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We consider the problem of shared randomness-assisted multiple access channel (MAC) simulation for product inputs and characterize the one-shot communication cost region via almost-matching inner and outer bounds in terms of the smooth max-information of the channel, featuring auxiliary random variables of bounded size. The achievability relies on a rejection-sampling algorithm to simulate an auxiliary channel between each sender and the decoder, and producing the final output based on the output of these intermediate channels. The converse follows via information-spectrum based arguments. To bound the cardinality of the auxiliary random variables, we employ the perturbation method from [Anantharam et al., IEEE Trans. Inf. Theory (2019)] in the one-shot setting. For the asymptotic setting and vanishing errors, our result expands to a tight single-letter rate characterization and consequently extends a special case of the simulation results of [Kurri et al., IEEE Trans. Inf. Theory (2022)] for fixed, independent and identically distributed (iid) product inputs to universal simulation for any product inputs. We broaden our discussion into the quantum realm by studying feedback simulation of quantum-to-classical (QC) MACs with product measurements [Atif et al., IEEE Trans. Inf. Theory (2022)]. For fixed product inputs and with shared randomness assistance, we give a quasi tight one-shot communication cost region with corresponding single-letter asymptotic iid expansion., Comment: Total 42 pages, main text 23 pages, References and Appendices 19 pages, 2 Figures

The trade-offs between error probabilities in quantum hypothesis testing are by now well-understood in the centralized setting, but much less is known for distributed settings. Here, we study a distributed binary hypothesis testing problem to infer a bipartite quantum state shared between two remote parties, where one of these parties communicates classical information to the tester at zero-rate (while the other party communicates classical or quantum information to the tester at zero-rate or higher). As our main contribution, we derive an efficiently computable single-letter formula for the Stein's exponent of this problem, when the state under the alternative is product. For the general case, we show that the Stein's exponent is given by a multi-letter expression involving max-min optimization of regularized measured relative entropy. While this becomes single-letter for the fully classical case, we further prove that this already does not happen in the same way for classical-quantum states in general. As a key tool for proving the converse direction of our results, we develop a quantum version of the blowing-up lemma which may be of independent interest.

The assembly of galaxy clusters is understood to be a hierarchical process with a continuous accretion of galaxies over time, which increases the cluster size and mass. Late-type galaxies that fall into clusters can undergo ram-pressure stripping, forming extended gas tails within which star formation can happen. The number, location, and tail orientations of such galaxies provide clues about the galaxy infall process, the assembly of the cluster over time, and the consequences of infall for galaxy evolution. Here, we utilise the $\sim$ 0.5 degree diameter circular field of the Ultraviolet Imaging Telescope to image six galaxy clusters at z < 0.06 that are known to contain jellyfish galaxies. We searched for stripping candidates in the ultraviolet images of these clusters, which revealed 54 candidates showing signs of unilateral extra-planar emission, due to ram-pressure stripping. Seven candidates had already been identified as likely stripping based on optical B-band imaging. We identified 47 new candidates through UV imaging. Spectroscopic redshift information is available for 39 of these candidate galaxies, of which 19 are associated with six clusters. The galaxies with spectroscopic redshifts that are not part of the clusters appear to be within structures at different redshifts identified as additional peaks in the redshift distribution of galaxies, indicating that they might be ram-pressure stripped or disturbed galaxies in other structures along the line of sight. We examine the orbital history of these galaxies based on their location in the position-velocity phase-space diagram and explore a possible connection to the orientation of the tail direction among cluster member candidates. The tails of confirmed cluster member galaxies are found to be oriented away from the cluster centre., Comment: Accepted for publication in A&A

Let $\mathscr{B}_n = \{ \pm x_1, \pm x_2, \pm x_3, \cdots, \pm x_{n-1}, x_n \}$ where $n>1$ is fixed, $x_i \in \mathbb{R}^+$, $i = 1, 2, 3, \cdots, n$ and $x_1 < x_2 < x_3 < \cdots < x_n$. Let $\phi(\mathscr{B}_n)$ be the set of all non-empty subsets $S = \{u_1, u_2,\cdots, u_t\}$ of $\mathscr{B}_n$ such that $|u_1|<|u_2|<\cdots <|u_{t-1}|

We perform the constraint analysis of a three (2 + 1)-dimensional (3D) field-theoretic example for Hodge theory $(i)$ at the classical level within the ambit of Lagrangian formulation, and $(ii)$ at the quantum level within the framework of Becchi-Rouet-Stora-Tyutin (BRST) formalism. We derive the conserved charges corresponding to the six continuous symmetries of our present theory. These six continuous summery transformations are the nilpotent (anti-)BRST and (anti-)co-BRST symmetries, a unique bosonic symmetry and the ghost-scale symmetry. It turns out that the Noether conserved (anti-)BRST charges are found to be non-nilpotent even though they are derived from the off-shell nilpotent versions of the continuous and infinitesimal (anti-)BRST symmetry transformations. We obtain the nilpotent versions of the (anti-)BRST charges from the non-nilpotent Noether (anti-)BRST charges and discuss the physicality criteria w.r.t. the latter to demonstrate that the operator forms of the first-class constraints (of the classical gauge theory) annihilate the physical states at the quantum level. This observation is consistent with Dirac's quantization conditions for the systems that are endowed with the constraints. We lay emphasis on the existence of a single (anti-)BRST invariant Curci-Ferrari (CF) type restriction in our theory and derive it from various theoretical angles., Comment: LaTeX file, 38 pages, version to appear in NPB

Serverless computing is an emerging cloud computing abstraction wherein the cloud platform transparently manages all resources, including explicitly provisioning resources and geographical load balancing when the demand for service spikes. Users provide code as functions, and the cloud platform runs these functions handling all aspects of function execution. While prior work has primarily focused on optimizing performance, this paper focuses on reducing the carbon footprint of these systems making variations in grid carbon intensity and intermittency from renewables transparent to the user. We introduce GreenWhisk, a carbon-aware serverless computing platform built upon Apache OpenWhisk, operating in two modes - grid-connected and grid-isolated - addressing intermittency challenges arising from renewables and the grid's carbon footprint. Moreover, we develop carbon-aware load balancing algorithms that leverage energy and carbon information to reduce the carbon footprint. Our evaluation results show that GreenWhisk can easily incorporate carbon-aware algorithms, thereby reducing the carbon footprint of functions without significantly impacting the performance of function execution. In doing so, our system design enables the integration of new carbon-aware strategies into a serverless computing platform., Comment: 11 pages, 13 figures, IC2E 2024

One of the neutron capture processes in lithium or lithium-doped materials leads to the production of secondary alpha-triton pairs. The counts of these secondary charged particle pairs in the presence of thermal/epithermal neutrons indirectly give access to detecting the primary incident neutrons with the aid of the plastic scintillators by reminding that both these secondary alphas and tritons deposit their entire kinetic energy within a short range. In this study, we modify the polystyrene fiber core of Kuraray Y11-200(M) by doping with 10 wt.$\%$ LiO$_{2}$. We investigate a fiber bundle system hinged on 10 wt.$\%$ LiO$_{2}$-doped Kuraray Y11-200(M) fibers via the GEANT4 simulations by targeting the detection of the incident thermal/epithermal neutrons through the generation of the secondary alpha-triton pairs. First, we estimate an optimal number of fiber arrays by using a monoenergetic beam of thermal neutrons with a kinetic energy of 0.025 eV and we count the number of secondary alpha-triton pairs. Subsequently, we insert a pixelated photo-sensor on both ends of the fiber bundle in order to collect the entrapped optical photons. In addition to the thermal neutrons, we also explore the detection of epithermal neutrons with a kinetic energy of 1 eV by utilizing a combination of the 10 wt.$\%$ LiO$_{2}$-doped Kuraray Y11-200(M) fiber bundles and the neutron absorption layers. We demonstrate that the LiO$_{2}$-doped plastic scintillating fiber bundles are instrumental for the detection of thermal/epithermal neutrons in diverse applications., Comment: 9 pages, 6 figures, 2 tables

An experimental investigation was carried out to study heat transfer rates in a high-temperature, high-pressure region generated using the shock focusing technique. A shock tube test facility with a specially designed spherically converging test section was used in the present study. Two test cases, a shock of initial strength Mach 2 and Mach 4, were investigated. An in-house 10 developed K -type thermocouple was used in the present investigations, and the measured heat transfer rates were of the order of KW/cm2.

We propose an extension of the classical R\'enyi divergences to quantum states through an optimization over probability distributions induced by restricted sets of measurements. In particular, we define the notion of locally-measured R\'enyi divergences, where the set of allowed measurements originates from variants of locality constraints between (distant) parties $A$ and $B$. We then derive variational bounds on the locally-measured R\'enyi divergences and systematically discuss when these bounds become exact characterizations. As an application, we evaluate the locally-measured R\'enyi divergences on variants of highly symmetric data-hiding states, showcasing the reduced distinguishing power of locality-constrained measurements. For $n$-fold tensor powers, we further employ our variational formulae to derive corresponding additivity results, which gives the locally-measured R\'enyi divergences operational meaning as optimal rate exponents in asymptotic locally-measured hypothesis testing., Comment: 35+11 pages

In this paper, we derive maximal acceleration of a massive particle in Rainbow gravity. Using eight-dimensional phase-space metric compatible with Rainbow gravity, we obtain the maximal acceleration, valid up to first order in the Rainbow gravity parameter $\eta$. Using the positivity condition on maximal acceleration, we find the upper bound on the Rainbow gravity parameter is of the order of $~10^{22}$ for positron and $10^{-44}$ for a black hole. After obtaining the expression for maximal acceleration for different choices of Rainbow functions, we derive corresponding modifications to Unruh temperature. Comparing with the observational value of the Unruh temperature, we find the upper bound on $\eta$ as $~10^{32}$ for positron radiation. %and of the order of $10^{-100}$ for radiation from a black hole. We then derive geodesic equations for different choices of Rainbow functions and also obtain Newtonian limit of these geodesic equations. We find that the changes in the value of maximum acceleration, maximum temperature and Newtonian force equation are dependent on the choices of Rainbow functions., Comment: 12 pages

We study a model of the neutron star in $\kappa$-deformed space-time in the presence of the cosmological constant ($\Lambda$). The Einstein tensor and the energy-momentum tensor are generalized to $\kappa$-deformed space-time and we construct the field equations with the cosmological constant. Considering the interior of the star to be a perfect fluid as in the commutative case, we find the Tolman-Oppenheimer-Volkoff equations with the inclusion of the cosmological constant in $\kappa$-deformed space-time. The behavior of the maximum allowed mass of the star and its radius are studied with the variation in the cosmological constant as well as the deformation parameter. We see that the non-commutativity enhances the mass of the star and its maximum mass increases with a decrease in the cosmological constant. The maximum mass varies from $3.44M_{\odot}$ to $3.68M_{\odot}$ as $\Lambda$ varies from $10^{-10}m^{-2}$ to $10^{-15}m^{-2}$. We also obtain the compactness factor and surface redshift of the star. We observe that the compactness of the star increases as the cosmological constant decreases, whereas the surface redshift of the star decreases with a decrease in the cosmological constant. The compactness factor and surface redshift corresponding to the maximum mass of the neutron star remains almost constant as $\Lambda$ decreases., Comment: 18 pages, 2 figures, 4 tables

Understanding what makes a video memorable has important applications in advertising or education technology. Towards this goal, we investigate spatio-temporal attention mechanisms underlying video memorability. Different from previous works that fuse multiple features, we adopt a simple CNN+Transformer architecture that enables analysis of spatio-temporal attention while matching state-of-the-art (SoTA) performance on video memorability prediction. We compare model attention against human gaze fixations collected through a small-scale eye-tracking study where humans perform the video memory task. We uncover the following insights: (i) Quantitative saliency metrics show that our model, trained only to predict a memorability score, exhibits similar spatial attention patterns to human gaze, especially for more memorable videos. (ii) The model assigns greater importance to initial frames in a video, mimicking human attention patterns. (iii) Panoptic segmentation reveals that both (model and humans) assign a greater share of attention to things and less attention to stuff as compared to their occurrence probability., Comment: Accepted to WACV 2025

Estimation of quantum relative entropy and its R\'{e}nyi generalizations is a fundamental statistical task in quantum information theory, physics, and beyond. While several estimators of these divergences have been proposed in the literature along with their computational complexities explored, a limit distribution theory which characterizes the asymptotic fluctuations of the estimation error is still premature. As our main contribution, we characterize these asymptotic distributions in terms of Fr\'{e}chet derivatives of elementary operator-valued functions. We achieve this by leveraging an operator version of Taylor's theorem and identifying the regularity conditions needed. As an application of our results, we consider an estimator of quantum relative entropy based on Pauli tomography of quantum states and show that the resulting asymptotic distribution is a centered normal, with its variance characterized in terms of the Pauli operators and states. We utilize the knowledge of the aforementioned limit distribution to obtain asymptotic performance guarantees for a multi-hypothesis testing problem., Comment: 14+30 pages

For the purpose of high-fidelity aircraft cabin noise simulations during early design phases, we study three efficient solving approaches for the fully coupled finite element model of an aircraft fuselage segment. Obtaining an efficient solution with respect to consumed computational time and resources is challenging within a conventional simulation pipeline, as large-scale and complex vibroacoustic models demand crucially high computational costs with increasing frequency. In this contribution, we adopt (1) frequency and domain-adaptive discretisation, (2) domain-decomposition techniques, and (3) model order reduction with rational Arnoldi Krylov subspace methods for an aircraft fuselage model. The three approaches have shown remarkable advantage thereby reducing the solving time as well as the memory requirement that are essential when solving large-scale models. While the discretisation and the model order reduction approaches accelerate the solving process by efficiently handling the complexity of the system to be solved, domain-decomposition techniques further handle the aspect of reducing the overall memory consumption. Finally with the help of active research aircraft models, we implement and showcase the achieved efficiency., Comment: 18 pages, 7 figures, Submitted to IJNME

The neural mechanisms underlying the comprehension of meaningful sounds are yet to be fully understood. While previous research has shown that the auditory cortex can classify auditory stimuli into distinct semantic categories, the specific contributions of the primary (A1) and the secondary auditory cortex (A2) to this process are not well understood. We used songbirds as a model species, and analyzed their neural responses as they listened to their entire vocal repertoire (\(\sim \)10 types of vocalizations). We first demonstrate that the distances between the call types in the neural representation spaces of A1 and A2 are correlated with their respective distances in the acoustic feature space. Then, we show that while the neural activity in both A1 and A2 is equally informative of the acoustic category of the vocalizations, A2 is significantly more informative of the semantic category of those vocalizations. Additionally, we show that the semantic categories are more separated in A2. These findings suggest that as the incoming signal moves downstream within the auditory cortex, its acoustic information is preserved, whereas its semantic information is enhanced.

Spurious correlations occur when a model learns unreliable features from the data and are a well-known drawback of data-driven learning. Although there are several algorithms proposed to mitigate it, we are yet to jointly derive the indicators of spurious correlations. As a result, the solutions built upon standalone hypotheses fail to beat simple ERM baselines. We collect some of the commonly studied hypotheses behind the occurrence of spurious correlations and investigate their influence on standard ERM baselines using synthetic datasets generated from causal graphs. Subsequently, we observe patterns connecting these hypotheses and model design choices., Comment: 2nd Workshop on SCIS, ICML 2023

There has been tremendous progress in generating realistic faces with high fidelity over the past few years. Despite this progress, a crucial question remains unanswered: "Given a generative face model, how many unique identities can it generate?" In other words, what is the biometric capacity of the generative face model? A scientific basis for answering this question will benefit evaluating and comparing different generative face models and establish an upper bound on their scalability. This paper proposes a statistical approach to estimate the biometric capacity of generated face images in a hyperspherical feature space. We employ our approach on multiple generative models, including unconditional generators like StyleGAN, Latent Diffusion Model, and "Generated Photos," as well as DCFace, a class-conditional generator. We also estimate capacity w.r.t. demographic attributes such as gender and age. Our capacity estimates indicate that (a) under ArcFace representation at a false acceptance rate (FAR) of 0.1%, StyleGAN3 and DCFace have a capacity upper bound of $1.43\times10^6$ and $1.190\times10^4$, respectively; (b) the capacity reduces drastically as we lower the desired FAR with an estimate of $1.796\times10^4$ and $562$ at FAR of 1% and 10%, respectively, for StyleGAN3; (c) there is no discernible disparity in the capacity w.r.t gender; and (d) for some generative models, there is an appreciable disparity in the capacity w.r.t age. Code is available at https://github.com/human-analysis/capacity-generative-face-models., Comment: IJCB 2023

Entropy measures quantify the amount of information and correlation present in a quantum system. In practice, when the quantum state is unknown and only copies thereof are available, one must resort to the estimation of such entropy measures. Here we propose a variational quantum algorithm for estimating the von Neumann and R\'enyi entropies, as well as the measured relative entropy and measured R\'enyi relative entropy. Our approach first parameterizes a variational formula for the measure of interest by a quantum circuit and a classical neural network, and then optimizes the resulting objective over parameter space. Numerical simulations of our quantum algorithm are provided, using a noiseless quantum simulator. The algorithm provides accurate estimates of the various entropy measures for the examples tested, which renders it as a promising approach for usage in downstream tasks., Comment: 14 pages, 2 figures; see also independent works of Shin, Lee, and Jeong at arXiv:2306.14566v1 and Lee, Kwon, and Lee at arXiv:2307.13511v2