Miracle-dz/UniFAR-FTUs huggingface dataset:
An efficient tight-binding mode-space NEGF model enabling up to million atoms III-V nanowire MOSFETs and TFETs simulations

We report the capability to simulate in a quantum mechanical tight-binding (TB) atomistic fashion NW devices featuring several hundred to millions of atoms and diameter up to 18 nm. Such simulations go far beyond what is typically affordable with today’s supercomputers using a traditional real space (RS) TB Hamiltonian technique. We have employed an innovative TB mode space (MS) technique instead and demonstrate large speedup (up to 10,000×) while keeping good accuracy (error < 1%) compared to the RS NEGF method. Such technique and capability open new avenues to explore and understand the physics of nanoscale and mesoscopic devices dominated by quantum effects. In particular, our method addresses in an unprecedented way the technological relevant case of band-to-band tunneling (BTBT) in III-V nanowire MOSFETs and broken gap heterojunction tunnel-FETs (TFETs). We demonstrate an accurate match of simulated BTBT currents to experimental measurements in a [111] InAs NW having a 12 nm diameter and a 300 nm long channel. We apply the predictivity of our TB MS simulations and report an in-depth atomistic study of the scaling potential of III-V GAA nanowire heterojunction n and pTFETs quantifying the benefits of this technology for low-power, low-voltage CMOS application. At VDD = 0.3 V and IOFF = 50 pA/m, the on-current (Ion) and energy-delay product (ETP) gain over a Si NW GAA MOSFET are 58× and 56× respectively.
EmoInHindi: A Multi-label Emotion and Intensity Annotated Dataset in Hindi for Emotion Recognition in Dialogues

The long-standing goal of Artificial Intelligence (AI) has been to create human-like conversational systems. Such systems should have the ability to develop an emotional connection with the users, consequently, emotion recognition in dialogues has gained popularity. Emotion detection in dialogues is a challenging task because humans usually convey multiple emotions with varying degrees of intensities in a single utterance. Moreover, emotion in an utterance of a dialogue may be dependent on previous utterances making the task more complex. Recently, emotion recognition in low-resource languages like Hindi has been in great demand. However, most of the existing datasets for multi-label emotion and intensity detection in conversations are in English. To this end, we propose a large conversational dataset in Hindi named EmoInHindi for multi-label emotion and intensity recognition in conversations containing 1,814 dialogues with a total of 44,247 utterances. We prepare our dataset in a Wizard-of-Oz manner for mental health and legal counselling of crime victims. Each utterance of dialogue is annotated with one or more emotion categories from 16 emotion labels including neutral and their corresponding intensity. We further propose strong contextual baselines that can detect the emotion(s) and corresponding emotional intensity of an utterance given the conversational context.
Control of a pneumatic actuator system using enhanced nonlinear proportional integral derivative controller algorithm

Pneumatic actuators offer several advantages such as low cost, simple to maintain, high power to weight ratio, fast motion, free from overheating and reliable. Due to these advantages, this actuator continues to generate significant research interests and it has been promoted as an alternative to hydraulics and electric servo motors in many automated tasks. However, it exhibits high nonlinearities due to high friction forces, compressibility of air and dead band of the spool movement in the valve. These nonlinearities make an accurate position difficult to achieve and it requires an appropriate controller for better performance. This thesis presents a new approach to control the pneumatic positioning system. The mathematical modeling is a crucial part to be established before the control algorithm can be designed. Initially, the modeling of the system is presented based on physical derivation. The parameters of the system are identified, and comparison between system identification by means of the MATLAB software is performed. The result obtained from the modeling process is validated with experimental data. Subsequently, two new controller techniques are proposed based on the enhancement of the Nonlinear Proportional Integral Derivative (N-PID) controller. The first technique is called Multi-rate Nonlinear PID (MN-PID), which is performed by utilizing the characteristics of rate variation in nonlinear gain. Fuzzy logic is used to perform this task. Meanwhile, for the second technique, a Self-regulation Nonlinear (SN) function, is introduced to reprocess the error signal to continuously generate the values for rate variation. The proposed controllers are implemented to the system, and their performances are analyzed for both cases, with and without load. Simulation and experimental tests are conducted with different input, namely step, multistep, sinusoidal and S-curve waveforms to evaluate the performance of the proposed techniques. The existing techniques that include PID, N-PID, and Sliding Mode Control (SMC) are also tested to the system as a comparison. The results indicate that the system with MN-PID and SN-PID exhibits improvement of dynamic performance criterion exceeding 34% and 59%, respectively. In addition, both techniques succesfully provide fast response without overshoot where the rise time reduces more than 87%. It proves that the novel initiative is capable of examining and identifying the characteristics of rate variation based on a new controller that was derived from N-PID controller. Moreover, the system performance is successfully accomplished for each position and direction as well as under various loads
Multi-dimensional clearing functions for aggregate capacity modelling in multi-stage production systems

Nonlinear clearing functions have been proposed in the literature as metamodels to represent the behaviour of production resources that can be embedded in optimisation models for production planning. However, most clearing functions tested to date use a single-state variable to represent aggregate system workload over all products, which performs poorly when product mix affects system throughput. Clearing functions using multiple-state variables have shown promise, but require significant computational effort to fit the functions and to solve the resulting optimisation models. This paper examines the impact of aggregation in state variables on solution time and quality in multi-item multi-stage production systems with differing degrees of manufacturing flexibility. We propose multi-dimensional clearing functions using alternative aggregations of state variables, and evaluate their performance in computational experiments. We find that at low utilisation, aggregation of state variables has little effect on system performance; multi-dimensional clearing functions outperform single-dimensional ones in general; and increasing manufacturing flexibility allows the use of aggregate clearing functions with little loss of solution quality.
Developing Innovation Competence Profile For Teaching Staff In Higher Education In Uganda

Higher education across the globe is under increasing pressure to prepare students with innovation capacities to address challenges facing humanity in the 21st century and beyond. A call for innovative graduates without first understanding the factors that impede higher education institutions from acting as catalysts of innovation is not judicious. One of the notable factors that has been advanced is the lack of adequate competent teachers to let students develop innovation skills. This paper explores competence domains with their underlying competencies teaching staff require to act competently in the innovation field as well as being able to prepare students with the capability to foster innovation at the place of work. The study employed a mixed research design involving systematic literature search, exploratory survey, and three focus group discussions. The study was conducted in four stages. After stage one, four teacher innovation competence domains and 17 underlying competencies were identified. After the consultation questionnaire and focus group discussions, one competence domain was added to the profile and two competencies considered irrelevant were omitted. The study presents five innovation competence domains (teaching staff as: an innovator, knowledge society developer, networker and collaborator, higher education designer and developer, and entrepreneur) and15 underlying competencies.
New methods for in situ measurement of mechanical root-reinforcement on slopes

Vegetation can increase the resistance of slopes against landsliding. The mechanical contribution of roots to the shear strength of the soil is however difficult to measure in situ. Existing methodologies are time-consuming and therefore not suitable to quantify spatial variability on the slope. Furthermore, some existing methods, for example large in situ shear box testing, can be difficult to apply on remote sites with difficult access, e.g. steep slopes. Therefore in this thesis novel, simple and portable methods to quantify mechanical root-reinforcement in the field were developed. The ‘blade penetrometer method’, one of these new methods, was based on standard penetrometer testing but used an adapted tip shape to increase sensitivity to roots. Root depths and diameters could be quantified based on characteristics of the depth–resistance trace, both in the laboratory and in the field. Several new analytical interpretive models were developed to predict the force–displacement behaviour of roots loaded under various conditions: one assuming roots broke in tension and another assuming roots broke in pure bending. Bothmethods did take root–soil interaction into account. Based on thesemodels, some roots were shown to have broken in bending and others in tension, depending on plant species and root diameter. Two new methods were developed to measure the root-reinforced soil strength directly. The ‘pin vane’ was an adaptation of a standard field shear vane, replacing the cruciform blades of the latter by prongs to minimise the effects of soil disturbance and root breakage during installation. This was one of the main problems encountered when using standard vanes in rooted soil. This ‘pin vane’ method was qualitatively shown to be able to measure the reinforcing effects of both fine and thick roots (or root analogues), both in the laboratory and the field. This method will be most useful when the strength of densely rooted surface layers is to be analysed, e.g. for erosion resistance purposes. Another newly developed shear device was the ‘corkscrew’. Rotational installation of the screw ensured minimal soil and root disturbance. During vertical extraction the root-reinforced shear strength was mobilised along the interface of the soil plug caught within the screw. The measured extraction force could be related to the reinforced soil strength. This method underestimated the strength in surface layers (especially at 0–125 mm and less so at 125–250 mm depth) but functioned well in deeper soil layers important for landsliding. Although laboratory results were promising, during in situ testing in deeper layers (>125 mm) local variation in soil stress, gravel content and water content, combined with low root volumes, made it difficult to accurately quantify the effect of the
Meta-learning from sparse recovery

Meta-learning aims to train a model on various tasks so that given sample data from a task, even if unforeseen, it can adapt fast and perform well. We apply techniques from compressed sensing to shed light on the effect of inner-loop regularization in meta-learning, with an algorithm that minimizes cross-task interference without compromising weight-sharing. In our algorithm, which is representative of numerous similar variations, the model is explicitly trained such that upon adding a pertinent sparse output layer, it can perform well on a new task with very few number of updates, where cross-task interference is minimized by the sparse recovery of output layer. We demonstrate that this approach produces good results on few-shot regression, classification and reinforcement learning, with several benefits in terms of training efficiency, stability and generalization.
Application of Liquid-Metal GaIn Alloys to Soft-matter Capacitance and Related Stretchable Electronics

Stretchable electronics is an exciting new field of developing technology, allowing devices to undergo large deformations such as, bending, twisting, stretching and compression. As such, they can be easily interfaced with the human body, conforming to its contours and enabling a range of advances in electronic skins. Creating stretchable circuits, however, is not straight forward, as most electrically conductive materials are rigid. Because of this, researchers have developed a number of methods in which conductive materials can support deformation. These include deterministic architectures (e.g. wavy circuits), polymer composites, and electrically conductivid fluids. The focus of this work is the latter technique, utilizing highly conductive gallium-indium (GaIn) metal alloys. Since they are liquid at room temperature, these metals are inherently deformable with no defined shape, and can create highly stretchable electronics when encapsulated within an elastomer. Using these materials, we target stretchable capacitance and related technologies, with capacitive elements playing a vital role in modern electronics and sensors. A family of highly stretchable micro uidic planar capacitors and inductors are introduced, which can be elongated by at least 200% of their initial length. The capacitance-strain response is examined using kinematic modeling and through experiment, and shows that these devices are viable as softmatter strain gauges. Next, new fabrication methods are introduced that incorporate freezing of the GaIn alloys. This enables the creation of tall, 3D circuit features that can be used to tune the capacitance of a given device. Additionally, freezing improves the ease with which the metal can be handled, manipulated, and altered. Conductive materials can be incorporated into polymers, creating composites with improved permittivity, which in turn will improve capacitance. By using GaIn alloys as the conductive inclusion and forming liquid metal embedded elastomers, highly stretchable dielectric materials can be fabricated. These composites can be elongated to as high as 600% strain and with dielectric improvements of over 400%. Continued advances of GaIn based technology can enable inherently compliant electronic skins and soft capacitive sensor arrays that can be incorporated into stretchable bio-compatible devices or bio-mimetic robots.
KVAZIDIMENZIJSKI MODEL ZA NUMERIČKE SIMULACIJE BRODSKOGA DVOTAKTNOGA DIZELSKOG MOTORA

In doctoral thesis, quasidimensional (QD) combustion model was developed, and successfully implemented in existing zerodimensional (0D) numerical model. Compared to another similar scientific researches of diesel internal combustion engines, with developed quasidimensional model, a number of improvements in the field of mathematical modeling were made. Primarily, the developed numerical model monitors the operating parameters of the diesel engine at the moment when they are changing, therefore the calculation of pressure and temperature as the basic variables of each numerical simulation, in this case is performed directly, without using iterative methods. In numerical sub models which are integral parts of the quasidimensional numerical model, there was observed a large number of experimentally determined constants which are valid for certain modeled diesel engine, respectively, they have a narrow application. In this thesis a correction of these constants was presented. Constants now enable more accurate simulation of the observed phenomena, and they are valid for the whole range of diesel engines. In this thesis are used properties of real gases and vapors for thermodynamic calculations in the engine cylinder, which makes that the mathematical model is extremely complex. Because of real, not an idealized working fluid properties, it was necessary to reduce the integration step, within usual integration step, used by other authors in their quasidimensional modeling research. This requirement creates an additional complication, because it necessarily requires a different way of defining volumes, for volumes generated in diesel engine cylinder, according to the quasidimensional modeling settings. Presented solution of this problem, not only makes the mathematical scheme stable and robust, but does not slow down the simulations, and the obtained results are very accurate and precise in relation to the measurements of the same operating parameters on a real engine. For numerical model validation, measurements were performed on diesel engine MAN D 0826 LOH15 in the Laboratory for Internal Combustion Engines, Faculty of Mechanical Engineering, University of Ljubljana. Since the developed numerical model showed good matching with measured values, there are shown a number of other operating parameters of the tested engine which would be difficult or impossible to measure on the real engine. Numerical modeling allows simulation of various engine operating parameters, with acceptable accuracy and precision, and an acceptable time span required for a simulation execution. Finally, developed quasidimensional model is adapted for simulation of the ship's slow speed two-stroke engine. Simulations were performed and compared with available measurements. Also in this case, very good matching of the measured values compared to those calculated by simulation were obtained.
Towards Asking Clariﬁcation Questions in Task-Oriented Dialogue

Task-oriented dialogues aim at providing users 001 with task-speciﬁc services. To provide satis-002 factory services, two major challenges exist: 003 1) users are not able to fully describe their 004 complex needs due to lack of task knowledge, 005 and; 2) systems need to personalize the ser-006 vice to their users since different users have 007 different proﬁles and preferences. In order 008 to solve these challenges, systems need to 009 be able to ask questions so as to clarify the 010 user’s proﬁle and needs. However, existing 011 task-oriented dialogue systems ignore this as-012 pect. In this paper, we formulate the prob-013 lem of asking clariﬁcation questions in task-014 oriented dialogue systems. To this end, we 015 propose a dialogue-based user simulator to col-016 lect a dataset, called TaskClariQ 1 . We further 017 propose a new System Ask paradigm and a 018 Multi-Attention Seq2Seq Networks (MAS2S) 019 that implements it. Experimental results on 020 TaskClariQ show that MAS2S outperforms 021 competitive baselines. 022
Prediction of air pollutant concentration in Jinchang City

Air pollution has aroused widespread concern. Predicting air pollution can help people prepare for preventive responses. However, the existing air quality prediction methods are still in their infancy. In this paper, the LSTM (Long Short-Term Memory) sequence-to-sequence model based on the attention mechanism is applied to the prediction of air pollution. We used the meteorological and air pollutant observation data and pollutant discharge point monitoring data of Jinchang City, Gansu Province, and fused the meteorological and air pollution data of the neighboring areas to analyze the six basic air pollutants (PM2.5, PM10 , SO2, NO2, O3, CO), followed by model training and prediction evaluation. The results show that the prediction performance of the model for each air pollutant is different. For the prediction of short-term NO2, O3 and CO concentrations in the future, the A-LSTM-Seq2Seq model designed in this paper performs better; for PM2.5, PM10 and SO2, the prediction error of the model is larger than the previous three, because The observed values of these three are widely distributed and the data is discrete. In the future, it is necessary to improve the prediction performance from the perspective of improving the model. The model pays attention to all six basic air pollutants, and has certain practical value and application potential in the prediction of air pollution on the hourly time scale, and also has reference significance for the prediction of AQI.
Can We Algorithmize Politics? The Promise and Perils of Computerized Text Analysis in Political Research

ABSTRACT In recent years, political scientists increasingly have used data-science tools to research political processes, positions, and behaviors. Because both domestic and international politics are grounded in oral and written texts, computerized text analysis (CTA)—typically based on natural-language processing—has become one of the most notable applications of data-science tools in political research. This article explores the promises and perils of using CTA methods in political research and, specifically, the study of international relations. We highlight fundamental analytical and methodological gaps that hinder application and review processes. Whereas we acknowledge the significant contribution of CTA to political research, we identify a dual “engagement deficit” that may distance those without prior background in data science: (1) the tendency to prioritize methodological innovation over analytical and theoretical insights; and (2) the scholarly and political costs of requiring high proficiency levels and training to comprehend, assess, and use advanced research models.
Evaluation of the Effectiveness and Efficiency of Atmospheric Particulates Reduction Policy: The Case of South Korea

In a situation where various policy measures can be used to reduce atmospheric particulates, effectiveness and efficiency may vary depending on how the policy is designed. Therefore, this study evaluated the effectiveness and efficiency of atmospheric particulates reduction policy in order to contribute to effective and efficient policy design. To this end, this study demonstrated the effectiveness of 1st Basic Plan on Metropolitan Area Air Quality Improvement and explored the cause of the effectiveness. As a result of the study, this study did not confirm that the effect of reducing PM10 caused by the plan in the metropolitan area was significantly different from that of the non-metropolitan area where the policy was not implemented. In particular, distinct effect was not confirmed on the installation of DPF, which required a large number of costs. Based on the results, more effective and efficient policy measures will be used based on the causal relationship of atmospheric particulates generation.
The principle of prudence in public sector accounting—a comparative analysis of cautious and asymmetric prudence

IMPACT The principle of prudence plays a significant role in the design of the EPSAS. In Germany it has been argued that a lack of consideration of the principle of prudence in the IPSASB’s Conceptual Framework and in the IPSAS mean that IPSAS are not an ideal reference framework for the EPSAS. Based on a comparative analysis of the IPSAS principle of prudence and the principle of prudence as it is applied in the German Standards of Governmental Accrual Accounting the authors examine the role and the design of the principle of prudence within public sector accounting systems. The findings of this article will be of value to national and international public sector accounting standard-setters. ABSTRACT The draft EPSAS Conceptual Framework proposes the prudence principle as a qualitative characteristic of financial reporting. Limited academic debate has taken place on the role of the prudence principle in public sector accounting. This article critically discusses the principle of prudence with its antagonistic interpretations ‘cautious prudence’ and ‘asymmetric prudence’ and its role in the design of public sector accounting in the context of the emerging EPSAS.