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Anomaly detection in real-world time-series data is a challenging task due to the complex and nonlinear temporal dynamics involved. This paper introduces KoopAGRU, a new deep learning model designed to tackle this problem by combining Fast Fourier Transform (FFT), Deep Dynamic Mode Decomposition (DeepDMD), and Koopman theory. FFT allows KoopAGRU to decompose temporal data into time-variant and time-invariant components providing precise modeling of complex patterns. To better control these two components, KoopAGRU utilizes Gate Recurrent Unit (GRU) encoders to learn Koopman observables, enhancing the detection capability across multiple temporal scales. KoopAGRU is trained in a single process and offers fast inference times. Extensive tests on various benchmark datasets show that KoopAGRU outperforms other leading methods, achieving a new average F1-score of 90.88\% on the well-known anomalies detection task of times series datasets, and proves to be efficient and reliable in detecting anomalies in real-world scenarios.

Rivers are well-known to exhibit fractal-like properties that lead to existence of scaling laws that link network geometry and size. However, these geometric relations might not necessarily capture the spatial features that most directly influence ecological processes such as the generation and maintenance of biodiversity. Connectivity metrics, on the other hand, have been shown both theoretically and empirically to influence relevant large-scale ecological outcomes. Therefore, in this work, we establish how river network connectivity scales with system size by analyzing networks obtained for more than 1000 rivers across the globe. Specifically, we found clear power-law scaling of both the Harmonic and Betweenness centrality network connectivity metrics. We then created a synthetic river network dataset by fitting an Optimal Channel Network (OCN) model to each real river, to ask if OCNs, which are widely used to represent rivers in many theoretical studies, exhibit the same connectivity scaling laws as empirical river networks. We found excellent agreement between empirical and OCN-based scaling laws, supporting the notion that OCNs can accurately represent rivers in network-based models and analyses. Finally, we examined the robustness of the connectivity scaling laws by varying species movement strategies from perfect shortest-path navigation to unbiased random walks. Surprisingly, we found that random walk movement breaks the power-law scaling relationship for harmonic, but not for betweenness centrality.

Current unsupervised domain adaptation (UDA) methods for semantic segmentation typically assume identical class labels between the source and target domains. This assumption ignores the label-level domain gap, which is common in real-world scenarios, thus limiting their ability to identify finer-grained or novel categories without requiring extensive manual annotation. A promising direction to address this limitation lies in recent advancements in foundation models, which exhibit strong generalization abilities due to their rich prior knowledge. However, these models often struggle with domain-specific nuances and underrepresented fine-grained categories. To address these challenges, we introduce DynAlign, a framework that integrates UDA with foundation models to bridge both the image-level and label-level domain gaps. Our approach leverages prior semantic knowledge to align source categories with target categories that can be novel, more fine-grained, or named differently (e.g., vehicle to {car, truck, bus}). Foundation models are then employed for precise segmentation and category reassignment. To further enhance accuracy, we propose a knowledge fusion approach that dynamically adapts to varying scene contexts. DynAlign generates accurate predictions in a new target label space without requiring any manual annotations, allowing seamless adaptation to new taxonomies through either model retraining or direct inference. Experiments on the street scene semantic segmentation benchmarks GTA to Mapillary Vistas and GTA to IDD validate the effectiveness of our approach, achieving a significant improvement over existing methods. Our code will be publicly available.

Autonomous drone racing has risen as a challenging robotic benchmark for testing the limits of learning, perception, planning, and control. Expert human pilots are able to agilely fly a drone through a race track by mapping the real-time feed from a single onboard camera directly to control commands. Recent works in autonomous drone racing attempting direct pixel-to-commands control policies (without explicit state estimation) have relied on either intermediate representations that simplify the observation space or performed extensive bootstrapping using Imitation Learning (IL). This paper introduces an approach that learns policies from scratch, allowing a quadrotor to autonomously navigate a race track by directly mapping raw onboard camera pixels to control commands, just as human pilots do. By leveraging model-based reinforcement learning~(RL) - specifically DreamerV3 - we train visuomotor policies capable of agile flight through a race track using only raw pixel observations. While model-free RL methods such as PPO struggle to learn under these conditions, DreamerV3 efficiently acquires complex visuomotor behaviors. Moreover, because our policies learn directly from pixel inputs, the perception-aware reward term employed in previous RL approaches to guide the training process is no longer needed. Our experiments demonstrate in both simulation and real-world flight how the proposed approach can be deployed on agile quadrotors. This approach advances the frontier of vision-based autonomous flight and shows that model-based RL is a promising direction for real-world robotics., Comment: 11 pages, 7 Figures

Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achieve over 90\% accuracy on popular benchmarks like MMLU, limiting informed measurement of state-of-the-art LLM capabilities. In response, we introduce Humanity's Last Exam (HLE), a multi-modal benchmark at the frontier of human knowledge, designed to be the final closed-ended academic benchmark of its kind with broad subject coverage. HLE consists of 3,000 questions across dozens of subjects, including mathematics, humanities, and the natural sciences. HLE is developed globally by subject-matter experts and consists of multiple-choice and short-answer questions suitable for automated grading. Each question has a known solution that is unambiguous and easily verifiable, but cannot be quickly answered via internet retrieval. State-of-the-art LLMs demonstrate low accuracy and calibration on HLE, highlighting a significant gap between current LLM capabilities and the expert human frontier on closed-ended academic questions. To inform research and policymaking upon a clear understanding of model capabilities, we publicly release HLE at https://lastexam.ai., Comment: 25 pages, 6 figures

In this paper, we investigate the problem of remote estimation of a discrete-time joint Markov process using multiple sensors. Each sensor observes a different component of the joint Markov process, and in each time slot, the monitor obtains a partial state value by sending a pull request to one of the sensors. The monitor chooses the sequence of sensors to observe with the goal of minimizing the mean of age of incorrect information (MAoII) by using the partial state observations obtained, which have different freshness levels. For instance, a monitor may be interested in tracking the location of an object by obtaining observations from two sensors, which observe the $x$ and $y$ coordinates of the object separately, in different time slots. The monitor, then, needs to decide which coordinate to observe in the next time slot given the history. In addition to this partial observability of the state of Markov process, there is an erasure channel with a fixed one-slot delay between each sensor and the monitor. First, we obtain a sufficient statistic, namely the \emph{belief}, representing the joint distribution of the age of incorrect information (AoII) and the current state of the observed process by using the history of all pull requests and observations. Then, we formulate the problem with a continuous state-space Markov decision problem (MDP), namely belief MDP. To solve the problem, we propose two model predictive control (MPC) methods, namely MPC without terminal costs (MPC-WTC) and reinforcement learning MPC (RL-MPC), that have different advantages in implementation.

The task of converting natural language questions (NLQs) into executable SQL queries, known as text-to-SQL, has gained significant interest in recent years, as it enables non-technical users to interact with relational databases. Many benchmarks, such as SPIDER and WikiSQL, have contributed to the development of new models and the evaluation of their performance. In addition, other datasets, like SEDE and BIRD, have introduced more challenges and complexities to better map real-world scenarios. However, these datasets primarily focus on high-resource languages such as English and Chinese. In this work, we introduce Dialect2SQL, the first large-scale, cross-domain text-to-SQL dataset in an Arabic dialect. It consists of 9,428 NLQ-SQL pairs across 69 databases in various domains. Along with SQL-related challenges such as long schemas, dirty values, and complex queries, our dataset also incorporates the complexities of the Moroccan dialect, which is known for its diverse source languages, numerous borrowed words, and unique expressions. This demonstrates that our dataset will be a valuable contribution to both the text-to-SQL community and the development of resources for low-resource languages.

In the QCD instanton vacuum, the C-even and C-odd nucleon Pauli form factors receive a large contribution from the underlying ensemble of pseudoparticles, that is sensitive to a finite QCD vacuum angle $\theta$. This observation is used to derive explicitly the electric dipole form factor for light quark flavors, and estimate the proton and neutron electric dipole moment induced by a small CP violating $\theta$. The results are in the range of some recently reported lattice simulations., Comment: 13 pages, 3 figures

Suicidal ideation detection is crucial for preventing suicides, a leading cause of death worldwide. Many individuals express suicidal thoughts on social media, offering a vital opportunity for early detection through advanced machine learning techniques. The identification of suicidal ideation in social media text is improved by utilising a hybrid framework that integrates Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM), enhanced with an attention mechanism. To enhance the interpretability of the model's predictions, Explainable AI (XAI) methods are applied, with a particular focus on SHapley Additive exPlanations (SHAP), are incorporated. At first, the model managed to reach an accuracy of 92.81%. By applying fine-tuning and early stopping techniques, the accuracy improved to 94.29%. The SHAP analysis revealed key features influencing the model's predictions, such as terms related to mental health struggles. This level of transparency boosts the model's credibility while helping mental health professionals understand and trust the predictions. This work highlights the potential for improving the accuracy and interpretability of detecting suicidal tendencies, making a valuable contribution to the progress of mental health monitoring systems. It emphasizes the significance of blending powerful machine learning methods with explainability to develop reliable and impactful mental health solutions.

This review underscores the critical need for effective strategies to identify and support individuals with suicidal ideation, exploiting technological innovations in ML and DL to further suicide prevention efforts. The study details the application of these technologies in analyzing vast amounts of unstructured social media data to detect linguistic patterns, keywords, phrases, tones, and contextual cues associated with suicidal thoughts. It explores various ML and DL models like SVMs, CNNs, LSTM, neural networks, and their effectiveness in interpreting complex data patterns and emotional nuances within text data. The review discusses the potential of these technologies to serve as a life-saving tool by identifying at-risk individuals through their digital traces. Furthermore, it evaluates the real-world effectiveness, limitations, and ethical considerations of employing these technologies for suicide prevention, stressing the importance of responsible development and usage. The study aims to fill critical knowledge gaps by analyzing recent studies, methodologies, tools, and techniques in this field. It highlights the importance of synthesizing current literature to inform practical tools and suicide prevention efforts, guiding innovation in reliable, ethical systems for early intervention. This research synthesis evaluates the intersection of technology and mental health, advocating for the ethical and responsible application of ML, DL, and NLP to offer life-saving potential worldwide while addressing challenges like generalizability, biases, privacy, and the need for further research to ensure these technologies do not exacerbate existing inequities and harms.

We present the strongest limits to date on the mixing angle, $\theta$, with which a new scalar particle, $S$, mixes with the Higgs field in the mass range $100$ $MeV

This paper presents an innovative method for automatically detecting dust accumulation on a PV system and notifying the user to clean it instantly. The accumulation of dust, bird, or insect droppings on the surface of photovoltaic (PV) panels creates a barrier between the solar energy and the panel's surface to receive sufficient energy to generate electricity. The study investigates the effects of dust on PV panel output and visible sunlight (VSL) block amounts to utilize the necessity of cleaning and detection. The amount of blocked visible sunlight while passing through glass due to dust determines the accumulated dust level. Visible sunlight can easily pass through the clean, transparent glass but reflects when something like dust obstructs it. Based on those concepts, a system is designed with a light sensor that is simple, effective, easy to install, hassle-free, and can spread the technology. The study also explores the effectiveness of the detection system developed by using image processing and machine learning algorithms to identify dust levels and bird or insect droppings accurately. The experimental setup in Gazipur, Bangladesh, found that excessive dust can block up to 55% of visible sunlight, wasting 55% of solar energy in the visible spectrum, and cleaning can recover 3% of power weekly. The data from the dust detection system is correlated with the 400W capacity solar panels' naturally lost efficiency data to validate the system. This research measured visible sunlight obstruction and loss due to dust. However, the addition of an infrared radiation sensor can draw the entire scenario of energy loss by doing more research.

Hate speech and abusive language are global phenomena that need socio-cultural background knowledge to be understood, identified, and moderated. However, in many regions of the Global South, there have been several documented occurrences of (1) absence of moderation and (2) censorship due to the reliance on keyword spotting out of context. Further, high-profile individuals have frequently been at the center of the moderation process, while large and targeted hate speech campaigns against minorities have been overlooked. These limitations are mainly due to the lack of high-quality data in the local languages and the failure to include local communities in the collection, annotation, and moderation processes. To address this issue, we present AfriHate: a multilingual collection of hate speech and abusive language datasets in 15 African languages. Each instance in AfriHate is annotated by native speakers familiar with the local culture. We report the challenges related to the construction of the datasets and present various classification baseline results with and without using LLMs. The datasets, individual annotations, and hate speech and offensive language lexicons are available on https://github.com/AfriHate/AfriHate

Indoor localization in challenging non-line-of-sight (NLOS) environments often leads to mediocre accuracy with traditional approaches. Deep learning (DL) has been applied to tackle these challenges; however, many DL approaches overlook computational complexity, especially for floating-point operations (FLOPs), making them unsuitable for resource-limited devices. Transformer-based models have achieved remarkable success in natural language processing (NLP) and computer vision (CV) tasks, motivating their use in wireless applications. However, their use in indoor localization remains nascent, and directly applying Transformers for indoor localization can be both computationally intensive and exhibit limitations in accuracy. To address these challenges, in this work, we introduce a novel tokenization approach, referred to as Sensor Snapshot Tokenization (SST), which preserves variable-specific representations of power delay profile (PDP) and enhances attention mechanisms by effectively capturing multi-variate correlation. Complementing this, we propose a lightweight Swish-Gated Linear Unit-based Transformer (L-SwiGLU Transformer) model, designed to reduce computational complexity without compromising localization accuracy. Together, these contributions mitigate the computational burden and dependency on large datasets, making Transformer models more efficient and suitable for resource-constrained scenarios. The proposed tokenization method enables the Vanilla Transformer to achieve a 90th percentile positioning error of 0.388 m in a highly NLOS indoor factory, surpassing conventional tokenization methods. The L-SwiGLU ViT further reduces the error to 0.355 m, achieving an 8.51% improvement. Additionally, the proposed model outperforms a 14.1 times larger model with a 46.13% improvement, underscoring its computational efficiency., Comment: The paper has been submitted to IEEE Transactions on Machine Learning in Communications and Networking

Tennis, like other games and sports, is governed by rules, including the rules that determine the winner of points, games, sets, and matches. If the two players are equally skilled -- each has the same probability of winning a point when serving or when receiving -- we show that each has an equal chance of winning games, sets, and matches, whether or not sets go to a tiebreak. However, in a women's match that is decided by 2 out of 3 sets, and a men's match that is decided by 3 out of 5 sets, it is possible that the player who wins the most games may not be the player who wins the match. We calculate the probability that this happens and show that it has actually occurred -- most notably, in the 2019 men's Wimbledon final between Novak Djokovic and Roger Federer, which took almost five hours to complete and is considered one of the greatest tennis matches ever (Djokovic won). We argue that the discrepancy between the game winner and the match winner, when it occurs, should be resolved by a Grand Tiebreak (GT) -- played according to the rules of tiebreaks in sets -- because each player has a valid claim to being called the rightful winner. A GT would have the salutary effect of -- even every point -- lest he/she win in sets but lose more games. This would make competition keener throughout a match and probably decrease the need for a GT, because the game and set winner would more likely coincide when the players fight hard for every point.

We propose a novel Bregman descent algorithm for minimizing a convex function that is expressed as the sum of a differentiable part (defined over an open set) and a possibly nonsmooth term. The approach, referred to as the Variable Bregman Majorization-Minimization (VBMM) algorithm, extends the Bregman Proximal Gradient method by allowing the Bregman function used in the divergence to adaptively vary at each iteration, provided it satisfies a majorizing condition on the objective function. This adaptive framework enables the algorithm to approximate the objective more precisely at each iteration, thereby allowing for accelerated convergence compared to the traditional Bregman Proximal Gradient descent. We establish the convergence of the VBMM algorithm to a minimizer under mild assumptions on the family of metrics used. Furthermore, we introduce a novel application of both the Bregman Proximal Gradient method and the VBMM algorithm to the estimation of the multidimensional parameters of a Dirichlet distribution through the maximization of its log-likelihood. Numerical experiments confirm that the VBMM algorithm outperforms existing approaches in terms of convergence speed.

Let $\mathbb{F}_{p^m}$ be a finite field of cardinality $p^m$, where $p$ is a prime number and $m$ is a positive integer. Self-dual constacyclic codes of length \( p^s \) over \( \frac{\mathbb{F}_{p^m}[u]}{\langle u^3 \rangle} \) exist only when \( p = 2 \). In this work, we classify and enumerate all self-dual cyclic codes of length \( 2^s \) over \( \frac{\mathbb{F}_{2^m}[u]}{\langle u^3 \rangle} \), thereby completing the classification and enumeration of self-dual constacyclic codes of length \( p^s \) over \( \frac{\mathbb{F}_{p^m}[u]}{\langle u^3 \rangle} \). Additionally, we correct and improve results from B. Kim and Y. Lee (2020) in \cite{kim2020classification}.

The performance of pavement under loading depends on the strength of the subgrade. However, experimental estimation of properties of pavement strengths such as California bearing ratio (CBR), unconfined compressive strength (UCS) and resistance value (R) are often tedious, time-consuming and costly, thereby inspiring a growing interest in machine learning based tools which are simple, cheap and fast alternatives. Thus, the potential application of two boosting techniques; categorical boosting (CatBoost) and extreme gradient boosting (XGBoost) and support vector regression (SVR), is similarly explored in this study for estimation of properties of subgrade soil modified with hydrated lime activated rice husk ash (HARSH). Using 121 experimental data samples of varying proportions of HARSH, plastic limit, liquid limit, plasticity index, clay activity, optimum moisture content, and maximum dry density as input for CBR, UCS and R estimation, four evaluation metrics namely coefficient of determination (R2), root mean squared error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) are used to evaluate the models' performance. The results indicate that XGBoost outperformed CatBoost and SVR in estimating these properties, yielding R2 of 0.9994, 0.9995 and 0.9999 in estimating the CBR, UCS and R respectively. Also, SVR outperformed CatBoost in estimating the CBR and R with R2 of 0.9997 respectively. On the other hand, CatBoost outperformed SVR in estimating the UCS with R2 of 0.9994. Feature sensitivity analysis shows that the three machine learning techniques are unanimous that increasing HARSH proportion lead to values of the estimated properties respectively. A comparison with previous results also shows superiority of XGBoost in estimating subgrade properties., Comment: 17 pages

Recognizing the same faces with and without masks is important for ensuring consistent identification in security, access control, and public safety. This capability is crucial in scenarios like law enforcement, healthcare, and surveillance, where accurate recognition must be maintained despite facial occlusion. This research focuses on the challenge of recognizing the same faces with and without masks by employing cosine similarity as the primary technique. With the increased use of masks, traditional facial recognition systems face significant accuracy issues, making it crucial to develop methods that can reliably identify individuals in masked conditions. For that reason, this study proposed Masked-Unmasked Face Matching Model (MUFM). This model employs transfer learning using the Visual Geometry Group (VGG16) model to extract significant facial features, which are subsequently classified utilizing the K-Nearest Neighbors (K-NN) algorithm. The cosine similarity metric is employed to compare masked and unmasked faces of the same individuals. This approach represents a novel contribution, as the task of recognizing the same individual with and without a mask using cosine similarity has not been previously addressed. By integrating these advanced methodologies, the research demonstrates effective identification of individuals despite the presence of masks, addressing a significant limitation in traditional systems. Using data is another essential part of this work, by collecting and preparing an image dataset from three different sources especially some of those data are real provided a comprehensive power of this research. The image dataset used were already collected in three different datasets of masked and unmasked for the same faces.

The aim of this study is to assess the impact of information technology (IT) on university graduates in terms of employment development, which will aid in economic issues. This study uses a descriptive research methodology and a quantitative approach to understand variables. The focus of this study is to ascertain how graduates of Kurdistan regional universities might use IT to secure employment and significantly contribute to the nation's economic revival. The sample size was established by the use of judgmental sampling procedure and consisted of 314 people. The researcher prepared the questionnaire to collect data, and then SPSS statistical software, version 22, and Excel 2010 were used to modify, compile, and tabulate the results. The study's outcome showed that information technology is incredibly inventive, has a promising future, and makes life much easier for everyone. It also proved that a deep academic understanding of information technology and its constituent parts helps graduates of Kurdistan Regional University find suitable careers. More importantly, though, anyone looking for work or a means of support will find great benefit from possessing credentials and understanding of IT. The study's final finding was that information technology has actively advanced the country's economy. Not only is IT helping to boost youth employment, but it is also turning into a worthwhile investment for economic growth.

The zero-shot capabilities of Vision-Language Models (VLMs) have been widely leveraged to improve predictive performance. However, previous works on transductive or test-time adaptation (TTA) often make strong assumptions about the data distribution, such as the presence of all classes. Our work challenges these favorable deployment scenarios, and introduces a more realistic evaluation framework, including: (i) a variable number of effective classes for adaptation within a single batch, and (ii) non-i.i.d. batches of test samples in online adaptation settings. We provide comprehensive evaluations, comparisons, and ablation studies that demonstrate how current transductive or TTA methods for VLMs systematically compromise the models' initial zero-shot robustness across various realistic scenarios, favoring performance gains under advantageous assumptions about the test samples' distributions. Furthermore, we introduce StatA, a versatile method that could handle a wide range of deployment scenarios, including those with a variable number of effective classes at test time. Our approach incorporates a novel regularization term designed specifically for VLMs, which acts as a statistical anchor preserving the initial text-encoder knowledge, particularly in low-data regimes. Code available at https://github.com/MaxZanella/StatA.

This work introduces an integrated approach to optimizing urban traffic by combining predictive modeling of vehicle flow, adaptive traffic signal control, and a modular integration architecture through distributed messaging. Using real-time data from various sensors, the system anticipates traffic fluctuations and dynamically adjusts signal phase durations to minimize delays and improve traffic flow. This proactive adjustment, supported by algorithms inspired by simulated annealing and reinforcement learning, also enhances energy efficiency, reduces pollutant emissions, and responds effectively to unexpected events (adverse weather, accidents, or temporary gatherings). Preliminary simulations conducted in a realistic urban environment demonstrate a significant reduction in average waiting times. Future developments include incorporating data from connected vehicles, integrating new modes of transport, and continuously refining predictive models to address the growing challenges of urban mobility.

Continuous gravitational waves (CWs) emission from neutron stars carries information about their internal structure and equation of state, and it can provide tests of General Relativity. We present a search for CWs from a set of 45 known pulsars in the first part of the fourth LIGO--Virgo--KAGRA observing run, known as O4a. We conducted a targeted search for each pulsar using three independent analysis methods considering the single-harmonic and the dual-harmonic emission models. We find no evidence of a CW signal in O4a data for both models and set upper limits on the signal amplitude and on the ellipticity, which quantifies the asymmetry in the neutron star mass distribution. For the single-harmonic emission model, 29 targets have the upper limit on the amplitude below the theoretical spin-down limit. The lowest upper limit on the amplitude is $6.4\!\times\!10^{-27}$ for the young energetic pulsar J0537-6910, while the lowest constraint on the ellipticity is $8.8\!\times\!10^{-9}$ for the bright nearby millisecond pulsar J0437-4715. Additionally, for a subset of 16 targets we performed a narrowband search that is more robust regarding the emission model, with no evidence of a signal. We also found no evidence of non-standard polarizations as predicted by the Brans-Dicke theory., Comment: main paper: 12 pages, 6 figures, 4 tables

We outline a general framework for evaluating the non-perturbative soft functions in the QCD instanton vacuum. In particular, from the soft function we derive the Collins-Soper (CS) kernel, which drives the rapidity evolution of the transverse-momentum-dependent parton distributions. The resulting CS kernel, when supplemented with the perturbative contribution, agrees well with recent lattice results and some phenomenological parameterizations. Moreover, our CS kernel depends logarithmically on the large quark transverse separation, providing a key constraint on its phenomenological parametrization. Finally, a lattice calculation can be directly compared to our generic results in Euclidean signature, thus providing a new approach for evaluating the soft function and extracting the CS kernel by analytical continuation., Comment: 19 pages, 8 figures

Star clusters, including high-mass stars, form within hub-filament systems (HFSs). Observations of HFSs that remain unaffected by feedback from embedded stars are rare yet crucial for understanding the mass inflow process in high-mass star formation. Using the JWST NIRCAM images, Dewangan et al. 2024, reported that the high-mass protostar G11P1 is embedded in a candidate HFS (G11P1-HFS; $<0.6$ pc). Utilizing ALMA N$_{2}$H$^{+}$(1-0) data, we confirm the presence of G11P1-HFS and study the dense gas kinematics. We analyzed the position-position-velocity (PPV) map and estimated on-sky velocity gradient ($V_g$) and gravity ($\mathcal{F}_{g}$) vectors. The spatial distribution of gas velocity and H$_2$ column density was examined. The steep $V_g$ of 5 km s$^{-1}$ pc$^{-1}$ and $-$7 km s$^{-1}$ pc$^{-1}$ toward either side of G11P1-hub, and the decreasing $V_g$ toward the hub, identify G11P1-HFS as a small-scale HFS in its nascent phase. $V_g$ and $\mathcal{F}_{g}$ align along the filaments, indicating gravity-driven flows. This work highlights the wiggled, funnel-shaped morphology of a HFS in PPV space, suggesting the importance of subfilaments or transverse gas flows in mass transportation to the hub., Comment: 10 pages, 11 figures, 1 table, Accepted for publication in A&A Letters

Machine Unlearning has emerged as a critical area in artificial intelligence, addressing the need to selectively remove learned data from machine learning models in response to data privacy regulations. This paper provides a comprehensive comparative analysis of six state-of-theart unlearning techniques applied to image and text classification tasks. We evaluate their performance, efficiency, and compliance with regulatory requirements, highlighting their strengths and limitations in practical scenarios. By systematically analyzing these methods, we aim to provide insights into their applicability, challenges,and tradeoffs, fostering advancements in the field of ethical and adaptable machine learning.

Memory-centric computing aims to enable computation capability in and near all places where data is generated and stored. As such, it can greatly reduce the large negative performance and energy impact of data access and data movement, by 1) fundamentally avoiding data movement, 2) reducing data access latency & energy, and 3) exploiting large parallelism of memory arrays. Many recent studies show that memory-centric computing can largely improve system performance & energy efficiency. Major industrial vendors and startup companies have recently introduced memory chips with sophisticated computation capabilities. Going forward, both hardware and software stack should be revisited and designed carefully to take advantage of memory-centric computing. This work describes several major recent advances in memory-centric computing, specifically in Processing-in-DRAM, a paradigm where the operational characteristics of a DRAM chip are exploited and enhanced to perform computation on data stored in DRAM. Specifically, we describe 1) new techniques that slightly modify DRAM chips to enable both enhanced computation capability and easier programmability, 2) new experimental studies that demonstrate the functionally-complete bulk-bitwise computational capability of real commercial off-the-shelf DRAM chips, without any modifications to the DRAM chip or the interface, and 3) new DRAM designs that improve access granularity & efficiency, unleashing the true potential of Processing-in-DRAM., Comment: This paper is an extended version of an IEDM 2024 Invited Paper in the AI Memory focus session

The ever-changing world of disease study heavily relies on mathematical models. They are key in finding and controlling infectious diseases. We aim to explore these mathematical tools used for studying disease spread in biology. The SEIR model holds our focus. It is a super important tool known for being flexible and useful. We look at the modified SEIR models' design and analysis. We dive right into vital parts like the equations that make the modified SEIR model work, setting parameter identities, and then checking its solutions' positivity and limits. The study begins with a detailed examination of the design and analysis of a modified SEIR model, demonstrating its angularity. We delve into the model's heart, dealing with critical issues such as the equations that drive the modified SEIR model, establishing parameter identities, and ensuring the positivity and boundlessness of its solutions. Basic Reproduction Number marks a significant milestone. We investigate the local stability, DFE, and EE. Global stability, a paramount consideration in understanding the long-term behaviors of the systems, is scrutinized by employing the Lyapunov stability theorem. The bifurcation analysis classifies and elucidates the fundamental concepts therein. One-dimensional bifurcation and forward and backward bifurcation analyses are intricately examined, providing a comprehensive understanding of the dynamical behavior and basic concepts. In summary, we offer a thorough description and analysis of the SEIR model but also lay the groundwork for advancing mathematical modeling in epidemiology. By bridging theoretical insights with practical implications, this study strives to empower researchers and policymakers with a deep understanding of infectious disease dynamics, thereby contributing to targeted public health strategies., Comment: 27 pages, 7 figures, 7 tables

This research proposes a novel approach utilizing Orbital Angular Momentum (OAM) beams to enhance Radar Cross Section (RCS) diversity for target detection in future transportation systems. Unlike conventional OAM beams with hollow-shaped divergence patterns, the new proposed OAM beams provide uniform illumination across the target without a central energy void, but keep the inherent phase gradient of vortex property. We utilize waveguide slot antennas to generate four different modes of these novel OAM beams at X-band frequency. Furthermore, these different mode OAM beams are used to illuminate metal models, and the resulting RCS is compared with that obtained using plane waves. The findings reveal that the novel OAM beams produce significant azimuthal RCS diversity, providing a new approach for the detection of weak and small targets.This study not only reveals the RCS diversity phenomenon based on novel OAM beams of different modes but also addresses the issue of energy divergence that hinders traditional OAM beams in long-range detection applications.

Many real-world complex systems, such as epidemic spreading networks and ecosystems, can be modeled as networked dynamical systems that produce multivariate time series. Learning the intrinsic dynamics from observational data is pivotal for forecasting system behaviors and making informed decisions. However, existing methods for modeling networked time series often assume known topologies, whereas real-world networks are typically incomplete or inaccurate, with missing or spurious links that hinder precise predictions. Moreover, while networked time series often originate from diverse topologies, the ability of models to generalize across topologies has not been systematically evaluated. To address these gaps, we propose a novel framework for learning network dynamics directly from observed time-series data, when prior knowledge of graph topology or governing dynamical equations is absent. Our approach leverages continuous graph neural networks with an attention mechanism to construct a latent topology, enabling accurate reconstruction of future trajectories for network states. Extensive experiments on real and synthetic networks demonstrate that our model not only captures dynamics effectively without topology knowledge but also generalizes to unseen time series originating from diverse topologies.

In today's digital age, information systems (IS) are indispensable tools for organizations of all sizes. The quality of these systems, encompassing system, information, and service dimensions, significantly impacts organizational performance. This study investigates the intricate relationships between these three quality dimensions and their collective influence on key performance indicators such as customer satisfaction and operational efficiency. By conducting a comparative analysis of various quality metrics, we aim to identify the most effective indicators for assessing IS quality. Our research contributes to the field by providing actionable insights for researchers or practitioners to develop the implementation, evaluation and design of information systems. Also, a quantitative study employing a structured questionnaire survey was conducted to achieve primary data from respondents across various sectors. Statistical analysis, including Cronbach's Alpha (0.953) and factor analysis (KMO = 0.965, Bartlett's Test p < 0.000), revealed strong interdependencies among System Quality (SQ), Information Quality (IQ), and Service Quality (SerQ). The results demonstrate that high SQ leads to improved IQ, which in turn contributes to enhanced SerQ and user satisfaction. While all three qualities are crucial, SerQ emerges as the most relevant indicator of overall system performance due to its broader representation of quality dimensions

Transductive few-shot learning has recently triggered wide attention in computer vision. Yet, current methods introduce key hyper-parameters, which control the prediction statistics of the test batches, such as the level of class balance, affecting performances significantly. Such hyper-parameters are empirically grid-searched over validation data, and their configurations may vary substantially with the target dataset and pre-training model, making such empirical searches both sub-optimal and computationally intractable. In this work, we advocate and introduce the unrolling paradigm, also referred to as "learning to optimize", in the context of few-shot learning, thereby learning efficiently and effectively a set of optimized hyper-parameters. Specifically, we unroll a generalization of the ubiquitous Expectation-Maximization (EM) optimizer into a neural network architecture, mapping each of its iterates to a layer and learning a set of key hyper-parameters over validation data. Our unrolling approach covers various statistical feature distributions and pre-training paradigms, including recent foundational vision-language models and standard vision-only classifiers. We report comprehensive experiments, which cover a breadth of fine-grained downstream image classification tasks, showing significant gains brought by the proposed unrolled EM algorithm over iterative variants. The achieved improvements reach up to 10% and 7.5% on vision-only and vision-language benchmarks, respectively.

This Letter presents an investigation of low-energy electron-neutrino interactions in the Fermilab Booster Neutrino Beam by the MicroBooNE experiment, motivated by the excess of electron-neutrino-like events observed by the MiniBooNE experiment. This is the first measurement to use data from all five years of operation of the MicroBooNE experiment, corresponding to an exposure of $1.11\times 10^{21}$ protons on target, a $70\%$ increase on past results. Two samples of electron neutrino interactions without visible pions are used, one with visible protons and one without any visible protons. MicroBooNE data is compared to two empirical models that modify the predicted rate of electron-neutrino interactions in different variables in the simulation to match the unfolded MiniBooNE low energy excess. In the first model, this unfolding is performed as a function of electron neutrino energy, while the second model aims to match the observed shower energy and angle distributions of the MiniBooNE excess. This measurement excludes an electron-like interpretation of the MiniBooNE excess based on these models at $> 99\%$ CL$_\mathrm{s}$ in all kinematic variables., Comment: 8 pages, 5 figures, 1 table

Reinforcement learning (RL) has shown great effectiveness in quadrotor control, enabling specialized policies to develop even human-champion-level performance in single-task scenarios. However, these specialized policies often struggle with novel tasks, requiring a complete retraining of the policy from scratch. To address this limitation, this paper presents a novel multi-task reinforcement learning (MTRL) framework tailored for quadrotor control, leveraging the shared physical dynamics of the platform to enhance sample efficiency and task performance. By employing a multi-critic architecture and shared task encoders, our framework facilitates knowledge transfer across tasks, enabling a single policy to execute diverse maneuvers, including high-speed stabilization, velocity tracking, and autonomous racing. Our experimental results, validated both in simulation and real-world scenarios, demonstrate that our framework outperforms baseline approaches in terms of sample efficiency and overall task performance.

Due to its peculiar and highly variable nature, the blazar 3C 454.3 has been extensively monitored by the WEBT team. Here, we present for the first time these long-term optical flux and color variability results using data acquired in B, V, R, and I bands over a time span of $\sim$ 2 decades. We include data from WEBT collaborators and public archives such as SMARTS, Steward Observatory, and ZTF. The data are binned and segmented to study the source over this long term when more regular sampling was available. During our study, the long-term spectral variability reveals a redder when brighter (RWB) trend, which, however, stabilizes at a particular brightness cutoff $\sim$ 14.5 mag in the I-band, after which it saturates and evolves into a complex state. This trend indicates increasing jet emission dominance over accretion disk emission until jet emission completely dominates. Plots of the spectral index variation (following $F_{\nu} \propto \nu^{-\alpha}$) reveal a bimodal distribution using a one-day binning. These correlate with two extreme phases of 3C 454.3, an outburst or high flux state and quiescent or low flux state, which are respectively jet and accretion disk dominated. We have also conducted intra-day variability studies of nine light curves and found that six of them are variable. Discrete Correlation Function (DCF) analysis between different optical waveband pairs peak at zero lags, indicating co-spatial emission in different optical bands., Comment: 18 pages, 6 figures, 5 tables

In this paper, we study a class of orthogonal polynomials defined by a three-term recurrence relation with periodic coefficients. We derive explicit formulas for the generating function, the associated continued fraction, the orthogonality measure of these polynomials, as well as the spectral measure for the associated doubly infinite tridiagonal Jacobi matrix. Notably, while the orthogonality measure may include discrete mass points, the spectral measure(s) of the doubly infinite Jacobi matrix are absolutely continuous. Additionally, we uncover an intrinsic connection between these new orthogonal polynomials and Chebyshev polynomials through a nonlinear transformation of the polynomial variables., Comment: 26 pages, 1 figure

Intermediate mass black holes (10^2 < M_BH< 10^5 Msun) are an open question in our understanding of black hole evolution and growth. They have long been linked to dense star cluster environments thanks to cluster dynamics, but there are a limited number of secure detections. We leverage existing X-ray observations from Chandra X-ray Observatory and optical catalogs from Hubble Space Telescope with new radio observations from the Karl G. Jansky Very Large Array to search for any evidence of accreting black holes in young massive clusters in the nearby galaxy M51. We find that of 43 bright ($L_X > 10^{38}$ erg/s) X-ray point sources in M51, 24 had probable matches to objects including possible associated star clusters in the HST Legacy Extragalactic UV Survey catalog, seven of which were classified as contaminants (background galaxies or foreground stars). We explore the optical properties of the remaining 17 sources, including cluster age and mass estimates, and search for radio counterparts in the 8-12 GHz band. The lack of radio counterparts to X-ray sources we know to be associated with young massive clusters in M51 suggests that we do not significantly detect hard-state IMBHs ~ 10^4 Msun or above. However, more sensitive radio facilities like the Square Kilometre Array and next generation Very Large Array may be able to provide evidence for IMBHs with masses down to ~ 10^3 Msun., Comment: 18 pages, 7 figures, accepted to ApJ

Recent advances in self-supervision and constrastive learning have brought the performance of foundation models to unprecedented levels in a variety of tasks. Fueled by this progress, these models are becoming the prevailing approach for a wide array of real-world vision problems, including risk-sensitive and high-stakes applications. However, ensuring safe deployment in these scenarios requires a more comprehensive understanding of their uncertainty modeling capabilities, which has been barely explored. In this work, we delve into the behavior of vision and vision-language foundation models under Conformal Prediction (CP), a statistical framework that provides theoretical guarantees of marginal coverage of the true class. Across extensive experiments including popular vision classification benchmarks, well-known foundation vision models, and three CP methods, our findings reveal that foundation models are well-suited for conformalization procedures, particularly those integrating Vision Transformers. Furthermore, we show that calibrating the confidence predictions of these models leads to efficiency degradation of the conformal set on adaptive CP methods. In contrast, few-shot adaptation to downstream tasks generally enhances conformal scores, where we identify Adapters as a better conformable alternative compared to Prompt Learning strategies. Our empirical study identifies APS as particularly promising in the context of vision foundation models, as it does not violate the marginal coverage property across multiple challenging, yet realistic scenarios.

The vehicular Metaverse represents an emerging paradigm that merges vehicular communications with virtual environments, integrating real-world data to enhance in-vehicle services. However, this integration faces critical security challenges, particularly in the data collection layer where malicious sensing IoT (SIoT) devices can compromise service quality through data poisoning attacks. The security aspects of the Metaverse services should be well addressed both when creating the digital twins of the physical systems and when delivering the virtual service to the vehicular Metaverse users (VMUs). This paper introduces vehicular Metaverse guard (VMGuard), a novel four-layer security framework that protects vehicular Metaverse systems from data poisoning attacks. Specifically, when the virtual service providers (VSPs) collect data about physical environment through SIoT devices in the field, the delivered content might be tampered. Malicious SIoT devices with moral hazard might have private incentives to provide poisoned data to the VSP to degrade the service quality (QoS) and user experience (QoE) of the VMUs. The proposed framework implements a reputation-based incentive mechanism that leverages user feedback and subjective logic modeling to assess the trustworthiness of participating SIoT devices. More precisely, the framework entails the use of reputation scores assigned to participating SIoT devices based on their historical engagements with the VSPs. Ultimately, we validate our proposed model using comprehensive simulations. Our key findings indicate that our mechanism effectively prevents the initiation of poisoning attacks by malicious SIoT devices. Additionally, our system ensures that reliable SIoT devices, previously missclassified, are not barred from participating in future rounds of the market., Comment: submitted to IEEE TVT

We calculate energy correlators in a general holographic model of confinement, involving an asymptotically anti-de Sitter (AdS) warped extra dimension. Building on a recent computation in a minimal hard-wall model of confinement, we show that the shockwave method for efficiently computing energy correlators in AdS generalizes to an arbitrary warped geometry. This is possible because exact, linear shockwave solutions to the 5D field equations exist in any warped background. We apply our formalism to compute the two-point energy correlator for two simple models of confinement with interesting infrared spectra -- one with a gapped continuum spectrum and one with linear Regge trajectories. The results differ from the simple hard-wall model and from each other, demonstrating that the details of the confining dynamics affect the shape of the energy correlator observables., Comment: 17 + 7 pages, 3 figures

A multi-wavelength, multi-scale study of the Mon R2 hub-filament system (HFS) reveals a spiral structure, with the central hub containing more mass than its filaments. ALMA C$^{18}$O(1-0) emission reveals several accreting filaments connected to a molecular ring (size $\sim$0.18 pc $\times$ 0.26 pc). The molecular ring surrounds the infrared (IR) ring (size $\sim$0.12 pc $\times$ 0.16 pc), which is not usually observed. The IR ring encircles IR dark regions and a population of embedded near-IR sources, including the massive stars IRS 1 and IRS 2. ALMA HNC(3-2) line data reveal a mirrored B-shaped feature (extent $\sim$19000 AU $\times$ 39000 AU) toward the eastern part of the molecular ring, suggesting expansion at $\sim$2.25 km s$^{-1}$. Distinct HNC sub-structures in both redshifted and blueshifted velocity components are investigated toward the B-shaped feature. The presence of these braid-like substructures in each velocity component strongly suggests instability in photon-dominated regions. A dusty shell-like feature (extent $\sim$0.04 pc $\times$ 0.07 pc; mass $\sim$7 M$_{\odot}$) hosting IRS 1 is identified in the ALMA 1.14 mm continuum map, centered toward the base of the B-shaped feature. The IR and dense molecular rings are likely shaped by feedback from massive stars, driven by high pressure values between 10$^{-8}$-10$^{-10}$ dynes cm$^{-2}$, observed within a 1 pc range of the B0 ZAMS star powering the ultracompact HII region. Overall, these outcomes support that the Mon R2 HFS transitioned from IR-quiet to IR-bright, driven by the interaction between gas accretion and feedback from massive stars., Comment: Accepted for publication in The Astronomical Journal, the manuscript consists of 23 pages, 12 figures, and 2 tables

The Naira is Nigeria's official currency in daily transactions. This study presents the deployment and evaluation of Deep Learning (DL) models to classify Currency Notes (Naira) by denomination. Using a diverse dataset of 1,808 images of Naira notes captured under different conditions, trained the models employing different architectures and got the highest accuracy with MobileNetV2, the model achieved a high accuracy rate of in training of 90.75% and validation accuracy of 87.04% in classification tasks and demonstrated substantial performance across various scenarios. This model holds significant potential for practical applications, including automated cash handling systems, sorting systems, and assistive technology for the visually impaired. The results demonstrate how the model could boost the Nigerian economy's security and efficiency of financial transactions.