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A novel method to reconstruct the energy of hadronic showers in the CMS High Granularity Calorimeter (HGCAL) is presented. The HGCAL is a sampling calorimeter with very fine transverse and longitudinal granularity. The active media are silicon sensors and scintillator tiles readout by SiPMs and the absorbers are a combination of lead and Cu/CuW in the electromagnetic section, and steel in the hadronic section. The shower reconstruction method is based on graph neural networks and it makes use of a dynamic reduction network architecture. It is shown that the algorithm is able to capture and mitigate the main effects that normally hinder the reconstruction of hadronic showers using classical reconstruction methods, by compensating for fluctuations in the multiplicity, energy, and spatial distributions of the shower's constituents. The performance of the algorithm is evaluated using test beam data collected in 2018 prototype of the CMS HGCAL accompanied by a section of the CALICE AHCAL prototype. The capability of the method to mitigate the impact of energy leakage from the calorimeter is also demonstrated., Comment: Prepared for submission to JINST

When Large Language Models (LLMs) are compressed using techniques such as quantization, the predominant way to demonstrate the validity of such techniques is by measuring the model's accuracy on various benchmarks.If the accuracies of the baseline model and the compressed model are close, it is assumed that there was negligible degradation in quality.However, even when the accuracy of baseline and compressed model are similar, we observe the phenomenon of flips, wherein answers change from correct to incorrect and vice versa in proportion.We conduct a detailed study of metrics across multiple compression techniques, models and datasets, demonstrating that the behavior of compressed models as visible to end-users is often significantly different from the baseline model, even when accuracy is similar.We further evaluate compressed models qualitatively and quantitatively using MT-Bench and show that compressed models are significantly worse than baseline models in this free-form generative task.Thus, we argue that compression techniques should also be evaluated using distance metrics.We propose two such metrics, KL-Divergence and flips, and show that they are well correlated.

In the background of homogeneous and isotropic flat FLRW space-time, both classical and quantum cosmology has been studied for teleparallel dark energy (DE) model. Using Noether symmetry analysis, not only the symmetry vector but also the coupling function in the Lagrangian and the potential of the scalar field has been determined. Also symmetry analysis identifies a cyclic variable in the Lagrangian along the symmetry vector and as a result the Lagrangian simplifies to a great extend so that classical solution is obtained. Subsequently, in quantum cosmology Wheeler-DeWitt(WD) equation has been constructed and the quantum version of the conserved momenta corresponding to Noether symmetry identifies the periodic part of the wave function of the universe and as a result the Wheeler-DeWitt equation becomes solvable. Finally, quantum description shows finite non-zero probability at the classical big-bang singularity., Comment: 16 Pages, 4 figures

The present work deals with Einstein-aether Scalar tensor gravity in the background of homogeneous and isotropic flat FLRW space-time model. The Noether symmetry vector identifies a transformation in the augmented space so that the field equations become solvable. The cosmological solutions are analyzed from the observational point of view. Finally, for quantum cosmology, the Wheeler-DeWitt (WD) has been formulated and solutions have been determined by identifying the periodic nature of the wave function using conserved (Noether) charge., Comment: 15 pages, 4 figures

Large Deep Neural Networks (DNNs) are often data hungry and need high-quality labeled data in copious amounts for learning to converge. This is a challenge in the field of medicine since high quality labeled data is often scarce. Data programming has been the ray of hope in this regard, since it allows us to label unlabeled data using multiple weak labeling functions. Such functions are often supplied by a domain expert. Data-programming can combine multiple weak labeling functions and suggest labels better than simple majority voting over the different functions. However, it is not straightforward to express such weak labeling functions, especially in high-dimensional settings such as images and time-series data. What we propose in this paper is a way to bypass this issue, using distance functions. In high-dimensional spaces, it is easier to find meaningful distance metrics which can generalize across different labeling tasks. We propose an algorithm that queries an expert for labels of a few representative samples of the dataset. These samples are carefully chosen by the algorithm to capture the distribution of the dataset. The labels assigned by the expert on the representative subset induce a labeling on the full dataset, thereby generating weak labels to be used in the data programming pipeline. In our medical time series case study, labeling a subset of 50 to 130 out of 3,265 samples showed 17-28% improvement in accuracy and 13-28% improvement in F1 over the baseline using clinician-defined labeling functions. In our medical image case study, labeling a subset of about 50 to 120 images from 6,293 unlabeled medical images using our approach showed significant improvement over the baseline method, Snuba, with an increase of approximately 5-15% in accuracy and 12-19% in F1 score.

The present work deals with a multi-field cosmological model in a spatially flat FLRW space-time geometry. The usual Brans-Dicke(BD) field and another scalar field are minimally coupled to gravity while they interact with each other through the Kinetic terms. {The main aim of the present work is to examine whether the model is compatible with cosmic observations. So cosmological solutions are obtained using symmetry analysis only.} By imposing Noether Symmetry to the Lagrangian of the system the potential of the scalar field as well as the coupling function has been determined. The classical solutions are determined after simplifying the Lagrangian using cyclic variables. Finally, Wheeler-DeWitt(WD) equation in quantum cosmology has been formulated and conserved momenta corresponding to Noether symmetry shows the periodic part of the wave function and it helps to have the complete integral for the wave function., Comment: 14 pages, 4 figures

Reinforcement learning (RL) methods for social robot navigation show great success navigating robots through large crowds of people, but the performance of these learning-based methods tends to degrade in particularly challenging or unfamiliar situations due to the models' dependency on representative training data. To ensure human safety and comfort, it is critical that these algorithms handle uncommon cases appropriately, but the low frequency and wide diversity of such situations present a significant challenge for these data-driven methods. To overcome this challenge, we propose modifications to the learning process that encourage these RL policies to maintain additional caution in unfamiliar situations. Specifically, we improve the Socially Attentive Reinforcement Learning (SARL) policy by (1) modifying the training process to systematically introduce deviations into a pedestrian model, (2) updating the value network to estimate and utilize pedestrian-unpredictability features, and (3) implementing a reward function to learn an effective response to pedestrian unpredictability. Compared to the original SARL policy, our modified policy maintains similar navigation times and path lengths, while reducing the number of collisions by 82% and reducing the proportion of time spent in the pedestrians' personal space by up to 19 percentage points for the most difficult cases. We also describe how to apply these modifications to other RL policies and demonstrate that some key high-level behaviors of our approach transfer to a physical robot.

Misinformation is still a major societal problem and the arrival of Large Language Models (LLMs) only added to it. This paper analyzes synthetic, false, and genuine information in the form of text from spectral analysis, visualization, and explainability perspectives to find the answer to why the problem is still unsolved despite multiple years of research and a plethora of solutions in the literature. Various embedding techniques on multiple datasets are used to represent information for the purpose. The diverse spectral and non-spectral methods used on these embeddings include t-distributed Stochastic Neighbor Embedding (t-SNE), Principal Component Analysis (PCA), and Variational Autoencoders (VAEs). Classification is done using multiple machine learning algorithms. Local Interpretable Model-Agnostic Explanations (LIME), SHapley Additive exPlanations (SHAP), and Integrated Gradients are used for the explanation of the classification. The analysis and the explanations generated show that misinformation is quite closely intertwined with genuine information and the machine learning algorithms are not as effective in separating the two despite the claims in the literature.

Fine-tuning large language models (LLMs) on limited tabular data for classification tasks can lead to \textit{fine-tuning multiplicity}, where equally well-performing models make conflicting predictions on the same inputs due to variations in the training process (i.e., seed, random weight initialization, retraining on additional or deleted samples). This raises critical concerns about the robustness and reliability of Tabular LLMs, particularly when deployed for high-stakes decision-making, such as finance, hiring, education, healthcare, etc. This work formalizes the challenge of fine-tuning multiplicity in Tabular LLMs and proposes a novel metric to quantify the robustness of individual predictions without expensive model retraining. Our metric quantifies a prediction's stability by analyzing (sampling) the model's local behavior around the input in the embedding space. Interestingly, we show that sampling in the local neighborhood can be leveraged to provide probabilistic robustness guarantees against a broad class of fine-tuned models. By leveraging Bernstein's Inequality, we show that predictions with sufficiently high robustness (as defined by our measure) will remain consistent with high probability. We also provide empirical evaluation on real-world datasets to support our theoretical results. Our work highlights the importance of addressing fine-tuning instabilities to enable trustworthy deployment of LLMs in high-stakes and safety-critical applications.

This paper establishes non-uniform continuity of the data-to-solution map in the periodic case, for the two-component Fornberg-Whitham system in Besov spaces $B^s_{p,r}(\mathbb{T}) \times B^{s-1}_{p,r}(\mathbb{T})$ for $s> \max\{2+\frac{1}{p}, \frac{5}{2}\}$. In particular, when $p=2$ and $r=2$, this proves the non-uniform dependence on initial data for the system in Sobolev spaces $H^s(\mathbb{T})\times H^{s-1}(\mathbb{T})$ for $s> \frac{5}{2}$.

Existing algorithms for human body part segmentation have shown promising results on challenging datasets, primarily relying on end-to-end supervision. However, these algorithms exhibit severe performance drops in the face of domain shifts, leading to inaccurate segmentation masks. To tackle this issue, we introduce POSTURE: \underline{Po}se Guided Un\underline{s}upervised Domain Adap\underline{t}ation for H\underline{u}man Body Pa\underline{r}t S\underline{e}gmentation - an innovative pseudo-labelling approach designed to improve segmentation performance on the unlabeled target data. Distinct from conventional domain adaptive methods for general semantic segmentation, POSTURE stands out by considering the underlying structure of the human body and uses anatomical guidance from pose keypoints to drive the adaptation process. This strong inductive prior translates to impressive performance improvements, averaging 8\% over existing state-of-the-art domain adaptive semantic segmentation methods across three benchmark datasets. Furthermore, the inherent flexibility of our proposed approach facilitates seamless extension to source-free settings (SF-POSTURE), effectively mitigating potential privacy and computational concerns, with negligible drop in performance.

One of the primary areas of interest in High Performance Computing is the improvement of performance of parallel workloads. Nowadays, compilable source code-based optimization tasks that employ deep learning often exploit LLVM Intermediate Representations (IRs) for extracting features from source code. Most such works target specific tasks, or are designed with a pre-defined set of heuristics. So far, pre-trained models are rare in this domain, but the possibilities have been widely discussed. Especially approaches mimicking large-language models (LLMs) have been proposed. But these have prohibitively large training costs. In this paper, we propose MIREncoder, a M}ulti-modal IR-based Auto-Encoder that can be pre-trained to generate a learned embedding space to be used for downstream tasks by machine learning-based approaches. A multi-modal approach enables us to better extract features from compilable programs. It allows us to better model code syntax, semantics and structure. For code-based performance optimizations, these features are very important while making optimization decisions. A pre-trained model/embedding implicitly enables the usage of transfer learning, and helps move away from task-specific trained models. Additionally, a pre-trained model used for downstream performance optimization should itself have reduced overhead, and be easily usable. These considerations have led us to propose a modeling approach that i) understands code semantics and structure, ii) enables use of transfer learning, and iii) is small and simple enough to be easily re-purposed or reused even with low resource availability. Our evaluations will show that our proposed approach can outperform the state of the art while reducing overhead., Comment: 12 pages, 6 figures, 9 tables, PACT '24 conference

Active Galactic Nuclei (AGN) are prime candidate sources of the high-energy, astrophysical neutrinos detected by IceCube. This is demonstrated by the real-time multi-messenger detection of the blazar TXS 0506+056 and the recent evidence of neutrino emission from NGC 1068 from a separate time-averaged study. However, the production mechanism of the astrophysical neutrinos in AGN is not well established which can be resolved via correlation studies with photon observations. For neutrinos produced due to photohadronic interactions in AGN, in addition to a correlation of neutrinos with high-energy photons, there would also be a correlation of neutrinos with photons emitted at radio wavelengths. In this work, we perform an in-depth stacking study of the correlation between 15 GHz radio observations of AGN reported in the MOJAVE XV catalog, and ten years of neutrino data from IceCube. We also use a time-dependent approach which improves the statistical power of the stacking analysis. No significant correlation was found for both analyses and upper limits are reported. When compared to the IceCube diffuse flux, at 100 TeV and for a spectral index of 2.5, the upper limits derived are $\sim3\%$ and $\sim9\%$ for the time-averaged and time-dependent case, respectively., Comment: 14 Pages 7 Figures

We present a search for an eV-scale sterile neutrino using 7.5 years of data from the IceCube DeepCore detector. The analysis uses a sample of 21,914 events with energies between 5 and 150 GeV to search for sterile neutrinos through atmospheric muon neutrino disappearance. Improvements in event selection and treatment of systematic uncertainties provide greater statistical power compared to previous DeepCore sterile neutrino searches. Our results are compatible with the absence of mixing between active and sterile neutrino states, and we place constraints on the mixing matrix elements $|U_{\mu 4}|^2 < 0.0534$ and $|U_{\tau 4}|^2 < 0.0574$ at 90% CL under the assumption that $\Delta m^2_{41}\geq 1\;\mathrm{eV^2}$. These null results add to the growing tension between anomalous appearance results and constraints from disappearance searches in the 3+1 sterile neutrino landscape., Comment: 11 pages, 5 figures. To be submitted to Physical Review D

A reentrant behavior of temperature dependent magnetic ac-susceptibility (or excess susceptibility(ES)) at lower temperature is observed in a composite made of superconductor $La_{1.85}Sr_{0.15}CuO_4$ (LCu) and an insulating paramagnetic salt $Gd_2O_3$ (GdO). The ES exhibits an exponential characteristic that varies with temperature ($\exp,[\frac{T_0}{T}]$), T0 is characteristics temperature. The characteristics temperature,T$_0$, decreases as the effective interface diminishes and the amplitude of the dc magnetic field increases. The creation of ferromagnetic dimers between Gd$^{+3}$ ions in GdO is observed as a result of vortex-dipole interaction, which causes the observation of this unusual ES at temperatures much lower than the superconducting onset temperature T$_{S}^{onset}$. This type of ferromagnetic dimer formation much below superconducting transition temperature is found comparable with the formation of Yu-Shiba-Rusinov (YSR) state and interaction between these YSR state., Comment: 12 pages, 8 figures

Spurious patterns refer to a mathematical association between two or more variables in a dataset that are not causally related. However, this notion of spuriousness, which is usually introduced due to sampling biases in the dataset, has classically lacked a formal definition. To address this gap, this work presents the first information-theoretic formalization of spuriousness in a dataset (given a split of spurious and core features) using a mathematical framework called Partial Information Decomposition (PID). Specifically, we disentangle the joint information content that the spurious and core features share about another target variable (e.g., the prediction label) into distinct components, namely unique, redundant, and synergistic information. We propose the use of unique information, with roots in Blackwell Sufficiency, as a novel metric to formally quantify dataset spuriousness and derive its desirable properties. We empirically demonstrate how higher unique information in the spurious features in a dataset could lead a model into choosing the spurious features over the core features for inference, often having low worst-group-accuracy. We also propose a novel autoencoder-based estimator for computing unique information that is able to handle high-dimensional image data. Finally, we also show how this unique information in the spurious feature is reduced across several dataset-based spurious-pattern-mitigation techniques such as data reweighting and varying levels of background mixing, demonstrating a novel tradeoff between unique information (spuriousness) and worst-group-accuracy., Comment: Accepted at ICML 2024 Workshop on Data-centric Machine Learning Research (DMLR): Datasets for Foundation Models

The deviations of the recent measurements of the muon magnetic moment and the $W-$boson mass from their SM predictions hint to new physics beyond the SM. In this article, we address the observed discrepancies in the $W$-boson mass and muon anomalous magnetic moment in the Inert Two Higgs Doublet Model (I2HDM) extended by a complex scalar field singlet under the SM gauge group. The model is constrained from the existing LEP data and the measurements of partial decay widths to gauge bosons at LHC. It is shown that a large subset of this constrained parameter space of the model can simultaneously accommodate the $W$-boson mass and also explain the muon $g-2$ anomaly., Comment: 15 pages, 5 figures

Reinforcement Learning from AI Feedback (RLAIF) has demonstrated significant potential across various domains, including mitigating harm in LLM outputs, enhancing text summarization, and mathematical reasoning. This paper introduces an RLAIF framework for improving the code generation abilities of lightweight (<1B parameters) LLMs. We specifically focus on code generation tasks that require writing appropriate API calls, which is challenging due to the well-known issue of hallucination in LLMs. Our framework extracts AI feedback from a larger LLM (e.g., GPT-3.5) through a specialized prompting strategy and uses this data to train a reward model towards better alignment from smaller LLMs. We run our experiments on the Gorilla dataset and meticulously assess the quality of the model-generated code across various metrics, including AST, ROUGE, and Code-BLEU, and develop a pipeline to compute its executability rate accurately. Our approach significantly enhances the fine-tuned LLM baseline's performance, achieving a 4.5% improvement in executability rate. Notably, a smaller LLM model (780M parameters) trained with RLAIF surpasses a much larger fine-tuned baseline with 7B parameters, achieving a 1.0% higher code executability rate.

Visual perception tasks are predominantly solved by Vision Transformer (ViT) architectures, which, despite their effectiveness, encounter a computational bottleneck due to the quadratic complexity of computing self-attention. This inefficiency is largely due to the self-attention heads capturing redundant token interactions, reflecting inherent redundancy within visual data. Many works have aimed to reduce the computational complexity of self-attention in ViTs, leading to the development of efficient and sparse transformer architectures. In this paper, viewing through the efficiency lens, we realized that introducing any sparse self-attention strategy in ViTs can keep the computational overhead low. However, these strategies are sub-optimal as they often fail to capture fine-grained visual details. This observation leads us to propose a general, efficient, sparse architecture, named Fibottention, for approximating self-attention with superlinear complexity that is built upon Fibonacci sequences. The key strategies in Fibottention include: it excludes proximate tokens to reduce redundancy, employs structured sparsity by design to decrease computational demands, and incorporates inception-like diversity across attention heads. This diversity ensures the capture of complementary information through non-overlapping token interactions, optimizing both performance and resource utilization in ViTs for visual representation learning. We embed our Fibottention mechanism into multiple state-of-the-art transformer architectures dedicated to visual tasks. Leveraging only 2-6% of the elements in the self-attention heads, Fibottention in conjunction with ViT and its variants, consistently achieves significant performance boosts compared to standard ViTs in nine datasets across three domains $\unicode{x2013}$ image classification, video understanding, and robot learning tasks., Comment: The code is publicly available at https://github.com/Charlotte-CharMLab/Fibottention

Given an Abelian group G, a Boolean-valued function f: G -> {-1,+1}, is said to be s-sparse, if it has at most s-many non-zero Fourier coefficients over the domain G. In a seminal paper, Gopalan et al. proved "Granularity" for Fourier coefficients of Boolean valued functions over Z_2^n, that have found many diverse applications in theoretical computer science and combinatorics. They also studied structural results for Boolean functions over Z_2^n which are approximately Fourier-sparse. In this work, we obtain structural results for approximately Fourier-sparse Boolean valued functions over Abelian groups G of the form,G:= Z_{p_1}^{n_1} \times ... \times Z_{p_t}^{n_t}, for distinct primes p_i. We also obtain a lower bound of the form 1/(m^{2}s)^ceiling(phi(m)/2), on the absolute value of the smallest non-zero Fourier coefficient of an s-sparse function, where m=p_1 ... p_t, and phi(m)=(p_1-1) ... (p_t-1). We carefully apply probabilistic techniques from Gopalan et al., to obtain our structural results, and use some non-trivial results from algebraic number theory to get the lower bound. We construct a family of at most s-sparse Boolean functions over Z_p^n, where p > 2, for arbitrarily large enough s, where the minimum non-zero Fourier coefficient is 1/omega(n). The "Granularity" result of Gopalan et al. implies that the absolute values of non-zero Fourier coefficients of any s-sparse Boolean valued function over Z_2^n are 1/O(s). So, our result shows that one cannot expect such a lower bound for general Abelian groups. Using our new structural results on the Fourier coefficients of sparse functions, we design an efficient testing algorithm for Fourier-sparse Boolean functions, thata requires poly((ms)^phi(m),1/epsilon)-many queries. Further, we prove an Omega(sqrt{s}) lower bound on the query complexity of any adaptive sparsity testing algorithm.

Observations with FAST recently detected HI 21-cm emission near M94, revealing an intriguing object, Cloud-9, without an optical counterpart. Subsequent analysis suggests Cloud-9 is consistent with a gas-rich ($M_{\rm HI} \approx 10^{6} \ M_{\odot}$), starless dark matter (DM) halo of mass $M_{200} \approx 5 \times 10^{9} \ M_{\odot}$. Using VLA in D-array configuration, we present interferometric observations of Cloud-9 revealing it as a dynamically cold ($W_{50} \approx 12 \rm \ km \ s^{-1}$), non-rotating, and spatially-asymmetric system, exhibiting gas compression on one side and a tail-like structure towards the other, features likely originating from ram pressure. Our observations suggest Cloud-9 is consistent with a starless $\Lambda$CDM dark matter halo if the gas is largely isothermal. If interpreted as a faint dwarf, Cloud-9 is similar to Leo T, a nearby gas-rich galaxy that would fall below current optical detection limits at Cloud-9's distance ($d\approx 5 \rm \ Mpc$). Further observations with HST reaching magnitudes $m_{g} \approx 30$ would help identify such a galaxy or dramatically lower current limits to its stellar mass ($M_{\rm gal} \lesssim 10^{5} \ M_{\odot}$). Cloud-9 thus stands as the firmest starless DM halo candidate to date or the faintest galaxy known at its distance., Comment: Submitted to ApJ

Filaments connecting halos are a long-standing prediction of cold dark matter theories. We present a novel detection of the cosmic web emission connecting two massive quasar-host galaxies at cosmic noon in the MUSE Ultra Deep Field (MUDF) using unprecedentedly deep observations that unlock a high-definition view of the filament morphology, a measure of the transition radius between the intergalactic and circumgalactic medium, and the characterization of the surface brightness profiles along the filament and in the transverse direction. Through systematic comparisons with simulations, we validate the filaments' typical density predicted in the current cold dark matter model. Our analysis of the MUDF field, an excellent laboratory for quantitatively studying filaments in emission, opens a new avenue to understanding the cosmic web that, being a fundamental prediction of cosmology, bears key information on the essence of dark matter., Comment: submitted, comments welcome!

The advancement of developing efficient medical image segmentation has evolved from initial dependence on Convolutional Neural Networks (CNNs) to the present investigation of hybrid models that combine CNNs with Vision Transformers. Furthermore, there is an increasing focus on creating architectures that are both high-performing in medical image segmentation tasks and computationally efficient to be deployed on systems with limited resources. Although transformers have several advantages like capturing global dependencies in the input data, they face challenges such as high computational and memory complexity. This paper investigates the integration of CNNs and Vision Extended Long Short-Term Memory (Vision-xLSTM) models by introducing a novel approach called UVixLSTM. The Vision-xLSTM blocks captures temporal and global relationships within the patches extracted from the CNN feature maps. The convolutional feature reconstruction path upsamples the output volume from the Vision-xLSTM blocks to produce the segmentation output. Our primary objective is to propose that Vision-xLSTM forms a reliable backbone for medical image segmentation tasks, offering excellent segmentation performance and reduced computational complexity. UVixLSTM exhibits superior performance compared to state-of-the-art networks on the publicly-available Synapse dataset. Code is available at: https://github.com/duttapallabi2907/UVixLSTM

Human feedback plays a critical role in learning and refining reward models for text-to-image generation, but the optimal form the feedback should take for learning an accurate reward function has not been conclusively established. This paper investigates the effectiveness of fine-grained feedback which captures nuanced distinctions in image quality and prompt-alignment, compared to traditional coarse-grained feedback (for example, thumbs up/down or ranking between a set of options). While fine-grained feedback holds promise, particularly for systems catering to diverse societal preferences, we show that demonstrating its superiority to coarse-grained feedback is not automatic. Through experiments on real and synthetic preference data, we surface the complexities of building effective models due to the interplay of model choice, feedback type, and the alignment between human judgment and computational interpretation. We identify key challenges in eliciting and utilizing fine-grained feedback, prompting a reassessment of its assumed benefits and practicality. Our findings -- e.g., that fine-grained feedback can lead to worse models for a fixed budget, in some settings; however, in controlled settings with known attributes, fine grained rewards can indeed be more helpful -- call for careful consideration of feedback attributes and potentially beckon novel modeling approaches to appropriately unlock the potential value of fine-grained feedback in-the-wild.

Increasing heterogeneity in HPC architectures and compiler advancements have led to OpenMP being frequently used to enable computations on heterogeneous devices. However, the efficient movement of data on heterogeneous computing platforms is crucial for achieving high utilization. The implicit OpenMP data-mapping rules often result in redundant data transfer, which can be a bottleneck for program performance. Programmers must explicitly map data between the host and connected accelerator devices to achieve efficient data movement. For this, OpenMP offers the target data and target update constructs. Ensuring efficient data transfer requires programmers to reason about complex data flow. This can be a laborious and error-prone process since the programmer must keep a mental model of data validity and lifetime spanning multiple data environments. Any automated analysis should maximize data reuse, minimize data transfer, and must consider control flow and context from function call sites, making the analysis interprocedural and context sensitive. In this paper, we present a static analysis tool, OMPDart (OpenMP DAta Reduction Tool), for OpenMP programs that models data dependencies between host and device regions and applies source code transformations to achieve efficient data transfer. The analysis is based on a hybrid data structure that joins an Abstract Syntax Tree (AST) with a Control Flow Graph (CFG). Our evaluations on nine HPC benchmarks demonstrate that OMPDart is capable of generating effective data mapping constructs that substantially reduce data transfer between host and device. OMPDart helps reduce data transfers by 85% and improves runtime performance by 1.6x over an expert-defined implementation of LULESH 2.0., Comment: Accepted to the 2024 International Conference for High Performance Computing, Networking, Storage, and Analysis (SC24)

We show that a Bose-Einstein Condensate (BEC) launched with non-zero initial momentum into a periodically kicked optical lattice creates an asymmetrically localized momentum distribution in a moving frame with a small initial current. This asymmetric localization is investigated under two scenarios; (a) when the BEC is in motion in the laboratory frame and, (b) when the optical lattice is in motion in the laboratory frame. The asymmetric features are shown to arise from the early-time dynamics induced by the broken parity symmetry and, asymptotically, freeze as the dynamical localization stabilizes. The micromotion of BEC is measured using the early-time asymmetry. In this context, micromotion refers to the extremely low initial velocity of the BEC along the lattice direction. This originates from the jitter when the hybrid trap potential is turned off. By employing BEC in a kicked and moving optical lattice, the asymmetry in early-time dynamics is measured to precisely characterize and quantify the micromotion phenomena in the quantum system. Micromotion measurement has applications in quantifying systematic shifts and uncertainties in light-pulse interferometers.

Recently proposed quasi-Bayesian (QB) methods initiated a new era in Bayesian computation by directly constructing the Bayesian predictive distribution through recursion, removing the need for expensive computations involved in sampling the Bayesian posterior distribution. This has proved to be data-efficient for univariate predictions, but extensions to multiple dimensions rely on a conditional decomposition resulting from predefined assumptions on the kernel of the Dirichlet Process Mixture Model, which is the implicit nonparametric model used. Here, we propose a different way to extend Quasi-Bayesian prediction to high dimensions through the use of Sklar's theorem by decomposing the predictive distribution into one-dimensional predictive marginals and a high-dimensional copula. Thus, we use the efficient recursive QB construction for the one-dimensional marginals and model the dependence using highly expressive vine copulas. Further, we tune hyperparameters using robust divergences (eg. energy score) and show that our proposed Quasi-Bayesian Vine (QB-Vine) is a fully non-parametric density estimator with \emph{an analytical form} and convergence rate independent of the dimension of data in some situations. Our experiments illustrate that the QB-Vine is appropriate for high dimensional distributions ($\sim$64), needs very few samples to train ($\sim$200) and outperforms state-of-the-art methods with analytical forms for density estimation and supervised tasks by a considerable margin., Comment: 36 pages, 2 figures

Large language models (LLMs) are capable of producing documents, and retrieval augmented generation (RAG) has shown itself to be a powerful method for improving accuracy without sacrificing fluency. However, not all information can be retrieved from text. We propose an approach that uses the analysis of structured data to generate fact sets that are used to guide generation in much the same way that retrieved documents are used in RAG. This analytics augmented generation (AAG) approach supports the ability to utilize standard analytic techniques to generate facts that are then converted to text and passed to an LLM. We present a neurosymbolic platform, Satyrn that leverages AAG to produce accurate, fluent, and coherent reports grounded in large scale databases. In our experiments, we find that Satyrn generates reports in which over 86% accurate claims while maintaining high levels of fluency and coherence, even when using smaller language models such as Mistral-7B, as compared to GPT-4 Code Interpreter in which just 57% of claims are accurate.

We present a novel dataset that extends our view of the cosmic gas around z$\approx$3-4 Ly$\alpha$ emitting galaxies (LAEs) in the Muse Analysis of Gas around Galaxies (MAGG) survey by tracing a cool and enriched gas phase through 47 MgII absorbers identified in newly-obtained VLT/XSHOOTER near-infrared quasar spectra. Jointly with the more ionized gas traced by CIV systems and the neutral HI from previous work, we find that LAEs are distributed inside cosmic structures that contain multiphase gas in composition and temperature. All gas phases are a strong function of the large-scale galaxy environment: the MgII and the CIV strength and kinematics positively correlate with the number of associated galaxies, and it is $\approx$3-4 times more likely to detect metal absorbers around group than isolated LAEs. Exploring the redshift evolution, the covering factor of MgII around group and isolated LAEs remains approximately constant from z$\approx$3-4 to z<2, but the one of CIV around group galaxies drops by z<2. Adding the cool enriched gas traced by the MgII absorbers to the results we obtained for the HI and CIV gas, we put forward a picture in which LAEs lie along gas filaments that contain high column-density HI systems and are enriched by strong CIV and MgII absorbers. While the MgII gas appears to be more centrally concentrated near LAEs, weaker CIV systems trace instead a more diffuse gas phase extended up to larger distances around the galaxies., Comment: Accepted for publication on A&A

The focus of this note is on the Chow group problem over ramified regular local rings $(R, m)$. Our goal is threefold: i) to introduce a characterization of a ramified regular local ring essentially of finite type over a dvr, ii) to address the question whether $(i-1)!$ $\mathbb{A}^i(U)=0$ for specific open subsets $U$ of Spec$R$ and iii) to establish a constructive relation between Chow groups of the henselization $(R^h, m^h)$ and Chow groups of the completion $(\hat{R}, \hat{m})$ of $(R, m)$.

Quantum communication represents a revolutionary advancement over classical information theory, which leverages unique quantum mechanics properties like entanglement to achieve unprecedented capabilities in secure and efficient information transmission. Unlike bits in classical communication, quantum communication utilizes qubits in superposition states, allowing for novel information storage and processing. Entanglement, a key quantum phenomenon, enables advanced protocols with enhanced security and processing power. This paper provides a comprehensive overview of quantum communication, emphasizing the role of entanglement in theoretical foundations, practical protocols, experimental progress, and security implications. It contrasts quantum communications potential applications with classical networks, identifying areas where entanglement offers significant advantages. The paper explores the fundamentals of quantum mechanics in communication, the physical realization of quantum information, and the formation of secure quantum networks through entanglement-based strategies like Quantum Key Distribution (QKD) and teleportation. It addresses the challenges of long-distance quantum communication, the role of quantum repeaters in scaling networks, and the conceptualization of interconnected quantum networks. Additionally, it discusses strides towards the Quantum Internet, Quantum Error-Correcting codes, and quantum cryptographys role in ensuring secure communication. By highlighting the role of entanglement, this paper aims to inspire further research and innovation in secure and efficient information exchange within quantum networks., Comment: 56 pages, 11 figures, 3 tables

Blueberry galaxies (BBs) are fainter, less massive, and lower redshift counterparts of the Green pea galaxies. They are thought to be the nearest analogues of the high redshift Lyman Alpha (Ly$\alpha$) emitters. We report the interferometric imaging of HI 21 cm emission from a Blueberry galaxy, J1509+3731, at redshift, z = 0.03259, using the Giant Metrewave Radio Telescope (GMRT). We find that this Blueberry galaxy has an HI mass of $M_{\text{HI}} \approx 3\times 10^8 \, M_{\odot}$ and an HI-to-stellar mass ratio $M_{\text{HI}}/M_* \approx$ 2.4. Using SFR estimates from the H$\beta$ emission line, we find that it has a short HI depletion time scale of $\approx 0.2$ Gyr, which indicates a significantly higher star-formation efficiency compared to typical star-forming galaxies at the present epoch. Interestingly, we find an offset of $\approx 2$ kpc between the peak of the HI 21 cm emission and the optical centre which suggests a merger event in the past. Our study highlights the important role of mergers in triggering the starburst in BBs and their role in the possible leakage of Lyman-$\alpha$ and Lyman-continuum photons which is consistent with the previous studies on BB galaxies., Comment: 8 pages, 4 figures, accepted for publication in MNRAS

High-momentum two-particle correlations are a useful tool for studying jet-quenching effects in the quark-gluon plasma. Angular correlations between neutral-pion triggers and charged hadrons with transverse momenta in the range 4--12~GeV/$c$ and 0.5--7~GeV/$c$, respectively, have been measured by the PHENIX experiment in 2014 for Au$+$Au collisions at $\sqrt{s_{_{NN}}}=200$~GeV. Suppression is observed in the yield of high-momentum jet fragments opposite the trigger particle, which indicates jet suppression stemming from in-medium partonic energy loss, while enhancement is observed for low-momentum particles. The ratio and differences between the yield in Au$+$Au collisions and $p$$+$$p$ collisions, $I_{AA}$ and $\Delta_{AA}$, as a function of the trigger-hadron azimuthal separation, $\Delta\phi$, are measured for the first time at the Relativistic Heavy Ion Collider. These results better quantify how the yield of low-$p_T$ associated hadrons is enhanced at wide angle, which is crucial for studying energy loss as well as medium-response effects., Comment: 534 authors from 83 institutions, 12 pages, 7 figures. v1 is version submitted to Physical Review C. HEPdata tables for the points plotted in figures for this and previous PHENIX publications are (or will be) publicly available at http://www.phenix.bnl.gov/papers.html

The recently computed post-Newtonian (PN) gravitational-wave phasing up to 4.5PN order accounts for several novel physical effects in compact binary dynamics such as the {\it tail of the memory, tails of tails of tails and tails of mass hexadecupole and current octupole moments}. Therefore, it is instructive to assess the ability of current-generation (2G) detectors such as LIGO/Virgo, next-generation (XG) ground-based gravitational wave detectors such as Cosmic Explorer/Einstein Telescope and space-based detectors like LISA to test the predictions of PN theory at these orders. Employing Fisher information matrix, we find that the projected bounds on the deviations from the logarithmic PN phasing coefficient at 4PN is ${\cal O}(10^{-2})$ and ${\cal O}(10^{-1})$ for XG and 2G detectors, respectively. Similarly, the projected bounds on other three PN coefficients that appear at 4PN and 4.5PN are ${\cal O}(10^{-1}-10^{-2})$ for XG and ${\cal O}(1)$ for 2G detectors. LISA observations of supermassive BHs could provide the tightest constraints on these four parameters ranging from ${\cal O}(10^{-4}-10^{-2})$. The variation in these bounds are studied as a function of total mass and the mass ratio of the binaries in quasi-circular orbits. These new tests are unique probes of higher order nonlinear interactions in compact binary dynamics and their consistency with the predictions of general relativity., Comment: 11 pages, 2 figures, 2 tables

The recent IceCube detection of TeV neutrino emission from the nearby active galaxy NGC 1068 suggests that active galactic nuclei (AGN) could make a sizable contribution to the diffuse flux of astrophysical neutrinos. The absence of TeV $\gamma$-rays from NGC 1068 indicates neutrino production in the vicinity of the supermassive black hole, where the high radiation density leads to $\gamma$-ray attenuation. Therefore, any potential neutrino emission from similar sources is not expected to correlate with high-energy $\gamma$-rays. Disk-corona models predict neutrino emission from Seyfert galaxies to correlate with keV X-rays, as they are tracers of coronal activity. Using through-going track events from the Northern Sky recorded by IceCube between 2011 and 2021, we report results from a search for individual and aggregated neutrino signals from 27 additional Seyfert galaxies that are contained in the BAT AGN Spectroscopic Survey (BASS). Besides the generic single power-law, we evaluate the spectra predicted by the disk-corona model. Assuming all sources to be intrinsically similar to NGC 1068, our findings constrain the collective neutrino emission from X-ray bright Seyfert galaxies in the Northern Hemisphere, but, at the same time, show excesses of neutrinos that could be associated with the objects NGC 4151 and CGCG 420-015. These excesses result in a 2.7$\sigma$ significance with respect to background expectations., Comment: 17 pages, 9 figures

Active Galactic Nuclei (AGN) are promising candidate sources of high-energy astrophysical neutrinos since they provide environments rich in matter and photon targets where cosmic ray interactions may lead to the production of gamma rays and neutrinos. We searched for high-energy neutrino emission from AGN using the $\textit{Swift}$-BAT Spectroscopic Survey (BASS) catalog of hard X-ray sources and 12 years of IceCube muon track data. First, upon performing a stacked search, no significant emission was found. Second, we searched for neutrinos from a list of 43 candidate sources and found an excess from the direction of two sources, Seyfert galaxies NGC 1068 and NGC 4151. We observed NGC 1068 at flux $\phi_{\nu_{\mu}+\bar{\nu}_{\mu}}$ = $4.02_{-1.52}^{+1.58} \times 10^{-11}$ TeV$^{-1}$ cm$^{-2}$ s$^{-1}$ normalized at 1 TeV, with power-law spectral index, $\gamma$ = 3.10$^{+0.26}_{-0.22}$, consistent with previous IceCube results. The observation of a neutrino excess from the direction of NGC 4151 is at a post-trial significance of 2.9$\sigma$. If interpreted as an astrophysical signal, the excess observed from NGC 4151 corresponds to a flux $\phi_{\nu_{\mu}+\bar{\nu}_{\mu}}$ = $1.51_{-0.81}^{+0.99} \times 10^{-11}$ TeV$^{-1}$ cm$^{-2}$ s$^{-1}$ normalized at 1 TeV and $\gamma$ = 2.83$^{+0.35}_{-0.28}$.

A structural relationship between the centrosome and cytoskeleton has been recognized for many years. Centrosomes typically reside near the nucleus, establishing and maintaining the nucleus-centrosome axis. This spatial arrangement is critical for determining cell polarity during interphase and ensuring the proper assembly of the spindle apparatus during mitosis. Centrosomes also engage in physical interactions with various components of the cytoskeleton, balancing internal cellular architecture and polarity in a manner specific to tissue type and developmental stage. Numerous crosslinking proteins facilitate these interactions, promoting both cytoskeletal and centrosomal nucleation. This article provides an overview of how cytoskeletal elements and centrosomes coordinate their actions to regulate complex cellular functions such as cell migration, adhesion, and division. The reciprocal influence between cytoskeletal dynamics and centrosomal positioning underscores their integral roles in cellular organization and function., Comment: 15 pages, 1 Figure

We study the nonequilibrium stationary state of a one-dimensional inertial run-and-tumble particle (IRTP) trapped in a harmonic potential. We find that the presence of inertia leads to two distinct dynamical scenarios, namely, overdamped and underdamped, characterized by the relative strength of the viscous and the trap time-scales. We also find that inertial nature of the active dynamics leads to the particle being confined in specific regions of the phase plane in the overdamped and underdamped cases, which we compute analytically. Moreover, the interplay of the inertial and active time-scales gives rise to several sub-regimes, which are characterized by very different behaviour of position and velocity fluctuations of the IRTP. In particular, in the underdamped regime, both the position and velocity undergoes transitions from a novel multi-peaked structure in the strongly active limit to a single peaked Gaussian-like distribution in the passive limit. On the other hand, in the overdamped scenario, the position distribution shows a transition from a U-shape to a dome-shape, as activity is decreased. Interestingly, the velocity distribution in the overdamped scenario shows two transitions -- from a single-peaked shape with an algebraic divergence at the origin in the strongly active regime to a double peaked one in the moderately active regime to a dome-shaped one in the passive regime., Comment: 20 pages, 21 figures. Comments or suggestions are welcome

In recent years, there has been a growing emphasis on compressing large pre-trained transformer models for resource-constrained devices. However, traditional pruning methods often leave the embedding layer untouched, leading to model over-parameterization. Additionally, they require extensive compression time with large datasets to maintain performance in pruned models. To address these challenges, we propose VTrans, an iterative pruning framework guided by the Variational Information Bottleneck (VIB) principle. Our method compresses all structural components, including embeddings, attention heads, and layers using VIB-trained masks. This approach retains only essential weights in each layer, ensuring compliance with specified model size or computational constraints. Notably, our method achieves upto 70% more compression than prior state-of-the-art approaches, both task-agnostic and task-specific. We further propose faster variants of our method: Fast-VTrans utilizing only 3% of the data and Faster-VTrans, a time efficient alternative that involves exclusive finetuning of VIB masks, accelerating compression by upto 25 times with minimal performance loss compared to previous methods. Extensive experiments on BERT, ROBERTa, and GPT-2 models substantiate the efficacy of our method. Moreover, our method demonstrates scalability in compressing large models such as LLaMA-2-7B, achieving superior performance compared to previous pruning methods. Additionally, we use attention-based probing to qualitatively assess model redundancy and interpret the efficiency of our approach. Notably, our method considers heads with high attention to special and current tokens in un-pruned model as foremost candidates for pruning while retained heads are observed to attend more to task-critical keywords.

We investigate the phenomenon of transition to synchronization in Sakaguchi-Kuramoto model in the presence of higher-order interactions and global order parameter adaptation. The investigation is done by performing extensive numerical simulations and low dimensional modeling of the system. Numerical simulations of the full system show both continuous (second order) as well as discontinuous transitions. The discontinuous transitions can either be associated with explosive (first order) or with tiered synchronization states depending on the choice of parameters. To develop an in depth understanding of the transition scenario in the parameter space we derive a reduced order model (ROM) using the Ott-Antonsen ansatz, the results of which closely matches with that of the numerical simulations of the full system. The simplicity and analytical accessibility of the ROM helps to conveniently unfold the transition scenario in the system having complex dependence on the parameters. Simultaneous analysis of the full system and the ROM clearly identifies the regions of the parameter space exhibiting different types of transitions. It is observed that the second order continuous transition is connected with a supercritical pitchfork bifurcation (PB) of the ROM. On the other hand, the discontinuous teired transition is associated with multiple saddle-node (SN) bifurcations along with a supercritical PB and the first order explosive transition involves a subcritical PB alongside a SN bifurcation., Comment: 11 pages, 11 figures

This paper introduces a novel information-theoretic perspective on the relationship between prominent group fairness notions in machine learning, namely statistical parity, equalized odds, and predictive parity. It is well known that simultaneous satisfiability of these three fairness notions is usually impossible, motivating practitioners to resort to approximate fairness solutions rather than stringent satisfiability of these definitions. However, a comprehensive analysis of their interrelations, particularly when they are not exactly satisfied, remains largely unexplored. Our main contribution lies in elucidating an exact relationship between these three measures of (un)fairness by leveraging a body of work in information theory called partial information decomposition (PID). In this work, we leverage PID to identify the granular regions where these three measures of (un)fairness overlap and where they disagree with each other leading to potential tradeoffs. We also include numerical simulations to complement our results., Comment: Published as a conference paper at 2024 IEEE International Symposium on Information Theory (ISIT 2024)

With the recent advancements and developments in quantum technologies, the emerging field of quantum communication and networking has gained the attention of the researchers. Owing to the unique properties of quantum mechanics, viz., quantum superposition and entanglement, this new area of quantum communication has shown potential to replace modernday communication technologies. The enhanced security and high information sharing ability using principles of quantum mechanics has encouraged networking engineers and physicists to develop this technology for next generation wireless systems. However, a conceptual bridge between the fundamentals of quantum mechanics, photonics and the deployability of a quantum communication infrastructure is not well founded in the current literature. This paper aims to fill this gap by merging the theoretical concepts from quantum physics to the engineering and computing perspectives of quantum technology. This paper builds the fundamental concepts required for understanding quantum communication, reviews the key concepts and demonstrates how these concepts can be leveraged for accomplishing successful communication. The paper delves into implementation advancements for executing quantum communication protocols, explaining how hardware implementation enables the achievement of all basic quantum computing operations. Finally, the paper provides a comprehensive and critical review of the state-of-the-art advancements in the field of quantum communication and quantum internet; and points out the recent trends, challenges and open problems for the real-world realization of next generation networking systems.

We define the notion of dextral symmetric algebras (not necessarily associative), motivated by the idea of symmetric rings. We derive a complete classification of dextral symmetric algebras of Leavitt path algebras, and right Leibniz algebras up to dimension $4$. We also obtain that a finite-dimensional dextral symmetric right Leibniz algebra is solvable if and only if it satisfies a weaker notion of nilpotency.

One of the most important tasks in mathematics and physics is to connect differential geometry and nonlinear differential equations. In the study of nonlinear optics, integrable nonlinear differential equations such as the nonlinear Schr\"odinger equation (NLSE) and higher-order NLSE (HNLSE) play crucial roles. Because of the medium's balance between dispersion and nonlinearity, all of these systems display soliton solutions. The soliton surfaces, or manifolds, connected to these integrable systems hold significance in numerous areas of mathematics and physics. We examine the use of soliton theory in differential geometry in this paper. We build the two-dimensional soliton surface in the three-dimensional Euclidean space by taking into account the Fokas-Lenells Derivative nonlinear Schr\"odinger equation (also known as the gauged Fokas-Lenells equation). The same is constructed by us using the Sym-Tafel formula. The first and second fundamental forms, surface area, and Gaussian curvature are obtained using a Lax representation of the gauged FLE.

In this study, we investigate the spin dynamics of a frustrated Kagom\'e lattice system, focusing on the nonlinearity of spin oscillations induced by a harmonic magnetic field under varying strengths of Dzyaloshinskii-Moriya interaction (DMI), exchange field, and anisotropy energy. We have utilized Poincar\'e Surface Sections (PSS) and Power Spectra (PS) for different DMI and anisotropy energy to study the spin dynamics. Our findings reveal that when the DMI strength, external field, anisotropy, and applied magnetic field are weak, the oscillations are quasi-periodic, mostly dominated by the exchange field. With the increase in the DMI strength, the oscillation of the system becomes highly aperiodic. Strong anisotropy tends to induce periodic oscillations, but increasing DMI eventually leads to chaotic behaviour. Additionally, the external magnetic field destabilizes the periodicity of oscillations in systems with weak easy-axis anisotropy, but the systems with strong anisotropy, the oscillations remain unaffected by the external field's strength. Our analysis of magnon dispersion and magnetic resonance (MR) spectra reveals multiple resonance peaks at higher DMI strengths, indicating a complex interplay between spin wave excitation and system parameters. These results underscore the importance of understanding the inherent DMI and anisotropy in the Kagom\'e lattice during fabrication for various applications. Moreover, our comprehensive analysis of spin dynamics in a Kagom\'e lattice system demonstrates a clear transition from quasi-periodic to chaotic oscillations with the increase in DMI strength.

Quantum error correction (QEC) plays a critical role in preventing information loss in quantum systems and provides a framework for reliable quantum computation. Identifying quantum codes with nice code parameters for physically motivated noise models remains an interesting challenge. Going beyond qubit codes, here we propose a class of qudit error correcting codes tailored to protect against amplitude-damping noise. Specifically, we construct a class of four-qudit codes that satisfies the error correction conditions for all single-qudit and a few two-qudit damping errors up to the leading order in the damping parameter $\gamma$. We devise a protocol to extract syndromes that identify this set of errors unambiguously, leading to a noise-adapted recovery scheme that achieves a fidelity loss of $\cO(\gamma^{2})$. For the $d=2$ case, our QEC scheme is identical to the known example of the $4$-qubit code and the associated syndrome-based recovery. We also assess the performance of our class of codes using the Petz recovery map and note some interesting deviations from the qubit case.

In this study, the particle motion around the naked singularity and black hole of Kerr-Newman spacetime is investigated with a special attention on the closed timelike orbits. It is found that both in the naked singularity (NS) and in black hole (BH), the singularity is concealed by causality violating regions, and the Cauchy surface consistently resides inside the inner horizon in non-extremal black holes. For neutral particles and particles with an identical charge to the source, only particles with positive angular momentum are permitted to traverse the closed timelike curves. Conversely, for particles with the opposite charge to the source, the strong Coulomb attraction draws all particles inside the Cauchy surface, allowing them to be present in the closed timelike curves irrespective of their angular momentum. However, in both the NS and BH (both extremal and non-extremal), test particles are confined at a considerable distance from the singular point such that there always exists an empty region surrounding the singularity which prevents particles from interacting with it. The radius of the empty surface that depends on the source parameters and the particle characteristics, is investigated with an accurate expression., Comment: 17 pages, 9 figures, accepted in IJMPD

Motivated by recent experiments realizing long-lived non-equilibrium states in aperiodically driven quantum many-body systems, we investigate the dynamics of a quasiperiodically driven Rydberg atom chain in the strong Rydberg blockage regime. In this regime, the system is kinetically constrained and the `PXP' model describes its dynamics. Even without driving, the PXP model exhibits many-body scarring and resultant persistent oscillations for dynamics originating from the N\'{e}el-ordered initial state. We demonstrate that a rich array of dynamical behaviors emerge when the system is subjected to a continuous drive. In the high-frequency regime, the system exhibits revivals and oscillations for the N\'{e}el ordered initial state both for periodic and quasi-periodic drives. We trace the origin of this non-ergodicity to an effective PXP Hamiltonian for both of these driving protocols in this regime. Furthermore, we demonstrate that the behavior of the fidelity and the entanglement entropy is non-monotonic at low frequencies in the high-amplitude regime. This leads to several re-entrant scarring transitions both for both the N\'{e}el-ordered and the fully polarized initial state. Our results demonstrate that continuous quasi-periodic drive protocols can provide a promising route to realize prethermal phases of matter in kinetically constrained systems., Comment: 14 pages, 6 figures

The 2nd SNMMI Artificial Intelligence (AI) Summit, organized by the SNMMI AI Task Force, took place in Bethesda, MD, on February 29 - March 1, 2024. Bringing together various community members and stakeholders, and following up on a prior successful 2022 AI Summit, the summit theme was: AI in Action. Six key topics included (i) an overview of prior and ongoing efforts by the AI task force, (ii) emerging needs and tools for computational nuclear oncology, (iii) new frontiers in large language and generative models, (iv) defining the value proposition for the use of AI in nuclear medicine, (v) open science including efforts for data and model repositories, and (vi) issues of reimbursement and funding. The primary efforts, findings, challenges, and next steps are summarized in this manuscript.