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Our research presents a comprehensive approach to leveraging mobile camera image data for real-time air quality assessment and recommendation. We develop a regression-based Convolutional Neural Network model and tailor it explicitly for air quality prediction by exploiting the inherent relationship between output parameters. As a result, the Mean Squared Error of 0.0077 and 0.0112 obtained for 2 and 5 pollutants respectively outperforms existing models. Furthermore, we aim to verify the common practice of augmenting the original dataset with a view to introducing more variation in the training phase. It is one of our most significant contributions that our experimental results demonstrate minimal accuracy differences between the original and augmented datasets. Finally, a real-time, user-friendly dashboard is implemented which dynamically displays the Air Quality Index and pollutant values derived from captured mobile camera images. Users' health conditions are considered to recommend whether a location is suitable based on current air quality metrics. Overall, this research contributes to verification of data augmentation techniques, CNN-based regression modelling for air quality prediction, and user-centric air quality monitoring through mobile technology. The proposed system offers practical solutions for individuals to make informed environmental health and well-being decisions.

This work presents a novel architecture for building Retrieval-Augmented Generation (RAG) systems to improve Question Answering (QA) tasks from a target corpus. Large Language Models (LLMs) have revolutionized the analyzing and generation of human-like text. These models rely on pre-trained data and lack real-time updates unless integrated with live data tools. RAG enhances LLMs by integrating online resources and databases to generate contextually appropriate responses. However, traditional RAG still encounters challenges like information dilution and hallucinations when handling vast amounts of data. Our approach addresses these challenges by converting corpora into a domain-specific dataset and RAG architecture is constructed to generate responses from the target document. We introduce QuIM-RAG (Question-to-question Inverted Index Matching), a novel approach for the retrieval mechanism in our system. This strategy generates potential questions from document chunks and matches these with user queries to identify the most relevant text chunks for generating accurate answers. We have implemented our RAG system on top of the open-source Meta-LLaMA3-8B-instruct model by Meta Inc. that is available on Hugging Face. We constructed a custom corpus of 500+ pages from a high-traffic website accessed thousands of times daily for answering complex questions, along with manually prepared ground truth QA for evaluation. We compared our approach with traditional RAG models using BERT-Score and RAGAS, state-of-the-art metrics for evaluating LLM applications. Our evaluation demonstrates that our approach outperforms traditional RAG architectures on both metrics.

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

In recent years, Large Language Models (LLMs) have emerged as a transformative development in artificial intelligence (AI), drawing significant attention from industry and academia. Trained on vast datasets, these sophisticated AI systems exhibit impressive natural language processing and content generation capabilities. This paper explores the potential of LLMs to address key challenges in personal finance, focusing on the United States. We evaluate several leading LLMs, including OpenAI's ChatGPT, Google's Gemini, Anthropic's Claude, and Meta's Llama, to assess their effectiveness in providing accurate financial advice on topics such as mortgages, taxes, loans, and investments. Our findings show that while these models achieve an average accuracy rate of approximately 70%, they also display notable limitations in certain areas. Specifically, LLMs struggle to provide accurate responses for complex financial queries, with performance varying significantly across different topics. Despite these limitations, the analysis reveals notable improvements in newer versions of these models, highlighting their growing utility for individuals and financial advisors. As these AI systems continue to evolve, their potential for advancing AI-driven applications in personal finance becomes increasingly promising.

Starting with the Dirac equation for an electron in a constant electromagnetic background on a noncommutative (NC) plane, we obtain a gauge invariant description of the system. Surprisingly, the dynamics of the system is dictated by the standard form of Lorentz force law, once the effective magnetic and electric fields $\left( B^{NC}, \, E^{NC} \right)$ correct up to leading order in the NC parameter are identified. The Hall effect is studied using the NC corrected fields in the non-relativistic (NR) limit. This shows that noncommutativity affects the cyclotron frequency, but leaves the Hall conductivity unaffected at least to first order in the NC parameter. Owing to the NC corrected magnetic field, the hyperfine splitting of Hydrogen atom spectrum also shows a first order correction which helps establish an upper bound on the spatial NC parameter., Comment: 9 pages, laTex, no figures

This study presents a detailed analysis of longitudinal flow decorrelations in light ion collisions at both Relativistic Heavy Ion Collider (RHIC) and Large Hadron Collider(LHC) energies, focusing on various \textit{ab-initio} models of oxygen structure. We identify that the correlator $V_{2\Delta}$ effectively differentiates between the variational Monte Carlo (VMC) structure and other models, such as Nuclear Lattice Effective Field Theory (NLEFT) and the Projected Generator Coordinate Method (PGCM), revealing similar behaviors between the latter two. By utilizing the isolated non-spherical components, we highlight significant negative contributions in the structure generated by VMC compared to the other configurations. Additionally, we show that employing decorrelation $r_2$ to mitigate non-flow effects proves beneficial in identifying various modeled structures. We further examine flow decorrelations across different systems, including d+Au and O+O collisions at 200 GeV, as well as Ne+Ne and O+O collisions at 6.37 TeV, indicating a hierarchy in decorrelations that underscores the complexity of these interactions. Our findings emphasize that the role of asymmetry in d+Au collisions and its impact on flow correlations is mitigated by the correlator $V_{2\Delta} (\eta, \eta_{\text{ref}} )$. Additionally, our analysis of flow angle decorrelations shows diverse distributions of $\cos(2\Delta\psi_2)$ vs. overall flow magnitude in d+Au collisions compared to those in other collision systems, as indicated by the mean value of $\cos(2\Delta\psi_2)$., Comment: 11 pages, 10 figures

CubeSat technology is an emerging alternative to large-scale space telescopes due to its short development time and cost-effectiveness. MeVCube is a proposed CubeSat mission to study the least explored MeV gamma-ray sky, also known as the `MeV gap'. Besides being sensitive to a plethora of astrophysical phenomena, MeVCube can also be important in the hunt for dark matter. If dark matter is made up of evaporating primordial black holes, then it can produce photons in the sensitivity range of MeVCube. Besides, particle dark matter can also decay or annihilate to produce final state gamma-ray photons. We perform the first comprehensive study of dark matter discovery potential of a near-future MeVCube CubeSat mission. In all cases, we find that MeVCube will have much better discovery reach compared to existing limits in the parameter space. This may be an important step towards discovering dark matter through its non-gravitational interactions., Comment: 17 pages, 7 figures. Comments are welcome

The variational autoencoder (VAE) is a popular, deep, latent-variable model (DLVM) due to its simple yet effective formulation for modeling the data distribution. Moreover, optimizing the VAE objective function is more manageable than other DLVMs. The bottleneck dimension of the VAE is a crucial design choice, and it has strong ramifications for the model's performance, such as finding the hidden explanatory factors of a dataset using the representations learned by the VAE. However, the size of the latent dimension of the VAE is often treated as a hyperparameter estimated empirically through trial and error. To this end, we propose a statistical formulation to discover the relevant latent factors required for modeling a dataset. In this work, we use a hierarchical prior in the latent space that estimates the variance of the latent axes using the encoded data, which identifies the relevant latent dimensions. For this, we replace the fixed prior in the VAE objective function with a hierarchical prior, keeping the remainder of the formulation unchanged. We call the proposed method the automatic relevancy detection in the variational autoencoder (ARD-VAE). We demonstrate the efficacy of the ARD-VAE on multiple benchmark datasets in finding the relevant latent dimensions and their effect on different evaluation metrics, such as FID score and disentanglement analysis.

Assuming Galactic cosmic rays originate in supernovae and the winds of massive stars, starburst galaxies should produce very-high-energy (VHE; E$>$100 GeV) gamma-ray emission via the interaction of their copious quantities of cosmic rays with the large reservoirs of dense gas within the galaxies. Such VHE emission was detected by VERITAS from the starburst galaxy M 82 in 2008-09. An extensive, multi-year campaign followed these initial observations, yielding a total of 254 h of good quality VERITAS data on M 82. Leveraging modern analysis techniques and the larger exposure, these VERITAS data show a more statistically significant VHE signal ($\sim$6.5 standard deviations ($\sigma$)). The corresponding photon spectrum is well fit by a power law ($\Gamma = 2.3 \pm 0.3_{stat} \pm0.2_{sys}$) and the observed integral flux is F($>$450 GeV) = $(3.2 \pm0.6_{stat} \pm 0.6_{sys}) \times 10^{-13}~\mathrm{cm^{-2}~s}^{-1}$, or $\sim$0.4\% of the Crab Nebula flux above the same energy threshold. The improved VERITAS measurements, when combined with various multi-wavelength data, enable modeling of the underlying emission and transport processes. A purely leptonic scenario is found to be a poor representation of the gamma-ray spectral energy distribution (SED). A lepto-hadronic scenario with cosmic rays following a power-law spectrum in momentum (index $s\simeq 2.25$), and with significant bremsstrahlung below $1$~GeV, provides a good match to the observed SED. The synchrotron emission from the secondary electrons indicates that efficient non-radiative losses of cosmic-ray electrons may be related to advective escape from the starburst core., Comment: 15 pages, 7 figures; Accepted for the publication in The Astrophysical Journal (ApJ)

This is the detailed system description of the IITKGP-ABSP lab's submission to the NIST language recognition evaluation (LRE) 2022. The objective of this LRE (LRE22) is focused on recognizing 14 low-resourced African languages. Even though NIST has provided additional training and development data, we develop our systems with additional constraints of extreme low-resource. Our primary fixed-set submission ensures the usage of only the LRE 22 development data that contains the utterances of 14 target languages. We further restrict our system from using any pre-trained models for feature extraction or classifier fine-tuning. To address the issue of low-resource, our system relies on diverse audio augmentations followed by classifier fusions. Abiding by all the constraints, the proposed methods achieve an EER of 11.43% and cost metric of 0.41 in the LRE22 development set. For users with limited computational resources or limited storage/network capabilities, the proposed system will help achieve efficient LID performance., Comment: Submission description for NIST LRE 2022

Genuine network non-locality (GNN) refers to the existence of quantum correlations in a network with independent sources that cannot be explained by local hidden-variables (LHV) models. Even in the simplest scenario, determining whether these quantum correlations remain genuinely network non-local when derived from entangled states that deviate from their ideal forms is highly challenging due to the non-convex nature of local correlations. Understanding the boundary of these correlations thus becomes a hard problem, but one that raises academic interest specifically its robustness to noise. To address this problem, we introduce a causal domain-informed learning algorithm called the LHV k-rank neural network, which assesses the rank parameter of the non-ideal combined state produced by sources. Applied to the triangle network scenario with the three sources generating a class of quantum states known as X states, the neural network reveals that non-locality persists only if the states remain pure. Remarkably, we find that even slight deviations from ideal Bell states due to noise cause GNN to vanish, exhibiting a discrete behavior that hasn't been witnessed in the standard bell scenario. This finding thus raises a fundamental question as to whether GNN in the triangle network is exclusive to pure states or not. Additionally, we explore the case of the three sources producing dissimilar states, indicating that GNN requires all its sources to send pure entangled states with joint entangled measurements as resources. Apart from these results, this work succeeds in showing that machine learning approaches with domain-specific constraints can greatly benefit the field of quantum foundations., Comment: 14 pages, 16 figures. Presented at the 16th International Conference on Quantum Communication, Measurement and Computing (QCMC 24). For associated code file, see https://github.com/ananthrishna/GNN-LHV-k-triangle

The rapid advancement of Large Vision-Language Models (LVLMs) has enhanced capabilities offering potential applications from content creation to productivity enhancement. Despite their innovative potential, LVLMs exhibit vulnerabilities, especially in generating potentially toxic or unsafe responses. Malicious actors can exploit these vulnerabilities to propagate toxic content in an automated (or semi-) manner, leveraging the susceptibility of LVLMs to deception via strategically crafted prompts without fine-tuning or compute-intensive procedures. Despite the red-teaming efforts and inherent potential risks associated with the LVLMs, exploring vulnerabilities of LVLMs remains nascent and yet to be fully addressed in a systematic manner. This study systematically examines the vulnerabilities of open-source LVLMs, including LLaVA, InstructBLIP, Fuyu, and Qwen, using adversarial prompt strategies that simulate real-world social manipulation tactics informed by social theories. Our findings show that (i) toxicity and insulting are the most prevalent behaviors, with the mean rates of 16.13% and 9.75%, respectively; (ii) Qwen-VL-Chat, LLaVA-v1.6-Vicuna-7b, and InstructBLIP-Vicuna-7b are the most vulnerable models, exhibiting toxic response rates of 21.50%, 18.30% and 17.90%, and insulting responses of 13.40%, 11.70% and 10.10%, respectively; (iii) prompting strategies incorporating dark humor and multimodal toxic prompt completion significantly elevated these vulnerabilities. Despite being fine-tuned for safety, these models still generate content with varying degrees of toxicity when prompted with adversarial inputs, highlighting the urgent need for enhanced safety mechanisms and robust guardrails in LVLM development.

The status of the two-point connected correlator is holographically probed by using the well-known prescription of holographic mutual information. In particular, we calculate the two-point connected correlator for a specific bulk spacetime geometry which was not done explicitly earlier. We then carefully study the inequality between the mutual information and the two-point connected correlator, namely, $I(A:B)\ge\frac{(\expval{\mathcal{O}_{A}\mathcal{O}_{B}}-\expval{\mathcal{O}_{A}}\expval{\mathcal{O}_{B}})^2}{2\expval{\mathcal{O}_{A}^2}\expval{\mathcal{O}_{B}^2}}$. We observe that by considering the standard set up of two strip-like subsystems which are separated by a distance, one can show that there exists two critical separation distances, namely, $sT|_c$ and $sT|_I$ depicting the vanishing of quantum dependencies and classical dependencies between the subsystems respectively. We also make a proposal in this context., Comment: 7 pages LaTex and 2 figures, comments are welcome

Text-to-image (T2I) generation has made significant advances in recent years, but challenges still remain in the generation of perceptual artifacts, misalignment with complex prompts, and safety. The prevailing approach to address these issues involves collecting human feedback on generated images, training reward models to estimate human feedback, and then fine-tuning T2I models based on the reward models to align them with human preferences. However, while existing reward fine-tuning methods can produce images with higher rewards, they may change model behavior in unexpected ways. For example, fine-tuning for one quality aspect (e.g., safety) may degrade other aspects (e.g., prompt alignment), or may lead to reward hacking (e.g., finding a way to increase rewards without having the intended effect). In this paper, we propose Focus-N-Fix, a region-aware fine-tuning method that trains models to correct only previously problematic image regions. The resulting fine-tuned model generates images with the same high-level structure as the original model but shows significant improvements in regions where the original model was deficient in safety (over-sexualization and violence), plausibility, or other criteria. Our experiments demonstrate that Focus-N-Fix improves these localized quality aspects with little or no degradation to others and typically imperceptible changes in the rest of the image. Disclaimer: This paper contains images that may be overly sexual, violent, offensive, or harmful.

The equation of state is fundamental in describing matter under the extreme conditions characteristic of neutron stars and is central to advancing our understanding of dense matter physics. A critical challenge, however, lies in accurately modelling first-order phase transitions while ensuring thermodynamic consistency and aligning with astrophysical observations. This study explores two frameworks for constructing EoSs with first-order phase transitions: the polytropic interpolation method and the randomized speed-of-sound interpolation approach. It is found that the mass-radius relation and pressure vs. energy density relation are blind towards the thermodynamic consistency check. The polytropic interpolation method can exhibit discontinuities in the chemical potential for first-order phase transition, raising concerns regarding potential causality violations and thermodynamic inconsistencies. In contrast, the speed of sound interpolation approach ensures continuity in the chemical potential, offering a more thermodynamically consistent and reliable framework. Moreover, the sound speed method effectively captures the softer segment of the mass-radius spectrum, a capability not achieved by the consistent piecewise-polytropic approach due to its monotonic stiffness constraints. The speed of sound definition involving number density and chemical potential reveals the thermodynamic inconsistency, making it a more consistent and robust definition. These findings underscore the importance of thermodynamic consistency in EoS construction and highlight the advantages of the randomized speed-of-sound method for modelling phase transitions in dense matter., Comment: 10 pages, 5 figures

Transductive zero-shot learning with vision-language models leverages image-image similarities within the dataset to achieve better classification accuracy compared to the inductive setting. However, there is little work that explores the structure of the language space in this context. We propose GTA-CLIP, a novel technique that incorporates supervision from language models for joint transduction in language and vision spaces. Our approach is iterative and consists of three steps: (i) incrementally exploring the attribute space by querying language models, (ii) an attribute-augmented transductive inference procedure, and (iii) fine-tuning the language and vision encoders based on inferred labels within the dataset. Through experiments with CLIP encoders, we demonstrate that GTA-CLIP, yields an average performance improvement of 8.6% and 3.7% across 12 datasets and 3 encoders, over CLIP and transductive CLIP respectively in the zero-shot setting. We also observe similar improvements in a few-shot setting. We present ablation studies that demonstrate the value of each step and visualize how the vision and language spaces evolve over iterations driven by the transductive learning., Comment: Code will be released at https://github.com/cvl-umass/GTA-CLIP

The task of text-to-image generation has encountered significant challenges when applied to literary works, especially poetry. Poems are a distinct form of literature, with meanings that frequently transcend beyond the literal words. To address this shortcoming, we propose a PoemToPixel framework designed to generate images that visually represent the inherent meanings of poems. Our approach incorporates the concept of prompt tuning in our image generation framework to ensure that the resulting images closely align with the poetic content. In addition, we propose the PoeKey algorithm, which extracts three key elements in the form of emotions, visual elements, and themes from poems to form instructions which are subsequently provided to a diffusion model for generating corresponding images. Furthermore, to expand the diversity of the poetry dataset across different genres and ages, we introduce MiniPo, a novel multimodal dataset comprising 1001 children's poems and images. Leveraging this dataset alongside PoemSum, we conducted both quantitative and qualitative evaluations of image generation using our PoemToPixel framework. This paper demonstrates the effectiveness of our approach and offers a fresh perspective on generating images from literary sources.

Extensive research in pedestrian dynamics has primarily focused on crowded conditions and associated phenomena, such as lane formation, evacuation, etc. Several force-based models have been developed to predict the behavior in these situations. In contrast, there is a notable gap in terms of investigations of the moderate-to-low density situations. These scenarios are extremely commonplace across the world, including the highly populated nations like India. Additionally, the details of force-based models are expected to show significant effects at these densities, whereas the crowded, nearly packed, conditions may be expected to be governed largely by contact forces. In this study, we address this gap and comprehensively evaluate the performance of different force-based models in some common scenarios. Towards this, we perform controlled experiments in four situations: avoiding a stationary obstacle, position-swapping by walking toward each other, overtaking to reach a common goal, and navigating through a maze of obstacles. The performance evaluation consists of two stages and six evaluating parameters - successful trajectories, overlapping proportion, oscillation strength, path smoothness, speed deviation, and travel time. Firstly, models must meet an eligibility criterion of at least 80\% successful trajectories and secondly, the models are scored based on the cutoff values established from the experimental data. We evaluated five force-based models where the best one scored 57.14\%. Thus, our findings reveal significant shortcomings in the ability of these models to yield accurate predictions of pedestrian dynamics in these common situations.

In the existing literature, pedestrian dynamics models have successfully captured various complex scenarios such as lane formation, evacuation, bottlenecks, crowd intersections, etc. However, many models, particularly force-based models, struggle to replicate simple yet real-life scenarios due to the absence of human intelligence during the processes. In this study, we present a novel Variable Goal Approach (VGA), which incorporates human intelligence into pedestrian dynamics models, significantly enhancing their efficiency and realism. VGA introduces the concept of multiple intermediate goals, termed variable goals, which guide pedestrians toward their final destination while minimizing interactions and disruptions. These variable goals act as a guidance system, allowing smoother transitions and adaptive navigation. By strategically positioning variable goals, VGA introduces an element of stochasticity. This allows the model to simulate varied pedestrian paths under identical conditions, reflecting the diversity in human decision-making. In addition to its effectiveness in simple scenarios, VGA demonstrates strong performance in replicating high-density scenarios, such as lane formation, providing results that closely match real-world data.

PSR J2030+4415 is a gamma-ray pulsar with an X-ray pulsar wind nebula elongated along the north-south direction. The system shows a prominent X-ray filament oriented at an angle of 130{\deg} to the nebula axis. To improve our understanding of the non-thermal processes occurring in the pulsar wind nebula, we attempted to determine the possible existence of a radio counterpart, study its morphology, and obtain restrictive upper limits of the pulsar and filament emission at radio wavelengths. We performed observations of the pulsar PSR J2030+4415 and its surroundings with the upgraded Giant Metrewave Radio Telescope (uGMRT) at two frequency bands, and put the results in context with findings at other wavelengths. We obtained radio images at 736 and 1274 MHz that reveal a structure trailing the pulsar, with a morphology overlapping the X-ray nebula. This radio structure is the radio counterpart of the X-ray pulsar wind nebula. The derived spectral index along this structure shows spatial variation. There are no hints of the pulsar and the filament at any of the explored radio frequencies, but we obtained restrictive upper limits. A physical scenario that combines the radio and the X-ray observations, and consistent with IR data, of the nebula and the filament is presented. We propose that particle acceleration occurs in the nebula tail due to the presence of a re-collimation shock, and the highest energy particles gradually escape from it through energy-dependent diffusion. We also find a lower limit in the energy of the particles escaping along the X-ray filament of ~GeV., Comment: 8 pages, 6 figures. Accepted for publication in A&A

Despite being the most common types of stars in the Galaxy, the physical properties of late M dwarfs are often poorly constrained. A trend of radius inflation compared to evolutionary models has been observed for earlier type M dwarfs in eclipsing binaries, possibly caused by magnetic activity. It is currently unclear whether this trend also extends to later type M dwarfs below the convective boundary. This makes the discovery of lower-mass, fully convective, M dwarfs in eclipsing binaries valuable for testing evolutionary models especially in longer-period binaries where tidal interaction between the primary and secondary is negligible. With this context, we present the discovery of the NGTS-EB-7 AB system, an eclipsing binary containing a late M dwarf secondary and an evolved G-type primary star. The secondary star has a radius of $0.125 \pm 0.006 R_\odot$ , a mass of $0.096 \pm 0.004 M_\odot$ and follows a highly eccentric $(e=0.71436 \pm 0.00085)$ orbit every $193.35875 \pm 0.00034$ days. This makes NGTS-EB-7 AB the third longest-period eclipsing binary system with a secondary smaller than $200 M_J$ with the mass and radius constrained to better than $5 \%$. In addition, NGTS-EB-7 is situated near the centre of the proposed LOPS2 southern field of the upcoming PLATO mission, allowing for detection of the secondary eclipse and measurement of the companion`s temperature. With its long-period and well-constrained physical properties - NGTS-EB-7 B will make a valuable addition to the sample of M dwarfs in eclipsing binaries and help in determining accurate empirical mass/radius relations for later M dwarf stars., Comment: Main body: 14 pages, 6 figures, 5 tables. Appendices: 7 pages, 5 figures, 5 tables

In this work, we provide the first example of an infinite family of branch groups in the class of non-contracting self-similar groups. We show that these groups are very strongly fractal, not regular branch, and of exponential growth. Further, we prove that these groups do not have the congruence subgroup property by explicitly calculating the structure of their rigid kernels. This class of groups is also the first example of branch groups with non-torsion rigid kernels. As a consequence of these results, we also determine the Hausdorff dimension of these groups.

Reinforcement Learning with Human Feedback (RLHF) and its variants have made huge strides toward the effective alignment of large language models (LLMs) to follow instructions and reflect human values. More recently, Direct Alignment Algorithms (DAAs) have emerged in which the reward modeling stage of RLHF is skipped by characterizing the reward directly as a function of the policy being learned. Examples include Direct Preference Optimization (DPO) and Simple Preference Optimization (SimPO). These methods often suffer from likelihood displacement, a phenomenon by which the probabilities of preferred responses are often reduced undesirably. In this paper, we argue that, for DAAs the reward (function) shape matters. We introduce AlphaPO, a new DAA method that leverages an $\alpha$-parameter to help change the shape of the reward function beyond the standard log reward. AlphaPO helps maintain fine-grained control over likelihood displacement and over-optimization. Compared to SimPO, one of the best performing DAAs, AlphaPO leads to about 7\% to 10\% relative improvement in alignment performance for the instruct versions of Mistral-7B and Llama3-8B. The analysis and results presented highlight the importance of the reward shape, and how one can systematically change it to affect training dynamics, as well as improve alignment performance., Comment: Preprint. Work in progress

Acknowledging the effects of outdoor air pollution, the literature inadequately addresses indoor air pollution's impacts. Despite daily health risks, existing research primarily focused on monitoring, lacking accuracy in pinpointing indoor pollution sources. In our research work, we thoroughly investigated the influence of indoor activities on pollution levels. A survey of 143 participants revealed limited awareness of indoor air pollution. Leveraging 65 days of diverse data encompassing activities like incense stick usage, indoor smoking, inadequately ventilated cooking, excessive AC usage, and accidental paper burning, we developed a comprehensive monitoring system. We identify pollutant sources and effects with high precision through clustering analysis and interpretability models (LIME and SHAP). Our method integrates Decision Trees, Random Forest, Naive Bayes, and SVM models, excelling at 99.8% accuracy with Decision Trees. Continuous 24-hour data allows personalized assessments for targeted pollution reduction strategies, achieving 91% accuracy in predicting activities and pollution exposure.

In this paper, we establish a priori log-concavity estimates for the first Dirichlet eigenfunction of convex domains of a Riemannian manifold. Specifically, we focus on cases where the principal eigenfunction $u$ is assumed to be log-concave and our primary goal is to obtain quantitative estimates for the Hessian of $\log u$., Comment: 13 pages

Honorifics serve as powerful linguistic markers that reflect social hierarchies and cultural values. This paper presents a large-scale, cross-linguistic exploration of usage of honorific pronouns in Bengali and Hindi Wikipedia articles, shedding light on how socio-cultural factors shape language. Using LLM (GPT-4o), we annotated 10, 000 articles of real and fictional beings in each language for several sociodemographic features such as gender, age, fame, and exoticness, and the use of honorifics. We find that across all feature combinations, use of honorifics is consistently more common in Bengali than Hindi. For both languages, the use non-honorific pronouns is more commonly observed for infamous, juvenile, and exotic beings. Notably, we observe a gender bias in use of honorifics in Hindi, with men being more commonly referred to with honorifics than women.

A diffusive system coupled to unequal boundary reservoirs eventually reaches a non-equilibrium steady state. While the full-counting-statistics of current in these non-equilibrium states are well understood for generic systems, results for steady-state density fluctuations remain limited to a few integrable models. By employing an exact solution of the Macroscopic Fluctuation Theory, we characterize steady-state density fluctuations in terms of large deviations for a class of systems. Additionally, we quantitatively describe the development of these rare fluctuations. For generic diffusive systems in arbitrary dimensions, we use a perturbation around the equilibrium state to solve for the large deviation functional and the corresponding path to fluctuations, up to non-trivial orders where the non-locality of fluctuations unveils., Comment: 6 pages, 2 figures, 1 table (+ 5 supplementary pages)

There are various examples of phenotypic plasticity in ecosystems that serve as the basis for a wide range of inducible defences against predation. These strategies include camouflage, burrowing, mimicry, evasive actions, and even counterattacks that enhance survival under fluctuating predatory threats. Additionally, the ability to exhibit plastic responses often influences ecological balances, shaping predator-prey coexistence over time. This study introduces a predator-prey model where prey species show inducible defences, providing new insights into the role of adaptive strategies in these complex interactions. The stabilizing impact of the defensive mechanism is one of several intriguing outcomes produced by the dynamics. Moreover, the predator population rises when the interference rate increases to a moderate value even in the presence of lower prey defence but decreases monotonically for stronger defence levels. Furthermore, we identify a bistable domain when the handling rate is used as a control parameter, emphasizing the critical role of initial population sizes in determining system outcomes. By considering the species diffusion in a bounded region, the study is expanded into a spatio-temporal model. The numerical simulation reveals that the Turing domain decreases as the level of protection increases. The study is subsequently extended to incorporate taxis, known as the directed movement of species toward or away from another species. Our investigation identifies the conditions under which pattern formation emerges, driven by the interplay of inducible defences, taxis as well as species diffusion. Numerical simulations demonstrate that including taxis within the spatio-temporal model exerts a stabilizing influence, thereby diminishing the potential for pattern formation in the system., Comment: 31 pages, 30 figures

The collective chasing dynamics of non-reciprocally coupled densities leads to stable travelling waves which can be mapped to a model for emergent flocking. In this work, we couple the non-reciprocal Cahn-Hilliard model (NRCH) to a fluid to minimally describe scalar active mixtures in a suspension, with the aim to explore the stability of the waves, i.e. the emergent flock in the presence of self-generated fluid flows. We show that the emergent polarity is linearly unstable to perturbations for a specific sign of the active stress recalling instabilities of orientational order in a fluid. Using numerical simulations, we find however that non-reciprocity stabilizes the waves against the linear instability in a large region of the phase space.

For $t\geq 2$, the $t$-independence complex of a graph $G$ is the collection of all $A\subseteq V(G)$ such that each connected component of the induced subgraph $G[A]$ has at most $t-1$ vertices. The Stanley-Reisner ideal $I_{t}(G)$ of the $t$-independence complex of $G$, called $t$-connected ideal, is generated by monomials in a polynomial ring $R$ corresponding to all $A\subseteq V(G)$ of size $t$ such that $G[A]$ is connected. This class of ideals is a natural generalization of the edge ideals of graphs. In this paper, we investigate the $t$-connected ideals of chordal graphs. In particular, we prove that for a chordal graph $G$ and for all $t$ \[ \mathrm{reg}(R/I_{t}(G))=(t-1)\nu_{t}(G) \text{ and } \mathrm{pd}(R/I_{t}(G))=\mathrm{bight}(I_{t}(G)), \] where $\nu_{t}(G)$ denotes the induced matching number of the corresponding hypergraph of $I_{t}(G)$, and $\mathrm{reg}$, $\mathrm{pd}$ and $\mathrm{bight}$ stand for the regularity, projective dimension, and big height, respectively. As a consequence of the above results, we completely characterize when the $t$-connected ideal of a chordal graph has a linear resolution as well as when it satisfies the Cohen-Macaulay property. The above formulas and their consequences can be seen as a nice generalization of the classical results corresponding to the edge ideals of chordal graphs., Comment: 14 pages, 1 figure

Optically active defects often play a crucial role in governing the light emission as well as the electronic properties of materials. Moreover, defect-mediated states in the mid-gap region can trap electrons, thus opening a path for the recombination of electrons and holes in lower energy states that may require phonons in the process. Considering this, we have probed electron-phonon interaction in halide perovskite systems with the introduction of defects and investigated the thermal effect on this interaction. Here, we report Raman spectroscopy study of the thermal evolution of electron-phonon coupling, which is tunable with the crystal growth conditions, in the halide perovskite systems Cs2AgInCl6 and Cs2NaInCl6. The signature of electron-phonon coupling is observed as a Fano anomaly in the lowest frequency phonon mode (51 cm-1) which evolves with temperature. In addition, we observe a broad band in the photoluminescence (PL) measurements for the defect-mediated systems, which is otherwise absent in defect-free halide perovskite. The simultaneous observation of the Fano anomaly in the Raman spectrum and the emergence of the PL band suggests the defect-mediated mid-gap states and the consequent existence of electron-phonon coupling in the double perovskite., Comment: 16 pages, 9 figures, Accepted in Applied Physics Letters

Supersinglets are states of spin zero of $d \ge 3$ particles of $d$ levels. They are invariant under unitary transformations of the form $U^{\otimes d}$ and have applications in metrology, error protection, and communication. They also violate some specific Bell inequalities. However, neither any of these applications {\em require} supersinglets nor any of these of these Bell inequality violations capture the unique properties of the supersinglets. This leads to two questions. Question 1 is whether there exists a task that can only be solved with supersinglets. Question 2 is whether supersinglets can produce a unique $d$-partite, $d$-dimensional nonlocal signature. Here, we answer both questions affirmatively by presenting a protocol that self-test all supersinglets by producing $d$-partite, $d$-dimensional {\em perfect} quantum strategies for any $d \ge 3$., Comment: 3+8 pages, 1 figure

Dynamic magnetic response in a trilayered structure with non-equivalent layers (ABA type) has been studied with Monte Carlo simulation using Metropolis algorithm. In each layer, ferromagnetic (FM) nearest neighbour Ising interactions are present along with antiferromagnetic (AFM) nearest neighbour coupling across different layers. The system is studied under a harmonically oscillating external magnetic field. It is revealed that along with dynamic phase transition (DPT), compensation phenomenon emerges in this system under dynamic scenario too. This feature in dynamic case is unique for such trilayered systems only, in contrast to the bulk system reported earlier. The temporal behaviour of the magnetisation of each individual layer shows that different magnetic response of the non-equivalent layers results into such dynamic compensation phenomenon. The difference in response also results into warping of the dynamic hysteresis loops, under various external parameter values, such as amplitude of the oscillating field and temperature., Comment: 18 pages, 13 figures

This article presents the Keystroke Verification Challenge - onGoing (KVC-onGoing), on which researchers can easily benchmark their systems in a common platform using large-scale public databases, the Aalto University Keystroke databases, and a standard experimental protocol. The keystroke data consist of tweet-long sequences of variable transcript text from over 185,000 subjects, acquired through desktop and mobile keyboards simulating real-life conditions. The results on the evaluation set of KVC-onGoing have proved the high discriminative power of keystroke dynamics, reaching values as low as 3.33% of Equal Error Rate (EER) and 11.96% of False Non-Match Rate (FNMR) @1% False Match Rate (FMR) in the desktop scenario, and 3.61% of EER and 17.44% of FNMR @1% at FMR in the mobile scenario, significantly improving previous state-of-the-art results. Concerning demographic fairness, the analyzed scores reflect the subjects' age and gender to various extents, not negligible in a few cases. The framework runs on CodaLab., Comment: arXiv admin note: substantial text overlap with arXiv:2401.16559, arXiv:2311.06000

We study the $q$-state Potts model on a square lattice with directed nearest-neighbor spin-spin interactions that are inherently non-reciprocal. Both equilibrium and non-equilibrium dynamics are investigated. Analytically, we demonstrate that non-reciprocal interactions do not alter the critical exponents of the model under equilibrium dynamics. In contrast, numerical simulations with selfish non-equilibrium dynamics reveal distinctive behavior. For $q=2$ (non-reciprocal non-equilibrium Ising model), the critical exponents remain consistent with those of the equilibrium Ising universality class. However, for $q=3$ and $q=4$, the critical exponents vary continuously. Remarkably, a super-universal scaling function -- Binder cumulant as a function of $\xi_2/\xi_0$, where $\xi_2$ is the second moment correlation length and $\xi_0$ its maximum value -- remains identical to that of the equilibrium $q=3,4$ Potts models. These findings indicate that non-reciprocal Potts models belong to the superuniversality class of their respective equilibrium counterparts., Comment: 6 pages, 3 Figs, Supp. Material (4 pages)

The prevailing educational methods predominantly rely on traditional classroom instruction or online delivery, often limiting the teachers' ability to engage effectively with all the students simultaneously. A more intrinsic method of evaluating student attentiveness during lectures can enable the educators to tailor the course materials and their teaching styles in order to better meet the students' needs. The aim of this paper is to enhance teaching quality in real time, thereby fostering a higher student engagement in the classroom activities. By monitoring the students' electroencephalography (EEG) signals and employing machine learning algorithms, this study proposes a comprehensive solution for addressing this challenge. Machine learning has emerged as a powerful tool for simplifying the analysis of complex variables, enabling the effective assessment of the students' concentration levels based on specific parameters. However, the real-time impact of machine learning models necessitates a careful consideration as their deployment is concerned. This study proposes a machine learning-based approach for predicting the level of students' comprehension with regard to a certain topic. A browser interface was introduced that accesses the values of the system's parameters to determine a student's level of concentration on a chosen topic. The deployment of the proposed system made it necessary to address the real-time challenges faced by the students, consider the system's cost, and establish trust in its efficacy. This paper presents the efforts made for approaching this pertinent issue through the implementation of innovative technologies and provides a framework for addressing key considerations for future research directions., Comment: Published in Romanian Journal of Information Technology and Automatic Control

This study investigates the transport parameters in multi-Weyl semimetals, focusing on their magneto-optical properties and the role of chiral states. The tilting parameter is identified as a key factor in higher-order Weyl nodes, significantly influencing the magneto-optical response. We obtain a generic Landau-level expression for multi-Weyl semimetals, establishing a robust framework for analyzing their quantum transport properties. A comprehensive expression for the conductivity tensor components is presented, uncovering distinctive low-frequency peaks and other features shaped by the tilting parameter. Our findings reveal that the signatures of chiral states in the conductivity tensors become increasingly pronounced with the Weyl node order. Particularly, the tilting parameter is shown to impact Faraday rotation, at energies near the tilted Dirac cone energies. These results provide critical insights into the magneto-optical behavior of multi-Weyl semimetals and their potential for exploring topological phenomena., Comment: 8 pages, 6 Figures

Conversational question answering (ConvQA) is a convenient means of searching over RDF knowledge graphs (KGs), where a prevalent approach is to translate natural language questions to SPARQL queries. However, SPARQL has certain shortcomings: (i) it is brittle for complex intents and conversational questions, and (ii) it is not suitable for more abstract needs. Instead, we propose a novel two-pronged system where we fuse: (i) SQL-query results over a database automatically derived from the KG, and (ii) text-search results over verbalizations of KG facts. Our pipeline supports iterative retrieval: when the results of any branch are found to be unsatisfactory, the system can automatically opt for further rounds. We put everything together in a retrieval augmented generation (RAG) setup, where an LLM generates a coherent response from accumulated search results. We demonstrate the superiority of our proposed system over several baselines on a knowledge graph of BMW automobiles., Comment: Accepted at BTW 2025, 10 pages

We investigate the effects of local features in the inflationary potential on the preheating dynamics after inflation. We show that a small feature in the potential can enhance the resonance and bring the radiation-like state equation during preheating despite the inflationary potential being a quadratic one. Such localized features may naturally arise due to various physical effects without altering the large-scale predictions of the original model for cosmic microwave background (CMB) observables. We demonstrate that these features effectively introduce localized higher-power terms in the potential, significantly influencing the preheating dynamics $\unicode{x2013}$ a phenomenon we term potential surge preheating. We outline the resulting modifications in energy distribution among different components. We further show that these small-scale features leave detectable imprints in the form of gravitational wave signals. These signals influence CMB measurements of the effective number of relativistic species, $N_{\mathrm{eff}}$, offering a way to reconstruct the shape of the inflaton potential at small scales. Finally, we argue that these modifications to the scalar potential provide a framework to explore preheating dynamics and the fragmentation of scalar fields using simple scalar potentials., Comment: 30 pages, 14 figures

We explore the gravitational wave probes of a two-component dark matter framework, consisting of an $SU(2)_L$ triplet scalar and a Standard Model singlet fermion. The triplet scalar dark matter typically remains underabundant in the region below $\sim 1.9$ TeV, due to the strong $SU(2)_L$ gauge mediated interactions. We introduce a second dark matter component, an $SU(2)_L$ singlet vector-like Dirac fermion, to address this deficit in the dark matter relic abundance within a sub-TeV range. A key aspect of the proposed setup is the potential dark matter inter-conversion between the two components, which impacts the dark matter freeze-out dynamics and relic density of individual dark matter components. In such a scenario, we examine the properties of electroweak phase transition and identify the regions of parameter space that exhibit strong first-order phase transition. We estimate the resulting gravitational wave spectrum and its detectability, which could be probed through the conventional power-law-integrated sensitivity limits and the recently proposed peak-integrated sensitivity curves. Our analysis reveals that a novel region of the model's parameter space, compatible with dark matter observables, can generate a detectable gravitational wave spectrum, observable by upcoming space-based gravitational wave detectors such as LISA, BBO, DECIGO, and DECIGOcorr, while also offering complementary detection prospects in the dark matter and collider experiments., Comment: 87 Pages, 24 Figures

In dynamical systems theory, there is a lack of a straightforward rule to distinguish exact center solutions from decaying center-like solutions, as both require the damping force function to be zero [1, 2]. By adopting a multi-scale perturbative method, we have demonstrated a general rule for the decaying center-like power law behavior, characterized by an exponent of 1/3 . The investigation began with a physical question about the higher-order nonlinearity in a damping force function, which exhibits birhythmic and trirhythmic behavior under a transition to a decaying center-type solution. Using numerical optimization algorithms, we identified the power law exponent for decaying center-type behavior across various rhythmic conditions. For all scenarios, we consistently observed a decaying power law with an exponent of 1/3 .Our study aims to elucidate their dynamical differences, contributing to theoretical insights and practical applications where distinguishing between different types of center-like behaviour is crucial. This key result would be beneficial for studying the multi-rhythmic nature of biological and engineering systems., Comment: 6 pages, 6 figures

The rise in cybercrime and the complexity of multilingual and code-mixed complaints present significant challenges for law enforcement and cybersecurity agencies. These organizations need automated, scalable methods to identify crime types, enabling efficient processing and prioritization of large complaint volumes. Manual triaging is inefficient, and traditional machine learning methods fail to capture the semantic and contextual nuances of textual cybercrime complaints. Moreover, the lack of publicly available datasets and privacy concerns hinder the research to present robust solutions. To address these challenges, we propose a framework for automated cybercrime complaint classification. The framework leverages Hinglish-adapted transformers, such as HingBERT and HingRoBERTa, to handle code-mixed inputs effectively. We employ the real-world dataset provided by Indian Cybercrime Coordination Centre (I4C) during CyberGuard AI Hackathon 2024. We employ GenAI open source model-based data augmentation method to address class imbalance. We also employ privacy-aware preprocessing to ensure compliance with ethical standards while maintaining data integrity. Our solution achieves significant performance improvements, with HingRoBERTa attaining an accuracy of 74.41% and an F1-score of 71.49%. We also develop ready-to-use tool by integrating Django REST backend with a modern frontend. The developed tool is scalable and ready for real-world deployment in platforms like the National Cyber Crime Reporting Portal. This work bridges critical gaps in cybercrime complaint management, offering a scalable, privacy-conscious, and adaptable solution for modern cybersecurity challenges.

As climate change drives an increase in global extremes, it is critical for Bangladesh, a nation highly vulnerable to these impacts, to assess future risks for effective adaptation and mitigation planning. Downscaling coarse-resolution climate models to finer scales is essential for accurately evaluating the risk of extremes. In this study, we apply our downscaling method, which integrates data, physics, and machine learning, to quantify the risks of extreme precipitation in Bangladesh. The proposed approach successfully captures the observed spatial patterns and risks of extreme rainfall in the current climate while generating risk and uncertainty estimates by efficiently downscaling multiple models under future climate scenarios. Our findings project that the risk of extreme rainfall rises across the country, with the most significant increases in the northeastern hilly and southeastern coastal areas. Projections show that the daily maximum rainfall for a 100-year return period could increase by approximately 50 mm/day by mid-century and around 100 mm/day by the end of the century. However, substantial uncertainties remain due to variations across multiple climate models and scenarios.

Even though the Dissipative Particle Dynamics (DPD) has shown its worth in a variety of research areas, it has been rarely used for polymer dynamics, particularly in dilute and semi-dilute conditions and under imposed flow fields. For such applications, the most popular technique has been Brownian dynamics (BD), even though the formulation of the same may be complicated for flow in complex geometries, which is straightforward for DPD. This is partly due to the flexibility of BD simulations to mimic any dynamic regime for polymer solutions by independently tuning hydrodynamic interactions (HI) and excluded volume (EV). In this study, we reveal that DPD also offers a similar flexibility and the regimes with respect to dominant EV and HI can be selected as conveniently as BD. This flexibility is achieved by tuning the repulsive interaction parameter of polymer beads and the spring length (which determines the chain resolution). Our results show that the former sets the chain size (and thus, EV) while the latter can be used to set the HI, nearly independently of each other. Thus, any rheological regime of certain level of EV and HI can be attained by appropriately tuning only these two parameters, providing a flexibility of similar levels as BD simulations. We further indicate the suitability of DPD by comparing rheological predictions with equivalent models in BD. For this, we imposed startup uniaxial extensional flows and steady shear flows on the system. Our results indicate the consistency of DPD with BD simulations, which is known to agree well with experiments.

Conventional calibration of Baryon Acoustic Oscillations (BAO) data relies on estimation of the sound horizon at drag epoch $r_d$ from early universe observations by assuming a cosmological model. We present a recalibration of two independent BAO datasets, SDSS and DESI, by employing deep learning techniques for model-independent estimation of $r_d$, and explore the impacts on $\Lambda$CDM cosmological parameters. Significant reductions in both Hubble ($H_0$) and clustering ($S_8$) tensions are observed for both the recalibrated datasets. Moderate shifts in some other parameters hint towards further exploration of such data-driven approaches., Comment: 5 pages, 2 figures, 2 tables. Comments are welcome

Ads demand forecasting for Walmart's ad products plays a critical role in enabling effective resource planning, allocation, and management of ads performance. In this paper, we introduce a comprehensive demand forecasting system that tackles hierarchical time series forecasting in business settings. Though traditional hierarchical reconciliation methods ensure forecasting coherence, they often trade off accuracy for coherence especially at lower levels and fail to capture the seasonality unique to each time-series in the hierarchy. Thus, we propose a novel framework "Multi-Stage Hierarchical Forecasting Reconciliation and Adjustment (Multi-Stage HiFoReAd)" to address the challenges of preserving seasonality, ensuring coherence, and improving accuracy. Our system first utilizes diverse models, ensembled through Bayesian Optimization (BO), achieving base forecasts. The generated base forecasts are then passed into the Multi-Stage HiFoReAd framework. The initial stage refines the hierarchy using Top-Down forecasts and "harmonic alignment." The second stage aligns the higher levels' forecasts using MinTrace algorithm, following which the last two levels undergo "harmonic alignment" and "stratified scaling", to eventually achieve accurate and coherent forecasts across the whole hierarchy. Our experiments on Walmart's internal Ads-demand dataset and 3 other public datasets, each with 4 hierarchical levels, demonstrate that the average Absolute Percentage Error from the cross-validation sets improve from 3% to 40% across levels against BO-ensemble of models (LGBM, MSTL+ETS, Prophet) as well as from 1.2% to 92.9% against State-Of-The-Art models. In addition, the forecasts at all hierarchical levels are proved to be coherent. The proposed framework has been deployed and leveraged by Walmart's ads, sales and operations teams to track future demands, make informed decisions and plan resources., Comment: Published in 2024 IEEE International Conference on Big Data (BigData)

Large Vision-Language Models typically require large text and image datasets for effective fine-tuning. However, collecting data from various sites, especially in healthcare, is challenging due to strict privacy regulations. An alternative is to fine-tune these models on end-user devices, such as in medical clinics, without sending data to a server. These local clients typically have limited computing power and small datasets, which are not enough for fully fine-tuning large VLMs on their own. A naive solution to these scenarios is to leverage parameter-efficient fine-tuning (PEFT) strategies and apply federated learning (FL) algorithms to combine the learned adapter weights, thereby respecting the resource limitations and data privacy. However, this approach does not fully leverage the knowledge from multiple adapters trained on diverse data distributions and for diverse tasks. The adapters are adversely impacted by data heterogeneity and task heterogeneity across clients resulting in suboptimal convergence. To this end, we propose a novel framework called FedPIA that improves upon the naive combinations of FL and PEFT by introducing Permutation and Integration of the local Adapters in the server and global Adapters in the clients exploiting Wasserstein barycenters for improved blending of client-specific and client-agnostic knowledge. This layerwise permutation helps to bridge the gap in the parameter space of local and global adapters before integration. We conduct over 2000 client-level experiments utilizing 48 medical image datasets across five different medical vision-language FL task settings encompassing visual question answering as well as image and report-based multi-label disease detection. Our experiments involving diverse client settings, ten different modalities, and two VLM backbones demonstrate that FedPIA consistently outperforms the state-of-the-art PEFT-FL baselines., Comment: Accepted for publication in AAAI 2025 (Main Track)

We investigate various properties of extremal dyonic static black holes in Einstein-Maxwell-Dilaton-Axion theory. We obtain a simple first-order ordinary differential equation for the black hole mass in terms of its electric and magnetic charges, which we can solve explicitly for certain special values of the scalar couplings. For one such case we also construct new dyonic black hole solutions, making use of the presence of an enhanced SL(2,R) symmetry. Finally, we investigate the structure of long range forces and binding energies between non-equivalent extremal black holes. For certain special cases, we can identify regions of parameter space where the force is always attractive or repulsive. Unlike in the case without an axion, the force and binding energies between distinct black holes are not always correlated with each other. Our work is motivated in part by the question of whether long range forces between non-identical states can potentially encode information about UV constraints on low-energy physics., Comment: 29 pages, no figures

Affleck-Dine (AD) baryogenesis is compelling yet challenging to probe because of the high energy physics involved. We demonstrate that this mechanism can be realized generically with low-energy new physics without supersymmetry while producing detectable gravitational waves (GWs) sourced by parametric resonance of a light scalar field. In viable benchmark models, the scalar has a mass of ${\cal O}(0.1-10)$ GeV, yielding GWs with peak frequencies of ${\cal O}(10-100)$ Hz. This study further reveals a new complementarity between upcoming LIGO-frequency GW detectors and laboratory searches across multiple frontiers of particle physics., Comment: 9 pages, 4 figures including supplemental material

We study the problem of robust influence maximization in dynamic diffusion networks. In line with recent works, we consider the scenario where the network can undergo insertion and removal of nodes and edges, in discrete time steps, and the influence weights are determined by the features of the corresponding nodes and a global hyperparameter. Given this, our goal is to find, at every time step, the seed set maximizing the worst-case influence spread across all possible values of the hyperparameter. We propose an approximate solution using multiplicative weight updates and a greedy algorithm, with provable quality guarantees. Our experiments validate the effectiveness and efficiency of the proposed methods., Comment: AAAI Conference on Artificial Intelligence 2025 (Main Technical Track)