Miracle-dz/UniFAR-FTUs huggingface dataset:
ESAIM: Control, Optimisation and Calculus of Variations

Electromagnetic interrogation techniques have numerous useful applications, including locating mines or bunkers beneath the ground and detecting abnormal tissue noninvasively within the body. (See [1, 4, 11, 12], and [13] for some examples of the applications of electromagnetic interrogation.) In one such class of electromagnetic interrogation techniques, one uses a superconductive (also referred to as supraconductive or perfectly conductive) backing, such as metal, as a reflector for an oncoming electromagnetic wave. These electromagnetic wave reflections are then used to identify dielectric properties (conductivity and polarization) of the target materials. However, there are many applications (for example finding a nonmetal object underground or detecting a brain tumor) in which this is impossible or even harmful. In these cases, it would be beneficial for a traveling acoustic wave, perhaps even one occurring naturally, to serve as a virtual interface. In [4] the authors describe models and applications for techniques which employ superconductive metal backings and standing acoustic waves as reflectors for the electromagnetic waves. In addition, they propose a configuration in which a traveling acoustic wave might be used as a virtual interface to reflect an oncoming electromagnetic wave. Electromagnetic interrogation techniques such as these may be modeled, under certain assumptions, by the one dimensional Maxwell’s equations with temporally and spatially varying coefficients. Here we present a general model that describes, as a special case, the interaction between the electromagnetic wave and a traveling acoustic wave, formulated as pressure-dependent Debye polarization. We note that the models derived in [4] to describe the cases where perfectly conductive backings or standing acoustic waves are used as reflectors are also special (simpler) cases of this model. The main purpose of this paper is to provide a general theoretical foundation for a class of electromagnetic/acoustic interaction problems. Specifically, in Section 2 we consider the well-posedness of a general variational form of the model. We consider the equation in the domain 0 ≤ z ≤ 1 and assume that the boundary
RESPONSE OF “WONDERFUL” POMEGRANATE CULTIVAR TO THE FOLIAR APPLICATION OF SOME BIOSTIMULANTS

This experiment was carried out during 2019 and 2020 seasons on eight years old "Wonderful" pomegranate cultivar (Punica granatum L.) at Burg El-Arab, Alexandria governorate, Egypt. The experiment was performed on eighty trees, which were trained as vase shaped system on single-trunk with four branches, grown in a sandy soil under drip irrigation system and were grown at 4 m between trees and 5 m between rows. The trees were also similar in their size as possible and subjected to the same horticultural practiced in the orchard. They were sprayed at the beginning of flowering, full bloom and one month later three times by the following treatments: control (water only), humic acid at 0.2%, 0.3% and 0.4%, fulvic acid at 0.2%, 0.3% and 0.4%, yeast extract at 0.2%, 0.3% and 0.4%, moringa leaf extract at 0.2%, 0.4% and 0.6%. Moreover, the trees also sprayed by different combinations; 0.2% humic acid +0.2% fulvic acid + 0.2%yeast extract + 0.2%moringaleaf extract,0.3% humic acid + 0.3% fulvic acid + 0.3%yeast extract + 0.4%moringa leaf extract and 0.4% humic acid + 0.4% fulvic acid + 0.4% yeast extract+ 0.6% moringa leaf extract. The obtained results showed that the combination of 0.4% humic acid + 0.4% fulvic acid + 0.4% yeast extract+ 0.6% moringa leaf extract was the best treatment, which significantly improved shoot length, and diameter, leaf total chlorophyll content, fruit set percentage, weight, size, length, number and width of fruits, and fruit yield. Besides, it increased also the leaf mineral from N, P and K in the two seasons, as compared to control or the other treatments. Moreover, it also improved significantly fruit firmness, anthocyanin content, total soluble solids total and reducing sugars, TSS/acid ratio. On the other hand, the same combination decreased the percentages of fruit drop, cracking, sunburn and total acidity in the two seasons as compared to the control.
Dynamical Basis for Drug Resistance of HIV-1 Protease

Background: Protease inhibitors designed to bind to protease have become major anti-AIDS drugs. Unfortunately, the emergence of viral mutations severely limits the long-term efficiency of the inhibitors. The resistance mechanism of these diversely located mutations remains unclear. Results: Here I use an elastic network model to probe the connection between the global dynamics of HIV-1 protease and the structural distribution of drug-resistance mutations. The models for study are the crystal structures of unbounded and bound (with the substrate and nine FDA approved inhibitors) forms of HIV-1 protease. Coarsegrained modeling uncovers two groups that couple either with the active site or the flap. These two groups constitute a majority of the drug-resistance residues. In addition, the significance of residues is found to be correlated with their dynamical changes in binding and the results agree well with the complete mutagenesis experiment of HIV-1 protease. Conclusions: The dynamic study of HIV-1 protease elucidates the functional importance of common drugresistance mutations and suggests a unifying mechanism for drug-resistance residues based on their dynamical properties. The results support the robustness of the elastic network model as a potential predictive tool for drug resistance.
Explainable Zero-Shot Modelling of Clinical Depression Symptoms from Text

We focus on exploring various approaches of Zero-Shot Learning (ZSL) and their explainability for a challenging yet important supervised learning task, notorious for training data scarcity, i.e. Depression Symptoms Detection (DSD) from text. We start with a comprehensive synthesis of different components of our ZSL modelling and analysis of our ground truth samples and Depression symptom clues curation process with the help of a practicing Clinician. We next analyze the accuracy of various state-of-the-art ZSL models and their potential enhancements for our task. Further, we sketch a framework for the use of ZSL for hierarchical text-based explanation mechanism, which we call, Syntax Tree-Guided Semantic Explanation (STEP). Finally, we summarize experiments from which we conclude that we can use ZSL models and achieve reasonable accuracy and explainability, measured by a proposed Explainability Index (EI). This work is, to our knowledge, the first work to exhaustively explore the efficacy of ZSL models for DSD task, both in terms of accuracy and explainability.
Unsupervised Domain Adaptation Method with Semantic-Structural Alignment for Dependency Parsing

Unsupervised cross-domain dependency parsing is to accomplish domain adaptation for dependency parsing without using labeled data in target domain. Existing methods are often of the pseudo-annotation type, which generates data through self-annotation of the base model and performing iterative training. However, these methods fail to consider the change of model structure for domain adaptation. In addition, the structural information contained in the text cannot be fully exploited. To remedy these drawbacks, we propose a Semantics-Structure Adaptative Dependency Parser (SSADP), which accomplishes unsupervised cross-domain dependency parsing without relying on pseudo-annotation or data selection. In particular, we design two feature extractors to extract semantic and structural features respectively. For each type of features, a corresponding feature adaptation method is utilized to achieve domain adaptation to align the domain distribution, which effectively enhances the unsupervised crossdomain transfer capability of the model. We validate the effectiveness of our model by conducting experiments on the CODT1 and CTB9 respectively, and the results demonstrate that our model can achieve consistent performance improvement. Besides, we verify the structure transfer ability of the proposed model by introducing Weisfeiler-Lehman Test.
A Unified 3D Human Motion Synthesis Model via Conditional Variational Auto-Encoder∗

We present a unified and flexible framework to address the generalized problem of 3D motion synthesis that covers the tasks of motion prediction, completion, interpolation, and spatial-temporal recovery. Since these tasks have different input constraints and various fidelity and diversity requirements, most existing approaches only cater to a specific task or use different architectures to address various tasks. Here we propose a unified framework based on Conditional Variational Auto-Encoder (CVAE), where we treat any arbitrary input as a masked motion series. Notably, by considering this problem as a conditional generation process, we estimate a parametric distribution of the missing regions based on the input conditions, from which to sample and synthesize the full motion series. To further allow the flexibility of manipulating the motion style of the generated series, we design an Action-Adaptive Modulation (AAM) to propagate the given semantic guidance through the whole sequence. We also introduce a cross-attention mechanism to exploit distant relations among decoder and encoder features for better realism and global consistency. We conducted extensive experiments on Human 3.6M and CMU-Mocap. The results show that our method produces coherent and realistic results for various motion synthesis tasks, with the synthesized motions distinctly adapted by the given action labels.
Applications of customer outage costs in system planning, design and operation

Although the basic function of an electric utility company is to supply power economically and at acceptable levels of continuity and quality, the emphasis placed on these aspects during planning, design and operation depends on the level of power system development. Further development of developed power networks is dominated by technological changes and concern for strict economic operation, quality of service and reliability. The authors only address issues related to reliability and consider distribution systems which are a part of developed networks from this vantage point. It has been suggested that existing standards do not adequately address customers' concerns over reliability. To redress this, the authors address such concerns by presenting customer outage costs (COC), which are considered as proxies for reliability worth, as a criterion for providing additional justification for alternative distribution system plans, designs or operating policies. To demonstrate the application in these respects, two distribution networks based on realistic 33 kV power systems are analysed, and the effects of network modifications or changes in operating policies on traditional reliability indices and COC studied.
Body-on-a-Chip Systems for Drug Development and in vitro Interactions

Organ-on-a-chips are microsystems that through tissue-engineering can model human organs, representing both structure and function [1]. Human cell-based multi-organ on-a-chip systems can be used for drug development [2], studying metastasis and chemotherapy. The microscale biomimetics of human organs with organ-organ interactions can be used to model human physiology and disease progression, and thus offer more accurate predictions of human responses to therapeutics and provide mechanistic insights into human diseases. Also, these models can significantly reduce drug development cost and animal usage [3-5]. Currently, we are developing several microfluidic systems, which are fabricated with tools at Cornell NanoScale Facility (CNF) and will be used to study chemotherapeutic toxicity, model cancer cell metastasis, and simulate immune responses. Summary of Research: Figure 1: Design of the single pass colorectal/liver metastatic device. Schematic of the single pass device. Single-Pass Chip. We have designed and modified a gravitydriven microfluidic model for studying the extravasation of circulating tumor cells (CTC) through endothelium (either primary human liver sinusoidal endothelial cells (LSEC) or human umbilical cord endothelial cells (HUVEC)) into a chamber with HepG2 C3A (liver cells). The frame of this device is made from PMMA layers patterned using a CO2 laser (VersaLaser VLS3.50), and bonded together using a hot press after a 15 min UV/Ozone (Samco UV& Ozone stripper) exposure at CNF. The propeller stirring lid of the device is fabricated using the Objet30 Pro 3D Printer from the CNF. Clear silicone sheets are also patterned with laser ablation using the VeraLaser CO2 laser cutter. The stirring is driven by a small stir bar on a magnetic stir plate. This device allows for a continuous operation up to 24 hours while maintaining a constant concentration of CTC and evaluate of the capability of CTC (large and small clusters) from various sources to enter into the liver chamber. [Fig.1] Unidirectional Chip Devices. We have two unidirectional chip devices (two-chamber and three-chamber chips) for studying the metastasis and drug effects. Chambers are interconnected and perfused with gravity-driven flow at physiological perfusion rates [3-5]. The flow dynamics are characterized computationally and experimentally. Our pumpless gravity-driven flow is created by using a customized programmable rocker where a common media is recirculated between the two reservoirs. Flow rates were measured to be within 15% of the designed values [6,7]. The prototype devices are initially tested for viability where the goal is to maintain a viability greater than 85%. Two-Chamber Unidirectional Chip. We have developed and modified our original colon-liver two-chamber unidirectional chip system to model CTC liver metastasis. This two organ system interconnects colon and liver
PERFORMANCE ANALYSIS OF TWO NOVEL CONVOLUTIONAL NEURAL NETWORK MODELS FOR AUTOMATED LEAF IDENTIFICATION

One of the main reasons for scientists and botanists to emphasize the importance of plant species conservation is the rapid deterioration of the green cover around the planet. With the extinction of many significant plant species, plant identification is becoming increasingly crucial. The field of automated plant species identification has benefited from advances in computer vision. Two well-planned convolutional neural networks with great accuracy are shown in this research. The innovative models were influenced by the well-known VGG-16 and AlexNet models[1][2]. On a fresh dataset of ninety-five plant species, both models obtained over 90% accuracy, according to previous research. These two models are analyzed for their accuracy on two datasets, one being the popular opensource Swedish dataset and the second one is the novel Western Ghats dataset which has been created by us.
Exploring Feature Selection Scenarios for Deep Learning-based Side-Channel Analysis

One of the main promoted advantages of deep learning in profiling sidechannel analysis is the possibility of skipping the feature engineering process. Despite that, most recent publications consider feature selection as the attacked interval from the side-channel measurements is pre-selected. This is similar to the worst-case security assumptions in security evaluations when the random secret shares (e.g., mask shares) are known during the profiling phase: an evaluator can identify points ofinterest locations and efficiently trim the trace interval. To broadly understand how feature selection impacts the performance of deep learning-based profiling attacks, this paper investigates three different feature selection scenarios that could be realistically used in practical security evaluations. The scenarios range from the minimum possible number of features (worst-case security assumptions) to the whole available traces. Our results emphasize that deep neural networks as profiling models show successful key recovery independently of explored feature selection scenarios against first-order masked software implementations of AES-128. First, we show that feature selection with the worst-case security assumptions results in optimal profiling models that are highly dependent on the number of features and signal-to-noise ratio levels. Second, we demonstrate that attacking raw side-channel measurements with small deep neural networks also provides optimal models, that shortens the gap between worst-case security evaluations and online (realistic) profiling attacks. In all explored feature selection scenarios, the hyperparameter search always indicates a successful model with up to eight hidden layers for MLPs and CNNs, suggesting that complex models are not required for the considered datasets. Our results demonstrate the key recovery with less than ten attack traces for all datasets for at least one of the feature selection scenarios. Additionally, in several cases, we can recover the target key with a single attack trace.
A new way to test the Cosmological Principle: measuring our peculiar velocity and the large-scale anisotropy independently

We present a novel approach to disentangle two key contributions to the largest-scale anisotropy of the galaxy distribution: (i) the intrinsic dipole due to clustering and anisotropic geometry, and (ii) the kinematic dipole due to our peculiar velocity. Including the redshift and angular size of galaxies, in addition to their fluxes and positions allows us to measure both the direction and amplitude of our velocity independently of the intrinsic dipole of the source distribution. We find that this new approach applied to future galaxy surveys (LSST and Euclid) and a SKA radio continuum survey will allow to measure our velocity (β = v/c) with a relative error in the amplitude σ(β)/β ∼ (1.3–4.5)% and in direction, θβ ∼ 0.9°–3.9°, well beyond what can be achieved when analysing only the number count dipole. We also find that galaxy surveys are able to measure the intrinsic large-scale anisotropy with a relative uncertainty of ≲5% (measurement error, not including cosmic variance). Our method enables two simultaneous tests of the Cosmological Principle: comparing the observations of our peculiar velocity with the CMB dipole, and testing for a significant intrinsic anisotropy on large scales which would indicate effects beyond the standard cosmological model.
Artificial neural network prediction of microbiological quality of beef minced meat processed for fast-food meals

In this study, the microbiological quality of 72 minced beef meat samples collected during six months from a local butcher was defined after laboratory analysis and developing advanced mathematical models. This new simultaneous approach provided adequate precision for the prediction of the microbiological profile of minced beef meat as one of the easily spoiled products with a short shelf life. For the first time, an artificial network model was developed to predict the microbiological profile of beef minced meat in a fast-food restaurant according to meat and storage temperatures, butcher identification, and work shift. A concurrent statistical study of practical analysis and the developing mathematical models provided adequate precision for the prediction of the microbiological profile of minced beef meat. The developed ANN provided a good prediction of the microbiological profile of beef minced meat with an overall R2 of 0.867 during the training cycle.
Strategy for the study of the proteome in animal muscle tissue

The existing approaches to the methodology of using proteomic and mass-selective methods for the analysis of the component composition of meat product proteins are systematized, formalized and modified. The scientific and practical foundations of a systemic proteomic strategy for identifying the protein composition of meat raw materials and the authenticity of meat products have been developed. A scheme for constructing proteomic maps and spectra of identified proteins and peptides of meat and meat products using bioinformatic data processing has been developed, and a scheme for choosing a research methodology as a tool for identifying and confirming the composition of meat products has been formed.
Arugula and pre-converted arugula extract as natural Nitrate/Nitrite sources for heat-treated sausages

The aim of this study was to investigate using arugula or pre-converted extracts as nitrite alternatives in heat-treated fermented sausages. Sausages with nitrite, 150 mg/kg of NaNO2, 1.2% arugula extract, and 1.5% pre-converted arugula extract were formulated. Natural nitrate sources added resulted in significantly lower oxidation content compared to negative control groups at initial storage. The addition of natural nitrate sources influenced colour, pigments, and conversion rate of sausages. Pre-converted arugula extract showed little effect on the residual nitrite content. The result of colour, oxidation, and nitrite analysis suggest pre-converted arugula is a potential nitrite replacer, but arugula as a nitrate source is limited to provide the functions of nitrite.
Multipacting suppression of the HOM-damped 166.6-MHz β=1 quarter-wave superconducting cavities at HEPS

A higher-order-mode (HOM) damped 166.6-MHz β=1 quarter-wave superconducting cavity has been designed for the High Energy Photon Source project. Dangerous multipacting barriers have to be removed by geometry optimizations in superconducting cavity design while retaining favorable radiofrequency and mechanical properties as well as sufficient HOM damping. Multipacting analysis has been conducted on the cavity by using coupled simulation codes comprising electromagnetic calculations and particle tracking. A circular transition structure between the cavity and the beam pipe was proposed to remove the high-field multipacting band by eliminating the local minimum of the electric field thus preventing the field-induced electrons from being trapped into the potential well. The impacts of the geometric change on the cavities' electromagnetic and mechanical properties as well as HOM damping were examined. The deteriorated impedance of the first monopole HOM can be recovered by a slightly enlarged beam pipe, while the total loss factor has little change. The compact footprint of the cavity module was maintained as required for a limited straight section with a fixed length. Mechanical properties of the cavity were also retained with an improved tuning sensitivity and frequency stability. These constitute a first HOM-damped 166.6 MHz β=1 quarter-wave superconducting cavity with multipacting suppressed.
Spore-forming bacteria in the dairy chain

Spore-forming bacteria form the most diverse and most complex group of bacteria in terms of their elimination from the dairy chain, due to their ability to form highly resistant spores. As ubiquitous microorganisms, spore-formers can enter the product along the milk-processing continuum from different sources, and subsequently cause spoilage in various types of dairy products. The most important classes of spore-forming bacteria relevant to the dairy industry are Bacilli and Clostridia. Bacilli are responsible mainly for the spoilage and decreased shelf-life of fluid milk, while Clostridia cause late gas blowing in cheese. Spore-forming microorganisms contaminate raw milk primarily at the farm level, with potential for recontamination to occur at various points along the dairy production continuum. The most effective measure in reducing spore load at the farm level is adequate pre-milking teat preparation, while at the dairy plant level, bactofugation and microfiltration are applied. Understanding the ecology of spore-formers can improve application of systematic approaches for controlling the spoilage bacteria in dairy processing systems. Also, novel technologies, such as high-pressure processing, ultrasound treatment, irradiation etc., could provide the dairy industry with the powerful tools to eliminate these bacteria from the dairy chain.