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Tiny Machine Learning (TinyML) enables efficient, lowcost, and privacy preserving machine learning inference directly on microcontroller units (MCUs) connected to sensors. Optimizing models for these constrained environments is crucial. This paper investigates how reducing data acquisition rates affects TinyML models for time series classification, focusing on resource-constrained, battery operated IoT devices. By lowering data sampling frequency, we aim to reduce computational demands RAM usage, energy consumption, latency, and MAC operations by approximately fourfold while maintaining similar classification accuracies. Our experiments with six benchmark datasets (UCIHAR, WISDM, PAMAP2, MHEALTH, MITBIH, and PTB) showed that reducing data acquisition rates significantly cut energy consumption and computational load, with minimal accuracy loss. For example, a 75\% reduction in acquisition rate for MITBIH and PTB datasets led to a 60\% decrease in RAM usage, 75\% reduction in MAC operations, 74\% decrease in latency, and 70\% reduction in energy consumption, without accuracy loss. These results offer valuable insights for deploying efficient TinyML models in constrained environments.

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

Conditional Tabular Generative Adversarial Networks (CTGAN) and their various derivatives are attractive for their ability to efficiently and flexibly create synthetic tabular data, showcasing strong performance and adaptability. However, there are certain critical limitations to such models. The first is their inability to preserve the semantic integrity of contextually correlated words or phrases. For instance, skillset in freelancer profiles is one such attribute where individual skills are semantically interconnected and indicative of specific domain interests or qualifications. The second challenge of traditional approaches is that, when applied to generate contextually correlated tabular content, besides generating semantically shallow content, they consume huge memory resources and CPU time during the training stage. To address these problems, we introduce a novel framework, CTGKrEW (Conditional Tabular GAN with KMeans Clustering and Word Embedding), which is adept at generating realistic synthetic tabular data where attributes are collections of semantically and contextually coherent words. CTGKrEW is trained and evaluated using a dataset from Upwork, a realworld freelancing platform. Comprehensive experiments were conducted to analyze the variability, contextual similarity, frequency distribution, and associativity of the generated data, along with testing the framework's system feasibility. CTGKrEW also takes around 99\% less CPU time and 33\% less memory footprints than the conventional approach. Furthermore, we developed KrEW, a web application to facilitate the generation of realistic data containing skill-related information. This application, available at https://riyasamanta.github.io/krew.html, is freely accessible to both the general public and the research community.

In this work, we present TinyTNAS, a novel hardware-aware multi-objective Neural Architecture Search (NAS) tool specifically designed for TinyML time series classification. Unlike traditional NAS methods that rely on GPU capabilities, TinyTNAS operates efficiently on CPUs, making it accessible for a broader range of applications. Users can define constraints on RAM, FLASH, and MAC operations to discover optimal neural network architectures within these parameters. Additionally, the tool allows for time-bound searches, ensuring the best possible model is found within a user-specified duration. By experimenting with benchmark dataset UCI HAR, PAMAP2, WISDM, MIT BIH, and PTB Diagnostic ECG Databas TinyTNAS demonstrates state-of-the-art accuracy with significant reductions in RAM, FLASH, MAC usage, and latency. For example, on the UCI HAR dataset, TinyTNAS achieves a 12x reduction in RAM usage, a 144x reduction in MAC operations, and a 78x reduction in FLASH memory while maintaining superior accuracy and reducing latency by 149x. Similarly, on the PAMAP2 and WISDM datasets, it achieves a 6x reduction in RAM usage, a 40x reduction in MAC operations, an 83x reduction in FLASH, and a 67x reduction in latency, all while maintaining superior accuracy. Notably, the search process completes within 10 minutes in a CPU environment. These results highlight TinyTNAS's capability to optimize neural network architectures effectively for resource-constrained TinyML applications, ensuring both efficiency and high performance. The code for TinyTNAS is available at the GitHub repository and can be accessed at https://github.com/BidyutSaha/TinyTNAS.git.

Human activity recognition (HAR) holds immense potential for transforming health and fitness monitoring, yet challenges persist in achieving personalized outcomes and sustainability for on-device continuous inferences. This work introduces a wrist-worn smart band designed to address these challenges through a novel combination of on-device TinyML-driven computing and cloud-enabled auto-deployment. Leveraging inertial measurement unit (IMU) sensors and a customized 1D Convolutional Neural Network (CNN) for personalized HAR, users can tailor activity classes to their unique movement styles with minimal calibration. By utilising TinyML for local computations, the smart band reduces the necessity for constant data transmission and radio communication, which in turn lowers power consumption and reduces carbon footprint. This method also enhances the privacy and security of user data by limiting its transmission. Through transfer learning and fine-tuning on user-specific data, the system achieves a 37\% increase in accuracy over generalized models in personalized settings. Evaluation using three benchmark datasets, WISDM, PAMAP2, and the BandX demonstrates its effectiveness across various activity domains. Additionally, this work presents a cloud-supported framework for the automatic deployment of TinyML models to remote wearables, enabling seamless customization and on-device inference, even with limited target data. By combining personalized HAR with sustainable strategies for on-device continuous inferences, this system represents a promising step towards fostering healthier and more sustainable societies worldwide.

Volunteer crowdsourcing (VCS) leverages citizen interaction to address challenges by utilizing individuals' knowledge and skills. Complex social tasks often require collaboration among volunteers with diverse skill sets, and their willingness to engage is crucial. Matching tasks with the most suitable volunteers remains a significant challenge. VCS platforms face unpredictable demands in terms of tasks and volunteer requests, complicating the prediction of resource requirements for the volunteer-to-task assignment process. To address these challenges, we introduce the Skill and Willingness-Aware Volunteer Matching (SWAM) algorithm, which allocates volunteers to tasks based on skills, willingness, and task requirements. We also developed a serverless framework to deploy SWAM. Our method outperforms conventional solutions, achieving a 71% improvement in end-to-end latency efficiency. We achieved a 92% task completion ratio and reduced task waiting time by 56%, with an overall utility gain 30% higher than state-of-the-art baseline methods. This framework contributes to generating effective volunteer and task matches, supporting grassroots community coordination and fostering citizen involvement, ultimately contributing to social good.

Crowdsourcing (CS) faces the challenge of managing complex, skill-demanding tasks, which requires effective task assignment and retention strategies to sustain a balanced workforce. This challenge has become more significant in Volunteer Crowdsourcing Services (VCS). This study introduces Workforce Composition Balance (WCB), a novel framework designed to maintain workforce diversity in VCS by dynamically adjusting retention decisions. The WCB framework integrates the Volunteer Retention and Value Enhancement (VRAVE) algorithm with advanced skill-based task assignment methods. It ensures efficient remuneration policy for both assigned and unassigned potential volunteers by incorporating their potential levels, participation dividends, and satisfaction scores. Comparative analysis with three state-of-the-art baselines on real dataset shows that our WCB framework achieves 1.4 times better volunteer satisfaction and a 20% higher task retention rate, with only a 12% increase in remuneration. The effectiveness of the proposed WCB approach is to enhance the volunteer engagement and their long-term retention, thus making it suitable for functioning of social good applications where a potential and skilled volunteer workforce is crucial for sustainable community services.

In this paper we introduce a new measure of conditional dependence between two random vectors ${\boldsymbol X}$ and ${\boldsymbol Y}$ given another random vector $\boldsymbol Z$ using the ball divergence. Our measure characterizes conditional independence and does not require any moment assumptions. We propose a consistent estimator of the measure using a kernel averaging technique and derive its asymptotic distribution. Using this statistic we construct two tests for conditional independence, one in the model-${\boldsymbol X}$ framework and the other based on a novel local wild bootstrap algorithm. In the model-${\boldsymbol X}$ framework, which assumes the knowledge of the distribution of ${\boldsymbol X}|{\boldsymbol Z}$, applying the conditional randomization test we obtain a method that controls Type I error in finite samples and is asymptotically consistent, even if the distribution of ${\boldsymbol X}|{\boldsymbol Z}$ is incorrectly specified up to distance preserving transformations. More generally, in situations where ${\boldsymbol X}|{\boldsymbol Z}$ is unknown or hard to estimate, we design a double-bandwidth based local wild bootstrap algorithm that asymptotically controls both Type I error and power. We illustrate the advantage of our method, both in terms of Type I error and power, in a range of simulation settings and also in a real data example. A consequence of our theoretical results is a general framework for studying the asymptotic properties of a 2-sample conditional $V$-statistic, which is of independent interest.

The exceptional physicochemical properties and versatile biological activities of ionic liquids (ILs) have propelled their potential applications in various industries, including pharmaceuticals and green chemistry. However, their widespread use is limited by concerns over toxicity, particularly due to interactions with cell membranes. This study examines the effects of imidazolium-based ILs on the microscopic structure and phase behavior of a model cell membrane composed of zwitterionic dipalmitoylphosphatidylcholine (DPPC) lipid. Small-angle neutron scattering and dynamic light scattering reveal that the shorter chain IL, 1-hexyl-3-methylimidazolium bromide (HMIM[Br]), induces aggregation of DPPC unilamellar vesicles. In contrast, this aggregation is absent with the longer alkyl chain IL, 1-decyl-3-methylimidazolium bromide (DMIM[Br]). Instead, DMIM[Br] incorporation leads to the formation of distinct IL-poor and IL-rich nanodomains within the DPPC membrane, as evidenced by X-ray reflectivity, differential scanning calorimetry, and molecular dynamics simulation. The less evident nanodomain formation with HMIM[Br] underscores the role of hydrophobic interactions between lipid alkyl tails and ILs. Our findings demonstrate that longer alkyl chains in ILs significantly enhance their propensity to form membrane nanodomains and increase membrane permeability, directly correlating with higher cytotoxicity. This crucial link between nanodomains and toxicity provides valuable insights for designing safer, more environmentally friendly ILs, and promoting their use in biomedical applications and sustainable industrial processes.

Nearest neighbor classifier is arguably the most simple and popular nonparametric classifier available in the literature. However, due to the concentration of pairwise distances and the violation of the neighborhood structure, this classifier often suffers in high-dimension, low-sample size (HDLSS) situations, especially when the scale difference between the competing classes dominates their location difference. Several attempts have been made in the literature to take care of this problem. In this article, we discuss some of these existing methods and propose some new ones. We carry out some theoretical investigations in this regard and analyze several simulated and benchmark datasets to compare the empirical performances of proposed methods with some of the existing ones.

The advent of wearable and sensor technologies now leads to functional predictors which are intrinsically infinite dimensional. While the existing approaches for functional data and survival outcomes lean on the well-established Cox model, the proportional hazard (PH) assumption might not always be suitable in real-world applications. Motivated by physiological signals encountered in digital medicine, we develop a more general and flexible functional time-transformation model for estimating the conditional survival function with both functional and scalar covariates. A partially functional regression model is used to directly model the survival time on the covariates through an unknown monotone transformation and a known error distribution. We use Bernstein polynomials to model the monotone transformation function and the smooth functional coefficients. A sieve method of maximum likelihood is employed for estimation. Numerical simulations illustrate a satisfactory performance of the proposed method in estimation and inference. We demonstrate the application of the proposed model through two case studies involving wearable data i) Understanding the association between diurnal physical activity pattern and all-cause mortality based on accelerometer data from the National Health and Nutrition Examination Survey (NHANES) 2011-2014 and ii) Modelling Time-to-Hypoglycemia events in a cohort of diabetic patients based on distributional representation of continuous glucose monitoring (CGM) data. The results provide important epidemiological insights into the direct association between survival times and the physiological signals and also exhibit superior predictive performance compared to traditional summary based biomarkers in the CGM study.

Neural networks have emerged as a tool for solving differential equations in many branches of engineering and science. But their progress in frequency domain acoustics is limited by the vanishing gradient problem that occurs at higher frequencies. This paper discusses a formulation that can address this issue. The problem of solving the governing differential equation along with the boundary conditions is posed as an unconstrained optimization problem. The acoustic field is approximated to the output of a neural network which is constructed in such a way that it always satisfies the boundary conditions. The applicability of the formulation is demonstrated on popular problems in plane wave acoustic theory. The predicted solution from the neural network formulation is compared with those obtained from the analytical solution. A good agreement is observed between the two solutions. The method of transfer learning to calculate the particle velocity from the existing acoustic pressure field is demonstrated with and without mean flow effects. The sensitivity of the training process to the choice of the activation function and the number of collocation points is studied., Comment: Published Journal Article

The bactericidal potency of ionic liquids (ILs) is well-established, yet their precise mechanism of action remains elusive. Here, we show evidence that the bactericidal action of ILs primarily involves permeabilizing the bacterial cell membrane. Our findings reveal that ILs exert their effects by directly interacting with the lipid bilayer and enhancing the membrane dynamics. Lateral lipid diffusion is accelerated which in turn augments membrane permeability, ultimately leading to bacterial death. Furthermore, our results establish a significant connection: an increase in the alkyl chain length of ILs correlates with a notable enhancement in both lipid lateral diffusion and antimicrobial potency. This underscores a compelling correlation between membrane dynamics and antimicrobial effectiveness, providing valuable insights for the rational design and optimization of IL-based antimicrobial agents in healthcare applications.

We develop a test for spherical symmetry of a multivariate distribution $P$ that works even when the dimension of the data $d$ is larger than the sample size $n$. We propose a non-negative measure $\zeta(P)$ such that $\zeta(P)=0$ if and only if $P$ is spherically symmetric. We construct a consistent estimator of $\zeta(P)$ using the data augmentation method and investigate its large sample properties. The proposed test based on this estimator is calibrated using a novel resampling algorithm. Our test controls the Type-I error, and it is consistent against general alternatives. We also study its behaviour for a sequence of alternatives $(1-\delta_n) F+\delta_n G$, where $\zeta(G)=0$ but $\zeta(F)>0$, and $\delta_n \in [0,1]$. When $\lim\sup\delta_n<1$, for any $G$, the power of our test converges to unity as $n$ increases. However, if $\lim\sup\delta_n=1$, the asymptotic power of our test depends on $\lim n(1-\delta_n)^2$. We establish this by proving the minimax rate optimality of our test over a suitable class of alternatives and showing that it is Pitman efficient when $\lim n(1-\delta_n)^2>0$. Moreover, our test is provably consistent for high-dimensional data even when $d$ is larger than $n$. Our numerical results amply demonstrate the superiority of the proposed test over some state-of-the-art methods.

This paper introduces FlexNN, a Flexible Neural Network accelerator, which adopts agile design principles to enable versatile dataflows, enhancing energy efficiency. Unlike conventional convolutional neural network accelerator architectures that adhere to fixed dataflows (such as input, weight, output, or row stationary) for transferring activations and weights between storage and compute units, our design revolutionizes by enabling adaptable dataflows of any type through software configurable descriptors. Considering that data movement costs considerably outweigh compute costs from an energy perspective, the flexibility in dataflow allows us to optimize the movement per layer for minimal data transfer and energy consumption, a capability unattainable in fixed dataflow architectures. To further enhance throughput and reduce energy consumption in the FlexNN architecture, we propose a novel sparsity-based acceleration logic that utilizes fine-grained sparsity in both the activation and weight tensors to bypass redundant computations, thus optimizing the convolution engine within the hardware accelerator. Extensive experimental results underscore a significant enhancement in the performance and energy efficiency of FlexNN relative to existing DNN accelerators., Comment: Version 1. Work started in 2019

To enhance the handover performance in fifth generation (5G) cellular systems, conditional handover (CHO) has been evolved as a promising solution. Unlike A3 based handover where handover execution is certain after receiving handover command from the serving access network, in CHO, handover execution is conditional on the RSRP measurements from both current and target access networks, as well as on mobility parameters such as preparation and execution offsets. Analytic evaluation of conditional handover performance is unprecedented in literature. In this work, handover performance of CHO has been carried out in terms of handover latency, handover packet loss and handover failure probability. A Markov model accounting the effect of different mobility parameters (e.g., execution offset, preparation offset, time-to-preparation and time-to-execution), UE velocity and channel fading characteristics; has been proposed to characterize handover failure. Results obtained from the analytic model has been validated against extensive simulation results. Our study reveal that optimal configuration of $O_{exec}$, $O_{prep}$, $T_{exec}$ and $T_{prep}$ is actually conditional on underlying UE velocity and fading characteristics. This study will be helpful for the mobile operators to choose appropriate thresholds of the mobility parameters under different channel condition and UE velocities.

Polymer translocation in crowded environments is a ubiquitous phenomenon in biological systems. We studied polymer translocation through a pore in free, one-sided (asymmetric), and two-sided (symmetric) crowded environments. Extensive Langevin dynamics simulation is employed to model the dynamics of the flexible polymer and crowding particles. We studied how crowding size and packing fraction play a crucial role in the translocation process. After determining the standard scaling properties of the translocation probability, time, and MSD, we observed that the translocation rate and bead velocities are location-dependent as we move along the polymer backbone, even in a crowd-free environment. Counter-intuitively, translocation rate and bead velocities showed the opposite behavior; for example, middle monomers near the pore exhibit maximum bead velocity and minimum translocation rate. Free energy calculation for asymmetrically placed polymer indicates there exists a critical number of segments that make the polymer to prefer the receiver side for translocation. For one-sided crowding, we have identified a critical crowding size above which there exists a non-zero probability to translocate into the crowding side instead of the free side. Moreover, we have observed that shifting the polymer towards the crowded side compensates for one-sided crowding, yielding an equal probability akin to a crowder-free system. Under two-sided crowding, the mechanism of how a slight variation in crowder size and packing fraction can force a polymer to switch its translocation direction is proposed, which has not been explored before. Using this control we achieved an equal translocation probability like a crowd-free scenario. These conspicuous yet counter-intuitive phenomena are rationalized by simple theoretical arguments based on osmotic pressure and radial entropic forces.

In this investigation, the distribution of the ratio of two independently distributed xgamma (Sen et al. 2016) random variables X and Y , with different parameters, is proposed and studied. The related distributional properties such as, moments, entropy measures, are investigated. We have also shown a unique characterization of the proposed distribution based on truncated incomplete moments.

We propose a novel semiparametric classifier based on Mahalanobis distances of an observation from the competing classes. Our tool is a generalized additive model with the logistic link function that uses these distances as features to estimate the posterior probabilities of different classes. While popular parametric classifiers like linear and quadratic discriminant analyses are mainly motivated by the normality of the underlying distributions, the proposed classifier is more flexible and free from such parametric modeling assumptions. Since the densities of elliptic distributions are functions of Mahalanobis distances, this classifier works well when the competing classes are (nearly) elliptic. In such cases, it often outperforms popular nonparametric classifiers, especially when the sample size is small compared to the dimension of the data. To cope with non-elliptic and possibly multimodal distributions, we propose a local version of the Mahalanobis distance. Subsequently, we propose another classifier based on a generalized additive model that uses the local Mahalanobis distances as features. This nonparametric classifier usually performs like the Mahalanobis distance based semiparametric classifier when the underlying distributions are elliptic, but outperforms it for several non-elliptic and multimodal distributions. We also investigate the behaviour of these two classifiers in high dimension, low sample size situations. A thorough numerical study involving several simulated and real datasets demonstrate the usefulness of the proposed classifiers in comparison to many state-of-the-art methods.

This work presents the first calibration experiment of a 3 MV Tandetron accelerator, FRENA, performed in May 2022. The $^7$Li(p,n) reaction threshold was measured to calibrate the terminal voltage measuring device. A LiF target of thickness 175 $\mu$g/cm$^2$ was used in the experiment. The measured threshold was 1872$\pm$2.7 keV, indicating 6$-$10 keV energy shift.

The safety of autonomous driving systems, particularly self-driving vehicles, remains of paramount concern. These systems exhibit affine nonlinear dynamics and face the challenge of executing predefined control tasks while adhering to state and input constraints to mitigate risks. However, achieving safety control within the framework of control input constraints, such as collision avoidance and maintaining system states within secure boundaries, presents challenges due to limited options. In this study, we introduce a novel approach to address safety concerns by transforming safety conditions into control constraints with a relative degree of 1. This transformation is facilitated through the design of control barrier functions, enabling the creation of a safety control system for affine nonlinear networks. Subsequently, we formulate a robust control strategy that incorporates safety protocols and conduct a comprehensive analysis of its stability and reliability. To illustrate the effectiveness of our approach, we apply it to a specific problem involving adaptive cruise control. Through simulations, we validate the efficiency of our model in ensuring safety without compromising control performance. Our approach signifies significant progress in the field, providing a practical solution to enhance safety for autonomous driving systems operating within the context of affine nonlinear dynamics., Comment: 11pages,9figures

Over the past six decades, the computing systems field has experienced significant transformations, profoundly impacting society with transformational developments, such as the Internet and the commodification of computing. Underpinned by technological advancements, computer systems, far from being static, have been continuously evolving and adapting to cover multifaceted societal niches. This has led to new paradigms such as cloud, fog, edge computing, and the Internet of Things (IoT), which offer fresh economic and creative opportunities. Nevertheless, this rapid change poses complex research challenges, especially in maximizing potential and enhancing functionality. As such, to maintain an economical level of performance that meets ever-tighter requirements, one must understand the drivers of new model emergence and expansion, and how contemporary challenges differ from past ones. To that end, this article investigates and assesses the factors influencing the evolution of computing systems, covering established systems and architectures as well as newer developments, such as serverless computing, quantum computing, and on-device AI on edge devices. Trends emerge when one traces technological trajectory, which includes the rapid obsolescence of frameworks due to business and technical constraints, a move towards specialized systems and models, and varying approaches to centralized and decentralized control. This comprehensive review of modern computing systems looks ahead to the future of research in the field, highlighting key challenges and emerging trends, and underscoring their importance in cost-effectively driving technological progress., Comment: Preprint submitted to Telematics and Informatics Reports, Elsevier (2024)

We present a general construction of pseudo-hermitian matrices in an arbitrary large, but finite dimensional vector space. The positive-definite metric which ensures reality of the entire spectra of a pseudo-hermitian operator, and is used for defining a modified inner-product in the associated vector space is also presented. The construction for an N dimensional vector space is based on the generators of SU (N ) in the fundamental representation and the identity operator. We apply the results to construct a generic pseudo-hermitian lattice model of size N with balanced loss-gain. The system is amenable to periodic as well as open boundary conditions and by construction, admits entirely real spectra along with unitary time-evolution. The tight binding and Su-Schrieffer-Heeger(SSH) models with nearest neighbour(NN) and next-nearest neighbour(NNN) interaction with balanced loss-gain appear as limiting cases., Comment: 10 pages, no figure

Biased enhanced sampling methods utilizing collective variables (CVs) are powerful tools for sampling conformational ensembles. Due to high intrinsic dimensions, efficiently generating conformational ensembles for complex systems requires enhanced sampling on high-dimensional free energy surfaces. While methods like temperature-accelerated molecular dynamics (TAMD) can adopt many CVs in a simulation, unbiasing the simulation requires accurate modeling of a high-dimensional CV probability distribution, which is challenging for traditional density estimation techniques. Here we propose an unbiasing method based on the score-based diffusion model, a deep generative learning method that excels in density estimation across complex data landscapes. We test the score-based diffusion unbiasing method on TAMD simulations. The results demonstrate that this unbiasing approach significantly outperforms traditional unbiasing methods, and can generate accurate unbiased conformational ensembles for simulations with a number of CVs higher than usual ranges.

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

We present a multiwavelength investigation of the hub-filament system RCW 117 (IRAS 17059-4132), which shows intricate filamentary features in the far-infrared, mapped using Herschel images. We obtain the column density and dust temperature maps for the region using the Herschel images, and identify 88 cores and 12 filaments from the column density map of the region ($18'\times18'$). We employ the ThrUMMS $^{13}$CO (J=1-0) data for probing the kinematics in RCW 117, and find velocity gradients ($\sim 0.3-1$ km s$^{-1}$ pc$^{-1}$) with hints of matter inflow along the filamentary structures. Ionised gas emission from the associated HII region is examined using the Giant Metrewave Radio Telescope (GMRT) at 610 and 1280 MHz, and is found to be of extent $5 \times 3$ pc$^2$ with intensity being brightest towards the hub. We estimate the peak electron density towards the hub to be $\sim 750$ cm$^{-3}$. Thirty four Class 0/I young stellar objects (YSOs) have been identified in the region using the Spitzer GLIMPSE colour-colour diagram, with many lying along the filamentary structures. Based on the (i) presence of filamentary structures, (ii) distribution of cores across the region, with $\sim39$% found along the filamentary structures, (iii) massive star-formation tracers in the hub, and (iv) the kinematics, we believe that global hierarchical collapse can plausibly explain the observed features in RCW 117., Comment: 15 pages, 11 figures, 6 tables, Accepted for publication in Monthly Notices of the Royal Astronomical Society (MNRAS)

We consider equal-mass quantum Toda lattice with balanced loss-gain for two and three particles. The two-particle Toda lattice is integrable and two integrals of motion which are in involution have been found. The bound-state energy and the corresponding eigenfunctions have been obtained numerically for a few low-lying states. The three-particle quantum Toda lattice with balanced loss-gain and velocity mediated coupling admits mixed phases of integrability and chaos depending on the value of the loss-gain parameter. We have obtained analytic expressions for two integrals of motion which are in involution. Although an analytic expression for the third integral has not been found, the numerical investigation suggests integrability below a critical value of the loss-gain strength and chaos above this critical value. The level spacing distribution changes from the Wigner-Dyson to the Poisson distribution as the loss-gain parameter passes through this critical value and approaches zero. An identical behaviour is seen in terms of the gap-ratio distribution of the energy levels. The existence of mixed phases of quantum integrability and chaos in the specified ranges of the loss-gain parameter has also been confirmed independently via the study of level repulsion and complexity in higher order excited states., Comment: 12 pages, 12 figures, two column

We numerically examine the driven transport of an overdamped self-propelled particle through a two-dimensional array of circular obstacles. A detailed analysis of transport quantifiers (mobility and diffusivity) has been performed for two types of channels, {\it channel I} and {\it channel II}, that respectively correspond to the parallel and diagonal drives with respect to the array axis. Our simulation results show that the signatures of pinning actions and depinning processes in the array of obstacles are manifested through excess diffusion peaks or sudden drops in diffusivity, and abrupt jumps in mobility with varying amplitude of the drive. The underlying depinning mechanisms and the associated threshold driving strength largely depend on the persistent length of self-propulsion. For low driving strength, both diffusivity and mobility are noticeably suppressed by the array of obstacles, irrespective of the self-propulsion parameters and direction of the drive. When self-propulsion length is larger than a channel compartment size, transport quantifiers are insensitive to the rotational relaxation time. Transport with diagonal drives features self-propulsion-dependent negative differential mobility. The amplitude of the negative differential mobility of an active particle is much larger than that of a passive one. The present analysis aims at understanding the driven transport of active species like, bacteria, virus, Janus Particle etc. in porous medium., Comment: Accepted for publication in JCP (October 2023)

The silicide superconductors (Ta, Nb, Zr)OsSi are among the best candidate materials for investigating the interplay of topological order and superconductivity. Here, we investigate in detail the normal-state topological properties of (Ta, Nb, Zr)OsSi, focusing on ZrOsSi, by employing a combination of $^{29}$Si nuclear magnetic resonance (NMR) measurements and first-principles band-structure calculations. We show that, while (Ta, Nb)OsSi behave as almost ideal metals, characterized by weak electronic correlations and a relatively low density of states, the replacement of Ta (or Nb) with Zr expands the crystal lattice and shifts ZrOsSi towards an insulator. Our ab initio calculations indicate that ZrOsSi is a $Z_2$ topological metal with clear surface Dirac cones and properties similar to a doped strong topological insulator., Comment: 15 pages, 9 figures

Fundamental to our understanding of planetary bulk compositions is the relationship between their masses and radii, two properties that are often not simultaneously known for most exoplanets. However, while many previous studies have modeled the two-dimensional relationship between planetary mass and radii, this approach largely ignores the dependencies on other properties that may have influenced the formation and evolution of the planets. In this work, we extend the existing nonparametric and probabilistic framework of \texttt{MRExo} to jointly model distributions beyond two dimensions. Our updated framework can now simultaneously model up to four observables, while also incorporating asymmetric measurement uncertainties and upper limits in the data. We showcase the potential of this multi-dimensional approach to three science cases: (i) a 4-dimensional joint fit to planetary mass, radius, insolation, and stellar mass, hinting of changes in planetary bulk density across insolation and stellar mass; (ii) a 3-dimensional fit to the California Kepler Survey sample showing how the planet radius valley evolves across different stellar masses; and (iii) a 2-dimensional fit to a sample of Class-II protoplanetary disks in Lupus while incorporating the upper-limits in dust mass measurements. In addition, we employ bootstrap and Monte-Carlo sampling to quantify the impact of the finite sample size as well as measurement uncertainties on the predicted quantities. We update our existing open-source user-friendly \texttt{MRExo} \texttt{Python} package with these changes, which allows users to apply this highly flexible framework to a variety of datasets beyond what we have shown here., Comment: Accepted in ApJ. Updated MRExo package and sample scripts available here: https://github.com/shbhuk/mrexo/tree/v1.0dev. Package will be released on PyPI (pip) along with full documentation upon publication in ApJ

Molecular polaritons arise when molecules interact so strongly with light that they become entangled with each other. This light-matter hybridization alters the chemical and physical properties of the molecular system and allows chemical reactions to be controlled without the use of external fields. We investigate the impact of strong light-matter coupling on the electronic structure using perturbative approaches and demonstrate that Rayleigh-Schr\"odinger perturbation theory can reproduce the ground state energies in optical cavities to comparable accuracy as ab initio cavity quantum electrodynamics methodologies for currently relevant coupling strengths. The method is effective in both low and high cavity frequency regimes and straightforward to implement via response functions. Furthermore, we establish simple relations between cavity-induced intermolecular forces and van der Waals forces. These findings provide valuable insight into the manipulation of ground-state polaritonic energy landscapes, shedding light on the systems and conditions in which modifications can be achieved., Comment: 11 pages, 3 Figures

We propose a generalization of the stochastic resetting mechanism for a Brownian particle diffusing in a one-dimensional periodic potential: randomly in time, the particle gets reset at the bottom of the potential well it was in. Numerical simulations show that in mirror asymmetric potentials, stochastic resetting rectifies the particle's dynamics, with maximum drift speed for an optimal average resetting time. Accordingly, an unbiased Brownian tracer diffusing on an asymmetric substrate can rectify its motion by adopting an adaptive stop-and-go strategy. Our proposed ratchet mechanism can model directed autonomous motion of molecular motors and micro-organisms, Comment: Journal of Chemical Physics 2023 (in press)