Miracle-dz/UniFAR-FTUs huggingface dataset:
Ranking and Rejecting of Pre-Trained Deep Neural Networks in Transfer Learning based on Separation Index

Automated ranking of pre-trained Deep Neural Networks (DNNs) reduces the required time for selecting optimal pre-trained DNN and boost the classification performance in transfer learning. In this paper, we introduce a novel algorithm to rank pre-trained DNNs by applying a straightforward distance-based complexity measure named Separation Index (SI) to the target dataset. For this purpose, at first, a background about the SI is given and then the automated ranking algorithm is explained. In this algorithm, the SI is computed for the target dataset which passes from the feature extracting parts of pre-trained DNNs. Then, by descending sort of the computed SIs, the pre-trained DNNs are ranked, easily. In this ranking method, the best DNN makes maximum SI on the target dataset and a few pre-trained DNNs may be rejected in the case of their sufficiently low computed SIs. The efficiency of the proposed algorithm is evaluated by using three challenging datasets including Linnaeus 5, Breast Cancer Images, and COVID-CT. For the two first case studies, the results of the proposed algorithm exactly match with the ranking of the trained DNNs by the accuracy on the target dataset. For the third case study, despite using different preprocessing on the target data, the ranking of the algorithm has a high correlation with the ranking resulted from classification accuracy. Keywords—Deep Neural Networks, Transfer Learning, Separation Index, Medical Imaging, Data Complexity 1 Department of Electrical Engineering Imam-Khomeini International University Qazvin, Iran 2 Corresponding Author, School of Electrical and Computer Engineering, University of Tehran E-mail address: akalhor@ut.ac. ir (A. Kalhor)
High intrinsic lattice thermal conductivity in monolayer MoSi2N4

Very recently, a novel two-dimension (2D) MXene, MoSi2N4, was successfully synthesized with excellent ambient stability, high carrier mobility, and moderate band gap (2020 Science 369 670). In this work, the intrinsic lattice thermal conductivity of monolayer MoSi2N4 is predicted by solving the phonon Boltzmann transport equation based on the first-principles calculations. Despite the heavy atomic mass of Mo and complex crystal structure, the monolayer MoSi2N4 unexpectedly exhibits a quite high lattice thermal conductivity over a wide temperature range between 300 to 800 K. At 300 K, its in-plane lattice thermal conductivity is 224 Wm−1 K−1. The detailed analysis indicates that the large group velocities and small anharmonicity are the main reasons for its high lattice thermal conductivity. We also calculate the lattice thermal conductivity of monolayer WSi2N4, which is only a little smaller than that of MoSi2N4. Our findings suggest that monolayer MoSi2N4 and WSi2N4 are potential 2D materials for thermal transport in future nano-electronic devices.
$T\bar{T}$-like Flows in Non-linear Electrodynamic Theories and S-duality

We investigate the T T̄ -like flows for non-linear electrodynamic theories in D(=2n)-dimensional spacetime. Our analysis is restricted to the deformation problem of the classical free action by employing the proposed T T̄ operator from a simple integration technique. We show that this flow equation is compatible with T T̄ deformation of a scalar field theory in D=2 and of a non-linear Born-Infeld type theory in D=4 dimensions. However, our computation discloses that this kind of T T̄ flow in higher dimensions is essentially different from deformation that has been derived from the AdS/CFT interpretations. Indeed, the gravity that may be exist as a holographic dual theory of this kind of effective Born-Infeld action is not necessarily an AdS space. As an illustrative investigation in D=4, we shall also show that our construction for the T T̄ operator preserves the original SL(2,R) symmetry of a non-supersymmetric Born-Infeld theory, as well as N = 2 supersymmetric model. It is shown that the corresponding SL(2,R) invariant action fixes the relationship between the T T̄ operator and quadratic form of the energy-momentum tensor in D=4.
TransPose: Towards Explainable Human Pose Estimation by Transformer

Deep Convolutional Neural Networks (CNNs) have made remarkable progress on human pose estimation task. However, there is no explicit understanding of how the locations of body keypoints are predicted by CNN, and it is also unknown what spatial dependency relationships be-tween structural variables are learned in the model. To explore these questions, we construct an explainable model named TransPose based on Trans-former architecture and low-level convolutional blocks. Given an image, the attention layers built in Transformer can capture long-range spatial relationships between keypoints and explain what dependencies the predicted keypoints locations highly rely on. We analyze the rationality of us-ing attention as the explanation to reveal the spatial dependencies in this task. The revealed dependencies are image-speciﬁc and variable for different keypoint types, layer depths, or trained models. The experiments show that TransPose can accurately predict the positions of keypoints. It achieves state-of-the-art performance on COCO dataset, while being more interpretable, lightweight, and efﬁcient than mainstream fully convolutional architectures.
Achieving Real-Time LiDAR 3D Object Detection on a Mobile Device

3D object detection is an important task, especially in the autonomous driving application domain. However, it is challenging to support the real-time performance with the limited computation and memory resources on edge-computing devices in self-driving cars. To achieve this, we propose a compiler-aware unified framework incorporating network enhancement and pruning search with the reinforcement learning techniques, to enable real-time inference of 3D object detection on the resource-limited edge-computing devices. Specifically, a generator Recurrent Neural Network (RNN) is employed to provide the unified scheme for both network enhancement and pruning search automatically, without human expertise and assistance. And the evaluated performance of the unified schemes can be fed back to train the generator RNN. The experimental results demonstrate that the proposed framework firstly achieves real-time 3D object detection on mobile devices (Samsung Galaxy S20 phone) with competitive detection performance.
DeepHateExplainer: Explainable Hate Speech Detection in Under-resourced Bengali Language

In this paper, we propose an explainable approach for hate speech detection from the under-resourced Bengali language, which we called DeepHateExplainer. In our approach, Bengali texts are first comprehensively preprocessed, before classifying them into political, personal, geopolitical, and religious hates using a neural ensemble method of transformer-based neural architectures (i.e., monolingual Bangla BERT-base, multilingual BERT-cased/uncased, and XLM-RoBERTa). Subsequently, important (most and least) terms are identified using sensitivity analysis and layer-wise relevance propagation (LRP), before providing human-interpretable explanations11To foster reproducible research, we make available the data, source codes, models, and notebooks: https://github.com/rezacsedu/DeepHateExplainer. Finally, we compute comprehensiveness and sufficiency scores to measure the quality of explanations w.r. t faithfulness. Evaluations against machine learning (linear and tree-based models) and neural networks (i.e., CNN, Bi-LSTM, and Conv-LSTM with word embeddings) baselines yield F1-scores of 78%, 91%, 89%, and 84%, for political, personal, geopolitical, and religious hates, respectively, outperforming both ML and DNN baselines22Read an extended version of this paper: https://arxiv.org/abs/2012.14353.
A Simple Fine-tuning Is All You Need: Towards Robust Deep Learning Via Adversarial Fine-tuning

Adversarial Training (AT) with Projected Gradient Descent (PGD) is an effective approach for improving the robustness of the deep neural networks. However, PGD AT has been shown to suffer from two main limitations: i) high computational cost, and ii) extreme overfitting during training that leads to reduction in model generalization. While the effect of factors such as model capacity and scale of training data on adversarial robustness have been extensively studied, little attention has been paid to the effect of a very important parameter in every network optimization on adversarial robustness: the learning rate. In particular, we hypothesize that effective learning rate scheduling during adversarial training can significantly reduce the overfitting issue, to a degree where one does not even need to adversarially train a model from scratch but can instead simply adversarially fine-tune a pre-trained model. Motivated by this hypothesis, we propose a simple yet very effective adversarial fine-tuning approach based on a $\textit{slow start, fast decay}$ learning rate scheduling strategy which not only significantly decreases computational cost required, but also greatly improves the accuracy and robustness of a deep neural network. Experimental results show that the proposed adversarial fine-tuning approach outperforms the state-of-the-art methods on CIFAR-10, CIFAR-100 and ImageNet datasets in both test accuracy and the robustness, while reducing the computational cost by 8-10$\times$. Furthermore, a very important benefit of the proposed adversarial fine-tuning approach is that it enables the ability to improve the robustness of any pre-trained deep neural network without needing to train the model from scratch, which to the best of the authors' knowledge has not been previously demonstrated in research literature.
Efficient interdigitated back‐contacted silicon heterojunction solar cells

We present back‐contacted amorphous/crystalline silicon heterojunction solar cells (IBC‐SHJ) on n‐type substrates with fill factors exceeding 78% and high current densities, the latter enabled by a SiNx /SiO2 passivated phosphorus‐diffused front surface field. Voc calculations based on carrier lifetime data of reference samples indicate that for the IBC architecture and the given amorphous silicon layer qualities an emitter buffer layer is crucial to reach a high Voc, as known for both‐side contacted silicon heterojunction solar cells. A back surface field buffer layer has a minor influence. We observe a boost in solar cell Voc of 40 mV and a simultaneous fill factor reduction introducing the buffer layer. The aperture‐area efficiency increases from 19.8 ± 0.4% to 20.2 ± 0.4%. Both, efficiencies and fill factors constitute a significant improvement over previously reported values. (© 2011 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
Millimeter Wave Sensing: A Review of Application Pipelines and Building Blocks

The increasing bandwidth requirement of new wireless applications has lead to standardization of the millimeter wave spectrum for high-speed wireless communication. The millimeter wave spectrum is part of 5G and covers frequencies between 30 and 300 GHz that correspond to wavelengths ranging from 10 to 1 mm. Although millimeter wave is often considered as a communication medium, it has also proved to be an excellent ‘sensor’, thanks to its narrow beams, operation across a wide bandwidth, and interaction with atmospheric constituents. In this paper, which is to the best of our knowledge the first review that completely covers millimeter wave sensing application pipelines, we provide a comprehensive overview and analysis of different basic application pipeline building blocks, including hardware, algorithms, analytical models, and model evaluation techniques. The review also provides a taxonomy that highlights different millimeter wave sensing application domains. By performing a thorough analysis, complying with the systematic literature review methodology and reviewing 165 papers, we not only extend previous investigations focused only on communication aspects of the millimeter wave technology and using millimeter wave technology for active imaging, but also highlight scientific and technological challenges and trends, and provide a future perspective for applications of millimeter wave as a sensing technology.
Reactivity of Lignin Diphenylmethane Model Dimers. 1. Nitrobenzene Oxidation

A series of unsubstituted phenolic lignin diphenylmethane (DPM)-model dimers of the guaiacyl type has been shown to be quite reactive under alkaline nitrobenzene oxidation conditions resulting in the formation of monomeric products. The yield of vanillin varied considerably with the linkage type (4-32%), and decreases in the order of α-1, α-5, α-6 and 5,5'-DPM dimers. The reactions also included an oxidation of the α-methylene groups, leading to the formation of diphenylketone derivatives, which were obtained in 5-28% yield. In contrast. the etherified DPM-model dimers were relatively stable under nitrobenzene oxidation conditions.
Morphology and photoluminescence properties of ZnO nanostructures fabricated with different given time of Ar

ZnO nanostructures were grown on Au‐coated Si (100) substrates by carbonthermal reduction method with the help of Ar at the beginning of growth. The structural and optical properties of ZnO nanostructures strongly depended on the supply time of Ar. When the given time of Ar gas current was 90s, sample was ZnO nanowires with hexagonal morphology. The Raman spectroscopy revealed the low level of oxygen vacancies and Zn interstitials in samples. Room temperature photoluminescence (PL) spectra exhibited the intensity of green emission increased on the condition of rich oxygen (decrease given time of Ar) and the nanowire had strongest intensity of UV emission compared with other nanostructures. Green emission is ascribed to the electron transition from the bottom of the conduction band to the antisite defect OZn level. (© 2008 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
Relationships Between Soil pH, Available Calcium and Prevalence of Potato Scab

Studies were carried out on the interaction between potato scab, soil pH and exchangeable calcium in two different fields, one consisting of volcanic ash soil and the other, of red-yellow soil. Severity of potato scab in the field with red-yellow soil was correlated with the soil pH, unlike in the field with volcanic ash soil. The amount of exchangeable calcium in both soils was positively correlated with the scab index of potato tuber, when the content of exchangeable calcium in soil exceeded 150 mg per 100 g soil. From these results, it was concluded that the content of exchangeable calcium is as a more reliable parameter than the soil pH to evaluate the severity of potato scab.
Surface loss coefficients of CFx and F radicals on stainless steel

The surface loss coefficients of CFx (x=1–3) and F radicals have been measured on the stainless steel walls of the differential pumping systems of a plasma reactor. This measurement is made by comparing the beam to background signal ratio of the radicals measured by appearance potential mass spectrometry. The surface loss of the radicals in the differential pumping stages of the vacuum system, in addition to the pumping of the species due to the vacuum pump, increases the beam to background signal ratio of the radical species. The measured loss coefficients are found to be lower than those reported on surfaces that have been exposed to fluorocarbon plasmas. This suggests that the loss coefficients of the species are dependent on the surface coverage of the radicals (CFx and F), which are lower on the walls in the differential pumping system. The measured loss coefficients in the differential pumping system are an order of magnitude lower than the loss coefficients measured in the afterglow of plasma chamb.
Structure-function relationship in antimony corrole photosensitizers: Time-resolved electron paramagnetic resonance and optical study

Three photosensitizers based on tris-(pentafluorophenyl)antimony corroles that differ in oxidation state and axial ligands, namely, (pyridine)Sb(III)-, (oxo)Sb(V)- and (difluoro)Sb(V) complexes, were studied by time-resolved electron paramagnetic resonance spectroscopy and laser flash photolysis. The magnetic and orientational parameters of the corroles oriented in a nematic liquid crystal as well as their triplet lifetimes in liquid toluene were determined and interpreted in terms of their structure and geometry. The negative zero-field splitting parameter D assigned to all studied corroles is explained by the asymmetric π-electron withdrawal effect caused by perfluorinated peripheral aryl groups, which force the triplet electron spins to align in head-to-tail configuration. The effect of the axial ligands on the photoexcited triplet state properties of the corroles is correlated with their different efficiency to perform photoassisted aerobic oxygenation of some organic molecules. This is explained by the dependence of the main parameters of the photoexcited complexes on the interaction between the central ion and corrole π-system. This interaction is strongly influenced by axial ligands coordination, affecting the macrocycle symmetry, planarity, and rigidity.
Electron Spin Resonance in Polycrystalline Graphite

The electron spin resonance of polycrystalline graphite is shown to be due to charge carriers, as for single crystals. For lampblack‐base graphite, the variation in g value with temperature agrees approximately with that for single crystal graphite if it is assumed that the spins effectively average out the anisotropic interactions by rapid intercrystallite diffusion. The magnitude and temperature dependence of the spin susceptibility are found to be in excellent agreement with McClure's theoretical predictions for the charge carriers in perfect graphite. Complications of measuring these materials are discussed, including effects of skin depth and microwave heating and the dependence upon crystallite size and sample purity. It is found that two types of impurities can affect the resonance; those which become ionized and shift the Fermi level, and large un‐ionized atoms which do not shift the Fermi level but act as efficient scattering centers for shortening the spin—lattice relaxation time.
Burning Characteristics and Thermochemical Behavior of AP/HTPB Composite Propellant Using Coarse and Fine AP Particles

The burning rate of AP/HTPB composite propellant increases with increasing AP content and with decreasing AP size. In addition, the burning rate can be enhanced with the addition of Fe2O3. The burning characteristics and thermal decomposition behavior of AP/HTPB composite propellant using coarse and fine AP particles with and without Fe2O3 at various AP contents were investigated to obtain an exhaustive set of data. As the AP content decreased, the burning rate decreased and the propellants containing less than a certain AP content self-quenched or did not ignite. The self-quenched combustion began at both lower and higher pressures. The lower limit of AP content to burn the propellant with coarse AP was lower than that with fine AP. The lower limit of AP content to burn was decreased by the addition of Fe2O3. The thermal decomposition behavior of propellants prepared with 20–80 % AP was investigated. The decrease in the peak temperature of the exothermic decomposition suggested an increased burning rate. However, a quantitative relationship between the thermochemical behavior and the burning characteristics, such as the burning rate and the lower limit of AP content to burn, could not be determined.
Integrating the Long-Term Unemployed into the Job Market

1 This technical note was prepared by Julian Hiebl (Consultant, World Bank and Ministry of Labor, Social Affairs and Consumer Protection Austria) and Kevin Hempel (Consultant, World Bank and Prospera Consulting) under the guidance of Stefanie Brodmann (Senior Economist, World Bank and Task Team Leader). The team acknowledges financial support from the Rapid Social Response Multi-Donor Trust Fund. Report No: AUS0001359. Standard disclaimer: This technical note is a product of the staff of the International Bank for Reconstruction and Development/The World Bank. The findings, interpretations, and conclusions expressed in this paper do not necessarily reflect the views of the Executive Directors of The World Bank or the governments they represent. The World Bank does not guarantee the accuracy of the data presented in this work. P ub lic D is cl os ur e A ut ho riz ed
Study on Adsorption of Chlorobenzene on Marine Sediment

The sorption behavior of chlorobenzene on marine sediments treated by different methods was investigated. Through experiments on three factors (temperature, sediment organic carbon content, and media) that might affect the sorption behavior of chlorobenzene, some important conclusions were obtained. Three hours were required for adsorption equilibrium of chlorobenzene on marine sediments. Adsorption of chlorobenzene was completed mainly via the surface and microporosity action of sediment. Either the Freundlich model or the Langmuir model was fit for describing adsorption behavior of chlorobenzene on marine sediments. Temperature did not have a great influence on adsorption behavior of chlorobenzene in seawater, but the saturate sorption amount decreased as the temperature became high. Through study on adsorption of chlorobenzene in different media, we found that the saturate sorption amount of chlorobenzene in natural seawater was several times greater than that in artificial seawater or deionized water.
Anisotropic self-diffusion in the nematic phase of a thermotropic liquid crystal by 1H-spin-echo nuclear magnetic resonance

The orientation-dependent molecular diffusion in the nematic liquid crystal 4-pentyl-4′-cyanobiphenyl is measured to high accuracy. The applied nuclear magnetic resonance method combines multiple-pulse homonuclear dipolar decoupling with pulsed-field-gradient stimulated echo and slice selection. The obtained temperature dependencies of the principal diffusion coefficients are not described by a simple Arrhenius relationship but reflect the decrease of the molecular orientational order close to the nematic-to-isotropic phase transition. The geometric average of the principal diffusion coefficients is continuous with the diffusion coefficient in the isotropic phase. The results are best described in terms of the affine transformation model of diffusion in hard-ellipsoid nematics.The orientation-dependent molecular diffusion in the nematic liquid crystal 4-pentyl-4′-cyanobiphenyl is measured to high accuracy. The applied nuclear magnetic resonance method combines multiple-pulse homonuclear dipolar decoupling with pulsed-field-gradient stimulated echo and slice selection. The obtained temperature dependencies of the principal diffusion coefficients are not described by a simple Arrhenius relationship but reflect the decrease of the molecular orientational order close to the nematic-to-isotropic phase transition. The geometric average of the principal diffusion coefficients is continuous with the diffusion coefficient in the isotropic phase. The results are best described in terms of the affine transformation model of diffusion in hard-ellipsoid nematics.
Surface analysis of PECVD organosilicon films for corrosion protection of steel substrates

This study deals with the surface and electrochemical characterization of mild steel surfaces after plasma‐enhanced chemical vapour deposition of organosilicon films, performed in order to increase corrosion resistance. Coating deposition was performed in a home‐made reactor with hexamethyldisiloxane–oxygen–argon mixtures of different compositions and at different input power values on as‐received specimens and after a pretreatment step in oxygen‐containing plasma. Surface and morphological characterization of coatings was performed by x‐ray photoelectron spectroscopy (XPS), Fourier transform infrared spectroscopy (FTIR) and scanning electron microscopy. To evaluate the protective effectiveness, electrochemical characterization of the coatings was carried out in sulphate‐containing solutions by electrochemical impedance spectroscopy.