Miracle-dz/UniFAR-FTUs huggingface dataset:
Accelerating Training using Tucker Decomposition

Tensor decomposition is one of the well-known approaches to reduce the latency time and number of parameters of a pre-trained convolutional neural network (CNN) model. However, in this paper, we propose an approach to use tensor decomposition to reduce training time of training a model from scratch. This paper explores one type of tensor decomposition: Tucker decomposition. In our approach, we train the model from scratch (i.e., randomly initialized weights) with its original architecture for a small number of epochs, then the model is decomposed, and then continue training the decomposed model till the end. There is an optional step in our approach to convert the decomposed architecture back to the original architecture. We present results of using this approach on both CIFAR10 and Imagenet datasets, and show that there can be upto 1.65 × speed up in training time with accuracy drop of upto 0.82% only. The work in this paper is considered one step towards enabling training on IoT devices. This training acceleration approach is independent of hardware and is expected to have similar speed ups on both CPU and GPU platforms. The code is available online at https://github.com/mostafaelhoushi/tensor-decompositions.
Gear compound fault detection method based on improved multiscale permutation entropy and local mean decomposition

The traditional multiscale entropy algorithm shows inconsistency because some points are ignored when the signal is coarsened. To solve this problem, this paper proposes an improved multiscale permutation entropy (IMSPE). Firstly, the fault signal is decomposed into several product functions (PF) by local mean decomposition (LMD). Secondly, IMSPE is proposed to extract fault features of product functions. IMSPE integrates the information of multiple coarse sequences and solves problems of entropy inconsistency. Finally, the proposed method based on LMD and IMSPE is applied into gear fault diagnosis system. The experiment shows the proposed method can distinguish different gear fault types with a higher accuracy than traditional methods.
Preparation and Property of MXene/Copper Alloy Composites

: Cu/Ti 3 C 2 T x composites with Ti 3 C 2 T x content of 5vol%, 10vol% and 20vol% were prepared by using a molecular-level mixing process and spark plasma sintering (SPS). Influence of Ti 3 C 2 T x content on electrical, mechanical and tribological properties was investigated. The result showed that relative density and electrical conductivity of Cu/Ti 3 C 2 T x composites gradually decreased with the Ti 3 C 2 T x content increase, while the tensile strength of Cu/Ti 3 C 2 T x composites increased at first and then decreased. When the Ti 3 C 2 T x content was 5vol%, the tensile strength of Cu/ Ti 3 C 2 T x composites increased by 43% than that of pure copper. Tribological properties of Cu/ Ti 3 C 2 T x composites were enhanced by the addition of Ti 3 C 2 T x . When the Ti 3 C 2 T x content was 10vol%, the wear rate of Cu/ Ti 3 C 2 T x composites was 2.55×10 –7 mm 3 /(N·m), which was one magnitude lower than that of pure copper.
Minh Phuoc Nguyen

Roselle (Hibiscus sabdariffaL.) calyx has a special attention due to its nutritional composition, bioactive constituents and therapeutic applications. Fresh Roselle (Hibiscus sabdariffa L.) calyx has short shelf life due to its high moisture content. In order to extend its shelf life, as well as create one new kind of therapeutic herbal tea, we examined various parameters of blanching, vacuum drying, roasting to the total phenolic, total flavonoid content and sensory characteristics in the dried herbal tea. Our results proved that steaming at 115oC in 30 seconds, vacuum drying at 35oC under pressure -0.6 bar, roasting at 135oC in 9 minutes were adequate to produce one kind of herbal tea having good overall acceptance while preserving the highest ascorbic, total phenolic and flavonoid content. Our results supported available evident for manufacturers in processing of this fragrant calyx into functional herbal tea containing a great source of phytochemical constituents.
A 3D Contextual Shading Method for Visualization of Diecasting Defects

In many mechanical design-related activities, the visualization tool needs to convey not only the shape of the objects, but also their interior problem regions. Due to the binary nature of these models, existing shading models often fall short of supporting a realistic display. In this case study, we present several new contextual shading methods that we originally developed for our design visualization tools. The results are then compared with gray-scale shading applied to a gray-level version of the binary object. The comparison shows that our method can be applied to any binary object and yields promising results.
Changes in manuscript based on Associate Editor report and interactive comment by referee #2, Pearse

During the four most recent glacial maxima, atmospheric CO2 has been lowered by about 90–100 ppm with respect to interglacial concentrations. It is likely that most of the atmospheric CO2 deficit was stored in the ocean. Changes of the biological pump, which are related to the efficiency of the biological carbon uptake in the surface ocean and/or of the export of organic carbon to the deep ocean, have been proposed as a key mechanism for the increased glacial oceanic CO2 storage. The biological pump is strongly constrained by the amount of available surface nutrients. In models, it is generally assumed 5 that the ratio between elemental nutrients, such as phosphorus, and carbon (C/P ratio) in organic material is fixed according to the classical Redfield ratio. The constant Redfield ratio appears to hold approximately when averaged over basin scales, but observations document highly variable C/P ratios on regional scales and between species. If the C/P ratio increases when phosphate availability is scarce, as observations suggest, this has the potential to further increase glacial oceanic CO2 storage in response to changes in surface nutrient distributions. In the present study, we perform a sensitivity study to test how 10 a phosphate–concentration dependent C/P ratio influences the oceanic CO2 storage in an Earth system model of intermediate complexity (cGENIE). We carry out simulations of glacial–like changes in albedo, radiative forcing, wind–forced circulation, remineralisation depth of organic matter, and mineral dust deposition. Specifically, we compare model versions with the classical constant Redfield ratio and an observationally–motivated variable C/P ratio, in which the carbon uptake increases with decreasing phosphate concentration. While a flexible C/P ratio does not impact the model’s ability to simulate benthic C 15 patterns seen in observational data, our results indicate that, in production of organic matter, flexible C/P can further increase the oceanic storage of CO2 in glacial model simulations. Past and future changes in the C/P ratio thus have implications for correctly projecting changes in oceanic carbon storage in glacial–to–interglacial transitions as well as in the present context of increasing atmospheric CO2 concentrations.
Rethinking search

When experiencing an information need, users want to engage with a domain expert, but often turn to an information retrieval system, such as a search engine, instead. Classical information retrieval systems do not answer information needs directly, but instead provide references to (hopefully authoritative) answers. Successful question answering systems offer a limited corpus created on-demand by human experts, which is neither timely nor scalable. Pre-trained language models, by contrast, are capable of directly generating prose that may be responsive to an information need, but at present they are dilettantes rather than domain experts - they do not have a true understanding of the world, they are prone to hallucinating, and crucially they are incapable of justifying their utterances by referring to supporting documents in the corpus they were trained over. This paper examines how ideas from classical information retrieval and pre-trained language models can be synthesized and evolved into systems that truly deliver on the promise of domain expert advice.
Referenced Thermodynamic Integration for Bayesian Model Selection: Application to COVID-19 Model Selection

Model selection is a fundamental part of the applied Bayesian statistical methodology. Metrics such as the Akaike Information Criterion are commonly used in practice to select models but do not incorporate the uncertainty of the models' parameters and can give misleading choices. One approach that uses the full posterior distribution is to compute the ratio of two models' normalising constants, known as the Bayes factor. Often in realistic problems, this involves the integration of analytically intractable, high-dimensional distributions, and therefore requires the use of stochastic methods such as thermodynamic integration (TI). In this paper we apply a variation of the TI method, referred to as referenced TI, which computes a single model's normalising constant in an efficient way by using a judiciously chosen reference density. The advantages of the approach and theoretical considerations are set out, along with explicit pedagogical 1 and 2D examples. Benchmarking is presented with comparable methods and we find favourable convergence performance. The approach is shown to be useful in practice when applied to a real problem - to perform model selection for a semi-mechanistic hierarchical Bayesian model of COVID-19 transmission in South Korea involving the integration of a 200D density.
Imidazolium Chloride Immobilized Fly Ash as a Heterogenized Organocatalyst for Esterification Reaction under Microwave Irradiation Heating

An efficient solventless one-pot procedure for the synthesis of imidazolium-based ionic liquid under microwave irradiation is described in which 1-methylimidazole was modified by organosilane (3-chloropropyl triethoxysilane). This ionic liquid 1-methyl-3-[(triethoxysilyl)propyl]imidazolium chloride (TMICl) is immobilized on Mechanically activated Fly Ash (MFA) by co-condensation method to develop an active organocatalyst (TMICl/MFA). The mechanical activation has generated significant number of silanol groups suitable for immobilizing ionic liquids. For characterization of various materials, different techniques viz, XRD, FTIR, SEM-EDX, TGA, BET surface area, H1NMR are used. The prepared TMICl/MFA exhibited high catalytic activity for a series of microwave assisted esterification of aliphatic alcohols and salicylic acid for four successive reaction cycles under optimized conditions.
Optimization and integration of ultrathin e-beam grown HfO2 gate dielectrics in MoS2 transistors

Integration of high-k dielectrics with two-dimensional (2D) layered semiconductors is one of the bottleneck in realization of the devices in a viable technology. In this work we report on the optimization and integration of electron beam (e-beam) evaporated, ultrathin (5 nm) HfO2 thin films on a few layer (5–7 nm) MoS2 substrate, without the need for any functionalization and buffer layers. The effect of HfO2 film thickness on optical, compositional and electrical properties of the material has been evaluated using ellipsometry, Rutherford backscattering, x-ray photoelectron spectroscopy and electrical measurements. The estimated dielectric constant and leakage current density values have been correlated with the refractive index and density of the films. It has been observed that the dielectric constant decreased drastically in ultrathin (5 nm) HfO2 film in case of Al as a gate electrode due to the formation of interfacial layer. The effect of gate electrode material and its processing on dielectric properties of ultra-thin films has been studied in detail and it is found that e-beam evaporated Ni seems to be a promising gate electrode. Equivalent oxide thickness of 1.2 nm and leakage current density (J) of 1.1 × 10−4 A cm−2 have been achieved for 5 nm films with Ni as gate electrode. Top gated MoS2 transistors with, 5 nm HfO2 gate dielectric show very good performance with I ON to I OFF ratio of 106. We thus demonstrate e-beam evaporation as a promising technique to directly integrate high-k dielectrics (HfO2) with 2D materials.
Electron impact excitation of glutamine molecules irradiated with an M-30 microtron with an energy of 11.5 MeV

The process of excitations of the glutamine molecules in the gas phase during collisions with low-energy electrons (0–70 eV) for samples before and after irradiation (11.5 MeV) at the M-30 microtron with doses of 10 kGy have been investigated by the optical spectroscopy method. Optical spectra were studied in the wavelength range 200–520 nm; the effects caused by both external radiation and the scanning electron beam with the 30, 50, and 70 eV energy were investigated. For the most intense emission of the molecular fragments, their thresholds and excitations’ energy dependences were determined in the range of electron energies up to 30 eV. The new effect has been established: for λ > 310 nm, the irradiated samples’ emission increased both by increasing the scanning beam’s electron energy and the emitted light wavelength. However, for values of λ < 310 nm, there is an opposite dependence. The simulation of the glutamine transformations under irradiation was carried out within the method of structural combinations, which allows determining all sets of molecular structures of glutamine without and in the presence of ‘broken’ chemical bonds. The role of radiation in the formation of metastable molecular structures, which determine the features of the optical and mass spectra of irradiated glutamine samples, is investigated.
Correlation of Gluten Molecular Conformation with Dough Viscoelastic Properties during Storage

Abstract: Significant correlations exist for the total sample between the dough viscoelastic properties and quality, but little information about these links is available. This study investigates the relationship between the gluten molecular conformation and the dough viscoelastic properties of the Zhengmai 379 wheat variety with different relative humidity (RH) values during storage. The results showed that protein extractability, free sulfhydryl (SH) groups, and wet gluten contents decreased to a certain degree during storage, while the glutenin quantity significantly varied. Significant negative correlation coefficients were found between the dough viscosity at 50% RH and the β-turn contents at 40% RH (−0.918) or 60% RH (−0.949) (P Supported by National Key Research and Development Program (2016YFD0400200), National Natural Science Foundation of China (31571780 and 31371850), General Science and Technology Research Projects of Zhengzhou (N2013G0077), Key Scientific Research Project of Universities in Henan Province (16A210018), and the focus on Fostering Basic Research Fund of Henan University of Technology (2013JCYJ05)
Facial Expressions Recognition using EEG Based on Machine Learning and Deep Neural Network Methods

-Real-time expression recognition has been an active field of research over the past few years. Our research work aims to classify physically challenged individuals (deaf, dumb and laid up) and disorder children’s expressions using facial landmarks and electroencephalograph (EEG) signals. We employ a convolutional neural network (CNN) and long short-term memory (LSTM) classifiers. Here we develop a rule for emotion recognition using virtual markers through an optical flow rule. It works effectively in uneven lightning like head rotation (up to 25 o ), totally different backgrounds, and numerous skin tones. Six facial emotions (happiness, sadness, anger, fear, disgust, and surprise) are collected using 10 virtual markers. 55 college students (Thirty Five male and Twenty female) with a mean age of 22 are taken as samples. All the students participated in the experiment for facial expression recognition. 19 college students volunteered to gather EEG signals. Initially hair like features were used for facial and eye detection. Then, virtual markers are placed on outlined locations of the subjects face. It helped in retrieving the facial action victimization using the mathematical model approach known as caterpillar-tracked victimization the Lucas-Kande optical flow algorithm. The space between the middle of the subjects face and every marker position is employed as a feature for facial expression classification. This distance feature is statistically valid employing a unidirectional analysis of variance with a significance level of p < 0.01. In addition to that, the fourteen signals are collected from the EEG signal reader (EPOCþ) channels which are used as options for expression classification. Finally, the options are cross-validated and victimization of multiple cross-validations is given to the LSTM and CNN classifiers. We accomplish the recognition rate of 99.25% victimization CNN for expression detection using facial landmarks. However, the utmost recognition rate achieved in victimization of the LSTM classifier is 87.96% for feeling detection using victimization EEG signals.
Implicit Neural Representations for Deconvolving SAS Images

Synthetic aperture sonar (SAS) image resolution is constrained by waveform bandwidth and array geometry. Specifically, the waveform bandwidth determines a point spread function (PSF) that blurs the locations of point scatterers in the scene. In theory, deconvolving the reconstructed SAS image with the scene PSF restores the original distribution of scatterers and yields sharper reconstructions. However, deconvolution is an ill-posed operation that is highly sensitive to noise. In this work, we leverage implicit neural representations (INRs), shown to be strong priors for the natural image space, to deconvolve SAS images. Importantly, our method does not require training data, as we perform our deconvolution through an analysis-by-synthesis optimization in a self-supervised fashion. We validate our method on simulated SAS data created with a point scattering model and real data captured with an in-air circular SAS. This work is an important first step towards applying neural networks for SAS image deconvolution.
Sleep Staging Based on Multi Scale Dual Attention Network.

Sleep staging plays an important role on the diagnosis of sleep disorders. In general, experts classify sleep stages manually based on polysomnography (PSG), which is quite time-consuming. Meanwhile, the acquisition process of multiple signals is much complex, which can affect the subject's sleep. Therefore, the use of single-channel electroencephalogram (EEG) for automatic sleep staging has become a popular research topic. In the literature, a large number of sleep staging methods based on single-channel EEG have been proposed with promising results and achieve the preliminary automation of sleep staging. However, the performance for most of these methods in the N1 stage do not satisfy the needs of the diagnosis. In this paper, we propose a deep learning model multi scale dual attention network(MSDAN) based on raw EEG, which utilizes multi-scale convolution to extract features in different waveforms contained in the EEG signal, connects channel attention and spatial attention mechanisms in series to filter and highlight key information, and uses soft thresholding to remove redundant information. Experiments were conducted using two datasets with 5-fold cross-validation and hold-out validation method. The final average accuracy, overall accuracy, macro F1 score and Cohen's Kappa coefficient of the model reach 96.70%, 91.74%, 0.8231 and 0.8723 on the Sleep-EDF dataset, 96.14%, 90.35%, 0.7945 and 0.8284 on the Sleep-EDFx dataset. Significantly, our model performed superiorly in the N1 stage, with F1 scores of 54.41% and 52.79% on the two datasets respectively. The results show the superiority of our network over the existing methods, reaching a new state-of-the-art. In particular, the proposed method achieves excellent results in the N1 sleep stage compared to other methods.
Single-shot Hyperspectral-Depth Imaging with Learned Diffractive Optics

Imaging depth and spectrum have been extensively studied in isolation from each other for decades. Recently, hyperspectral-depth (HS-D) imaging emerges to capture both information simultaneously by combining two different imaging systems; one for depth, the other for spectrum. While being accurate, this combinational approach induces increased form factor, cost, capture time, and alignment/registration problems. In this work, departing from the combinational principle, we propose a compact single-shot monocular HS-D imaging method. Our method uses a diffractive optical element (DOE), the point spread function of which changes with respect to both depth and spectrum. This enables us to reconstruct spectrum and depth from a single captured image. To this end, we develop a differentiable simulator and a neural-network-based reconstruction method that are jointly optimized via automatic differentiation. To facilitate learning the DOE, we present a first HS-D dataset by building a benchtop HS-D imager that acquires high-quality ground truth. We evaluate our method with synthetic and real experiments by building an experimental prototype and achieve state-of-the-art HS-D imaging results.
Generalised vector products and metrical trigonometry of a tetrahedron

We study the general rational trigonometry of a tetrahedron, based on quadrances, spreads and solid spreads, using vector products associated to an arbitrary symmetric bilinear form over a general field, not of characteristic two. This gives us algebraic analogs of many classical formulas, as well as new insights and results. In particular we derive original relations for a tri-rectangular tetrahedron.
Knowledge-Based Construction of Confusion Matrices for Multi-Label Classification Algorithms using Semantic Similarity Measures

So far, multi-label classification algorithms have been evaluated using statistical methods that do not consider the semantics of the considered classes and that fully depend on abstract computations such as Bayesian Reasoning. Currently, there are several attempts to develop ontology-based methods for a better assessment of supervised classification algorithms. In this research paper, we define a novel approach that aligns expected labels with predicted labels in multi-label classification using ontology-driven feature-based semantic similarity measures and we use it to develop a method for creating precise confusion matrices for a more effective evaluation of multi-label classification algorithms.
AlphaZero Based Post-Storm Repair Crew Dispatch for Distribution Grid Restoration

Natural disasters such as storms usually bring significant damages to distribution grids. This paper investigates the optimal routing of utility vehicles to restore outages in the distribution grid as fast as possible after a storm. First, the poststorm repair crew dispatch task with multiple utility vehicles is formulated as a sequential stochastic optimization problem. In the formulated optimization model, the belief state of the power grid is updated according to the phone calls from customers and the information collected by utility vehicles. Second, an AlphaZero [1] based utility vehicle routing (AlphaZero-UVR) approach is developed to achieve the real-time dispatching of the repair crews. The proposed AlphaZero-UVR approach combines deep neural networks with stochastic Monte-Carlo tree search (MCTS) to give a lookahead search decisions, which can learn to navigate repair crews without human guidance. Simulation results show that the proposed approach can efficiently navigate crews to repair all outages.