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The concept of evolutionarily stability and its relation with the fixed points of the replicator equation are important aspects of evolutionary game dynamics. In the light of the fact that oscillating state of a population and individuals (or players) of different roles are quite natural occurrences, we ask the question how the concept of evolutionarily stability can be generalized so as to associate game-theoretic meaning to oscillatory behaviours of players asymmetrically interacting, i.e., if there are both intraspecific and interspecific interactions between two subpopulations in the population. We guide our scheme of generalization such that the evolutionary stability is related to the dynamic stability of the corresponding periodic orbits of a time-discrete replicator dynamics. We name the generalization of evolutionarily stable state as two-species heterogeneity stable orbit. Furthermore, we invoke the principle of decrease of relative entropy in order to associate the generalization of evolutionary stability with an information-theoretic meaning. This particular generalization is aptly termed as two-species information stable orbit.

The paper aims at deriving a curvature form of the famous Raychaudhuri equation (RE) and the associated criteria for focusing of a hyper-surface orthogonal congruence of time-like geodesic. Moreover, the paper identifies a transformation of variable related to the metric scalar of the hyper-surface which converts the first order RE into a second order differential equation that resembles an equation of a Harmonic oscillator and also gives a first integral that yields the analytic solution of the RE and Lagrangian of the dynamical system representing the congruence.

Evolutionarily stable strategy (ESS) is the defining concept of evolutionary game theory. It has a fairly unanimously accepted definition for the case of symmetric games which are played in a homogeneous population where all individuals are in same role. However, in asymmetric games, which are played in a population with multiple subpopulations (each of which has individuals in one particular role), situation is not as clear. Various generalizations of ESS defined for such cases differ in how they correspond to fixed points of replicator equation which models evolutionary dynamics of frequencies of strategies in the population. Moreover, some of the definitions may even be equivalent, and hence, redundant in the scheme of things. Along with reporting some new results, this paper is partly indented as a contextual mini-review of some of the most important definitions of ESS in asymmetric games. We present the definitions coherently and scrutinize them closely while establishing equivalences -- some of them hitherto unreported -- between them wherever possible. Since it is desirable that a definition of ESS should correspond to asymptotically stable fixed points of replicator dynamics, we bring forward the connections between various definitions and their dynamical stabilities. Furthermore, we find the use of principle of relative entropy to gain information-theoretic insights into the concept of ESS in asymmetric games, thereby establishing a three-fold connection between game theory, dynamical system theory, and information theory in this context. We discuss our conclusions also in the backdrop of asymmetric hypermatrix games where more than two individuals interact simultaneously in the course of getting payoffs., Comment: 22 pages, 3 figures

The present work deals with the Raychaudhuri equation (RE) to examine the space-time singularity both from classical and quantum point of view. The RE has been looked upon in terms of a classical linear Harmonic oscillator and Focusing theorem has been studied. Also, the Raychaudhuri scalar has been shown to affect the convergence and singularity formation in Black-holes and cosmology. Finally, resolution of initial big-bang singularity has been attempted quantum mechanically in two ways namely by canonical quantization and by formulation of Bohmian trajectories., Comment: 11 pages, 4 figures. arXiv admin note: substantial text overlap with arXiv:2402.17799

Checking for Non-Termination (NT) of a given program P, i.e., determining if P has at least one non-terminating run, is an undecidable problem that continues to garner significant research attention. While unintended NT is common in real-world software development, even the best-performing tools for NT checking are often ineffective on real-world programs and sometimes incorrect due to unrealistic assumptions such as absence of overflows. To address this, we propose a sound and efficient technique for NT checking that is also effective on realworld software. Given P, we encode the NT property as an assertion inside each loop of P to check for recurrent states in that loop, up to a fixed unwinding depth, using a Bounded Model Checker. The unwinding depth is increased iteratively until either NT is found or a predefined limit is reached. Our experiments on wide ranging software benchmarks show that the technique outperforms state-of-the-art NT checkers

An attempt has been made to have an analytical description for possible traversable wormhole in non-static spherically symmetric space-time supported by anisotropic fluid. Both trivial and non-trivial choices of the red-shift function result in identical WH configuration and it is possible to have emergent scenario for evolution of the background space-time in both the cases., Comment: 10 pages, 3 figures

An investigation has been done for possible existence of evolving wormhole (WH) solution in the background of inhomogeneous Lemaitre-Tolman-Bondi (LTB) space-time geometry. Using separable product form of the geometric (or area) radius, the shape function can be evaluated in terms of the functional part of the radial coordinate in the area radius. The conditions for a viable WH configuration have been examined., Comment: 11 pages, 3 figures

This work introduces Hierarchical Preference Optimization (HPO), a novel approach to hierarchical reinforcement learning (HRL) that addresses non-stationarity and infeasible subgoal generation issues when solving complex robotic control tasks. HPO leverages maximum entropy reinforcement learning combined with token-level Direct Preference Optimization (DPO), eliminating the need for pre-trained reference policies that are typically unavailable in challenging robotic scenarios. Mathematically, we formulate HRL as a bi-level optimization problem and transform it into a primitive-regularized DPO formulation, ensuring feasible subgoal generation and avoiding degenerate solutions. Extensive experiments on challenging robotic navigation and manipulation tasks demonstrate impressive performance of HPO, where it shows an improvement of up to 35% over the baselines. Furthermore, ablation studies validate our design choices, and quantitative analyses confirm the ability of HPO to mitigate non-stationarity and infeasible subgoal generation issues in HRL.

Almost four decades of intense research have been invested to study the physics of high-T$_c$ cuprate superconductors. The recent discovery of high-T$_c$ superconductivity in pressurized bilayer nickelates and its potential similarities with cuprate superconductors may open a new window to understand this long standing problem. Motivated by this we have assumed that nickelates belong to the category of strongly correlated systems, and considered the bilayer $t$-$J_\parallel$-$J_\perp$ model as a minimal model, where $J_\parallel$ and $J_\perp$ are the in-plane and out-of-plane magnetic exchange, respectively. We have studied the $t$-$J_\parallel$-$J_\perp$ model in a large-$N$ approach on the basis of the path integral representation for Hubbard operators, which allows to obtain results at mean-field and beyond mean-field level. We find that $J_\perp$ is a promising candidate for triggering high superconducting $T_c$ values at quarter filling (hole doping $\delta=0.5$) of the $d_{x^2-y^2}$ orbitals. Beyond mean-field level, we remarkably find a new phase, an out-of-plane bond-order phase (z-BOP), triggered also by $J_\perp$. z-BOP develops below a critical temperature which decreases with increasing doping and vanishes at a quantum critical point below quarter filling. The occurrence of this phase and its competition with superconductivity leads to a superconducting dome shaped behavior as a function of doping and as a function of $J_\perp$. Comparisons with the physics of cuprates and the recent literature on the new pressurized nickelates are given along the paper., Comment: 7 pages (main text) + 8 pages (references + appendices), 7 figures

The resilience of urban rapid transit systems (URTs) to a rapidly evolving threat space is of much concern. Extreme rainfall events are both intensifying and growing more frequent under continuing climate change, exposing transit systems to flooding, while cyber threats and emerging technologies such as unmanned aerial vehicles are exposing such systems to targeted disruptions. An imperative has emerged to model how networked infrastructure systems fail and devise strategies to efficiently recover from disruptions. Passenger flow approaches can quantify more dimensions of resilience than network science-based approaches, but the former typically requires granular data from automatic fare collection and suffers from large runtime complexities. Some attempts have been made to include accessible low-resolution ridership data in topological frameworks. However, there is yet to be a systematic investigation of the effects of incorporating low-dimensional, coarsely-averaged ridership volume into topological network science methodologies. We simulate targeted attack and recovery sequences using station-level ridership, four centrality measures, and weighted combinations thereof. Resilience is quantified using two topological measures of performance: the node count of a network's giant connected component (GCC), and a new measure termed the "highest ridership connected component" (HRCC). Three transit systems are used as case studies: the subways of Boston, New York, and Osaka. Results show that centrality-based strategies are most effective when measuring performance via GCC, while centrality-ridership hybrid strategies perform strongest by HRCC. We show that the most effective strategies vary by network characteristics and the mission goals of emergency managers, highlighting the need to plan for strategic adversaries and rapid recovery according to each city's unique needs.

Autonomous systems, such as self-driving cars and drones, have made significant strides in recent years by leveraging visual inputs and machine learning for decision-making and control. Despite their impressive performance, these vision-based controllers can make erroneous predictions when faced with novel or out-of-distribution inputs. Such errors can cascade into catastrophic system failures and compromise system safety. In this work, we compute Neural Reachable Tubes, which act as parameterized approximations of Backward Reachable Tubes to stress-test the vision-based controllers and mine their failure modes. The identified failures are then used to enhance the system safety through both offline and online methods. The online approach involves training a classifier as a run-time failure monitor to detect closed-loop, system-level failures, subsequently triggering a fallback controller that robustly handles these detected failures to preserve system safety. For the offline approach, we improve the original controller via incremental training using a carefully augmented failure dataset, resulting in a more robust controller that is resistant to the known failure modes. In either approach, the system is safeguarded against shortcomings that transcend the vision-based controller and pertain to the closed-loop safety of the overall system. We validate the proposed approaches on an autonomous aircraft taxiing task that involves using a vision-based controller to guide the aircraft towards the centerline of the runway. Our results show the efficacy of the proposed algorithms in identifying and handling system-level failures, outperforming methods that rely on controller prediction error or uncertainty quantification for identifying system failures.

This paper is about generating motion plans for high degree-of-freedom systems that account for collisions along the entire body. A particular class of mathematical programs with complementarity constraints become useful in this regard. Optimization-based planners can tackle confined-space trajectory planning while being cognizant of robot constraints. However, introducing obstacles in this setting transforms the formulation into a non-convex problem (oftentimes with ill-posed bilinear constraints), which is non-trivial in a real-time setting. To this end, we present the FLIQC (Fast LInear Quadratic Complementarity based) motion planner. Our planner employs a novel motion model that captures the entire rigid robot as well as the obstacle geometry and ensures non-penetration between the surfaces due to the imposed constraint. We perform thorough comparative studies with the state-of-the-art, which demonstrate improved performance. Extensive simulation and hardware experiments validate our claim of generating continuous and reactive motion plans at 1 kHz for modern collaborative robots with constant minimal parameters.

We present the results of our multi-wavelength (X-ray to radio) follow-up campaign of the Einstein Probe transient EP240408a. The initial 10 s trigger displayed bright soft X-ray (0.5-4 keV) radiation with peak luminosity $L_\textrm{X} \gtrsim 10^{49}$ ($10^{50}$) erg s$^{-1}$ for an assumed redshift z>0.5 (2.0). The Neil Gehrels Swift Observatory and Neutron star Interior Composition ExploreR discovered a fading X-ray counterpart lasting for $\sim$5 d (observer frame), which showed a long-lived (~4 d) plateau-like emission ($t^{-0.5}$) before a sharp powerlaw decline ($t^{-7}$). The plateau emission was in excess of $L_\textrm{X} \gtrsim 10^{46}$ ($10^{47}$) erg s$^{-1}$ at z>0.5 (2.0). Deep optical and radio observations resulted in non-detections of the transient. Our observations with Gemini South revealed a faint potential host galaxy ($r \approx 24$ AB mag) near the edge of the X-ray localization. The faint candidate host, and lack of other potential hosts ($r \gtrsim 26$ AB mag; $J \gtrsim 23$ AB mag), implies a higher redshift origin (z>0.5), which produces extreme X-ray properties that are inconsistent with many known extragalactic transient classes. In particular, the lack of a bright gamma-ray counterpart, with the isotropic-equivalent energy ($10 - 10,000$ keV) constrained by GECam and Konus-Wind to $E_{\gamma,\textrm{iso}} \lesssim 4\times10^{51}$ ($6\times10^{52}$) erg at z>0.5 (2.0), conflicts with known gamma-ray bursts (GRBs) of similar X-ray luminosities. We therefore favor a jetted tidal disruption event (TDE) as the progenitor of EP240408a at z>1.0, possibly caused by the disruption of a white dwarf by an intermediate mass black hole. The alternative is that EP240408a may represent a new, previously unknown class of transient., Comment: Submitted; 33 pages; 9 figures

Covariance matrix estimation is an important problem in multivariate data analysis, both from theoretical as well as applied points of view. Many simple and popular covariance matrix estimators are known to be severely affected by model misspecification and the presence of outliers in the data; on the other hand robust estimators with reasonably high efficiency are often computationally challenging for modern large and complex datasets. In this work, we propose a new, simple, robust and highly efficient method for estimation of the location vector and the scatter matrix for elliptically symmetric distributions. The proposed estimation procedure is designed in the spirit of the minimum density power divergence (DPD) estimation approach with appropriate modifications which makes our proposal (sequential minimum DPD estimation) computationally very economical and scalable to large as well as higher dimensional datasets. Consistency and asymptotic normality of the proposed sequential estimators of the multivariate location and scatter are established along with asymptotic positive definiteness of the estimated scatter matrix. Robustness of our estimators are studied by means of influence functions. All theoretical results are illustrated further under multivariate normality. A large-scale simulation study is presented to assess finite sample performances and scalability of our method in comparison to the usual maximum likelihood estimator (MLE), the ordinary minimum DPD estimator (MDPDE) and other popular non-parametric methods. The applicability of our method is further illustrated with a real dataset on credit card transactions.

Federated Learning (FL) has emerged as a groundbreaking paradigm in collaborative machine learning, emphasizing decentralized model training to address data privacy concerns. While significant progress has been made in optimizing federated learning, the exploration of generalization error, particularly in heterogeneous settings, has been limited, focusing mainly on parametric cases. This paper investigates the generalization properties of deep federated regression within a two-stage sampling model. Our findings highlight that the intrinsic dimension, defined by the entropic dimension, is crucial for determining convergence rates when appropriate network sizes are used. Specifically, if the true relationship between response and explanatory variables is charecterized by a $\beta$-H\"older function and there are $n$ independent and identically distributed (i.i.d.) samples from $m$ participating clients, the error rate for participating clients scales at most as $\tilde{O}\left((mn)^{-2\beta/(2\beta + \bar{d}_{2\beta}(\lambda))}\right)$, and for non-participating clients, it scales as $\tilde{O}\left(\Delta \cdot m^{-2\beta/(2\beta + \bar{d}_{2\beta}(\lambda))} + (mn)^{-2\beta/(2\beta + \bar{d}_{2\beta}(\lambda))}\right)$. Here, $\bar{d}_{2\beta}(\lambda)$ represents the $2\beta$-entropic dimension of $\lambda$, the marginal distribution of the explanatory variables, and $\Delta$ characterizes the dependence between the sampling stages. Our results explicitly account for the "closeness" of clients, demonstrating that the convergence rates of deep federated learners depend on intrinsic rather than nominal high-dimensionality.

We provide the first ab-initio calculation of the Euclidean long-distance window of the isospin symmetric light-quark connected contribution to the hadronic vacuum polarization for the muon $g-2$ and find $a_\mu^{\rm LD,iso,conn,ud} = 411.4(4.3)(2.4) \times 10^{-10}$. We also provide the currently most precise calculation of the total isospin symmetric light-quark connected contribution, $a_\mu^{\rm iso,conn,ud} = 666.2(4.3)(2.5) \times 10^{-10}$, which is more than 4$\sigma$ larger compared to the data-driven estimates of Boito et al. 2022 and 1.7$\sigma$ larger compared to the lattice QCD result of BMW20., Comment: 12 pages, 9 figures

We present a comprehensive investigation exploring the theoretical framework of Einstein-Aether gravity theory when combined with two novel cosmological paradigms: the Barrow Agegraphic Dark Energy (BADE) and its newer variant, the New Barrow Agegraphic Dark Energy (NBADE). Our study focuses on deriving the functional relationships within Einstein-Aether gravity as they emerge from these dark energy formulations. The parameter space of our theoretical models is rigorously constrained through statistical analysis employing the Markov Chain Monte Carlo (MCMC) methodology, utilizing multiple observational datasets, incorporating measurements from cosmic chronometers (CC), Baryon Acoustic Oscillations (BAO), and the combined Pantheon+SH0ES compilation. Based on our optimized parameter sets, we conduct an extensive analysis of fundamental cosmological indicators, including cosmographic parameter evolution, dark energy equation of state parameter ($\omega_{DE}$), evolution of the density parameter $\Omega(z)$, dynamical characteristics in the $\omega'_{DE}-\omega_{DE}$ space, behavior of statefinder diagnostic pairs $(r,s^*)$ and $(r,q)$, and Om(z) diagnostic trajectories. Our analysis demonstrates that the current cosmic expansion exhibits accelerated behavior, with the dark energy component manifesting quintessence-like properties in the present epoch while trending toward phantom behavior in future evolution. We additionally evaluate the viability of both BADE and NBADE frameworks through an examination of the squared sound speed ($v_s^2$) stability criterion. The cumulative evidence suggests that these models effectively characterize contemporary cosmic evolution while offering novel perspectives on dark energy phenomenology., Comment: 32 pages, 32 figures. All comments are welcome

Context: Transmission spectroscopy is a powerful tool for understanding exoplanet atmospheres. At optical wavelengths, it makes it possible to infer the composition and the presence of aerosols in the atmosphere. However, unocculted stellar activity can result in contamination of atmospheric transmission spectra by introducing spurious slopes and molecular signals. Aims: We aim to characterise the atmosphere of the transiting exoplanet WASP-69b, a hot Jupiter orbiting an active K star, and characterise the host star's activity levels. Methods: We obtained three nights of spectrophotometric data with the FORS2 instrument on the VLT, covering a wavelength range of 340-1100 nm. We performed retrievals on the full spectrum with combined stellar activity and planet atmosphere models. Results: We directly detect a facula in the form of a hot spot crossing event in one of the transits and indirectly detect unocculted faculae through an apparently decreasing radius towards the blue end of the transmission spectrum. We determine a facula temperature of $\Delta T=+644^{+427}_{-263}$ K for the former and a stellar coverage fraction of around 30% with a temperature of $\Delta T=+231\pm72$ K for the latter. The planetary atmosphere is best fit with a high-altitude cloud deck at 1.4 mbar that mutes atomic and molecular features. We find indications of water and ammonia with $log(H_2O)=-2.01^{+0.54}_{-0.86}$ and $log(NH_3)=-3.4^{+0.96}_{-5.20} respectively and place 3$\sigma$ upper limits on TiO ($10^{-7.65}$) and K ($10^{-7}$). Conclusions. The simultaneous multi-wavelength observations allow us to break the size-contrast degeneracy for facula-crossings, meaning we can obtain temperatures for both the directly and indirectly detected faculae, which are consistent with each other., Comment: Accepted in A\&A, 18 pages, 10 figures

We study the cogenesis of baryon and dark matter (DM) in an extended reheating period after the end of slow-roll inflation. Within the regime of perturbative reheating, we consider different monomial potential of the inflaton field during reheating era. The inflaton condensate reheats the Universe by decaying into the Standard Model (SM) bath either via fermionic or bosonic decay modes. Assuming the leptogenesis route to baryogenesis in a canonical seesaw framework, we consider the bath to produce such RHNs during the period of reheating when the maximum temperature $T_{\rm max}$ of the SM bath is well above the reheating temperature $T_{\rm rh}$. The DM, assumed to be a SM gauge singlet field, also gets produced from the bath during the reheating period via UV freeze-in. In addition to obtaining different parameter space for such non-thermal leptogenesis and DM for both bosonic and fermionic reheating modes and the type of monomial potential, we discuss the possibility of probing such scenarios via spectral shape of primordial gravitational waves., Comment: 25 pages, 2 tables and 6 figures

Computational fluid dynamics (CFD) solvers employing two-equation eddy viscosity models are the industry standard for simulating turbulent flows using the Reynolds-averaged Navier-Stokes (RANS) formulation. While these methods are computationally less expensive than direct numerical simulations, they can still incur significant computational costs to achieve the desired accuracy. In this context, physics-informed neural networks (PINNs) offer a promising approach for developing parametric surrogate models that leverage both existing, but limited CFD solutions and the governing differential equations to predict simulation outcomes in a computationally efficient, differentiable, and near real-time manner. In this work, we build upon the previously proposed RANS-PINN framework, which only focused on predicting flow over a cylinder. To investigate the efficacy of RANS-PINN as a viable approach to building parametric surrogate models, we investigate its accuracy in predicting relevant turbulent flow variables for both internal and external flows. To ensure training convergence with a more complex loss function, we adopt a novel sampling approach that exploits the domain geometry to ensure a proper balance among the contributions from various regions within the solution domain. The effectiveness of this framework is then demonstrated for two scenarios that represent a broad class of internal and external flow problems., Comment: To be presented at the Data-driven and Differentiable Simulations, Surrogates, and Solvers (D3S3) Workshop at NeurIPS'2024

Despite the advances in probabilistic model checking, the scalability of the verification methods remains limited. In particular, the state space often becomes extremely large when instantiating parameterized Markov decision processes (MDPs) even with moderate values. Synthesizing policies for such \emph{huge} MDPs is beyond the reach of available tools. We propose a learning-based approach to obtain a reasonable policy for such huge MDPs. The idea is to generalize optimal policies obtained by model-checking small instances to larger ones using decision-tree learning. Consequently, our method bypasses the need for explicit state-space exploration of large models, providing a practical solution to the state-space explosion problem. We demonstrate the efficacy of our approach by performing extensive experimentation on the relevant models from the quantitative verification benchmark set. The experimental results indicate that our policies perform well, even when the size of the model is orders of magnitude beyond the reach of state-of-the-art analysis tools., Comment: Preprint. Under review

In this paper, we study the problem of methodically obtaining a sufficient set of kinesthetic demonstrations, one at a time, such that a robot can be confident of its ability to perform a complex manipulation task in a given region of its workspace. Although Learning from Demonstrations has been an active area of research, the problems of checking whether a set of demonstrations is sufficient, and systematically seeking additional demonstrations have remained open. We present a novel approach to address these open problems using (i) a screw geometric representation to generate manipulation plans from demonstrations, which makes the sufficiency of a set of demonstrations measurable; (ii) a sampling strategy based on PAC-learning from multi-armed bandit optimization to evaluate the robot's ability to generate manipulation plans in a subregion of its task space; and (iii) a heuristic to seek additional demonstration from areas of weakness. Thus, we present an approach for the robot to incrementally and actively ask for new demonstration examples until the robot can assess with high confidence that it can perform the task successfully. We present experimental results on two example manipulation tasks, namely, pouring and scooping, to illustrate our approach. A short video on the method: https://youtu.be/R-qICICdEos, Comment: 8 pages, 6 figures, under review in IEEE Robotics and Automation Letters

Field-induced assembly of colloidal particles into structures of desired configurations is extremely relevant from the viewpoint of producing field-assembled micro-swimmers and reconfigurable smart materials. However, the behaviour of colloidal particles under the influence of alternating current (AC) electric fields remains a topic of ongoing investigation due to the complex effects of various control parameters. Here we examine the role of several factors including particle size, zeta potential, voltage and frequency of the applied field in the formation of different structural configurations ranging from crystals to glasses, and observe interesting and unexpected behaviours in the structure formation. Additionally, we investigate the dynamics of structure formation; the nature of diffusion and active motion in these out-of-equilibrium systems, and show how that leads to the formation of close-packed or open structures. Lastly, we investigate the frequency-driven disorder-order-disorder phase transition in colloidal crystals, which is a starting point for building reconfigurable systems. Our findings contribute to a deeper understanding of the significant roles of various factors in electric field-induced assembly of colloidal particles, as well as pave the way for potential applications in micro-robotics and next-generation materials., Comment: 10 pages, 7 figures

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

We study transmission stabilization of optical solitons against emission of radiation in nonlinear optical waveguides in the presence of weak linear gain-loss, cubic loss, and the collisional Raman frequency shift. We first show how the collisional Raman frequency shift perturbation arises in three different physical setups. We then show by numerical simulations with a perturbed nonlinear Schr\"odinger (NLS) model that transmission in waveguides with weak frequency-independent linear gain is unstable. The radiative instability is stronger than the radiative instabilities that were observed in earlier studies for soliton transmission in the presence of weak linear gain, cubic loss, and various frequency-shifting physical mechanisms. In particular, the Fourier spectrum of the radiation is significantly more spiky and broadband than the radiation's Fourier spectra in earlier studies. Moreover, we demonstrate by numerical simulations with another perturbed NLS model that transmission in waveguides with weak frequency-dependent linear gain-loss, cubic loss, and the collisional Raman frequency shift is stable. Despite the stronger radiative instability in the corresponding waveguide setup with weak linear gain, stabilization occurs via the same generic mechanism that was suggested in earlier studies. More precisely, the collisional Raman frequency shift experienced by the soliton leads to the separation of the soliton's and the radiation's Fourier spectra, while the frequency-dependent linear gain-loss leads to efficient suppression of radiation emission. Thus, our study demonstrates the robustness of the proposed generic soliton stabilization method, which is based on the interplay between perturbation-induced shifting of the soliton's frequency and frequency-dependent linear gain-loss., Comment: The paper demonstrates stabilization of optical solitons against radiation emission in the presence of weak linear gain-loss, cubic loss, and the collisional Raman frequency shift. It significantly extends the stabilization method that was proposed in arXiv:1804.03226 by showing that the proposed method works even when the underlying radiative instability due to linear gain is strong

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

We construct the first examples of genuine ergodic discrete measured groupoids that are not isomorphic to any equivalence relation or transformation groupoid. We use a construction due to B.H. Neumann of an uncountable family of pairwise non-isomorphic 2-generated groups for our result., Comment: 20 pages, comments welcome

We propose a novel dual physics-informed neural network for topology optimization (DPNN-TO), which merges physics-informed neural networks (PINNs) with the traditional SIMP-based topology optimization (TO) algorithm. This approach leverages two interlinked neural networks-a displacement network and an implicit density network-connected through an energy-minimization-based loss function derived from the variational principles of the governing equations. By embedding deep learning within the physical constraints of the problem, DPNN-TO eliminates the need for large-scale data and analytical sensitivity analysis, addressing key limitations of traditional methods. The framework efficiently minimizes compliance through energy-based objectives while enforcing volume fraction constraints, producing high-resolution designs for both 2D and 3D optimization problems. Extensive numerical validation demonstrates that DPNN-TO outperforms conventional methods, solving complex structural optimization scenarios with greater flexibility and computational efficiency, while addressing challenges such as multiple load cases and three-dimensional problems without compromising accuracy.

We revisit the problem of computing the determinant of Klein-Gordon operator $\Delta = -\nabla^2 + M^2$ on Euclideanized $AdS_3$ with the Euclideanized time coordinate compactified with period $\beta$, $H_3/Z$, by explicitly computing its eigenvalues and computing their product. Upon assuming that eigenfunctions are normalizable on $H_3/Z$, we found that there are no such eigenfunctions. Upon closer examination, we discover that the intuition that $H_3/Z$ is like a box with normalizable eigenfunctions was false, and that there is, instead, a set of eigenfunctions which forms a continuum. Somewhat to our surprise, we find that there is a different operator $\tilde \Delta = r^2 \Delta$, which has the property that (1) the determinant of $\Delta$ and the determinant of $r^2 \Delta$ have the same dependence on $\beta$, and that (2) the Green's function of $\Delta$ can be spectrally decomposed into eigenfunctions of $\tilde \Delta$. We identify the $\tilde \Delta$ operator as the ``weighted Laplacian'' in the context of warped compactifications, and comment on possible applications., Comment: 24 pages

Intercalation is a robust method for tuning the physical properties of a vast number of van der Waals (vdW) materials. However, the prospects of using intercalation to modify magnetism in vdWs systems and the associated mechanisms have not been investigated adequately. In this work, we modulate magnetic order in an XY antiferromagnet NiPS3 single crystals by introducing pyridine molecules into the vdWs gap under different thermal conditions. X-ray diffraction measurements indicated pronounced changes in the lattice parameter beta, while magnetization measurements at in-plane and out-of-plane configurations exposed reversal trends in the crystals Neel temperatures through intercalation-de-intercalation processes. The changes in magnetic ordering were also supported by three-dimensional thermal diffusivity experiments. The preferred orientation of the pyridine dipoles within vdW gaps was deciphered via polarized Raman spectroscopy. The results highlight the relation between the preferential alignment of the intercalants, thermal transport, and crystallographic disorder along with the modulation of anisotropy in the magnetic order. The theoretical concept of double-exchange interaction in NiPS3 was employed to explain the intercalation-induced magnetic ordering. The study uncovers the merit of intercalation as a foundation for spin switches and spin transistors in advanced quantum devices.

Gathering is a fundamental coordination problem in swarm robotics, where the objective is to bring robots together at a point not known to them at the beginning. While most research focuses on continuous domains, some studies also examine the discrete domain. This paper addresses the optimal gathering problem on an infinite grid, aiming to improve the energy efficiency by minimizing the maximum distance any robot must travel. The robots are autonomous, anonymous, homogeneous, identical, and oblivious. We identify all initial configurations where the optimal gathering problem is unsolvable. For the remaining configurations, we introduce a deterministic distributed algorithm that effectively gathers $n$ robots ($n\ge 9$). The algorithm ensures that the robots gathers at one of the designated min-max nodes in the grid. Additionally, we provide a comprehensive characterization of the subgraph formed by the min-max nodes in this infinite grid model.

Classifying nodes in a graph is a common problem. The ideal classifier must adapt to any imbalances in the class distribution. It must also use information in the clustering structure of real-world graphs. Existing Graph Neural Networks (GNNs) have not addressed both problems together. We propose the Enhanced Cluster-aware Graph Network (ECGN), a novel method that addresses these issues by integrating cluster-specific training with synthetic node generation. Unlike traditional GNNs that apply the same node update process for all nodes, ECGN learns different aggregations for different clusters. We also use the clusters to generate new minority-class nodes in a way that helps clarify the inter-class decision boundary. By combining cluster-aware embeddings with a global integration step, ECGN enhances the quality of the resulting node embeddings. Our method works with any underlying GNN and any cluster generation technique. Experimental results show that ECGN consistently outperforms its closest competitors by up to 11% on some widely studied benchmark datasets., Comment: 17 pages, 3 figures

Ionospheric irregularities and associated scintillations under geomagnetically active/quiet conditions have detrimental effects on the reliability and performance of space- and ground-based navigation satellite systems, especially over the low-latitude region. The current work investigates the low-latitude ionospheric irregularities using the phase screen theory and the corresponding temporal Power Spectral Density (PSD) analysis to present an estimate of the outer irregularity scale sizes over these locations. The study uses simultaneous L5 signal C/N$_o$ observations of NavIC (a set of GEO and GSO navigation satellite systems) near the northern crest of EIA (Indore: 22.52$^\circ$N, 75.92$^\circ$E, dip: 32.23$^\circ$N) and in between the crest and the dip equator (Hyderabad: 17.42$^\circ$N, 78.55$^\circ$E, dip: 21.69$^\circ$N). The study period (2017-2018) covers disturbed and quiet-time conditions in the declining phase of the solar cycle 24. The PSD analysis brings forward the presence of irregularities, of the order of a few hundred meters during weak-to-moderate and quiet-time conditions and up to a few km during the strong event, over both locations. The ROTI values validate the presence of such structures in the Indian region. Furthermore, only for the strong event, a time delay of scintillation occurrence over Indore, with values of 36 minutes and 50 minutes for NavIC satellites (PRNs) 5 and 6, respectively, from scintillation occurrence at Hyderabad is observed, suggesting a poleward evolution of irregularity structures. Further observations show a westward propagation of these structures on this day. This study brings forward the advantage of utilizing continuous data from the GEO and GSO satellite systems in understanding the evolution and propagation of the ionospheric irregularities over the low-latitude region., Comment: Accepted for publication in the Journal of Atmospheric and Solar-Terrestrial Physics (JASTP)

Arcing faults in low voltage (LV) distribution systems associated with arc-flash risk and potentially significant equipment damage are notoriously difficult to detect under some conditions. Especially so when attempting to detect using sensing at the line, high voltage side of a substation transformer. This paper presents an analytics-based physics-aware approach to detect high-impedance, low-current arcing faults from the primary side of the substation transformer at current thresholds, below normal operating events, along with transformer inrush currents. The proposed methodology leverages the Hankel Alternative View Of Koopman Operator approach to differentiate arcing faults from standard operations, while the Series2Graph method is employed to identify the time of fault occurrence and duration. Unlike prior studies that detect such faults at the device or secondary transformer side, this work demonstrates successful fault detection at the primary side of the distribution substation transformer for faults occurring on the secondary side. The approach addresses the practical challenges of differentiating primary side expected and acceptable transients from similar magnitude LV arcing fault currents that may occur on the secondary side. The results demonstrate the efficacy of the proposed method in accurately identifying fault occurrence and duration, minimizing the risk of false positives during similar characteristic events, thus improving the reliability and operational efficiency of power distribution systems. This approach can benefit both traditional and smart power grids that employ similar transformer configurations.

Microscopic examination of slides prepared from tissue samples is the primary tool for detecting and classifying cancerous lesions, a process that is time-consuming and requires the expertise of experienced pathologists. Recent advances in deep learning methods hold significant potential to enhance medical diagnostics and treatment planning by improving accuracy, reproducibility, and speed, thereby reducing clinicians' workloads and turnaround times. However, the necessity for vast amounts of labeled data to train these models remains a major obstacle to the development of effective clinical decision support systems. In this paper, we propose the integration of topological deep learning methods to enhance the accuracy and robustness of existing histopathological image analysis models. Topological data analysis (TDA) offers a unique approach by extracting essential information through the evaluation of topological patterns across different color channels. While deep learning methods capture local information from images, TDA features provide complementary global features. Our experiments on publicly available histopathological datasets demonstrate that the inclusion of topological features significantly improves the differentiation of tumor types in ovarian and breast cancers.

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

The eikonal correspondence between the quasi-normal modes (QNMs) of asymptotically flat static spherically symmetric black holes and the properties of unstable null circular geodesics is studied in the case of higher dimensional Lovelock black holes (BHs). It is known that such correspondence does not generically hold for gravitational QNMs associated with BHs in Lovelock theories. In the present work, we revisit this correspondence and establish the relationship between the eikonal QNMs and the causal properties of the gravitational field equations in Lovelock theories of gravity., Comment: 13 pages, 2 figures, 4 table

Machine learning (ML) models often require large amounts of data to perform well. When the available data is limited, model trainers may need to acquire more data from external sources. Often, useful data is held by private entities who are hesitant to share their data due to propriety and privacy concerns. This makes it challenging and expensive for model trainers to acquire the data they need to improve model performance. To address this challenge, we propose Mycroft, a data-efficient method that enables model trainers to evaluate the relative utility of different data sources while working with a constrained data-sharing budget. By leveraging feature space distances and gradient matching, Mycroft identifies small but informative data subsets from each owner, allowing model trainers to maximize performance with minimal data exposure. Experimental results across four tasks in two domains show that Mycroft converges rapidly to the performance of the full-information baseline, where all data is shared. Moreover, Mycroft is robust to noise and can effectively rank data owners by utility. Mycroft can pave the way for democratized training of high performance ML models., Comment: 10 pages, 3 figures, 3 tables

Consider the scenario where a human cleans a table and a robot observing the scene is instructed with the task "Remove the cloth using which I wiped the table". Instruction following with temporal reasoning requires the robot to identify the relevant past object interaction, ground the object of interest in the present scene, and execute the task according to the human's instruction. Directly grounding utterances referencing past interactions to grounded objects is challenging due to the multi-hop nature of references to past interactions and large space of object groundings in a video stream observing the robot's workspace. Our key insight is to factor the temporal reasoning task as (i) estimating the video interval associated with event reference, (ii) performing spatial reasoning over the interaction frames to infer the intended object (iii) semantically track the object's location till the current scene to enable future robot interactions. Our approach leverages existing large pre-trained models (which possess inherent generalization capabilities) and combines them appropriately for temporal grounding tasks. Evaluation on a video-language corpus acquired with a robot manipulator displaying rich temporal interactions in spatially-complex scenes displays an average accuracy of 70.10%. The dataset, code, and videos are available at https://reail-iitdelhi.github.io/temporalreasoning.github.io/ .

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

A typical manipulation task consists of a manipulator equipped with a gripper to grasp and move an object with constraints on the motion of the hand-held object, which may be due to the nature of the task itself or from object-environment contacts. In this paper, we study the problem of computing joint torques and grasping forces for time-optimal motion of an object, while ensuring that the grasp is not lost and any constraints on the motion of the object, either due to dynamics, environment contact, or no-slip requirements, are also satisfied. We present a second-order cone program (SOCP) formulation of the time-optimal trajectory planning problem that considers nonlinear friction cone constraints at the hand-object and object-environment contacts. Since SOCPs are convex optimization problems that can be solved optimally in polynomial time using interior point methods, we can solve the trajectory optimization problem efficiently. We present simulation results on three examples, including a non-prehensile manipulation task, which shows the generality and effectiveness of our approach.

This paper explores the thermodynamic properties and stability of two newly introduced gas models, namely the Modified Chaplygin Jacobi gas and the Modified Chaplygin Abel gas. To achieve this, we examine the behavior of relevant physical parameters to gain in depth information about the evolution of the universe. The specific heat formalism is employed to verify the applicability of the third law of thermodynamics. Furthermore, the equation of state for the thermal system is obtained by applying thermodynamic variables. The stability of the gas models is investigated within the framework of classical thermodynamics, focusing on adiabatic processes, specific heat capacities, and isothermal conditions. It is inferred that the proposed fluid configurations exhibit thermodynamic stability and undergo adiabatic expansion for suitable parameter choices. We then perform observational analysis using CC+BAO and Pantheon+SH0ES datasets to impose constraints on our model parameters using the Markov Chain Monte Carlo (MCMC) process., Comment: 37 pages, 30 figures. All comments are welcome

Assume that a target is known to be present at an unknown point among a finite set of locations in the plane. We search for it using a mobile robot that has imperfect sensing capabilities. It takes time for the robot to move between locations and search a location; we have a total time budget within which to conduct the search. We study the problem of computing a search path/strategy for the robot that maximizes the probability of detection of the target. Considering non-uniform travel times between points (e.g., based on the distance between them) is crucial for search and rescue applications; such problems have been investigated to a limited extent due to their inherent complexity. In this paper, we describe fast algorithms with performance guarantees for this search problem and some variants, complement them with complexity results, and perform experiments to observe their performance., Comment: 10 pages, 6 figures, 3 algorithms

Predicting the long-term behavior of chaotic systems remains a formidable challenge due to their extreme sensitivity to initial conditions and the inherent limitations of traditional data-driven modeling approaches. This paper introduces a novel framework that addresses these challenges by leveraging the recently proposed multi-step penalty (MP) optimization technique. Our approach extends the applicability of MP optimization to a wide range of deep learning architectures, including Fourier Neural Operators and UNETs. By introducing penalized local discontinuities in the forecast trajectory, we effectively handle the non-convexity of loss landscapes commonly encountered in training neural networks for chaotic systems. We demonstrate the effectiveness of our method through its application to two challenging use-cases: the prediction of flow velocity evolution in two-dimensional turbulence and ocean dynamics using reanalysis data. Our results highlight the potential of this approach for accurate and stable long-term prediction of chaotic dynamics, paving the way for new advancements in data-driven modeling of complex natural phenomena., Comment: 7 pages, 5 Figures, Submitted to CASML2024

Testing whether a sample survey is a credible representation of the population is an important question to ensure the validity of any downstream research. While this problem, in general, does not have an efficient solution, one might take a task-based approach and aim to understand whether a certain data analysis tool, like linear regression, would yield similar answers both on the population and the sample survey. In this paper, we design an algorithm to test the credibility of a sample survey in terms of linear regression. In other words, we design an algorithm that can certify if a sample survey is good enough to guarantee the correctness of data analysis done using linear regression tools. Nowadays, one is naturally concerned about data privacy in surveys. Thus, we further test the credibility of surveys published in a differentially private manner. Specifically, we focus on Local Differential Privacy (LDP), which is a standard technique to ensure privacy in surveys where the survey participants might not trust the aggregator. We extend our algorithm to work even when the data analysis has been done using surveys with LDP. In the process, we also propose an algorithm that learns with high probability the guarantees a linear regression model on a survey published with LDP. Our algorithm also serves as a mechanism to learn linear regression models from data corrupted with noise coming from any subexponential distribution. We prove that it achieves the optimal estimation error bound for $\ell_1$ linear regression, which might be of broader interest. We prove the theoretical correctness of our algorithms while trying to reduce the sample complexity for both public and private surveys. We also numerically demonstrate the performance of our algorithms on real and synthetic datasets.

In this study, we have examined the evolving wormhole solution within Einstein-massive gravity, considering traceless, barotropic, and anisotropic pressure fluids. We have conducted a comprehensive analysis of the constraints imposed by the constants derived from the wormhole solution. It is found that the wormhole throat, situated between two asymptotic universes, undergoes simultaneous expansion with acceleration. A detailed investigation of the energy conditions for traceless, barotropic, and anisotropic fluids suggests a wide range of possibilities for evolving wormhole configurations with non-exotic matter at the throat. The dependency of this feature on the various parameters arising from the study has also been examined., Comment: 17 pages, 5 figures, 4 tables. Accepted for publication in IJMPA

Large Language Models trained on web-scale text acquire language generation abilities that can solve a wide range of tasks, particularly when task knowledge is refined into the generative prior using in-context examples. However, spurious features learned from noisy data hinder their generalizability. Supervised finetuning can introduce task specificity, but introduce data inefficiency. Prior studies indicate that (i) noisy neural circuitries coexist with generalizable ones within LLMs, and (ii) finetuning typically enhances (or suppresses) existing abilities without introducing newer ones. Building upon these, we propose TaRot, a novel method for task adaptation. TaRot intervenes in the neural circuitries using learnable rotation matrices that are optimized using Bayesian Optimization, on labelled samples in the order of standard few-shot prompting examples. Experiments on multiple classification and generation tasks using LLMs of varying sizes reveal the efficacy of TaRot, improving upon both zero- as well as few-shot performance, with average improvements (across models and tasks) of 23.81% and 11.15%, respectively. The source code is available at https://github.com/joykirat18/TaRot

Reinforcement learning with general utilities has recently gained attention thanks to its ability to unify several problems, including imitation learning, pure exploration, and safe RL. However, prior work for solving this general problem in a unified way has mainly focused on the tabular setting. This is restrictive when considering larger state-action spaces because of the need to estimate occupancy measures during policy optimization. In this work, we address this issue and propose to approximate occupancy measures within a function approximation class using maximum likelihood estimation (MLE). We propose a simple policy gradient algorithm (PG-OMA) where an actor updates the policy parameters to maximize the general utility objective whereas a critic approximates the occupancy measure using MLE. We provide a sample complexity analysis of PG-OMA showing that our occupancy measure estimation error only scales with the dimension of our function approximation class rather than the size of the state action space. Under suitable assumptions, we establish first order stationarity and global optimality performance bounds for the proposed PG-OMA algorithm for nonconcave and concave general utilities respectively. We complement our methodological and theoretical findings with promising empirical results showing the scalability potential of our approach compared to existing tabular count-based approaches., Comment: 26 pages, 5 figures