Miracle-dz/UniFAR-FTUs huggingface dataset:
A Taxonomy of Cyber Defence Strategies Against False Data Attacks in Smart Grids

The modern electric power grid, known as the Smart Grid, has fast transformed the isolated and centrally controlled power system to a fast and massively connected cyber-physical system that benefits from the revolutions happening in communications (such as 5G/6G) and the fast adoption of Internet of Things devices (such as intelligent electronic devices and smart meters). While the synergy of a vast number of cyber-physical entities has allowed the Smart Grid to be much more effective and sustainable in meeting the growing global energy challenges, it has also brought with it a large number of vulnerabilities resulting in breaches of data integrity, confidentiality, and availability. False data injection (FDI) appears to be among the most critical cyberattacks and has been a focal point of interest for both research and industry. To this end, this article presents a comprehensive review of the recent advances in defence countermeasures of FDI attacks on the Smart Grid. Relevant existing works of literature are evaluated and compared in terms of their theoretical and practical significance to Smart Grid cybersecurity. In conclusion, a range of technical limitations of existing false data attack detection research is identified, and a number of future research directions are recommended.
On Capital Allocation for a Risk Measure Derived from Ruin Theory

This paper addresses allocation methodologies for a risk measure inherited from ruin theory. Specifically, we consider a dynamic value-at-risk (VaR) measure defined as the smallest initial capital needed to ensure that the ultimate ruin probability is less than a given threshold. We introduce an intuitively appealing, novel allocation method, with a focus on its application to capital reserves which are determined through the dynamic value-at-risk (VaR) measure. Various desirable properties of the presented approach are derived including a limit result when considering a large time horizon and the comparison with the frequently used gradient allocation method. In passing, we introduce a second allocation method and discuss its relation to the other allocation approaches. A number of examples illustrate the applicability and performance of the allocation approaches.
PyMAF: 3D Human Pose and Shape Regression with Pyramidal Mesh Alignment Feedback Loop

Regression-based methods have recently shown promising results in reconstructing human meshes from monocular images. By directly mapping raw pixels to model parameters, these methods can produce parametric models in a feed-forward manner via neural networks. However, minor deviation in parameters may lead to noticeable mis-alignment between the estimated meshes and image evidences. To address this issue, we propose a Pyramidal Mesh Alignment Feedback (PyMAF) loop to leverage a feature pyramid and rectify the predicted parameters explicitly based on the mesh-image alignment status in our deep regressor. In PyMAF, given the currently predicted parameters, mesh-aligned evidences will be extracted from finer-resolution features accordingly and fed back for parameter rectification. To reduce noise and enhance the reliability of these evidences, an auxiliary pixel-wise supervision is imposed on the feature encoder, which provides mesh-image correspondence guidance for our network to preserve the most related information in spatial features. The efficacy of our approach is validated on several benchmarks, including Human3.6M, 3DPW, LSP, and COCO, where experimental results show that our approach consistently improves the mesh-image alignment of the reconstruction. The project page with code and video results can be found at https://hongwenzhang.github. io/pymaf.
Inference in the stochastic Cox-Ingersol-Ross diffusion process with continuous sampling: Computational aspects and simulation

In this paper, we consider a stochastic model based on the CoxIngersollRoss model (CIR). The stochastic model is parameterized analytically by applying Itô’s calculus and the trend functions of the proposed process is calculated. The parameter estimators are then derived by means of two procedures : the first is used to estimate the parameters in the drift coefficient by the maximum likelihood (ML) method, based on continuous sampling, and the second procedure approximates the diffusion coefficient by two methods. Finally, a simulation of the process is presented. Thus, a typical simulated trajectory of the process and its estimators is obtained.
Memory Enhanced Embedding Learning for Cross-Modal Video-Text Retrieval

Cross-modal video-text retrieval, a challenging task in the field of vision and language, aims at retrieving corresponding instance giving sample from either modality. Existing approaches for this task all focus on how to design encoding model through a hard negative ranking loss, leaving two key problems unaddressed during this procedure. First, in the training stage, only a mini-batch of instance pairs is available in each iteration. Therefore, this kind of hard negatives is locally mined inside a mini-batch while ignoring the global negative samples among the dataset. Second, there are many text descriptions for one video and each text only describes certain local features of a video. Previous works for this task did not consider to fuse the multiply texts corresponding to a video during the training. In this paper, to solve the above two problems, we propose a novel memory enhanced embedding learning (MEEL) method for videotext retrieval. To be specific, we construct two kinds of memory banks respectively: cross-modal memory module and text center memory module. The cross-modal memory module is employed to record the instance embeddings of all the datasets for global negative mining. To avoid the fast evolving of the embedding in the memory bank during training, we utilize a momentum encoder to update the features by a moving-averaging strategy. The text center memory module is designed to record the center information of the multiple textual instances corresponding to a video, and aims at bridging these textual instances together. Extensive experimental results on two challenging benchmarks, i.e., MSR-VTT and VATEX, demonstrate the effectiveness of the proposed method.
Oxidation Kinetics of Chemically Vapor‐Deposited Silicon Carbide in Wet Oxygen

The oxidation kinetics of chemically vapor-deposited SiC in dry oxygen and wet oxygen at temperatures between 1,200 and 1,400 C were monitored using thermogravimetric analysis. It was found that in a clean environment, 10% water vapor enhanced the oxidation kinetics of SiC only very slightly compared to rates found in dry oxygen. Oxidation kinetics were examined in terms of the Deal and Grove model for oxidation of silicon. It was found that in an environment containing even small amounts of impurities, such as high-purity Al[sub 2]O[sub 3] reaction tubes containing 200 ppm Na, water vapor enhanced the transport of these impurities to the oxidation sample. Oxidation rates increased under these conditions presumably because of the formation of less protective sodium aluminosilicate scales.
Models relating soil pH measurements in water and calcium chloride that incorporate electrolyte concentration

Soil pH is the most routinely measured soil property for assessing plant nutrient availability. Nevertheless, there are various techniques for soil pH measurement, which vary with regard to the solution used and the soil‐to‐solution ratio. Soil pH is commonly measured in water or 0.01 m CaC12. Soil pH in CaCl2 is usually preferred as it is less affected by soil electrolyte concentration and provides a more consistent measurement. Therefore there is a need to convert measurement values between the two methods. Previous models reported linear and curvilinear relationships between the two measurements. However, the pH difference between measurements in water and CaCl2 is related to the soil solution electrolyte concentration. We observed that the pH difference between the two methods became smaller with increasing soil electrical conductivity (EC). We therefore developed models that relate pH in CaCl2 and water and incorporate EC values. We calibrated a linear and a non‐linear model (artificial neural networks, ANN) using 9817 soil samples from Queensland, Australia. Soil pH in water and CaCl2 and EC were measured with a 1:5 soil‐to‐solution ratio. The results show that incorporating EC in the prediction model improves the prediction of pH in CaCl2 significantly. We validated these models using 4576 independent samples obtained from a diverse range of soils across Australia. Although the linear and ANN models performed similarly, the ANN (which has a curvilinear relationship) provided a better prediction and aligns with the theory that for acid and alkaline pH values, the difference between pH in water and CaCl2 is less than that for pHs between 4.5 and 7.
The influence of cations on the dipole moments of neighboring polar molecules

In this review/perspective article we show how the dipole moment of a single molecule in a cluster can be calculated and used for describing the polarization effect. Additionally, we review the accuracy of the calculation of the dipole moment of several simple protic and aprotic molecules. It is shown that the dipole moment of polar (water, methanol, formamide, acetone and acetonitrile) molecules in the neighborhood of a cation is increased primarily by polarization from the bare electrostatic charge of the cation, although the effective value of the latter is somewhat reduced by “ back donation ” of electrons from neighboring polar molecules. In other words, the classical picture may be viewed as if a point charge slightly smaller than the nominal charge of the cation would be placed at the cation site. It was found that the geometrical arrangement of the polar molecules in the first solvation shell is such that their mutual polarization reduces the dipole moments of individual molecules, so that in some cases they become smaller than the dipole moment of the free protic or aprotic molecule. We conjecture, for the first time, that this behavior, namely the about 10% – 20% decrease of the dipole moment of water in the first shell of cations, with the cation itself removed, is essentially a manifestation of the Le Chatelier – Braun principle. We also remark that if the cation-molecule bond order is too large then the calculated dipole moment for these complexes can be questionable, due to the questionable definition of a single molecule within the “ supermolecule ” -like cluster.
Enhancing Segment-Based Speech Emotion Recognition by Deep Self-Learning

Despite the widespread utilization of deep neural networks (DNNs) for speech emotion recognition (SER), they are severely restricted due to the paucity of labeled data for training. Recently, segment-based approaches for SER have been evolving, which train backbone networks on shorter segments instead of whole utterances, and thus naturally augments training examples without additional resources. However, one core challenge remains for segment-based approaches: most emotional corpora do not provide ground-truth labels at the segment level. To supervisely train a segment-based emotion model on such datasets, the most common way assigns each segment the corresponding utterance's emotion label. However, this practice typically introduces noisy (incorrect) labels as emotional information is not uniformly distributed across the whole utterance. On the other hand, DNNs have been shown to easily over-fit a dataset when being trained with noisy labels. To this end, this work proposes a simple and effective deep self-learning (DSL) framework, which comprises a procedure to progressively correct segment-level labels in an iterative learning manner. The DSL method produces dynamically-generated and soft emotion labels, leading to significant performance improvements. Experiments on three well-known emotional corpora demonstrate noticeable gains using the proposed method.
Industry Scale Semi-Supervised Learning for Natural Language Understanding

This paper presents a production Semi-Supervised Learning (SSL) pipeline based on the student-teacher framework, which leverages millions of unlabeled examples to improve Natural Language Understanding (NLU) tasks. We investigate two questions related to the use of unlabeled data in production SSL context: 1) how to select samples from a huge unlabeled data pool that are beneficial for SSL training, and 2) how does the selected data affect the performance of different state-of-the-art SSL techniques. We compare four widely used SSL techniques, Pseudo-label (PL), Knowledge Distillation (KD), Virtual Adversarial Training (VAT) and Cross-View Training (CVT) in conjunction with two data selection methods including committee-based selection and submodular optimization based selection. We further examine the benefits and drawbacks of these techniques when applied to intent classification (IC) and named entity recognition (NER) tasks, and provide guidelines specifying when each of these methods might be beneficial to improve large scale NLU systems.
The Complexity of Nonconvex-Strongly-Concave Minimax Optimization

This paper studies the complexity for finding approximate stationary points of nonconvex-strongly-concave (NC-SC) smooth minimax problems, in both general and averaged smooth finite-sum settings. We establish nontrivial lower complexity bounds of $\Omega(\sqrt{\kappa}\Delta L\epsilon^{-2})$ and $\Omega(n+\sqrt{n\kappa}\Delta L\epsilon^{-2})$ for the two settings, respectively, where $\kappa$ is the condition number, $L$ is the smoothness constant, and $\Delta$ is the initial gap. Our result reveals substantial gaps between these limits and best-known upper bounds in the literature. To close these gaps, we introduce a generic acceleration scheme that deploys existing gradient-based methods to solve a sequence of crafted strongly-convex-strongly-concave subproblems. In the general setting, the complexity of our proposed algorithm nearly matches the lower bound; in particular, it removes an additional poly-logarithmic dependence on accuracy present in previous works. In the averaged smooth finite-sum setting, our proposed algorithm improves over previous algorithms by providing a nearly-tight dependence on the condition number.
Surface modification studies of Kapton® HN polyimide films

Surface imide hydrolysis of Kapton HN polyimide films in a 1 M KOH aqueous solution was studied using dynamic contact angle measurements, X-ray photoelectron spectroscopy (XPS) and time-of-flight secondary ion mass spectrometry (TOF-SIMS). The surface energy of Kapton HN increased quickly upon KOH treatment due to the formation of polar polyamate. Prolonged treatment led to a jump in surface energy that we ascribe to the onset of etching causing increased surface roughness. Potassium depth profiles of treated Kapton HN samples measured by XPS and TOF-SIMS demonstrated that during the first 10 minutes of 1 M KOH treatment the modification depth increased rapidly. Thereafter the thickness of the modified layer no longer increased. For Kapton H films, however, the development of etching was much slower and a steady increase in modification depth was observed even after long treatment times. The different behaviours between Kapton HN and Kapton H films in alkali solutions are analysed and discussed. Copyright © 2003 Society of Chemical Industry
Supported nickel catalyst from hydroxycarbonate of nickel and aluminium

The hydroxycarbonate of nickel and aluminium (Ni/ Al = 3) with a hydrotalcite-like structure is an outstanding precursor of the active component of supported nickel catalysts. Good mechanical strength and suitable nickel content of these catalysts, which are necessary for practical applications, can be achieved by mechanical mixing of this compound with an additional support. The catalyst prepared from a mixture of 56.5 wt-% of nickel-aluminium hydroxycarbonate and 43.5 wt-% of γ-alumina was proven to have a stable catalytic activity in the methanation reaction at 2 MPa and 800 K.
Gluing Karcher–Scherk saddle towers I: Triply periodic minimal surfaces

We construct minimal surfaces by gluing singly periodic Karcher–Scherk saddle towers along their wings. Such constructions were previously implemented assuming a horizontal reflection plane. We break this symmetry by prescribing phase differences between the saddle towers. It turns out that, in addition to the previously known horizontal balancing condition, the saddle towers must also be balanced under a subtle vertical interaction. This interaction vanishes in the presence of a horizontal reflection plane, hence was not perceived in previous works. Our construction will be presented in a series of papers. In this first paper of the series, we will explain the background of the project and establish the graph theoretical setup that will be useful for all papers in the series. The main task of the current paper is to glue saddle towers into triply periodic minimal surfaces (TPMSs). Our construction expands many previously known TPMSs into new 5-parameter families, therefore significantly advances our knowledge on the space of TPMSs.
Machine learning method for light field refocusing

two Neither of these two methods refocus the light ﬁeld in real-time without any pre-processing. In this paper we introduce a machine learning based refocusing technique that is capable of extracting 16 refocused images with refocusing parameters of α = 0 . 125 , 0 . 250 , 0 . 375 , . , 2 . 0 in real-time. We have trained our network, which is called RefNet, in two experiments. Once using the Fourier slice method as the training—i.e., “ground truth”—data and another using the shift-and-sum method as the training data. We showed that in both cases, not only is the RefNet method at least 134 × faster than previous approaches, but also the color prediction of RefNet is superior to both Fourier slice and shift-and-sum methods while having similar depth of ﬁeld and focus distance performance.
PIXLISE-C: Exploring The Data Analysis Needs of NASA Scientists for Mineral Identification

NASA JPL scientists working on the micro x-ray fluorescence (microXRF) spectroscopy data collected from Mars surface perform data analysis to look for signs of past microbial life on Mars. Their data analysis workflow mainly involves identifying mineral compounds through the element abundance in spatially distributed data points. Working with the NASA JPL team, we identified pain points and needs to further develop their existing data visualization and analysis tool. Specifically, the team desired improvements for the process of creating and interpreting mineral composition groups. To address this problem, we developed an interactive tool that enables scientists to (1) cluster the data using either manual lasso-tool selection or through various machine learning clustering algorithms, and (2) compare the clusters and individual data points to make informed decisions about mineral compositions. Our preliminary tool supports a hybrid data analysis workflow where the user can manually refine the machine-generated clusters.
Enabling Homomorphically Encrypted Inference for Large DNN Models

The proliferation of machine learning services in the last few years has raised data privacy concerns. Homomorphic encryption (HE) enables inference using encrypted data but it incurs 100x–10,000x memory and runtime overheads. Secure deep neural network (DNN) inference using HE is currently limited by computing and memory resources, with frameworks requiring hundreds of gigabytes of DRAM to evaluate small models. To overcome these limitations, in this paper we explore the feasibility of leveraging hybrid memory systems comprised of DRAM and persistent memory. In particular, we explore the recently-released Intel® Optane™ PMem technology and the Intel® HE-Transformer nGraph® to run large neural networks such as MobileNetV2 (in its largest variant) and ResNet-50 for the first time in the literature. We present an in-depth analysis of the efficiency of the executions with different hardware and software configurations. Our results conclude that DNN inference using HE incurs on friendly access patterns for this memory configuration, yielding efficient executions.
HST PanCET program: non-detection of atmospheric escape in the warm Saturn-sized planet WASP-29 b

Short-period gas giant exoplanets are susceptible to intense atmospheric escape due to their large scale heights and strong high-energy irradiation. This process is thought to occur ubiquitously, but to date we have only detected direct evidence of atmospheric escape in hot Jupiters and warm Neptunes. The latter planets are particularly more sensitive to escape-driven evolution as a result of their lower gravities with respect to Jupiter-sized planets. But the paucity of cases for intermediate, Saturn-sized exoplanets at varying levels of irradiation precludes a detailed understanding of the underlying physics in atmospheric escape of hot gas giants. Aiming to address this issue, our objectives here are to assess the high-energy environment of the warm (Teq = 970 K) Saturn WASP-29 b and search for signatures of atmospheric escape. We used far-ultraviolet observations from the Hubble Space Telescope to analyze the flux time series of H I, C II, Si III, Si IV, and N V during the transit of WASP-29 b. At 88 pc, a large portion of the Lyman-α core of the K4V-type host WASP-29 is attenuated by interstellar medium absorption, limiting our ability to probe the escape of H at velocities between −84 and +35 km s−1. At 3σ confidence, we rule out any in-transit absorption of H I larger than 92% in the Lyman-α blue wing and 19% in the red wing. We found an in-transit flux decrease of 39%−11%+12% in the ground-state C II emission line at 1334.5 Å. But due to this signal being significantly present in only one visit, it is difficult to attribute a planetary or stellar origin to the ground-state C II signal. We place 3σ absorption upper limits of 40, 49, and 24% on Si III, Si IV, and for excited-state C II at 1335.7 Å, respectively. Low activity levels and the faint X-ray luminosity suggest that WASP-29 is an old, inactive star. Nonetheless, an energy-limited approximation combined with the reconstructed EUV spectrum of the host suggests that the planet is losing its atmosphere at a relatively large rate of 4 × 109 g s−1. The non-detection at Lyman-α could be partly explained by a low fraction of escaping neutral hydrogen, or by the state of fast radiative blow-out we infer from the reconstructed Lyman-α line.
A comprehensive study of the physical properties of Nb2P5 via ab-initio technique

Binary metallic phosphide, Nb2P5, belongs to technologically important class of materials. Quite surprisingly, a large number of physical properties of Nb2P5, including elastic properties and their anisotropy, acoustic, electronic (DOS, charge density distribution, electron density difference), thermo-physical, bonding characteristics, and optical properties have not been investigated at all. In the present work we have explored all these properties in details for the first time employing density functional theory based first-principles method. Nb2P5 is found to be a mechanically stable, elastically anisotropic compound with weak brittle character. The bondings among the atoms are dominated by covalent and ionic contributions with small signature of metallic feature. The compound possesses high level of machinability. Nb2P5 is a moderately hard compound. The band structure calculations reveal metallic conduction with a large electronic density of states at the Fermi level. Calculated values of different thermal properties indicate that Nb2P5 has the potential to be used as a thermal barrier coating material. The energy dependent optical parameters show close agreement with the underlying electronic band structure. The optical absorption and reflectivity spectra and the static index of refraction of Nb2P5 show that the compound holds promise to be used in optoelectronic device sector. Unlike notable anisotropy in elastic and mechanical properties, the optical parameters are found to be almost isotropic.