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Energy correlators have recently come to the forefront of jet substructure studies at colliders due to their remarkable properties: they naturally separate physics at different scales, are robust to contamination from soft radiation, and offer a direct connection with quantum field theory. The current parametrization used for energy correlators, however, is based on redundant pairwise angles with complex phase space restrictions. In this Letter, we introduce a new parametrization of energy correlators that features a simpler phase space structure and preserves information about the orientation of jet constituents. Further, our parametrization drastically reduces the computational cost to compute energy correlators on experimental data; whereas the time to compute a traditional projected N-point energy correlator scales as M^N/N! on a jet with M particles, our new parametrization achieves a scaling of M^2 log(M) independently of N. Theoretical calculations for our new energy correlators differ from those of traditional parametrizations only at next-to-next-to-leading logarithmic accuracy and beyond, and we expect that our simpler phase space structure will simplify those calculations. We also discuss how to extend our parametrization to resolved N-point energy correlators that encode angular distances between greater numbers of particles, and we propose two possible generalizations for probing multi-prong jets and testing jet scaling behaviour., Comment: 5 pages, 4 figures + supplemental material with 10 figures. Associated code for our PENCs available at https://github.com/abudhraj/FastEEC/releases/tag/0.3 and for our PENCs and RENCs at https://github.com/samcaf/ResolvedEnergyCorrelators . A video of RENCs imaging jets from CMS Open Data is available at https://samcaf.github.io/assets/RENC_Intro.mp4

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

Our research introduces a groundbreaking approach to stress classification through Photoplethysmogram (PPG) signals. By combining Continuous Wavelet Transformation (CWT) with the proven VGG16 classifier, our method enhances stress assessment accuracy and reliability. Previous studies highlighted the importance of physiological signal analysis, yet precise stress classification remains a challenge. Our approach addresses this by incorporating robust data preprocessing with a Kalman filter and a sophisticated neural network architecture. Experimental results showcase exceptional performance, achieving a maximum training accuracy of 98% and maintaining an impressive average training accuracy of 96% across diverse stress scenarios. These results demonstrate the practicality and promise of our method in advancing stress monitoring systems and stress alarm sensors, contributing significantly to stress classification., Comment: 4 figures

Stress is one of the main issues of nowadays lifestyle. If it becomes chronic it can have adverse effects on the human body. Thus, the early detection of stress is crucial to prevent its hurting effects on the human body and have a healthier life. Stress can be assessed using physiological signals. To this end, Photoplethysmography (PPG) is one of the most favorable physiological signals for stress assessment. This research is focused on developing a novel technique to assess stressful events using raw PPG signals recorded by Empatica E4 sensor. To achieve this goal, an adaptive convolutional neural network (CNN) combined with Multilayer Perceptron (MLP) has been utilized to realize the detection of stressful events. This research will use a dataset that is publicly available and named wearable stress and effect detection (WESAD). This dataset will be used to simulate the proposed model and to examine the advantages of the proposed developed model. The proposed model in this research will be able to distinguish between normal events and stressful events. This model will be able to detect stressful events with an accuracy of 96.7%., Comment: 5 figures, 2 tables

This paper introduces a robust stress detection system utilizing a Convolutional Neural Network (CNN) designed for the analysis of Photoplethysmogram (PPG) signals. Employing the WESAD dataset, we applied Continuous Wavelet Transform (CWT) to extract informative features from wrist PPG signals, demonstrating enhanced stress detection and learning compared to conventional techniques. Notably, the CNN achieved an impressive accuracy of 93.7% after five epochs, post-implementation on a resource-constrained microcontroller. The optimization process, including pruning and Post-Train Quantization, was crucial to reduce the model size to 1.6 megabytes, overcoming the microcontroller's limited resources of 2 megabytes of Flash memory and 512 kilobytes of RAM. This optimized model not only addresses resource constraints but also outperforms traditional signal processing methods, positioning it as a promising solution for real-time stress monitoring on wearable devices., Comment: 5 figures, implemented on Microcontroller

Integrating large language models (LLMs) like ChatGPT is revolutionizing the field of computer science education. These models offer new possibilities for enriching student learning and supporting teaching assistants (TAs) in providing prompt feedback and supplementary learning resources. This research delves into the use of ChatGPT in a data structures and algorithms (DSA) course, particularly when combined with TA supervision. The findings demonstrate that incorporating ChatGPT with structured prompts and active TA guidance enhances students' understanding of intricate algorithmic concepts, boosts engagement, and elevates academic performance. However, challenges exist in addressing academic integrity and the limitations of LLMs in tackling complex problems. The study underscores the importance of active TA involvement in reducing students' reliance on AI-generated content and amplifying the overall educational impact. The results suggest that while LLMs can be advantageous for education, their successful integration demands continuous oversight and a thoughtful balance between AI and human guidance.

Stress has become a fact in people's lives. It has a significant effect on the function of body systems and many key systems of the body including respiratory, cardiovascular, and even reproductive systems are impacted by stress. It can be very helpful to detect stress episodes in early steps of its appearance to avoid damages it can cause to body systems. Using physiological signals can be useful for stress detection as they reflect very important information about the human body. PPG signal due to its advantages is one of the mostly used signal in this field. In this research work, we take advantage of PPG signals to detect stress events. The PPG signals used in this work are collected from one of the newest publicly available datasets named as UBFC-Phys and a model is developed by using CNN-MLP deep learning algorithm. The results obtained from the proposed model indicate that stress can be detected with an accuracy of approximately 82 percent., Comment: 5 figures , 2 tables

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

This paper introduces a novel control strategy for agile spacecraft attitude control, addressing reaction wheel-related input and state constraints. An optimal-decay control Lyapunov function quadratic program stabilizes the system and mitigates chattering at low sampling frequencies, while control barrier functions enforce hard state constraints. Numerical simulations validate the method's practicality and efficiency for real-time agile spacecraft attitude control.

Spaceborne microwave passive soil moisture products are known for their accuracy but are often limited by coarse spatial resolutions. This limits their ability to capture finer soil moisture gradients and hinders their applications. The Portable L band radiometer (PoLRa) offers soil moisture measurements from submeter to tens of meters depending on the altitude of measurement. Given that the assessments of soil moisture derived from this sensor are notably lacking, this study aims to evaluate the performance of submeter soil moisture retrieved from PoLRa mounted on poles at four different locations in central Illinois, USA. The evaluation focuses on the consistency of PoLRa measured brightness temperatures from different directions relative to the same area, and the accuracy of PoLRa derived soil moisture. As PoLRa shares many aspects of the L band radiometer onboard the NASA Soil Moisture Active Passive (SMAP) mission, two SMAP operational algorithms and the conventional dual channel algorithm were applied to calculate soil moisture from the measured brightness temperatures. The vertically polarized brightness temperatures from the PoLRa are typically more stable than their horizontally polarized counterparts. In each test period, the standard deviations of observed dual polarization brightness temperatures are generally less than 5 K. By comparing PoLRa based soil moisture retrievals against the moisture values obtained by handheld time domain reflectometry, the unbiased root mean square error and the Pearson correlation coefficient are mostly below 0.04 and above 0.75, confirming the high accuracy of PoLRa derived soil moisture retrievals and the feasibility of utilizing SMAP algorithms for PoLRa data. These findings highlight the significant potential of ground or drone based PoLRa measurements as a standalone reference for future spaceborne L band sensors., Comment: 13 pages, 7 figures

We explore the dynamics of qubit-state purity in the presence of transverse noise that is anisotropically distributed in the Bloch-sphere XY plane. We perform Ramsey experiments with noise injected along a fixed laboratory-frame axis and observe oscillations in the purity at twice the qubit frequency arising from the intrinsic qubit Larmor precession. We probe the oscillation dependence on the noise anisotropy, orientation, and power spectral density, using a low-frequency fluxonium qubit. Our results elucidate the impact of transverse noise anisotropy on qubit decoherence and may be useful to disentangle charge and flux noise in superconducting quantum circuits.

In recent years, inflationary models have been studied from different perspectives using different conditions. Now, in this paper, we want to investigate the inverse monomial Inflation $(IMI)$ on the brane. We limit our results to the potentials of the form $\frac{M^{4+\sigma}}{\phi^{\sigma}}$. We calculate some cosmological parameters and then we investigate the satisfaction of the model with some conjectures of the swampland program. We will check the model's compatibility with the refined dS swampland conjecture(RDSSC), further refining the de Sitter swampland conjecture (FRSDC), scalar weak gravity (SWGC), and strong scalar weak gravity conjecture (SSWGC). Despite the incompatibility with (DSSC), We find a specific region of compatibility with other ones which means it satisfied the (FRDSSC) for example with (a=0.982873, b=0.017127, q=2.2). Also, the model is compatible with the (SWGC) with the ($\phi\leq \sigma+2$) condition and consistent with the (SSWGC) with the ($\phi \leq \sqrt{(\sigma+2)(\sigma+1)}$) condition. Since inverse monomial Inflation(IMI) on the brane could satisfy some swampland conjecture simultaneously, it has the potential to be a "real" inflation model of the universe., Comment: 16 pages, 1 Table

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

The equation of motion is the important equation for obtain the extra force and Raychaudhuri equation. By considering an explicitly coupling between an arbitrary function of the scalar Gauss-Bonnet, G and the Lagrangian density of matter, it is shown that an extra force normal to their four-velocities arises. In this paper, we obtain the extra force and the generalized Raychaudhuri equation in F(G) modified theory of gravity in an imperfect fluid for the massive particle by divergence of energy momentum tensor so we earn extra force an Raychaudhuri equation in a compared with f(R) modified gravity for perfect fluid this conclusion giving the evolution of the kinematical quantities and describing the relative accelerations of nearby particles .

This work investigates the application of Machine Unlearning (MU) for mitigating the impact of trojans embedded in conventional large language models of natural language (Text-LLMs) and large language models of code (Code-LLMs) We propose a novel unlearning approach, LYA, that leverages both gradient ascent and elastic weight consolidation, a Fisher Information Matrix (FIM) based regularization technique, to unlearn trojans from poisoned models. We compare the effectiveness of LYA against conventional techniques like fine-tuning, retraining, and vanilla gradient ascent. The subject models we investigate are BERT and CodeBERT, for sentiment analysis and code defect detection tasks, respectively. Our findings demonstrate that the combination of gradient ascent and FIM-based regularization, as done in LYA, outperforms existing methods in removing the trojan's influence from the poisoned model, while preserving its original functionality. To the best of our knowledge, this is the first work that compares and contrasts MU of trojans in LLMs, in the NL and Coding domain.

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

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

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 differential rotation plays a crucial role in the dynamics of the Sun. We study the solar rotation and its correlation with solar activity by applying a modified machine learning algorithm to identify and track coronal bright points (CBPs) from the Solar Dynamics Observatory/Atmospheric Imaging Assembly observations at 193 \AA\ during cycle 24. For more than 321,440 CBPs, the sidereal and meridional velocities are computed. We find the occurring height of CBPs about 5627 km above the photosphere. We obtain a rotational map for the corona by tracking CBPs at the formation height of Fe\,{\sc xii} (193 \AA) emissions. The equator rotation (14.$^{\circ}$40 to 14.$^{\circ}$54 day$^{-1}$) and latitudinal gradient of rotation ($ - $3.$^{\circ}$0 to $ - $2.$^{\circ}$64 day$^{-1}$) show very slightly positive and negative trends with solar activity (sunspots and flares), respectively. For cycle 24, our investigations show that the northern hemisphere has more differential rotation than the southern hemisphere, confirmed by the asymmetry of the midlatitude rotation parameter. The asymmetry (ranked) of the latitudinal gradient of the rotation parameter is concordant with the sunspot numbers for 7 yr within the 9 yr of the cycle; however, for only 3 yr, it is concordant with the flare index. The minimum horizontal Reynolds stress changes from about $ - $2500 m$^{2}$ s$^{-2}$ (corresponding to high activity) in 2012 and 2014 to $ - $100 m$^{2}$ s$^{-2}$ (corresponding to low activity) in 2019 over 5$^{\circ}$ to 35$^{\circ}$ latitudes within cycle 24. We conclude that the negative horizontal Reynolds stress (momentum transfer toward the Sun's equator) is a helpful indication of solar activity.

Unit testing is crucial in software engineering for ensuring quality. However, it's not widely used in parallel and high-performance computing software, particularly scientific applications, due to their smaller, diverse user base and complex logic. These factors make unit testing challenging and expensive, as it requires specialized knowledge and existing automated tools are often ineffective. To address this, we propose an automated method for generating unit tests for such software, considering their unique features like complex logic and parallel processing. Recently, large language models (LLMs) have shown promise in coding and testing. We explored the capabilities of Davinci (text-davinci-002) and ChatGPT (gpt-3.5-turbo) in creating unit tests for C++ parallel programs. Our results show that LLMs can generate mostly correct and comprehensive unit tests, although they have some limitations, such as repetitive assertions and blank test cases.

This study proposes CGRclust, a novel combination of unsupervised twin contrastive clustering of Chaos Game Representations (CGR) of DNA sequences, with convolutional neural networks (CNNs). To the best of our knowledge, CGRclust is the first method to use unsupervised learning for image classification (herein applied to two-dimensional CGR images) for clustering datasets of DNA sequences. CGRclust overcomes the limitations of traditional sequence classification methods by leveraging unsupervised twin contrastive learning to detect distinctive sequence patterns, without requiring DNA sequence alignment or biological/taxonomic labels. CGRclust accurately clustered twenty-five diverse datasets, with sequence lengths ranging from 664 bp to 100 kbp, including mitochondrial genomes of fish, fungi, and protists, as well as viral whole genome assemblies and synthetic DNA sequences. Compared with three recent clustering methods for DNA sequences (DeLUCS, iDeLUCS, and MeShClust v3.0.), CGRclust is the only method that surpasses 81.70% accuracy across all four taxonomic levels tested for mitochondrial DNA genomes of fish. Moreover, CGRclust also consistently demonstrates superior performance across all the viral genomic datasets. The high clustering accuracy of CGRclust on these twenty-five datasets, which vary significantly in terms of sequence length, number of genomes, number of clusters, and level of taxonomy, demonstrates its robustness, scalability, and versatility., Comment: 29 pages, 4 figures

The new capabilities of generative artificial intelligence tools Generative AI, such as ChatGPT, allow users to interact with the system in intuitive ways, such as simple conversations, and receive (mostly) good-quality answers. These systems can support students' learning objectives by providing accessible explanations and examples even with vague queries. At the same time, they can encourage undesired help-seeking behaviors by providing solutions to the students' homework. Therefore, it is important to better understand how students approach such tools and the potential issues such approaches might present for the learners. In this paper, we present a case study for understanding student-AI collaboration to solve programming tasks in the CS1 introductory programming course. To this end, we recruited a gender-balanced majority non-white set of 15 CS1 students at a large public university in the US. We observed them solving programming tasks. We used a mixed-method approach to study their interactions as they tackled Python programming tasks, focusing on when and why they used ChatGPT for problem-solving. We analyze and classify the questions submitted by the 15 participants to ChatGPT. Additionally, we analyzed user interaction patterns, their reactions to ChatGPT's responses, and the potential impacts of Generative AI on their perception of self-efficacy. Our results suggest that in about a third of the cases, the student attempted to complete the task by submitting the full description of the tasks to ChatGPT without making any effort on their own. We also observed that few students verified their solutions. We discuss the results and their potential implications., Comment: Koli Calling 2024

In this paper, we investigate the weak cosmic censorship conjecture (WCCC) for the Reissner-Nordstrom (R-N) AdS black hole in a restricted phase space thermodynamics (RPST). Also here, we consider energy flux and equivalence mass-energy principle and examine the weak gravity conjecture (WGC) and the weak cosmic censorship conjecture. The incoming and outgoing energy flux leads to changes in the black hole. In that case, by applying the first law, we examined whether the second law of thermodynamics is valid. And also one can say that, in the case where absorption and superradiance are in the saturated to an equilibrium. Also, by using the thermodynamics of black holes in the restricted phase space, we show that if the black hole is in an extreme or close to an extreme state with radiation and particle absorption, the weak cosmic censorship conjecture is established. In addition, with the help of equivalence mass and energy principle and second-order approximation, in the near extremity, we find that when the black hole radiates and its central charge is greater than the scaled electric charge, the superradiance particles obey the weak gravity conjecture, and this causes the black hole to move further away from its extreme state. But when the particles that obey the weak gravity conjecture are attracted to the black hole when the black hole is very small. Then, in this case, we note that the black hole becomes closer to its extreme state., Comment: 15 pages, 1 table

The proper handling of out-of-distribution (OOD) samples in deep classifiers is a critical concern for ensuring the suitability of deep neural networks in safety-critical systems. Existing approaches developed for robust OOD detection in the presence of adversarial attacks lose their performance by increasing the perturbation levels. This study proposes a method for robust classification in the presence of OOD samples and adversarial attacks with high perturbation levels. The proposed approach utilizes a fully connected neural network that is trained to use training samples as its attractors, enhancing its robustness. This network has the ability to classify inputs and identify OOD samples as well. To evaluate this method, the network is trained on the MNIST dataset, and its performance is tested on adversarial examples. The results indicate that the network maintains its performance even when classifying adversarial examples, achieving 87.13% accuracy when dealing with highly perturbed MNIST test data. Furthermore, by using fashion-MNIST and CIFAR-10-bw as OOD samples, the network can distinguish these samples from MNIST samples with an accuracy of 98.84% and 99.28%, respectively. In the presence of severe adversarial attacks, these measures decrease slightly to 98.48% and 98.88%, indicating the robustness of the proposed method.

General-purpose motion planners for automated/autonomous vehicles promise to handle the task of motion planning (including tactical decision-making and trajectory generation) for various automated driving functions (ADF) in a diverse range of operational design domains (ODDs). The challenges of designing a general-purpose motion planner arise from several factors: a) A plethora of scenarios with different semantic information in each driving scene should be addressed, b) a strong coupling between long-term decision-making and short-term trajectory generation shall be taken into account, c) the nonholonomic constraints of the vehicle dynamics must be considered, and d) the motion planner must be computationally efficient to run in real-time. The existing methods in the literature are either limited to specific scenarios (logic-based) or are data-driven (learning-based) and therefore lack explainability, which is important for safety-critical automated driving systems (ADS). This paper proposes a novel general-purpose motion planning solution for ADS inspired by the theory of fluid mechanics. A computationally efficient technique, i.e., the lattice Boltzmann method, is then adopted to generate a spatiotemporal vector field, which in accordance with the nonholonomic dynamic model of the Ego vehicle is employed to generate feasible candidate trajectories. The trajectory optimising ride quality, efficiency and safety is finally selected to calculate the imminent control signals, i.e., throttle/brake and steering angle. The performance of the proposed approach is evaluated by simulations in highway driving, on-ramp merging, and intersection crossing scenarios, and it is found to outperform traditional motion planning solutions based on model predictive control (MPC).

Motion planning is an essential element of the modular architecture of autonomous vehicles, serving as a bridge between upstream perception modules and downstream low-level control signals. Traditional motion planners were initially designed for specific Automated Driving Functions (ADFs), yet the evolving landscape of highly automated driving systems (ADS) requires motion for a wide range of ADFs, including unforeseen ones. This need has motivated the development of the ``hybrid" approach in the literature, seeking to enhance motion planning performance by combining diverse techniques, such as data-driven (learning-based) and logic-driven (analytic) methodologies. Recent research endeavours have significantly contributed to the development of more efficient, accurate, and safe hybrid methods for Tactical Decision Making (TDM) and Trajectory Generation (TG), as well as integrating these algorithms into the motion planning module. Owing to the extensive variety and potential of hybrid methods, a timely and comprehensive review of the current literature is undertaken in this survey article. We classify the hybrid motion planners based on the types of components they incorporate, such as combinations of sampling-based with optimization-based/learning-based motion planners. The comparison of different classes is conducted by evaluating the addressed challenges and limitations, as well as assessing whether they focus on TG and/or TDM. We hope this approach will enable the researchers in this field to gain in-depth insights into the identification of current trends in hybrid motion planning and shed light on promising areas for future research.

Information Foraging Theory's (IFT) framing of human information seeking choices as decision-theoretic cost-value judgments has successfully explained how people seek information among linked patches of information (e.g., linked webpages). However, the theory has to be adopted and validated in non-patchy LLM-based chatbot environments, before its postulates can be reliably applied to the design of such chat-based information seeking environments. This paper is a thought experiment that applies the IFT cost-value proposition to LLM-based chatbots and presents a set of preliminary hypotheses to guide future theory-building efforts for how people seek information in such environments.

Large language models (LLMs) have revolutionized software development practices, yet concerns about their safety have arisen, particularly regarding hidden backdoors, aka trojans. Backdoor attacks involve the insertion of triggers into training data, allowing attackers to manipulate the behavior of the model maliciously. In this paper, we focus on analyzing the model parameters to detect potential backdoor signals in code models. Specifically, we examine attention weights and biases, and context embeddings of the clean and poisoned CodeBERT and CodeT5 models. Our results suggest noticeable patterns in context embeddings of poisoned samples for both the poisoned models; however, attention weights and biases do not show any significant differences. This work contributes to ongoing efforts in white-box detection of backdoor signals in LLMs of code through the analysis of parameters and embeddings., Comment: This work has been accepted at the 1st ACM International Conference on AI-powered Software (AIware), co-located with the ACM International Conference on the Foundations of Software Engineering (FSE) 2024, Porto de Galinhas, Brazil. arXiv admin note: substantial text overlap with arXiv:2402.12936

Large language models (LLMs) have provided a lot of exciting new capabilities in software development. However, the opaque nature of these models makes them difficult to reason about and inspect. Their opacity gives rise to potential security risks, as adversaries can train and deploy compromised models to disrupt the software development process in the victims' organization. This work presents an overview of the current state-of-the-art trojan attacks on large language models of code, with a focus on triggers -- the main design point of trojans -- with the aid of a novel unifying trigger taxonomy framework. We also aim to provide a uniform definition of the fundamental concepts in the area of trojans in Code LLMs. Finally, we draw implications of findings on how code models learn on trigger design., Comment: arXiv admin note: substantial text overlap with arXiv:2305.03803

Traditional static methods in phase transition studies, such as the swallowtail bifurcation diagram, provide good insights into the thermodynamics of black holes. However, they practically lose sight of the dynamic aspects and temporal sequence of events. The Kramers escape rate, central to our research, offers a somewhat dynamic approach to phase transition. We examine the free energy landscapes for black holes under the influence of ’dark’ and ’stringy+dark’ structures, assessing how additional parameters affect the escape rates and dynamics of the transition during the first-order phase transition from small to large black holes. In our analysis, we consider the escape rate as a function of the black hole radius and study its variations. We will observe that, on one hand, the escape rate well represents our assumption based on the movement from zero, increasing to a maximum point, and then decreasing back to zero as reactive structures become active during the phase transition interval. However, the critical point in this method is the encounter with a specific and distinct point. This is where the diagram of the direct process (escape rate from small to large black holes) intersects with the reverse process (large to small black holes), becoming equally probable (contact point). The point, which seems improbable at the onset of the phase transition or very negligible, gains more significance as the process progresses. This increase indicates the dominance of a region where the escape rate from larger black holes to smaller ones prevails. The predominance of the reverse process, which increases as we approach the end of the process and is necessarily accompanied by a variation in radius, may be considered as a natural reaction of the black hole against the ’phase change’ action. A reaction which attempting to prevent any uncontrolled radial growth that could jeopardize the stability of the black hole., Comment: 32 pages, 11 figures. The updated version of manuscript includes new calculations, discussions, and optimization of the results

In this paper, we explore the intriguing interplay between fundamental theoretical physics concepts within the context of charged black holes. Specifically, we focus on the consistency of the weak gravity conjecture (WGC) and weak cosmic censorship conjecture (WCCC) in the thermodynamics of conformal field theory (CFT), and restricted phase space (RPS) for AdS Reissner-Nordstr$\"o$m black holes with a perfect fluid dark matter (RN-PFDM). The WGC ensures that gravity remains the weakest force in the system. Meanwhile, the WCCC addresses the cosmic censorship problem by preventing the violation of fundamental physical laws near the black hole singularity. First, we analyze the RN black hole's free energy in both spaces, revealing a distinctive swallowtail pattern indicative of a first-order phase transition when certain free parameter conditions are met. We explore the WGC across different phase spaces, emphasizing the need for certain conditions in EPST, RPST, and CFT. We demonstrate that PFDM $( \gamma )$ and the radius of AdS ($l$) have a vital role in proving the satisfaction of the WGC. Also, these values have a linear relation with the range compatibility of WGC. The range of compatibility for WGC in RPST and EPST is the same while for CFT, this range is larger than EPST, and RPST. It means somehow the WGC and CFT are more consistent. The WCCC was examined at the critical juncture, confirming its validity with $( f(M_c, Q_c, r_c) = 0 )$. We conclude that the WGC is supported at the critical point of black holes, and the WCCC is also maintained, demonstrating the robustness of these conjectures within the critical ranges of black hole parameters., Comment: 29 pages, 7 figures

Soil moisture sensing through biomass or vegetation canopy has challenged researchers, even those who use SAR sensors with penetration capabilities. This is mainly due to the imposed extra time and phase offsets on Radio Frequency (RF) signals as they travel through the canopy. These offsets depend on the vegetation canopy moisture and height, both of which are typically unknown in agricultural and forest fields. In this paper, we leverage the mobility of an unmanned aerial system (UAS) to collect spatially-diverse radar measurements, enabling the joint estimation of soil moisture, above-ground biomass moisture, and biomass height, all without assuming any calibration steps. We leverage the changes in time-of-flight (ToF) and angle-of-arrival (AoA) measurements of reflected radar signals as the UAS flies above a reflector buried under the soil. We demonstrate the effectiveness of our algorithm by simulating its performance under realistic measurement noises as well as conducting lab experiments with different types of above-ground biomass. Our simulation results conclude that our algorithm is capable of estimating volumetric soil moisture to less than 1% median absolute error (MAE), vegetation height to 11.1cm MAE, and vegetation relative permittivity to 0.32 MAE. Our experimental results demonstrate the effectiveness of the proposed method in practical scenarios for varying biomass moistures and heights.

Portfolio management is one of the unresponded problems of the Motion Pictures Industry (MPI). To design an optimal portfolio for an MPI distributor, it is essential to predict the box office of each project. Moreover, for an accurate box office prediction, it is critical to consider the effect of the celebrities involved in each MPI project, which was impossible with any precedent expert-based method. Additionally, the asymmetric characteristic of MPI data decreases the performance of any predictive algorithm. In this paper, firstly, the fame score of the celebrities is determined using a large language model. Then, to tackle the asymmetric character of MPI's data, projects are classified. Furthermore, the box office prediction takes place for each class of projects. Finally, using a hybrid multi-attribute decision-making technique, the preferability of each project for the distributor is calculated, and benefiting from a bi-objective optimization model, the optimal portfolio is designed.

Moisture estimation of sub-surface soil and the overlaying biomass layer is pivotal in precision agriculture and wildfire risk assessment. However, the characterization of layered material is nontrivial due to the radar penetration-resolution tradeoff. Here, a waveform inversion-based method was proposed for predicting the dielectric permittivity (as a moisture proxy) of the bottom soil layer and the top biomass layer from radar signals. Specifically, the use of a combination of a higher and a lower frequency radar compared to a single frequency in predicting the permittivity of both the soil and the overlaying layer was investigated in this study. The results show that each layer was best characterized via one of the frequencies. However, for the simultaneous prediction of both layers permittivity, the most consistent results were achieved by inversion of data from a combination of both frequencies, showing better correlation with in situ permittivity and reduced prediction errors., Comment: There are 5 pages, 5 figures and 1 table. This study has been accepted at 2024 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

Accurate velocity estimation of surrounding moving objects and their trajectories are critical elements of perception systems in Automated/Autonomous Vehicles (AVs) with a direct impact on their safety. These are non-trivial problems due to the diverse types and sizes of such objects and their dynamic and random behaviour. Recent point cloud based solutions often use Iterative Closest Point (ICP) techniques, which are known to have certain limitations. For example, their computational costs are high due to their iterative nature, and their estimation error often deteriorates as the relative velocities of the target objects increase (>2 m/sec). Motivated by such shortcomings, this paper first proposes a novel Detection and Tracking of Moving Objects (DATMO) for AVs based on an optical flow technique, which is proven to be computationally efficient and highly accurate for such problems. \textcolor{black}{This is achieved by representing the driving scenario as a vector field and applying vector calculus theories to ensure spatiotemporal continuity.} We also report the results of a comprehensive performance evaluation of the proposed DATMO technique, carried out in this study using synthetic and real-world data. The results of this study demonstrate the superiority of the proposed technique, compared to the DATMO techniques in the literature, in terms of estimation accuracy and processing time in a wide range of relative velocities of moving objects. Finally, we evaluate and discuss the sensitivity of the estimation error of the proposed DATMO technique to various system and environmental parameters, as well as the relative velocities of the moving objects., Comment: This manuscript has been accepted as a regular paper in Transactions on Intelligent Transportation Systems (DOI: 10.1109/TITS.2024.3382495)

In recent times, the grandeur of Large Language Models (LLMs) has not only shone in the realm of natural language processing but has also cast its brilliance across a vast array of applications. This remarkable display of LLM capabilities has ignited a surge in research contributions within this domain, spanning a diverse spectrum of topics. These contributions encompass advancements in neural network architecture, context length enhancements, model alignment, training datasets, benchmarking, efficiency improvements, and more. Recent years have witnessed a dynamic synergy between academia and industry, propelling the field of LLM research to new heights. A notable milestone in this journey is the introduction of ChatGPT, a powerful AI chatbot grounded in LLMs, which has garnered widespread societal attention. The evolving technology of LLMs has begun to reshape the landscape of the entire AI community, promising a revolutionary shift in the way we create and employ AI algorithms. Given this swift-paced technical evolution, our survey embarks on a journey to encapsulate the recent strides made in the world of LLMs. Through an exploration of the background, key discoveries, and prevailing methodologies, we offer an up-to-the-minute review of the literature. By examining multiple LLM models, our paper not only presents a comprehensive overview but also charts a course that identifies existing challenges and points toward potential future research trajectories. This survey furnishes a well-rounded perspective on the current state of generative AI, shedding light on opportunities for further exploration, enhancement, and innovation.

Accurate assessment of fuel conditions is a prerequisite for fire ignition and behavior prediction, and risk management. The method proposed herein leverages diverse data sources including Landsat-8 optical imagery, Sentinel-1 (C-band) Synthetic Aperture Radar (SAR) imagery, PALSAR (L-band) SAR imagery, and terrain features to capture comprehensive information about fuel types and distributions. An ensemble model was trained to predict landscape-scale fuels such as the 'Scott and Burgan 40' using the as-received Forest Inventory and Analysis (FIA) field survey plot data obtained from the USDA Forest Service. However, this basic approach yielded relatively poor results due to the inadequate amount of training data. Pseudo-labeled and fully synthetic datasets were developed using generative AI approaches to address the limitations of ground truth data availability. These synthetic datasets were used for augmenting the FIA data from California to enhance the robustness and coverage of model training. The use of an ensemble of methods including deep learning neural networks, decision trees, and gradient boosting offered a fuel mapping accuracy of nearly 80\%. Through extensive experimentation and evaluation, the effectiveness of the proposed approach was validated for regions of the 2021 Dixie and Caldor fires. Comparative analyses against high-resolution data from the National Agriculture Imagery Program (NAIP) and timber harvest maps affirmed the robustness and reliability of the proposed approach, which is capable of near-real-time fuel mapping., Comment: 40 pages

The rapid proliferation of the Internet of Things (IoT) has ushered in transformative connectivity between physical devices and the digital realm. Nonetheless, the escalating threat of Distributed Denial of Service (DDoS) attacks jeopardizes the integrity and reliability of IoT networks. Conventional DDoS mitigation approaches are ill-equipped to handle the intricacies of IoT ecosystems, potentially compromising data privacy. This paper introduces an innovative strategy to bolster the security of IoT networks against DDoS attacks by harnessing the power of Federated Learning that allows multiple IoT devices or edge nodes to collaboratively build a global model while preserving data privacy and minimizing communication overhead. The research aims to investigate Federated Learning's effectiveness in detecting and mitigating DDoS attacks in IoT. Our proposed framework leverages IoT devices' collective intelligence for real-time attack detection without compromising sensitive data. This study proposes innovative deep autoencoder approaches for data dimensionality reduction, retraining, and partial selection to enhance the performance and stability of the proposed model. Additionally, two renowned aggregation algorithms, FedAvg and FedAvgM, are employed in this research. Various metrics, including true positive rate, false positive rate, and F1-score, are employed to evaluate the model. The dataset utilized in this research, N-BaIoT, exhibits non-IID data distribution, where data categories are distributed quite differently. The negative impact of these distribution disparities is managed by employing retraining and partial selection techniques, enhancing the final model's stability. Furthermore, evaluation results demonstrate that the FedAvgM aggregation algorithm outperforms FedAvg, indicating that in non-IID datasets, FedAvgM provides better stability and performance.