Miracle-dz/UniFAR-FTUs huggingface dataset:
A CROSS-CULTURAL COMPARISON OF ATTITUDES TOWARD ROBOTS AMONG A JAPANESE AND US UNIVERSITY FACULTY USING IMPLICIT AND EXPLICIT MEASURES

Vasudevan, Sandosh K. MS, Indiana University, May, 2008. A Cross-Cultural Comparison of Attitudes toward Robots among a Japanese and US University Faculty using Implicit and Explicit Measures. Major Professor: Karl F. MacDorman. Japan has more robots than any other country, and robots play a role in many areas of Japanese society, including manufacturing, healthcare, and entertainment. However, there have been relatively few cross-cultural studies to examine Japan’s robot-oriented culture, and the studies there have been tend to suffer from a selfselection bias by sampling among participants who are visiting robotics exhibits or robot-related news groups. In addition, no studies have considered implicit measures of attitudes toward robots, which are indicative of uncontrolled cognitive processes, thus reducing self-presentational bias. This study presents a cross-cultural comparison of attitudes toward robots at a US and Japanese faculty using explicit and implicit measures. The results of this study indicate Japanese faculty had many more robot-related experiences than the US faculty. Although US participants reported a slightly stronger preference for people than the Japanese participants did, and Japanese participants reported slightly warmer feelings toward robots, implicit measures showed virtually no difference between the groups. Both groups had significantly more pleasant associations with humans than with robots. Both the Japanese and US faculty reported people as being more dangerous than robots but implicitly associated robots more strongly with weapons than people. This gap was bigger for the US faculty. It may indicate a conflict between rational fears of people and unconscious fears of robots. Given the higher prevalence of violent crime in the US, people are a known danger, but the danger of robots is harder to assess owing to a lack of experience with them.
The effects of parental alcoholism and childhood conduct disorder symptoms on early-, middle-, and late-adolescence-onset alcoholism in young adults

Fox, L. W. ; Bierut, L. J.; Reich, W. ; Bucholz, Kathleen K. ; Constantino, J.; Crowe, R. ; Hesselbrock, V. ; Kramer, J.; Kuperman, S. ;Nurnberger, Jr., J.; Schuckit, M. ; and Begleiter, H. , "The effects of parental alcoholism and childhood conduct disorder symptoms onearly-, middle-, and late-adolescence-onset alcoholism in young adults" (2005).
Evolutionary algorithm for prioritized pairwise test data generation

Combinatorial Interaction Testing (CIT) is a technique used to discover faults caused by parameter interactions in highly configurable systems. These systems tend to be large and exhaustive testing is generally impractical. Indeed, when the resources are limited, prioritization of test cases is a must. Important test cases are assigned a high priority and should be executed earlier. On the one hand, the prioritization of test cases may reveal faults in early stages of the testing phase. But, on the other hand the generation of minimal test suites that fulfill the demanded coverage criteria is an NP-hard problem. Therefore, search based approaches are required to find the (near) optimal test suites. In this work we present a novel evolutionary algorithm to deal with this problem. The experimental analysis compares five techniques on a set of benchmarks. It reveals that the evolutionary approach is clearly the best in our comparison. The presented algorithm can be integrated into a professional tool for CIT.
Medical Students’ Beliefs and Attitudes Towards Schizophrenia Before and After Psychiatric Training in Almaarefa Colleges, Riyadh, Saudi Arabia

Background: Public surveys have shown that the general population has limited knowledge about mental illness and holds unfavorable attitudes towards the people who suffer from it in various, culturally diverse, countries. Aims: The aim of the present study was to explore medical students’ beliefs and attitudes towards people with schizophrenia (PwS) prior to receiving any training in psychiatry and to assess the impact of the psychiatric placement on changing them. Methods: A questionnaire addressing beliefs, attitudes and desired social distance from PwS was distributed to all final year medical students before the beginning of their 4-week undergraduate psychiatric placement and upon its completion. Results: Students did not endorse stereotypes commonly attached to PwS, such as being dangerous, lazy or of lower intelligence, but they held the view that PwS are unpredictable and suffer from split personality. Furthermore, the baseline level of desired social distance was found to increase as the intimacy of the interaction increased, and the only variable associated with it was personal experience of serious mental illness. Moreover, the placement was found to have either no influence at all or in a negative direction. Upon its completion more students were found to believe that PwS couldn’t recover, have no insight into their condition, cannot make reasonable decisions, cannot work in regular jobs and are dangerous to the public. No difference was recorded in social distance scores. Conclusions: A close and critical examination of the various elements of the undergraduate placement in psychiatry is needed in order to develop an evidence based, fully rounded education with an anti-stigma orientation.
Mesh-Based Inverse Kinematics

2 Feature Vectors 6 2.1 The deformation gradient . 6 2.2 Extending the triangle representation . 6 2.3 Extracting Feature Vectors . 7 2.4 Reconstructing the Mesh . 8
Computational models: Bottom-up and top-down aspects

Computational models of visual attention have become popular over the past decade, we believe primarily for two reasons: First, models make testable predictions that can be explored by experimentalists as well as theoreticians, second, models have practical and technological applications of interest to the applied science and engineering communities. In this chapter, we take a critical look at recent attention modeling efforts. We focus on {\em computational models of attention} as defined by Tsotsos \& Rothenstein \shortcite{Tsotsos_Rothenstein11}: Models which can process any visual stimulus (typically, an image or video clip), which can possibly also be given some task definition, and which make predictions that can be compared to human or animal behavioral or physiological responses elicited by the same stimulus and task. Thus, we here place less emphasis on abstract models, phenomenological models, purely data-driven fitting or extrapolation models, or models specifically designed for a single task or for a restricted class of stimuli. For theoretical models, we refer the reader to a number of previous reviews that address attention theories and models more generally \cite{Itti_Koch01nrn,Paletta_etal05,Frintrop_etal10,Rothenstein_Tsotsos08,Gottlieb_Balan10,Toet11,Borji_Itti12pami}.
Economics of Crime and Enforcement

Introduction Part I: Fundamental Economics of Crime and Enforcement 1. The Economic Rationale for the Criminal Law 2. Benefit/Cost Analysis of the Enforcement Decision 3. Measuring the Amount and Cost of Crime Part II: Applying Economic Theory to Crime 4. The Market for "Victimless" Crime 5. The Market for Crime with Victims 6. Risk Preferences and the Supply of Offenses 7. The State Preference Model and the Economics of Tax Evasion 8. Modeling Neighborhood Crime and Self-Enforcement 9. Enforcement Games Part III: Use of Statistical Analysis in Research on Crime 10. Statistical Problems in Testing Models of Crime 11. Implicit Market Measures of the Benefits of Crime Control Part IV: Economics of Specific Enforcement Issues 12. Economics of Incarceration: Deterrence and Incapacitation 13. Economics of Three-Strikes Legislation 14. Juvenile Crime 15. Neighborhood Gangs 16. Economic Effects of Private Enforcement 17. The Broken Windows Hypothesis 18. Crime and Economic Development 19. Guns and Crime 20. Drugs and Crime 21. Economics of Profiling 22. Deterrence and Capital Punishment
Peptide binding to major histocompatibility complex molecules.

INTRODUCTION . 163 THE STRUCTURE OF MHC MOLECULES . 166 THE CHARACTERIZATION OF PEPTIDES BOUND BY MHC MOLECULES , . 173 ASSEMBLY OF MHC: PEPTIDE COMPLEXES . 182 In Vivo Binding of Peptide to MHC Class I Molecules . 183 In Vivo Binding of Peptide to MHC Class II Molecules. 189 SUMMARY AND PERSPECTIVES . 196
Design and Evaluation of a Configurable Hardware Merge Sorter for Various Output Records

Sorting is one of the fundamental operations that are important in many applications such as image processing and database. Many researches have been developed to improve the performance of sorting. One of the most promising techniques is FPGA-based hardware merge sorters (HMS). While previous studies on HMS achieved a very high throughput, most of them could output only power of two records per clock cycle. Moreover, they couldn't evaluate the performance of HMS configuration that outputs more than 32 records per clock cycle due to hardware resources limitation. In this paper, we propose an HMS architecture that can be configured to output not only power of two records but various outputs e.g., 3, 7, and 12. In addition, our proposed HMS can be configured to output more than 32 records such as 40, 48, and 56 records per clock cycle. Finally, we study the performance evaluation for different configurations of key and data widths that can be required by different sorting applications.
S 1 for Simple topological features reflect dynamics and modularity in protein interaction networks

S1 Supplementary Results 1 S1.1 Hub classification analysis . 1 S1.2 Analysis for a relaxed definition of hubs . 2 S1.3 Potential biases and confounding factors in the correlation analysis of hub characteristics . 2 S1.4 GO annotations of hubs . 2 S1.5 Correction for essentiality when studying the number of genetic interactions of genes . 3 S1.6 Yeast two-hybrid and co-complex interaction networks . 3 S1.7 Comparison of network topology properties for orthologs between organisms . 3 S1.8 Correction for signal from random networks for genetic interactions and essentiality 3
Label-Free Fluorescent Aptasensor for Small Targets via Displacement of Groove Bound Curcumin Molecules

Signal transduction based on fluorescence is one of the most common optical aptasensors for small molecules. Sensors with a number of unique features including high sensitivity, low cost, and simple operation can be constructed easily. However, the label-free fluorescent approach is limited to synthetic dyes that bind strongly to the aptamer sequence and result in a diminished sensor operation with high detection limits. In this study, we report the use of curcumin as a fluorescent probe to signal aptamer/small target binding events. A substantial enhancement in curcumin’s fluorescent emission was observed when bound into the grooves of vitamin D3 (VTD3) binding aptamer, as an example. However, the introduction of the target molecule causes the aptamer to undergo a conformational change that favors complexing the target molecule over binding the curcumin dye. The sensor was able to detect VTD3 down to 1 fM concentration in buffer solutions and extracted blood samples, operate at a wide dynamic range, and discriminate against potential biological interfering molecules including VTD2. The operation of the curcumin based fluorescent sensor is at least six orders of magnitude more sensitive than a VTD3 sensor constructed with the synthetic dye SYBR Green I. The generality of the reported label-free approach was applied with a previously isolated 75-mer bisphenol-A (BPA) aptamer, confirming that the reported sensing strategy is not confined on a particular aptamer sequence. Our work not only reports a novel sensor format for the detection of small molecules, but also serves fluorescent sensor’s most pressing need being novel fluorophores for multiplex targets detection.
On Lp-Generalization of a Theorem of Adamyan, Arov, and Krein

The paper deals with problems relating to the theory of Hankel operators. Let G be a bounded simple connected domain with the boundary ? consisting of a closed analytic Jordan curve. Denote by Mn,p(G), 1? p<∞, the class of all meromorphic functions on G that can be represented in the form h=β/? , where β belongs to the Smirnov class Ep(G), ? is a polynomial degree at most n, ? 0. We obtain estimates of s-numbers of the Hankel operator Af constructed from f? Lp(? ), 1? p<∞, in terms of the best approximation ? n,p of f in the space Lp(? ) by functions belonging to the class Mn,p(G).
Kernel Embedding Linear Response

In the paper, we study the problem of estimating linear response statistics under external perturbations using time series of unperturbed dynamics. A standard approach to this estimation problem is to employ the Fluctuation-Dissipation Theory, which, in turn, requires the knowledge of the functional form of the underlying unperturbed density that is not available in general. To overcome this issue, we consider a nonparametric density estimator formulated by the kernel embedding of distribution. To avoid the computational expense arises using radial type kernels, we consider the "Mercer-type" kernels constructed based on the classical orthogonal bases defined on non-compact domains, such as the Hermite and Laguerre polynomials. We establish the uniform convergence of the estimator, which justifies the use of the estimator for interpolation. Given a target function with a specific decaying property quantified by the available data, our framework allows one to choose the appropriate hypothesis space (an RKHS) that is "rich" enough for consistent estimation. For the linear response estimation, our study provides practical conditions for the well-posedness of both the estimator and the underlying response statistics. We offer a theoretical guarantee for the convergence of the estimator to the underlying actual linear response statistics. Finally, we provide a statistical error bound for the density estimation that accounts for the Monte-Carlo averaging over non-i.i. d time series and biases due to finite basis truncation. This error bound provides a mean to understand the feasibility as well as limitation of the kernel embedding with Mercer-type kernel. Numerically, we verify the effectiveness of the kernel embedding linear response estimator on two stochastic dynamics with known, yet, non-trivial equilibrium densities.
A New FGF21 Analog for the Treatment of Fatty Liver Disease

Nonalcoholic fatty liver disease (NAFLD) describes a non–alcohol-induced cluster of conditions related to excessive fat stored in the liver. Defined as 5% or more hepatic fat content, NAFLD is prevalent, affecting ∼25% of the general population (1). Approximately 20% of individuals with NAFLD have a more aggressive form of the disease, termed nonalcoholic steatohepatitis (NASH), which is in addition characterized by liver inflammation and extensive scarring (cirrhosis) (1,2). NASH is a growing cause of liver cancer and a leading indication for liver transplantation (2). In addition, NAFLD and NASH are risk factors for chronic kidney disease (3), type 2 diabetes (4), and cardiovascular disease (5). Despite being serious afflictions, NAFLD and in particular NASH face two major headwinds. One, they are difficult to diagnose because they require liver biopsies for conclusive diagnosis. Two, there are currently no approved medications for treatment. Lifestyle intervention to reduce body weight is the primary strategy to manage the disease. However, while reducing caloric intake and increasing physical activity are obvious remedies, such lifestyle modification has very limited efficacy in the long run, with typically no more than 3–4% of weight loss after 4 years (6). With a quickly growing population of patients with NAFLD/NASH (2) and no approved therapies, there is an unmet yet steadily increasing medical need for safe and effective pharmacotherapy. Unfortunately, resembling the quest for the Holy Grail, the development of such a therapy seemed, as of today, a …
Modeling Axisymmetric Centrifugal Compressor Characteristics From First Principles

Turbochargers are a vital component for aiding engine manufacturers in meeting the latest emissions standards. However, their range of operation is limited for low mass flows by compressor surge. Operation in surge results in pressure and mass flow oscillations that are often damaging to the compressor and its installation. Since surge is a highly complex flow regime, full unsteady three-dimensional models are generally too computationally expensive to run. The majority of current low-dimensional surge models use a cubic compressor characteristic that needs to be fitted to experimental data. Therefore, each time a compressor is studied using these models, costly experimental testing is required. In this paper, a new technique for obtaining an axisymmetric centrifugal compressor characteristic is presented. This characteristic is built using the equations of mass, momentum, and energy from first principles in order to provide a more complete model than those currently obtained via experimental data. This approach enables us to explain the resulting cubic-like shape of the characteristic and hence to identify impeller inlet stall as a route into surge. The characteristic is used within a quasi-steady, map-based surge model in order to demonstrate its ability to predict the onset of surge while only providing geometric data as input. Validation is provided for this model by a discussion of the qualitative flow dynamics and a good fit to experimental data, especially for low impeller speeds and pressure ratios.
Performance of the Panbio COVID-19 Rapid Antigen Test Device for infection control purposes

Objective: We assessed the performance characteristics of the Panbio COVID-19 Rapid Antigen Test Device in field conditions and its potential use as a point-of-care test for isolation and cohorting patients at risk. Methods: We reviewed our laboratory records from January 2021 to May 2021 to identify subjects with a Panbio COVID-19 Rapid Antigen Test Device (index test) and a real-time reverse transcriptase-polymerase chain reaction (RT-PCR) (reference test) test carried out on the same date. Results: In total, 634 subjects met inclusion criteria. Three (0.2%) samples of the index test were not interpretable. A total of 51 (8.08%) subjects had SARS-CoV-2 infection. The index test had sensitivity 78.85% (95% CI: 66.30-88.94%), specificity 91.58% (95% CI: 89.02-93.71), and accuracy 90.54% (95% CI: 87.99-92.70%). A total of 11 (1.73%) subjects (one asymptomatic, five with respiratory symptoms, and five with pneumonia) had a false-negative result of the index test with median (range) cycle threshold RT-PCR of 25 (21-30), indicating the presence of high viral load in the nasopharyngeal sample. Conclusions: The Panbio COVID-19 Rapid Antigen Test Device cannot replace SARS-CoV-2 RT-PCR for infection control purposes.
Stochastic 2D Signal Generative Model with Wavelet Packets Basis Regarded as a Random Variable and Bayes Optimal Processing

This study deals with two-dimensional (2D) signal processing using the wavelet packet transform. When the basis is unknown the candidate of basis increases in exponential order with respect to the signal size. Previous studies do not consider the basis as a random vaiables. Therefore, the cost function needs to be used to select a basis. However, this method is often a heuristic and a greedy search because it is impossible to search all the candidates for a huge number of bases. Therefore, it is difficult to evaluate the entire signal processing under a criterion and also it does not always gurantee the optimality of the entire signal processing. In this study, we propose a stochastic generative model in which the basis is regarded as a random variable. This makes it possible to evaluate entire signal processing under a unified criterion i.e. Bayes criterion. Moreover we can derive an optimal signal processing scheme that achieves the theoretical limit. This derived scheme shows that all the bases should be combined according to the posterior in stead of selecting a single basis. Although exponential order calculations is required for this scheme, we have derived a recursive algorithm for this scheme, which successfully reduces the computational complexity from the exponential order to the polynomial order.
Effect of binary stars on the dynamical evolution of stellar clusters. II. Analytic evolutionary models

We use analytic models to compute the evolution of the core of a stellar system due simultaneously to stellar evaporation which causes the system (core) to contract and to its binaries which cause it to expand by progressively decreasing its binding energy. The evolution of the system is determined by two parameters: the initial number of stars in the system N$sub 0$, and the fraction f/subb/ of its stars which are binaries. For a fixed f/subb/, stellar evaporation initially dominates the dynamical evolution if N$sub 0$ is sufficiently large due to the fact that the rate of evaporation is determined chiefly by long-range encounters which increase in importance as the number of stars in the system increases. If stellar evaporation initially dominates, the system first contracts, but as N/subc/, the number of remaining stars in the system, decreases by evaporation, the system reaches a minimum radius and a maximum density and then it expands monotonically as N/subc/ decreases further. Open clusters expand monotonically from the beginning if they have anything approaching average Population I binary frequencies. Globular clusters are highly deficient in binaries in order to have formed and retained the high- density stellar cores observed in most of them.more » We estimate that for these system f/subb/ < or = 0.15. (AIP)« less
A functional approach to understanding how temperature and habitat dimensionality drive universality and variation in ecological systems

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