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Post-quantum cryptographic (PQC) algorithms, especially those based on the learning with errors (LWE) problem, have been subjected to several physical attacks in the recent past. Although the attacks broadly belong to two classes - passive side-channel attacks and active fault attacks, the attack strategies vary significantly due to the inherent complexities of such algorithms. Exploring further attack surfaces is, therefore, an important step for eventually securing the deployment of these algorithms. Also, it is important to test the robustness of the already proposed countermeasures in this regard. In this work, we propose a new fault attack on side-channel secure masked implementation of LWE-based key-encapsulation mechanisms (KEMs) exploiting fault propagation. The attack typically originates due to an algorithmic modification widely used to enable masking, namely the Arithmetic-to-Boolean (A2B) conversion. We exploit the data dependency of the adder carry chain in A2B and extract sensitive information, albeit masking (of arbitrary order) being present. As a practical demonstration of the exploitability of this information leakage, we show key recovery attacks of Kyber, although the leakage also exists for other schemes like Saber. The attack on Kyber targets the decapsulation module and utilizes Belief Propagation (BP) for key recovery. To the best of our knowledge, it is the first attack exploiting an algorithmic component introduced to ease masking rather than only exploiting the randomness introduced by masking to obtain desired faults (as done by Delvaux). Finally, we performed both simulated and electromagnetic (EM) fault-based practical validation of the attack for an open-source first-order secure Kyber implementation running on an STM32 platform.

Learning accurate, data-driven predictive models for multiple interacting agents following unknown dynamics is crucial in many real-world physical and social systems. In many scenarios, dynamics prediction must be performed under incomplete observations, i.e., only a subset of agents are known and observable from a larger topological system while the behaviors of the unobserved agents and their interactions with the observed agents are not known. When only incomplete observations of a dynamical system are available, so that some states remain hidden, it is generally not possible to learn a closed-form model in these variables using either analytic or data-driven techniques. In this work, we propose STEMFold, a spatiotemporal attention-based generative model, to learn a stochastic manifold to predict the underlying unmeasured dynamics of the multi-agent system from observations of only visible agents. Our analytical results motivate STEMFold design using a spatiotemporal graph with time anchors to effectively map the observations of visible agents to a stochastic manifold with no prior information about interaction graph topology. We empirically evaluated our method on two simulations and two real-world datasets, where it outperformed existing networks in predicting complex multiagent interactions, even with many unobserved agents., Comment: Accepted as a conference paper at $6^{th}$ Annual Learning for Dynamics & Control Conference 2024

Early observations with the James Webb Space Telescope (JWST) have revealed the existence of an unexpectedly large abundance of extremely massive galaxies at redshifts $z \gtrsim 5$: these observations are in tension with the predictions not only of the standard $\Lambda$CDM cosmology, but also with those of a wide class of dynamical dark energy (DE) models, and are generally in better agreement with models characterized by a phantom behaviour. Here we consider a model, inspired by string theory and the ubiquity of anti-de Sitter vacua therein, featuring an evolving DE component with positive energy density on top of a negative cosmological constant, argued in an earlier exploratory analysis to potentially be able to explain the JWST observations. We perform a robust comparison of this model against JWST data, considering both photometric observations from the CEERS program, and spectroscopic observations from the FRESCO survey. We show that the model is able to accommodate the JWST observations, with a consistency probability of up to $98\%$, even in the presence of an evolving component with a quintessence-like behaviour (easier to accommodate theoretically compared to phantom DE), while remaining consistent with standard low-redshift probes. Our results showcase the tremendous potential of measurements of high-redshift galaxy abundances in tests of fundamental physics, and their valuable complementarity with standard cosmological probes., Comment: 32 pages, 3 figures. v2: references added, minor edits to figures, clarified certain aspects of the analysis. Version accepted for publication in JCAP

In a modern DBMS, working memory is frequently the limiting factor when processing in-memory analytic query operations such as joins, sorting, and aggregation. Existing resource estimation approaches for a DBMS estimate the resource consumption of a query by computing an estimate of each individual database operator in the query execution plan. Such an approach is slow and error-prone as it relies upon simplifying assumptions, such as uniformity and independence of the underlying data. Additionally, the existing approach focuses on individual queries separately and does not factor in other queries in the workload that may be executed concurrently. In this research, we are interested in query performance optimization under concurrent execution of a batch of queries (a workload). Specifically, we focus on predicting the memory demand for a workload rather than providing separate estimates for each query within it. We introduce the problem of workload memory prediction and formalize it as a distribution regression problem. We propose Learned Workload Memory Prediction (LearnedWMP) to improve and simplify estimating the working memory demands of workloads. Through a comprehensive experimental evaluation, we show that LearnedWMP reduces the memory estimation error of the state-of-the-practice method by up to 47.6%. Compared to an alternative single-query model, during training and inferencing, the LearnedWMP model and its variants were 3x to 10x faster. Moreover, LearnedWMP-based models were at least 50% smaller in most cases. Overall, the results demonstrate the advantages of the LearnedWMP approach and its potential for a broader impact on query performance optimization.

Despite considerable success, large Denoising Diffusion Models (DDMs) with UNet backbone pose practical challenges, particularly on limited hardware and in processing gigapixel images. To address these limitations, we introduce two Neural Cellular Automata (NCA)-based DDMs: Diff-NCA and FourierDiff-NCA. Capitalizing on the local communication capabilities of NCA, Diff-NCA significantly reduces the parameter counts of NCA-based DDMs. Integrating Fourier-based diffusion enables global communication early in the diffusion process. This feature is particularly valuable in synthesizing complex images with important global features, such as the CelebA dataset. We demonstrate that even a 331k parameter Diff-NCA can generate 512x512 pathology slices, while FourierDiff-NCA (1.1m parameters) reaches a three times lower FID score of 43.86, compared to the four times bigger UNet (3.94m parameters) with a score of 128.2. Additionally, FourierDiff-NCA can perform diverse tasks such as super-resolution, out-of-distribution image synthesis, and inpainting without explicit training.

Fog harvesting relies on intercepting atmospheric or industrial fog by placing a porous obstacle, e.g., a mesh and collecting the deposited water. In the face of global water scarcity, such fog harvesting has emerged as a viable alternative source of potable water. Typical fog harvesting meshes suffer from poor collection efficiency due to aerodynamic bypassing of the oncoming fog stream and poor collection of the deposited water from the mesh. One pestering challenge in this context is the frequent clogging up of mesh pores by the deposited fog water, which not only yields low drainage efficiency but also generates high aerodynamic resistance to the oncoming fog stream, thereby negatively impacting the fog collection efficiency. Minimizing the clogging is possible by rendering the mesh fiber superhydrophobic, but that entails other detrimental effects like premature dripping and flow-induced re-entrainment of water droplets into the fog stream from the mesh fiber. Herein, we improvise on the traditional interweaved metal mesh designs by defining critical parameters, viz., mesh pitch, shade coefficient, and fiber wettability, and deduce their optimal values from numerically and experimentally observed morphology of collected fog-water droplets under various operating scenarios. We extend our investigations over a varying range of mesh-wettability, including superhydrophilic and hydrophobic fibers, and go on to find optimal shade coefficients which would theoretically render clog-proof fog harvesting meshes. The aerodynamic, deposition, and overall collection efficiencies are characterized. Hydrophobic meshes with square pores, having fiber diameters smaller than the capillary length scale of water, and an optimal shade coefficient, are found to be the most effective design of such clog-proof meshes., Comment: Arani and Arkadeep contributed equally. Corresponding author: Prof. Ranjan Ganguly (Email: ranjan.ganguly@jadavpuruniversity.in). All work carried out in the Advanced Materials Research and Applications (AMRA) Laboratory, India

We present a four-component relativistic unitary coupled cluster method for molecules. We have used commutator-based non-perturbative approximation using the ''Bernoulli expansion'' to derive an approximation to the relativistic unitary coupled cluster method. The performance of the full quadratic unitary coupled-cluster singles and doubles method \left ( qUCCSD \right ), as well as a perturbative approximation variant \left ( UCC3 \right ), has been reported for both energies and properties. It can be seen that both methods give results comparable to those of the standard relativistic coupled cluster method. The qUCCSD method shows better agreement with experimental results due to better inclusion of the relaxation effects. A natural spinor-based scheme to reduce the computation cost of relativistic UCC3 and qUCCSD methods has been discussed., Comment: 34 pages, 10 figures, 4 tables

This article aims to study efficient/trace optimal designs for crossover trials with multiple responses recorded from each subject in the time periods. A multivariate fixed effects model is proposed with direct and carryover effects corresponding to the multiple responses. The corresponding error dispersion matrix is chosen to be either of the proportional or the generalized Markov covariance type, permitting the existence of direct and cross-correlations within and between the multiple responses. The corresponding information matrices for direct effects under the two types of dispersions are used to determine efficient designs. The efficiency of orthogonal array designs of Type $I$ and strength $2$ is investigated for a wide choice of covariance functions, namely, Mat($0.5$), Mat($1.5$) and Mat($\infty$). To motivate these multivariate crossover designs, a gene expression dataset in a $3 \times 3$ framework is utilized.

Sequential decision-making under uncertainty is present in many important problems. Two popular approaches for tackling such problems are reinforcement learning and online search (e.g., Monte Carlo tree search). While the former learns a policy by interacting with the environment (typically done before execution), the latter uses a generative model of the environment to sample promising action trajectories at decision time. Decision-making is particularly challenging in non-stationary environments, where the environment in which an agent operates can change over time. Both approaches have shortcomings in such settings -- on the one hand, policies learned before execution become stale when the environment changes and relearning takes both time and computational effort. Online search, on the other hand, can return sub-optimal actions when there are limitations on allowed runtime. In this paper, we introduce \textit{Policy-Augmented Monte Carlo tree search} (PA-MCTS), which combines action-value estimates from an out-of-date policy with an online search using an up-to-date model of the environment. We prove theoretical results showing conditions under which PA-MCTS selects the one-step optimal action and also bound the error accrued while following PA-MCTS as a policy. We compare and contrast our approach with AlphaZero, another hybrid planning approach, and Deep Q Learning on several OpenAI Gym environments. Through extensive experiments, we show that under non-stationary settings with limited time constraints, PA-MCTS outperforms these baselines., Comment: Extended Abstract accepted for presentation at AAMAS 2024

A fundamental (and largely open) challenge in sequential decision-making is dealing with non-stationary environments, where exogenous environmental conditions change over time. Such problems are traditionally modeled as non-stationary Markov decision processes (NSMDP). However, existing approaches for decision-making in NSMDPs have two major shortcomings: first, they assume that the updated environmental dynamics at the current time are known (although future dynamics can change); and second, planning is largely pessimistic, i.e., the agent acts ``safely'' to account for the non-stationary evolution of the environment. We argue that both these assumptions are invalid in practice -- updated environmental conditions are rarely known, and as the agent interacts with the environment, it can learn about the updated dynamics and avoid being pessimistic, at least in states whose dynamics it is confident about. We present a heuristic search algorithm called \textit{Adaptive Monte Carlo Tree Search (ADA-MCTS)} that addresses these challenges. We show that the agent can learn the updated dynamics of the environment over time and then act as it learns, i.e., if the agent is in a region of the state space about which it has updated knowledge, it can avoid being pessimistic. To quantify ``updated knowledge,'' we disintegrate the aleatoric and epistemic uncertainty in the agent's updated belief and show how the agent can use these estimates for decision-making. We compare the proposed approach with the multiple state-of-the-art approaches in decision-making across multiple well-established open-source problems and empirically show that our approach is faster and highly adaptive without sacrificing safety., Comment: Accepted for publication at the International Conference on Autonomous Agents and MultiAgent Systems (AAMAS), 2024

Several works related to spatial crowdsourcing have been proposed in the direction where the task executers are to perform the tasks within the stipulated deadlines. Though the deadlines are set, it may be a practical scenario that majority of the task executers submit the tasks as late as possible. This situation where the task executers may delay their task submission is termed as procrastination in behavioural economics. In many applications, these late submission of tasks may be problematic for task providers. So here, the participating agents (both task providers and task executers) are articulated with the procrastination issue. In literature, how to prevent this procrastination within the deadline is not addressed in spatial crowdsourcing scenario. However, in a bipartite graph setting one procrastination aware scheduling is proposed but balanced job (task and job will synonymously be used) distribution in different slots (also termed as schedules) is not considered there. In this paper, a procrastination aware scheduling of jobs is proliferated by proposing an (randomized) algorithm in spatial crowdsourcing scenario. Our algorithm ensures that balancing of jobs in different schedules are maintained. Our scheme is compared with the existing algorithm through extensive simulation and in terms of balancing effect, our proposed algorithm outperforms the existing one. Analytically it is shown that our proposed algorithm maintains the balanced distribution.

Open Source Intelligence (OSINT) investigations, which rely entirely on publicly available data such as social media, play an increasingly important role in solving crimes and holding governments accountable. The growing volume of data and complex nature of tasks, however, means there is a pressing need to scale and speed up OSINT investigations. Expert-led crowdsourcing approaches show promise but tend to either focus on narrow tasks or domains or require resource-intense, long-term relationships between expert investigators and crowds. We address this gap by providing a flexible framework that enables investigators across domains to enlist crowdsourced support for the discovery and verification of OSINT. We use a design-based research (DBR) approach to develop OSINT Research Studios (ORS), a sociotechnical system in which novice crowds are trained to support professional investigators with complex OSINT investigations. Through our qualitative evaluation, we found that ORS facilitates ethical and effective OSINT investigations across multiple domains. We also discuss broader implications of expert-crowd collaboration and opportunities for future work., Comment: To be published in CSCW 2024

More than 5 million children under five years die from largely preventable or treatable medical conditions every year, with an overwhelmingly large proportion of deaths occurring in underdeveloped countries with low vaccination uptake. One of the United Nations' sustainable development goals (SDG 3) aims to end preventable deaths of newborns and children under five years of age. We focus on Nigeria, where the rate of infant mortality is appalling. In particular, low vaccination uptake in Nigeria is a major driver of more than 2,000 daily deaths of children under the age of five years. In this paper, we describe our collaboration with government partners in Nigeria to deploy ADVISER: AI-Driven Vaccination Intervention Optimiser. The framework, based on an integer linear program that seeks to maximize the cumulative probability of successful vaccination, is the first successful deployment of an AI-enabled toolchain for optimizing the allocation of health interventions in Nigeria. In this paper, we provide a background of the ADVISER framework and present results, lessons, and success stories of deploying ADVISER to more than 13,000 families in the state of Oyo, Nigeria., Comment: Accepted for publication at the AAAI Conference on Artificial Intelligence (AAAI-24)

When a star undergoes core collapse, a vast amount of energy is released in a ~10 s long burst of neutrinos of all species. Inverse beta decay in the star's hydrogen envelope causes an electromagnetic cascade which ultimately results in a flare of gamma rays - an "echo" of the neutrino burst - at the characteristic energy of 0.511 MeV. We study the phenomenology and detectability of this flare. Its luminosity curve is characterized by a fast, seconds-long, rise and an equally fast decline, with a minute- or hour-long plateau in between. For a near-Earth star (distance D<1 kpc) the echo will be observable at near future gamma ray telescopes with an effective area of 10^3 cm^2 or larger. Its observation will inform us on the envelope size and composition. In conjunction with the direct detection of the neutrino burst, it will also give information on the neutrino emission away from the line of sight and will enable tests of neutrino propagation effects between the stellar surface and Earth., Comment: 10 pages, 5 figures and 1 table. Description of the microphysics has been expanded; minor edits were made for clarity. This version matches the one to be published in the Astrophysical Journal

The initial density of dark matter (DM) particles, otherwise secluded from the standard model (SM), may be generated at reheating, with an initial temperature ratio for internal thermalizations, $\xi_i=T_{\rm DM,i}/T_{\rm SM,i}$. This scenario necessarily implies inflaton-mediated scatterings between DM and SM after reheating, with a rate fixed by the relic abundance of DM and the reheat temperature. These scatterings can be important for an inflaton mass and reheat temperature as high as $\mathcal{O}(10^7 {~\rm GeV})$ and $\mathcal{O}(10^9{~\rm GeV})$, respectively, since the thermally averaged collision terms become approximately independent of the inflaton mass when the bath temperature is larger than the mass. The impact of these scatterings on DM cosmology is studied modeling the perturbative reheating physics by a gauge-invariant set of inflaton interactions upto dimension-5 with the SM gauge bosons, fermions and the Higgs fields. It is observed that an initially lower (higher) DM temperature will rapidly increase (decrease), even with very small couplings to the inflaton. There is a sharp lower bound on the DM mass below which the relic abundance cannot be satisfied due to faster back-scatterings depleting DM quanta to SM particles. For low DM masses, the CMB constraints become stronger due to the collisions for $\xi_i<1$, probing values as small as $\mathcal{O}(10^{-4})$, and weaker for $\xi_i>1$. The BBN constraints become stronger due to the collisions for lower DM masses, probing $\xi_i$ as small as $\mathcal{O}(0.1)$, and weaker for higher DM mass. Thus inflaton-mediated collisions with predictable rates, relevant even for high-scale inflation models, can significantly impact the cosmology of light DM., Comment: 19 pages, 10 figures; v2: comments added, version as published in Physical Review D

The Join-the-Shortest-Queue (JSQ) load balancing scheme is widely acknowledged for its effectiveness in minimizing the average response time for jobs in systems with identical servers. However, when applied to a heterogeneous server system with servers of different processing speeds, the JSQ scheme exhibits suboptimal performance. Recently, a variation of JSQ called the Speed-Aware-Join-the-Shortest-Queue (SA-JSQ) scheme has been shown to attain fluid limit optimality for systems with heterogeneous servers. In this paper, we examine the SA-JSQ scheme for heterogeneous server systems under the Halfin-Whitt regime. Our analysis begins by establishing that the scaled and centered version of the system state weakly converges to a diffusion process characterized by stochastic integral equations. Furthermore, we prove that the diffusion process is positive recurrent and the sequence of stationary measures for the scaled and centered queue length processes converge to the stationary measure for the limiting diffusion process. To achieve this result, we employ Stein's method with a generator expansion approach.

We apply machine learning to understand fundamental aspects of holographic duality, specifically the entropies obtained from the apparent and event horizon areas. We show that simple features of only the time series of the pressure anisotropy, namely the values and half-widths of the maxima and minima, the times these are attained, and the times of the first zeroes can predict the areas of the apparent and event horizons in the dual bulk geometry at all times with a fixed maximum length (30) of the input vector. Given that simple Vaidya-type metrics constructed just from the apparent and event horizon areas can be used to approximately obtain unequal time correlation functions, we argue that the corresponding entropy functions are the measures of information that need to be extracted from simple one-point functions to reconstruct specific aspects of correlation functions of the dual state with the best possible approximations., Comment: 10+10 pages, 1 Figure; Discussion improved, k-fold cross validation added

We present a systematic investigation of the quantum games, constructed using a novel repeated game protocol, when played repeatedly ad infinitum. We focus on establishing that such repeated games -- by virtue of inherent quantum-mechanical randomness -- can be mapped to the paradigm of stochastic games. Subsequently, using the setup of two-player--two-action games, we explore the pure reactive strategies belonging to the set of reactive strategies, whose support in the quantum games is no longer countably finite but rather non-denumerably infinite. We find that how two pure strategies fare against each other is crucially dependent on the discount factor (the probability of occurrence of every subsequent round) and how much entangled the quantum states of the players are. We contrast the results obtained with the corresponding results in the classical setup and find fundamental differences between them: e.g, when the underlying game is the prisoner's dilemma, in the quantum game setup, always-defect strategy can be beaten by the tit-for-tat strategy for high enough discount factor.

The origin of the solar system abundances of several proton-rich isotopes, especially $^{92,94}$Mo and $^{96,98}$Ru, has been an enduring mystery in nuclear astrophysics. An attractive proposal to solve this problem is the $\nu p$-process, which can operate in neutrino-driven outflows in a core-collapse supernova after the shock is launched. Years of detailed studies, however, have cast doubt over the ability of this process to generate sufficiently high absolute and relative amounts of various $p$-nuclei. The $\nu p$-process is also thought to be excluded by arguments based on the long-lived radionuclide $^{92}$Nb. Here, we present explicit calculations, in which both the abundance ratios and the absolute yields of the $p$-nuclei up to $A\lesssim105$ are successfully reproduced, even when using the modern (medium enhanced) triple-$\alpha$ reaction rates. The process is also shown to produce the necessary amounts of $^{92}$Nb. The models are characterized by subsonic outflows and by the protoneutron star masses in the {$\gtrsim1.7 M_\odot$ range}. This suggests that the Mo and Ru $p$-nuclides observed in the Solar System were made in CCSN explosions characterized by an extended accretion stage., Comment: 13 pages, 7 figures

Quantum many-body systems undergoing phase transitions have been proposed as probes enabling beyond-classical enhancement of sensing precision. However, this enhancement is usually limited to a very narrow region around the critical point. Here, we systematically develop a modular approach for introducing multiple phase transitions in a many-body system. This naturally allows us to enlarge the region of quantum-enhanced precision by encompassing the newly created phase boundaries. Our approach is general and can be applied to both symmetry-breaking and topological quantum sensors. In symmetry-breaking sensors, we show that the newly created critical points inherit the original universality class and a simple total magnetization measurement already suffices to locate them. In topological sensors, our modular construction creates multiple bands which leads to a rich phase diagram. In both cases, Heisenberg scaling for Hamiltonian parameter estimation is achieved at all the phase boundaries. This can be exploited to create a global sensor which significantly outperforms a uniform probe., Comment: 4+6 pages, 6+4 figures, accepted for publication in Physical Review Letters

While many unsupervised learning models focus on one family of tasks, either generative or discriminative, we explore the possibility of a unified representation learner: a model which addresses both families of tasks simultaneously. We identify diffusion models, a state-of-the-art method for generative tasks, as a prime candidate. Such models involve training a U-Net to iteratively predict and remove noise, and the resulting model can synthesize high-fidelity, diverse, novel images. We find that the intermediate feature maps of the U-Net are diverse, discriminative feature representations. We propose a novel attention mechanism for pooling feature maps and further leverage this mechanism as DifFormer, a transformer feature fusion of features from different diffusion U-Net blocks and noise steps. We also develop DifFeed, a novel feedback mechanism tailored to diffusion. We find that diffusion models are better than GANs, and, with our fusion and feedback mechanisms, can compete with state-of-the-art unsupervised image representation learning methods for discriminative tasks - image classification with full and semi-supervision, transfer for fine-grained classification, object detection and segmentation, and semantic segmentation. Our project website (https://mgwillia.github.io/diffssl/) and code (https://github.com/soumik-kanad/diffssl) are available publicly., Comment: Website: see https://mgwillia.github.io/diffssl/ . Code: see https://github.com/soumik-kanad/diffssl . The first two authors contributed equally. 27 pages, 10 figures, 17 tables. Submission under review. (this article supersedes arXiv:2307.08702)

Optimal choice of spin polarization enables electron injection into the helical edge state at a precise position, despite the uncertainty principle, permitting access to specific nonlocal Green's functions. We show, within 1D effective description, that this fact facilitates a direct identification of odd-frequency pairing through parity measurement (under frequency reversal) of the nonlocal differential conductance in a setup comprising the Josephson junction on the helical edge state of a 2D topological insulator with two spin-polarized probes tunnel-coupled to the junction region. A 2D numerical simulation has also been conducted to confirm theoretical predictions as well as to demonstrate the experimental feasibility of the proposal., Comment: 11 pages, 3 figures

In this article, universally optimal multivariate crossover designs are studied. The multiple response crossover design is motivated by a $3 \times 3$ crossover setup, where the effect of $3$ doses of an oral drug are studied on gene expressions related to mucosal inflammation. Subjects are assigned to three treatment sequences and response measurements on $5$ different gene expressions are taken from each subject in each of the $3$ time periods. To model multiple or $g$ responses, where $g>1$, in a crossover setup, a multivariate fixed effect model with both direct and carryover treatment effects is considered. It is assumed that there are non zero within response correlations, while between response correlations are taken to be zero. The information matrix corresponding to the direct effects is obtained and some results are studied. The information matrix in the multivariate case is shown to differ from the univariate case, particularly in the completely symmetric property. For the $g>1$ case, with $t$ treatments and $p$ periods, for $p=t \geq 3$, the design represented by a Type $\rm{I}$ orthogonal array of strength $2$ is proved to be universally optimal over the class of binary designs, for the direct treatment effects., Comment: 18 Pages, 2 Figures

In-beam $ \gamma $-ray spectroscopy of $^{131}$Xe has been carried out to study the structure of the intruder $ \nu h_{11/2} $ band. Excited states were populated via an $ \alpha $-induced fusion-evaporation reaction at E$ _{\alpha} = 38 $ MeV. Inspection of $ \gamma \gamma $-coincidence data resulted in the identification of a new rotational sequence. Based on the systematics of excitation energy, assigned spin-parity, decay pattern, and the electromagnetic character of the inter-band $ \Delta I = 1 $ $ \gamma $-transitions, this sequence is proposed as the unfavoured signature partner of the $ \nu h_{11/2} $ band. The structure of this band is further illuminated in the light of the triaxial particle rotor model (TPRM). The possibility of wobbling excitation in $ N = 77 $ Xe-Ba-Ce isotones has been explored in a systematic manner.

The radiative decay of the Hoyle state is the doorway to the production of heavier elements in stellar environment. Here we report, an exclusive measurement of electric quadruple (E$_2$) transitions of the Hoyle state to the ground state of $^{12}$C through the $^{12}$C(p, p$^\prime$$\gamma$$\gamma$)$^{12}$C reaction. Triple coincidence measurement yields a value of radiative branching ratio $\Gamma_{rad}$/$\Gamma$ = 4.01 (30) $\times$ 10$^{-4}$. The result has been corroborated by an independent experiment based on the complete kinematical measurement $via.$ $^{12}$C(p, p$^\prime$)$^{12}$C reaction ($\Gamma_{rad}$/$\Gamma$ = 4.04 (30) $\times$ 10$^{-4}$). Using our results together with the currently adopted values of $\Gamma_{\pi}$(E$_0$)/$\Gamma$ and $\Gamma_{\pi}$($E_0$), the radiative width of the Hoyle state is found to be 3.75 (40) $\times$ 10$^{-3}$ eV. We emphasize here that our result is not in agreement with 34 $\%$ increase in the radiative decay width of the Hoyle state measured recently but consistent with the currently adopted value., Comment: 7 pages, 5 figures Submitted to Phys. Rev. Lett

MoEDAL's Apparatus for Penetrating Particles (MAPP) Experiment is designed to expand the search for new physics at the LHC, significantly extending the physics program of the baseline MoEDAL Experiment. The Phase-1 MAPP detector (MAPP-1) is currently undergoing installation at the LHC's UA83 gallery adjacent to the LHCb/MoEDAL region at Interaction Point 8 and will begin data-taking in early 2024. The focus of the MAPP experiment is on the quest for new feebly interacting particles$\unicode{x2014}$avatars of new physics with extremely small Standard Model couplings, such as minicharged particles (mCPs). In this study, we present the results of a comprehensive analysis of MAPP-1's sensitivity to mCPs arising in the canonical model involving the kinetic mixing of a massless dark $U(1)$ gauge field with the Standard Model hypercharge gauge field. We focus on several dominant production mechanisms of mCPs at the LHC across the mass$\unicode{x2013}$mixing parameter space of interest to MAPP: Drell$\unicode{x2013}$Yan pair production, direct decays of heavy quarkonia and light vector mesons, and single Dalitz decays of pseudoscalar mesons. The $95\%$ confidence level background-free sensitivity of MAPP-1 for mCPs produced at the LHC's Run 3 and the HL-LHC through these mechanisms, along with projected constraints on the minicharged strongly interacting dark matter window, are reported. Our results indicate that MAPP-1 exhibits sensitivity to sizable regions of unconstrained parameter space and can probe effective charges as low as $8 \times 10^{-4}\:e$ and $6 \times 10^{-4}\:e$ for Run 3 and the HL-LHC, respectively., Comment: 11 pages, 7 figures

The densities in the cores of the neutron stars (NSs) can reach several times that of the nuclear saturation density. The exact nature of matter at these densities is still virtually unknown. We consider a number of proposed, phenomenological relativistic mean-field equations of state to construct theoretical models of NSs. We find that, based on our selected set of models, the emergence of exotic matter at these high densities restricts the mass of NSs to $\simeq 2.2 M_\odot$. However, the presence of magnetic fields and a model anisotropy significantly increases the star's mass, placing it within the observational mass gap that separates the heaviest NSs from the lightest black holes. Therefore, we propose that gravitational wave observations, like GW190814, and other potential candidates within this mass gap, may actually represent massive, magnetized NSs., Comment: 18 pages including 26 figures and 12 tables; this version published in Phys. Rev. D

Continual learning (CL) methods designed for natural image classification often fail to reach basic quality standards for medical image segmentation. Atlas-based segmentation, a well-established approach in medical imaging, incorporates domain knowledge on the region of interest, leading to semantically coherent predictions. This is especially promising for CL, as it allows us to leverage structural information and strike an optimal balance between model rigidity and plasticity over time. When combined with privacy-preserving prototypes, this process offers the advantages of rehearsal-based CL without compromising patient privacy. We propose Atlas Replay, an atlas-based segmentation approach that uses prototypes to generate high-quality segmentation masks through image registration that maintain consistency even as the training distribution changes. We explore how our proposed method performs compared to state-of-the-art CL methods in terms of knowledge transferability across seven publicly available prostate segmentation datasets. Prostate segmentation plays a vital role in diagnosing prostate cancer, however, it poses challenges due to substantial anatomical variations, benign structural differences in older age groups, and fluctuating acquisition parameters. Our results show that Atlas Replay is both robust and generalizes well to yet-unseen domains while being able to maintain knowledge, unlike end-to-end segmentation methods. Our code base is available under https://github.com/MECLabTUDA/Atlas-Replay.