Miracle-dz/UniFAR-FTUs huggingface dataset:
Models for the Growth of a Solid Tumor by Diffusion

A simple mathematical model of tumor growth by diffusion is constructed in order to examine and evaluate different hypotheses concerning the evolution of a solid carcinoma. A primary objective is to infer the chemical source of growth inhibition from the most easily obtained data, namely, the outer radius of the nodule as a function of time and a histological examination of the final dormant state. In section 6 some of the conclusions of this study relating to a prototype experiment and described with as little mathematics as possible.
Hyperbolic metamaterial filter for angle-independent TM transmission in the infrared regime (Conference Presentation)

In this project we propose and fabricate a hyperbolic metamaterials-based narrowband notch filter for the infrared regime with a center wavelength that remains fixed as the angle of incidence changes from 0 to 30 degrees for TM polarization. This novel device modifies a conventional Bragg reflector by including a middle resonance layer that opens up a narrow, highly transmissive band. To achieve angular independence, a subwavelength sized array of silver wires are inserted in a vertical orientation and permeate all 7 Si and SiO2 layers of the structure. In this work the theoretical underpinnings are explored using Maxwell-Garnett Theory, and simulated with 3D finite element full wave electromagnetic modeling software. Simulations demonstrate that the device is capable of up to 60% transmission at a fixed center wavelength for TM polarization in the infrared. The device is fabricated using typical microfabrication techniques. The silver nanowires are fabricated via DC electrodeposition. The angle and polarization dependent transmission, reflection and absorption of the device are experimentally measured, and scanning electron microscopy images of the structure are shown. Though the experimental validation of this device is performed for the infrared regime, scaling the structural sizes can extend the operating regime to higher and lower wavelengths. Whether used as a stand-alone filter, or integrated into a hyperspectral array, the angle-independent response of this filter has many uses in remote sensing applications.
Genetic variation in the ovine KAP22-1 gene and its effect on wool traits in Egyptian sheep

Abstract The objective of this study was to investigate the genetic polymorphisms in the keratin-associated protein (KAP22-1) gene in Barki (n=206), Rahmani (n=28) and Ossimi (n=28) as the three major sheep breeds in Egypt. Subsequently, the detected variants were correlated with important wool traits. The traits included greasy fleece weight (GFW, g), staple length (SL, cm), prickle factor (PF, %), medullated fiber (MF, %), fiber diameter (FD, µm), crimp percentage (CR, %) and the standard deviation of FD (SDfd, µm), as well as the subjectively assessed traits of kemp score (KS), handle grade (HG), greasy color grade (GCG), bulk grade (BG), luster grade (LG) and staple structure (SST). Animals were genotyped by polymerase chain reaction (PCR) – single strand conformation polymorphism (SSCP). Five SSCP banding patterns representing three different nucleotide variants (A, B and C) were detected. DNA sequencing confirmed three single nucleotide polymorphisms (SNPs). Animal age significantly affected GFW (P=0.007), SDfd (P=0.006), SL (P=0.002), CR (P=0.006), KS (P=0.001), LG (P=0.006) and SST (P=0.013). Likewise, the breed had a significant effect on all studied traits except HG and BG, which was not significant. Results showed significant associations between the KAP22-1 variants and CR (P=0.01), SL (P=0.012), KS (P<0.001) and GCG (P=0.01). Interestingly, animals with BB genotypes tended to produce more wool yield (1163.63±65.91 g) with high SL (8.38±0.20 cm), CR (8.38±0.21 %) and KS (1.98±1.88). Results of this study strongly recommend the KAP22-1 gene as a candidate gene for wool production traits in Egyptian sheep, with new useful insights into the visually assessed wool traits. The identified genetic markers may be incorporated into breeding strategies and genetic improvement programs of wool traits in Egyptian sheep.
Anti-diabetic and Lipidemic Effect of Muskmelon Fruits and Seeds on Streptozocin Induced Diabetic Rat

Background: The increasing prevalence of diabetes mellitus Worldwide and its complex nature of predisposing one to different ailments like obesity, eye defect, cardiovascular diseases etc. calls for alternative measures in the management of the disease. Muskmelon (Cucumis melo L.) is an underutilized food with a lot of nutritional and medical potential which are used traditionally in the management of different ailments like diabetes mellitus. Objective: The aim of this study was to evaluate the anti-diabetes and anti-lipidemic effects of muskmelon fruits and seeds in streptozotocin induced-diabetic rats. Methods: Diabetes was induced using intravenous Streptozotocin at a dose of 42 mg/kg of body weight into the tail veins to groups 2-5. Twenty-five male albino rats were divided into 5 groups, group 1 normal control, group 2- diabetic control, group 3- Glucophage treatment, group 4-500mg/kg BW muskmelon fruits extract, and group 5- 500mg/kg bw muskmelon seeds extract and were treated for 2 weeks. Fasting blood glucose, body weight, triglyceride, LDL, HDL and total cholesterol were evaluated. Results: The result shows that the extract caused a significant increase in the body weight, HDL-cholesterol, and a significant decrease in triglyceride, LDL-cholesterol, total cholesterol, and fasting blood glucose. Although the extract performed well there is a significant difference between the group that took Glucophage and the groups that ate muskmelon fruits and seed extract p<0.05 Conclusion: The result proved the anti-diabetic and anti-lipidemic effect of muskmelon fruits and seeds which could be added to the pool of other food used in the management of diabetes mellitus which will lead to diet diversification.
A control system for MR damper-based suspension of front-loaded washing machines featuring magnetic induction coils and phase-lead compensator

This paper studies a semi-active control system for magneto-rheological (MR) damper-based suspension of front-loaded washing machines. In this system, a permanent magnet is fastened to the shaft end of a shear-mode MR damper and an induction coil is wound directly on the slots of damper housing. Vibration of the washing machine tube leads to the relative reciprocal movement between the magnet and induction coil, which results in an induction voltage. The induced voltage is then served as an input signal to control the damping force of the MR damper. Because of the MR fluid hysteresis, a phase-lead compensator and an amplifier are employed in the controller of the MR damper. After the controller design, simulations and experiments are performed to evaluate the control system effectiveness. From the experimental results, performance characteristics of the proposed control system are discussed and compared with those of constant current and uncontrolled states.
The Tryptophan Catabolite or Kynurenine Pathway in a Major Depressive Episode with Melancholia, Psychotic Features and Suicidal Behaviors: A Systematic Review and Meta-Analysis

Background: Major depressive disorder (MDD) and bipolar disorder (BD) with melancholia and psychotic features and suicidal behaviors are accompanied by activated immune-inflammatory and oxidative pathways which may stimulate indoleamine 2,3-dioxygenase (IDO), the first and rate-limiting enzyme of the tryptophan catabolite (TRYCAT) pathway resulting in increased tryptophan degradation and elevated tryptophan catabolites (TRYCTAs). Objective: The purpose of the current study is to systematically review and meta-analyze levels of TRP, its competing amino-acids (CAAs) and TRYCATs in patients with severe affective disorders. Methods: PubMed, Google Scholar and SciFinder were searched in the present study and we recruited 35 studies to examine 4,647 participants including 2,332 unipolar (MDD) and bipolar (BD) depressed patients and 2,315 healthy controls. Results: Severe patients showed significant lower (p<0.0001) TRP (standardized mean difference, SMD=-0.517, 95% confidence interval, CI: -0.735; -0.299) and TRP/CAA (SMD= -0.617, CI: -0.957; -0.277) levels with moderate effect sizes, while no significant difference in CAAs were found. Kynurenine (KYN) levels were unaltered in severe MDD/BD phenotypes, while the KYN/TRP ratio showed a significant increase only in patients with psychotic features (SMD= 0.224, CI: 0.012; 0.436). Quinolinic acid (QA) was significantly increased (SMD= 0.358, CI: 0.015; 0.701) and kynurenic acid (KA) significantly decreased (SMD= -0.260, CI: -0.487; -0.034) in severe MDD/BD. Conclusion: Patients with affective disorders with melancholic and psychotic features and suicidal behaviors show normal IDO enzyme activity but a lowered availability of plasma/serum TRP to the brain, which is probably due to other processes such as low albumin levels
Rapid threat detection in SARS-CoV-2

This paper presents a novel virus surveillance framework, completely independent of phylogeny-based methods. The framework issues timely alerts with an accuracy exceeding 85% that are based on the co-evolutionary relations between sites of the viral multiple sequence array (MSA). This set of relations is formalized via a motif complex, whose dynamics contains key information about the emergence of viral threats without the referencing of strain prevalence. Our notion of threat is centered at the emergence of a certain type of critical cluster consisting of key co-evolving sites. We present three case studies, based on GISAID data from UK, US and New York, where we perform our surveillance. We alert on May 16, 2022, based on GISAID data from New York, to a critical cluster of co-evolving sites mapping to the Pango-designation, BA.5. The alert specifies a cluster of seven genomic sites, one of which exhibits D3N on the M (membrane) protein{the distinguishing mutation of BA.5, three encoding ORF6:D61L and the remaining three exhibiting the synonymous mutations C26858T, C27889T and A27259C. New insight is obtained: when projected onto sequences, this cluster splits into two, mutually exclusive blocks of co-evolving sites (m:D3N,nuc:C27889T) linked to the five reverse mutations (nuc:C26858T,nuc:A27259C,ORF6:D61L). We furthermore provide an in depth analysis of all major signaled threats, during which we discover a specific signature concerning linked reverse mutation in the critical cluster.
A Forward-secure Efficient Two-factor Authentication Protocol

Two-factor authentication (2FA) schemes that rely on a combination of knowledge factors (e.g., PIN) and device possession have gained popularity. Some of these schemes remain secure even against strong adversaries that (a) observe the traffic between a client and server, and (b) have physical access to the client's device, or its PIN, or breach the server. However, these solutions have several shortcomings; namely, they (i) require a client to remember multiple secret values to prove its identity, (ii) involve several modular exponentiations, and (iii) are in the non-standard random oracle model. In this work, we present a 2FA protocol that resists such a strong adversary while addressing the above shortcomings. Our protocol requires a client to remember only a single secret value/PIN, does not involve any modular exponentiations, and is in a standard model. It is the first one that offers these features without using trusted chipsets. This protocol also imposes up to 40% lower communication overhead than the state-of-the-art solutions do.
Minimizing Mindless Mentions: Recommendation with Minimal Necessary User Reviews

Recently, researchers have turned their attention to recommender systems that use only minimal necessary data. This trend is informed by the idea that recommender systems should use no more user interactions than are needed in order to provide users with useful recommendations. In this position paper, we make the case for applying the idea of minimal necessary data to recommender systems that use user reviews. We argue that the content of individual user reviews should be subject to minimization. Specifically, reviews used as training data to generate recommendations or reviews used to help users decide on purchases or consumption should be automatically edited to contain only the information that is needed.
A Stable Jacobi polynomials based least squares regression estimator associated with an ANOVA decomposition model

In this work, we construct a stable and fairly fast estimator for solving non-parametric multidimensional regression problems. The proposed estimator is based on the use of multivariate Jacobi polynomials that generate a basis for a reduced size of $d-$variate finite dimensional polynomial space. An ANOVA decomposition trick has been used for building this later polynomial space. Also, by using some results from the theory of positive definite random matrices, we show that the proposed estimator is stable under the condition that the i.i. d. random sampling points for the different covariates of the regression problem, follow a $d-$dimensional Beta distribution. Also, we provide the reader with an estimate for the $L^2-$risk error of the estimator. Moreover, a more precise estimate of the quality of the approximation is provided under the condition that the regression function belongs to some weighted Sobolev space. Finally, the various theoretical results of this work are supported by numerical simulations.
Fisher Discriminative Sparse Representation Based on DBN for Fault Diagnosis of Complex System

Fault detection and diagnosis in the chemical industry is a challenging task due to the large number of measured variables and complex interactions among them. To solve this problem, a new fault diagnosis method named Fisher discriminative sparse representation (FDSR), based on deep belief network (DBN), is proposed in this paper. We used DBN to extract the features of all faulty and normal modes. Features extracted by the DBN were used to learn subdictionaries, then the overcomplete dictionary was constructed by cascading all subdictionaries in order, and each dictionary atom corresponded to class labels. The Fisher discrimination criterion (FDC) was applied to the dictionary learning to ensure smaller within-class scatter but greater between-class scatter. The quadratic programming method was applied to estimate the sparse coefficients simultaneously class by class. Therefore, both the reconstruction error and sparse coefficients were discriminative, so that the reconstruction error after sparse coding can be used for pattern classification. An experiment performed on the Tennessee Eastman (TE) process indicated that compared with the traditional monitoring methods, the FDSR based on DBN produced more interpretable results and achieved superior performance for feature extraction and classification in the field of complex system fault diagnosis. T-distributed stochastic neighbor embedding (t-SNE) appropriately visualized the performance of the proposed method and produced reliable fault diagnosis results.
ResVGAE: Going Deeper with Residual Modules for Link Prediction

Graph autoencoders are efficient at embedding graph-based data sets. Most graph autoencoder architectures have shallow depths which limits their ability to capture meaningful relations between nodes separated by multi-hops. In this paper, we propose Residual Variational Graph Autoencoder, ResVGAE, a deep variational graph autoencoder model with multiple residual modules. We show that our multiple residual modules, a convolutional layer with residual connection, improve the average precision of the graph autoencoders. Experimental results suggest that our proposed model with residual modules outperforms the models without residual modules and achieves similar results when compared with other state-of-the-art methods.
Social network structure and the spread of complex contagions from a population genetics perspective.

Ideas, behaviors, and opinions spread through social networks. If the probability of spreading to a new individual is a nonlinear function of the fraction of the individuals' affected neighbors, such a spreading process becomes a "complex contagion. " This nonlinearity does not typically appear with physically spreading infections, but instead can emerge when the concept that is spreading is subject to game theoretical considerations (e.g., for choices of strategy or behavior) or psychological effects such as social reinforcement and other forms of peer influence (e.g., for ideas, preferences, or opinions). Here we study how the stochastic dynamics of such complex contagions are affected by the underlying network structure. Motivated by simulations of complex contagions on real social networks, we present a framework for analyzing the statistics of contagions with arbitrary nonlinear adoption probabilities based on the mathematical tools of population genetics. The central idea is to use an effective lower-dimensional diffusion process to approximate the statistics of the contagion. This leads to a tradeoff between the effects of "selection" (microscopic tendencies for an idea to spread or die out), random drift, and network structure. Our framework illustrates intuitively several key properties of complex contagions: stronger community structure and network sparsity can significantly enhance the spread, while broad degree distributions dampen the effect of selection compared to random drift. Finally, we show that some structural features can exhibit critical values that demarcate regimes where global contagions become possible for networks of arbitrary size. Our results draw parallels between the competition of genes in a population and memes in a world of minds and ideas. Our tools provide insight into the spread of information, behaviors, and ideas via social influence, and highlight the role of macroscopic network structure in determining their fate.
ACE: Adaptive Constraint-aware Early Stopping in Hyperparameter Optimization

Deploying machine learning models requires high model quality and needs to comply with application constraints. That motivates hyperparameter optimization (HPO) to tune model conﬁgurations under deployment constraints. The constraints often require additional computation cost to evaluate, and training ineligible conﬁgurations can waste a large amount to tuning cost. In this work, we propose an Adaptive Constraint-aware Early stopping (ACE) method to incorporate constraint evaluation into trial pruning during HPO. To minimize the overall optimization cost, ACE estimates the cost-effective constraint evaluation interval based on a theoretical analysis of the expected evaluation cost. Meanwhile, we propose a stratum early stopping criterion in ACE, which considers both optimization and constraint metrics in pruning and does not require regularization hyperparameters. Our experiments demonstrate superior performance of ACE in hyperparameter tuning of classiﬁcation tasks under fairness or under robustness constraints.
Learning New Skills after Deployment: Improving open-domain internet-driven dialogue with human feedback

Frozen models trained to mimic static datasets can never improve their performance. Models that can employ internet-retrieval for up-to-date information and obtain feedback from humans during deployment provide the promise of both adapting to new information, and improving their performance. In this work we study how to improve internet-driven conversational skills in such a learning framework. We collect deployment data, which we make publicly available, of human interactions, and collect various types of human feedback – including binary quality measurements, free-form text feedback, and fine-grained reasons for failure. We then study various algorithms for improving from such feedback, including standard supervised learning, rejection sampling, model-guiding and reward-based learning, in order to make recommendations on which type of feed- back and algorithms work best. We find the recently introduced DIRECTOR model (Arora et al., 2022) shows significant improvements over other existing approaches.
Towards Antisymmetric Neural Ansatz Separation

We study separations between two fundamental models (or \emph{Ans\"atze}) of antisymmetric functions, that is, functions $f$ of the form $f(x_{\sigma(1)}, \ldots, x_{\sigma(N)}) = \text{sign}(\sigma)f(x_1, \ldots, x_N)$, where $\sigma$ is any permutation. These arise in the context of quantum chemistry, and are the basic modeling tool for wavefunctions of Fermionic systems. Specifically, we consider two popular antisymmetric Ans\"atze: the Slater representation, which leverages the alternating structure of determinants, and the Jastrow ansatz, which augments Slater determinants with a product by an arbitrary symmetric function. We construct an antisymmetric function in $N$ dimensions that can be efficiently expressed in Jastrow form, yet provably cannot be approximated by Slater determinants unless there are exponentially (in $N^2$) many terms. This represents the first explicit quantitative separation between these two Ans\"atze.
ULX pulsar Swift J0243.6+6124 observations with NuSTAR– dominance of reflected emission in the super-Eddington state

We report the discovery of the bright reflected emission component in the super-Eddington state of the ULX pulsar Swift J0243.6+6124, based on the NuSTAR observations of the source during its 2017 outburst. The flux of the reflected emission is weakly variable over the pulsar phase while the direct emission shows significantly larger pulsation amplitude. We propose that in this system the neutron star finds itself in the centre of the well formed by the inner edge of the geometrically thick super-Eddington accretion disc truncated by the magnetic field of the pulsar. The aspect ratio of the well is H/R ∼ 1. The inner edge of the truncated disc is continuously illuminated by the emission of the accretion column giving rise to the weakly variable reflected emission. As the neutron star rotates, its emission sweeps through the line of sight, giving rise to the pulsating direct emission. From Doppler broadening of the iron line, we measure the truncation radius of the accretion disc ∼ 50 Rg. The inferred dipole component of the magnetic field is consistent with previous estimates favouring a not very strong field. The uniqueness of this system is determined by its moderately super-Eddington accretion rate and the moderate magnetic field so that the inner edge of the truncated geometrically thick accretion disc is seen from the neutron star at a large solid angle.
Interpretable Uncertainty Quantification in AI for HEP

Estimating uncertainty is at the core of performing scientific measurements in HEP: a measurement is not useful without an estimate of its uncertainty. The goal of uncertainty quantification (UQ) is inextricably linked to the question,"how do we physically and statistically interpret these uncertainties? "The answer to this question depends not only on the computational task we aim to undertake, but also on the methods we use for that task. For artificial intelligence (AI) applications in HEP, there are several areas where interpretable methods for UQ are essential, including inference, simulation, and control/decision-making. There exist some methods for each of these areas, but they have not yet been demonstrated to be as trustworthy as more traditional approaches currently employed in physics (e.g., non-AI frequentist and Bayesian methods). Shedding light on the questions above requires additional understanding of the interplay of AI systems and uncertainty quantification. We briefly discuss the existing methods in each area and relate them to tasks across HEP. We then discuss recommendations for avenues to pursue to develop the necessary techniques for reliable widespread usage of AI with UQ over the next decade.