Miracle-dz/UniFAR-FTUs huggingface dataset:
Social robots as assistants for autism therapy in Iran: Research in progress

Autistic children are often impaired in initiating and responding to Joint Attention. In recent years, there has been an increase in the application of robots in diagnosis and treatment of autism. The purpose of the current research has been primarily to originate the proper therapeutic scenarios and to implement two interactive humanoid robots as therapy assistants in autism treatment in Iran. To this end, the humanoid robots were programmed and teleoperated via Microsoft Kinect Sensor and PhantomOmni Haptic Robot to elicit reactions consisting of imitation of humans by the humanoid robots and vice versa. In this paper, we elaborate on the therapeutic items that we have designed to improve joint attention and imitation in autistic children through using humanoid robots. Moreover, the fairly promising results of some interventions conducted in a pilot study on four autistic cases will be addressed and discussed. Our research target is to increase social interaction and involve autistic children in dyadic/triadic interactions which seems quite possible due to the findings of the pilot study conducted.
Activation of the FGFR1 signalling pathway by the Epstein–Barr virus‐encoded LMP1 promotes aerobic glycolysis and transformation of human nasopharyngeal epithelial cells

Non‐keratinizing nasopharyngeal carcinoma (NPC) is closely associated with Epstein–Barr virus (EBV) infection. The EBV‐encoded latent membrane protein 1 (LMP1) is believed to play an important role in NPC pathogenesis by virtue of its ability to activate multiple cell signalling pathways which collectively promote cell proliferation, transformation, angiogenesis, and invasiveness, as well as modulation of energy metabolism. In this study, we report that LMP1 increases cellular uptake of glucose and glutamine, enhances LDHA activity and lactate production, but reduces pyruvate kinase activity and pyruvate concentrations. LMP1 also increases the phosphorylation of PKM2, LDHA, and FGFR1, as well as the expression of PDHK1, FGFR1, c‐Myc, and HIF‐1α, regardless of oxygen availability. Collectively, these findings suggest that LMP1 promotes aerobic glycolysis. With respect to FGFR1 signalling, LMP1 not only increases FGFR1 expression, but also up‐regulates FGF2, leading to constitutive activation of the FGFR1 signalling pathway. Furthermore, two inhibitors of FGFR1 (PD161570 and SU5402) attenuate LMP1‐mediated aerobic glycolysis, cellular transformation (proliferation and anchorage‐independent growth), cell migration, and invasion in nasopharyngeal epithelial cells, identifying FGFR1 signalling as a key pathway in LMP1‐mediated growth transformation. Immunohistochemical staining revealed that high levels of phosphorylated FGFR1 are common in primary NPC specimens and that this correlated with the expression of LMP1. In addition, FGFR1 inhibitors suppress cell proliferation and anchorage‐independent growth of NPC cells. Our current findings demonstrate that LMP1‐mediated FGFR1 activation contributes to aerobic glycolysis and transformation of epithelial cells, thereby implicating FGF2/FGFR1 signalling activation in the EBV‐driven pathogenesis of NPC. Copyright © 2015 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.
Symbols Among the Neurons: Details of a Connectionist Inference Architecture

Pattern matching and variable binding are easily implemented in conventional computer architectures, but not necessarily in all architectures. In a distributed neural network architecture each symbol is represented by activity in many units and each unit contributes to the representation of many symbols. Manipulating symbols using this type of distributed representation is not as easy as with a local representation whore each unit denotes one symbol, but there is evidence that the distributed approach is the one chosen by nature. We describe a working implementation of a production system interpreter in a neural network using distributed representations for both symbols and rules. The research provides a detailed account of two important symbolic reasoning operations, pattern matching and variable binding, as emergent properties of collections of neuron-like elements. The success of our production system implementation goes some way towards answering a common criticism of connectionist theories: that they aren't powerful enough to do symbolic reasoning.
Dry Machining of Ti-6 Al-4 V using PVD Coated TiAlN Tools

This paper deals with machining Ti6Al4V material. The experimental analysis was carried out using Response Surface Methodology (RSM). The detailed experiments under dry conditions using the PVD coated TiAlN tools. In the present work the relationship of Ti6Al4V’s surface roughness and cutting forces with critical machining parameters and conditions, based on experimental input and output data, has been derived during the turning operation. It has been found through design of experiments technique that linear model is best fitted for predicting feed force and surface roughness dry cutting environment. Linear model is also fitted for thrust force prediction during dry cutting. 2FI (2 Factor Interaction) model is found to be fitted for cutting force prediction under dry cutting environment
Taken by Storm

We use Hurricane Katrina's damage to the Mississippi coast in 2005 as a natural experiment to study business survival in the aftermath of a cost shock. We find that damaged establishments that returned to operation were more resilient than those that had never been damaged. This effect is particularly strong for establishments belonging to younger and smaller firms. The effect of damage on establishments in older and larger chains was more limited, and they were subsequently less resilient having survived the damage. These selection effects persist up to five years after the initial shock. We interpret these findings as evidence that the effect of the shock is tied to the presence of financial and other constraints. Keyword: Retail, chain, credit constraints, hurricane, Katrina, natural disaster, exit JEL Classification: D22, L11, L81, L83, Q54
Influence of Nationality and Profession on Intercultural Sensitivity

A survey of 833 college English major and non-English major students as well as multinational employees from China's Mainland and Thailand was conducted.In this study,independent variables include nationality and profession;dependent variable is intercultural sensitivity,which is conceptualized as consisting of five dimensions,namely,interaction engagement,respect for cultural differences,interaction confidence,interaction enjoyment,and interaction attentiveness.Scale examples of each dimension are as follows: Interaction engagement: I enjoy interacting with people from different cultures;Respect for cultural differences: I think people from other cultures are narrow-minded;Interaction confidence: I am pretty sure of myself in interacting with people from different cultures;Interaction enjoyment: I get upset easily when interacting with people from different cultures;Interaction attentiveness: I am very observant when interacting with people from different cultures.Major results of multivariate analysis of variance indicate that Chinese subjects had a significantly higher level of intercultural sensitivity than Thai subjects.The between-subjects differences show that English major students and multinational employees had a significantly higher level of intercultural sensitivity than non-English major students.The results of this study imply that foreign language learning experience is equally important to the building of intercultural sensitivity as is intercultural communication experience.The interaction effect of nationality and profession was not discovered from data analysis.Although data analysis did not generate an ideal model(R~2=0.098),from the various levels of intercultural sensitivity of the subjects,we can clearly see the possible impact of foreign language competence on intercultural sensitivity.Both the English major students of China and Thailand have higher levels of intercultural sensitivity.This fact suggests that language learning,especially foreign language learning is positively related with intercultural sensitivity.This study also discovered that intercultural communication practice is related with intercultural sensitivity.In fact,the intercultural sensitivity of multinational employees mainly comes from their intercultural communication practice;whereas the intercultural sensitivity of English major students mainly comes from their foreign language learning.Both China and Thailand are collectivistic countries with a lot of cultural and traditional similarities.However,the overall results of this study confirm that cultural groups with the same or similar value orientation may differ significantly from each other in cultural and communication behavior in one way or another,a fact that classic cultural theories do not attempt to explain.It is the limit of this study that English proficiency was not conceptualized and operationalized for a comparison across subjects.
Sustained Release of Prindopril Erbumine from Its Chitosan-Coated Magnetic Nanoparticles for Biomedical Applications

The preparation of magnetic nanoparticles coated with chitosan-prindopril erbumine was accomplished and confirmed by X-ray diffraction, TEM, magnetic measurements, thermal analysis and infrared spectroscopic studies. X-ray diffraction and TEM results demonstrated that the magnetic nanoparticles were pure iron oxide phase, having a spherical shape with a mean diameter of 6 nm, compared to 15 nm after coating with chitosan-prindopril erbumine (FCPE). Fourier transform infrared spectroscopy study shows that the coating of iron oxide nanoparticles takes place due to the presence of some bands that were emerging after the coating process, which belong to the prindopril erbumine (PE). The thermal stability of the PE in an FCPE nanocomposite was remarkably enhanced. The release study showed that around 89% of PE could be released within about 93 hours by a phosphate buffer solution at pH 7.4, which was found to be of sustained manner governed by first order kinetic. Compared to the control (untreated), cell viability study in 3T3 cells at 72 h post exposure to both the nanoparticles and the pure drug was found to be sustained above 80% using different doses.
Corporate bankruptcy prediction using data mining techniques

The interest in the prediction of corporate bankruptcy is increasing due to the implications associated with this phenomenon (e.g. economic, and social) for investors, creditors, competitors, government, although this is a classical problem in the financial literature. Two kinds of models are generally adopted for bankruptcy prediction: (i) accounting ratios based models and (ii) market based models. In the former, classical statistical techniques such as discriminant analysis or logistic regression models have been used, while in the latter the Moody’s KMV model was adopted. This paper follows the first approach (i), and it is based on the analysis of the evolution of several financial indicators during a three-year period. A framework was developed, encompassing a total of 16 models. These differ in the data mining algorithm (e.g. Artificial Neural Networks or Decision Trees), the data used (all three years or just the last one) and the input attributes adopted (e.g. all accounting ratios or just the most significant ones). The experiments were conducted using the new Business Intelligence Development Studio of the Microsoft SQL Server. Very good results were achieved, with performances between 86% and 99% for all 16 models.
Reynolds stress and heat flux in spherical shell convection

Context. Turbulent fluxes of angular momentum and enthalpy or heat due to rotationally affected convection play a key role in determining differential rotation of stars. Their dependence on latitude and depth has been determined in the past from convection simulations in Cartesian or spherical simulations. Here we perform a systematic comparison between the two geometries as a function of the rotation rate. Aims. Here we want to extend the earlier studies by using spherical wedges to obtain turbulent angular momentum and heat transport as functions of the rotation rate from stratified convection. We compare results from spherical and Cartesian models in the same parameter regime in order to study whether restricted geometry introduces artefacts into the results. In particular, we want to clarify whether the sharp equatorial profile of the horizontal Reynolds stress found in earlier Cartesian models is also reproduced in spherical geometry. Methods. We employ direct numerical simulations of turbulent convection in spherical and Cartesian geometries. In order to alleviate the computational cost in the spherical runs, and to reach as high spatial resolution as possible, we model only parts of the latitude and longitude. The rotational influence, measured by the Coriolis number or inverse Rossby number, is varied from zero to roughly seven, which is the regime that is likely to be realised in the solar convection zone. Cartesian simulations are performed in overlapping parameter regimes. Results. For slow rotation we find that the radial and latitudinal turbulent angular momentum fluxes are directed inward and equatorward, respectively. In the rapid rotation regime the radial flux changes sign in accordance with earlier numerical results, but in contradiction with theory. The latitudinal flux remains mostly equatorward and develops a maximum close to the equator. In Cartesian simulations this peak can be explained by the strong banana cells. Their effect in the spherical case does not appear to be as large. The latitudinal heat flux is mostly equatorward for slow rotation but changes sign for rapid rotation. Longitudinal heat flux is always in the retrograde direction. The rotation profiles vary from anti-solar (slow equator) for slow and intermediate rotation to solar-like (fast equator) for rapid rotation. The solar-like profiles are dominated by the Taylor-Proudman balance.
Deep Discriminative and Generative Models for Pattern Recognition

In this chapter we describe deep generative and discriminative models as they have been applied to speech recognition. The former models describe the distribution of data, whereas the latter models describe the distribution of targets conditioned on data. Both models are characterized as being ‘deep’ as they use layers of latent or hidden variables. Understanding and exploiting tradeoffs between deep generative and discriminative models is a fascinating area of research and it forms the background of this chapter. We focus on speech recognition but our analysis is applicable to other domains. We suggest ways in which deep generative models can be beneficially integrated with deep discriminative models based on their respective strengths. We also examine the recent advances in endto-end optimization, a hallmark of deep learning that differentiates it from most standard pattern recognition practices.
Piecewise Polynomial Lyapunov Functions for Global Asymptotic Stability of Saturated Uncertain Systems

Abstract In this paper we provide sufficient conditions for global asymptotic and global exponential stability of linear systems subject to saturations and/or deadzones. These conditions rely on piecewise polynomial Lyapunov functions and are formulated in terms of linear matrix inequalities, by using sum-of-squares relaxations. It is also shown that the proposed approach can be extended to deal with robust stability of saturated systems affected by structured parametric uncertainties. Example studies are presented to illustrate the reduced conservativeness of the proposed conditions as compared to existing results.
Clathrin heavy chain plays multiple roles in polarizing the Drosophila oocyte downstream of Bic-D

Bicaudal-D (Bic-D), Egalitarian (Egl), microtubules and their motors form a transport machinery that localizes a remarkable diversity of mRNAs to specific cellular regions during oogenesis and embryogenesis. Bic-D family proteins also promote dynein-dependent transport of Golgi vesicles, lipid droplets, synaptic vesicles and nuclei. However, the transport of these different cargoes is still poorly understood. We searched for novel proteins that either mediate Bic-D-dependent transport processes or are transported by them. Clathrin heavy chain (Chc) co-immunopurifies with Bic-D in embryos and ovaries, and a fraction of Chc colocalizes with Bic-D. Both proteins control posterior patterning of the Drosophila oocyte and endocytosis. Although the role of Chc in endocytosis is well established, our results show that Bic-D is also needed for the elevated endocytic activity at the posterior of the oocyte. Apart from affecting endocytosis indirectly by its role in osk mRNA localization, Bic-D is also required to transport Chc mRNA into the oocyte and for transport and proper localization of Chc protein to the oocyte cortex, pointing to an additional, more direct role of Bic-D in the endocytic pathway. Furthermore, similar to Bic-D, Chc also contributes to proper localization of osk mRNA and to oocyte growth. However, in contrast to other endocytic components and factors of the endocytic recycling pathway, such as Rabenosyn-5 (Rbsn-5) and Rab11, Chc is needed during early stages of oogenesis (from stage 6 onwards) to localize osk mRNA correctly. Moreover, we also uncovered a novel, presumably endocytosis-independent, role of Chc in the establishment of microtubule polarity in stage 6 oocytes.
Efficient Computation of ℓ1 Regularized Estimates in Gaussian Graphical Models

In this article, I propose an effcient algorithm to compute ℓ1 regularized maximum likelihood estimates in the Gaussian graphical model. These estimators, recently proposed in an earlier article by Yuan and Lin, conduct parameter estimation and model selection simultaneously and have been shown to enjoy nice properties in both large and finite samples. To compute the estimates, however, can be very challenging in practice because of the high dimensionality and positive definiteness constraint on the covariance matrix. Taking advantage of the recent advance in semidefinite programming, Yuan and Lin suggested a sophisticated interior-point algorithm to solve the optimization problem. Although a polynomial time algorithm, the optimization technique is known not to be scalable for high-dimensional problems. Alternatively, this article shows that the estimates can be computed by iteratively solving a sequence of ℓ1 regularized quadratic programs. By effectively exploiting the sparsity of the graphical structure, I propose a new algorithm that can be applied to problems of larger scale. When combined with a path-following strategy, the new algorithm can be used to efficiently approximate the entire solution path of the ℓ1 regularized maximum likelihood estimates, which also facilitates the choice of tuning parameter. I demonstrate the efficacy and usefulness of the proposed algorithm on a few simulations and real datasets.
On the flow anonymity problem in Network Coding

In this paper, we aim at protecting the privacy of the communicating parties while ensuring the authenticity of source nodes. In particular, we exploit intra-flow network coding to preserve the anonymity of communicating parties. Towards this objective, we propose an anonymity preservation scheme, namely closed group anonymity (CGA) that preserves the anonymity of the communicating parties via mixing their flows. Afterwards, we explore an instance of the Authentication-Privacy Trade-off in the context of Network Coding. We analyze the trade-off with the aid of the proposed anonymity scheme and a previously proposed Source Authentication Scheme using Network Coding (SANC). We present simulation results showing that the proposed algorithm successfully leverages network coding to preserve anonymity against traffic analysis attacks. Finally, we not only confirm the fundamental authentication-privacy trade-off in the context of intra-flow network coding but also parameterize it via introducing a tunable parameter to dynamically control, and potentially balance, this trade-off depending on the security provisions dictated by the operational scenario and application of interest.
Learning to Love Guns? Gun-Based Gameplay’s Links to Gun Attitudes

Although there is a long empirical record exploring links between violent videogame play and aggression, little is known about how these games potentially affect players’ political attitudes. Specifically, with firearms frequently featured in videogames, including games where players are required to use firearms to succeed during gameplay, it is worth examining whether players’ experience with firearms relates to their attitudes toward guns and gun policy. Utilizing the General Learning Model, this survey explores whether public policy outcomes regarding gun control and public safety are related to exposure to violent video games, first-person shooter games, and realistic gun controllers. Results show that increased exposure to first-person shooter games was related to more negative attitudes concerning gun control. In addition, more experience using realistic gun controllers was associated with negative attitudes toward gun control and greater support for the idea that greater gun availability can help guarantee public safety. Thus, video game exposure may shape the gun attitudes of young people in small but important ways.
Orphans in Africa: parental death, poverty, and school enrollment

We examine the impact of orphanhood on children’s school enrollment in 10 sub-Saharan African countries. Although poorer children in Africa are less likely to attend school, the lower enrollment of orphans is not accounted for solely by their poverty. We find that orphans are less likely to be enrolled than are nonorphans with whom they live. Consistent with Hamilton’s rule, the theory that the closeness of biological ties governs altruistic behavior, outcomes for orphans depend on the relatedness of orphans to their household heads. The lower enrollment of orphans is largely explained by the greater tendency of orphans to live with distant relatives or unrelated caregivers.
On the Structure of Dark Matter Halos at the Damping Scale of the Power Spectrum with and without Relict Velocities

We report a series of high-resolution cosmological N-body simulations designed to explore the formation and properties of dark matter halos with masses close to the damping scale of the primordial power spectrum of density fluctuations. We further investigate the effect that the addition of a random component, vrms, into the particle velocity field has on the structure of halos. We adopted as a fiducial model a ΛWDM cosmology with a nonthermal sterile neutrino mass of 0.5 keV. The filtering mass corresponds then to Mf = 2.6 × 1012 h−1 M☉. Halos of masses close to Mf were simulated with several million particles. The results show that, on the one hand, the inner density slope of these halos (at radii ≲0.02 the virial radius Rv) is systematically steeper than the one corresponding to the NFW fit or to the CDM counterpart. On the other hand, the overall density profile (radii >0.02Rv) is less curved and less concentrated than the NFW fit, with an outer slope shallower than –3. For simulations with vrms, the inner halo density profiles flatten significantly at radii smaller than 2-3 h−1 kpc (≲0.010Rv-0.015Rv). A constant density core is not detected in our simulations, with the exception of one halo for which the flat core radius is ≈1 h−1 kpc. Nevertheless, if “cored” density profiles are used to fit the halo profiles, the inferred core radii are ≈(0.1–0.8) h−1 kpc, in rough agreement with theoretical predictions based on phase-space constraints and on dynamical models of warm gravitational collapse. A reduction of vrms by a factor of 3 produces a modest decrease in core radii, by less than a factor of 1.5. We discuss the extension of our results into several contexts, for example, to the structure of the cold DM microhalos at the damping scale of this model.