Miracle-dz/UniFAR-FTUs huggingface dataset:
Noise Reduction and Simulation in Avalanche Photodiodes

One of the most important devices used in optical communication systems are Avalanche Photo Diodes (APDs). APDs are the proper devices in signal detection because of their wide bandwidth, low noise operation and sensitive detection respect to other detectors. In this paper, an overview of the noise specification is presented for them. Some structures such as thin multiplication layer APDs, impact ionization engineering, ultra-low noise APD with a centered-well multiplication region and some theories such as dead space multiplication theory (DSMT) and modified DSMT (MDSMT) are studied. The numerical simulation of nonlocal ionization and dead space effects in homojunction APDs are reviewed based on the history dependent multiplication theory (HDMT). Finally, we discussed the low noise CMOS APDs. Keywords—Avalanche photodiodes, simulation, low noise operation, dead space, noise equivalent power.
WORKPLACE SOCIAL SUPPORT AND PERSONALITY TYPE AS PREDICTORS OF JOB SATISFACTION

Investigated in the present study were workplace social support variables and personality types as predictors of job satisfaction. Two hundred and fifty staff of Nigeria Breweries Ama Enugu State who volunteered participated in the study. Their average age was 28 years and they were mainly junior and medium level staff. Valid questionnaire were used to collected raw data about the study variables and multiple regression analysis using SPSS version 16 was used to analyze the data. The results showed that perceived workplace social support contributed to 13% of the models significant of F 3,249 = 13.8, P < . 001. Only supervisors support and organization support were significant predictors of job satisfaction, betas . 33 and . 13, p< . 001 and . 05 respectively. Colleagues support was not found to be significant predictor of job satisfaction. Also personality type contributed to 22% of the models significant of F 5,249 = 14.00, p < . 001. Only extraversion, neuroticism and conscientiousness were significant personality traits predictors of job satisfaction at betas . 29, -. 19, and . 27, p< . 001 and . 05 respectively. Agreeableness and openness were not significant predictors of job satisfaction. Discussion was made drawing strength from literature review; implications of the results were highlighted. While limitation and suggestion for future research was stated.
Effects of Phytoestrogens on the Developing Brain, Gut Microbiota, and Risk for Neurobehavioral Disorders

Many pregnant and nursing women consume high amounts of soy and other plant products that contain phytoestrogens, such as genistein (GEN) and daidzein. Infants may also be provided soy based formulas. With their ability to bind and activate estrogen receptors (ESR) in the brain, such compounds can disrupt normal brain programming and lead to later neurobehavioral disruptions. However, other studies suggest that maternal consumption of soy and soy based formulas containing such phytoestrogens might lead to beneficial behavioral effects. Select gut microbes might also convert daidzein and to a lesser extent genistein to even more potent forms, e.g., equol derivatives. Thus, infant exposure to phytoestrogens may result in contrasting effects dependent upon the gut flora. It is also becoming apparent that consumption or exposure to these xenoestrogens may lead to gut dysbiosis. Phytoestrogen-induced changes in gut bacteria might in turn affect the brain through various mechanisms. This review will consider the evidence to date in rodent and other animal models and human epidemiological data as to whether developmental exposure to phytoestrogens, in particular genistein and daidzein, adversely or beneficially impact offspring neurobehavioral programming. Consideration will be given to potential mechanisms by which such compounds might affect neurobehavioral responses. A better understanding of effects perinatal exposure to phytoestrogen can exert on brain programming will permit pregnant women and those seeking to become pregnant to make better-educated choices. If phytoestrogen-induced gut dysbiosis contributes to neurobehavioral disruptions, remediation strategies may be designed to prevent such gut microbiota alterations and thereby improve neurobehavioral outcomes.
Mapping Quality of Government in the European Union : A Study of National and Sub-National Variation

This study maps-out the variation in quality of government (‘QoG’ e.g. low levels or corruption, protection of the rule of law, government effectiveness and accountability) for the national and regional level in the 27 EU countries. We begin with a national level assessment of QoG in the EU, elucidating the three significantly different cluster groupings of states that emerge using existing data from the World Bank. We then present a novel sub-national-level data for 172 EU regions based on a survey of 34,000 residents across 18 countries, which constitutes the largest multi-country survey on QoG at the regional level to date. We combine the two measures to conduct a large multilevel assessment of QoG in Europe. The paper shows, first, that there are notable differences in the level of quality of government across EU regions; second, that those differences, despite being inherently difficult to capture, can be measured with sufficient accuracy and are resistant to the most stringent robustness checks available; third, that those differences in quality of government are highly correlated with the available indicators of differences in socio-economic development of EU regions. Nicholas Charron The Quality of Government Institute Department of Political Science University of Gothenburg nicholas.charron@pol.gu. se Lewis Dijkstra European Commission DG Regional Policy lewis.dijkstra@ec.europa. eu Victor Lapuente The Quality of Government Institute Department of Political Science University of Gothenburg victor.lapuente@pol.gu. se
A web-based feedback study on optimization-based training and analysis of human decision making

The question “How can humans learn efficiently to make decisions in a complex, dynamic, and uncertain environment” is still a very open question. We investigate what effects arise when feedback is given in a computer-simulated microworld that is controlled by participants. This has a direct impact on training simulators that are already in standard use in many professions, e.g., for flight simulators for pilots, and a potential impact on a better understanding of human decision making in general. Our study is based on a benchmark microworld with an economic framing, the IWR Tailorshop. N=94 participants played four rounds of the microworld, each 10 months, via a web interface. We propose a new approach to quantify performance and learning, which is based on a mathematical model of the microworld and optimization. Six participant groups receive different kinds of feedback in a training phase, then results in a performance phase without feedback are analyzed. As a main result, feedback of optimal solutions in training rounds improved model knowledge, early learning, and performance, especially when this information is encoded in a graphical representation (arrows).
Diagnostic Tumor Markers in Head and Neck Squamous Cell Carcinoma (HNSCC) in the Clinical Setting

Head and neck squamous cell carcinoma (HNSCC) represents a group of tumors arising in the oral cavity, oropharynx, and larynx. Although HNSCC is traditionally associated with tobacco and alcohol consumption, a growing proportion of head and neck tumors, mainly of the oropharynx, are associated with Human Papilloma Virus (HPV). Recurrent/metastatic disease is characterized by dismal prognosis and there is an unmet need for the development of biomarkers for detection of early disease, accurate prediction of prognosis, and appropriate selection of therapy. Based on the REMARK guidelines, a variety of diagnostic and prognostic biomarkers are being evaluated in clinical trials but their clinical significance is doubtful. Herein, we will focus on biomarkers in HNSCC used in the clinical setting and we will illustrate their clinical relevance.
An improved algorithm for personalized recommendation on MOOCs

Purpose In the past few years, millions of people started to acquire knowledge from the Massive Open Online Courses (MOOCs). MOOCs contain massive video courses produced by instructors, and learners all over the world can get access to these courses via the internet. However, faced with massive courses, learners often waste much time finding courses they like. This paper aims to explore the problem that how to make accurate personalized recommendations for MOOC users. Design/methodology/approach This paper proposes a multi-attribute weight algorithm based on collaborative filtering (CF) to select a recommendation set of courses for target MOOC users. Findings The recall of the proposed algorithm in this paper is higher than both the traditional CF and a CF-based algorithm – uncertain neighbors’ collaborative filtering recommendation algorithm. The higher the recall is, the more accurate the recommendation result is. Originality/value This paper reflects the target users’ preferences for the first time by calculating separately the weight of the attributes and the weight of attribute values of the courses.
A Random Opposition-Based Learning Grey Wolf Optimizer

Grey wolf optimizer (GWO) algorithm is a swarm intelligence optimization technique that is recently developed to mimic the hunting behavior and leadership hierarchy of grey wolves in nature. It has been successfully applied to many real world applications. In the GWO algorithm, “C” is an important parameter which favoring exploration. At present, the researchers are few study the parameter “C” in GWO algorithm. In addition, during the evolution process, the other individuals in the population move towards to the <inline-formula> <tex-math notation="LaTeX">$=alpha $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$\delta $ </tex-math></inline-formula> wolves which are to accelerate convergence. However, GWO is easy to trap in the local optima. This paper presents a modified parameter “C” strategy to balance between exploration and exploitation of GWO. Simultaneously, a new random opposition-based learning strategy is proposed to help the population jump out of the local optima. The experiments on 23 widely used benchmark test functions with various features, 30 benchmark problems from IEEE CEC 2014 Special Session, and three engineering design optimization problems. The results reveal that the proposed algorithm shows better or at least competitive performance against other compared algorithms on not only global optimization but also engineering design optimization problems.
APPROPRIATION OF YOUTUBE FOR CHRONIC ILLNESS MANAGEMENT

With the rapid increase of social media adoption globally, there has been a surge in the number of chronically ill adults who utilize social media tools as part of their illness management practice. While there have been numerous conceptual papers discussing the potential benefits of appropriation in this context, there seem to be limited studies that have explored the appropriation of social media platforms by this cohort. This paper addresses this gap in knowledge through a case study with a mixed method approach to examine the appropriation of YouTube by chronically ill adults to better understand and manage their Fibromyalgia illness. Our results highlight the process of social media appropriation which include: viewing to vlogging; implementing workarounds and; borderless appropriation of social media tools. In addition, self-presentation, building transactive relationships, engagement in illness advocacy and technology features were found to influence appropriation outcomes for chronically ill adults on social media.
Adaptive Learning and Survey Expectations of Ination

The use of survey information on ination expectations as an observable in a DSGE model can re ne substantially the identi cation of the shocks that drive the ination process. An optimal integration of the survey information improves the model forecast for both ination and the other macroeconomic variables. Models with expectations based on an Adaptive Learning setup can exploit the survey information more ef ciently than their Rational Expectations counterparts. The resulting time-variation in the perceived ination target, in the ination persistence and in the sensitivity of ination to various shocks provide a rich and consistent description of the joint dynamics in realized and expected ination. JEL Classi cation: C5, D84, E3
Toward High Accuracy Positioning in 5G via Passive Synchronization of Base Stations Using Thermally-Insensitive Optical Fibers

Positioning accuracy in 5G networks (achieved via techniques based on observed time difference of arrival (OTDoA)) is limited by the synchronization error between the cellular base stations. Here, we demonstrate that these base stations can be synchronized entirely passively through the use of emerging forms of hollow core fiber (HCF) as the data transmission medium in the 5G front-haul network. This is possible due to the excellent thermal stability of HCF which allows the synchronization error among cellular base stations to be reduced significantly as compared to systems based on standard single mode fibers. Reducing this synchronization error is necessary to meet the strict timing requirements envisaged for 5G networks. We analyze the polarization mode dispersion, chromatic dispersion, and thermal stability of the HCF and give suggestions on how to use the HCF to balance overall radio over fiber (RoF) link performance in 5G front-haul networks. In a proof of concept experiment we show that HCF links enable the positioning error (calculated with the OTDoA method) to be reduced down to the centimeter level even when subject to tens of degrees Celsius temperature variations. This represents a 20-fold improvement over standard single mode fiber systems which would require active compensation schemes to achieve similar levels of time synchronization accuracy.
A contravariant formulation of Non-linear Shallow Water Equations

An Upwind Weighted Essentially Non-Oscillatory scheme for thesolution of the Shallow Water Equations on generalized curvilinearcoordinate systems is proposed. The Shallow Water Equations are expressed in a contravariant formulation in which Christoffelsymbols are avoided. The equations are solved by us ing ahigh-r esolu t ion f inite-volume method incorporated with an exactRiemann Solver .
Effects of nitrogen levels on plant growth and leaf yield of off season coriander under shade nets

Present investigation was carried out on influence of nitrogen levels on plant growth and leaf yield of off season coriander cultivar Ajmer Green Coriander-1 (AGCr-1) under shade nets at ICAR-National Research Centre on Seed Spices, Ajmer, Rajasthan during 2015-16. The experiment comprised five levels of nitrogen (30, 40, 50, 60 and 70 kg N ha) with four replications under randomized block design keeping plot size 3.0m x 2.5m. Observations with respect to seed germination, plant growth, leaf yield in each treatment were recorded. The crop was cut four times during the cropping season and computed the total yield of green leaves. Results revealed that plant height, number of leaves per plant, leaf size and yield of green biomass of coriander crop was influenced significantly with the application of nitrogen and increased upto 60 kg N ha and then decreased. Maximum total leaf yield (2599 g plot and 6930.67 kg ha) with other parameters at highest level was recorded under the application of 60 kg N ha (N 4 treatment) as compared to minimum (1640 g plant and 4373.33 kg ha) with lowest level of other parameters under the application of 30 kg N ha 1 (N 1 treatment).
Wireless Power Transfer with Information Asymmetry: A Public Goods Perspective

Wireless power transfer (WPT) technology enables a cost-effective and sustainable energy supply in wireless networks. However, the broadcast nature of wireless signals makes them non-excludable public goods, which leads to potential free-riders among energy receivers. In this study, we formulate the wireless power provision problem as a public goods provision problem, aiming to maximize the social welfare of a system of an energy transmitter (ET) and all the energy users (EUs), while considering their heterogeneous valuations, private information, and self-interested behaviors. We propose a two-phase all-or-none scheme involving a low-complexity Power And Taxation (PAT) mechanism, which ensures voluntary participation, truthfulness, budget balance, and social optimality at every Nash equilibrium (NE). We propose a distributed PAT (D-PAT) algorithm to reach an NE, and prove its convergence by connecting the structure of NEs and that of the optimal solution to a related optimization problem. We further extend the analysis to a multi-channel system, which brings a further challenge of non-strictly concave agents’ payoffs. We propose a Multi-Channel PAT (M-PAT) mechanism and a distributed M-PAT (D-MPAT) algorithm to address the challenge. Simulation results show that, our design is most beneficial when there are more EUs and more homogeneous channel gains.
Computation and Analysis of Explicit Formulae for the Circumradius of Cyclic Polygons ∗

This paper describes computations of the circumradius of cyclic polygons given by the lengths of the sides. Extending the author’s previous paper in 2011, we mainly discuss the computation and analysis of the formulae for cyclic heptagons and octagons. As a result of the present work, we have succeeded in explicitly computing the circumradius of cyclic heptagons, which is converted into an expression in the form of elementary symmetric polynomials for the first time. We have also succeeded in computing 25 out of 39 coefficients in the circumradius formula for cyclic octagons. Moreover, investigating the formulae by the total degree of each term, from triangles to octagons, we have discovered a characteristic structure in common among them, which should be helpful for computing the other huge coefficients remaining in the octagon formula.
A Lightweight Neural Network for Monocular View Generation With Occlusion Handling

In this article, we present a very lightweight neural network architecture, trained on stereo data pairs, which performs view synthesis from one single image. With the growing success of multi-view formats, this problem is indeed increasingly relevant. The network returns a prediction built from disparity estimation, which fills in wrongly predicted regions using a occlusion handling technique. To do so, during training, the network learns to estimate the left-right consistency structural constraint on the pair of stereo input images, to be able to replicate it at test time from one single image. The method is built upon the idea of blending two predictions: a prediction based on disparity estimation and a prediction based on direct minimization in occluded regions. The network is also able to identify these occluded areas at training and at test time by checking the pixelwise left-right consistency of the produced disparity maps. At test time, the approach can thus generate a left-side and a right-side view from one input image, as well as a depth map and a pixelwise confidence measure in the prediction. The work outperforms visually and metric-wise state-of-the-art approaches on the challenging KITTI dataset, all while reducing by a very significant order of magnitude (5 or 10 times) the required number of parameters (6.5 M).
Intra-Lingual and Cross-Lingual Prosody Transformation

Speech technologies have a major role to play in future generation technologies like spoken language translation, intelligent virtual assistants, gaming, language learning etc., Text-to-speech synthesis (TTS) is one of the main components in these technologies, often acting as the interface between the rest of the software and the end-user. It is incumbent on TTS researchers now more than ever to make synthetic speech output more natural and appropriate in the wide variety of spoken language application domains. The main criticism of the state-of-the-art in speech synthesis is the lack of expression and personality in synthetic speech outputs. This proposal takes a step closer in the direction of expressive and flexible speech synthesis by improving the current prosodic models used within TTS. In preliminary work, we propose a Statistical Phrase/Accent Model (SPAM) for intonation that can generate natural and expressive intonation contours within TTS. Through subjective evaluation, the model is shown to be significantly better than currently used intonation models. Future work within intonation modeling will include empirical investigation into optimal prosodic representations like segments, syllables and higher level phonological units like metrical feet. The efficiency of the proposed model will be tested on prosodically rich and diverse tasks like and newscasts and audiobooks. We then propose techniques for conversion of a speaker’s intonation to that of a target speaker or style. This is done within a voice conversion framework to match a target speaker’s spectral and prosodic characteristics. Further, we extend the proposed model and techniques within a cross-lingual setting, where we learn from a parallel speech corpus and attempt to transfer affect (including prominence patterns) in a speechto-speech machine translation task. We apply the cross-lingual transformation on lecture style TED videos for the case of English↔Portuguese translation. Evaluation will be conducted through large-scale perceptual tests and appropriate objective comparisons.
Explaining the low luminosity of Uranus: a self-consistent thermal and structural evolution

The low luminosity of Uranus is a long-standing challenge in planetary science. Simple adiabatic models are inconsistent with the measured luminosity, which indicates that Uranus is non-adiabatic because it has thermal boundary layers and/or conductive regions. A gradual composition distribution acts as a thermal boundary to suppress convection and slow down the internal cooling. Here we investigate whether composition gradients in the deep interior of Uranus can explain its low luminosity, the required composition gradient, and whether it is stable for convective mixing on a timescale of some billion years. We varied the primordial composition distribution and the initial energy budget of the planet, and chose the models that fit the currently measured properties (radius, luminosity, and moment of inertia) of Uranus. We present several alternative non-adiabatic internal structures that fit the Uranus measurements. We found that convective mixing is limited to the interior of Uranus, and a composition gradient is stable and sufficient to explain its current luminosity. As a result, the interior of Uranus might still be very hot, in spite of its low luminosity. The stable composition gradient also indicates that the current internal structure of Uranus is similar to its primordial structure. Moreover, we suggest that the initial energy content of Uranus cannot be greater than 20% of its formation (accretion) energy. We also find that an interior with a mixture of ice and rock, rather than separated ice and rock shells, is consistent with measurements, suggesting that Uranus might not be “differentiated”. Our models can explain the luminosity of Uranus, and they are also consistent with its metal-rich atmosphere and with the predictions for the location where its magnetic field is generated.