Search

The dynamics and detection possibility of a pseudo-FIMP (pFIMP) dark matter (DM) in the presence of a thermal DM have been studied in different contexts. The pFIMP phenomenology largely depends on the WIMP-like partner DM, as pFIMP interacts with the standard model (SM) particles only via the partner DM loop. Introducing a lepton portal interaction, which connects DM directly to the SM lepton sector, improves its detection prospects. However, such possibilities are constrained strongly by the non-observation of lepton flavor-violating decays. Interestingly, this also makes it possible to probe such models in future low-energy experiments. In this article, we have tried to establish such connections and find parameter space which respects the limits from DM relic, direct, indirect, and lepton flavor violation (LFV). We also recast the constraints from di-lepton/di-tau plus missing energy signal at the LHC on our model and provide projections for HL-LHC and future lepton colliders. Although the LFV and collider limits mainly concern WIMPs, the parameter space for pFIMPs is also constrained due to its strong connection to WIMPs through DM relic density and detection prospects., Comment: 26 pages, 13 figures, 3 tables

Molecular communication (MC) is a model of information transmission where the signal is transmitted by information-carrying molecules through their physical transport from a transmitter to a receiver through a communication channel. Prior efforts have identified suitable "information molecules" whose efficacy for signal transmission has been studied extensively in diffusive channels (DC). Although easy to implement, DCs are inefficient for distances longer than tens of nanometers. In contrast, molecular motor-driven nonequilibrium or active transport can drastically increase the range of communication and may permit efficient communication up to tens of micrometers. In this paper, we investigate how active transport influences the efficacy of molecular communication, quantified by the mutual information between transmitted and received signals. We consider two specific scenarios: (a) active transport through relays and (b) active transport through a mixture of active and diffusing particles. In each case, we discuss the efficacy of the communication channel and discuss their potential pitfalls., Comment: 8 pages, 5 figures

Among the many narratives of the transformative power of Generative AI is one that sees in the world a latent nation of programmers who need to wield nothing but intentions and natural language to render their ideas in software. In this paper, this outlook is problematised in two ways. First, it is observed that generative AI is not a neutral vehicle of intention. Multiple recent studies paint a picture of the "mechanised convergence" phenomenon, namely, that generative AI has a homogenising effect on intention. Second, it is observed that the formation of intention itself is immensely challenging. Constraints, materiality, and resistance can offer paths to design metaphors for intentional tools. Finally, existentialist approaches to intention are discussed and possible implications for programming are proposed in the form of a speculative, illustrative set of intentional programming practices., Comment: Proceedings of the 35th Annual Conference of the Psychology of Programming Interest Group (PPIG 2024)

Hyperspectral images enable precise identification of ground objects by capturing their spectral signatures with fine spectral resolution.While high spatial resolution further enhances this capability, increasing spatial resolution through hardware like larger telescopes is costly and inefficient. A more optimal solution is using ground processing techniques, such as hypersharpening, to merge high spectral and spatial resolution data. However, this method works best when datasets are captured under similar conditions, which is difficult when using data from different times. In this work, we propose a superresolution approach to enhance hyperspectral data's spatial resolution without auxiliary input. Our method estimates the high-resolution point spread function (PSF) using blind deconvolution and corrects for sampling-related blur using a model-based superresolution framework. This differs from previous approaches by not assuming a known highresolution blur. We also introduce an adaptive prior that improves performance compared to existing methods. Applied to the visible and near-infrared (VNIR) spectrometer of HySIS, ISRO hyperspectral sensor, our algorithm removes aliasing and boosts resolution by approximately 1.3 times. It is versatile and can be applied to similar systems., Comment: Preprint

The Ocean Color Monitor-3 (OCM-3) sensor is instrumental in Earth observation, achieving a critical balance between high-resolution imaging and broad coverage. This paper explores innovative imaging methods employed in OCM-3 and the transformative potential of super-resolution techniques to enhance image quality. The super-resolution model for OCM-3 (SOCM-3) addresses the challenges of contemporary satellite imaging by effectively navigating the trade-off between image clarity and swath width. With resolutions below 240 meters in Local Area Coverage (LAC) mode and below 750 meters in Global Area Coverage (GAC) mode, coupled with a wide 1550-kilometer swath and a 2-day revisit time, SOCM-3 emerges as a leading asset in remote sensing. The paper details the intricate interplay of atmospheric, motion, optical, and detector effects that impact image quality, emphasizing the necessity for advanced computational techniques and sophisticated algorithms for effective image reconstruction. Evaluation methods are thoroughly discussed, incorporating visual assessments using the Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) metric and computational metrics such as Line Spread Function (LSF), Full Width at Half Maximum (FWHM), and Super-Resolution (SR) ratio. Additionally, statistical analyses, including power spectrum evaluations and target-wise spectral signatures, are employed to gauge the efficacy of super-resolution techniques. By enhancing both spatial resolution and revisit frequency, this study highlights significant advancements in remote sensing capabilities, providing valuable insights for applications across cryospheric, vegetation, oceanic, coastal, and domains. Ultimately, the findings underscore the potential of SOCM-3 to contribute meaningfully to our understanding of finescale oceanic phenomena and environmental monitoring., Comment: Preprint

Dark matter (DM) genesis via Ultraviolet (UV) freeze-in embeds the seed of reheating temperature and dynamics in its relic density. Thus, discovery of such a DM candidate can possibly open the window for post-inflationary dynamics. However, there are several challenges in this exercise, as freezing-in DM possesses feeble interaction with the visible sector and therefore very low production cross-section at the collider. We show that mono-photon (and dilepton) signal at the ILC, arising from DM effective operators connected to the SM field strength tensors, can still warrant a signal discovery. We study both the scalar and fermionic DM production during reheating via UV freeze-in, when the inflaton oscillates at the bottom of a general monomial potential. Interestingly, we see, right DM abundance can be achieved only in the case of bosonic reheating scenario, satisfying bounds from big bang nucleosynthesis (BBN). This provides a unique correlation between collider signal and the post-inflationary dynamics of the Universe within single-field inflationary models., Comment: 28 pages, 10 figures and 4 table

The autonomous vehicle industry is rapidly expanding, requiring significant computational resources for tasks like perception and decision-making. Vehicular edge computing has emerged to meet this need, utilizing roadside computational units (roadside edge servers) to support autonomous vehicles. Aligning with the trend of green cloud computing, these roadside edge servers often get energy from solar power. Additionally, each roadside computational unit is equipped with a battery for storing solar power, ensuring continuous computational operation during periods of low solar energy availability. In our research, we address the scheduling of computational tasks generated by autonomous vehicles to roadside units with power consumption proportional to the cube of the computational load of the server. Each computational task is associated with a revenue, dependent on its computational needs and deadline. Our objective is to maximize the total revenue of the system of roadside computational units. We propose an offline heuristics approach based on predicted solar energy and incoming task patterns for different time slots. Additionally, we present heuristics for real-time adaptation to varying solar energy and task patterns from predicted values for different time slots. Our comparative analysis shows that our methods outperform state-of-the-art approaches upto 40\% for real-life datasets.

Using the Israel-Darmois junction conditions, we obtain a class of regular dynamical interiors to the recently proposed black-bounce spacetimes which regularises the Schwarzschild singularity by introducing a regularisation parameter. We show that a regularised Friedmann-Lemaitre-Robertson-Walker like interior geometry can not be matched smoothly with the exterior black-bounce spacetime through a timelike hypersurface, as there always exists a thin shell of non-zero energy-momentum tensor at the matching hypersurface. We obtain the expressions for the energy density and pressure of the thin shell energy-momentum tensor in terms of the regularisation parameter and derive an evolution equation for the scale factor of the interior geometry by imposing physical conditions on these components of the surface energy-momentum tensor. We also discuss the formation of the event horizon inside the interior in the case when the initial conditions are such that the situation describes a collapsing matter cloud. We elaborate upon the physical implications of these results.

We study the effects of magnetic field in the formation of a radiatively inefficient accretion flow (RIAF) in the presence of Bremsstrahlung cooling, which facilitates the formation of a geometrically thin, optically thick accretion disk surrounded by a hot corona. We have performed axis-symmetric magnetohydrodynamic (MHD) simulations of an initial accretion torus with a $1/r$ dependant local poloidal field in the presence of a pseudo-Newtonian potential, taking into account optically thin cooling, resistivity and viscosity. We observe the formation of persistent jets and magnetised outflows from the corona surrounding a thin disk with an increase in the magnetic diffusivity parameter. We have defined an equivalent time scale ($\tau_{eq}$) which takes into account the heating time scales due to viscosity, resistivity, magnetic reconnection and magneto-rotational instability turbulence such that the thin disk is formed if the cooling time scale ($\tau_{cool}$) is lower than this equivalent time scale ($\tau_{cool}/\tau_{eq}<1$). Using this condition, for the first time, we found that the thin disk exists when the initial ratio of plasma pressure to magnetic pressure (plasma beta) exceeds a range of $600-800$ for the gas obeying a polytropic equation of state accreting at $10^{-5}\ M_{\odot}/year$, Comment: Comments are welcome

Diffusion models have emerged as a powerful tool for image generation and denoising. Typically, generative models learn a trajectory between the starting noise distribution and the target data distribution. Recently Liu et al. (2023b) designed a novel alternative generative model Rectified Flow (RF), which aims to learn straight flow trajectories from noise to data using a sequence of convex optimization problems with close ties to optimal transport. If the trajectory is curved, one must use many Euler discretization steps or novel strategies, such as exponential integrators, to achieve a satisfactory generation quality. In contrast, RF has been shown to theoretically straighten the trajectory through successive rectifications, reducing the number of function evaluations (NFEs) while sampling. It has also been shown empirically that RF may improve the straightness in two rectifications if one can solve the underlying optimization problem within a sufficiently small error. In this paper, we make two key theoretical contributions: 1) we provide the first theoretical analysis of the Wasserstein distance between the sampling distribution of RF and the target distribution. Our error rate is characterized by the number of discretization steps and a new formulation of straightness stronger than that in the original work. 2) In line with the previous empirical findings, we show that, for a rectified flow from a Gaussian to a mixture of two Gaussians, two rectifications are sufficient to achieve a straight flow. Additionally, we also present empirical results on both simulated and real datasets to validate our theoretical findings.

Context. Gamma-ray bursts (GRBs), observed at redshifts as high as 9.4, could serve as valuable probes for investigating the distant Universe. However, this necessitates an increase in the number of GRBs with determined redshifts, as currently, only 12% of GRBs have known redshifts due to observational biases. Aims. We aim to address the shortage of GRBs with measured redshifts, enabling us to fully realize their potential as valuable cosmological probes Methods. Following Dainotti et al. (2024c), we have taken a second step to overcome this issue by adding 30 more GRBs to our ensemble supervised machine learning training sample, an increase of 20%, which will help us obtain better redshift estimates. In addition, we have built a freely accessible and user-friendly web app that infers the redshift of long GRBs (LGRBs) with plateau emission using our machine learning model. The web app is the first of its kind for such a study and will allow the community to obtain redshift estimates by entering the GRB parameters in the app. Results. Through our machine learning model, we have successfully estimated redshifts for 276 LGRBs using X-ray afterglow parameters detected by the Neil Gehrels Swift Observatory and increased the sample of LGRBs with known redshifts by 110%. We also perform Monte Carlo simulations to demonstrate the future applicability of this research. Conclusions. The results presented in this research will enable the community to increase the sample of GRBs with known redshift estimates. This can help address many outstanding issues, such as GRB formation rate, luminosity function, and the true nature of low-luminosity GRBs, and enable the application of GRBs as standard candles, Comment: 18 Figures, 3 tables. Submitted for publication in Astronomy and Astrophysics journal

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.

Clinicians generally diagnose cardiovascular diseases (CVDs) using standard 12-Lead electrocardiogram (ECG). However, for smartphone-based public healthcare systems, a reduced 3-lead system may be preferred because of (i) increased portability, and (ii) reduced requirement for power, storage and bandwidth. Subsequently, clinicians require accurate 3-lead to 12-Lead ECG reconstruction, which has so far been studied only in the personalized setting. When each device is dedicated to one individual, artificial intelligence (AI) methods such as temporal long short-term memory (LSTM) and a further improved spatio-temporal LSTM-UNet combine have proven effective. In contrast, in the current smartphone-based public health setting where a common device is shared by many, developing an AI lead-reconstruction model that caters to the extensive ECG signal variability in the general population appears a far greater challenge. In this direction, we take a first step, and observe that the performance improvement achieved by a generative model, specifically, 1D Pix2Pix GAN (generative adversarial network), over LSTM-UNet is encouraging., Comment: Accepted to IEEE Healthcom 2024 for presentation as a Main Conference Paper

This research paper explores the privacy and security threats posed to an Agentic AI system with direct access to database systems. Such access introduces significant risks, including unauthorized retrieval of sensitive information, potential exploitation of system vulnerabilities, and misuse of personal or confidential data. The complexity of AI systems combined with their ability to process and analyze large volumes of data increases the chances of data leaks or breaches, which could occur unintentionally or through adversarial manipulation. Furthermore, as AI agents evolve with greater autonomy, their capacity to bypass or exploit security measures becomes a growing concern, heightening the need to address these critical vulnerabilities in agentic systems., Comment: 8 pages, 3 figures

Relativistic accretors are cosmic objects that pull matter from their surroundings at speeds almost equal to the light's speed. Because of the tremendous gravitational force from the accretors and the angular momentum of infalling material, which often result in discs of gas and dust that are heated to extremely high temperatures. We encounter strong radiation throughout the electromagnetic spectrum, including intense X-rays. The X-ray view provides a unique window into the behavior of accretors. In this review, we discuss different accretors, particularly binaries, and their origin, involved mechanisms, and properties, including energy spectra and the variability of X-rays from the accretors. This X-ray perspective gives a unique insight into the evolution and connections of these systems with their environment. Future research in this area is necessary to fully understand the process underlying X-ray emission from relativistic accretors., Comment: 8 pages, 3 figures, 2 tables, The manuscript is accepted for publication as a conference paper in National Conference "Physics Frontiers: Bridging Theory and Experiment-2024" organized by Bhawanipur Anchalik College, Assam, India

Automatic question generation is a critical task that involves evaluating question quality by considering factors such as engagement, pedagogical value, and the ability to stimulate critical thinking. These aspects require human-like understanding and judgment, which automated systems currently lack. However, human evaluations are costly and impractical for large-scale samples of generated questions. Therefore, we propose a novel system, MIRROR (Multi-LLM Iterative Review and Response for Optimized Rating), which leverages large language models (LLMs) to automate the evaluation process for questions generated by automated question generation systems. We experimented with several state-of-the-art LLMs, such as GPT-4, Gemini, and Llama2-70b. We observed that the scores of human evaluation metrics, namely relevance, appropriateness, novelty, complexity, and grammaticality, improved when using the feedback-based approach called MIRROR, tending to be closer to the human baseline scores. Furthermore, we observed that Pearson's correlation coefficient between GPT-4 and human experts improved when using our proposed feedback-based approach, MIRROR, compared to direct prompting for evaluation. Error analysis shows that our proposed approach, MIRROR, significantly helps to improve relevance and appropriateness., Comment: Accepted at FM-Eduassess @ NEURIPS 2024 (ORAL Paper)

We present the results of a suite of numerical simulations to study partial tidal disruption events (TDEs) of white dwarfs (WDs) in off-equatorial planes in intermediate mass spinning (Kerr) black hole backgrounds. We carry out this analysis for both parabolic and eccentric WD orbits and also take into account possible initial WD spins. Our objective here is to quantify the differences in variables like the mass of the self-bound core, the peak fallback rate of debris and gravitational wave signature in off-equatorial orbits compared to equatorial ones. The analysis is carried out using a hybrid numerical scheme, one which involves integrating the exact Kerr geodesics while adopting a Newtonian formalism for the stellar fluid dynamics, justified by our choice of simulation parameters. We find that the physics of TDEs in off-equatorial orbits present several interesting and novel features due to black hole spin, which in some cases enhances when coupled with the rotation of the WD. However, numerical values of observable quantities in TDEs involving off-equatorial orbits cannot possibly distinguish between such orbits from equatorial ones. We further comment on the genericness of our results and argue that these should extend to a general TDE scenario involving a spinning BH., Comment: 15 Pages

Quantum sensors based on critical many-body systems are known to exhibit enhanced sensing capability. Such enhancements typically scale algebraically with the probe size. Going beyond algebraic advantage and reaching exponential scaling has remained elusive when all the resources, such as the preparation time, are taken into account. In this work, we show that systems with first order quantum phase transitions can indeed achieve exponential scaling of sensitivity, thanks to their exponential energy gap closing. Remarkably, even after considering the preparation time using local adiabatic driving, the exponential scaling is sustained. Our results are demonstrated through comprehensive analysis of three paradigmatic models exhibiting first order phase transitions, namely Grover, $p$-spin, and biclique models. We show that this scaling survives moderate decoherence during state preparation and also can be optimally measured in experimentally available basis., Comment: 9 pages, 7 figures. Comments are welcome

Missing frequency channels pose a problem for estimating $P(k_\perp,k_\parallel)$ the redshifted 21-cm power spectrum (PS) from radio-interferometric visibility data. This is particularly severe for the Murchison Widefield Array (MWA), which has a periodic pattern of missing channels that introduce spikes along $k_\parallel$. The Tracking Tapered Gridded Estimator (TTGE) overcomes this by first correlating the visibilities in the frequency domain to estimate the multi-frequency angular power spectrum (MAPS) $C_\ell(\Delta\nu)$ that has no missing frequency separation $\Delta\nu$. We perform a Fourier transform along $\Delta\nu$ to estimate $P(k_\perp,k_\parallel)$. Considering our earlier work, simulations demonstrate that the TTGE can estimate $P(k_\perp,k_\parallel)$ without any artifacts due to the missing channels. However, the spikes were still found to persist for the actual data, which is foreground-dominated. The current work presents a detailed investigation considering both simulations and actual data. We find that the spikes arise due to a combination of the missing channels and the strong spectral dependence of the foregrounds. Based on this, we propose and demonstrate a technique to mitigate the spikes. Applying this, we find the values of $P(k_\perp,k_\parallel)$ in the region $0.004 \leq k_\perp \leq 0.048\,{\rm Mpc^{-1}}$ and $k_\parallel > 0.35 \,{\rm Mpc^{-1}}$ to be consistent with zero within the expected statistical fluctuations. We obtain the $2\sigma$ upper limit of $\Delta_{\rm UL}^2(k)=(918.17)^2\,{\rm mK^2}$ at $k=0.404\,{\rm Mpc^{-1}}$ for the mean squared brightness temperature fluctuations of the $z=8.2$ epoch of reionization (EoR) 21-cm signal. This upper limit is from just $\sim 17$ minutes of observation for a single pointing direction. We expect tighter constraints when we combine all $162$ different pointing directions of the drift scan observation., Comment: 11 pages, 16 figures, comments are welcome

The multiplicities of positive and negative pions, kaons and unidentified hadrons produced in deep-inelastic scattering are measured in bins of the Bjorken scaling variable $x$, the relative virtual-photon energy $y$ and the fraction of the virtual-photon energy transferred to the final-state hadron $z$. Data were obtained by the COMPASS Collaboration using a 160 GeV muon beam of both electric charges and a liquid hydrogen target. These measurements cover the kinematic domain with photon virtuality $Q^2 > 1$ (GeV/$c)^2$, $0.004 < x < 0.4$, $0.1 < y < 0.7$ and $0.2 < z < 0.85$, in accordance with the kinematic domain used in earlier published COMPASS multiplicity measurements with an isoscalar target. The calculation of radiative corrections was improved by using the Monte Carlo generator DJANGOH, which results in up to 12\% larger corrections in the low-$x$ region., Comment: 19 pages, 29 figures

We investigate the effect of the spin-orbit (SO) and Rabi couplings on the localization of the spin-1/2 condensate trapped in a one-dimensional random potential. Our studies reveal that the spin-dependent couplings create distinct localization regimes, resulting in various relations between localization and spin-related properties. First, we examine the localization in the linear condensate and find that the SO coupling can lead to a transition of the localized state from the "basin-like" to the "void" region of the potential. For a weak random potential upon an increase in the SO coupling, we find a re-entrant transition from a broad to narrow localized state and back at a higher SO coupling. Further, we analyze the competing role of inter-species and intra-species interactions on the localization of the condensate. We find the appearance of spin-dependent localization as the interactions increase beyond threshold values for a sufficiently strong disorder. Our findings on controlling spin-dependent localization may be useful for future ultracold atomic experiments and corresponding spin-related quantum technologies., Comment: 18pages, 21figures

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

Micro-Pattern Gas Detectors (MPGDs) represent a category of gaseous ionization detectors that utilize microelectronics. They feature a remarkably small distance between the high potential difference anode and cathode electrodes and are typically filled with gases. When a high-energy particle interacts with the gas medium, it generates ions and electrons, which are subsequently accelerated in opposite directions due to the applied electric field. Deflected electrons trigger further ionization to create electron-ion pairs through an avalanche process. These particles can be detected with very high precision at the readout. The Gas Electron Multiplier (GEM) is one type of MPGD constructed with a polyimide film sandwiched between two conductors under a high voltage difference. Microscopic holes in the foil facilitate electron avalanche. However, the current geometry of the GEM detector used in various experiments is sub-optimal for the gain and performance. In this study, we have modified the geometry of the GEM detector to enhance the gain, reduce ions backflow, and enhance the performance of the detector. We are proposing a new geometry of the GEM detector foil for higher gain, better performance, and durability. For this study, the geometry has been constructed in ANSYS, and further studies have been performed using Garfield$^{++}$.

Sequential memory, the ability to form and accurately recall a sequence of events or stimuli in the correct order, is a fundamental prerequisite for biological and artificial intelligence as it underpins numerous cognitive functions (e.g., language comprehension, planning, episodic memory formation, etc.) However, existing methods of sequential memory suffer from catastrophic forgetting, limited capacity, slow iterative learning procedures, low-order Markov memory, and, most importantly, the inability to represent and generate multiple valid future possibilities stemming from the same context. Inspired by biologically plausible neuroscience theories of cognition, we propose \textit{Predictive Attractor Models (PAM)}, a novel sequence memory architecture with desirable generative properties. PAM is a streaming model that learns a sequence in an online, continuous manner by observing each input \textit{only once}. Additionally, we find that PAM avoids catastrophic forgetting by uniquely representing past context through lateral inhibition in cortical minicolumns, which prevents new memories from overwriting previously learned knowledge. PAM generates future predictions by sampling from a union set of predicted possibilities; this generative ability is realized through an attractor model trained alongside the predictor. We show that PAM is trained with local computations through Hebbian plasticity rules in a biologically plausible framework. Other desirable traits (e.g., noise tolerance, CPU-based learning, capacity scaling) are discussed throughout the paper. Our findings suggest that PAM represents a significant step forward in the pursuit of biologically plausible and computationally efficient sequential memory models, with broad implications for cognitive science and artificial intelligence research., Comment: Accepted to NeurIPS 2024

We present the results of an asteroseismic study of HD 118660 (TIC 171729860), being a chemically peculiar (mild Am) star exhibiting $\delta$ Scuti ($\delta$ Sct) pulsations. It is based on the analysis of two sectors of time-series photometry from the space mission TESS and seismic modelling. It yielded the detection of 15 and 16 frequencies for TESS sectors 23 and 50, respectively. The identified pulsation modes include four radial ($\ell=0$) and five dipolar ($\ell=1$) ones. The radial modes are overtones with order $n$ ranging from $3$ and $6$. Such high values of $n$ are theoretically not expected for stars with the effective temperature of HD 118660 ($\rm T_{\rm eff}\approx 7550 \rm K$ ) located near the red edge of the $\delta$ Sct instability strip. To estimate the asteroseismic parameters, we have generated a grid of stellar models assuming a solar metallicity ($Z=0.014$) and different values for the convective overshooting parameter ($0.1\leq \alpha_{\rm ov}\leq 0.3$). We conclude that the analysis of the radial modes is insufficient to constrain $\alpha_{\rm ov}$ and $Z$ for $\delta$ Sct stars. The value for the equatorial velocity of HD 118660 derived from the seismic radius and the rotational frequency is consistent with values found in the literature., Comment: 10 pages, 7 figures, Accepted for publication in MNRAS

Origami principles are used to create strong, lightweight structures with complex mechanical response. However, identifying the fundamental physical principles that determine a sheet's behavior remains a challenge. We introduce a new analytic theory in which commonly studied origami sheets fall into distinct topological classes that predict sharply varying mechanical behavior, including effective stiffness and smoothness of mechanical response under external loads. Origami sheets with negative Poisson's ratios, such as the Miura ori, have conventional, smooth mechanical response amenable to continuum-based approaches. In contrast, positive Poisson's ratio, as in the Eggbox ori, generates a topological transition to lines of doubly degenerate zero modes that lead to dramatically softer structures with uneven, complex patterns of spatial response. These patterns interact in complicated ways with origami boundary conditions and source terms, leading to rich physical phenomena in experimentally accessible systems. This approach highlights topological mechanics, with deep connections to topologically protected quantum-mechanical systems, as a design principle for controlling the mechanical response of thin, complex sheets.

Volumetric modeling and neural radiance field representations have revolutionized 3D face capture and photorealistic novel view synthesis. However, these methods often require hundreds of multi-view input images and are thus inapplicable to cases with less than a handful of inputs. We present a novel volumetric prior on human faces that allows for high-fidelity expressive face modeling from as few as three input views captured in the wild. Our key insight is that an implicit prior trained on synthetic data alone can generalize to extremely challenging real-world identities and expressions and render novel views with fine idiosyncratic details like wrinkles and eyelashes. We leverage a 3D Morphable Face Model to synthesize a large training set, rendering each identity with different expressions, hair, clothing, and other assets. We then train a conditional Neural Radiance Field prior on this synthetic dataset and, at inference time, fine-tune the model on a very sparse set of real images of a single subject. On average, the fine-tuning requires only three inputs to cross the synthetic-to-real domain gap. The resulting personalized 3D model reconstructs strong idiosyncratic facial expressions and outperforms the state-of-the-art in high-quality novel view synthesis of faces from sparse inputs in terms of perceptual and photo-metric quality., Comment: Siggraph Asia Conference Papers 2024

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

For a $G$-space $X$, we introduce the notion of sectional category with respect to $G$. We establish properties analogous to those of the classical sectional category. As a consequence of the sectional category with respect to the group $G$, we obtain $G$-homotopy invariants: the LS category with respect to $G$, the sequential topological complexity with respect to $G$, and the strong sequential topological complexity with respect to $G$, denoted by $\mathrm{cat}_G^{\#}(X)$, $\mathrm{TC}_{k,G}^{\#}(X)$, and $\mathrm{TC}_{k,G}^{\#,*}(X)$, respectively. We explore several relationships between these invariants and well-known ones, such as the LS category, the sequential (equivariant) topological complexity, and the sequential strong equivariant topological complexity. We also prove some fundamental properties of these invariants. Additionally, we derive several additive upper bounds on the sequential topological complexity of the total spaces of fibre bundles involving these newly introduced invariants. As applications of these results, we compute the sequential topological complexity of various generalized projective product spaces., Comment: 24 pages. Comments are welcome

Working memory $\unicode{x2013}$ the ability to remember recent events as they recede continuously into the past $\unicode{x2013}$ requires the ability to represent any stimulus at any time delay. This property requires neurons coding working memory to show mixed selectivity, with conjunctive receptive fields (RFs) for stimuli and time, forming a representation of 'what' $\times$ 'when'. We study the properties of such a working memory in simple experiments where a single stimulus must be remembered for a short time. The requirement of conjunctive receptive fields allows the covariance matrix of the network to decouple neatly, allowing an understanding of the low-dimensional dynamics of the population. Different choices of temporal basis functions lead to qualitatively different dynamics. We study a specific choice $\unicode{x2013}$ a Laplace space with exponential basis functions for time coupled to an "Inverse Laplace" space with circumscribed basis functions in time. We refer to this choice with basis functions that evenly tile log time as a Laplace Neural Manifold. Despite the fact that they are related to one another by a linear projection, the Laplace population shows a stable stimulus-specific subspace whereas the Inverse Laplace population shows rotational dynamics. The growth of the rank of the covariance matrix with time depends on the density of the temporal basis set; logarithmic tiling shows good agreement with data. We sketch a continuous attractor CANN that constructs a Laplace Neural Manifold. The attractor in the Laplace space appears as an edge; the attractor for the inverse space appears as a bump. This work provides a map for going from more abstract cognitive models of WM to circuit-level implementation using continuous attractor neural networks, and places constraints on the types of neural dynamics that support working memory.

The secretary problem is one of the fundamental problems in online decision making; a tight competitive ratio for this problem of $1/\mathrm{e} \approx 0.368$ has been known since the 1960s. Much more recently, the study of algorithms with predictions was introduced: The algorithm is equipped with a (possibly erroneous) additional piece of information upfront which can be used to improve the algorithm's performance. Complementing previous work on secretary problems with prior knowledge, we tackle the following question: What is the weakest piece of information that allows us to break the $1/\mathrm{e}$ barrier? To this end, we introduce the secretary problem with predicted additive gap. As in the classical problem, weights are fixed by an adversary and elements appear in random order. In contrast to previous variants of predictions, our algorithm only has access to a much weaker piece of information: an \emph{additive gap} $c$. This gap is the difference between the highest and $k$-th highest weight in the sequence. Unlike previous pieces of advice, knowing an exact additive gap does not make the problem trivial. Our contribution is twofold. First, we show that for any index $k$ and any gap $c$, we can obtain a competitive ratio of $0.4$ when knowing the exact gap (even if we do not know $k$), hence beating the prevalent bound for the classical problem by a constant. Second, a slightly modified version of our algorithm allows to prove standard robustness-consistency properties as well as improved guarantees when knowing a range for the error of the prediction., Comment: Full version of NeurIPS 2024 paper

We present a fully discrete Crank-Nicolson Fourier-spectral-Galerkin (FSG) scheme for approximating solutions of the fractional Korteweg-de Vries (KdV) equation, which involves a fractional Laplacian with exponent $\alpha \in [1,2]$ and a small dispersion coefficient of order $\varepsilon^2$. The solution in the limit as $\varepsilon \to 0$ is known as the zero dispersion limit. We demonstrate that the semi-discrete FSG scheme conserves the first three integral invariants, thereby structure preserving, and that the fully discrete FSG scheme is $L^2$-conservative, ensuring stability. Using a compactness argument, we constructively prove the convergence of the approximate solution to the unique solution of the fractional KdV equation in $C([0,T]; H_p^{1+\alpha}(\mathbb{R}))$ for the periodic initial data in $H_p^{1+\alpha}(\mathbb{R})$. The devised scheme achieves spectral accuracy for the initial data in $H_p^r,$ $r \geq 1+\alpha$ and exponential accuracy for the analytic initial data. Additionally, we establish that the approximation of the zero dispersion limit obtained from the fully discrete FSG scheme converges to the solution of the Hopf equation in $L^2$ as $\varepsilon \to 0$, up to the gradient catastrophe time $t_c$. Beyond $t_c$, numerical investigations reveal that the approximation converges to the asymptotic solution, which is weakly described by the Whitham's averaged equation within the oscillatory zone for $\alpha = 2$. Numerical results are provided to demonstrate the convergence of the scheme and to validate the theoretical findings.

Bose-Fermi mixtures naturally appear in various physical systems. In semiconductor heterostructures, such mixtures can be realized, with bosons as excitons and fermions as dopant charges. However, the complexity of these hybrid systems challenges the comprehension of the mechanisms that determine physical properties such as mobility. In this study, we investigate interlayer exciton diffusion in an H-stacked WSe$_2$/WS$_2$ heterobilayer. Our measurements are performed in the dilute exciton density limit at low temperatures to examine how the presence of charges affects exciton mobility. Remarkably, for charge doping near the Mott insulator phase, we observe a giant enhancement of exciton diffusion of three orders of magnitude compared to charge neutrality. We attribute this observation to mobile valence holes, which experience a suppressed moir\'e potential due to the electronic charge order in the conduction band, and recombine with any conduction electron in a non-monogamous manner. This new mechanism emerges for sufficiently large fillings in the vicinity of correlated generalized Wigner crystal and Mott insulating states. Our results demonstrate the potential to characterize correlated electron states through exciton diffusion and provide insights into the rich interplay of bosons and fermions in semiconductor heterostructures., Comment: Main text: 19 pages, 4 figures. Supplementary Material: 9 pages, 6 figures

Simulating fluid flow around arbitrary shapes is key to solving various engineering problems. However, simulating flow physics across complex geometries remains numerically challenging and computationally resource-intensive, particularly when using conventional PDE solvers. Machine learning methods offer attractive opportunities to create fast and adaptable PDE solvers. However, benchmark datasets to measure the performance of such methods are scarce, especially for flow physics across complex geometries. We introduce FlowBench, a dataset for neural simulators with over 10K samples, which is currently larger than any publicly available flow physics dataset. FlowBench contains flow simulation data across complex geometries (\textit{parametric vs. non-parametric}), spanning a range of flow conditions (\textit{Reynolds number and Grashoff number}), capturing a diverse array of flow phenomena (\textit{steady vs. transient; forced vs. free convection}), and for both 2D and 3D. FlowBench contains over 10K data samples, with each sample the outcome of a fully resolved, direct numerical simulation using a well-validated simulator framework designed for modeling transport phenomena in complex geometries. For each sample, we include velocity, pressure, and temperature field data at 3 different resolutions and several summary statistics features of engineering relevance (such as coefficients of lift and drag, and Nusselt numbers). %Additionally, we include masks and signed distance fields for each shape. We envision that FlowBench will enable evaluating the interplay between complex geometry, coupled flow phenomena, and data sufficiency on the performance of current, and future, neural PDE solvers. We enumerate several evaluation metrics to help rank order the performance of neural PDE solvers. We benchmark the performance of several baseline methods including FNO, CNO, WNO, and DeepONet.

Astrophysical and cosmological observations strongly suggest the existence of Dark Matter. However, it's fundamental nature is still elusive. Collider experiments at Large Hadron Collider (LHC) offer a promising way to reveal the particle nature of the dark matter. In such an endeavour, we investigate the potential of the mono-Higgs plus missing $E_T$ signature at the LHC to search for dark matter. Without going in a particular Ultra-Violet complete model of dark matter, we have used the framework of Effective Field Theory to describe the dynamics of a relatively light fermionic dark matter candidate, which interacts with the Standard Model via dimension-6 and dimension-7 operators involving the Higgs and the gauge bosons. Both cut-based and Boosted Decision Tree (BDT) algorithms have been used to extract the signal for dark matter production over the Standard Model backgrounds, assuming an integrated luminosity of $3000~fb^{-1}$ at $\sqrt{s}~=~14$ TeV at the High Luminosity phase of the LHC (HL-LHC). The BDT is seen to separate the dark matter signal at $5\sigma$ significance, for masses below 200 GeV, showcasing the prospects of this search at the HL-LHC., Comment: 19 Pages, 8 Figures, 8 Tables

Current video-based computer vision (CV) applications typically suffer from high energy consumption due to reading and processing all pixels in a frame, regardless of their significance. While previous works have attempted to reduce this energy by skipping input patches or pixels and using feedback from the end task to guide the skipping algorithm, the skipping is not performed during the sensor read phase. As a result, these methods can not optimize the front-end sensor energy. Moreover, they may not be suitable for real-time applications due to the long latency of modern CV networks that are deployed in the back-end. To address this challenge, this paper presents a custom-designed reconfigurable CMOS image sensor (CIS) system that improves energy efficiency by selectively skipping uneventful regions or rows within a frame during the sensor's readout phase, and the subsequent analog-to-digital conversion (ADC) phase. A novel masking algorithm intelligently directs the skipping process in real-time, optimizing both the front-end sensor and back-end neural networks for applications including autonomous driving and augmented/virtual reality (AR/VR). Our system can also operate in standard mode without skipping, depending on application needs. We evaluate our hardware-algorithm co-design framework on object detection based on BDD100K and ImageNetVID, and gaze estimation based on OpenEDS, achieving up to 53% reduction in front-end sensor energy while maintaining state-of-the-art (SOTA) accuracy., Comment: Under review

Incorporating higher curvature terms into gravity theories modifies the classical field equations, potentially leading to theoretical issues like Shapiro time advancements that violate the Camanho, Edelstein, Maldacena, and Zhiboedov (CEMZ) causality criterion. We explore this criterion within the context of Generalised Quadratic Gravity (GQG), a higher curvature theory with a distinct graviton three-point coupling from General Relativity (GR). By constructing an exact shock wave solution of GQG and calculating the Shapiro time shift for a massless probe graviton, we demonstrate that it can remain strictly positive within a classically allowed parameter space of couplings, ensuring the theory's adherence to the CEMZ criterion. This finding indicates that GQG can offer a causal extension beyond GR, paving the way for further exploration into the consistency of classical higher curvature gravity theories., Comment: 5 pages, 2 figures

We investigate the transport properties of a two-dimensional Su-Schrieffer-Heeger (2D SSH) model in the quantum Hall regime using non-equilibrium Green's function formalism (NEGF). The device Hamiltonian, where the 2D SSH model serves as the channel, is constructed using a nearest-neighbor tight-binding model. The effect of an external perpendicular magnetic field is incorporated into the contacts via Peierls substitution. We observe a transition from a gapped phase to a flat band regime at zero energy by varying the magnetic field. This transition is characterized by the emergence of highly localized states in the bulk or edges, which we observe by calculating local density-of-states (LDOS). We analyze transport in the system along two directions ($x$ and $y$) via transmission measurements, indicating a magnetic field-induced transition from insulating to metallic phase. The study of the energy spectrum of the system shows the formation of Landau levels. Moreover, the quantum number of the non-degenerate and degenerate Landau levels (transmission modes) can be any integer or only an odd integer, depending on diagonal, inter-cell, and intra-cell hopping strengths. From the analysis of the transport properties along $y$-direction, we find that edge modes play a crucial role in facilitating ballistic transport., Comment: 8 pages, 9 figures

This research focuses on constructing $q$-ary functions for complete complementary codes (CCCs) with flexible parameters. Most existing work has primarily identified sufficient conditions for $q$-ary functions related to $q$-ary CCCs. To the best of the authors' knowledge, this study is the first to establish both the necessary and sufficient conditions for $q$-ary functions, encompassing most existing CCCs constructions as special cases. For $q$-ary CCCs with a length of $q^m$ and a set size of $q^{n+1}$, we begin by analyzing the necessary and sufficient conditions for $q$-ary functions defined over the domain $\mathbb{Z}_q^m$. Additionally, we construct CCCs with lengths given by $L = \prod_{i=1}^k p_i^{m_i}$, set sizes given by $K = \prod_{i=1}^k p_i^{n_i+1}$, and an alphabet size of $\nu = \prod_{i=1}^k p_i$, where $p_1 < p_2 < \cdots < p_k$. To achieve these specific parameters, we examine the necessary and sufficient conditions for $\nu$-ary functions over the domain $\mathbf{Z}_{p_1}^{m_1} \times \cdots \times \mathbf{Z}_{p_k}^{m_k}$, which is a subset of $\mathbb{Z}_{\nu}^m$ and contains $\prod_{i=1}^k p_i^{m_i}$ vectors. In this context, $\mathbf{Z}_{p_i}^{m_i} = \{0, 1, \ldots, p_i - 1\}^{m_i}$, and $m$ is the sum of $m_1, m_2, \ldots, m_k$. The $q$-ary and $\nu$-ary functions allow us to cover all possible length sequences. However, we find that the proposed $\nu$-ary functions are more suitable for generating CCCs with a length of $L = \prod_{i=1}^k p_i^{m_i}$, particularly when $m_i$ is coprime to $m_j$ for some $1 \leq i \neq j \leq k$. While the proposed $q$-ary functions can also produce CCCs of the same length $L$, the set size and alphabet size become as large as $L$, since in this case, the only choice for $q$ is $L$. In contrast, the proposed $\nu$-ary functions yield CCCs with a more flexible set size $K\leq L$ and an alphabet size of $\nu

Accretion physics studies the process of gravitational capture of ambient matter by massive stars. The background processes are very challenging to observe and measure due to the extreme conditions in these systems. Numerical simulations play a crucial role in accretion physics because they provide the only practical method to model the complex processes occurring in accretion disks. In this review, we outline different branches of numerical simulations, such as hydrodynamic simulations, magnetohydrodynamic simulations, and Monte-Carlo simulations, and their methodology, and we discuss possible implications for modeling accretion physics around black holes, neutron stars, and protoplanetary disks., Comment: The manuscript is accepted for publication as a Conference paper in Physics Frontiers, Bhawanipur Anchalik College, 2024, 5 pages, 1 figure