblastic leukemia cells is accompanied by overexpression of bcl-2 protein.

Overexpression of bcl-2 delays the onset of apoptosis in lymphohematopoietic cells. We measured levels of bcl-2 protein in normal and leukemic human B-cell progenitors with a specific monoclonal antibody and flow cytometry. Normal immature B cells had low levels of bcl-2 protein; the intensity of fluorescence, expressed as molecules of soluble fluorochrome per cell, within CD10+ cells was 3,460 +/- 1,050 (mean +/- SD; 5 samples). In 16 cases of B-lineage acute lymphoblastic leukemia (ALL), cells had levels of bcl-2 that were strikingly higher than those of their normal counterparts (33,560 +/- 14,570; P < .001 by t-test analysis). We next investigated whether the intensity of bcl-2 expression correlated with the resistance of immature B cells to in vitro culture. In 12 cases of B-lineage ALL, the cells recovered after 7 days of culture on allogeneic bone marrow stromal layers were 69% to 178% (median, 95.5%) of those originally seeded. Prolonged survival of leukemic cells in vitro was observed even in the absence of stromal layers in 6 of these 12 cases; the intensity of bcl-2 protein expression in these cases was 45,000 +/- 13,270, compared with 21,500 +/- 7,260 in the 6 cases in which greater than 99.5% of cells rapidly died by apoptosis under the same culture conditions (P = .003). Five immature B-cell lines, continuously growing in the absence of stroma, had the highest bcl-2 expression (79,400 +/- 20,330). By contrast, most normal CD19+, sIg-immature B cells died despite the presence of bone marrow stromal layers; 9.7% to 28.2% were recovered after 7 days of culture in three experiments. We conclude that abnormal bcl-2 gene expression influences the survival ability of B-cell progenitors. This may contribute to leukemogenesis and explain the aptitude of leukemic lymphoblasts to expand outside the bone marrow microenvironment.
Radiative feedback from quasars and the growth of massive black holes in stellar spheroids

We discuss the importance of feedback via photoionization and Compton heating on the co-evolution of massive black holes (MBHs) at the centre of spheroidal galaxies, and their stellar and gaseous components. We first assess the energetics of the radiative feedback from a typical quasar on the ambient interstellar medium (ISM). We then demonstrate that the observed M BH -σ relation could be established following the conversion of most of the gas of an elliptical progenitor into stars, specifically when the gas-to-stars mass ratio in the central regions has dropped to a low level ∼0.01 or less, so that gas cooling is no longer able to keep up with the radiative heating by the growing central massive black hole (MBH). A considerable amount of the remaining gas will be expelled and both MBH accretion and star formation will proceed at significantly reduced rates thereafter, in agreement with observations of present-day ellipticals. We find further support for this scenario by evolving over an equivalent Hubble time a simple, physically based toy model that additionally takes into account the mass and energy return for the spheroid evolving stellar population, a physical ingredient often neglected in similar approaches.
Modeling and analysis of soft-test/repair for CCD-based digital X-ray systems

Modern X-ray imaging systems evolve toward digitization for reduced cost, faster time-to-diagnosis, and improved diagnostic confidence. For the digital X-ray systems, charge coupled device (CCD) technology is commonly used to detect and digitize optical X-ray image. This paper presents a novel soft-test/repair approach to overcome the defective pixel problem in CCD-based digital X-ray systems through theoretical modeling and analysis of the test/repair process. There are two possible solutions to cope with the defective pixel problem in CCDs: one is the hard-repair approach and another is the proposed soft-test/repair approach. Hard-repair approach employs a high-yield, expensive reparable CCD to minimize the impact of hard defects on the CCD, which occur in the form of noise propagated through A/D converter to the frame memory. Therefore, less work is needed to filter and correct the image at the end-user level while it maybe exceedingly expensive to practice. On the other hand, the proposed soft-test/repair approach is to detect and tolerate defective pixels at the digitized image level; thereby, it is inexpensive to practice and on-line repair can be done for noninterrupted service. It tests the images to detect the detective pixels and filter noise at the frame memory level and caches them in a flash memory in the controller for future repair. The controller cache keeps accumulating all the noise coordinates and preprocesses the incoming image data from the A/D converter by repairing them. The proposed soft-test/repair approach is particularly devised to facilitate hardware level implementation ultimately for real-time telediagnosis. Parametric simulation results demonstrate the speed and virtual yield enhancement by using the proposed approach; thereby highly reliable, yet inexpensive, soft-test/repair of CCD-based digital X-ray systems can be ultimately realized.
Increased Levels of Nuclear SREBP-1c Associated with Fatty Livers in Two Mouse Models of Diabetes Mellitus*

Hepatic steatosis is common in non-insulin-dependent diabetes and can be associated with fibrosis and cirrhosis in a subset of individuals. Increased rates of fatty acid synthesis have been reported in livers from rodent models of diabetes and may contribute to the development of steatosis. Sterol regulatory element-binding proteins (SREBPs) are a family of regulated transcription factors that stimulate lipid synthesis in liver. In the current studies, we measured the content of SREBPs in livers from two mouse models of diabetes, obese ob/ob mice and transgenic aP2-SREBP-1c436 (aP2-SREBP-1c) mice that overexpress nuclear SREBP-1c only in adipose tissue. The aP2-SREBP-1c mice exhibit a syndrome that resembles congenital generalized lipodystrophy in humans. Both lines of mice develop hyperinsulinemia, hyperglycemia, and hepatic steatosis. Nuclear SREBP-1c protein levels were significantly elevated in livers from ob/ob and aP2-SREBP-1c mice compared with wild-type mice. Increased nuclear SREBP-1c protein was associated with elevated mRNA levels for known SREBP target genes involved in fatty acid biosynthesis, which led to significantly higher rates of hepatic fatty acid synthesis in vivo. These studies suggest that increased levels of nuclear SREBP-1c contribute to the elevated rates of hepatic fatty acid synthesis that leads to steatosis in diabetic mice.