Miracle-dz/UniFAR-FTUs huggingface dataset:
Testing Neuronal Accounts of Anisotropic Motion Perception with Computational Modelling

There is an over-representation of neurons in early visual cortical areas that respond most strongly to cardinal (horizontal and vertical) orientations and directions of visual stimuli, and cardinal- and oblique-preferring neurons are reported to have different tuning curves. Collectively, these neuronal anisotropies can explain two commonly-reported phenomena of motion perception – the oblique effect and reference repulsion – but it remains unclear whether neuronal anisotropies can simultaneously account for both perceptual effects. We show in psychophysical experiments that reference repulsion and the oblique effect do not depend on the duration of a moving stimulus, and that brief adaptation to a single direction simultaneously causes a reference repulsion in the orientation domain, and the inverse of the oblique effect in the direction domain. We attempted to link these results to underlying neuronal anisotropies by implementing a large family of neuronal decoding models with parametrically varied levels of anisotropy in neuronal direction-tuning preferences, tuning bandwidths and spiking rates. Surprisingly, no model instantiation was able to satisfactorily explain our perceptual data. We argue that the oblique effect arises from the anisotropic distribution of preferred directions evident in V1 and MT, but that reference repulsion occurs separately, perhaps reflecting a process of categorisation occurring in higher-order cortical areas.
Information infrastructure for text mining of the primary scientific literature : Principles and Practice

We present an informatics infrastructure for biocuration, based on a combination of techniques from Information Extraction (IE) and Knowledge Engineering (KE). We describe the high-level design of this infrastructure which we base on the concept of ‘experimental type’. Here, we treat each experiment as a specific type of knowledge statement determined by the experiment’s design. We provide a preliminary, detailed example of the use of the infrastructure to support the construction of a database pertaining to neuroanatomical tract-tracing experiments. This work could generalize to provide support for other experimental types and could be used to make biocuration efforts more efficient. We also discuss how the process of annotating text for IE directly supports designing schema for databases.
Importance sampling simulation in the presence of heavy tails

We consider importance sampling simulation for estimating rare event probabilities in the presence of heavy-tailed distributions that have polynomial-like tails. In particular, we prove the following negative result: there does not exist an asymptotically optimal state-independent change-of-measure for estimating the probability that a random walk (respectively, queue length for a single server queue) exceeds a "high" threshold before going below zero (respectively, becoming empty). Furthermore, we derive explicit bounds on the best asymptotic variance reduction achieved by importance sampling relative to naive simulation. We illustrate through a simple numerical example that a "good" state-dependent change-of-measure may be developed based on an approximation of the zero-variance measure.
Bioequivalence and comparative pharmacodynamics of insulin lispro 200 U/mL relative to insulin lispro (Humalog®) 100 U/mL

Insulin lispro 200 U/mL (IL200) is a new strength formulation of insulin lispro (Humalog®, IL100), developed as an option for diabetic patients on higher daily mealtime insulin doses. This phase 1, open‐label, 2‐sequence, 4‐period crossover, randomized, 8‐hour euglycemic clamp study aimed to demonstrate the bioequivalence of IL200 and IL100 after subcutaneous administration of 20 U (U) to healthy subjects (n = 38). Pharmacokinetic (PK) and pharmacodynamic (PD) responses were similar in both formulations. All 90%CIs for the ratios of area under the concentration‐versus‐time curve from time zero to the time of the last measurable concentration (AUC0–tlast) and maximum observed drug concentration (Cmax), as well as the total glucose infused throughout the clamp (Gtot) and the maximum glucose infusion rate (Rmax), were contained within 0.80 and 1.25. Time of maximum observed drug concentration (tmax) was similar between formulations, with a median difference of 15 minutes and a 95%CI of the difference that included zero. Inter‐ and intrasubject variability estimates were similar for both formulations. Both formulations were well tolerated. IL200 was bioequivalent to IL100 after subcutaneous administration of 20‐U single doses, and PD responses were comparable between formulation strengths.
Automated Tissue Classification of noisy MR Images of the Brain Using Constrained Multiple Multivariate Gaussian Mixture Model ( CGMM )

We present a fully automated algorithm for tissue segmentation of noisy, low contrast magnetic resonance (MR) images of the brain. We use a mixture model composed of a large number of Gaussians to represent the brain image. Each tissue is represented by a large number of the Gaussian components in order to capture the complex tissue spatial layout. The intensity of a tissue is considered a global feature and is incorporated into the model through parameter tying. The intensity parameter is shared by all the Gaussians that are related to the same tissue. The EM algorithm is utilized to learn the parameter-tied Gaussian mixture model. A new initialization method is applied to guarantee the convergence of the EM algorithm to the global maximum likelihood. Segmentation of the brain image is achieved by the affiliation of each pixel to a selected tissue class. The presented algorithm is used to segment 2D, T1–weighted, simulated and real MR images of the brain into three different tissues, under varying noise conditions. Quantitative results are compared with other segmentation results reported in the literature.
Graph state data structures

The discovery of a polynomial time quantum algorithm for factoring integers [1] sparked a great interest in the study and implementation of quantum computation, and quantum computers. Despite this interest, the number of distinct quantum algorithms has grown relatively slowly. Moreover, the algorithms that do exist are based on an even smaller number of distinct techniques. One feature common to all of these algorithms, however, is while they perform information processing in a quantum manner, the data structures they exploit are essentially classical. The question then arises as to whether it is possible to develop an intrinsically quantum data structure; one that is not merely a superposition of classical data. Here, we answer this in the affirmative. We develop a fully quantum data structure, that can represent graphs, based on quantum graph states. Graph states have been studied heavily before, but generally in the context of use as a computational resource for one-way quantum computation[2–7]. Here, we study them as a method to represent graphs. As a data-structure, graph states have no classical analogue: they are not simply a quantum superposition of classical states representing graphs.
THE FREQUENCY AND INTENSITY OF EXPERIENCE IN ONLINE BANKING USE

The literature on online banking adoption generally finds experience to be a positive factor in online banking use. In contrast, this research suggests a more complex relationship between experience and online banking. Experience is found to have a negative effect on online banking use through its impact on the perceived ease of use (PEOU) and perceived usefulness (PU) of online banking. While counterintuitive, these finding are hypothesized to reflect the fact that greater intensity of use diminishes PEOU because as an individual gains experience with more complex and sophisticated applications in the bank web site, the user becomes increasingly aware of the difficulty of making the most of that technology. It is further hypothesized that greater frequency of use diminishes the PU of a web site because the perception of usefulness of a web site depends on marginal utility, not total utility.
Effects of a Selective Cannabinoid CB2 Agonist and Antagonist on Intravenous Nicotine Self Administration and Reinstatement of Nicotine Seeking

Over the last decade there have been significant advances in the discovery and understanding of the cannabinoid system along with the development of pharmacologic tools that modulate its function. Characterization of the crosstalk between nicotine addiction and the cannabinoid system may have significant implications on our understanding of the neurobiological mechanisms underlying nicotine dependence. Two types of cannabinoid receptors (CB1 and CB2) have been identified. CB1 receptors are expressed in the brain and modulate drug taking and drug seeking for various drugs of abuse, including nicotine. CB2 receptors have been recently identified in the brain and have been proposed to play a functional role in mental disorders and drug addiction. Our objective was to explore the role of CB2 receptors on intravenous nicotine self administration under two schedules of reinforcement (fixed and progressive ratio) and on nicotine seeking induced by nicotine priming or by nicotine associated cues. For this, we evaluated the effects of various doses of the selective CB2 antagonist AM630 (1.25 to 5 mg/kg) and CB2 agonist AM1241 (1 to 10 mg/kg) on these behavioral responses in rats. Different groups of male Long Evans rats were trained to lever press for nicotine at a unit dose of 30 µg/kg/infusion. Subsequently, animals were randomized using a Latin-square design and injected with either AM1241 or AM630 using a counterbalanced within subject design. Administration of the CB2 ligands did not affect either nicotine-taking nicotine-seeking behavior. Our results do not support the involvement of CB2 receptors in nicotine-taking or nicotine-seeking behavior.
TYPE-II PROGRESSIVE CENSORING AND RELATED PROCESSES

Progressive censoring schemes are very useful in life-test experiments and in clinical studies. Montanari and Cacciari [13] reported results of progressively censored data aging tests on XLPE-insulated cable models under combined thermal-electrical stresses. Bhattacharya [5] gives an example, in a clinical trial study. The monograph of Balakrishnan and Aggarwala [2] (see also [5]) gives an interesting review of the background and of developments in this eld. Progressive censoring is a particular model based on order statistics and record values, see for instance [12]. Let X1, . , Xn be independent and identically distributed (i.i.d.) random lifetimes of n items. A progressive type-II right censored sample may be obtained in the following way: at the time of the rst failure, noted X1:m:n, r1 surviving items are removed at random from the n− 1 remaining surviving items, at the time of the next failure, noted X2:m:n, r2 surviving items are removed at random from the n− r1− 2 remaining items, and so on. At the time of the mth failure, all the remaining rm = n −m− r1 − · · · − rm−1 surviving items are censored. Our rst result concerns the asymptotic behaviour of the [αm]th order statistic of a type-II progressively censored sample of size m (quantile process), de ned by (X[αm]:m:n)α∈[0,1], where [x] is the greatest integer satisfying [x] ≤ x < [x] + 1. Our aim is to establish a result of the type √ m ( X[αm]:m:n − uα ) = CαW ′(α) + O ( δ′ m )
Nobody’s talking to the mentees

ABSTRACT Although researchers support the notion that college- and university-sponsored mentoring programs effectively academically transition and retain postsecondary students from a variety of sociocultural backgrounds, researchers have not examined how mentoring programs address the ‘mentorability’ or the ‘ability to be mentored’ of their mentees. Employing a quantitative content analysis, we examined postsecondary mentoring program websites (n= 187) at public, four-year institutions in Texas (n = 44). Findings suggest only 19% of all mentoring programs address ‘mentorability’ by defining mentee characteristics and expectations, compared to 37% of all mentoring programs which define mentor characteristics and expectations. Furthermore, only 4% of mentoring program websites included mentee applications, and mentoring programs were four times more likely to address their mentors (1,023 occurrences) than ‘mentees/protégés’ (237 occurrences), speaking to the lack of focus on mentee ‘mentorability. ’ Implications for practitioners and future research is addressed.
A SIFT-based image fingerprinting approach robust to geometric transformations

Among approaches in implementing Digital Rights Management, image fingerprinting technique is considered to be one of the most attractive solutions, especially in detecting illegal use of image works. The deficiency of the existing image fingerprinting methods is that they can not deal with geometric transformations, such as aspect ratio changes, rotations, cropping, combining, etc. Aiming at this shortcoming, we propose a SIFT-based image fingerprinting algorithm which is robust to geometric transformations. Firstly, we introduce SIFT-based algorithm to extract features as a unique fingerprint. Secondly, a method based on area ratio invariance of affine transformation is utilized to verify valid matched keypoint pairs between the queried image and the pre-registered image. Finally, by counting the valid matched pairs, we estimate whether the two images are homologous or not. Experimental results demonstrate that the proposed method exhibits an excellent performance when geometric transformation occurs.
2016 Fifth International Conference on Recent Trends in Information Technology Alleviating Data Sparsity and Cold Start in Recommender Systems Using Social Behaviour

—Recommender systems are used to find preferences of people or to predict the ratings with the help of information available from other users. The most widely used collaborative filtering recommender system by the e-commerce sites suffers from both the sparsity and cold-start problem due to insufficient data. Most of the existing systems consider only the ratings of the similar users and they do not give any preferences to the social behavior of users which shall aid the recommendations made to the user to a great extent. In this paper, instead of finding similarity from rating information, we propose a new approach which predicts the ratings of items by considering directed and transitive trust with timestamps and profile similarity from the social network along with the user-rated information. In cases where the trust and the rating details of users from the system is absent, we still make use of the social data of the users like the products liked by the user, user's social profile-education status, location etc. to make recommendation. Experimental analysis proves that our approach can improve the user recommendations at the extreme levels of sparsity in user-rating data. We also show that our approach works considerably well for cold-start users under the circumstances where collaborative filtering approach fails. I. INTRODUCTION Since internet has become part of daily life of millions of users, most of the sellers are trying to make internet as their selling platform to reach more and more customers. Thus many e-commerce websites are springing up day by day. All of these websites want to provide better services and experiences to their customers to attract more and more of them and thereby increase their revenue. Understanding the interests of users and recommending products to the users based on their individual interests have become a major challenge which has given birth to the recommender systems.
Top-K interesting subgraph discovery in information networks

In the real world, various systems can be modeled using heterogeneous networks which consist of entities of different types. Many problems on such networks can be mapped to an underlying critical problem of discovering top-K subgraphs of entities with rare and surprising associations. Answering such subgraph queries efficiently involves two main challenges: (1) computing all matching subgraphs which satisfy the query and (2) ranking such results based on the rarity and the interestingness of the associations among entities in the subgraphs. Previous work on the matching problem can be harnessed for a naïve ranking-after-matching solution. However, for large graphs, subgraph queries may have enormous number of matches, and so it is inefficient to compute all matches when only the top-K matches are desired. In this paper, we address the two challenges of matching and ranking in top-K subgraph discovery as follows. First, we introduce two index structures for the network: topology index, and graph maximum metapath weight index, which are both computed offline. Second, we propose novel top-K mechanisms to exploit these indexes for answering interesting subgraph queries online efficiently. Experimental results on several synthetic datasets and the DBLP and Wikipedia datasets containing thousands of entities show the efficiency and the effectiveness of the proposed approach in computing interesting subgraphs.
Automatic muscle generation for physically-based facial animation

Physically-based facial animation (FA) techniques are notoriously difficult to create, reuse, and art-direct. We address these shortcomings by proposing a rig-builder that automatically generates bony and soft-tissue substructures for any given head model. In an earlier work, [Aina 2009] presented a method for fitting a generic skull to any given head model as a first step toward automated rig-building. Here, we outline work done since, and give an overview of a method for creating muscles of facial expression (mimic muscles), and other soft-tissues in the gap between a given head model and a fitted generic skull.
The Effects of an Interactive Software Agent on Student Affective Dynamics while Using ;an Intelligent Tutoring System

We study the affective states exhibited by students using an intelligent tutoring system for Scatterplots with and without an interactive software agent, Scooter the Tutor. Scooter the Tutor had been previously shown to lead to improved learning outcomes as compared to the same tutoring system without Scooter. We found that affective states and transitions between affective states were very similar among students in both conditions. With the exception of the "neutral state,” no affective state occurred significantly more in one condition over the other. Boredom, confusion, and engaged concentration persisted in both conditions, representing both "virtuous cycles” and "vicious cycles” that did not appear to differ by condition. These findings imply that - although Scooter is well liked by students and improves student learning outcomes relative to the original tutor - Scooter does not have a large effect on students' affective states or their dynamics.
Native accent classification via i-vectors and speaker compensation fusion

We present a comprehensive analysis of the use of I-vector based classiﬁers for the classiﬁcation of unlabelled acoustic data as native British accents. We demonstrate the different behaviours of various popular dimensionality reduction techniques that have been previously used in problems such as speaker and language classiﬁcation. Our results show that a fusion of I-vector based systems gives state-of-the-art performance for unlabelled classiﬁcation of British accent speech data, reaching ∼ 81% accuracy.
Morphology of Mandibular Incisors: A Study on CBCT

Summary Background The aim of the study was to identify the number of root canals and examine root canal morphology of permanent mandibular incisors in an Indian sub-population of Pune, Maharashtra, India using cone-beam computed tomography (CBCT). Material/Methods This study was conducted at Elite CBCT & Dental Diagnostics, Pune. One hundred mandibular incisors were evaluated for the number of root, root canals and root morphology. Results In the present study, amongst 102 mandibular incisors, all had one root, 36% of them had a second canal, and Vertucci Type I was the most common type. Conclusions CBCT imaging is an excellent method for detection of different canal configurations of mandibular incisors.
Production of poly(vinyl alcohol)/Nafion® nanofibers and their stability assessment for the use in direct methanol fuel cells

The aim of this study is to investigate the electrospinning of Nafion® nanofibers with poly(vinyl alcohol) (PVA) as a carrier polymer and to assess the thermal and chemical stability of resultant PVA/Nafion® nanofibers for the use in direct methanol fuel cells, in simulated conditions. Bead-free PVA/Nafion® nanofibers were produced using higher molecular weight PVA. Resultant PVA and PVA/Nafion® nanofibers were stabilized using two different methods which are BTCA crosslinking and thermal stabilization, followed by sulfonation of the PVA part. FT-IR analysis demonstrated that the membranes were stabilized and sulfonated successfully. Thermal, water, methanol and oxidative stability of the membranes were tested in addition to ion-exchange capacity. Morphological changes in the structure were analyzed using SEM analysis. Thermally stabilized PVA/Nafion® nanofibrous membrane was found to be stable against water, methanol and oxidative effects. The nanofibrous structure was well preserved after treatments, while the other membranes became a film-like material. Thermal stability of the PVA/Nafion® nanofibrous membrane was similar to that of commercial Nafion® 115 membrane up to 200℃.
Prevalence and Comparison of Problematic Cell Phone Use amongst University Students in Western U.P Region in India: Correlates with Stress and Anxiety

Introduction: Since their introduction 12 years ago, there has been a wide proliferation of uptake of cell phone usage in India. Studies acknowledging excessive and problematic use of cell phone have also been reported from various parts of world. Systematic studies from India are still sparse. Aims: The present study aims to target the young cohort of university students pursuing education in a prominent deemed university of Northern India and to find the prevalence of cell phone addiction in the various groups of students belonging to various streams of education. Materials and methods: 50 students with age groups ranging from 18-24 were selected on a random basis by lucky draw method. They were explained the nature of study and its purpose and a 24 item self report questionnaire prepared on lines from ICD-X and CAGE questionnaire was administered to them after taking a informed consent. They were also administered Perceived Stress Scale and Hamiltons Anxiety Rating Scale to measure stress and anxiety. Results: Female students scored significantly higher on the dependence criteria. Female students scored significantly higher on the dependence criteria. Stream of study, type of family and monthly allowance of the students had no major role in the development of cell phone dependence in the sample. There was no significant correlation between the perceived stress of the students and the development of cell phone dependence. There was a significant relation between anxiety and high level of cell phone dependence Conclusion: Increasing cell phone usage among young students to the levels of cell phone dependence is a cause of much concern for the society especially when seen in conjunction with the higher levels of anxiety seen in this group.