Search

We present the results of a search for gravitational-wave transients associated with core-collapse supernova SN 2023ixf, which was observed in the galaxy Messier 101 via optical emission on 2023 May 19th, during the LIGO-Virgo-KAGRA 15th Engineering Run. We define a five-day on-source window during which an accompanying gravitational-wave signal may have occurred. No gravitational waves have been identified in data when at least two gravitational-wave observatories were operating, which covered $\sim 14\%$ of this five-day window. We report the search detection efficiency for various possible gravitational-wave emission models. Considering the distance to M101 (6.7 Mpc), we derive constraints on the gravitational-wave emission mechanism of core-collapse supernovae across a broad frequency spectrum, ranging from 50 Hz to 2 kHz where we assume the GW emission occurred when coincident data are available in the on-source window. Considering an ellipsoid model for a rotating proto-neutron star, our search is sensitive to gravitational-wave energy $1 \times 10^{-5} M_{\odot} c^2$ and luminosity $4 \times 10^{-5} M_{\odot} c^2/\text{s}$ for a source emitting at 50 Hz. These constraints are around an order of magnitude more stringent than those obtained so far with gravitational-wave data. The constraint on the ellipticity of the proto-neutron star that is formed is as low as $1.04$, at frequencies above $1200$ Hz, surpassing results from SN 2019ejj., Comment: Main paper: 6 pages, 4 figures and 1 table. Total with appendices: 20 pages, 4 figures, and 1 table

Pressure-induced emission enhancement and bandgap narrowing in vacancy-ordered halide double perovskite $Cs_2TeBr_6$ are extensively investigated through photoluminescence and absorption experiments. The below bandgap broad emission is attributed to self-trapped excitons recombination. The $Cs_2TeBr_6$ crystal, consisting of undistorted octahedra, exhibits substantial emission enhancement due to the lowering of the energy barrier between $^3P_1$ and self-trapped exciton states, as well as the suppression of nonradiative energy loss with increasing pressure. In the Raman measurements, the observed behavior of full width at half maximum of all Raman modes implies dominant electron-phonon interactions rather than anharmonic interactions between phonons. The pressure-dependent X-ray diffraction measurements reveal an anomalous behaviour in the normalized pressure as a function of the Eulerian strain.

The magnetar SGR 1935+2154 is the only known Galactic source of fast radio bursts (FRBs). FRBs from SGR 1935+2154 were first detected by CHIME/FRB and STARE2 in 2020 April, after the conclusion of the LIGO, Virgo, and KAGRA Collaborations' O3 observing run. Here we analyze four periods of gravitational wave (GW) data from the GEO600 detector coincident with four periods of FRB activity detected by CHIME/FRB, as well as X-ray glitches and X-ray bursts detected by NICER and NuSTAR close to the time of one of the FRBs. We do not detect any significant GW emission from any of the events. Instead, using a short-duration GW search (for bursts $\leq$ 1 s) we derive 50\% (90\%) upper limits of $10^{48}$ ($10^{49}$) erg for GWs at 300 Hz and $10^{49}$ ($10^{50}$) erg at 2 kHz, and constrain the GW-to-radio energy ratio to $\leq 10^{14} - 10^{16}$. We also derive upper limits from a long-duration search for bursts with durations between 1 and 10 s. These represent the strictest upper limits on concurrent GW emission from FRBs., Comment: 15 pages of text including references, 4 figures, 5 tables

Optical clocks have improved their frequency stability and estimated accuracy by more than two orders of magnitude over the best caesium microwave clocks that realise the SI second. Accordingly, an optical redefinition of the second has been widely discussed, prompting a need for the consistency of optical clocks to be verified worldwide. While satellite frequency links are sufficient to compare microwave clocks, a suitable method for comparing high-performance optical clocks over intercontinental distances is missing. Furthermore, remote comparisons over frequency links face fractional uncertainties of a few $10^{-18}$ due to imprecise knowledge of each clock's relativistic redshift, which stems from uncertainty in the geopotential determined at each distant location. Here, we report a landmark campaign towards the era of optical clocks, where, for the first time, state-of-the-art transportable optical clocks from Japan and Europe are brought together to demonstrate international comparisons that require neither a high-performance frequency link nor information on the geopotential difference between remote sites. Conversely, the reproducibility of the clocks after being transported between countries was sufficient to determine geopotential height offsets at the level of 4 cm. Our campaign paves the way for redefining the SI second and has a significant impact on various applications, including tests of general relativity, geodetic sensing for geosciences, precise navigation, and future timing networks., Comment: 29 pages, 5 figures

We explore the stationary densities in totally asymmetric exclusion processes (TASEP) with open boundary conditions and spatially inhomogeneous hopping rates. We calculate the steady state density profiles that characterise the associated phases. We show that in the contrast to the low and high density phases, the stationary density profile in the maximal current phase can be discontinuous, even when the space-dependent hopping rate is continuous. The phase diagrams in the plane of the control parameters show universal topology. The associated phase transitions are explored. We further investigate the domain walls, which are delocalised and calculate their envelops, which reveal their dependence on the spatial nonuniformity of the hopping rates., Comment: 17 pages, 16 figures, preliminary version

ATi$_3$Bi$_5$ (A = Cs or Rb) are emerging topological kagome metals that exhibit superconductivity and nematicity without intertwining translational symmetry-breaking charge orders. In this work, we explore the Fermionology of their titanium kagome electrons and identify a set of sublattice-pure, high-order van Hove singularities (VHSs) that can suppress charge ordering and enhance electronic correlations and superconductivity. Our calculations of charge susceptibility for kagome bands with both normal and high-order VHSs emphasize the role of these VHSs in driving electronic nematicity in CsTi$_3$Bi$_5$. Additionally, we compute the phonon spectrum and electron-phonon interactions for CsTi$_3$Bi$_5$ under pristine, doped, and kagome-exposed surface conditions, revealing its robustness against structural instabilities while enhancing the superconducting transition temperature. Our work positions ATi$_3$Bi$_5$ as a key platform for investigating superconductivity and electronic nematicity without translational symmetry-breaking states in kagome metals.

We investigate the probability that a random polynomial with independent, mean-zero and finite variance coefficients has no real zeros. Specifically, we consider a random polynomial of degree $2n$ with coefficients given by an i.i.d. sequence of mean-zero, variance-1 random variables, multiplied by an $\frac{\alpha}{2}$-regularly varying sequence for $\alpha>-1$. We show that the probability of no real zeros is asymptotically $n^{-2(b_{\alpha}+b_0)}$, where $b_{\alpha}$ is the persistence exponents of a mean-zero, one-dimensional stationary Gaussian processes with covariance function as $\mathrm{sech}((t-s)/2)^{\alpha+1}$. Our work generalizes the previous results of Dembo et al. [DPSZ02] and Dembo \& Mukherjee [DM15] by removing the requirement of finite moments of all order or Gaussianity. In particular, in the special case $\alpha = 0$, our findings confirm a conjecture by Poonen and Stoll [PS99, Section 9.1] concerning random polynomials with i.i.d. coefficients., Comment: 62 pages, 2 figures

In the Horava Lifshitz gravity scenario, we have described the mass accretion process of a 4 dimensional Kehagias Sfetsos black hole caused by some candidates of dark energy in detail. Firstly, we have obtained the equation of mass for the black hole with respect to the cosmic scale parameter a in the Horava Lifshitz gravity framework. Then, one by one, we have considered dark energies like Generalized Cosmic Chaplygin gas, Variable Modified Chaplygin gas, New Variable Modified Chaplygin gas, Modified Chaplygin Jacobi gas, and Modified Chaplygin Abel gas, respectively, and established their equation of mass in terms of the redshift function z. In addition, we have presented the results with graphical representations of the mass and redshift functions. Finally, we came to the conclusion that, due to the accretion of all of the dark energy candidates mentioned above, the mass of a 4-dimensional Kehagias-Sfetsos Black Hole will increase gradually in correspondence to the evolution of the universe.

In this paper, we consider the problem of estimating the interaction parameter $p$ of a $p$-spin Curie-Weiss model at inverse temperature $\beta$, given a single observation from this model. We show, by a contiguity argument, that joint estimation of the parameters $\beta$ and $p$ is impossible, which implies that estimation of $p$ is impossible if $\beta$ is unknown. These impossibility results are also extended to the more general $p$-spin Erd\H{o}s-R\'enyi Ising model. The situation is more delicate when $\beta$ is known. In this case, we show that there exists an increasing threshold function $\beta^*(p)$, such that for all $\beta$, consistent estimation of $p$ is impossible when $\beta^*(p) > \beta$, and for almost all $\beta$, consistent estimation of $p$ is possible for $\beta^*(p)<\beta$., Comment: 8 pages

Here, we report the full-fledged journey towards the material synthesis and characterization of few-layered/thin WSe$_2$ using sputtered W-films on SiO$_2$/Si substrates followed by electrical studies under dark and illumination conditions. Growth temperature 500oC and gas pressure 55 sccm are found to be the optimized parameters for formation of thermodynamically stable WSe$_{2-x}$ with dominant Raman peak at 265 cm-1. XRD and HR-TEM measurement clarify the formation of high crystallinity along the c-axis and quasi-crystallinity along a and b axes respectively. Lower intensities from Raman-measurement and PL-peak at 768 nm (with 532 nm excitation wavelength) infers the thin nature of the grown film, along with strong second harmonic emission with excitation wavelength varying from 350nm to 450 nm. This work also retracks the controlled etching by reactive ions to achieve large area bi/tri-layer films to fabricate advanced devices. We also have fabricated an advanced MOS structure on SiO$_2$/p-Si substrate which shows tremendous performance by means of photo-capacitance under illumination condition where photo-carriers can survive the higher probe frequencies (> 1MHz). Under illumination condition, HfO$_2$/WSe$_2$ embedded MOS shows its dominance showing a huge electron-inversion region over HfO$_2$/ SiO$_2$/p-Si and SiO$_2$/p-Si MOS devices even at high frequencies (1-10 MHz). Thereby, this work also reveals a possible route for capacitance based highly sensitive photodetection using conventional Si-technology with integration of such WSe$_2$/W as an active material., Comment: 23, 7

We investigate the prospect of using ultracold alkali diatomic molecules to implement many-body quantum systems with SU($N$) symmetry. Experimentally accessible molecules offer large $N$ for both bosonic and fermionic systems, with both attractive and repulsive interactions. We carry out coupled-channel scattering calculations on pairs of NaK, NaRb and NaCs molecules that are shielded from destructive collisions with static electric fields. We develop new methods to handle the very large basis sets required to include nuclear spins. We show that all the molecules studied have the properties required for SU($N$) symmetry: the collisions are principally elastic, and the scattering lengths depend only weakly on the spin states of the molecules. The rates of spin-changing inelastic collisions are very low. We develop and test a semiclassical model of the spin dependence and find that it performs well., Comment: 15 pages, 12 figures, and 6 tables

Despite seemingly performant web agents on the task-completion benchmarks, most existing methods evaluate the agents based on a presupposition: the web navigation task consists of linear sequence of actions with an end state that marks task completion. In contrast, our work focuses on web navigation for information aggregation, wherein the agent must explore different websites to gather information for a complex query. We consider web information aggregation from two different perspectives: (i) Direct API-driven Access relies on a text-only view of the Web, leveraging external tools such as Google Search API to navigate the web and a scraper to extract website contents. (ii) Interactive Visual Access uses screenshots of the webpages and requires interaction with the browser to navigate and access information. Motivated by these diverse information access settings, we introduce Infogent, a novel modular framework for web information aggregation involving three distinct components: Navigator, Extractor and Aggregator. Experiments on different information access settings demonstrate Infogent beats an existing SOTA multi-agent search framework by 7% under Direct API-Driven Access on FRAMES, and improves over an existing information-seeking web agent by 4.3% under Interactive Visual Access on AssistantBench., Comment: Preprint

We present spectroscopic properties of 22 Lyman-alpha emitters(LAEs) at z=5.5-6.6 with Lyman-alpha(Lya) luminosity log($L_{Lya}$[$ergs^{-1}$])=42.4-43.5, obtained using VLT/MUSE as part of the Middle Ages Galaxy Properties with Integral Field Spectroscopy(MAGPI) survey. Additionally, we incorporate broad-band photometric data from the Subaru Hyper Suprime-Cam(HSC) for 17 LAEs in our sample. The HSC-y band magnitudes show that our LAEs are UV-bright, with rest-frame absolute UV magnitudes -19.7 < $M_{UV}$ < -23.3. We find that the Lya line width increases with luminosity, and this trend becomes more prominent at z > 6 where Lya lines become significantly broadened (> 260 $kms^{-1}$) at luminosities log($L_{Lya}$[$ergs^{-1}$]) > 43. This broadening is consistent with previous studies, suggesting that these sources are located inside larger ionized bubbles. We observe a slightly elevated ionizing photon production efficiency estimated for LAEs at z > 6, indicating that younger galaxies could be producing more ionizing photons per UV luminosity. A tentative anti-correlation between ionizing photon production efficiency and Lya rest-frame equivalent width is noticed, which could indicate a time delay between production and escape of ionizing photon primarily due to supernovae activity. Furthermore, we find a positive correlation between bubble radius and Lya line width, which again suggests that large ionized bubbles are created around these LAEs, allowing them to self-shield from the scattering effects of the intergalactic medium (IGM). We also detect two closely separated LAEs at z=6.046 (projected spatial separation is 15.92 kpc). The size of their respective bubbles suggests that they likely sit inside a common large ionized region. Such a closely-separated LAE pair increases the size of ionized bubble, potentially allowing a boosted transmission of Lya through neutral IGM. (Abridged), Comment: 14 pages, 6 figures, 2 tables, 1st revision submitted to PASA

Automatic Speech Recognition (ASR) systems have been examined and shown to exhibit biases toward particular groups of individuals, influenced by factors such as demographic traits, accents, and speech styles. Noise can disproportionately impact speakers with certain accents, dialects, or speaking styles, leading to biased error rates. In this work, we introduce a novel framework DENOASR, which is a selective denoising technique to reduce the disparity in the word error rates between the two gender groups, male and female. We find that a combination of two popular speech denoising techniques, viz. DEMUCS and LE, can be effectively used to mitigate ASR disparity without compromising their overall performance. Experiments using two state-of-the-art open-source ASRs - OpenAI WHISPER and NVIDIA NEMO - on multiple benchmark datasets, including TIE, VOX-POPULI, TEDLIUM, and FLEURS, show that there is a promising reduction in the average word error rate gap across the two gender groups. For a given dataset, the denoising is selectively applied on speech samples having speech intelligibility below a certain threshold, estimated using a small validation sample, thus ameliorating the need for large-scale human-written ground-truth transcripts. Our findings suggest that selective denoising can be an elegant approach to mitigate biases in present-day ASR systems., Comment: Paper accepted at IEEE ICKG 2024

A primary goal of characterizing exoplanet atmospheres is to constrain planetary bulk properties, such as their metallicity, C/O ratio, and intrinsic heat. However, there are significant uncertainties in many aspects of atmospheric physics, such as the strength of vertical mixing. Here we use PICASO and the photochem model to explore how atmospheric chemistry is influenced by planetary properties like metallicity, C/O ratio, $T_{\rm int}$, $T_{\rm eq}$, and $K_{\rm zz}$ in hydrogen-dominated atmospheres. We vary the $T_{\rm eq}$ of the planets between 400 K- 1600 K, across ``cold",``warm," and `hot" objects. We also explore an extensive range of $T_{\rm int}$ values between 30-500 K, representing sub-Neptunes to massive gas giants. We find that gases like CO and CO$_2$ show a drastically different dependence on $K_{\rm zz}$ and C/O for planets with cold interiors (e.g., sub-Neptunes) compared to planets with hotter interiors (e.g., Jupiter mass planets), for the same $T_{\rm eq}$. We also find that gases like CS and CS$_2$ can carry a significant portion of the S- inventory in the upper atmosphere near $T_{\rm eq}$ $\le$ 600 K, below which SO$_2$ ceases to be abundant. For solar C/O, we show that the CO/CH$_4$ ratio in the upper atmospheres of planets can become $\le$1 for planets with low $T_{\rm eq}$, but only if their interiors are cold ($T_{\rm int}$$\le$100 K). We find that photochemical haze precursor molecules in the upper atmosphere show very complex dependence on C/O, $K_{\rm zz}$, $T_{\rm eq}$, and $T_{\rm int}$ for planets with cold interiors (e.g., sub-Neptunes). We also briefly explore fully coupling PICASO and photochem to generate self-consistent radiative-convective-photochemical-equilibrium models., Comment: Revised and re-submitted to ApJ, Main text: Pages 1 to 34, 25 Figures, Appendix: Pages 35 to 36, 1 Figure

Young supernova remnants (SNRs) are believed to be the origin of energetic cosmic rays (CRs) below the "knee" of their spectrum at $\sim3$ petaelectronvolt (PeV, $10^{15}$ eV). Nevertheless, the precise location, duration, and operation of CR acceleration in young SNRs are open questions. Here, we report on multi-epoch X-ray observations of Cassiopeia A (Cas A), a 350-year-old SNR, in the 15-50 keV band that probes the most energetic CR electrons. The observed X-ray flux decrease $(15\pm1\%)$, contrary to the expected $>$90\% decrease based on previous radio, X-ray, and gamma-ray observations, provides unambiguous evidence for CR electron acceleration operating in Cas A. A temporal model for the radio and X-ray data accounting for electron cooling and continuous injection finds that the freshly injected electron spectrum is significantly harder (exponential cutoff power law index $q=2.15$), and its cutoff energy is much higher ($E_{cut}=36$ TeV) than the relic electron spectrum ($q=2.44\pm0.03$, $E_{cut}=4\pm1$ TeV). Both electron spectra are naturally explained by the recently developed modified nonlinear diffusive shock acceleration (mNLDSA) mechanism. The CR protons producing the observed gamma rays are likely accelerated at the same location by the same mechanism as those for the injected electron. The Cas A observations and spectral modeling represent the first time radio, X-ray, gamma ray and CR spectra have been self-consistently tied to a specific acceleration mechanism -- mNLDSA -- in a young SNR., Comment: Submitted to ApJ. 12 pages, 4 figures, 2 tables

This paper significantly advances the application of Quantum Key Distribution (QKD) in Free- Space Optics (FSO) satellite-based quantum communication. We propose an innovative satellite quantum channel model and derive the secret quantum key distribution rate achievable through this channel. Unlike existing models that approximate the noise in quantum channels as merely Gaussian distributed, our model incorporates a hybrid noise analysis, accounting for both quantum Poissonian noise and classical Additive-White-Gaussian Noise (AWGN). This hybrid approach acknowledges the dual vulnerability of continuous variables (CV) Gaussian quantum channels to both quantum and classical noise, thereby offering a more realistic assessment of the quantum Secret Key Rate (SKR). This paper delves into the variation of SKR with the Signal-to-Noise Ratio (SNR) under various influencing parameters. We identify and analyze critical factors such as reconciliation efficiency, transmission coefficient, transmission efficiency, the quantum Poissonian noise parameter, and the satellite altitude. These parameters are pivotal in determining the SKR in FSO satellite quantum channels, highlighting the challenges of satellitebased quantum communication. Our work provides a comprehensive framework for understanding and optimizing SKR in satellite-based QKD systems, paving the way for more efficient and secure quantum communication networks.

Entanglement and nonlocality are two important nonclassical features of quantum correlations. Recently the study of quantum correlations in networks has undergone remarkable progress owing to technological development towards scalable quantum networks. However, compared to standard Bell scenario, manifestation of the interplay between these two aspects has received less attention in network scenarios featuring independent sources. In this work we have analyzed the relation between entanglement content of the sources and detectable non n-locality in two distinct network topologies(linear and star). We have studied the extremal violations of n-local inequalities(compatible with linear and star network) for any fixed amount of entanglement(in terms of concurrence) of the independent sources. It is observed that each of the sources must be entangled for detecting non n-locality in linear network. However, the same is not true for star n-local network. Present analysis is revealing that entanglement of all the independent sources is not a necessity for generation of non n-local correlations in star topology. Characterization of sources in terms of minimum entanglement requirement for any fixed violation amount of the n-local inequalities is also provided. Interestingly, detection of non n-locality is ensured in the network if product of concurrence of all the sources involved exceeds 1/2. ., Comment: Preliminary Version

We theoretically investigate the resonant inelastic X-ray scattering (RIXS) spectra in a quasi-1D chain of weakly coupled frustrated spin-1/2 trimers, as realized in Na$_{2}$Cu$_{3}$Ge$_{4}$O$_{12}$, with Cu $d^{9}$ 1/2 spins. We compute multi-spin correlations contributing to spin-conserving (SC) and spin non-conserving (NSC) RIXS cross-sections using ultra-short core-hole lifetime expansion within the Kramer-Heisenberg formalism. These excitations involve flipping spins of up to three spin-1/2 trimers and include the inelastic neutron scattering (INS) single spin-flip excitations in the lowest order of the NSC channel. We identify the fractionalization of two coupled frustrated trimers in terms of spinons, doublons, and quartons in the spectra evaluated using exact diagonalization, complementing prior studies single spin-spin flip excitation in inelastic neutron scattering. Specifically, we uncover two new high-energy modes at $\omega \approx 2.4J_1$ and $3.0 J_1$ in the NSC and SC channels that are accessible at the Cu $K$-edge and $L$-edge RIXS spectra, which were missing in the INS study. This, therefore, provides pathways to uncover all the possible excitations in coupled trimers. Our work opens new opportunities for understanding the nature of fractionalization and RIXS spectra of frustrated, low-dimensional spin chains.

We present \synver{}, a novel synthesis and verification framework for C programs, that deploys a Large Language Model (LLM) to search for a candidate program that satisfies the given specification. Our key idea is to impose syntactic and semantic biases on programs generated by LLMs, such that the synthesized program is more amenable to automated verification. Based on this idea, we propose a novel specification-verification tool, built on top of Verified Software Toolchain, that help automate the process. Our experiments on a diverse set of benchmarks drawn from the deductive program synthesis community, shows that this approach is scalable and extensible. The benchmarks constitute of specifications comprising of basic coding examples, Separation Logic based assertions, and API specifications.

We consider a few online and offline games under actual playing conditions. Generally it is expected that initially a player obtains additional skill with experience of playing more number of games and then it should finally saturate. This phase is identified when a player, with the experience of very few games, loses more when she plays against players with much longer history. Then the winning proportion curve moves up and finally it saturates. We benchmark our analysis and discussion against Chess, the most skilled one among the games we consider here. We use proprietary data from actual games (online and offline) as well as experiments for our statistical analysis. In this regard, we show that Rummy has stronger skill and learning effects. Ludo has similar characteristics as Rummy, but at a weaker level. Similarly, a game that is perceived as almost no skill such as Teen Patti indeed presents much less skill in the analysis. In the next section we describe the game structures., Comment: 25 pages, 9 figures

The nonequilibrium turbulent response of driven three-dimensional dipolar gases is monitored by dynamically crossing the supersolid-to-superfluid phase transition and vice versa utilizing a periodically modulated scattering length. Following the generation of vortical defects in the bulk, a direct cascade front emerges transporting energy from larger to smaller length scales and leading to a nonequilibrium quasi-steady state at long evolution times. This quasi-steady state is evidenced by the self-similar character of the momentum distributions featuring an algebraic reduction at large momenta with scaling exponents supporting the existence of wave turbulence. We observe that supersolidity sustaining higher-lying momenta, associated with the roton minimum, promotes the emergence of turbulence. Our results provide a stepping stone for unravelling and exploiting turbulent and self-similar behavior in anisotropically interacting quantum gases amenable in current experiments., Comment: 6 pages, 4 figures, 4 pages Supplemental material

We develop a new longitudinal count data regression model that accounts for zero-inflation and spatio-temporal correlation across responses. This project is motivated by an analysis of Iowa Fluoride Study (IFS) data, a longitudinal cohort study with data on caries (cavity) experience scores measured for each tooth across five time points. To that end, we use a hurdle model for zero-inflation with two parts: the presence model indicating whether a count is non-zero through logistic regression and the severity model that considers the non-zero counts through a shifted Negative Binomial distribution allowing overdispersion. To incorporate dependence across measurement occasion and teeth, these marginal models are embedded within a Gaussian copula that introduces spatio-temporal correlations. A distinct advantage of this formulation is that it allows us to determine covariate effects with population-level (marginal) interpretations in contrast to mixed model choices. Standard Bayesian sampling from such a model is infeasible, so we use approximate Bayesian computing for inference. This approach is applied to the IFS data to gain insight into the risk factors for dental caries and the correlation structure across teeth and time.

In retail sales forecasting, accurately predicting future sales is crucial for inventory management and strategic planning. Traditional methods like LR often fall short due to the complexity of sales data, which includes seasonality and numerous product families. Recent advancements in machine learning (ML) provide more robust alternatives. This research benefits from the power of ML, particularly Random Forest (RF), Gradient Boosting (GB), Support Vector Regression (SVR), and XGBoost, to improve prediction accuracy. Despite advancements, a significant gap exists in handling complex datasets with high seasonality and multiple product families. The proposed solution involves implementing and optimizing a RF model, leveraging hyperparameter tuning through randomized search cross-validation. This approach addresses the complexities of the dataset, capturing intricate patterns that traditional methods miss. The optimized RF model achieved an R-squared value of 0.945, substantially higher than the initial RF model and traditional LR, which had an R-squared of 0.531. The model reduced the root mean squared logarithmic error (RMSLE) to 1.172, demonstrating its superior predictive capability. The optimized RF model did better than cutting-edge models like Gradient Boosting (R-squared: 0.942), SVR (R-squared: 0.940), and XGBoost (R-squared: 0.939), with more minor mean squared error (MSE) and mean absolute error (MAE) numbers. The results demonstrate that the optimized RF model excels in forecasting retail sales, handling the datasets complexity with higher accuracy and reliability. This research highlights the importance of advanced ML techniques in predictive analytics, offering a significant improvement over traditional methods and other contemporary models., Comment: IEEE 4th ICSES 2024

We address the optimization problem in a data-driven variational reconstruction framework, where the regularizer is parameterized by an input-convex neural network (ICNN). While gradient-based methods are commonly used to solve such problems, they struggle to effectively handle non-smoothness which often leads to slow convergence. Moreover, the nested structure of the neural network complicates the application of standard non-smooth optimization techniques, such as proximal algorithms. To overcome these challenges, we reformulate the problem and eliminate the network's nested structure. By relating this reformulation to epigraphical projections of the activation functions, we transform the problem into a convex optimization problem that can be efficiently solved using a primal-dual algorithm. We also prove that this reformulation is equivalent to the original variational problem. Through experiments on several imaging tasks, we demonstrate that the proposed approach outperforms subgradient methods in terms of both speed and stability.

We develop a canonical form for congruence of max plus symmetric matrices. We use the same canonical form to get results in the generalized eigenvector problem. We have also utilized the canonical form to find all symmetric matrices that commute with a given symmetric matrix.

Analyzing longitudinal electronic health records (EHR) data is challenging due to clinically informative observation processes, where the timing and frequency of patient visits often depend on their underlying health condition. Traditional longitudinal data analysis rarely considers observation times as stochastic or models them as functions of covariates. In this work, we evaluate the impact of informative visiting processes on two common statistical tasks: (1) estimating the effect of an exposure on a longitudinal biomarker, and (2) assessing the effect of longitudinal biomarkers on a time-to-event outcome, such as disease diagnosis. The methods we consider range from using simple summaries of the observed longitudinal data, imputation, inversely weighting by the estimated intensity of the visit process, and joint modeling. To our knowledge, this is the most comprehensive evaluation of inferential methods specifically tailored for EHR-like visiting processes. For the first task, we improve certain methods around 18 times faster, making them more suitable for large-scale EHR data analysis. For the second task, where no method currently accounts for the visiting process in joint models of longitudinal and time-to-event data, we propose incorporating the historical number of visits to adjust for informative visiting. Using data from the longitudinal biobank at the University of Michigan Health System, we investigate two case studies: 1) the association between genetic variants and lab markers with repeated measures (known as the LabWAS); and 2) the association between cardiometabolic health markers and time to hypertension diagnosis. We show how accounting for informative visiting processes affects the analysis results. We develop an R package CIMPLE (Clinically Informative Missingness handled through Probabilities, Likelihood, and Estimating equations) that integrates all these methods.

Quadratic Unconstrained Binary Optimization (QUBO) sits at the heart of many industries and academic fields such as logistics, supply chain, finance, pharmaceutical science, chemistry, IT, and energy sectors, among others. These problems typically involve optimizing a large number of binary variables, which makes finding exact solutions exponentially more difficult. Consequently, most QUBO problems are classified as NP-hard. To address this challenge, we developed a powerful feedforward neural network (FNN) optimizer for arbitrary QUBO problems. In this work, we demonstrate that the FNN optimizer can provide high-quality approximate solutions for large problems, including dense 80-variable weighted MaxCut and random QUBOs, achieving an average accuracy of over 99% in less than 1.1 seconds on an 8-core CPU. Additionally, the FNN optimizer outperformed the Gurobi optimizer by 72% on 200-variable random QUBO problems within a 100-second computation time limit, exhibiting strong potential for real-time optimization tasks. Building on this model, we explored the novel approach of integrating FNNs with a quantum annealer-based activation function to create a quantum-classical encoder-decoder (QCED) optimizer, aiming to further enhance the performance of FNNs in QUBO optimization.

Auto-regressive Large Language Models (LLMs) demonstrate remarkable performance across domanins such as vision and language processing. However, due to sequential processing through a stack of transformer layers, autoregressive decoding faces significant computation/latency challenges, particularly in resource constrained environments like mobile and edge devices. Existing approaches in literature that aim to improve latency via skipping layers have two distinct flavors - 1) Early exit 2) Input-agnostic heuristics where tokens exit at pre-determined layers irrespective of input sequence. Both the above strategies have limitations - the former cannot be applied to handle KV Caching necessary for speed-ups in modern framework and the latter does not capture the variation in layer importance across tasks or more generally, across input sequences. To address both limitations, we propose FIRST, an algorithm that reduces inference latency by using layer-specific routers to select a subset of transformer layers adaptively for each input sequence - the prompt (during prefill stage) decides which layers will be skipped during decoding. FIRST preserves compatibility with KV caching enabling faster inference while being quality-aware. FIRST is model-agnostic and can be easily enabled on any pre-trained LLM. We further improve performance by incorporating LoRA adapters for fine-tuning on external datasets, enhancing task-specific accuracy while maintaining latency benefits. Our approach reveals that input adaptivity is critical - indeed, different task-specific middle layers play a crucial role in evolving hidden representations depending on task. Extensive experiments show that FIRST significantly reduces latency while retaining competitive performance (as compared to baselines), making our approach an efficient solution for LLM deployment in low-resource environments., Comment: 17 pages, 6 figures, Submitted to ICLR 2025

The objective of this study is applying a utility based analysis to a comparatively efficient design experiment which can capture people's perception towards the various components of a commodity. Here we studied the multi-dimensional poverty index and the relative importance of its components and their two-factor interaction effects. We also discussed how to model a choice based conjoint data for determining the utility of the components and their interactions. Empirical results from survey data shows the nature of coefficients, in terms of utility derived by the individuals, their statistical significance and validity in the present framework. There has been some discrepancies in the results between the bootstrap model and the original model, which can be understood by surveying more people, and ensuring comparative homogeneity in the data.

As LLMs are increasingly deployed in global applications, the importance of cultural sensitivity becomes paramount, ensuring that users from diverse backgrounds feel respected and understood. Cultural harm can arise when these models fail to align with specific cultural norms, resulting in misrepresentations or violations of cultural values. This work addresses the challenges of ensuring cultural sensitivity in LLMs, especially in small-parameter models that often lack the extensive training data needed to capture global cultural nuances. We present two key contributions: (1) A cultural harm test dataset, created to assess model outputs across different cultural contexts through scenarios that expose potential cultural insensitivities, and (2) A culturally aligned preference dataset, aimed at restoring cultural sensitivity through fine-tuning based on feedback from diverse annotators. These datasets facilitate the evaluation and enhancement of LLMs, ensuring their ethical and safe deployment across different cultural landscapes. Our results show that integrating culturally aligned feedback leads to a marked improvement in model behavior, significantly reducing the likelihood of generating culturally insensitive or harmful content. Ultimately, this work paves the way for more inclusive and respectful AI systems, fostering a future where LLMs can safely and ethically navigate the complexities of diverse cultural landscapes.

Magnetic Resonance Imaging (MRI) is the gold standard for measuring fat and iron content non-invasively in the body via measures known as Proton Density Fat Fraction (PDFF) and $R_2^*$, respectively. However, conventional PDFF and $R_2^*$ quantification methods operate on MR images voxel-wise and require at least three measurements to estimate three quantities: water, fat, and $R_2^*$. Alternatively, the two-point Dixon MRI protocol is widely used and fast because it acquires only two measurements; however, these cannot be used to estimate three quantities voxel-wise. Leveraging the fact that neighboring voxels have similar values, we propose using a generative machine learning approach to learn PDFF and $R_2^*$ from Dixon MRI. We use paired Dixon-IDEAL data from UK Biobank in the liver and a Pix2Pix conditional GAN to demonstrate the first large-scale $R_2^*$ imputation from two-point Dixon MRIs. Using our proposed approach, we synthesize PDFF and $R_2^*$ maps that show significantly greater correlation with ground-truth than conventional voxel-wise baselines.

Located 10.888 pc from Earth, COCONUTS-2b is a planetary-mass companion to a young (150-800 Myr) M3 star, with a wide orbital separation (6471 au) and a low companion-to-host mass ratio ($0.021\pm0.005$). We have studied the atmospheric properties of COCONUTS-2b using newly acquired 1.0-2.5 $\mu$m spectroscopy from Gemini/Flamingos-2. The spectral type of COCONUTS-2b is refined to T$9.5 \pm 0.5$ based on comparisons with T/Y dwarf spectral templates. We have conducted an extensive forward-modeling analysis, comparing the near-infrared spectrum and mid-infrared broadband photometry with sixteen state-of-the-art atmospheric model grids developed for brown dwarfs and self-luminous exoplanets near the T/Y transition. The PH$_{3}$-free ATMO2020++, ATMO2020++, and Exo-REM models best match the specific observations of COCONUTS-2b, regardless of variations in the input spectrophotometry. This analysis suggests the presence of disequilibrium chemistry, along with a diabatic thermal structure and/or clouds, in the atmosphere of COCONUTS-2b. All models predict fainter $Y$-band fluxes than observed, highlighting uncertainties in the alkali chemistry models and opacities. We determine a bolometric luminosity of $\log{(L_{\rm bol}/L_{\odot})}=-6.18$ dex, with a 0.5 dex-wide range of $[-6.43,-5.93]$ dex that accounts for various assumptions of models. Using thermal evolution models, we derive an effective temperature of $T_{\rm eff}=483^{+44}_{-53}$ K, a surface gravity of $\log{(g)}=4.19^{+0.18}_{-0.13}$ dex, a radius of $R=1.11^{+0.03}_{-0.04}$ R$_{\rm Jup}$, and a mass of $M=8 \pm 2$ M$_{\rm Jup}$. Various atmospheric model grids consistently indicate that COCONUTS-2b's atmosphere has sub- or near-solar metallicity and C/O. These findings provide valuable insights into COCONUTS-2b's formation history and the potential outward migration to its current wide orbit., Comment: Accepted for publication in AJ. Main text: Pages 1-25, Figures 1-11, Tables 1-4; Appendix: Pages 26-43, Figures 12-15. Mostly unchanged from the previous version, except for footnotes 6-15, which were updated based on suggestions from the AJ data editor. The Gemini/F2 spectrum of COCONUTS-2b is accessible via https://doi.org/10.5281/zenodo.13975825

This paper considers the problem of combinatorial multi-armed bandits with semi-bandit feedback and a cardinality constraint on the super-arm size. Existing algorithms for solving this problem typically involve two key sub-routines: (1) a parameter estimation routine that sequentially estimates a set of base-arm parameters, and (2) a super-arm selection policy for selecting a subset of base arms deemed optimal based on these parameters. State-of-the-art algorithms assume access to an exact oracle for super-arm selection with unbounded computational power. At each instance, this oracle evaluates a list of score functions, the number of which grows as low as linearly and as high as exponentially with the number of arms. This can be prohibitive in the regime of a large number of arms. This paper introduces a novel realistic alternative to the perfect oracle. This algorithm uses a combination of group-testing for selecting the super arms and quantized Thompson sampling for parameter estimation. Under a general separability assumption on the reward function, the proposed algorithm reduces the complexity of the super-arm-selection oracle to be logarithmic in the number of base arms while achieving the same regret order as the state-of-the-art algorithms that use exact oracles. This translates to at least an exponential reduction in complexity compared to the oracle-based approaches., Comment: 26 pages

The mixture of experts (MoE) model is a versatile framework for predictive modeling that has gained renewed interest in the age of large language models. A collection of predictive ``experts'' is learned along with a ``gating function'' that controls how much influence each expert is given when a prediction is made. This structure allows relatively simple models to excel in complex, heterogeneous data settings. In many contemporary settings, unlabeled data are widely available while labeled data are difficult to obtain. Semi-supervised learning methods seek to leverage the unlabeled data. We propose a novel method for semi-supervised learning of MoE models. We start from a semi-supervised MoE model that was developed by oceanographers that makes the strong assumption that the latent clustering structure in unlabeled data maps directly to the influence that the gating function should give each expert in the supervised task. We relax this assumption, imagining a noisy connection between the two, and propose an algorithm based on least trimmed squares, which succeeds even in the presence of misaligned data. Our theoretical analysis characterizes the conditions under which our approach yields estimators with a near-parametric rate of convergence. Simulated and real data examples demonstrate the method's efficacy.

Models for quantum state reduction address the quantum measurement problem by suggesting weak modifications to Schr\"odinger's equation that have no observable effect at microscopic scales, but dominate the dynamics of macroscopic objects. Enforcing linearity of the master equation for such models ensures that modifications to Schr\"odinger's equation do not introduce a possibility for superluminal signalling. In large classes of quantum state reduction models, however, and in particular in all models employing correlated noise, formulating a master equation for the quantum state is prohibitively difficult or impossible. Here, we formulate a witness for superluminal signalling that is applicable to generic quantum state reduction models, including those involving correlated noise. We apply the witness to several relevant cases, and find that correlated-noise models in general allow for superluminal signalling. We suggest how specific models may be able to avoid it, and that the witness introduced here provides a stringent guide to constructing such models., Comment: 6 pages, 2 figures

We propose an architecture with accompanying protocol for on-board satellite data infrastructure designed for Low Earth Orbit (LEO) constellations offering communication services, such as direct-to-cell connectivity. Our design leverages the unused or under-used computing and communication resources of LEO satellites that are orbiting over uninhabited parts of the earth, like the oceans. We show how blockchain-backed distributed transactions can be run efficiently on this architecture to offer smart contract services. A key aspect of the proposed architecture that sets it apart from other blockchain systems is that migration of the ledger is not done solely to recover from failures. Rather, migration is also performed periodically and continuously as the satellites circle around in their orbits and enter and leave the blockchain service area. We show in simulations how message and blockchain processing overhead can be contained using different sizes of dynamic geo-aware service areas.

Gravitational microlensing occurs when the Schwarzschild radius of the lensing object is smaller or nearly equal to the wavelength of the incoming waves and can produce a chromatic amplitude and phase modulation in the Gravitational Waves (GWs) signal. In contrast, strong gravitational lensing magnifies the incoming gravitational wave in an achromatic way, but can also lead to a constant phase shift. In reality, an observed GW signal can be lensed by both microlensing and strong lensing. To detect and characterize both microlensed and strong lensed GW events together, we have developed a novel method $\mu$-GLANCE (Micro-Gravitational Lensing Authenticator using Non-modelled Cross-correlation Exploration). In this technique, we calculate the cross-correlation of the residual between different detectors with respect to the best fit and use a generalized template to search for both GW source and lensing parameters. We assign a false alarm rate to each candidate from a statistical viewpoint, depending on how many times the noise cross-correlation matches the strength of the residual cross-correlation of the candidate. We show that for an event with a matched-filtering signal-to-noise ratio (SNR) close to thirty, a residual due to microlensing with an amplitude about $10\%$ of strong lensing magnification $\mu \approx 3.2$ will start to show deviation from noise distribution at more than $68\%$ C.I with the LIGO-Virgo-KAGRA sensitivity for the fourth observation run. If the strong lensing or microlensing amplitude is higher (or lower), a lower (or higher) matched-filtering SNR will be required to identify any deviation. This method provides the first technique to detect both strong lensing and micro-lensing without assuming any specific lensing model, and its application on the current and future GW data can identify events with both chromatic and achromatic lensed scenarios., Comment: 30 pages, 16 figures (including 3 appendices)

The data-hungry approach of supervised classification drives the interest of the researchers toward unsupervised approaches, especially for problems such as medical image segmentation, where labeled data are difficult to get. Motivated by the recent success of Vision transformers (ViT) in various computer vision tasks, we propose an unsupervised segmentation framework with a pre-trained ViT. Moreover, by harnessing the graph structure inherent within the image, the proposed method achieves a notable performance in segmentation, especially in medical images. We further introduce a modularity-based loss function coupled with an Auto-Regressive Moving Average (ARMA) filter to capture the inherent graph topology within the image. Finally, we observe that employing Scaled Exponential Linear Unit (SELU) and SILU (Swish) activation functions within the proposed Graph Neural Network (GNN) architecture enhances the performance of segmentation. The proposed method provides state-of-the-art performance (even comparable to supervised methods) on benchmark image segmentation datasets such as ECSSD, DUTS, and CUB, as well as challenging medical image segmentation datasets such as KVASIR, CVC-ClinicDB, ISIC-2018. The github repository of the code is available on \url{https://github.com/ksgr5566/UnSeGArmaNet}., Comment: Accepted at BMVC-2024. arXiv admin note: text overlap with arXiv:2405.06057

In this paper, we establish the existence of positive ground state solutions for a class of mixed Schr\"{o}dinger systems with concave-convex nonlinearities in $\mathbb{R}^2$, subject to $L^2$-norm constraints; that is, \[ \left\{ \begin{aligned} -\partial_{xx} u + (-\Delta)_y^s u + \lambda_1 u &= \mu_1 u^{p-1} + \beta r_1 u^{r_1-1} v^{r_2}, && -\partial_{xx} v + (-\Delta)_y^s v + \lambda_2 v &= \mu_2 v^{q-1} + \beta r_2 u^{r_1} v^{r_2-1}, && \end{aligned} \right. \] subject to the $L^2$-norm constraints: \[ \int_{\mathbb{R}^2} u^2 \,\mathrm{d}x\mathrm{d}y = a \quad \text{and} \quad \int_{\mathbb{R}^2} v^2 \,\mathrm{d}x\mathrm{d}y = b, \] where $(x,y)\in \mathbb{R}^2$, $u, v \geq 0$, $s \in \left(1/2, 1 \right)$, $\mu_1, \mu_2, \beta > 0$, $r_1, r_2 > 1$, the prescribed masses $a, b > 0$, and the parameters $\lambda_1, \lambda_2$ appear as Lagrange multipliers. Moreover, the exponents $p, q, r_1 + r_2$ satisfy: \[ \frac{2(1+3s)}{1+s} < p, q, r_1 + r_2 < 2_s, \] where $2_s = \frac{2(1+s)}{1-s}$. To obtain our main existence results, we employ variational techniques such as the Mountain Pass Theorem, the Pohozaev manifold, Steiner rearrangement, and others, consolidating the works of Louis Jeanjean et al. \cite{jeanjean2024normalized}.

Restless multi-armed bandits (RMAB) has been widely used to model constrained sequential decision making problems, where the state of each restless arm evolves according to a Markov chain and each state transition generates a scalar reward. However, the success of RMAB crucially relies on the availability and quality of reward signals. Unfortunately, specifying an exact reward function in practice can be challenging and even infeasible. In this paper, we introduce Pref-RMAB, a new RMAB model in the presence of preference signals, where the decision maker only observes pairwise preference feedback rather than scalar reward from the activated arms at each decision epoch. Preference feedback, however, arguably contains less information than the scalar reward, which makes Pref-RMAB seemingly more difficult. To address this challenge, we present a direct online preference learning (DOPL) algorithm for Pref-RMAB to efficiently explore the unknown environments, adaptively collect preference data in an online manner, and directly leverage the preference feedback for decision-makings. We prove that DOPL yields a sublinear regret. To our best knowledge, this is the first algorithm to ensure $\tilde{\mathcal{O}}(\sqrt{T\ln T})$ regret for RMAB with preference feedback. Experimental results further demonstrate the effectiveness of DOPL.

Extensive recent research on Lieb and kagome lattices highlights their unique physics characterized by the coexistence of Dirac points, van Hove singularities, and flat bands. In these models, flat bands are typically pinned at the center of the Lieb spectrum or the extrema of kagome bands, offering limited tunability. In this work, we investigate the square-octagon lattice and demonstrate that flat bands generated through next-nearest-neighbor (NNN) hoppings can be tuned with intercell hoppings or staggered magnetic fluxes. Importantly, the introduction of staggered magnetic fluxes leads to the emergence of a Chern insulator phase and a higher-order topological insulator (HOTI) state at half-filling. An appropriate magnetic flux combined with NNN hopping can generate topological flat bands in the Chern insulator phase, exhibiting nontrivial Chern number and chiral edge states. The HOTI phase, in contrast, is characterized by topological corner states with quantized quadrupole moment within the bulk and edge states gap. We also present phase diagrams for the square-octagon lattice as functions of NNN and intercell hoppings under staggered magnetic fluxes. Our results indicate that the square-octagon lattice offers a promising platform for realizing topological flat bands, HOTI, and other topological and nontopological phases., Comment: 8 pages, 7 figures

Terahertz microscopy has attracted attention owing to distinctive characteristics of the THz frequency region, particularly non-ionizing photon energy, spectral fingerprint, and transparency to most nonpolar materials. Nevertheless, the well-known Rayleigh diffraction limit imposed on THz waves commonly constrains the resultant imaging resolution to values beyond the millimeter scale, consequently limiting the applicability in numerous emerging applications for chemical sensing and complex media imaging. In this theoretical and numerical work, we address this challenge by introducing a new imaging approach, based on acquiring high-spatial frequencies by adapting the Fourier synthetic aperture approach to the terahertz spectral range, thus surpassing the diffraction-limited resolution. Our methodology combines multi-angle terahertz pulsed illumination with time-resolved field measurements, as enabled by the state-of-the-art time-domain spectroscopy technique. We demonstrate the potential of the approach for hyperspectral terahertz imaging of semi-transparent samples and show that the technique can reconstruct spatial and temporal features of complex inhomogeneous samples with subwavelength resolution., Comment: 15 pages, 5 figures

In this work, we present a lattice QCD calculation of the Mellin moments of the twist-2 axial-vector generalized parton distribution (GPD), $\widetilde{H}(x,\xi,t)$, at zero skewness, $\xi$, with multiple values of the momentum transfer, $t$. Our analysis employs the short-distance factorization framework on ratio-scheme renormalized quasi-GPD matrix elements. The calculations are based on an $N_f=2+1+1$ twisted mass fermions ensemble with clover improvement, a lattice spacing of $a = 0.093$ fm, and a pion mass of $m_\pi = 260$ MeV. We consider both the iso-vector and iso-scalar cases, utilizing next-to-leading-order perturbative matching while ignoring the disconnected contributions and gluon mixing in the iso-scalar case. For the first time, we determine the Mellin moments of $\widetilde{H}$ up to the fifth order. From these moments, we discuss the quark helicity and orbital angular momentum contributions to the nucleon spin, as well as the spin-orbit correlations of the quarks. Additionally, we perform a Fourier transform over the momentum transfer, which allows us to explore the spin structure in the impact-parameter space., Comment: 17 pages, 13 figures

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

The current accelerated expansion of the Universe remains ones of the most intriguing topics in modern cosmology, driving the search for innovative statistical techniques. Recent advancements in machine learning have significantly enhanced its application across various scientific fields, including physics, and particularly cosmology, where data analysis plays a crucial role in problem-solving. In this work, a non-parametric regression method with Gaussian processes is presented along with several applications to reconstruct some cosmological observables, such as the deceleration parameter and the dark energy equation of state, in order to contribute with some information that helps to clarify the behavior of the Universe. It was found that the results are consistent with $\Lambda$CDM and the predicted value of the Hubble parameter at redshift zero is $H_{0}=68.798\pm 6.340(1\sigma) \text{ km}\text{ Mpc}^{-1} \text{ s}^{-1}$., Comment: 17 pages, 18 figures

We characterize steady-state static and dynamic properties in a broad class of mass transport processes on a periodic hypercubic lattice of volume $L^d$, where both mass and {\it center-of-mass} (CoM) remain conserved and detailed balance is violated in the bulk; we specifically consider these models in $d=1$ and $2$ dimensions. Using a microscopic approach, we exactly determine the decay (or, growth) exponents for various dynamic and static correlation functions. We show that, despite constrained dynamics due to the CoM conservation (CoMC), the density relaxation is indeed diffusive. However, fluctuation properties are strikingly different from that in the diffusive systems with a single (mass) conservation law. In the thermodynamic limit, the steady-state variance $\langle {\cal Q}^2(T) \rangle_c$ of time-integrated bond current ${\cal Q}(T)$ across a bond in time interval $T$ exhibits the following long-time behavior: $\langle {\cal Q}^2(T) \rangle_c \simeq A_1 T + A_2 + A_3 T^{-d/2}$. Remarkably, depending on dimensions and microscopic details, the prefactor $A_1$ can vanish (e.g., for $d=1$), causing the variance to eventually {\it saturate}. The exponents governing the small-frequency behavior of the power spectrum $S_J(f) \sim f^{\psi_J}$ for bond current are exactly determined as $\psi_J=3/2$ and $2$ in $d=1$ and $2$ dimensions, respectively, implying a ``dynamic hyperuniformity''. We also compute the static structure factor $S(q)$, which, in the small-$q$ limit, varies as the square of wave number $q$, i.e., $S(q) \sim q^2$. Indeed, both dynamic and static fluctuations are anomalously suppressed, resulting in an extreme form of (``class I'') hyperuniformity in the systems., Comment: 19 pages, 10 figures