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The growing integration of Artificial Intelligence (AI) into Human Resources (HR) processes has transformed the way organizations manage recruitment, performance evaluation, and employee engagement. While AI offers numerous advantages, such as improved efficiency, reduced bias, and hyper-personalization, it raises significant concerns about employee well-being, job security, fairness, and transparency. This study examines how AI shapes employee perceptions, job satisfaction, mental health, and retention. Key findings reveal that while AI can enhance efficiency and reduce bias, it also raises concerns about job security, fairness, and privacy. Transparency in AI systems emerges as a critical factor in fostering trust and positive employee attitudes. AI systems can both support and undermine employee well-being, depending on how they are implemented and perceived. The research introduces an AI-employee well-being Interaction Framework, illustrating how AI influences employee perceptions, behaviors, and outcomes. Organizational strategies, such as clear communication, upskilling programs, and employee involvement in AI implementation, are identified as crucial for mitigating negative impacts and enhancing positive outcomes. The study concludes that the successful integration of AI in HR requires a balanced approach that prioritizes employee well-being, facilitates human-AI collaboration, and ensures ethical and transparent AI practices alongside technological advancement.

In the dynamic landscape of project management, scope changes are an inevitable reality that can significantly impact project performance. These changes, whether initiated by stakeholders, external factors, or internal project dynamics, can lead to cost overruns and schedule delays. Accurately predicting the consequences of these changes is crucial for effective project control and informed decision-making. This study aims to develop predictive models to estimate the impact of scope changes on project cost and schedule using machine learning techniques. The research utilizes a comprehensive dataset containing detailed information on project tasks, including the Work Breakdown Structure (WBS), task type, productivity rate, estimated cost, actual cost, duration, task dependencies, scope change magnitude, and scope change timing. Multiple machine learning models are developed and evaluated to predict the impact of scope changes on project cost and schedule. These models include Linear Regression, Decision Tree, Ridge Regression, Random Forest, Gradient Boosting, and XGBoost. The dataset is split into training and testing sets, and the models are trained using the preprocessed data. Model robustness and generalization are assessed using cross-validation techniques. To evaluate the performance of models, we use Mean Squared Error (MSE) and R2. Residual plots are generated to assess the goodness of fit and identify any patterns or outliers. Hyperparameter tuning is performed to optimize the XGBoost model and improve its predictive accuracy. The study identifies the most influential project attributes in determining the magnitude of cost and schedule deviations caused by scope modifications. It is identified that productivity rate, scope change magnitude, task dependencies, estimated cost, actual cost, duration, and specific WBS elements are powerful predictors.

In this paper, we examine Einstein-Hilbert gravity featuring a cosmological constant and a non-abelian nonlinear electromagnetic field that is minimally coupled to gravity. We first present the black brane solution for this model and subsequently calculate the color non-abelian DC conductivity for this solution using AdS/CFT duality. Our results retrieve the Yang-Mills model in the limit as $q_1$ approaches zero., Comment: 12 Pages, no figure, accepted for publication in CJP

We investigate the interaction of spontaneous emission photons generated by a strongly driven laser-cooled atom sample with that same sample after a time delay, which is important for establishing long-distance entanglement between quantum systems. The photons are emitted into an optical nanofiber, connected to a length of conventional optical fiber and reflected back using a Fiber-Bragg Grating mirror. We show that the photon count rates as a function of exciting laser frequency and intensity follow a simple model.

The integration of non-commutative geometry and Gauss-Bonnet corrections in an action and the study of their black hole responses can provide highly intriguing insights. Our primary motivation for this study is to understand the interplay of these two parameters on the geodesics of spacetime, including photon spheres and time-like orbits. In this study, we found that this integration, in its initial form, can limit the value of the Gauss-Bonnet parameter ($\alpha$), creating a critical threshold beyond which changes in the non-commutative parameter ($\Xi$) become ineffective, and the structure can only manifest as a naked singularity. Furthermore, we found that using a more complex model, which includes additional factors such as a cloud of strings and linear charge, as a sample for studying spacetime geodesics, yield different and varied results. In this scenario, negative $\alpha$ values can also play a role, notably preserving the black hole form even with a super-extremal charge ($q > m$). For $\alpha> 0.1$, the black hole mass parameter becomes significantly influential, with a critical mass below which the impact of other parameter changes is nullified. Interestingly, considering a more massive black hole, this high-mass state also maintains its black hole form within the super-extremal charge range. The existence of these two models led us to our main goal. By examining the temperature for these two cases, we find that both situations are suitable for studying the WGC. Finally, based on the behavior of these two models, we will explain how the WGC acts as a logical solution and a protector for the WCCC., Comment: 22 pages, 14 figures

Accurate forecasting of project performance metrics is crucial for successfully managing and delivering urban road reconstruction projects. Traditional methods often rely on static baseline plans and fail to consider the dynamic nature of project progress and external factors. This research proposes a machine learning-based approach to forecast project performance metrics, such as cost variance and earned value, for each Work Breakdown Structure (WBS) category in an urban road reconstruction project. The proposed model utilizes time series forecasting techniques, including Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) networks, to predict future performance based on historical data and project progress. The model also incorporates external factors, such as weather patterns and resource availability, as features to enhance the accuracy of forecasts. By applying the predictive power of machine learning, the performance forecasting model enables proactive identification of potential deviations from the baseline plan, which allows project managers to take timely corrective actions. The research aims to validate the effectiveness of the proposed approach using a case study of an urban road reconstruction project, comparing the model's forecasts with actual project performance data. The findings of this research contribute to the advancement of project management practices in the construction industry, offering a data-driven solution for improving project performance monitoring and control.

This study evaluates metrics for tasks such as classification, regression, clustering, correlation analysis, statistical tests, segmentation, and image-to-image (I2I) translation. Metrics were compared across Python libraries, R packages, and Matlab functions to assess their consistency and highlight discrepancies. The findings underscore the need for a unified roadmap to standardize metrics, ensuring reliable and reproducible ML evaluations across platforms. This study examined a wide range of evaluation metrics across various tasks and found only some to be consistent across platforms, such as (i) Accuracy, Balanced Accuracy, Cohens Kappa, F-beta Score, MCC, Geometric Mean, AUC, and Log Loss in binary classification; (ii) Accuracy, Cohens Kappa, and F-beta Score in multi-class classification; (iii) MAE, MSE, RMSE, MAPE, Explained Variance, Median AE, MSLE, and Huber in regression; (iv) Davies-Bouldin Index and Calinski-Harabasz Index in clustering; (v) Pearson, Spearman, Kendall's Tau, Mutual Information, Distance Correlation, Percbend, Shepherd, and Partial Correlation in correlation analysis; (vi) Paired t-test, Chi-Square Test, ANOVA, Kruskal-Wallis Test, Shapiro-Wilk Test, Welchs t-test, and Bartlett's test in statistical tests; (vii) Accuracy, Precision, and Recall in 2D segmentation; (viii) Accuracy in 3D segmentation; (ix) MAE, MSE, RMSE, and R-Squared in 2D-I2I translation; and (x) MAE, MSE, and RMSE in 3D-I2I translation. Given observation of discrepancies in a number of metrics (e.g. precision, recall and F1 score in binary classification, WCSS in clustering, multiple statistical tests, and IoU in segmentation, amongst multiple metrics), this study concludes that ML evaluation metrics require standardization and recommends that future research use consistent metrics for different tasks to effectively compare ML techniques and solutions., Comment: This paper is 12 pages with 1 table and 10 figures

We study the collective behavior of clusters of cylinders placed in the wake of a fixed cylinder and free to move in a direction perpendicular to that of the incoming flow, with no structural damping or stiffness. We keep the Reynolds number, defined based on the cylinder diameter, at 100 and consider five different configurations for the initial positions of the cluster cylinders: linear, rectangular, V-shaped, triangular, and circular. In each configuration, we consider progressively increasing number of cylinders in the cluster. We show that overall, the cylinders tend to form final linear configurations, in which, after their transition, the cylinders form one or more lines. Some free-to-move cylinders might take the lead position in some of these linear formations depending on the initial configuration. These steady-state positions are achieved when the mean value of lift that acts on the cylinders becomes negligible. As a byproduct of these reconfigurations, the overall drag force that acts on the collection of cylinders reduces at their final steady-state locations in comparison with their original configurations. The complicated wakes that are observed in the fixed counterparts of these configurations are replaced by a series of vortex rows in the wake of separate lines of cylinders. Reducing the mass ratio allows the cylinders to oscillate about their mean displacement paths, but their transient paths and their final steady-state positions are not affected significantly by the decrease in the mass ratio.

Non-commutative black holes(NCBH), due to the non-commutativity of spacetime coordinates, lead to a modification of the spacetime metric. By replacing the Dirac delta function with a Gaussian distribution, the mass is effectively smeared, eliminating point-like singularities. Our objective is to investigate the impact of this change on spacetime geodesics, including photon spheres and time-like orbits. We will demonstrate how the photon sphere can serve as a tool to classify spacetime, illustrating the influence of the NC parameter and constraining its values in various modes of these black holes. Additionally, using this classification, we will show how the addition of the nonlinear Einstein-Born-Infeld(BI) field to the model enhances its physical alignment with reality compared to the charged model. In the dS BI model, we will show how the study of the effective potential and photon sphere can provide insights into the initial structural status of the model, thereby establishing this potential as an effective tool for examining the initial conditions of black holes. Finally, by examining super-extremality conditions, we will show that the AdS BI model, with the necessary conditions, can be a suitable candidate for studying and observing the effects of the Weak Gravity Conjecture (WGC)., Comment: 24 pages, 15 figures

Topology Bench is a comprehensive topology dataset designed to accelerate benchmarking studies in optical networks. The dataset, focusing on core optical networks, comprises publicly accessible and ready-to-use topologies, including (a) 105 georeferenced real-world optical networks and (b) 270,900 validated synthetic topologies. Prior research on real-world core optical networks has been characterised by fragmented open data sources and disparate individual studies. Moreover, previous efforts have notably failed to provide synthetic data at a scale comparable to our present study. Topology Bench addresses this limitation, offering a unified resource and represents a 61.5% increase in spatially-referenced real world optical networks. To benchmark and identify the fundamental nature of optical network topologies through the lens of graph-theoretical analysis, we analyse both real and synthetic networks using structural, spatial and spectral metrics. Our comparative analysis identifies constraints in real optical network diversity and illustrates how synthetic networks can complement and expand the range of topologies available for use. Currently, topologies are selected based on subjective criteria, such as preference, data availability, or perceived suitability, leading to potential biases and limited representativeness. Our framework enhances the generalisability of optical network research by providing a more objective and systematic approach to topology selection. A statistical and correlation analysis reveals the quantitative range of all of these graph metrics and the relationships between them. Finally, we apply unsupervised machine learning to cluster real-world topologies into distinctive groups using nine optimal graph metrics using K-means. We conclude the analysis by providing guidance on how to use such clusters to select a diverse set of topologies for future studies.

Distant-microphone meeting transcription is a challenging task. State-of-the-art end-to-end speaker-attributed automatic speech recognition (SA-ASR) architectures lack a multichannel noise and reverberation reduction front-end, which limits their performance. In this paper, we introduce a joint beamforming and SA-ASR approach for real meeting transcription. We first describe a data alignment and augmentation method to pretrain a neural beamformer on real meeting data. We then compare fixed, hybrid, and fully neural beamformers as front-ends to the SA-ASR model. Finally, we jointly optimize the fully neural beamformer and the SA-ASR model. Experiments on the real AMI corpus show that,while state-of-the-art multi-frame cross-channel attention based channel fusion fails to improve ASR performance, fine-tuning SA-ASR on the fixed beamformer's output and jointly fine-tuning SA-ASR with the neural beamformer reduce the word error rate by 8% and 9% relative, respectively.

Hardware-level memory vulnerabilities severely threaten computing systems. However, hardware patching is inefficient or difficult postfabrication. We investigate the effectiveness of hardware fuzzing in detecting hardware memory vulnerabilities and highlight challenges and potential future research directions to enhance hardware fuzzing for memory safety.

Microarchitectural attacks represent a challenging and persistent threat to modern processors, exploiting inherent design vulnerabilities in processors to leak sensitive information or compromise systems. Of particular concern is the susceptibility of Speculative Execution, a fundamental part of performance enhancement, to such attacks. We introduce Specure, a novel pre-silicon verification method composing hardware fuzzing with Information Flow Tracking (IFT) to address speculative execution leakages. Integrating IFT enables two significant and non-trivial enhancements over the existing fuzzing approaches: i) automatic detection of microarchitectural information leakages vulnerabilities without golden model and ii) a novel Leakage Path coverage metric for efficient vulnerability detection. Specure identifies previously overlooked speculative execution vulnerabilities on the RISC-V BOOM processor and explores the vulnerability search space 6.45x faster than existing fuzzing techniques. Moreover, Specure detected known vulnerabilities 20x faster.

In an era where global connectivity has become critical, satellite communication is essential for businesses, governments, and individuals. Widely used services with satellite communication such as climate change monitoring, military surveillance and real-time event broadcasting, involve data in the form of images rather text. Therefore, securing image transmission in satellite communication using efficient and effective encryption approaches, has gained a significant attention from academia as well as the industry. In this paper, we specifically focus on chaos based image encryption as one of the key privacy-preserving techniques for satellite communication. While there are several privacy enhancing techniques for protecting image data but chaos based encryption has distinct advantages such as high flexibility, high security, less computational overheads, less computing power and ease of implementation. First, we present a solid background about satellite communication and image encryption in satellite communication, covering theoretical aspects of chaotic systems and their practical usage for image encryption. Next we present a comprehensive literature review on all state-of-the-art studies specifically for chaos based satellite image encryption, with a detailed analysis of the evaluation process, including evaluation parameters and conditions. Finally, we discuss about existing challenges and open research problems for chaos based satellite image encryption.

Efficiently managing papillary thyroid microcarcinoma (PTMC) while minimizing patient discomfort poses a significant clinical challenge. Radiofrequency ablation (RFA) offers a less invasive alternative to surgery and radiation therapy for PTMC treatment, characterized by shorter recovery times and reduced pain. As an image-guided procedure, RFA generates localized heat by delivering high-frequency electrical currents through electrodes to the targeted area under ultrasound imaging guidance. However, the precision and skill required by operators for accurate guidance using current ultrasound B-mode imaging technologies remain significant challenges. To address these challenges, we develop a novel AI segmentation model, E2E-Swin-Unet++. This model enhances ultrasound B-mode imaging by enabling real-time identification and segmentation of PTMC tumors and monitoring of the region of interest for precise targeting during treatment. E2E-Swin- Unet++ is an advanced end-to-end extension of the Swin-Unet architecture, incorporating thyroid region information to minimize the risk of false PTMC segmentation while providing fast inference capabilities. Experimental results on a real clinical RFA dataset demonstrate the superior performance of E2E-Swin-Unet++ compared to related models. Our proposed solution significantly improves the precision and control of RFA ablation treatment by enabling real-time identification and segmentation of PTMC margins during the procedure.

In this study, we investigate the Weak Gravity Conjecture (WGC) in the context of quantum-corrected AdS-Reissner-Nordstrom (AdS-RN) black holes within Kiselev spacetime. Our focus is on photon spheres, which serve as markers for stable and unstable photon spheres. We confirm the validity of the WGC by demonstrating that quantum corrections do not alter the essential charge-to-mass ratio, thereby supporting the conjecture's universality. Our analysis reveals that black holes with a charge greater than their mass ($Q > M$) possess photon spheres or exhibit a total topological charge of the photon sphere (PS = -1), which upholds the WGC. This finding is significant as it reinforces the conjecture's applicability even in the presence of quantum corrections. Furthermore, we examine various parameter configurations to understand their impact on the WGC. Specifically, we find that configurations with $\omega = -\frac{1}{3}$ and $\omega = -1$ maintain the conjecture, indicating that these values do not disrupt the charge-to-mass ratio required by the WGC. However, for $\omega = -\frac{4}{3}$, the conjecture does not hold, suggesting that this particular parameter value leads to deviations from the expected behavior. These results open new directions for research in quantum gravity, as they highlight the importance of specific parameter values in maintaining the WGC. The findings suggest that while the WGC is robust under certain conditions, there are scenarios where it may be challenged, prompting further investigation into the underlying principles of quantum gravity, Comment: 13 pages, 6 figures, 1 Table

This paper presents Auriga Robotics' autonomous vehicle, developed at Shahid Beheshti University's Robotics and Intelligent Automation Lab, as part of the team's entry for the 2024 RoboCup IranOpen competition. The vehicle is a 1:10 scale car equipped with a custom-designed chassis, a stepper motor for precision, and a range of sensors for autonomous navigation. Key hardware includes ESP32 microcontrollers that manage motor control and sensor data acquisition. The software system integrates computer vision, including YOLOv8 for sign detection and PiNet for lane detection, combined with control algorithms such as the Stanley, PID, and Pure Pursuit controllers. The vehicle's design emphasizes real-time decision-making, environmental mapping, and efficient localization, ensuring its ability to navigate complex driving scenarios.

Let $R$ be a commutative ring and $S \subseteq R$ be a multiplicative subset. We introduce and study the concept of $S$-purity based on the notion of $S$-strongly flat modules. The class of $S$-pure injective modules will be studied. We demonstrate that this class is enveloping and explore its closedness under extension. The concept of purity is closely connected to the existence of phantom maps. So we will delve into the study of the $S$-phantom morphisms. We will establish that the $S$-phantom ideal is a precovering ideal and examine the situations where it becomes a covering ideal. Finally, in the last section, we will investigate an ideal version of the `Optimistic Conjecture', raised by Positselski and Sl\'{a}vik.

This paper proposes a new unsupervised audiovisual speech enhancement (AVSE) approach that combines a diffusion-based audio-visual speech generative model with a non-negative matrix factorization (NMF) noise model. First, the diffusion model is pre-trained on clean speech conditioned on corresponding video data to simulate the speech generative distribution. This pre-trained model is then paired with the NMF-based noise model to iteratively estimate clean speech. Specifically, a diffusion-based posterior sampling approach is implemented within the reverse diffusion process, where after each iteration, a speech estimate is obtained and used to update the noise parameters. Experimental results confirm that the proposed AVSE approach not only outperforms its audio-only counterpart but also generalizes better than a recent supervisedgenerative AVSE method. Additionally, the new inference algorithm offers a better balance between inference speed and performance compared to the previous diffusion-based method.

In this paper, we investigate the topological charge and the conditions for the existence of the photon sphere (PS) in Kiselev-AdS black holes within \(f(R, T)\) gravity. We employ two different methods based on Duan's topological current \(\phi\)-mapping theory viz analize of temperature and the generalized Helmholtz free energy methods to study the topological classes of our black hole. By considering the mentioned black hole, we discuss the critical and zero points (topological charges and topological numbers) for different parameters. Our findings reveal that the Kiselev parameter \(\omega\) and the \(f(R, T)\) gravity parameter \(\gamma\) influence the number of topological charges of black holes, leading to novel insights into topological classifications. We observe that for given values of the free parameters, there exist total topological charges (\(Q_{total} = -1\)) for T-method and total topological numbers (\(W = +1\)) for the generalized Helmholtz free energy method. Our research findings elucidate that, in contrast to the scenario where \(\omega = 1/3\), in other cases, increasing the parameter \(\gamma\) increases the number of total topological charges for the black hole. Interestingly, for the phantom field (\(\omega = -4/3\)), we observed that decreasing the parameter \(\gamma\) increases the number of topological charges. Additionally, we study the results for the photon sphere. The studied models clearly reveal that the simultaneous presence of \(\gamma\) and \(\omega\) effectively expands the permissible range for \(\gamma\). In other words, the model can exhibit black hole behavior over a larger domain. Additionally, it is evident that with the stepwise reduction of \(\omega\), the region covered by singularity also diminishes and becomes more restricted. However, An interesting point about all three ranges is the elimination of the forbidden region in this model., Comment: 25 pages, 19 figures, 4 table

We present a method for a precise determination of magnetic anisotropy and anisotropy of quadratic magneto-optical response of thin films of ferromagnetic and ferrimagnetic materials. The method is based on measurements of a magneto-optical response for light close to the normal incidence on the sample with a fixed position. The measurement is performed for a set of orientations of an external magnetic field and a series of incident light linear polarizations beyond the standard s and p orientations. Based on the symmetry of the signal, we are able to separate the part of magneto-optical response that is even with respect to magnetization and, in turn, to exclude all non-magnetic contributions which come from imperfections of the experimental setup or from the sample itself. It is, therefore, possible to study the sample placed inside a cryostat: the polarization changes due to cryostat windows and possible strain-induced optical anisotropy of the sample are removed by the applied data processing. Thanks to this, we can perform measurements on low or elevated temperatures (from 15 to 800 K in our case), making it possible to study the behavior of magnetic materials in different magnetic phases and/or close to phase transitions. The applicability of this experimental technique was tested by measuring the low-temperature response of two samples of ferromagnetic semiconductor (Ga,Mn)As with a different Mn content at several wavelengths, which enabled us to deduce the magnetic and quadratic magneto-optical anisotropies in this material. In particular, we observed that the anisotropy of quadratic magneto-optical coefficients in (Ga,Mn)As is much weaker than that reported previously for other magnetic material systems., Comment: 41 pages, 16 figures

In this paper, we propose a framework to enhance the robustness of the neural models by mitigating the effects of process-induced and aging-related variations of analog computing components on the accuracy of the analog neural networks. We model these variations as the noise affecting the precision of the activations and introduce a denoising block inserted between selected layers of a pre-trained model. We demonstrate that training the denoising block significantly increases the model's robustness against various noise levels. To minimize the overhead associated with adding these blocks, we present an exploration algorithm to identify optimal insertion points for the denoising blocks. Additionally, we propose a specialized architecture to efficiently execute the denoising blocks, which can be integrated into mixed-signal accelerators. We evaluate the effectiveness of our approach using Deep Neural Network (DNN) models trained on the ImageNet and CIFAR-10 datasets. The results show that on average, by accepting 2.03% parameter count overhead, the accuracy drop due to the variations reduces from 31.7% to 1.15%.

In this paper, we explore a modified black brane within AdS spacetime, characterized by the Lagrangian density $\frac{1}{q} \text{arcsin}(qR)-2\Lambda$. Due to the absence of an analytic solution, we approach the Einstein equations using a perturbative method, extending our analysis to the second order in $q$. Subsequently, we compute the ratio of shear viscosity to entropy density. Our results suggest that the KSS Bound is not saturated in this model., Comment: 10 Pages, no figure, accepted for publication in Int. J. Mod. Phys. A (IJMPA)

We study the problem of system identification for stochastic continuous-time dynamics, based on a single finite-length state trajectory. We present a method for estimating the possibly unstable open-loop matrix by employing properly randomized control inputs. Then, we establish theoretical performance guarantees showing that the estimation error decays with trajectory length, a measure of excitability, and the signal-to-noise ratio, while it grows with dimension. Numerical illustrations that showcase the rates of learning the dynamics, will be provided as well. To perform the theoretical analysis, we develop new technical tools that are of independent interest. That includes non-asymptotic stochastic bounds for highly non-stationary martingales and generalized laws of iterated logarithms, among others.

This paper describes Auriga's @Work team and their robot, developed at Shahid Beheshti University Faculty of Electrical Engineering's Robotics and Intelligent Automation Lab for RoboCup 2024 competitions. The robot is designed for industrial tasks, optimizing efficiency in repetitive or hazardous environments. It features a 4-wheel Mecanum system for omnidirectional movement and a 5-degree-of-freedom manipulator arm with a 3D-printed gripper for object handling and navigation. The electronics include custom boards with ESP32 microcontrollers and an Nvidia Jetson Nano for real-time control. Key software components include Hector SLAM for mapping, A* path planning, and YOLO for object detection, supported by integrated sensors for enhanced navigation and collision avoidance.

The topology of black hole thermodynamics is a fascinating area of study that explores the connections between thermodynamic properties and topological features of black holes. This paper has led to several significant findings: We successfully derive the field equations for $F(R)$-Euler-Heisenberg theory, providing a framework for studying the interplay between modified gravity and non-linear electromagnetic effects. We obtain an analytical solution for a static, spherically symmetric, energy-dependent black hole with constant scalar curvature. Also, our analysis of black holes in F(R)-Euler-Heisenberg gravity's Rainbow reveals significant insights into their topological properties. We identified the total topological charges by examining the normalized field lines along various free parameters. Our findings indicate that the parameters $( R_0 )$ and $( f_{\epsilon} = g_{\epsilon} )$ influence the topological charges. These results are comprehensively summarized in Table I. Additionally, a general overview of Tables II, III, and IV related to the photon sphere of the mentioned black hole reveals that with an increase in $f_{\varepsilon}$, the permissible range of negative $\lambda$ in the first case gradually transitions into a non-permissible region in the third case. On the other hand, it is known that the QED parameter, which measures the strength of nonlinear effects, can be either positive or negative. A positive QED parameter reduces the electric field near the horizon and increases the black hole's mass, whereas a negative QED parameter increases the electric field and decreases the mass. According to the two statements above, it can be concluded that the increase in $f_{\varepsilon}$ actually decreases the strength of the electric field near the horizon and strengthens the effects of gravity., Comment: 21 pages, 11 figures, 4 Tables

In this paper, we extend the framework of quantitative information flow (QIF) to include adversaries that use Kolmogorov-Nagumo $f$-mean to infer secrets of a private system. Specifically, in our setting, an adversary uses Kolmogorov-Nagumo $f$-mean to compute its best actions before and after observing the system's randomized outputs. This leads to generalized notions of prior and posterior vulnerability and generalized axiomatic relations that we will derive to elucidate how these $f$-mean based vulnerabilities interact with each other. We demonstrate usefulness of this framework by showing how some notions of leakage that had been derived outside of the QIF framework and so far seemed incompatible with it are indeed explainable via such extension of QIF. These leakage measures include $\alpha$-leakage, which is the same as Arimoto mutual information of order $\alpha$, maximal $\alpha$-leakage which is the $\alpha$-leakage capacity, and $(\alpha,\beta)$ leakage, which is a generalization of the above and captures local differential privacy as a special case. We also propose a new pointwise notion of gain function, which we coin pointwise information gain. We show that this pointwise information gain can explain R\'eyni divergence and Sibson mutual information of order $\alpha \in [0,\infty]$ as the Kolmogorov-Nagumo average of the gain with a proper choice of function $f$.

Deep Neural Networks (DNNs) can handle increasingly complex tasks, albeit they require rapidly expanding training datasets. Collecting data from platforms with user-generated content, such as social networks, has significantly eased the acquisition of large datasets for training DNNs. Despite these advancements, the manual labeling process remains a substantial challenge in terms of both time and cost. In response, Semi-Supervised Learning (SSL) approaches have emerged, where only a small fraction of the dataset needs to be labeled, leaving the majority unlabeled. However, leveraging data from untrusted sources like social networks also creates new security risks, as potential attackers can easily inject manipulated samples. Previous research on the security of SSL primarily focused on injecting backdoors into trained models, while less attention was given to the more challenging untargeted poisoning attacks. In this paper, we introduce Phantom, the first untargeted poisoning attack in SSL that disrupts the training process by injecting a small number of manipulated images into the unlabeled dataset. Unlike existing attacks, our approach only requires adding few manipulated samples, such as posting images on social networks, without the need to control the victim. Phantom causes SSL algorithms to overlook the actual images' pixels and to rely only on maliciously crafted patterns that \ourname superimposed on the real images. We show Phantom's effectiveness for 6 different datasets and 3 real-world social-media platforms (Facebook, Instagram, Pinterest). Already small fractions of manipulated samples (e.g., 5\%) reduce the accuracy of the resulting model by 10\%, with higher percentages leading to a performance comparable to a naive classifier. Our findings demonstrate the threat of poisoning user-generated content platforms, rendering them unsuitable for SSL in specific tasks., Comment: To Appear at ACM CCS 2024

In this paper, we delve into the intricate thermodynamic topology of quantum-corrected Anti-de Sitter-Reissner-Nordstrm (AdS-RN) black hole within the framework of Kiselev spacetime. By employing the generalized off-shell Helmholtz free energy approach, we meticulously compute the thermodynamic topology of these selected black holes. Furthermore, we establish their topological classifications. Our findings reveal that quantum correction terms influence the topological charges of black holes in Kiselev spacetime, leading to novel insights into topological classifications. Our research findings elucidate that, in contrast to the scenario in which $\omega=0$ and $a=0.7$ with total topological charge $W=0$ and $\omega=-4/3$ with total topological charge $W=-1$, in other cases, the total topological charge for the black hole under consideration predominantly stabilizes at +1. This stabilization occurs with the significant influence of the parameters a, c, and $\omega$ on the number of topological charges. Specifically, when $\omega$ assumes the values of $\omega=-1/3$, $\omega=-2/3$ , $\omega=-1$, the total topological charge consistently be to W = +1., Comment: 17 pages, 5 figures. Accepted for publication in Chinese Physics C

This paper investigates the privacy funnel, a privacy-utility tradeoff problem in which mutual information quantifies both privacy and utility. The objective is to maximize utility while adhering to a specified privacy budget. However, the privacy funnel represents a non-convex optimization problem, making it challenging to achieve an optimal solution. An existing proposed approach to this problem involves substituting the mutual information with the lift (the exponent of information density) and then solving the optimization. Since mutual information is the expectation of the information density, this substitution overestimates the privacy loss and results in a final smaller bound on the privacy of mutual information than what is allowed in the budget. This significantly compromises the utility. To overcome this limitation, we propose using a privacy measure that is more relaxed than the lift but stricter than mutual information while still allowing the optimization to be efficiently solved. Instead of directly using information density, our proposed measure is the average of information density over the sensitive data distribution for each observed data realization. We then introduce a heuristic algorithm capable of achieving solutions that produce extreme privacy values, which enhances utility. The numerical results confirm improved utility at the same privacy budget compared to existing solutions in the literature. Additionally, we explore two other privacy measures, $\ell_{1}$-norm and strong $\chi^2$-divergence, demonstrating the applicability of our algorithm to these lift-based measures. We evaluate the performance of our method by comparing its output with previous works. Finally, we validate our heuristic approach with a theoretical framework that estimates the optimal utility for strong $\chi^2$-divergence, numerically showing a perfect match.

By representing any constraint-based causal learning algorithm via a placeholder property, we decompose the correctness condition into a part relating the distribution and the true causal graph, and a part that depends solely on the distribution. This provides a general framework to obtain correctness conditions for causal learning, and has the following implications. We provide exact correctness conditions for the PC algorithm, which are then related to correctness conditions of some other existing causal discovery algorithms. We show that the sparsest Markov representation condition is the weakest correctness condition resulting from existing notions of minimality for maximal ancestral graphs and directed acyclic graphs. We also reason that additional knowledge than just Pearl-minimality is necessary for causal learning beyond faithfulness.

Solar winds originate from the Sun and can be classified as fast or slow. Fast solar winds come from coronal holes at the solar poles, while slow solar winds may originate from the equatorial region or streamers. Spicules are jet-like structures observed in the Sun's chromosphere and transition region. Some spicules exhibit rotating motion, potentially indicating vorticity and Alfven waves. Machine learning and the Hough algorithm were used to analyze over 3000 frames of the Sun, identifying spicules and their characteristics. The study found that rotating spicules, accounting for 21 percent at the poles and 4 percent at the equator, play a role in energy transfer to the upper solar atmosphere. The observations suggest connections between spicules, mini-loops, magnetic reconnection, and the acceleration of fast solar winds. Understanding these small-scale structures is crucial for comprehending the origin and heating of the fast solar wind., Comment: 8 pages, 6 figs. accepted in IAU365

In this paper, we explore the interplay between topological structures and phase retrieval in the context of projective Hilbert spaces. This work provides not only a deeper understanding and a new classification of the phase retrieval property in Hilbert spaces but also a way for further investigations into the topological underpinnings of quantum states.

In this paper, we delve into the study of thermodynamics and phase transition of charged Gauss-Bonnet black holes within the context of anti-de Sitter (AdS) space, with particular emphasis on the central charge's role within the dual conformal field theory (CFT). We employ a holographic methodology that interprets the cosmological constant and the Newton constant as thermodynamic variables, leading to the derivation of a modified first law of thermodynamics that incorporates the thermodynamic volume and pressure. Our findings reveal that the central charge of the CFT is intrinsically linked to the variation of these constants, and its stability can be ensured by simultaneous adjustment of these constants. We further explore the phase structures of the black holes, utilizing the free energy. Our research uncovers the existence of a critical value of the central charge, beyond which the phase diagram displays a first-order phase transition between small and large black holes. We also delve into the implications of our findings on the complexity of the CFT. Our conclusions underscore the significant role of the central charge in the holographic thermodynamics and phase transition of charged Gauss-Bonnet black holes. Furthermore, we conclude that while the central charge considered provides suitable and satisfactory solutions for this black hole in 4 and 5 dimensions, it becomes necessary to introduce a unique central charge for this structure of modified gravity. In essence, the central charge in holographic thermodynamics is not a universal value and requires modification in accordance with different modified gravities. Consequently, the physics of the problem will significantly deviate from the one discussed in this article, indicating a rich and complex landscape for future work., Comment: 26 pages, 4 figures, accepted for publication in General Relativity and Gravitation

This study analyzed the Doppler shift in the solar spectrum using the Interface Region Imaging Spectrograph (IRIS). Two types of oscillations were investigated: long period damp and short period damp. The researchers observed periodic perturbations in the Doppler velocity oscillations of bright points (BPs) in the chromosphere and transition region (TR). Deep learning techniques were used to examine the statistical properties of damping in different solar regions. The results showed variations in damping rates, with higher damping in coronal hole areas. The study provided insights into the damping behavior of BPs and contributed to our understanding of energy dissipation processes in the solar chromosphere and TR., Comment: 8 pages, 6 figs., accepted in IAU365

Vertebrate animals benefit from a combination of rigidity for structural support and softness for adaptation. Similarly, integrating rigidity and softness can enhance the versatility of soft robotics. However, the challenges associated with creating durable bonding interfaces between soft and rigid materials have limited the development of hybrid robots. Existing solutions require specialized machinery, such as polyjet 3D printers, which are not commonly available. In response to these challenges, we have developed a 3D printing technique that can be used with almost all commercially available FDM printers. This technique leverages the common issue of underextrusion to create a strong bond between soft and rigid materials. Underextrusion generates a porous structure, similar to fibrous connective tissues, that provides a robust interface with the rigid part through layer fusion, while the porosity enables interlocking with the soft material. Our experiments demonstrated that this method outperforms conventional adhesives commonly used in soft robotics, achieving nearly 200\% of the bonding strength in both lap shear and peeling tests. Additionally, we investigated how different porosity levels affect bonding strength. We tested the technique under pressure scenarios critical to soft and hybrid robots and achieved three times more pressure than the current adhesion solution. Finally, we fabricated various hybrid robots using this technique to demonstrate the wide range of capabilities this approach and hybridity can bring to soft robotics. has context menu

The Hulth\'{e}n potential is a short-range potential that has been widely used in various fields of physics. In this paper, we investigate the distribution functions for the Hulth\'{e}n potential by using statistical and superstatistical methods. We first review the ordinary statistics and superstatistics methods. We then consider some distribution functions, such as uniform, 2-level, gamma, and log-normal and F distributions. Finally, we investigate the behavior of the Hulth\'{e}n potential for statistical and superstatistical methods and compare the results with each other. We use the Tsallis statistics of the superstatistical system. We conclude that the Tsallis behavior of different distribution functions for the Hulth\'{e}n potential exhibits better results than the statistical method. We examined the thermal properties of the Hulth\'{e}n potential for five different distributions: Uniform, 2-level, Gamma, Log-normal, and F. We plotted the Helmholtz free energy and the entropy as functions of temperature for various values of q. It shows that the two uniform and 2-level distributions have the same results due to the universal relationship and that the F distribution does not become ordinary statistics at q=1. It also reveals that the curves of the Helmholtz free energy and the entropy change their order and behavior as q increases and that some distributions disappear or coincide at certain values of q. One can discuss the physical implications of our results and their applications in nuclear and atomic physics in the future., Comment: 27 pages, 10 figures

The absence of security measures between DNS recursive resolvers and authoritative nameservers has been exploited by both inline and off-path attacks. While many security proposals have been made in practice and previous literature, they typically suffer from deployability barriers and/or inadequate security properties. The absence of a broadly adopted security solution between resolvers and nameservers motivates a new scheme that mitigates these issues in previous proposals. We present DNSSEC+, which addresses security and deployability downsides of DNSSEC, while retaining its benefits. DNSSEC+ takes advantage of the existent DNSSEC trust model and authorizes the nameservers within a zone for short intervals to serve the zone data securely, facilitating real-time security properties for DNS responses, without requiring long-term private keys to be duplicated (thus put at risk) on authoritative nameservers. Regarding name resolution latency, DNSSEC+ offers a performance comparable to less secure schemes. We define nine security, privacy, and deployability properties for name resolution, and show how DNSSEC+ fulfills these properties., Comment: 15 pages, 6 figures

Understanding and controlling the growth of chalcogenide perovskite thin films through interface design is important for tailoring film properties. Here, the film and interface structure of BaZr(S,Se)$_3$ thin films grown on LaAlO$_3$ by molecular beam epitaxy and post-growth anion exchange is resolved using aberration-corrected scanning transmission electron microscopy. Epitaxial films are achieved from self-assembly of an interface ``buffer'' layer, which accommodates the large film/substrate lattice mismatch of nearly 40\% for the alloy film studied here. The self-assembled buffer layer, occurring for both the as-grown sulfide and post-selenization alloy films, is shown to have rock-salt-like atomic stacking akin to a Ruddlesden-Popper phase. Above this buffer, the film quickly transitions to the perovskite structure. Overall, these results provide insights into oxide-chalcogenide heteroepitaxial film growth, illustrating a process that yields relaxed, crystalline, epitaxial chalcogenide perovskite films that support ongoing studies of optoelectronic and device properties.

In this study, we investigate the dynamics and frame-by-frame phase transition of the first order in black hole thermodynamics. For our analysis, we will utilize the Kramers escape rate. Our focus is on charged anti-de Sitter (AdS) black holes influenced by Kaniadakis and Barrow statistics. The selection of these black holes aims to examine the effects of entropy variation on the dynamics of phase transition and to demonstrate that the Kramers escape rate, as an efficient tool, can effectively represent the dynamic transition from a small to a large black hole within the domain of first-order phase transitions. It is noteworthy that while the transition from small to large black holes should ostensibly dominate the entire process, our results indicate that the escape rate undergoes changes as it passes through the midpoint of the phase transition, leading to a reverse escape phenomenon. The findings suggest that the dynamic phase transition in charged AdS black holes affected by entropy change bears a significant resemblance to the outcomes of models influenced by Bekenstein-Hawking entropy\cite{23}. This similarity in results could serve as an additional motivation to further explore the potential capabilities of Kaniadakis and Barrow statistics in related cosmological fields. These capabilities could enhance our understanding of other cosmological properties, Comment: 20 pages, 9 figures

The rapid growth of image data has led to the development of advanced image processing and computer vision techniques, which are crucial in various applications such as image classification, image segmentation, and pattern recognition. Texture is an important feature that has been widely used in many image processing tasks. Therefore, analyzing and understanding texture plays a pivotal role in image analysis and understanding.Local binary pattern (LBP) is a powerful operator that describes the local texture features of images. This paper provides a novel mathematical representation of the LBP by separating the operator into three matrices, two of which are always fixed and do not depend on the input data. These fixed matrices are analyzed in depth, and a new algorithm is proposed to optimize them for improved classification performance. The optimization process is based on the singular value decomposition (SVD) algorithm. As a result, the authors present optimal LBPs that effectively describe the texture of human face images. Several experiment results presented in this paper convincingly verify the efficiency and superiority of the optimized LBPs for face detection and facial expression recognition tasks.

Depression is characterized by persistent sadness and loss of interest, significantly impairing daily functioning and now a widespread mental disorder. Traditional diagnostic methods rely on subjective assessments, necessitating objective approaches for accurate diagnosis. Our study investigates the use of facial action units (AUs) and emotions as biomarkers for depression. We analyzed facial expressions from video data of participants classified with or without depression. Our methodology involved detailed feature extraction, mean intensity comparisons of key AUs, and the application of time series classification models. Furthermore, we employed Principal Component Analysis (PCA) and various clustering algorithms to explore the variability in emotional expression patterns. Results indicate significant differences in the intensities of AUs associated with sadness and happiness between the groups, highlighting the potential of facial analysis in depression assessment.

Vision Transformers (ViTs) represent a groundbreaking shift in machine learning approaches to computer vision. Unlike traditional approaches, ViTs employ the self-attention mechanism, which has been widely used in natural language processing, to analyze image patches. Despite their advantages in modeling visual tasks, deploying ViTs on hardware platforms, notably Field-Programmable Gate Arrays (FPGAs), introduces considerable challenges. These challenges stem primarily from the non-linear calculations and high computational and memory demands of ViTs. This paper introduces CHOSEN, a software-hardware co-design framework to address these challenges and offer an automated framework for ViT deployment on the FPGAs in order to maximize performance. Our framework is built upon three fundamental contributions: multi-kernel design to maximize the bandwidth, mainly targeting benefits of multi DDR memory banks, approximate non-linear functions that exhibit minimal accuracy degradation, and efficient use of available logic blocks on the FPGA, and efficient compiler to maximize the performance and memory-efficiency of the computing kernels by presenting a novel algorithm for design space exploration to find optimal hardware configuration that achieves optimal throughput and latency. Compared to the state-of-the-art ViT accelerators, CHOSEN achieves a 1.5x and 1.42x improvement in the throughput on the DeiT-S and DeiT-B models.

Matroid theory is fundamentally connected with index coding and network coding problems. In fact, the reliance of linear index coding and network coding rates on the characteristic of a field has been demonstrated by using the two well-known matroid instances, namely the Fano and non-Fano matroids. This established the insufficiency of linear coding, one of the fundamental theorems in both index coding and network coding. While the Fano matroid is linearly representable only over fields with characteristic two, the non-Fano instance is linearly representable only over fields with odd characteristic. For fields with arbitrary characteristic $p$, the Fano and non-Fano matroids were extended to new classes of matroid instances whose linear representations are dependent on fields with characteristic $p$. However, these matroids have not been well appreciated nor cited in the fields of network coding and index coding. In this paper, we first reintroduce these matroids in a more structured way. Then, we provide a completely independent alternative proof with the main advantage of using only matrix manipulation rather than complex concepts in number theory and matroid theory. In this new proof, it is shown that while the class $p$-Fano matroid instances are linearly representable only over fields with characteristic $p$, the class $p$-non-Fano instances are representable over fields with any characteristic other than characteristic $p$. Finally, following the properties of the class $p$-Fano and $p$-non-Fano matroid instances, we characterize two new classes of index coding instances, respectively, referred to as the class $p$-Fano and $p$-non-Fano index coding, each with a size of $p^2 + 4p + 3$., Comment: arXiv admin note: text overlap with arXiv:2201.10057

Sentence embedding is a cornerstone in NLP. Whitening has been claimed to be an effective operation to improve embedding quality obtained from Large Language Models (LLMs). However, we find that the efficacy of whitening is model-dependent and task-dependent. In particular, whitening degenerates embeddings for classification tasks. The conclusion is supported by extensive experiments. We also explored a variety of whitening operations, including PCA, ZCA, PCA-Cor, ZCA-Cor and Cholesky whitenings. A by-product of our research is embedding evaluation platform for LLMs called SentEval+.

Detecting Machine-Generated Text (MGT) has emerged as a significant area of study within Natural Language Processing. While language models generate text, they often leave discernible traces, which can be scrutinized using either traditional feature-based methods or more advanced neural language models. In this research, we explore the effectiveness of fine-tuning a RoBERTa-base transformer, a powerful neural architecture, to address MGT detection as a binary classification task. Focusing specifically on Subtask A (Monolingual-English) within the SemEval-2024 competition framework, our proposed system achieves an accuracy of 78.9% on the test dataset, positioning us at 57th among participants. Our study addresses this challenge while considering the limited hardware resources, resulting in a system that excels at identifying human-written texts but encounters challenges in accurately discerning MGTs., Comment: 8 pages, 3 figures, 2 tables. Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)

This work proposes stochastic partial differential equations (SPDEs) as a practical tool to replicate clustering effects of more detailed particle-based dynamics. Inspired by membrane-mediated receptor dynamics on cell surfaces, we formulate a stochastic particle-based model for diffusion and pairwise interaction of particles, leading to intriguing clustering phenomena. Employing numerical simulation and cluster detection methods, we explore the approximation of the particle-based clustering dynamics through mean-field approaches. We find that SPDEs successfully reproduce spatiotemporal clustering dynamics, not only in the initial cluster formation period, but also on longer time scales where the successive merging of clusters cannot be tracked by deterministic mean-field models. The computational efficiency of the SPDE approach allows us to generate extensive statistical data for parameter estimation in a simpler model that uses a Markov jump process to capture the temporal evolution of the cluster number.

We consider a status update system consisting of a finite-state Markov source, an energy-harvesting-enabled transmitter, and a sink. The forward and feedback channels between the transmitter and the sink are error-prone. We study the problem of minimizing the long-term time average of a (generic) distortion function subject to an energy causality constraint. Since the feedback channel is error-prone, the transmitter has only partial knowledge about the transmission results and, consequently, about the estimate of the source state at the sink. Therefore, we model the problem as a partially observable Markov decision process (POMDP), which is then cast as a belief-MDP problem. The infinite belief space makes solving the belief-MDP difficult. Thus, by exploiting a specific property of the belief evolution, we truncate the state space and formulate a finite-state MDP problem, which is then solved using the relative value iteration algorithm (RVIA). Furthermore, we propose a low-complexity transmission policy in which the belief-MDP problem is transformed into a sequence of per-slot optimization problems. Simulation results show the effectiveness of the proposed policies and their superiority compared to a baseline policy. Moreover, we numerically show that the proposed policies have switching-type structures.

In this paper, we present an exponential modification for the action of an AdS black hole in the absence of a matter field. An approximated black hole solution is obtained up to the third order of perturbation coefficient. A thermodynamic investigation in canonical ensemble shows that the behavior of a Van der Waals fluid is not seen in this model. Nevertheless, the study of thermodynamic potentials and other related quantities suggests that the thermodynamic phase transitions of the first and second types can occur in this model. The forms of the phase transitions are more similar to the Hawking-Page phase transitions., Comment: 18 Pages, 9 figures, title changed, ref added