Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep convolutional models improve predictions of macaque V1 responses to natural images

Despite great efforts over several decades, our best models of primary visual cortex (V1) still predict spiking activity quite poorly when probed with natural stimuli, highlighting our limited understanding of the nonlinear computations in V1. Recently, two approaches based on deep learning have been successfully applied to neural data: On the one hand, transfer learning from networks trained on object recognition worked remarkably well for predicting neural responses in higher areas of the primate ventral stream, but has not yet been used to model spiking activity in early stages such as V1. On the other hand, data-driven models have been used to predict neural responses in the early visual system (retina and V1) of mice, but not primates. Here, we test the ability of both approaches to predict spiking activity in response to natural images in V1 of awake monkeys. Even though V1 is rather at an early to intermediate stage of the visual system, we found that the transfer learning approach performed similarly well to the data-driven approach and both outperformed classical linear-nonlinear and wavelet-based feature representations that build on existing theories of V1. Notably, transfer learning using a pre-trained feature space required substantially less experimental time to achieve the same performance. In conclusion, multi-layer convolutional neural networks (CNNs) set the new state of the art for predicting neural responses to natural images in primate V1 and deep features learned for object recognition are better explanations for V1 computation than all previous filter bank theories. This finding strengthens the necessity of V1 models that are multiple nonlinearities away from the image domain and it supports the idea of explaining early visual cortex based on high-level functional goals. Author summary Predicting the responses of sensory neurons to arbitrary natural stimuli is of major importance for understanding their function. Arguably the most studied cortical area is primary visual cortex (V1), where many models have been developed to explain its function. However, the most successful models built on neurophysiologists’ intuitions still fail to account for spiking responses to natural images. Here, we model spiking activity in primary visual cortex (V1) of monkeys using deep convolutional neural networks (CNNs), which have been successful in computer vision. We both trained CNNs directly to fit the data, and used CNNs trained to solve a high-level task (object categorization). With these approaches, we are able to outperform previous models and improve the state of the art in predicting the responses of early visual neurons to natural images. Our results have two important implications. First, since V1 is the result of several nonlinear stages, it should be modeled as such. Second, functional models of entire visual pathways, of which V1 is an early stage, do not only account for higher areas of such pathways, but also provide useful representations for V1 predictions.
Access, Retention and Course Choice in Online, Open and Distance Learning

Course choice is an important part of the progression of transforming a potential student into a successful undergraduate. Yet the processes and materials of course choice have attracted little attention, it being assumed that the activity will involve an adviser discussing choices and directions with a potential students using course descriptions. Such discussions are expensive and there is evidence that potential students do not always take advice. In any case vulnerable students are often the least able to access guidance and tension between the recruitment and retention functions of course descriptions may make a course appear more accessible than it is. This paper argues that course descriptions are inadequate to describe a course and that other materials are needed. These other materials are diagnostic, preview or 'taster' materials and students' views. Such materials have a vital role to play in ensuring that students get onto the right course for them thereby increasing their chances of retention. Courses will need such a 'set of competing perspectives' to describe them to any degree of accuracy. There is also evidence that such materials have a demystifying effect on potential students' perception of courses, enhancing their self confidence in the possibility of their ultimate success
EFL teachers ’ beliefs and practices regarding writing activities and class organization

This study aimed to find out the relationship among EFL university teachers’ code-based and meaning-based beliefs in writing instruction, classroom writing activities and classroom organization. To this end, 120 university teachers, who were teaching writing to EFL learners at different universities in Iran, participated in the study. For the data collection, a valid and reliable questionnaire on writing was used. Descriptive statistics and Pearson Product-Moment correlation were carried out for analyzing the data. The results showed that the correlations between both teachers’ meaning-based and code-based beliefs in writing and writing activities exist. It was also found that the correlations between meaningbased beliefs in writing and whole class activity and group work as ways of doing writing activities were significantly positive. However, meaning-based beliefs in writing and individual writing were not correlated significantly. Moreover, correlations between code-based beliefs in writing and individual writing and whole class were positively significant and there was a positive but not significant correlation between code-based beliefs in writing and group work. The results can be useful for
Resource Allocation for SC-FDMA Femtocell Networks

We consider joint subcarrier and power allocation problem for uplink femtocell networks. The air interface is single carrier frequency division multiple access. We consider zero forcing and minimum mean square error frequency domain equalizers. To formulate the resource allocation problem, we derive a closed-form expression for the received signal-to-interference-plus-noise ratio. Since the cross-tier interference can deteriorate the macrocell data rate, we impose a temperature limit on the interference arising from femtousers to the macro-base station. This constraint protects the data rate of the macrousers. Moreover, we consider the quality of service requirement constraint for the high data rate femtousers, and the adjacency of the assigned subcarriers to a user. We divide the optimization procedure into two steps: the power allocation and the subcarrier assignment. In the first step, we use the dual problem to obtain the optimal powers. In the second step, we formulate the problem of subcarrier allocation as binary integer programming (BIP). The BIP problem is NP-hard and cannot be solved in polynomial time. To reduce the complexity, we propose a practical suboptimal algorithm for the subcarrier assignment, which is significantly simpler than the BIP approach.
Do Community Members Have an Effective Voice in the Ethical Deliberation of a Behavioral Institutional Review Board?

Using concepts and methods from technical and professional communication and linguistics, the authors conducted an observational study of the voice of community members (CMs) in the deliberation of a behavioral institutional review board (IRB). In the discourse of deliberation, they found that CMs had an effective voice in constructing the compliance of individual research protocols under IRB review. But they also found that CMs had an ineffective voice in representing their African-American community, particularly in their efforts to advocate for more consideration of minority research sites and subjects and a fuller consideration of minority community attitudes.
Forecasting the Number of Children and Students Attending to School in Vietnam – The Interest in Gender Difference

Gender is considered as one of the important factors influencing the development of society, relating to many fields such as economy, politics, culture, education, etc. For the past years, Vietnam has actively promoted the gender equality, including the adjustment of the gender gap between the number of the children born throughout the years. However, the forecast results of the number of the children and students attending to school in Vietnam showed that there was quite large gender gap between boys and girls. This leads to the imbalance of gender; as a result, the assurance of the conditions for the education development becomes a challenge. Based on the analysis of the forecast results, the paper mentioned the responsibility of the education managers on the issue of the gender gap in the development of education and sustainable development.
Directed test generation and analysis for web applications

The advent of web technologies has led to the proliferation of modern web applications with enhanced user interaction and client-side execution. JavaScript (the most widely used programming language) is extensively used to build responsive modern web applications. The event-driven and dynamic nature of JavaScript, and its interaction with the Document Object Model (DOM), make it challenging to understand and test effectively. The ultimate goal of this thesis is to improve the quality of web applications through automated testing and maintenance. The work presented in this dissertation has focused on advancing the stateof-the-art in testing and maintaining web applications by proposing a new set of techniques and tools. We proposed (1) a feedback-directed exploration technique and a tool to cover a subset of the state-space of a given web application; the exploration is guided towards achieving higher functionality, navigational, and page structural coverage while reducing the test model size, (2) a technique and a tool to generate UI tests using existing tests; it mines the existing test suite to infer a model of the covered DOM states and event-based transitions including input values and assertions; it then expands the inferred model by exploring alternative paths and generates assertions for the new states; finally it generates a new test suite from the extended model, (3) the first empirical study on JavaScript tests to characterize their prevalence and quality metrics, and to find out root causes for the uncovered (missed) parts of the code under test, (4) a DOM-based JavaScript test fixture generation technique and a tool, which is based on dynamic symbolic execution; it guides the executing through different branches of a function by producing expected DOM instances, (5) a technique and a tool to detect JavaScript code smells using static and dynamic analysis. We evaluated the presented techniques by conducting various empirical studies and comparisons. The evaluation results point to the effectiveness of the proposed techniques in terms of fault detection capability and code coverage for test generation, and in terms of accuracy for code smell detection.
Evaluation of Terminology Translation in Instance-Based Neural MT Adaptation

We address the issues arising when a neural machine translation engine trained on generic data receives requests from a new domain that contains many specific technical terms. Given training data of the new domain, we consider two alternative methods to adapt the generic system: corpus-based and instance-based adaptation. While the first approach is computationally more intensive in generating a domain-customized network, the latter operates more efficiently at translation time and can handle on-the-fly adaptation to multiple domains. Besides evaluating the generic and the adapted networks with conventional translation quality metrics, in this paper we focus on their ability to properly handle domain-specific terms. We show that instance-based adaptation, by fine-tuning the model on-the-fly, is capable to significantly boost the accuracy of translated terms, producing translations of quality comparable to the expensive corpusbased method.
Comparative Analysis Between Robust SMC & Conventional PI Controllers Used in WECS Based on DFIG

In this paper, we present a direct and indirect stator powers control schemes of a doubly fed induction generator based wind energy conversion system (WECS), using a Proportional-Integrated (PI) and sliding mode (SMC) controllers. The classical PI controllers present many limitations due to the nonlinearity of the DFIG model. Therefore, in order to improve the system performances, a new algorithm that combines the nonlinear sliding mode control approach and field orientation scheme applied to the DFIG converters (e.g. rotor side converter and the grid side converter) is developed. The maximum power point tracking (MPPT) and the pitch angle algorithm control are used to extract the maximum power and to protect the wind turbine system from overloading respectively. The performances of the proposed controllers system were tested, analyzed and compared in dynamic and static regime by using Matlab/Simulink.
Typologically robust statistical machine translation : Understanding and exploiting differences and similarities between languages in machine translation

Machine translation systems often incorporate modeling assumptions motivated by properties of the language pairs they initially target. When such systems are applied to language families with considerably different properties, translation quality can deteriorate. Phrase-based machine translation systems, for instance, are ill-equipped to handle the challenges caused by relaxed word order constraints and productive word formation processes in morphologically rich languages. In this thesis, we ask what role the properties of languages, as studied in the field of linguistic typology, play in how well machine translation systems perform. We focus in particular on word order and morphology, and show that typological differences in these areas can be bridged by making certain linguistic phenomena overt to the translation system. Understanding and exploiting typological differences between languages enables improvements to the typological robustness of translation systems without significantly changing the assumptions of the underlying translation models. In the area of word order, we examine the influence of word order freedom on preordering, a popular technique to model word order in phrase-based machine translation, and propose a method to improve its typological robustness. For morphological complexity, we show that reducing the dissimilarity between the source and target language improves phrase-based machine translation for typologically diverse language pairs. Finally, we show that besides helping to bridge the performance gaps between typologically diverse languages, linguistic typology can also serve as a source of knowledge to guide reordering models and to facilitate universal reordering models applicable to multiple target languages.
Supervised association rules mining on pedestrian crashes in urban areas: identifying patterns for appropriate countermeasures

ABSTRACT In 2011, 4,432 pedestrians were killed (14% of total traffic crash fatalities), and 69,000 pedestrians were injured in vehicle-pedestrian crashes in the United States. Particularly in Louisiana, vehicle-pedestrian crashes have become a key concern because of the high percentage of fatalities in recent years. In 2012, pedestrians were accounted for 17% of all fatalities due to traffic crashes in Louisiana. Alcohol was involved in nearly 44% of these fatalities. This research utilized ‘a priori’ algorithm of supervised association mining technique to discover patterns from the vehicle-pedestrian crash database. By using association rules mining, this study aims to discover vehicle-pedestrian crash patterns using eight years of Louisiana crash data (2004–2011). The results indicated that roadway lighting at night helped in alleviating pedestrian crash severity. In addition, a few groups of interest were identified from this study: male pedestrians’ greater propensity towards severe and fatal crashes, younger female drivers (15–24) being more crash-prone than other age groups, vulnerable impaired pedestrians even on roadways with lighting at night, middle-aged male pedestrians (35–54) being inclined towards crash occurrence, and dominance of single vehicle crashes. Based on the recognized patterns, this study recommends several countermeasures to alleviate the safety concerns. The findings of this study will help traffic safety professionals in understanding significant patterns and relevant countermeasures to raise awareness and improvements for the potential decrease of pedestrian crashes.
It’s hip to be scene: authenticity, esoteric knowledge and subcultural style in Saint John and Portland

ii DEDICATION . iii TABLE OF CONTENTS. iv LIST OF TABLES.vi 1.0 Introduction to cities, scenes, subcultures, and terminology. 1 1.1 Scene Functionality. 4 1.2 The Terms. 6 2.0 The Research Methods. 10 2.1 Participant Observation as a Peripheral Scene Member. 13 2.2 Conclusion. 19 3.0 The Literature. 19 3.1 The Notion of Authenticity. 20 3.2 The Importance of the City for Music Scenes. 25 3.3 From Hip to Branded. 30 3.4 It is Hip to be Scene: The Role of Esoteric Knowledge. 32 3.5 Style and Symbolic Power.38 3.6 Conclusion.50 4.0 Venues, Style, and Esoteric Knowledge: An Ethnography of Scenes. 51 4.1 The Venues. 52 4.2 Peppers Pub in Saint John . 55 4.3 The Empire in Portland.57 4.4 Geno’s Rock Club in Portland. 59 4.5 The Importance of Subcultural Style. 60 4.6 Punk Style.61 4.7 Hipster Style.64 4.8 Demonstrating Esoteric Knowledge. 67 4.9 Conclusion. 72 v 5.0 CONCLUSION. 74 6.0 REFERENCES. 80 7.0 Glossary. 86 8.0 Appendix A: Map of Scene Spots in Saint John.90 Appendix B: Map of Scene Spots in Portland.91 CURRICULUM VITAE or CV
Radio resource allocation techniques for MISO downlink cellular networks

This thesis examines radio resource management techniques for multicell multi-input singleoutput (MISO) downlink networks. Specifically, the thesis focuses on developing linear transmit beamforming techniques by optimizing certain quality-of-service (QoS) features, including, spectral efficiency, fairness, and throughput. The problem of weighted sum-rate-maximization (WSRMax) has been identified as a central problem to many network optimization methods, and it is known to be NP-hard. An algorithm based on a branch and bound (BB) technique which globally solves the WSRMax problem with an optimality certificate is proposed. Novel bounding techniques via conic optimization are introduced and their efficiency is illustrated by numerical simulations. The proposed BB based algorithm is not limited to WSRMax only; it can be easily extended to maximize any system performance metric that can be expressed as a Lipschitz continuous and increasing function of the signal-to-interference-plus-noise (SINR) ratio. Beamforming techniques can provide higher spectral efficiency, only when the channel state information (CSI) of users is accurately known. However, in practice the CSI is not perfect. By using an ellipsoidal uncertainty model for CSI errors, both optimal and suboptimal robust beamforming techniques for the worst-case WSRMax problem are proposed. The optimal method is based on a BB technique. The suboptimal algorithm is derived using alternating optimization and sequential convex programming. Through a numerical example it is also shown how the proposed algorithms can be applied to a scenario with statistical channel errors. Next two decentralized algorithms for multicell MISO networks are proposed. The optimization problems considered are: P1) minimization of the total transmission power subject to minimum SINR constraints of each user, and P2) SINR balancing subject to the total transmit power constraint of the base stations. Problem P1 is of great interest for obtaining a transmission strategy with minimal transmission power that can guarantee QoS for users. In a system where the power constraint is a strict system restriction, problem P2 is useful in providing fairness among the users. Decentralized algorithms for both problems are derived by using a consensus based alternating direction method of multipliers. Finally, the problem of spectrum sharing between two wireless operators in a dynamic MISO network environment is investigated. The notion of a two-person bargaining problem is used to model the spectrum sharing problem, and it is cast as a stochastic optimization. For this problem, both centralized and distributed dynamic resource allocation algorithms are proposed. The proposed distributed algorithm is more suitable for sharing the spectrum between the operators, as it requires a lower signaling overhead, compared with centralized one. Numerical results show that the proposed distributed algorithm achieves almost the same performance as the centralized one.
CMOS 120 GHz Phase-Locked Loops Based on Two Different VCO Topologies

This work describes the development and comparison of two phase-locked loops (PLLs) based on a 65-nm CMOS technology. The PLLs incorporate two different topologies for the output voltage-controlled oscillator (VCO): LC cross-coupled and differential Colpitts. The measured locking ranges of the LC cross-coupled VCO-based phase-locked loop (PLL1) and the Colpitts VCO-based phase-locked loop (PLL2) are 119.84−122.61 GHz and 126.53−129.29 GHz, respectively. The output powers of PLL1 and PLL2 are −8.6 dBm and −10.5 dBm with DC power consumptions of 127.3 mW and 142.8 mW, respectively. Th e measured phase noise of PLL1 is −59.2 at 10 kHz offset and −104.5 at 10 MHz offset, and the phase noise of PLL2 is −60.9 dBc/Hz at 10 kHz offset and −104.4 dBc/Hz at 10 MHz offset. The chip sizes are 1,080 μ m × 760 μ m (PLL1) and 1,100 μ m × 800 μ m (PLL2), including the probing pads.
A Survey of Various Image De-noising Techniques

The image processing is the technology through which information of image get processed . the noises are the extra pixels which get added on the image to reduce image quality. The authors proposed various techniques which works to remove noisy pixels from the image. In this paper, various image de-nosing techniques has been reviewed and discuss in terms of their outcomes.
Breakdown in CO2 and CO2/C2F4 Mixtures at Elevated Temperatures in the Range 1000-4000 K

CO2 is a promising gas for replacement of SF6 in high voltage circuit breakers. The electric breakdown in CO2 and mixtures with C2F4 from PTFE nozzles is, however, still not fully understood. To understand the electric breakdown in CO2 and mixtures with C2F4 from PTFE nozzles fundamental parameters like the temperature and pressure dependence of critical electric fields are needed. Data on critical fields is usually available based on simulations only and experimental validation is lacking so far. Our present contribution aims to close this gap and presents experiments where the breakdown fields in uniform and weakly non-uniform electric fields are determined at various temperatures and pressures. The gas temperatures were estimated from measurements with pyrometers. The results are compared to theoretical predictions.