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Medical task-oriented dialogue systems can assist doctors by collecting patient medical history, aiding in diagnosis, or guiding treatment selection, thereby reducing doctor burnout and expanding access to medical services. However, doctor-patient dialogue datasets are not readily available, primarily due to privacy regulations. Moreover, existing datasets lack comprehensive annotations involving medical slots and their different attributes, such as symptoms and their onset, progression, and severity. These comprehensive annotations are crucial for accurate diagnosis. Finally, most existing datasets are non-English, limiting their utility for the larger research community. In response, we introduce MediTOD, a new dataset of doctor-patient dialogues in English for the medical history-taking task. Collaborating with doctors, we devise a questionnaire-based labeling scheme tailored to the medical domain. Then, medical professionals create the dataset with high-quality comprehensive annotations, capturing medical slots and their attributes. We establish benchmarks in supervised and few-shot settings on MediTOD for natural language understanding, policy learning, and natural language generation subtasks, evaluating models from both TOD and biomedical domains. We make MediTOD publicly available for future research., Comment: EMNLP2024 Camera Ready Version

Time series forecasting aids decision-making, especially for stakeholders who rely on accurate predictions, making it very important to understand and explain these models to ensure informed decisions. Traditional explainable AI (XAI) methods, which underline feature or temporal importance, often require expert knowledge. In contrast, natural language explanations (NLEs) are more accessible to laypeople. However, evaluating forecast NLEs is difficult due to the complex causal relationships in time series data. To address this, we introduce two new performance metrics based on simulatability, assessing how well a human surrogate can predict model forecasts using the explanations. Experiments show these metrics differentiate good from poor explanations and align with human judgments. Utilizing these metrics, we further evaluate the ability of state-of-the-art large language models (LLMs) to generate explanations for time series data, finding that numerical reasoning, rather than model size, is the main factor influencing explanation quality.

Over the past decade, predictive modeling of neural responses in the primate visual system has advanced significantly, largely driven by various DNN approaches. These include models optimized directly for visual recognition, cross-modal alignment through contrastive objectives, neural response prediction from scratch, and large language model embeddings.Likewise, different readout mechanisms, ranging from fully linear to spatial-feature factorized methods have been explored for mapping network activations to neural responses. Despite the diversity of these approaches, it remains unclear which method performs best across different visual regions. In this study, we systematically compare these approaches for modeling the human visual system and investigate alternative strategies to improve response predictions. Our findings reveal that for early to mid-level visual areas, response-optimized models with visual inputs offer superior prediction accuracy, while for higher visual regions, embeddings from LLMs based on detailed contextual descriptions of images and task-optimized models pretrained on large vision datasets provide the best fit. Through comparative analysis of these modeling approaches, we identified three distinct regions in the visual cortex: one sensitive primarily to perceptual features of the input that are not captured by linguistic descriptions, another attuned to fine-grained visual details representing semantic information, and a third responsive to abstract, global meanings aligned with linguistic content. We also highlight the critical role of readout mechanisms, proposing a novel scheme that modulates receptive fields and feature maps based on semantic content, resulting in an accuracy boost of 3-23% over existing SOTAs for all models and brain regions. Together, these findings offer key insights into building more precise models of the visual system.

The successful deployment of deep learning-based acoustic echo and noise reduction (AENR) methods in consumer devices has spurred interest in developing low-complexity solutions, while emphasizing the need for robust performance in real-life applications. In this work, we propose a hybrid approach to enhance the state-of-the-art (SOTA) ULCNet model by integrating time alignment and parallel encoder blocks for the model inputs, resulting in better echo reduction and comparable noise reduction performance to existing SOTA methods. We also propose a channel-wise sampling-based feature reorientation method, ensuring robust performance across many challenging scenarios, while maintaining overall low computational and memory requirements., Comment: 5 pages, 4 figures

Enhancing speech quality under adverse SNR conditions remains a significant challenge for discriminative deep neural network (DNN)-based approaches. In this work, we propose DisCoGAN, which is a time-frequency-domain generative adversarial network (GAN) conditioned by the latent features of a discriminative model pre-trained for speech enhancement in low SNR scenarios. Our proposed method achieves superior performance compared to state-of-the-arts discriminative methods and also surpasses end-to-end (E2E) trained GAN models. We also investigate the impact of various configurations for conditioning the proposed GAN model with the discriminative model and assess their influence on enhancing speech quality, Comment: 5 pages, 2 figures

The conditional autoregressive (CAR) model, simultaneous autoregressive (SAR) model, and its variants have become the predominant strategies for modeling regional or areal-referenced spatial data. The overwhelming wide-use of the CAR/SAR model motivates the need for new classes of models for areal-referenced data. Thus, we develop a novel class of Markov random fields based on truncating the full-conditional distribution. We define this truncation in two ways leading to versions of what we call the truncated autoregressive (TAR) model. First, we truncate the full conditional distribution so that a response at one location is close to the average of its neighbors. This strategy establishes relationships between TAR and CAR. Second, we truncate on the joint distribution of the data process in a similar way. This specification leads to connection between TAR and SAR model. Our Bayesian implementation does not use Markov chain Monte Carlo (MCMC) for Bayesian computation, and generates samples directly from the posterior distribution. Moreover, TAR does not have a range parameter that arises in the CAR/SAR models, which can be difficult to learn. We present the results of the proposed truncated autoregressive model on several simulated datasets and on a dataset of average property prices., Comment: 41 pages, 10 figures

Large language models (LLMs) have shown promise in many natural language understanding tasks, including content moderation. However, these models can be expensive to query in real-time and do not allow for a community-specific approach to content moderation. To address these challenges, we explore the use of open-source small language models (SLMs) for community-specific content moderation tasks. We fine-tune and evaluate SLMs (less than 15B parameters) by comparing their performance against much larger open- and closed-sourced models. Using 150K comments from 15 popular Reddit communities, we find that SLMs outperform LLMs at content moderation -- 11.5% higher accuracy and 25.7% higher recall on average across all communities. We further show the promise of cross-community content moderation, which has implications for new communities and the development of cross-platform moderation techniques. Finally, we outline directions for future work on language model based content moderation. Code and links to HuggingFace models can be found at https://github.com/AGoyal0512/SLM-Mod., Comment: Preprint: 15 pages, 8 figures, 8 pages

An $s$-sparse polynomial has at most $s$ monomials with nonzero coefficients. The Equivalence Testing problem for sparse polynomials (ETsparse) asks to decide if a given polynomial $f$ is equivalent to (i.e., in the orbit of) some $s$-sparse polynomial. In other words, given $f \in \mathbb{F}[\mathbf{x}]$ and $s \in \mathbb{N}$, ETsparse asks to check if there exist $A \in \mathrm{GL}(|\mathbf{x}|, \mathbb{F})$ and $\mathbf{b} \in \mathbb{F}^{|\mathbf{x}|}$ such that $f(A\mathbf{x} + \mathbf{b})$ is $s$-sparse. We show that ETsparse is NP-hard over any field $\mathbb{F}$, if $f$ is given in the sparse representation, i.e., as a list of nonzero coefficients and exponent vectors. This answers a question posed in [Gupta-Saha-Thankey, SODA'23] and [Baraskar-Dewan-Saha, STACS'24]. The result implies that the Minimum Circuit Size Problem (MCSP) is NP-hard for a dense subclass of depth-$3$ arithmetic circuits if the input is given in sparse representation. We also show that approximating the smallest $s_0$ such that a given $s$-sparse polynomial $f$ is in the orbit of some $s_0$-sparse polynomial to within a factor of $s^{\frac{1}{3} - \epsilon}$ is NP-hard for any $\epsilon > 0$; observe that $s$-factor approximation is trivial as the input is $s$-sparse. Finally, we show that for any constant $\sigma \geq 5$, checking if a polynomial (given in sparse representation) is in the orbit of some support-$\sigma$ polynomial is NP-hard. Support of a polynomial $f$ is the maximum number of variables present in any monomial of $f$. These results are obtained via direct reductions from the $3$-SAT problem., Comment: A preliminary version of the paper appeared in the proceedings of ICALP 2024. This version is slightly stronger than the ECCC one: a) We show it is NP-hard to test equivalence to support-5 polynomials (instead of support-6). b) We adapt the proof technique to show it is NP-hard to test equivalence to sparse polynomials under translations only; A suitable gap version of the result is also shown

Triangular cross-section nanodevices are among the leading approaches for integrating color centers with photonics for applications in quantum information processing. We design periodic and aperiodic fishbone triangular grating couplers in silicon carbide. We optimize the designs for achieving up to $\sim$ 31% collection efficiency from color center integrated triangular devices to a microscopy system. Using an ion beam angle etching process, we demonstrate proof-of-principle fabrication of the designed devices for future implementation in wafer-scale quantum nanophotonics.

This paper presents the results of the LegalLens Shared Task, focusing on detecting legal violations within text in the wild across two sub-tasks: LegalLens-NER for identifying legal violation entities and LegalLens-NLI for associating these violations with relevant legal contexts and affected individuals. Using an enhanced LegalLens dataset covering labor, privacy, and consumer protection domains, 38 teams participated in the task. Our analysis reveals that while a mix of approaches was used, the top-performing teams in both tasks consistently relied on fine-tuning pre-trained language models, outperforming legal-specific models and few-shot methods. The top-performing team achieved a 7.11% improvement in NER over the baseline, while NLI saw a more marginal improvement of 5.7%. Despite these gains, the complexity of legal texts leaves room for further advancements.

We theoretically investigate emergent topological phases in an extended spin-full Su-Schrieffer-Heeger (SSH) model considering Rashba spin-orbit interaction, all possible complex next to next nearest neighbor (NNNN) hopping preserving Chiral symmetry. Our analysis finds exact condition for which the topological phases of both the spin sectors could be independently varied. We show that it necessarily depends on complex NNNN only. We elaborate in detail the emergent topological phases, its criteria through analytic determination of non-trivial gap-closing condition due to the presence of $\cos 2k$ term. We also find that the profile of topological edge modes for finite winding numbers depend non-monotonously on the value of NNNN hopping elucidating competing effect of model parameters. We extend our study to few coupled chains and show explicitly that depending on the parameters all possible winding number ranging from zero to $2N$ could be obtained, where $N$ is the number of chains considered. Finally we incorporate the study of domain wall and remarkably we find that the location of mid-gap zero energy state by changing the values of model parameters. Our study could be of immensely useful for future applications in quantum technology., Comment: 19 pages, 19 figures

Among the surprising features of quantum measurements, the problem of distinguishing and anti-distinguishing general quantum measurements is fundamentally appealing. Quantum theory offers four distinct scenarios for distinguishing (and anti-distinguishing) quantum measurements - (i) probing single systems and without access to the post-measurement states, (ii) probing entangled systems and without access to the post-measurement states, (iii) probing single systems with access to the post-measurement states, and (iv) probing entangled systems with access to the post-measurement states. In these scenarios, we consider the probability of distinguishing (and anti-distinguishing) quantum measurements sampled from a given set in the single-shot regime. For some scenarios, we derive analytical expressions for the distinguishability (and anti-distinguishability) of qubit projective measurements. Notably, we demonstrate that in scenario (iii), the distinguishability of any pair of qubit projective measurements is always higher than in scenario (ii). Interestingly, for some qubit non-projective measurements, the highest distinguishability in scenario (ii) is achieved using a non-maximally entangled state. It turns out that, for any set of measurements, the distinguishability (and anti-distinguishability) in scenario (i) is always less than or equal to that in any other scenario, while it reaches its highest possible value in scenario (iv). We establish that these relations form a strict hierarchy, and there is no hierarchical relation between scenarios (ii) and (iii). Specifically, we construct pairs (and triples) of qubit measurements that are perfectly distinguishable (and anti-distinguishable) in scenario (ii) but not in scenario (iii), and vice versa. Furthermore, we identify qubit measurements that achieve perfect distinguishability (and anti-distinguishability) only in scenario (iv)., Comment: 21 pages, 11 figures. Comments are welcome

We report the detection of 5 newly identifed Lyman-continuum (LyC) leaker candidates at redshifts 0.99-1.42 in the AstroSat UV Deep Field South F154W image. We derive physical properties of these galaxies using a combination of spectral-energy distribution fitting and information from publicly available spectra. The estimated escape fraction of these objects vary from 14-85\% after accounting for the IGM attenuation. With only about a dozen known leakers at these redshifts, these detections significantly raise the fraction of LyC leakers in this redshift range. High-resolution HST UV imaging reveals that a subset of the galaxies in our sample have blue star-forming structures that are likely associated with harder ionizing sources. We find tentative evidence that the LyC emission is spatially offset from the non-ionizing UV continuum centers of these galaxies. The integrated properties of these galaxies, such as the UV continuum slope, dust attenuation, stellar mass, and $[\text{O III}] \lambda 5007/ [\text{O II}] \lambda 3727$ ratios, make them atypical compared to known LyC leakers. The leakage of LyC photons from these systems presents a compelling challenge., Comment: 12 figures, 3 tables, Submitted to ApJ, comments are welcome

Hallucinations (i.e., generating plausible but inaccurate content) and laziness (i.e. excessive refusals or defaulting to "I don't know") persist as major challenges in LLM reasoning. Current efforts to reduce hallucinations primarily focus on factual errors in knowledge-grounded tasks, often neglecting hallucinations related to faulty reasoning. Meanwhile, some approaches render LLMs overly conservative, limiting their problem-solving capabilities. To mitigate hallucination and laziness in reasoning tasks, we propose Automatic Curriculum Expert Iteration (Auto-CEI) to enhance LLM reasoning and align responses to the model's capabilities--assertively answering within its limits and declining when tasks exceed them. In our method, Expert Iteration explores the reasoning trajectories near the LLM policy, guiding incorrect paths back on track to reduce compounding errors and improve robustness; it also promotes appropriate "I don't know" responses after sufficient reasoning attempts. The curriculum automatically adjusts rewards, incentivizing extended reasoning before acknowledging incapability, thereby pushing the limits of LLM reasoning and aligning its behaviour with these limits. We compare Auto-CEI with various SOTA baselines across logical reasoning, mathematics, and planning tasks, where Auto-CEI achieves superior alignment by effectively balancing assertiveness and conservativeness., Comment: 20 pages

The striped patterns in rocks, characterized by thin dark bands and thick light bands, are a result of the natural processes that have shaped the rocks through in situ metamorphism. Notably, the dark and light bands are composed of similar minerals, with the dark bands containing additional trapped materials such as fluid pockets or extra mineral grains. Here we employed the Gray-Scott Reaction Diffusion model to investigate the interaction between two sets of virtual chemicals, denoted as 'u' and 'v'. The differing diffusion and reaction rates of 'u' and 'v' chemical orders lead to the formation of 180 degree out-of-phase chemical domains, resulting in striped patterns. Utilizing the Gray-Scott model in this manner, we gain valuable insights into the early microscopic stages of these geologically significant striped patterns in metamorphic rocks., Comment: 13 Pages, 4 figures

Feedback from active galactic nuclei (AGN) plays a critical role in shaping the matter distribution on scales comparable to and larger than individual galaxies. Upcoming surveys such as $\textit{Euclid}$ and LSST aim to precisely quantify the matter distribution on cosmological scales, making a detailed understanding of AGN feedback effects essential. Hydrodynamical simulations provide an informative framework for studying these effects, in particular by allowing us to vary the parameters that determine the strength of these feedback processes and, consequently, to predict their corresponding impact on the large-scale matter distribution. We use the EAGLE simulations to explore how changes in subgrid viscosity and AGN heating temperature affect the matter distribution, quantified via 2- and 3-point correlation functions, as well as higher order cumulants of the matter distribution. We find that varying viscosity has a small impact ($\approx 10\%$) on scales larger than $1 h^{-1}$ Mpc, while changes to the AGN heating temperature lead to substantial differences, with up to $70\%$ variation in gas clustering on small scales ($\lesssim 1 h^{-1}$ Mpc). By examining the suppression of the power spectrum as a function of time, we identify the redshift range $z = 1.5 - 1$ as a key epoch where AGN feedback begins to dominate in these simulations. The 3-point function provides complementary insight to the more familiar 2-point statistics, and shows more pronounced variations between models on the scale of individual haloes. On the other hand, we find that effects on even larger scales are largely comparable., Comment: Accepted for publication in MNRAS

Recent large language models (LLMs) have demonstrated versatile capabilities in long-context scenarios. Although some recent benchmarks have been developed to evaluate the long-context capabilities of LLMs, there is a lack of benchmarks evaluating the mathematical reasoning abilities of LLMs over long contexts, which is crucial for LLMs' application in real-world scenarios. In this paper, we introduce MathHay, an automated benchmark designed to assess the long-context mathematical reasoning capabilities of LLMs. Unlike previous benchmarks like Needle in a Haystack, which focus primarily on information retrieval within long texts, MathHay demands models with both information-seeking and complex mathematical reasoning abilities. We conduct extensive experiments on MathHay to assess the long-context mathematical reasoning abilities of eight top-performing LLMs. Even the best-performing model, Gemini-1.5-Pro-002, still struggles with mathematical reasoning over long contexts, achieving only 51.26% accuracy at 128K tokens. This highlights the significant room for improvement on the MathHay benchmark., Comment: Work-in-Progress

Topic modeling is a powerful technique for uncovering hidden themes within a collection of documents. However, the effectiveness of traditional topic models often relies on sufficient word co-occurrence, which is lacking in short texts. Therefore, existing approaches, whether probabilistic or neural, frequently struggle to extract meaningful patterns from such data, resulting in incoherent topics. To address this challenge, we propose a novel approach that leverages large language models (LLMs) to extend short texts into more detailed sequences before applying topic modeling. To further improve the efficiency and solve the problem of semantic inconsistency from LLM-generated texts, we propose to use prefix tuning to train a smaller language model coupled with a variational autoencoder for short-text topic modeling. Our method significantly improves short-text topic modeling performance, as demonstrated by extensive experiments on real-world datasets with extreme data sparsity, outperforming current state-of-the-art topic models., Comment: EMNLP Findings 2024. arXiv admin note: substantial text overlap with arXiv:2310.15420

We enhance machine learning algorithms for learning model parameters in complex systems represented by ordinary differential equations (ODEs) with domain decomposition methods. The study evaluates the performance of two approaches, namely (vanilla) Physics-Informed Neural Networks (PINNs) and Finite Basis Physics-Informed Neural Networks (FBPINNs), in learning the dynamics of test models with a quasi-stationary longtime behavior. We test the approaches for data sets in different dynamical regions and with varying noise level. As results, we find a better performance for the FBPINN approach compared to the vanilla PINN approach, even in cases with data from only a quasi-stationary time domain with few dynamics.

Counterspeech presents a viable alternative to banning or suspending users for hate speech while upholding freedom of expression. However, writing effective counterspeech is challenging for moderators/users. Hence, developing suggestion tools for writing counterspeech is the need of the hour. One critical challenge in developing such a tool is the lack of quality and diversity of the responses in the existing datasets. Hence, we introduce a new dataset - CrowdCounter containing 3,425 hate speech-counterspeech pairs spanning six different counterspeech types (empathy, humor, questioning, warning, shaming, contradiction), which is the first of its kind. The design of our annotation platform itself encourages annotators to write type-specific, non-redundant and high-quality counterspeech. We evaluate two frameworks for generating counterspeech responses - vanilla and type-controlled prompts - across four large language models. In terms of metrics, we evaluate the responses using relevance, diversity and quality. We observe that Flan-T5 is the best model in the vanilla framework across different models. Type-specific prompts enhance the relevance of the responses, although they might reduce the language quality. DialoGPT proves to be the best at following the instructions and generating the type-specific counterspeech accurately., Comment: 19 pages, 1 figure, 14 tables, Code available https://github.com/hate-alert/CrowdCounter

In this article, we consider weighted weak type $(1,1)$ inequality for certain square function associated to differences of ball averages and martingale in the non-commutative setting. This establishes a weighted version of main result of \cite{hong2021noncommutative}., Comment: 15 pages, typos fixed, more details are added

We study massive disk galaxies (stellar mass $>$= 10$^{11}$ M$_{\odot}$ ) at $z=0$ from IllustrisTNG simulation to detect galaxies that contain two exponential stellar disks - a central high surface brightness (HSB) disk surrounded by an extended low surface brightness (LSB) envelope. This is motivated by observation of several giant LSB galaxies, reported in literature, showing such complex layered structure (e.g, Malin 1, UGC 1378, UGC 1382 etc). We use the high resolution IllustrisTNG50 data, and perform Sersic plus exponential profile modeling on the idealised, synthetic SDSS-g, r band images of the massive disk galaxies using GALFIT. We identify 7 disk galaxies (12$\%$ of the parent sample of disk galaxies) that are best represented by a central Sersic plus a central HSB disk surrounded by an extended LSB disk. The radial scale lengths of the LSB disk lie in the range of $\sim$ 9.7-31.7 kpc in agreement to that found in literature. We study the star formation properties of these simulated double-disk galaxies to understand the distribution of these from blue star-forming to red-quenched region. Some of these double-disk galaxies display a characteristic minima in their (g-r) color radial profiles. The double-disk galaxies are found to lie within $\sim$ 1.5-$\sigma$ region of the Baryonic Tully-Fisher relation from observation. Our theoretical exploration will be potentially useful in exploring the faint outskirts of massive disk galaxies in upcoming deep surveys such as LSST., Comment: 15 pages, 7 figures, 2 tables, Submitted to ApJ, Comments welcome

Dark matter (DM), in spite of being stable or long-lived on cosmological scales, can decay in the early Universe due to finite-temperature effects. In particular, a first order phase transition (FOPT) in the early Universe can provide a finite window for such decay, guaranteeing DM stability at lower temperatures, consistent with observations. The FOPT can lead to the generation of stochastic gravitational waves (GW) with peak frequencies correlated with DM mass. On the other hand, early DM decay into neutrinos can create a large neutrino asymmetry which can have interesting cosmological consequences in terms of enhanced effective relativistic degrees of freedom $N_{\rm eff}$, providing a solution to the recently observed Helium anomaly among others. Allowing DM decay to occur below sphaleron decoupling temperature, thereby avoiding overproduction of baryon asymmetry, forces the FOPT to occur at sub-electroweak scale. This leaves the stochastic GW within range of experiments like LISA, $\mu$ARES, NANOGrav etc., Comment: 30 pages, 6 captioned figures

The manuscript focuses on the theoretical stability analysis of the viscous liquid over a vibrating inclined rigid bed when the fluid undergoes an impact of odd viscosity. Such an impact emerges in the classical fluid owing to the broken time-reversal symmetry. The rigid bottom vibrates in streamwise and cross-stream directions. The time-dependent Orr-Sommerfeld eigenvalue problem is obtained using the normal mode approach and resolved based on the Chebyshev-collocation method and the Floquet theory. The effect of the odd viscosity coefficient on the different types of instability gravitational, subharmonic, and harmonic are identified. The gravitational instability arises in the longwave region, whereas the resonated wave instability appears in the finite wavenumber region. The gravitational instability is generated in the fluid flow owing to the gravity driving force, whereas the subharmonic instability appears for lower forcing amplitude and the harmonic instability emerges for comparatively higher forcing amplitude. It is found that the subharmonic and harmonic resonances appear when the forcing amplitude surpasses its critical value. A higher odd viscosity leads to stabilizing the gravitational instability, whereas a larger odd viscosity diminishes the subharmonic resonance along with the harmonic resonance instigated at a high forcing amplitude. Further, a new instability, named shear instability in the finite wavenumber range emerges together with the aforementioned three instabilities when the Reynolds number is sufficiently high with a low angle of inclination and becomes weaker when the time-reversal symmetry breaks., Comment: 16 pages, 22 figures

Social media platform design often incorporates explicit signals of positive feedback. Some moderators provide positive feedback with the goal of positive reinforcement, but are often unsure of their ability to actually influence user behavior. Despite its widespread use and theory touting positive feedback as crucial for user motivation, its effect on recipients is relatively unknown. This paper examines how positive feedback impacts Reddit users and evaluates its differential effects to understand who benefits most from receiving positive feedback. Through a causal inference study of 11M posts across 4 months, we find that users who received positive feedback made more frequent (2% per day) and higher quality (57% higher score; 2% fewer removals per day) posts compared to a set of matched control users. Our findings highlight the need for platforms and communities to expand their perspective on moderation and complement punitive approaches with positive reinforcement strategies., Comment: 23 pages, 5 figures

Non-uniform quantization, such as power-of-two (PoT) quantization, matches data distributions better than uniform quantization, which reduces the quantization error of Deep Neural Networks (DNNs). PoT quantization also allows bit-shift operations to replace multiplications, but there are limited studies on the efficiency of shift-based accelerators for PoT quantization. Furthermore, existing pipelines for accelerating PoT-quantized DNNs on edge devices are not open-source. In this paper, we first design shift-based processing elements (shift-PE) for different PoT quantization methods and evaluate their efficiency using synthetic benchmarks. Then we design a shift-based accelerator using our most efficient shift-PE and propose PoTAcc, an open-source pipeline for end-to-end acceleration of PoT-quantized DNNs on resource-constrained edge devices. Using PoTAcc, we evaluate the performance of our shift-based accelerator across three DNNs. On average, it achieves a 1.23x speedup and 1.24x energy reduction compared to a multiplier-based accelerator, and a 2.46x speedup and 1.83x energy reduction compared to CPU-only execution. Our code is available at https://github.com/gicLAB/PoTAcc, Comment: Accepted at 31st IEEE International Conference on Electronics, Circuits and Systems (ICECS), 2024

Let $X$ be a normal projective variety with an action of a semisimple algebraic group $G$ such that $X$ contains a unique closed orbit. Let $B$ be a Borel subgroup of $G$ and let $E$ be a $B$-equivariant vector bundle on $X$. In this article, we prove that $E$ is ample (resp. nef) if and only if its restriction to the finite set of $B$-stable curves on $X$ is ample (resp. nef). Moreover, we calculate the nef cone of the blow-up of a nonsingular simple $G$-projective variety $X$ at a unique $B$-fixed point $x^-$, called the sink of $X$. As an application, when $X$ is nonsingular, we calculate the Seshadri constants of any ample line bundles (not necessarily $G$-equivariant) at $x^-$. Additionally, we compute the Seshadri constants of $B$-equivariant vector bundles at $x^{-}$., Comment: Comments welcome!

Hub-filament systems are considered as natural sites for high-mass star formation. Kinematic analysis of the surroundings of hub-filaments is essential to better understand high-mass star formation within such systems. In this work, we present a detailed study of the massive Galactic protocluster IRAS 15394$-$5358, using continuum and molecular line data from the ALMA Three-millimeter Observations of Massive Star-forming Regions (ATOMS) survey. The 3~mm dust continuum map reveals the fragmentation of the massive ($\rm M=843~M_{\odot}$) clump into six cores. The core C-1A is the largest (radius = 0.04~pc), the most massive ($\rm M=157~M_{\odot}$), and lies within the dense central region, along with two smaller cores ($\rm M=7~and~3~M_{\odot}$). The fragmentation process is consistent with the thermal Jeans fragmentation mechanism and virial analysis shows that all the cores have small virial parameter values ($\rm \alpha_{vir}<<2$), suggesting that the cores are gravitationally bound. The mass vs. radius relation indicates that three cores can potentially form at least a single massive star. The integrated intensity map of $\rm H^{13}CO^{+}$ shows that the massive clump is associated with a hub-filament system, where the central hub is linked with four filaments. A sharp velocity gradient is observed towards the hub, suggesting a global collapse where the filaments are actively feeding the hub. We discuss the role of global collapse and the possible driving mechanisms for the massive star formation activity in the protocluster., Comment: 23 pages, 19 figures, accepted for publication in MNRAS

The ion-lipid interface in Langmuir monolayers of Dipalmitoylphosphatidylcholine (DPPC) on pure water and 10 mM solutions of Na+ and K+ at different [K+]/[Na+] (a), atom/atom ratios, were studied initially by Surface Pressure (p) versus Specific Molecular Area (A) isotherms. The values of a were chosen as 0 (no K+), 0.43 ([K+]:[Na+] = 30:70) and 1.0 ([K+]:[Na+] = 50:50) These monolayers were studied through X-Ray Reflectivity (XRR) and Near Edge X-ray Absorption Fine Structure (NEXAFS) spectroscopy at the O K-edge. The two-dimensional rigidity of the monolayer was found to increase with Na+ ions with respect to the pristine monolayer but fall drastically and non-linearly below the pristine value with introduction of the K+ ions, as a was increased. Analysis of the XRR profiles provided the thickness, average electron density (aed) and the interfacial roughness of the phosphatidylcholine head group and the two hydrocarbon tails of the monolayers on Si (001), from which the angle (f) between the head and the tails was determined. This was also follow the same as former one. From NEXAFS, it was found that a linear increase in the cation ratio towards K led to a nonlinear variation in the P=O bond energy and a weakening of the P-O bond energy, the latter becoming more pronounced with K ions, consistent with Fajans rule. Also a split in the C=O p-bond peak was observed at a = 1.0. These results cannot be explained with the model of a uniform electric field due to the cations, which would fall linearly with increase in the K+ proportion, and rather suggest a structured field due a spatial variation in charge density in an interfacial layer of high ion concentration assembled by the counterionic attraction of the phosphatidylcholine head groups. Our results have important implications for the cell membrane, where such mixtures at high concentrations constitute the norm.

This paper introduces a novel track-length extension fitting algorithm for measuring the kinetic energies of inelastically interacting particles in liquid argon time projection chambers (LArTPCs). The algorithm finds the most probable offset in track length for a track-like object by comparing the measured ionization density as a function of position with a theoretical prediction of the energy loss as a function of the energy, including models of electron recombination and detector response. The algorithm can be used to measure the energies of particles that interact before they stop, such as charged pions that are absorbed by argon nuclei. The algorithm's energy measurement resolutions and fractional biases are presented as functions of particle kinetic energy and number of track hits using samples of stopping secondary charged pions in data collected by the ProtoDUNE-SP detector, and also in a detailed simulation. Additional studies describe the impact of the dE/dx model on energy measurement performance. The method described in this paper to characterize the energy measurement performance can be repeated in any LArTPC experiment using stopping secondary charged pions.

We present a new approach to bootstrapping string-like theories by exploiting a local crossing symmetric dispersion relation and field redefinition ambiguities. This approach enables us to use mass-level truncation and to go beyond the dual resonance hypothesis. We consider both open and closed strings, focusing mainly on open tree-level amplitudes with integer-spaced spectrum, and two leading Wilson coefficients as inputs. Using entanglement minimization in the form of the minimum of the first finite moment of linear entropy or entangling power, we get an excellent approximation to the superstring amplitudes, including the leading and sub-leading Regge trajectories. We find other interesting S-matrices which do not obey the duality hypothesis, but exhibit a transition from Regge behaviour to power law behaviour in the high energy limit. Finally, we also examine Machine-Learning techniques to do bootstrap and discuss potential advantages over the present approach., Comment: 59 pages, 34 figures. Now includes Regge theory investigations using ML, a unified dispersion relation and more explanations. References added

The current state of Advanced Persistent Threats (APT) attribution primarily relies on time-consuming manual processes. These include mapping incident artifacts onto threat attribution frameworks and employing expert reasoning to uncover the most likely responsible APT groups. This research aims to assist the threat analyst in the attribution process by presenting an attribution method named CAPTAIN (Comprehensive Advanced Persistent Threat AttrIbutioN). This novel APT attribution approach leverages the Tactics, Techniques, and Procedures (TTPs) employed by various APT groups in past attacks. CAPTAIN follows two significant development steps: baseline establishment and similarity measure for attack pattern matching. This method starts by maintaining a TTP database of APTs seen in past attacks as baseline behaviour of threat groups. The attribution process leverages the contextual information added by TTP sequences, which reflects the sequence of behaviours threat actors demonstrated during the attack on different kill-chain stages. Then, it compares the provided TTPs with established baseline to identify the most closely matching threat group. CAPTAIN introduces a novel similarity measure for APT group attack-pattern matching that calculates the similarity between TTP sequences. The proposed approach outperforms traditional similarity measures like Cosine, Euclidean, and Longest Common Subsequence (LCS) in performing attribution. Overall, CAPTAIN performs attribution with the precision of 61.36% (top-1) and 69.98% (top-2), surpassing the existing state-of-the-art attribution methods., Comment: Under Review

Extreme continuum variability in AGNs can indicate extreme changes in accretion flows onto supermassive black holes. We explore the multiwavelength nature of a continuum flare in the Seyfert LCRS B040659.9$-$385922. The all-sky X-ray surveys conducted by the eROSITA showed that its X-ray flux increased by a factor of roughly five over six months, and concurrent optical photometric monitoring with the ATLAS showed a simultaneous increase. We triggered a multiwavelength follow-up monitoring program (XMM, NICER; optical spectroscopy) to study the evolution of the accretion disk, broad-line region, and X-ray corona. During the campaign, X-ray and optical continuum flux subsided over roughly six months. We detected a soft X-ray excess near the flare peak and after it subsided, both exhibiting a power-law (nonthermal) behavior. We modeled the broadband optical/UV/X-ray spectral energy distribution at both the flare peak and post-flare times with the AGNSED model, incorporating thermal disk emission into the optical/UV and warm thermal Comptonization in the soft X-rays. Additionally, we find that the broad Heii $\lambda$4686 emission line fades significantly as the optical/UV/X-ray continuum fades, which could indicate a substantial flare of disk emission above 54 eV. We also observed a redshifted broad component in the H${\beta}$ emission line that is present during the high flux state of the source and disappears in subsequent observations. We witnessed a likely sudden strong increase in local accretion rate, which manifested itself via an increase in accretion disk emission and thermal Comptonization emission in the soft X-rays, followed by a decrease in accretion and Comptonized luminosity. The physical processes leading to such substantial variations are still an open question, and future continuous monitoring along with multi-wavelength studies will shed some light on it., Comment: 19 pages, 7 figures

Traffic routing is vital for the proper functioning of the Internet. As users and network traffic increase, researchers try to develop adaptive and intelligent routing algorithms that can fulfill various QoS requirements. Reinforcement Learning (RL) based routing algorithms have shown better performance than traditional approaches. We developed a QoS-aware, reusable RL routing algorithm, RLSR-Routing over SDN. During the learning process, our algorithm ensures loop-free path exploration. While finding the path for one traffic demand (a source destination pair with certain amount of traffic), RLSR-Routing learns the overall network QoS status, which can be used to speed up algorithm convergence when finding the path for other traffic demands. By adapting Segment Routing, our algorithm can achieve flow-based, source packet routing, and reduce communications required between SDN controller and network plane. Our algorithm shows better performance in terms of load balancing than the traditional approaches. It also has faster convergence than the non-reusable RL approach when finding paths for multiple traffic demands., Comment: 19 pages, 11 figures, Submitted to Elsevier Journal of Computer Network

This paper introduces a simple extension of mixup (Zhang et al., 2018) data augmentation to enhance generalization in visual recognition tasks. Unlike the vanilla mixup method, which blends entire images, our approach focuses on combining regions from multiple images., Comment: Published as a Tiny Paper at ICLR 2024

We present the first large-scale analysis of 339 cybercriminal activity channels (CACs). Followed by over 23.8 million users, these channels share a wide array of malicious and unethical content with their subscribers, including compromised credentials, pirated software and media, social media manipulation tools, and blackhat hacking resources such as malware, exploit kits, and social engineering scams. To evaluate these channels, we developed DarkGram, a BERT-based framework that automatically identifies malicious posts from the CACs with an accuracy of 96%. Using DarkGram, we conducted a quantitative analysis of 53,605 posts shared on these channels between February and May 2024, revealing key characteristics of the content. While much of this content is distributed for free, channel administrators frequently employ strategies such as promotions and giveaways to engage users and boost the sales of premium cybercriminal content. Interestingly, these channels sometimes pose significant risks to their own subscribers. Notably, 28.1% of the links shared in these channels contained phishing attacks, and 38% of executable files were bundled with malware. Analyzing how subscribers consume and positively react to the shared content paints a dangerous picture of the perpetuation of cybercriminal content at scale. We also found that the CACs can evade scrutiny or platform takedowns by quickly migrating to new channels with minimal subscriber loss, highlighting the resilience of this ecosystem. To counteract this, we utilized DarkGram to detect emerging channels and reported malicious content to Telegram and affected organizations. This resulted in the takedown of 196 channels over three months. Our findings underscore the urgent need for coordinated efforts to combat the growing threats posed by these channels. To aid this effort, we open-source our dataset and the DarkGram framework., Comment: To appear in USENIX Security 2025

In this paper, we present a novel hybrid deep learning model, named ConvLSTMTransNet, designed for time series prediction, with a specific application to internet traffic telemetry. This model integrates the strengths of Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Transformer encoders to capture complex spatial-temporal relationships inherent in time series data. The ConvLSTMTransNet model was evaluated against three baseline models: RNN, LSTM, and Gated Recurrent Unit (GRU), using real internet traffic data sampled from high-speed ports on a provider edge router. Performance metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Weighted Absolute Percentage Error (WAPE) were used to assess each model's accuracy. Our findings demonstrate that ConvLSTMTransNet significantly outperforms the baseline models by approximately 10% in terms of prediction accuracy. ConvLSTMTransNet surpasses traditional models due to its innovative architectural features, which enhance its ability to capture temporal dependencies and extract spatial features from internet traffic data. Overall, these findings underscore the importance of employing advanced architectures tailored to the complexities of internet traffic data for achieving more precise predictions., Comment: 6 pages, 1 figure, Submitted to IEEE Virtual Conference on Communications 2024

The exponential growth of the Internet of Things (IoT) has significantly increased the complexity and volume of cybersecurity threats, necessitating the development of advanced, scalable, and interpretable security frameworks. This paper presents an innovative, comprehensive framework for real-time IoT attack detection and response that leverages Machine Learning (ML), Explainable AI (XAI), and Large Language Models (LLM). By integrating XAI techniques such as SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) with a model-independent architecture, we ensure our framework's adaptability across various ML algorithms. Additionally, the incorporation of LLMs enhances the interpretability and accessibility of detection decisions, providing system administrators with actionable, human-understandable explanations of detected threats. Our end-to-end framework not only facilitates a seamless transition from model development to deployment but also represents a real-world application capability that is often lacking in existing research. Based on our experiments with the CIC-IOT-2023 dataset \cite{neto2023ciciot2023}, Gemini and OPENAI LLMS demonstrate unique strengths in attack mitigation: Gemini offers precise, focused strategies, while OPENAI provides extensive, in-depth security measures. Incorporating SHAP and LIME algorithms within XAI provides comprehensive insights into attack detection, emphasizing opportunities for model improvement through detailed feature analysis, fine-tuning, and the adaptation of misclassifications to enhance accuracy., Comment: 6 pages, 1 figure, Accepted in 2024 IEEE WF-IoT Conference

Effective internet traffic prediction in smaller ISP networks is challenged by limited data availability. This paper explores this issue using transfer learning and data augmentation techniques with two LSTM-based models, LSTMSeq2Seq and LSTMSeq2SeqAtn, initially trained on a comprehensive dataset provided by Juniper Networks and subsequently applied to smaller datasets. The datasets represent real internet traffic telemetry, offering insights into diverse traffic patterns across different network domains. Our study revealed that while both models performed well in single-step predictions, multi-step forecasts were challenging, particularly in terms of long-term accuracy. In smaller datasets, LSTMSeq2Seq generally outperformed LSTMSeq2SeqAtn, indicating that higher model complexity does not necessarily translate to better performance. The models' effectiveness varied across different network domains, reflecting the influence of distinct traffic characteristics. To address data scarcity, Discrete Wavelet Transform was used for data augmentation, leading to significant improvements in model performance, especially in shorter-term forecasts. Our analysis showed that data augmentation is crucial in scenarios with limited data. Additionally, the study included an analysis of the models' variability and consistency, with attention mechanisms in LSTMSeq2SeqAtn providing better short-term forecasting consistency but greater variability in longer forecasts. The results highlight the benefits and limitations of different modeling approaches in traffic prediction. Overall, this research underscores the importance of transfer learning and data augmentation in enhancing the accuracy of traffic prediction models, particularly in smaller ISP networks with limited data availability., Comment: 16 pages, 7 Figures, Submitted to Elsevier Journal of Computer Communication

The ribbon number of a knot is the minimum number of ribbon singularities among all ribbon disks bounded by that knot. In this paper, we build on the systematic treatment of this knot invariant initiated in recent work of Friedl, Misev, and Zupan. We show that the set of Alexander polynomials of knots with ribbon number at most four contains 56 polynomials, and we use this set to compute the ribbon numbers for many 12-crossing knots. We also study higher-genus ribbon numbers of knots, presenting some examples that exhibit interesting behavior and establishing that the success of the Alexander polynomial at controlling genus-0 ribbon numbers does not extend to higher genera., Comment: 32 pages, 12 figures, 7 tables

Systematic study of theoretical estimation of nuclear cross section of charged particle induced reactions on rare earth nuclei has been carried out. The production cross section of the $^{150,149,148,146,144,143,141}$Pm and $^{149,147}$Nd nuclei were calculated theoretically via proton induced reaction on neodymium using TALYS (version 1.96) code in the default mode, with different combination of nuclear models as well as using adjusted nuclear model parameters from reaction threshold to the proton energy upto 65 MeV. The theoretically computed results were compared with the experimental results taken from EXFOR database and literature, reported by various research groups. Moreover, the effect of the various Level Density Models (LDMs), Preequilibrium Models (PEs), Optical Model Potentials (OMPs), gamma Strength Functions ($\gamma$SFs) in the cross section calculation were taken into consideration. The present theoretical analysis will help to understand the theory of nuclear reaction models and improve the evaluated nuclear data libraries., Comment: 15 pages, 13 figures, to be published in Chinese Physics C

We theoretically investigate the thermoelectric response of Bogoliubov Fermi surfaces (BFSs) generated in a two dimensional unconventional $d$-wave superconductor subjected to an external in-plane Zeeman field. These BFSs exhibiting the same dimension as the underlying normal state Fermi surface are topologically protected by combinations of discrete symmetries. Utilizing the Blonder-Tinkham-Klapwijk formalism and considering normal-$d$-wave superconductor hybrid junction, we compute the thermoelectric coefficients including thermal conductance, Seebeck coefficient, figure of merit ($zT$), and examine the validation of Widemann-Franz law in the presence of both voltage and temperature bias. Importantly, as a signature of anisotropic nature of $d$-wave pairing, Andreev bound states (ABSs) formed at the normal-superconductor interface play a significant role in the thermoelectric response. In the presence of ABSs, we observe a substantial enhancement in Seebeck coefficient ($\sim 200\,\mu$V/K) and $zT$ ($\sim 3.5$) due to the generation of the BFSs and thus making such setup a potential candidate for device applications. Finally, we strengthen our continuum model results by computing the thermoelectric coefficients based on a lattice-regularized version of our continuum model., Comment: 14 pages; 8+1 figures; Published version

Large Language Models' (LLM) reasoning can be improved using test-time aggregation strategies, i.e., generating multiple samples and voting among generated samples. While these improve performance, they often reach a saturation point. Refinement offers an alternative by using LLM-generated feedback to improve solution quality. However, refinement introduces 3 key challenges: (1) Excessive refinement: Uniformly refining all instances can over-correct and reduce the overall performance. (2) Inability to localize and address errors: LLMs have a limited ability to self-correct and struggle to identify and correct their own mistakes. (3) Insufficient refinement: Deciding how many iterations of refinement are needed is non-trivial, and stopping too soon could leave errors unaddressed. To tackle these issues, we propose MAgICoRe, which avoids excessive refinement by categorizing problem difficulty as easy or hard, solving easy problems with coarse-grained aggregation and hard ones with fine-grained and iterative multi-agent refinement. To improve error localization, we incorporate external step-wise reward model (RM) scores. Moreover, to ensure effective refinement, we employ a multi-agent loop with three agents: Solver, Reviewer (which generates targeted feedback based on step-wise RM scores), and the Refiner (which incorporates feedback). To ensure sufficient refinement, we re-evaluate updated solutions, iteratively initiating further rounds of refinement. We evaluate MAgICoRe on Llama-3-8B and GPT-3.5 and show its effectiveness across 5 math datasets. Even one iteration of MAgICoRe beats Self-Consistency by 3.4%, Best-of-k by 3.2%, and Self-Refine by 4.0% while using less than half the samples. Unlike iterative refinement with baselines, MAgICoRe continues to improve with more iterations. Finally, our ablations highlight the importance of MAgICoRe's RMs and multi-agent communication., Comment: 22 pages, code: https://github.com/dinobby/MAgICoRe

Tiny Machine Learning (TinyML) enables efficient, lowcost, and privacy preserving machine learning inference directly on microcontroller units (MCUs) connected to sensors. Optimizing models for these constrained environments is crucial. This paper investigates how reducing data acquisition rates affects TinyML models for time series classification, focusing on resource-constrained, battery operated IoT devices. By lowering data sampling frequency, we aim to reduce computational demands RAM usage, energy consumption, latency, and MAC operations by approximately fourfold while maintaining similar classification accuracies. Our experiments with six benchmark datasets (UCIHAR, WISDM, PAMAP2, MHEALTH, MITBIH, and PTB) showed that reducing data acquisition rates significantly cut energy consumption and computational load, with minimal accuracy loss. For example, a 75\% reduction in acquisition rate for MITBIH and PTB datasets led to a 60\% decrease in RAM usage, 75\% reduction in MAC operations, 74\% decrease in latency, and 70\% reduction in energy consumption, without accuracy loss. These results offer valuable insights for deploying efficient TinyML models in constrained environments.

To explore the quantum chromodynamics (QCD) phase transitions and the properties of quark$-$gluon plasma, the ALICE collaboration at CERN has conducted an extensive analysis of the correlations among net-conserved quantities, namely net-baryon, net-charge, and net-strangeness. These correlations are essential for understanding the QCD phase structure, as they are directly connected to ratios of thermodynamic susceptibilities calculated in lattice QCD. This analysis focuses on the correlations between net-kaon and net-proton, as well as net-kaon and net-charge, in Pb$-$Pb collisions at $\sqrt{s_\mathrm{NN}} = 5.02$ TeV, where net-proton and net-kaon serve as effective proxies for net-baryon and net-strangeness, respectively. A comparison with theoretical predictions from the Thermal-FIST model sheds light on the role of resonance decays and the effects of charge conservation laws in shaping these correlations. Furthermore, the measurements show sensitivity to the correlation volume in which these conservation laws are applied, underscoring the importance of modeling the underlying dynamics to fully understand the experimental results on fluctuations and correlations in heavy-ion collisions., Comment: 6 pages, 4 figures; contribution to the 42nd International Conference on High Energy physics (ICHEP-2024)

Recovering the underlying clustering of a set $U$ of $n$ points by asking pair-wise same-cluster queries has garnered significant interest in the last decade. Given a query $S \subset U$, $|S|=2$, the oracle returns yes if the points are in the same cluster and no otherwise. For adaptive algorithms with pair-wise queries, the number of required queries is known to be $\Theta(nk)$, where $k$ is the number of clusters. However, non-adaptive schemes require $\Omega(n^2)$ queries, which matches the trivial $O(n^2)$ upper bound attained by querying every pair of points. To break the quadratic barrier for non-adaptive queries, we study a generalization of this problem to subset queries for $|S|>2$, where the oracle returns the number of clusters intersecting $S$. Allowing for subset queries of unbounded size, $O(n)$ queries is possible with an adaptive scheme (Chakrabarty-Liao, 2024). However, the realm of non-adaptive algorithms is completely unknown. In this paper, we give the first non-adaptive algorithms for clustering with subset queries. Our main result is a non-adaptive algorithm making $O(n \log k \cdot (\log k + \log\log n)^2)$ queries, which improves to $O(n \log \log n)$ when $k$ is a constant. We also consider algorithms with a restricted query size of at most $s$. In this setting we prove that $\Omega(\max(n^2/s^2,n))$ queries are necessary and obtain algorithms making $\tilde{O}(n^2k/s^2)$ queries for any $s \leq \sqrt{n}$ and $\tilde{O}(n^2/s)$ queries for any $s \leq n$. We also consider the natural special case when the clusters are balanced, obtaining non-adaptive algorithms which make $O(n \log k) + \tilde{O}(k)$ and $O(n\log^2 k)$ queries. Finally, allowing two rounds of adaptivity, we give an algorithm making $O(n \log k)$ queries in the general case and $O(n \log \log k)$ queries when the clusters are balanced.

In this report, we describe the speaker diarization (SD) and language diarization (LD) systems developed by our team for the Second DISPLACE Challenge, 2024. Our contributions were dedicated to Track 1 for SD and Track 2 for LD in multilingual and multi-speaker scenarios. We investigated different speech enhancement techniques, voice activity detection (VAD) techniques, unsupervised domain categorization, and neural embedding extraction architectures. We also exploited the fusion of various embedding extraction models. We implemented our system with the open-source SpeechBrain toolkit. Our final submissions use spectral clustering for both the speaker and language diarization. We achieve about $7\%$ relative improvement over the challenge baseline in Track 1. We did not obtain improvement over the challenge baseline in Track 2.

A very pressing question in contemporary physics is the identity of Dark Matter (DM), and one that has not been answered affirmatively to any degree so far. Primordial Black Holes (PBHs) are one of the most well-motivated DM candidates. Light enough PBHs have been constrained by either the non-detection of their Hawking radiation itself, or by the non-observation of any measurable effects of this radiation on astrophysical and cosmological observables. We constrain the PBH density by their Hawking radiation effect on the intergalactic medium (IGM) temperature evolution. We use the latest deductions of IGM temperature from Lyman-$\alpha$ forest observations. We put constraints on the fraction of PBH DM with masses $5 \times 10^{15}$ g - $10^{17}$ g separately for spinning and non-spinning BHs. We derive constraints by dealing with the heating effects of the astrophysical reionization of the IGM in two ways. In one way, we completely neglect this heating due to astrophysical sources, thus giving us weaker constraints, but completely robust to the reionization history of the universe. In the second way, we utilise some modelling of the ionization and temperature history, and use it to derive more stringent constraints. We find that for non-spinning PBHs of mass $10^{16}$ g, the current measurements can constrain the PBH-density to be $\lesssim$ 0.1% of the total DM. We find that these constraints from the latest Lyman-$\alpha$ forest temperature measurements are competitive, and hence provide a new observable to probe the nature of PBH DM. The systematics affecting Lyman-$\alpha$ forest measurements are different from other constraining observations, and thus this is a complementary probe., Comment: v1; 15 pages, 2 Figures. Comments are welcome

A system of interacting walkers on a two-dimensional space where the dynamics of each walker are governed by the opinions of agents of a three-state opinion dynamics model are considered. Such walks, inspired by Ising-like models and opinions dynamics models, are usually considered in one-dimensional virtual spaces. Here, the mapping is done in such a way that the walk is directed along the $y$ axis while it can move either way along the $x$ axis. We explore the properties of such walks as the parameter representing the noise in the opinion dynamics model, responsible for a continuous phase transition, is varied. The walk features show marked differences as the system crosses the critical point. The bivariate distribution of the displacements below the critical point is a modified biased Gaussian function of x and y which is symmetric about the X axis. The marginal probability distributions can be extracted and the scaling forms of different quantities, showing power law behaviour, are obtained. The directed nature of the walk is reflected in the marginal distributions as well as in the exponents.

The grand minimum in the Sun's activity is a distinctive mode characterized by a magnetic lull that almost completely lacks the emergence of sunspots on the solar surface for an extended duration. The factors driving this transition of an otherwise magnetically active star into a quiescent phase, the processes occurring within the solar interior and across the heliosphere during this period, and the mechanisms leading to the eventual resurgence of surface magnetic activity remain enigmatic. However, there have been sustained efforts in the past few decades to unravel these mysteries by employing a combination of observation, reconstruction and simulation of solar magnetic variability. Here, we summarize recent research on the solar grand minimum and highlight some outstanding challenges - both intellectual and practical - that necessitate further investigations., Comment: 12 pages, 7 figures, Proceedings IAU Symposium No. 365: Dynamics of Solar and Stellar Convection Zones and Atmospheres, Accepted for publication