Miracle-dz/UniFAR-FTUs huggingface dataset:
The role of presynaptic calcium in short-term enhancement at the hippocampal mossy fiber synapse

The mossy fiber synapse between dentate granule cells and CA3 pyramidal cells in the guinea pig hippocampus shows a robust short-term synaptic enhancement. We have simultaneously measured presynaptic residual free calcium ([Ca2+]i) and postsynaptic field potentials at this synapse to examine the role of [Ca2+]i in this enhancement. Single action potentials produced an increase in [Ca2+]i of 10–50 nM that decayed to resting levels with a time constant of about 1 sec. Trains of action potentials produced larger [Ca2+]i increases that returned more slowly to resting levels. Following the onset of moderate frequency stimulus trains (0.1–5 Hz), synaptic transmission and [Ca2+]i both increased and eventually plateaued. During the steady-state phase a linear relationship between [Ca2+]i and synaptic enhancement was observed. During the initial buildup, however, [Ca2+]i rose more rapidly than synaptic enhancement. Similarly, during the decay phase immediately following termination of a stimulus train, [Ca2+]i returned to prestimulus levels faster than synaptic enhancement. High concentrations of the calcium buffer EGTA in the presynaptic terminal slowed the buildup and decay of both [Ca2+]i and synaptic enhancement produced by stimulus trains. Under these conditions, the time course of [Ca2+]i and synaptic enhancement were well matched. This suggests that, despite the differences in kinetic rates observed for normal buffering conditions, increases in [Ca2+]i play a causal role in short-term enhancement. An increase in [Ca2+]i of 10–30 nM produced a twofold enhancement. We propose a simple kinetic model to explain these results. The model assumes that synaptic enhancement is controlled by a Ca-dependent first-order reaction. According to this scheme, a change in [Ca2+]i alters neurotransmitter release, but the slow kinetics of the underlying reaction introduces a temporal filter, producing a delay in the change in synaptic enhancement.
A tool for the rapid evaluation of input devices using Fitts' law models

A tool for building Fitts' law models is described. MODEL BUILDER runs on the Apple Macintosh using any device which connects to the Apple Desktop Bus. After 16 blocks of trials taking about 4--5 minutes, the program provides an immediate (albeit tentative) statistical analysis, showing the coefficients in the prediction equation, the coefficient of correlation, and a regression line with scatter points. MODEL BUILDER can be retrieved anonymously by researchers, educators, developers, or anyone with access to INTERNET through file-transfer-protocol (FTP).
An efficient video similarity search strategy for video-on-demand systems

The video-on-demand systems and video share web are more and more popular in recent years. Video similarity search for content-based video retrieval is important in web service and research field. There is still no satisfying search method for scalable fast similarity search in large video database. In order to solve two challenging problems: video similarity measure and fast search method in large database, a novel efficient video similarity search strategy for video-on-demand systems is proposed in this paper. A compact video signature was computed according to image histogram and spatial-temporal features of video. The video similarity is measured by the computation of the distance of video signature. For the scalable computing requirement, a new search method based on Clustering Index Table was presented by index clustering. The experimental results from the query tests in large database show this method is highly efficient and effective for similar video search.
Driving safety and traffic data collection - A laser scanner based approach

This research is motivated by two potential applications - enhancing driving safety and collecting traffic data in a large dynamic urban environment. A laser scanner based approach is proposed, in which SLAM (simultaneous localization and mapping) is developed with moving object detection and tracking using a laser scanner for perception, using GPS to achieve global accuracy, and using yaw rate and wheel speed to diagnose pose errors. Experiments are conducted to collect data along a course (4.5 km) with a test-bed vehicle run in a highly dynamic environment. The algorithms are examined, possibilities with respect to the two potential applications are demonstrated, and future works are discussed.
Upgrading of Farmed Salmon Oil Through Lipase-Catalyzed Hydrolysis

Upgrading of farmed salmon oil obtained from by-products was carried out by lipase-catalyzed hydrolysis to increase omega 3 polyunsaturated fatty acids (PUFA) content. The lipases tested were from Penicillium camembertii (PC), Rhizomucor javanicus (RJ), Rhizopus niveus (RN), Rhizopus delemar (RD), Burkholderia cepacia (BC), Rhizopus oryzae (RO), Candida rugosa (CR) and Rhizomucor miehei (RM). The lipases from PC, RJ and RN had lower hydrolysis degrees (HDs) compared to the rest of the lipases. The lipase from CR had the highest HD after 24 h (91.89%). Moreover, CR lipase was the most effective one in concentrating omega 3 PUFA. The final value was increased from 13.77% to 27.81% (wt%). The changes in omega 3 PUFA content were significantly different among the lipases although the HD values were similar at the end of the reactions, which was believed to be caused by the substrate specificities of the li- pases. The investigation of the relationship between HD and hydrolysis resistant value (HRV) for eicosapentaenoic acid (EPA, 20:5), docosahexaenoic acid (DHA, 22:6) and oleic acid (OA, 18:1) revealed that the fatty acid (FA) selectivity of the lipases were significantly different. CR lipase had the highest preference for hydrolyzing OA selectively over EPA and DHA. The reaction conditions, i.e. presence of surfactants, sonication, buffer-to-oil ratio, enzyme load, did not affect the selectivity. Investigation of the reaction conditions revealed that it was possible to obtain ~2.15-fold of the original omega 3 PUFA content by hydrolysis of salmon oil in the presence of CR lipase (4%, based on oil weight) with a buffer- to-oil ratio of 2:1 (v/v) at 37 o C for 4 h.
SSD: A Robust RF Location Fingerprint Addressing Mobile Devices' Heterogeneity

Fingerprint-based methods are widely adopted for indoor localization purpose because of their cost-effectiveness compared to other infrastructure-based positioning systems. However, the popular location fingerprint, Received Signal Strength (RSS), is observed to differ significantly across different devices' hardware even under the same wireless conditions. We derive analytically a robust location fingerprint definition, the Signal Strength Difference (SSD), and verify its performance experimentally using a number of different mobile devices with heterogeneous hardware. Our experiments have also considered both Wi-Fi and Bluetooth devices, as well as both Access-Point(AP)-based localization and Mobile-Node (MN)-assisted localization. We present the results of two well-known localization algorithms (K Nearest Neighbor and Bayesian Inference) when our proposed fingerprint is used, and demonstrate its robustness when the testing device differs from the training device. We also compare these SSD-based localization algorithms' performance against that of two other approaches in the literature that are designed to mitigate the effects of mobile node hardware variations, and show that SSD-based algorithms have better accuracy.
A Revisionist View of Blackboard Systems

Despite the continued popularity of the blackboard model of problem solving, recent AI texts hardly mention it. This is probably largely due to the decision to adopt formal frameworks into which the blackboard model does not appear to fit. In this paper, we explore the relationship between blackboard-based and behef network approaches to interpretation problems. In this way we begin to reinterpret the blackboard model, to show how it can be explained in terms of modern AI concepts.
Specification Testing for Nonparametric Structural Models with Monotonicity in Unobservables

Monotonicity in a scalar unobservable is a now common assumption in economic theory and applications. In nonseparable structures, this allows one to relate the regression quantiles to the underlying structural function. This strength of the monotonicity assumption is also its weakness; this assumption may simply impose too much structure in some contexts. Thus, tests for this hypothesis are essential. This paper provides such a test for cross-section data. We show how to exploit the power of an exclusion restriction together with a conditional independence assumption, plausible in a variety of applications, to construct a test statistic. We analyze the large sample behavior of our estimators and tests, and we study their finite sample behavior in Monte Carlo experiments. Our experiments show that a suitable bootstrap procedure yields tests with reasonably well behaved levels. Both theory and experiment show that the test has useful power. We apply our test to study the role of unobserved ability in determining Black-White wage differences and to study whether Engel curves are monotonically driven by a scalar unobservable.
Morphological Verb-Aware Tibetan Language Model

The Tibetan language model (TLM) is the key to Tibetan natural language processing. In this paper, we first observe that, different from widely used languages, Tibetan contains many morphological verbs that rarely appear in natural sentences but play a key role in accurate text prediction. This property is usually ignored by existing methods and makes traditional training strategies less effective in constructing accurate and robust TLMs. Hence, we propose a morphological verb-aware TLM by offline learning via a character frequency reweighting strategy and online tuning of discriminative weights conditioned on morphological verbs. However, because of the influence of morphological verbs on the tense and semantics of sentences, it is necessary to consider the morphological verbs in Tibetan. As a result, compared with state-of-the-art methods, our method not only reduces the perplexity but also improves the character error on tasks of the text prediction and automatic speech recognition (ASR).
DETERMINANTS OF PUPILS' EXAM PERFORMANCE IN THE UK AND PAKISTAN

This thesis consists of three chapters exploring the determinants of pupil performance in high-stakes exams in UK and Pakistan. The first empirical chapter looks at the effect of parents’ education on their children’s education adopting Instrumental Variable (IV) methodology. I exploit the 1972 UK RoSLA (raising of school leaving age) as a source of exogenous variation using the Longitudinal Study of Young People in England (LSYPE) dataset linked to the National Pupil Database (NPD). The results show that parental education has a significant and positive impact on their children’s educational outcomes, as measured by performance in GCSE examinations taken at age 16. The subsequent chapter explores key issues relating to teacher quality. Firstly, it investigates the impact of being taught by high quality or low quality teachers on students’ performance in exams, and secondly, which teacher characteristics are associated with student performance. It uses the survey data of 611 pupils from one region of Pakistan studying in Year 9 which is also linked to the administrative data on the students’ exam scores. The unique feature of the data tracks students’ scores across multiple subjects at a single point in time. Teacher fixed effects and pupil fixed effects methods are used. First stage results show that there are significant variations in teacher fixed effects within the schools, suggesting important unobservable differences among teachers. A good teacher, defined as being at the 75th percentile of the teacher fixed effects distribution, is related to an increase in score by 0.15 standard deviations relative to the omitted teacher, while a bad teacher decreases the score by 0.77 standard deviations. Therefore, a pupil having been taught by a good teacher scores 0.92 standard deviations more than the pupil who is taught by a bad teacher (25th percentile teacher) leaving a significant effect on pupil performance. Second stage results suggest that teacher observed characteristics do not explain the variation in teacher quality. The final chapter studies the effect of distance on participation in post-compulsory education in the Pakistan context, and whether socioeconomic characteristics have an effect on achievement in a value-added model taking selection into secondary education into account using a survey dataset on pupils studying in the post-compulsory grade (Year 12) in 2011-2012 from one district of the Punjab province of Pakistan. In this chapter I used two variables as instruments: one is the log of distance to nearest post-compulsory education institution, as a measure of proximity to an educational institution and the second is urban location. The results show that participation and performance in post-compulsory education are two different processes, with participation being driven by availability of post-compulsory institutions within travel distance, while performance once in post-compulsory education is determined by ability.
Dynamic Response Improvement of Fuel Cell Using Ultra Capacitor

As fuel cell systems, originally have a large time-constant to deliver energy into a load, it is very critical in distributed generation systems, especially those based on fuel cells to response more rapidly to disturbances. In this paper, a new method based on ultra capacitor systems and bidirectional buck-boost converter is proposed to improve voltage response of fuel cells under the condition of rapid load changes. To illustrate the validity of the method, the proposed system has been employed and simulated by MATLAB SIMULINK. The simulation results show that slow-response drawback of fuel is eliminated appropriately.
Did You Expect Your Users to Say This?: Distilling Unexpected Micro-reviews for Venue Owners

With social media platforms such as Foursquare, users can now generate concise reviews, i.e. micro-reviews, about entities such as venues (or products). From the venue owner's perspective, analysing these micro-reviews will offer interesting insights, useful for event detection and customer relationship management. However not all micro-reviews are equally important, especially since a venue owner should already be familiar with his venue's primary aspects. Instead we envisage that a venue owner will be interested in micro-reviews that are unexpected to him. These can arise in many ways, such as users focusing on easily overlooked aspects (by the venue owner), making comparisons with competitors, using unusual language or mentioning rare venue-related events, e.g. a dish being contaminated with bugs. Hence in this study, we propose to discover unexpected information in micro-reviews, primarily to serve the needs of venue owners. Our proposed solution is to score and rank micro-reviews, for which we design a novel topic model, Sparse Additive Micro-Review (SAMR). Our model surfaces micro-review topics related to the venues. By properly offsetting these topics, we then derive unexpected micro-reviews. Qualitatively, we observed reasonable results for many venues. We then evaluate ranking accuracy using both human annotation and an automated approach with synthesized data. Both sets of evaluation indicate that our novel topic model, Sparse Additive Micro-Review (SAMR) has the best ranking accuracy, outperforming baselines using chi-square statistics and the vector space model.
Insidious Assumptions

Pluralistic ignorance occurs when individuals misperceive a group norm and attempt to match the perceived—rather than actual—norm. Little is currently known about its role in the undergraduate classroom. The present research examined the pluralistic ignorance of studying behavior and its relationship with examination performance across four studies. Results suggested that students underestimate the amount of time their peers spend studying (Study 1), the extent of their estimation error is related to exam performance (Study 2), and that the relationship is mediated by a perceived lack of preparation (Study 3). Finally, a brief classroom intervention (Study 4) may be able to eliminate the pluralistic ignorance. Implications for the theory of pluralistic ignorance, and college teaching, are discussed.
Usage-Aware Average Clicks

A number of methods exists that measure the distance between two web pages. Average-Clicks [18] is a new measure of distance between web pages which fits user’s intuition of distance better than the traditional measure of clicks between two pages. Average-Clicks however assumes that the probability of the user following any link on a web page is the same and gives equal weights to each of the out-going links. In our method “Usage Aware Average-Clicks” we have taken the user’s browsing behavior into account and assigned different weights to different links on a particular page based on how frequently users follow a particular link. Thus, Usage Aware Average-Clicks is an extension to the Average-Clicks Algorithm where the static web link structure graph is combined with the dynamic Usage Graph (built using the information available from the web logs) to assign different weights to links on a web page and hence capture the user’s intuition of distance more accurately. This method has been used as a new metric to calculate the page similarities in a recommendation engine to improve its predictive power.
Convolutional Pseudo-Prior for Structured Labeling

Current practice in convolutional neural networks (CNN) remains largely bottom-up and the role of top-down process in CNN for pattern analysis and visual inference is not very clear. In this paper, we propose a new method for structured labeling by developing convolutional pseudo-prior (ConvPP) on the ground-truth labels. Our method has several interesting properties: (1) compared with classical machine learning algorithms like CRFs and Structural SVM, ConvPP automatically learns rich convolutional kernels to capture both short- and long- range contexts; (2) compared with cascade classifiers like Auto-Context, ConvPP avoids the iterative steps of learning a series of discriminative classifiers and automatically learns contextual configurations; (3) compared with recent efforts combing CNN models with CRFs and RNNs, ConvPP learns convolution in the labeling space with much improved modeling capability and less manual specification; (4) compared with Bayesian models like MRFs, ConvPP capitalizes on the rich representation power of convolution by automatically learning priors built on convolutional filters. We accomplish our task using pseudo-likelihood approximation to the prior under a novel fixed-point network structure that facilitates an end-to-end learning process. We show state-of-the-art results on sequential labeling and image labeling benchmarks.
LAMP: A Hybrid Fog-Cloud Latency-Aware Module Placement Algorithm for IoT Applications

The delay in the transmission of data to the cloud and their return impacts on applications performance. Hence, Fog computing with transferring the cloud services to the edge of the network causes to decrease the delay in services of applications such as healthcare, online games, and other IoT applications. Moreover, Fog computing reduces the cost and energy consumption of cloud data centers. One of the most important issues in fog computing is the placement of modules of IoT application on fog devices at the edge of the network to have a minimum of latency and the highest performance. In this paper, we propose a latency-aware module placement algorithm in Fog-Cloud environment, called LAMP, which is an exploratory algorithm for the optimal application modules placement into devices. LAMP provides less delay for IoT applications with reducing the distance between the host device modules. Consequently, improves the functionality of IoT applications. We use iFogsim simulator and evaluate the proposed algorithm compared to four strategies of placing in the fog-cloud environment. According to our results of the simulation, the efficiency of the proposed algorithm is guaranteed.
Can you pick a broccoli? 3D-vision based detection and localisation of broccoli heads in the field

This paper presents a 3D vision system for robotic harvesting of broccoli using low-cost RGB-D sensors. The presented method addresses the tasks of detecting mature broccoli heads in the field and providing their 3D locations relative to the vehicle. The paper evaluates different 3D features, machine learning and temporal filtering methods for detection of broccoli heads. Our experiments show that a combination of Viewpoint Feature Histograms, Support Vector Machine classifier and a temporal filter to track the detected heads results in a system that detects broccoli heads with 95.2% precision. We also show that the temporal filtering can be used to generate a 3D map of the broccoli head positions in the field.
Effects of a cell phone conversation on cognitive processing performances.

BACKGROUND The ability to apportion cognitive resources to process multiple visual and auditory stimuli is essential for human communication in competing conditions. PURPOSE The purpose of the current research was to examine the effects of a cell phone conversation on a battery of cognitive tests, using both timing (RT) and accuracy (A') as dependent measures. RESEARCH DESIGN A repeated measures ANOVA was conducted. STUDY SAMPLE Forty-two college-age (mean 22 yr) adult females with normal hearing and cognitive function participated in the study. DATA COLLECTION AND ANALYSIS In one condition (quiet), a standardized cognitive assessment battery was administered to participants in a quiet room. In the (cell phone) condition, subjects were formulating and responding to specific questions about their travel experiences during administration of the same cognitive assessment battery. The computer automatically records subject performance. Post-hoc pairwise comparisons were conducted using the Bonferroni approach. The alpha level was set at . 05 for all data analysis. This method of analysis was repeated for each of the dependent measures, RT, and A'. RESULTS The results revealed a consistent, significant effect on reaction time between the two conditions. The same analysis was also conducted to examine the effect of participation in a cell phone discussion on accuracy. As with RT, results revealed a consistent, significant affect on A' between the two conditions. CONCLUSIONS Our study supports the notion that there are differential effects of auditory distracters across cognitive spheres. For simple automatic type visual cognitive tasks, the effect is minimal. However, as visual tasks increase in difficulty, the effect of the auditory distraction is magnified, particularly when the task requires extensive division of language resources.
Using the California Evidence-Based Clearinghouse for Child Welfare as a Tool for Teaching Evidence-Based Practice

ABSTRACT Using practical examples from a child welfare research capstone class, this article discusses how the California Evidence-Based Clearinghouse for Child Welfare (CEBC) can be used as a tool to teach social work students how to access, analyze, and interpret current research related to child welfare practice and help them apply their understanding of evidence-based practice to their work in the field. We showcase CEBC’s selection and implementation materials, including a framework for implementation, to help students learn how to apply research-supported interventions to real-world situations in child welfare or other family-serving organizations. We hope that by providing such an illustration, social work educators can apply the CEBC in their teachings on research-supported interventions.