Miracle-dz/UniFAR-FTUs huggingface dataset:
Modified SVPWM Techniques for Six-Phase Asymmetrical Induction Motor Drives

The six-phase asymmetrical machine is a contender for numerous high-power industrial applications. A substantial stator current harmonics are observed in this configuration when this machine is supplied by dual three-phase VSI inverters. In this paper, 24-sector based modified SVPWM control algorithms are developed to eliminate these harmonics. A comprehensive analysis of continuous and discontinuous PWM techniques including other 24 sector-based techniques is elaborated using the harmonic flux concept. Consequently, harmonic distortion factors are calculated. Theoretical findings are validated through simulations and experimentations.
Fine-Grained Diabetic Wound Depth and Granulation Tissue Amount Assessment Using Bilinear Convolutional Neural Network

Diabetes mellitus is a serious chronic disease that affects millions of people worldwide. In patients with diabetes, ulcers occur frequently and heal slowly. Grading and staging of diabetic ulcers is the first step of effective treatment and wound depth and granulation tissue amount are two important indicators of wound healing progress. However, wound depths and granulation tissue amount of different severities can visually appear quite similar, making accurate machine learning classification challenging. In this paper, we innovatively adopted the fine-grained classification idea for diabetic wound grading by using a Bilinear CNN (Bi-CNN) architecture to deal with highly similar images of five grades. Wound area extraction, sharpening, resizing and augmentation were used to pre-process images before being input to the Bi-CNN. Innovative modifications of the generic Bi-CNN network architecture are explored to improve its performance. Our research generated a valuable wound dataset. In collaboration with wound experts from University of Massachusetts Medical School, we collected a diabetic wound dataset of 1639 images and annotated them with wound depth and granulation tissue grades as labels for classification. Deep learning experiments were conducted using holdout validation on this diabetic wound dataset. Comparisons with widely used CNN classification architectures demonstrated that our Bi-CNN fine-grained classification approach outperformed prior work for the task of grading diabetic wounds.
Multi-codebook Fuzzy Neural Network Using Incremental Learning for Multimodal Data Classification

One of the challenge in classification is classification in multimodal data. This paper proposed multi-codebook fuzzy neural network by using incremental learning for multimodal data classification. There are 2 variations of the proposed method, one uses a static threshold, and the other uses a dynamic threshold. Based on the experiment result, the multicodebook FNGLVQ using dynamic incremental learning has the highest improvement compared to the original FNGLVQ. It achieves 15.65% margin in synthetic dataset, 5.02 % margin in benchmark dataset, and 11.30% on average all dataset.
Au/Al Wire Bond Interface Resistance Degradation Rate Simulations

Gold wires are often used to connect aluminum terminals on semiconductor chips for signal transmission. Au-Al bonds degrade over time with temperature due to formation of intermetallic compounds at the gold-aluminum interface. A literature overview of the research on the Au-Al intermetallic compounds is outlined. Formation of these intermetallic compounds is governed by diffusion rules; however, the observed interface failure rates do not always follow the expected Fickian t1/2 time dependence. Gold-aluminum intermetallic compounds in turn cause Kirkendall voiding. The proliferation of Kirkendall voids over time separates the two interfaces. A literature overview of research on Kirkendall voiding is also outlined. In this paper, time-dependence generated by simulations of the Au/Al interface degradation due to Kirkendall voids is compared with the observed data time-dependence and the empirical failure rate time-dependences and their diverse rates of progression.
Improved Differential Strobe Terminal Based Open-loop Control of Smart Power Factor Metering System

This paper presents a smart power factor metering system to measure the power factor of a load by taking the frequency of a main-line. The variation of frequency makes sure the poor power factor (PF) which increases the utility charge. The proposed system provides the accurate FF by plotting a real-time AC power triangle in MATLAB environment. The system uses one embedded system as a controller that able to read the mainline frequency, load voltage, current and their phase difference. A preliminary circuit is used to transform the high value of voltage and current to the embedded system (ES) readable form. It consists of a potential transformer (PT), a current transformer (CT), high differential strobe terminal based comparator and logic gate that detects the phase delay between the load voltage and current. A serial communication between the ES and the MATLAB is established for plotting the power triangle which confirm the accurate PF. The simulation and experimental results are studied to validate the effectiveness of proposed system.
Immobilization and Anaesthesia of Asiatic Lions (Panthera Leo Persica)

Asiatic lions (Panthera Leo Persica) and hybrid lions are often subjected to chemical immobilization and anaesthesia for various measures related to conservation and elective and emergency surgical procedures by veterinarians. Management of long term anaesthesia has been carried out under using injectable anaesthetics and ancillary drugs which pose problems due to change in depth of anaesthesia and alteration in homeostasis leading to stress and stress related complications. Significantly higher dose (p < 0.05) of xylazine and ketamine was required in sub-adult lions (1.08±0.10 and 2.70±0.26 mg/kg body weight) than adult lions (1.06±0.30 and 2.64±0.08 mg/kg body weight) for immobilization. Similarly lions required higher dose (p < 0.05) of xylazine and ketamine (1.08±0.07 and 2.71±0.16 mg/kg body weight) than lioness (1.04±0.40 and 2.61±0.10 mg/kg body weight) for immobilization irrespective of body weights. Ketamine and propofol (2 &1 mg/kg iv) could be used as induction agents. Propofol induction was safe for oral digital palpation of glottis and intubation. Isoflurane provided smooth maintenance. This work is useful for wildlife veterinarians.
An Investigation of SO3 Control Routes in Ultra-low Emission Coal-fired Power Plants

ABSTRACT With the implementation of ultra-low emission systems in coal-fired power plants in China, the emission of sulfur trioxide (SO3) has become an important issue in pollution control. However, systematic research and evaluation of SO3 control routes based on the existing ultra-low emission systems are still lacking. We assigned 148 coal-fired power plants to four categories based on their ultra-low emission control routes and selected a representative power plant from each category for comprehensive field testing. The results indicated great variability in the synergistic SO3 removal capability of different air pollution control devices and routes, resulting in removal efficiencies that ranged from 27% to 94%. Control Route 1, which lacked both a low-low temperature electrostatic precipitator (LLTESP) and a wet electrostatic precipitator (WESP), exhibited the lowest removal efficiency. The two routes equipped with either an LLTESP or a WESP (Control Routes 2 and 3) reduced the SO3 concentration in the flue gas produced by medium-sulfur-coal combustion to below 10 mg m–3, whereas Control Route 4, which utilized both an LLTESP and a WESP, reduced the SO3 concentration to below 5 mg m–3. Furthermore, sampling the emissions of the 148 power plants revealed that only 14% of the power plants complied with the 5 mg m–3 standard for SO3, although 44% and 64% of them complied with the 10 mg m–3 and the 20 mg m–3 standard, respectively. Our study evaluated the control routes within the context of the whole process, which can guide subsequent research and engineering practices.
Introducing the Robot Vulnerability Database (RVD)

Cybersecurity in robotics is an emerging topic that has gained significant traction. Researchers have demonstrated some of the potentials and effects of cyber attacks on robots lately. This implies safety related adverse consequences causing human harm, death or lead to significant integrity loss clearly overcoming the privacy concerns in classical IT world. In cybersecurity research, the use of vulnerability databases is a very reliable tool to responsibly disclose vulnerabilities in software products and raise willingness of vendors to address these issues. In this paper we argue, that existing vulnerability databases are of insufficient information density and show some biased content with respect to vulnerabilities in robots. This paper presents the Robot Vulnerability Database (RVD), a directory for responsible disclosure of bugs, weaknesses and vulnerabilities in robots. This article aims to describe the design and process as well as the associated disclosure policy behind RVD. Furthermore the authors present preliminary selected vulnerabilities already contained in RVD and call to the robotics and security communities for contribution to the endeavour of eliminating zero-day vulnerabilities in robotics.
Moderate deviations for diffusion in time dependent random media

The position x(t) of a particle diffusing in a one-dimensional uncorrelated and time dependent random medium is simply Gaussian distributed in the typical direction, i.e. along the ray x = v 0 t, where v 0 is the average drift. However, it has been found that it exhibits at large time sample to sample fluctuations characteristic of the Kardar–Parisi–Zhang (KPZ) universality class when observed in an atypical direction, i.e. along the ray x = v t with v ≠ v 0. Here we show, from exact solutions, that in the moderate deviation regime x − v 0 t ∝ t 3/4 these fluctuations are precisely described by the finite time KPZ equation, which thus describes the crossover between the Gaussian typical regime and the KPZ fixed point regime for the large deviations. This confirms heuristic arguments given in []. These exact results include the discrete model known as the Beta random walk in a time dependent random environment, and a continuum diffusion. They predict the behavior of the maximum of a large number of independent walkers, which should be easier to observe (e.g. in experiments) in this moderate deviations regime.
Enhanced thermoelectricity at the ultra-thin film limit

At the ultra-thin film limit, quantum confinement strongly improves thermoelectric figure of merit in materials such as Sb$_2$Te$_3$ and Bi$_2$Te$_3$. These high quality films have only been realized using well controlled techniques such as molecular beam epitaxy. We report a two fold increase in the Seebeck coefficient for both p-type Sb$_2$Te$_3$ and n-type Bi$_2$Te$_3$ using thermal co-evaporation, an affordable approach. At the thick film limit greater than 100 nm, their Seebeck coefficients are around 100 $\mu V/K$, similar to results obtained in other work. When the films are thinner than 50 nm, the Seebeck coefficient increases to about 500 $\mu V/K$. With a total Seebeck coefficient $\sim$ 1 mV/K and an estimate ZT $\sim$ 2, this pair of materials is the first step to a practical micro-cooler at room temperature.
High-resolution Near-infrared Polarimetry and Submillimeter Imaging of FS Tau A: Possible Streamers in Misaligned Circumbinary Disk System

We analyzed the young (2.8 Myr-old) binary system FS Tau A using near-infrared (H-band) high-contrast polarimetry data from Subaru/HiCIAO and submillimeter CO (J = 2–1) line emission data from Atacama Large Millimeter/submillimeter Array (ALMA). Both the near-infrared and submillimeter observations reveal several clear structures extending to ∼240 au from the stars. Based on these observations at different wavelengths, we report the following discoveries. One arm-like structure detected in the near-infrared band initially extends from the south of the binary with a subsequent turn to the northeast, corresponding to two bar-like structures detected in ALMA observations with an local standard of rest kinematic (LSRK) velocity of 1.19–5.64 km s−1. Another feature detected in the near-infrared band extends initially from the north of the binary, relating to an arm-like structure detected in ALMA observations with an LSRK velocity of 8.17–16.43 km s−1. From their shapes and velocities, we suggest that these structures can mostly be explained by two streamers that connect the outer circumbinary disk and the central binary components. These discoveries will be helpful for understanding the evolution of streamers and circumstellar disks in young binary systems.
Moiré potential impedes interlayer exciton diffusion in van der Waals heterostructures

Interlayer exciton diffusion in transition metal dichalcogenide heterostructures is controlled by the moiré potential. The properties of van der Waals heterostructures are drastically altered by a tunable moiré superlattice arising from periodically varying atomic alignment between the layers. Exciton diffusion represents an important channel of energy transport in transition metal dichalcogenides (TMDs). While early studies performed on TMD heterobilayers suggested that carriers and excitons exhibit long diffusion, a rich variety of scenarios can exist. In a moiré crystal with a large supercell and deep potential, interlayer excitons may be completely localized. As the moiré period reduces at a larger twist angle, excitons can tunnel between supercells and diffuse over a longer lifetime. The diffusion should be the longest in commensurate heterostructures where the moiré superlattice is completely absent. Here, we experimentally demonstrate the rich phenomena of interlayer exciton diffusion in WSe2/MoSe2 heterostructures by comparing several samples prepared with chemical vapor deposition and mechanical stacking with accurately controlled twist angles.
Big Picard theorem for moduli spaces of polarized manifolds

Consider a smooth projective family of complex polarized manifolds with semi-ample canonical sheaf over a quasi-projective manifold $V$. When the associated moduli map $V\to P_h$ from the base to coarse moduli space is quasi-finite, we prove that the generalized big Picard theorem holds for the base manifold $V$: for any projective compactification $Y$ of $V$, any holomorphic map $f:\Delta-\{0\}\to V$ from the punctured unit disk to $V$ extends to a holomorphic map of the unit disk $\Delta$ into $Y$. This result generalizes our previous work on the Brody hyperbolicity of $V$ (i.e. there are no entire curves on $V$), as well as a more recent work by Lu-Sun-Zuo on the Borel hyperbolicity of $V$ (i.e. any holomorphic map from a quasi-projective variety to $V$ is algebraic). We also obtain generalized big Picard theorem for bases of log Calabi-Yau families.
Some aspects of the Markovian SIRS epidemics on networks and its mean-field approximation

We study the spread of an SIRS-type epidemics with vaccination on network. Starting from an exact Markov description of the model we investigate the mean epidemic lifetime by providing a sufficient condition for the fast extinction, depending on the topology of the network. Then, we pass to consider a first-order mean-field approximation of the exact model. At this point, we dwell on the stability properties of the system by relying on the graph-theoretical notion of equitable partition. In the case of graphs possessing this kind of partition, we find a positively invariant set which contain the endemic equilibrium, that can be computed by using a lower-dimensional dynamical system. Finally, in the special case of regular graphs, we show that when the recovery rate is higher than the vaccination rate, the aforementioned invariant set is contained in the domain of attraction of the endemic equilibrium.
Entanglement branes and factorization in conformal field theory

In this work, we consider the question of local Hilbert space factorization in 2D conformal field theory in the presence of a Kac-Moody symmetry. Generalizing previous work on entanglement and open-closed TQFT, we interpret the factorization of CFT states in terms of path integral processes that split and join the Hilbert spaces of circles and intervals. More abstractly, these processes are cobordisms of an extended CFT which are defined purely in terms of the OPE data. In addition to the usual sewing axioms, we impose an entanglement boundary condition that is satisfied by the vacuum Ishibashi state. This choice of entanglement boundary state leads to reduced density matrices that sum over super-selection sectors, which we identify as the CFT edge modes. Finally, we relate our factorization map to the co-product formula for the Kac-Moody algebra, which we show is equivalent to a Boguliubov transformation in the case of a free boson.
Adversarial AutoAugment

Data augmentation (DA) has been widely utilized to improve generalization in training deep neural networks. Recently, human-designed data augmentation has been gradually replaced by automatically learned augmentation policy. Through finding the best policy in well-designed search space of data augmentation, AutoAugment can significantly improve validation accuracy on image classification tasks. However, this approach is not computationally practical for large-scale problems. In this paper, we develop an adversarial method to arrive at a computationally-affordable solution called Adversarial AutoAugment, which can simultaneously optimize target related object and augmentation policy search loss. The augmentation policy network attempts to increase the training loss of a target network through generating adversarial augmentation policies, while the target network can learn more robust features from harder examples to improve the generalization. In contrast to prior work, we reuse the computation in target network training for policy evaluation, and dispense with the retraining of the target network. Compared to AutoAugment, this leads to about 12x reduction in computing cost and 11x shortening in time overhead on ImageNet. We show experimental results of our approach on CIFAR-10/CIFAR-100, ImageNet, and demonstrate significant performance improvements over state-of-the-art. On CIFAR-10, we achieve a top-1 test error of 1.36%, which is the currently best performing single model. On ImageNet, we achieve a leading performance of top-1 accuracy 79.40% on ResNet-50 and 80.00% on ResNet-50-D without extra data.
The aBc Problem and Equator Sampling Renyi Divergences

We investigate the problem of approximating the product $a^TBc$, where $a,c\in S^{n-1}$ and $B\in O_n$, in models of communication complexity and streaming algorithms. The worst meaningful approximation is to simply decide whether the product is 1 or -1, given the promise that it is either. We call that problem the aBc problem. This is a modification of computing approximate inner products, by allowing a basis change. While very efficient streaming algorithms and one-way communication protocols are known for simple inner products (approximating $a^Tc$) we show that no efficient one-way protocols/streaming algorithms exist for the aBc problem. In communication complexity we consider the 3-player number-in-hand model. 1) In communication complexity $a^TBc$ can be approximated within additive error $\epsilon$ with communication $O(\sqrt n/\epsilon^2)$ by a one-way protocol Charlie to Bob to Alice. 2) The $aBc$ problem has a streaming algorithm that uses space $O(\sqrt n \log n)$. 3) Any one-way communication protocol for $aBc$ needs communication at least $\Omega(n^{1/3})$, and we prove a tight results regarding a communication tradeoff: if Charlie and Bob communicate over many rounds such that Charlie communicates $o(n^{2/3})$ and Bob $o(n^{1/3})$, and then the transcript is sent to Alice, the error will be large. 4) To establish our lower bound we show concentration results for Renyi divergences under the event of restricting a density function on the sphere to a random equator and subsequently normalizing the restricted density function. This extends previous results by Klartag and Regev for set sizes to Renyi divergences of arbitrary density functions. 5) We show a strong concentration result for conditional Renyi divergences on bipartite systems for all $\alpha>1$.
Cascading Convolutional Color Constancy

Regressing the illumination of a scene from the representations of object appearances is popularly adopted in computational color constancy. However, it's still challenging due to intrinsic appearance and label ambiguities caused by unknown illuminants, diverse reflection properties of materials and extrinsic imaging factors (such as different camera sensors). In this paper, we introduce a novel algorithm – Cascading Convolutional Color Constancy (in short, C4) to improve robustness of regression learning and achieve stable generalization capability across datasets (different cameras and scenes) in a unique framework. The proposed C4 method ensembles a series of dependent illumination hypotheses from each cascade stage via introducing a weighted multiply-accumulate loss function, which can inherently capture different modes of illuminations and explicitly enforce coarse-to-fine network optimization. Experimental results on the public Color Checker and NUS 8-Camera benchmarks demonstrate superior performance of the proposed algorithm in comparison with the state-of-the-art methods, especially for more difficult scenes.
Learning temporal coherence via self-supervision for GAN-based video generation

Our work explores temporal self-supervision for GAN-based video generation tasks. While adversarial training successfully yields generative models for a variety of areas, temporal relationships in the generated data are much less explored. Natural temporal changes are crucial for sequential generation tasks, e.g. video super-resolution and unpaired video translation. For the former, state-of-the-art methods often favor simpler norm losses such as L2 over adversarial training. However, their averaging nature easily leads to temporally smooth results with an undesirable lack of spatial detail. For unpaired video translation, existing approaches modify the generator networks to form spatio-temporal cycle consistencies. In contrast, we focus on improving learning objectives and propose a temporally self-supervised algorithm. For both tasks, we show that temporal adversarial learning is key to achieving temporally coherent solutions without sacrificing spatial detail. We also propose a novel Ping-Pong loss to improve the long-term temporal consistency. It effectively prevents recurrent networks from accumulating artifacts temporally without depressing detailed features. Additionally, we propose a first set of metrics to quantitatively evaluate the accuracy as well as the perceptual quality of the temporal evolution. A series of user studies confirm the rankings computed with these metrics. Code, data, models, and results are provided at https://github.com/thunil/TecoGAN.