Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of nonlocal electrical conductivity on near-field radiative heat transfer between graphene sheets

Graphene's near-field radiative heat transfer is determined from its electrical conductivity, commonly modeled using the local Kubo and Drude formulas. In this letter, we analyze the non-locality of graphene's electrical conductivity using the Lindhard model combined with the Mermin relaxation time approximation. We also study how the variation of electrical conductivity with wavevector affects near-field radiative conductance between two graphene sheets separated by a vacuum gap. It is shown that the variation of electrical conductivity with wavevector, $k_{\rho}$, is appreciable for $k_{\rho}$s greater than $100k_0$, where $k_0$ is the magnitude of the wavevector in the free space. The Kubo electrical conductivity provides an accurate estimation of the spectral radiative conductance between two graphene sheets except for around the surface-plasmon-polariton frequency of graphene and at separation gaps smaller than 20 nm where there is a non-negligible contribution from modes with $k_{\rho}>100k_0$ to the radiative conductance. The Drude formula proves to be inaccurate for modeling the electrical conductivity and radiative conductance of graphene except for at temperatures much below the Fermi temperature and frequencies much smaller than $2{\mu}_c/{\hbar}$, where ${\mu}_c$ and ${\hbar}$ are the chemical potential and reduced Planck's constant, respectively. It is also shown that the electronic scattering processes should be considered in the Lindhard model properly, such that the local electron number is conserved. A substitution of ${\omega}$ by ${\omega}+i{\gamma}$ (${\omega}$, $i$, and ${\gamma}$ being the angular frequency, imaginary unit, and scattering rate, respectively) in the collisionless Lindhard model does not satisfy the conservation of the local electron number and results in significant errors in computing graphene's electrical conductivity and radiative conductance.
Mining Code Review Data to Understand Waiting Times Between Acceptance and Merging: An Empirical Analysis

Increasing code velocity (or the speed with which code changes are reviewed and merged) is integral to speeding up development and contributes to the work satisfaction of engineers. While factors affecting code change acceptance have been investigated in the past, solutions to decrease the code review lifetime are less understood. This study investigates the code review process to quantify delays and investigate opportunities to potentially increase code velocity. We study the temporal characteristics of half a million code reviews hosted on Gerrit and Phabricator, starting from the first response, to a decision to accept or reject the changes, and until the changes are merged into a target branch. We identified two types of time delays: (a) the wait time from the proposal of code changes until first response, and (b) the wait time between acceptance and merging. Our study indicates that reducing the time between acceptance and merging has the potential to speed up Phabricator code reviews by 29-63%. Small code changes and changes made by authors with a large number of previously accepted code reviews have a higher chance of being immediately accepted, without code review iterations. Our analysis suggests that switching from manual to automatic merges can help increase code velocity.
Quantum yield of an emitter proximate to nanostructures: a quantitative understanding

Abstract: Exciton-surface plasmon coupling is at the heart of the most elementary light-matter interactions and is a result of not only an intrinsic property of the emitter but that of emitter-environment interaction. Thus, change of electromagnetic environment, as in case of metallic nanoplasmonic structures and an emitter, significantly modifies the near field light-matter interaction, which leads to energy transfer in the form of exciton between metallic nanostructure and the emitter. However, this mechanism remains largely unexplored. Here, we developed and applied semi-classical electrodynamics theory and modeling techniques to analyze the energy transfer mechanism in exciton-surface plasmon coupling. The quantum efficiency of an emitter was investigated as a function of the location of the emitter with respect to nanoparticles and their assembles whose local plasmonic field modified by forming complex coupling modes as well as the local dielectric environment. The research provided a theoretical insight into fundamental science of nanophotonics and shed light on unprecedented applications in wide range fields such as ultra-low power lasers, quantum information processing, photovoltaics, photocatalysis, and chemical sensing.
LSTM-driven Forecast of CO2 Injection in Porous Media

The ability to simulate the partial differential equations (PDE’s) that govern multiphase flow in porous media is essential for different applications such as geologic sequestration of CO2, groundwater flow monitoring and hydrocarbon recovery from geologic formations [1]. These multi-phase flow problems can be simulated by solving the governing PDE’s numerically, using various discretization schemes such as finite elements, finite volumes, spectral methods, etc. More recently, the application of Machine Learning (ML) to approximate the solutions to PDE’s has been a very active research area. However, most researchers have focused on the performance of their models within the time-space domain in which the models were trained. In this work, we apply ML techniques to approximate PDE solutions and focus on the forecasting problem outside of the training domain. To this end, we use two different ML architectures the feed forward neural (FFN) network and the long short-term memory (LSTM)-based neural network, to predict the PDE solutions in future times based on the knowledge of the solutions in the past. The results of our methodology are presented on two example PDE’s namely a form of PDE that models the underground injection of CO2 and its hyperbolic limit which is a common benchmark case. In both cases, the LSTM architecture shows a huge potential to predict the solution behavior at future times based on prior data.
Human-GDPR Interaction: Practical Experiences of Accessing Personal Data

In our data-centric world, most services rely on collecting and using personal data. The EU's General Data Protection Regulation (GDPR) aims to enhance individuals’ control over their data, but its practical impact is not well understood. We present a 10-participant study, where each participant filed 4-5 data access requests. Through interviews accompanying these requests and discussions scrutinising returned data, it appears that GDPR falls short of its goals due to non-compliance and low-quality responses. Participants found their hopes to understand providers’ data practices or harness their own data unmet. This causes increased distrust without any subjective improvement in power, although more transparent providers do earn greater trust. We propose designing more effective, data-inclusive and open policies and data access systems to improve both customer relations and individual agency, and also that wider public use of GDPR rights could help with delivering accountability and motivating providers to improve data practices.
Bayesian estimation of dynamic weights in Gaussian mixture models

This paper proposes a generalization of Gaussian mixture models, where the mixture weight is allowed to behave as an unknown function of time. This model is capable of successfully capturing the features of the data, as demonstrated by simulated and real datasets. It can be useful in studies such as clustering, change-point and process control. In order to estimate the mixture weight function, we propose two new Bayesian nonlinear dynamic approaches for polynomial models, that can be extended to other problems involving polynomial nonlinear dynamic models. One of the methods, called here component-wise Metropolis-Hastings, apply the Metropolis- Hastings algorithm to each local level component of the state equation. It is more general and can be used in any situation where the observation and state equa- tions are nonlinearly connected. The other method tends to be faster, but is applied speciﬁcally to binary data (using the probit link function). The performance of these methods of estimation, in the context of the proposed dynamic Gaussian mixture model, is evaluated through simulated datasets. Also, an application to an array Comparative Genomic Hybridization (aCGH) dataset from glioblastoma cancer il-lustrates our proposal, highlighting the ability of the method to detect chromosome aberrations.
Modelling the operation strategies of storages and hydro resources in adequacy analysis of power systems in presence of wind farms

This study addresses the adequacy of a generating system considering the impact of operation strategies of storages and hydro energy resources. It is assumed that the installed storage capacity of a system can be assigned for two purposes: first, economical operation which adopts storage to shift electric energy and smooth the load curve; and the second, reliability-based operation, which deploys storage to avoid load curtailments. The economical operation strategy is represented by starting from time domain data and using the load curve modification method to synthesise the overall impact of storage. The reliability-based operation is modelled with a new method based on Markov chain model that considers the dynamic behaviour of storage during time. Moreover, a novel state reduction method is presented for decreasing the number of Markov states in applications to large systems. The effectiveness of the presented methods is evaluated by running several simulation scenarios with results presented on three test systems. The results demonstrate that evaluating the system reliability in the presence of energy storage by considering the frequency and duration of system states is more effective than using methods based on the Capacity Outage Probability Table (COPT).
Research of Fuzzy Control Method for Electronic Hydraulic Hitch of Tractor

Based on the character of the electronic hydraulic hitch,this paper chose the right control method.Besides it also introduced the design method of electronic hydraulic hitch system fuzzy controller,built Fuzzy Inference System(FIS). The simulation model of hitch fuzzy control system and PID control system was built with MATLAB,and the simulation analysis indicated that fuzzy control method is more satisfied the request of system performance than PID control method.
A high resolution traction stress sensor array for use in robotic tactile determination

When two solid bodies are brought into physical contact, a stress profile is generated at the interface. This contact stress profile, termed traction stress, has three components: one component oriented normal to the surface (normal stress), and two components oriented tangent to the surface (shear stress). It has long been recognized that processing this contact stress information is important to the formation and maintenance of stable grasp configurations in dexterous robotic manipulation systems. Throughout the past two decades, many contact stress sensor arrays have been developed with the intent of conferring upon robotic manipulators the ability to sense tactile information. Indeed, a broad variety of transduction mechanisms have been harnessed to this end. However, much of the utility of tactile sensors in robotic manipulation has been demonstrated with relatively low spatial resolution arrays that are insensitive to shear stress. In fact, only one array capable of simultaneously measuring both shear and normal stress information has been demonstrated to date. Nonetheless, the successful development of a robust, high resolution, shear sensitive contact stress sensor would most certainly expand the range of manipulation tasks that may be performed by robotic systems. The first tactile sensing array capable of measuring triaxial contact stress profiles at a high spatial resolution has recently been developed at Stanford University. The array, composed of 4,096 (64 x 64) sensor elements, was constructed using VLSI circuit fabrication techniques, thereby allowing a sensor spacing comparable to that of the papillary ridges of the human dermis (300 μm). Digital CMOS circuitry has been integrated on the device substrate to allow sequentially scanning of the 1.92 x 1.92 cm active sensing area. Testing of the device indicates performance which is commensurate with that needed for a useful robotic tactile sensor. Characterization studies revealed that the sensor array was capable of measuring two dimensional contact stress profiles with normal and shear stress measurement resolutions of 0.20 kPa and 1.0 kPa respectively, as well as a two-point spatial resolution of 0.7 mm. In this thesis, the design, construction, fabrication, and characterization of the array are presented.
Towards better translation performance on spoken language

In this paper, we describe GTCOM’s neural machine translation(NMT) systems for the International Workshop on Spoken Language Translation(IWSLT) 2017. We participated in the English-to-Chinese and Chinese-to-English tracks in the small data condition of the bilingual task and the zero-shot condition of the multilingual task. Our systems are based on the encoder-decoder architecture with attention mechanism. We build byte pair encoding (BPE) models in parallel data and back-translated monolingual training data provided in the small data condition. Other techniques we explored in our system include two deep architectures, layer nomalization, weight normalization and training models with annealing Adam, etc. The official scores of English-to-Chinese, Chinese-to-English are 28.13 and 21.35 on test set 2016 and 28.30 and 22.16 on test set 2017. The official scores on German-to-Dutch, Dutch-to-German, Italian-to-Romanian and Romanian-to-Italian are 19.59, 17.95, 18.62 and 20.39 respectively.
FBK’s Multilingual Neural Machine Translation System for IWSLT 2017

Neural Machine Translation has been shown to enable inference and cross-lingual knowledge transfer across multiple language directions using a single multilingual model. Focusing on this multilingual translation scenario, this work summarizes FBK’s participation in the IWSLT 2017 shared task. Our submissions rely on two multilingual systems trained on five languages (English, Dutch, German, Italian, and Romanian). The first one is a 20 language direction model, which handles all possible combinations of the five languages. The second multilingual system is trained only on 16 directions, leaving the others as zero-shot translation directions (i.e representing a more complex inference task on language pairs not seen at training time). More specifically, our zero-shot directions are Dutch$German and Italian$Romanian (resulting in four language combinations). Despite the small amount of parallel data used for training these systems, the resulting multilingual models are effective, even in comparison with models trained separately for every language pair (i.e. in more favorable conditions). We compare and show the results of the two multilingual models against a baseline single language pair systems. Particularly, we focus on the four zero-shot directions and show how a multilingual model trained with small data can provide reasonable results. Furthermore, we investigate how pivoting (i.e using a bridge/pivot language for inference in a source! pivot! target translations) using a multilingual model can be an alternative to enable zero-shot translation in a low resource setting.
The 2016 KIT IWSLT Speech-to-Text Systems for English and German

This paper describes our German and English Speech-to-Text (STT) systems for the 2016 IWSLT evaluation campaign. The campaign focuses on the transcription of unsegmented TED talks. Our setup includes systems using both the Janus and Kaldi frameworks. We combined the outputs using both ROVER [1] and confusion network combination (CNC) [2] to archieve a good overall performance. The individual subsystems are built by using different speaker-adaptive feature combination (e.g., lMEL with i-vector or bottleneck speaker vector), acoustic models (GMM or DNN) and speaker adaption (MLLR or fMLLR). Decoding is performed in two stages, where the GMM and DNN systems are adapted on the combination of the first stage outputs using MLLR, and fMLLR. The combination setup produces a final hypothesis that has a significantly lower WER than any of the individual subsystems. For the English TED task, our best combination system has a WER of 7.8% on the development set while our other combinations gained 21.8% and 28.7% WERs for the English and German MSLT tasks.
Molecular phylogeography and evolutionary history of the pink rice borer (Lepidoptera: Noctuidae): Implications for refugia identification and pest management

Understanding phylogeography, demographic dynamics as well as evolutionary forces of agricultural pests can enhance our understanding of their distribution dynamics, which is critical for developing effective control strategies. The pink rice borer (PRB), Sesamia inferens (Walker), is a destructive pest of rice that is widely distributed in Asia; however, little is known regarding its phylogeography and evolutionary history. In this study, we investigated the genetic diversity, population genetic structure and demographic history of the PRB at the scale of its whole distribution in China. Both mitochondrial and microsatellite datasets indicated that PRB individuals exhibit high levels of genetic diversity. Two large phylogeographic clusters were identified, corresponding to Northern and Southern China regions, respectively. The glacial events during the penultimate glacial period likely led to the separation of these two clusters, and parts of Northern and Southern China areas acted as independent refugia for PRB during glacial period. Two microrefugia in the Sichuan Basin and Yunnan‐Guizhou Plateau were also identified, and the PRB from Sichuan Basin could be one of the ancestral sources for PRB individuals from the Northern China lineage. Our study not only provides information that has implications for refugia identification, but also provides an example on how to reveal the phylogeography, demography history and evolutionary forces of an agriculture pest.
High‐accuracy, direct aberration determination using self‐attention‐armed deep convolutional neural networks

Optical microscopes have long been essential for many scientific disciplines. However, the resolution and contrast of such microscopic images are dramatically affected by aberrations. In this study, compacted with adaptive optics, we propose a machine learning technique, called the ‘phase‐retrieval deep convolutional neural networks (PRDCNNs)’. This aberration determination architecture is direct and exhibits high accuracy and certain generalisation ability. Notably, its performance surpasses those of similar, existing methods, with fewer fluctuations and greater robustness against noise. We anticipate future application of the proposed PRDCNNs to super‐resolution microscopes.
Diversifying the clinical pipeline: The necessity of diverse, equitable, and inclusive initiatives to advance the placement of underrepresented minoritized students in postgraduate pharmacy training

Pharmacists are one of the most accessible health care professionals in the United States. As a result, pharmacists are in a unique position to provide direct patient care to the increasingly diverse racial and ethnic US population. However, the profession of pharmacy does not currently reflect the population in which it serves. Of the 341 000 pharmacists in the United States in 2019, approximately 64.5% selfidentified as White, 22.1% as Asian, 10.5% as Black and/or AfricanAmerican, and 2.7% as Hispanic and/or Latinx. Within the United States, racial and ethnic minorities are at disproportionate risk of lacking access to care and experiencing worse health outcomes from treatable and preventable conditions. For this reason, it is crucial to increase the diversity within the pharmacy workforce to enhance patient-provider communication, improve drug and followup adherence, and increase health care access for underrepresented minorities (URMs).
Perceptions of performativity in English Further Education

ABSTRACT The notions of performativity and the use of accountability practices within the UK education sector are contentious. Although some commentators suggest that statistically driven performativity measures do not align with practitioner values, little research has investigated any potential differences in relation to job role and level of management responsibility. This study focused on whether perceptions of performativity change according to someone’s job role and whether there is a differential between managers and teachers. An electronic questionnaire was disseminated at a single FE college, with 107 participants surveyed across a wide range of subject areas. Quantitative analysis revealed that perceptions of managers differ from those of teaching staff regarding the effectiveness of statistical performativity targets to drive factors which are integral to an efficacious learning environment. Results are far from unequivocal though. As practitioners take on more of a managerial emphasis within their role, the perceived benefit of and their affinity for target setting and performativity measures increase. However, the magnitude of this more favourable outlook towards performativity is limited, with managers also broadly sceptical concerning any benefit and positive impact that target setting practices can have.
JenTab Meets SemTab 2021's New Challenges

While tables are a rich source of structured information, their automated use is oftentimes prevented by the inherent ambiguity contained within. Issues ranging from mere typos over inconsistent naming conventions to homonymy among values pose substantial barriers to exploiting this source of knowledge. Although the Semantic Web can alleviate many of these issues, the actual annotation process remains challenging. To foster new ideas and the improvement of existing approaches, the Semantic Web Challenge on Tabular Data to Knowledge Graph Matching (SemTab) since 2019 hosts yearly competitions allowing systems to present their current capabilities. Datasets of different origins and characteristics highlight the various challenges present in this area. In this paper, we report on the evolution of our system, “JenTab”, during SemTab2021. We re-designed the system architecture, optimized individual modules, and developed various pipelines to target specific challenges posed throughout the challenge. JenTab is among the top 5 systems in the first two rounds of SemTab2021. The results demonstrate JenTab’s flexibility and its ability to quickly address new challenges.