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We study a monolayer of metal balls under periodic chiral driving in the horizontal plane. Energy dissipation occurs in this system via (i) inelastic collisions and (ii) frictional interaction with the substrate. We show that below a density-dependent critical drive, the system phase separates into a fluid phase coexisting with a solid phase. Unlike ordinary coexistence, however, the system does not obey the lever rule, as the fluid-phase density depends on the overall particle density. Additionally, the pressure is discontinuous across the fluid-solid interface, accompanied by a chiral edge current at the interface.

To understand how the interconnected and interdependent world of the twenty-first century operates and make model-based predictions, joint probability models for networks and interdependent outcomes are needed. We propose a comprehensive regression framework for networks and interdependent outcomes with multiple advantages, including interpretability, scalability, and provable theoretical guarantees. The regression framework can be used for studying relationships among attributes of connected units and captures complex dependencies among connections and attributes, while retaining the virtues of linear regression, logistic regression, and other regression models by being interpretable and widely applicable. On the computational side, we show that the regression framework is amenable to scalable statistical computing based on convex optimization of pseudo-likelihoods using minorization-maximization methods. On the theoretical side, we establish convergence rates for pseudo-likelihood estimators based on a single observation of dependent connections and attributes. We demonstrate the regression framework using simulations and an application to hate speech on the social media platform X in the six months preceding the insurrection at the U.S. Capitol on January 6, 2021.

Neal (1996) proved that infinitely wide shallow Bayesian neural networks (BNN) converge to Gaussian processes (GP), when the network weights have bounded prior variance. Cho & Saul (2009) provided a useful recursive formula for deep kernel processes for relating the covariance kernel of each layer to the layer immediately below. Moreover, they worked out the form of the layer-wise covariance kernel in an explicit manner for several common activation functions. Recent works, including Aitchison et al. (2021), have highlighted that the covariance kernels obtained in this manner are deterministic and hence, precludes any possibility of representation learning, which amounts to learning a non-degenerate posterior of a random kernel given the data. To address this, they propose adding artificial noise to the kernel to retain stochasticity, and develop deep kernel inverse Wishart processes. Nonetheless, this artificial noise injection could be critiqued in that it would not naturally emerge in a classic BNN architecture under an infinite-width limit. To address this, we show that a Bayesian deep neural network, where each layer width approaches infinity, and all network weights are elliptically distributed with infinite variance, converges to a process with $\alpha$-stable marginals in each layer that has a conditionally Gaussian representation. These conditional random covariance kernels could be recursively linked in the manner of Cho & Saul (2009), even though marginally the process exhibits stable behavior, and hence covariances are not even necessarily defined. We also provide useful generalizations of the recent results of Lor\'ia & Bhadra (2024) on shallow networks to multi-layer networks, and remedy the computational burden of their approach. The computational and statistical benefits over competing approaches stand out in simulations and in demonstrations on benchmark data sets., Comment: 21 pages, 11 figures

Deciding if there exists a path from one vertex to another in a graph is known as the s-t connectivity or the reachability problem. Reachability can be solved using graph traversal algorithms like Depth First Search(DFS) or Breadth First Search(BFS) in linear time but these algorithms also take linear space. On the other hand, Savitch's algorithm solves the same problem using O(log^2 n) space but takes quasipolynomial time. A path decomposition P of a directed graph G is a collection of simple directed paths such that every edge of G lies on exactly one path in P. A minimal path decomposition of G is a path decomposition of G having the smallest number of paths possible and the number of paths in a minimal path decomposition of G is called the path number of G. We show that if a path decomposition P of a directed graph G consisting of k directed paths is provided, then reachability in G can be decided simultaneously in O(klog n) space and polynomial time. In fact, our result holds even when a walk decomposition is provided (instead of a path decomposition) where the graph is decomposed into k directed walks (instead of paths) and the walks are not necessarily edge-disjoint. We further show that a minimal path decomposition can be computed in logspace for directed acyclic graphs. This leads to the conclusion that reachability in directed acyclic graphs having bounded path number is logspace computable.

Scent marks play a crucial role in both territorial and sexual communication in many species. We investigated how free-ranging dogs respond to scent marks from individuals of different identities in terms of sex and group, across varying strategic locations within their territory. Both male and female dogs showed heightened interest in scent marks compared to control, exhibiting stronger territorial responses,. with males being more territorial than females. Overmarking behaviour was predominantly observed in males, particularly in response to male scent marks and those from neighbouring groups. Behavioural cluster analysis revealed distinct responses to different scent marks, with neighbouring group male scents eliciting the most distinct reactions. Our findings highlight the multifaceted role of scent marks in free-ranging dog communication, mediating both territorial defence and intrasexual competition. The differential responses based on the identity and gender of the scent-marker emphasize the complexity of olfactory signalling in this species. This study contributes to understanding the social behaviour of dogs in their natural habitat, and opens up possibilities for future explorations in the role of olfactory cues in the social dynamics of the species.

In this paper, we present geometric scaling models for axial flux motors (AFMs) to be used for in-wheel powertrain design optimization purposes. We first present a vehicle and powertrain model, with emphasis on the electric motor model. We construct the latter by formulating the analytical scaling laws for AFMs, based on the scaling concept of RFMs from the literature, specifically deriving the model of the main loss component in electric motors: the copper losses. We further present separate scaling models of motor parameters, losses and thermal models, as well as the torque limits and cost, as a function of the design variables. Second, we validate these scaling laws with several experiments leveraging high-fidelity finite-element simulations. Finally, we define an optimization problem that minimizes the energy consumption over a drive cycle, optimizing the motor size and transmission ratio for a wide range of electric vehicle powertrain topologies. In our study, we observe that the all-wheel drive topology equipped with in-wheel AFMs is the most efficient, but also generates the highest material cost., Comment: 5 pages, 6 figures, 4 tables, 2024 IEEE Vehicle Power and Propulsion Conference, Washington DC, USA

We present ALMA observations of SiO, SiS, H$_2$O , NaCl, and SO line emission at ~30 to 50 mas resolution. These images map the molecular outflow and disk of Orion Source I (SrcI) on ~12 to 20 AU scales. Our observations show that the flow of material around SrcI creates a turbulent boundary layer in the outflow from SrcI which may dissipate angular momentum in the rotating molecular outflow into the surrounding medium. Additionally, the data suggests that the proper motion of SrcI may have a significant effect on the structure and evolution of SrcI and its molecular outflow. As the motion of SrcI funnels material between the disk and the outflow, some material may be entrained into the outflow and accrete onto the disk, creating shocks which excite the NaCl close to the disk surface., Comment: Accepted in The Astrophysical Journal

Palatability of food is driven by multiple factors like taste, smell, texture, freshness, etc. and can be very variable across species. There are classic examples of local adaptations leading to speciation, driven by food availability. Urbanization across the world is causing rapid decline of biodiversity, while also driving local adaptations in some species. Free-ranging dogs are an interesting example of adaptation to a human-dominated environment across varied habitats. They have co-existed with humans for centuries and are a perfect model system for studying local adaptations. We attempted to understand a specific aspect of their scavenging behaviour in India: citrus aversion. Pet dogs are known to avoid citrus fruits and food contaminated by them. In India, lemons are used widely in the cuisine, and discarded in the garbage. Hence, free-ranging dogs, that typically are scavengers of human leftovers, are likely to encounter lemons and lemon-contaminated food on a regular basis. We carried out a population level experiment to test response of free-ranging dogs to chicken contaminated with various parts of lemon. The dogs avoided chicken contaminated with lemon juice the most. Further, when provided with chicken dipped in three different concentrations of lemon juice, the lowest concentration was most preferred. A survey confirmed that the local people use lemon in their diet extensively and also discard these with the leftovers. People avoided giving citrus contaminated food to their pets but did not follow the same caution for free-ranging dogs. This study revealed that free-ranging dogs in West Bengal, India, are well adapted to scavenging among citrus-contaminated garbage and have their own strategies to avoid the contamination as far as possible, while maximizing their preferred food intake., Comment: Includes supplementary information

The human face is a multi-signal system continuously transmitting information of identity and emotion. In shared human-animal environments, the face becomes a reliable tool of heterospecific recognition. Because humans display mixed behaviour and pose differential risk to animals, adaptable decision-making based on recognition and classification of humans confer a fitness benefit. The human-dog dyad is an ideal model to study heterospecific recognition due to their shared history, niche overlap, and cognitive co-evolution. Multiple studies on pet dogs have examined their human facial information processing. However, no study has examined these perceptual abilities in free-living populations in their natural habitat where the human-dog relationship is more complex and impacts survival. Comprehensive behavioural analysis of 416 free-ranging dogs in an approach-based task with differential facial occlusion of a human, demonstrated that these dogs recognize and discriminate between familiar and unfamiliar people. Negative behaviours like aggression and avoidance were unlikely to be displayed. Inner facial components like eyes, nose and mouth were more important than outer components like hair in human recognition. Unlike in pet dogs, the occlusion of even a single inner component of the face prevented recognition by facial cue alone. Personality and habitat conditions influenced the behavioural strategy adopted by the dogs too. Considering the ambiguous nature of human interactions, recognition and response in free-ranging dogs relied on dual assessment of identity and intent of a human based, in part, on their ontogeny. Such a cue-processing system highlights the selection pressures inherent in the unpredictable environment of free-living populations., Comment: 6 figures, 2 tables, supplementary material included

Seasonal fairs, bustling with human activity, provide a unique environment for exploring the intricate interplay between humans and free-ranging dogs. This study investigated these interactions during seasonal fairs in Nadia and Bardhaman districts, West Bengal, India. Across 13 fairground sites, we explore how human footfall and resource availability impact dog behavior, with a particular emphasis on cognitive mechanisms. Data collection occurred from December to March, comprising three sessions - initial, middle, and end, each on three randomly selected days. Employing spot census and scan sampling methods, observers documented GPS locations, sex, and behaviors of free-ranging dogs. Videos captured interactions within the fair environment, revealing cognitive processes. Our analysis revealed a notable increase in human activity during the middle phase, coinciding with a rise in dog abundance. Dogs predominantly foraged, exhibited gait, and remained vigilant, their numbers positively associated with resource availability. Proximity to the fairground significantly shaped dog behavior, indicating cognitive processes in decision-making. Dogs closer to the fair demonstrated consistent behavior, likely due to immediate resource access, implying sophisticated cognitive mapping and resource utilization. Conversely, dogs from farther distances exhibited lower consistency and heightened aggression, intensifying foraging, gait, and vigilance activities, suggesting cognitive adaptations to resource scarcity and competition. These findings underscore the intricate relationship between human activity, resource availability, and the behavior and cognition of free-ranging dogs during seasonal fairs. They offer insights into the ecological dynamics of free-ranging dogs in human-influenced landscapes, emphasizing the necessity for comprehensive management strategies in urban and peri-urban environments., Comment: 3 figures, supplementary material included

Individual human recognition is important for species that live in close proximity to humans. Numerous studies on domesticated species and urban-adapted birds have highlighted this ability. One such species which is heavily reliant on humans is the free-ranging dog. Very little knowledge exists on the amount of time taken by free-ranging dogs to learn and remember individual humans. Due to their territorial nature, they have a high probability of encountering the same people multiple times on the streets. Being able to distinguish individual humans might be helpful in making decisions regarding people from whom to beg for food or social reward. We investigated if free-ranging dogs are capable of identifying the person rewarding them and the amount of time required for them to learn it. We conducted field trials on randomly selected adult free-ranging dogs in West Bengal, India. On Day 1, a choice test was conducted. The experimenter chosen did not provide reward while the other experimenter provided a piece of boiled chicken followed by petting. The person giving reward on Day 1 served as the correct choice on four subsequent days of training. Day 6 was the test day when none of the experimenters had a reward. We analyzed the choice made by the dogs, the time taken to approach during the choice tests, and the socialization index, which was calculated based on the intensity of affiliative behaviour shown towards the experimenters. The dogs made correct choices at a significantly higher rate on the fifth and sixth days, as compared to Day 2, suggesting learning. This is the first study aiming to understand the time taken for individual human recognition in free-ranging dogs and can serve as the scaffold for future studies to understand the dog-human relationship in open environments, like urban ecosystems.

We investigate the phenomena of crater formation and gas release caused by projectile impact on underwater beds, which occurs in many natural, geophysical, and industrial applications. The bed in our experiment is constructed of hydrophobic particles, which trap a substantial amount of air in its pores. In contrast to dry beds, the air-water interface in a submerged bed generates a granular skin that provides rigidity to the medium by producing skin over the bulk. The projectile's energy is used to reorganise the grains, which causes the skin to crack, allowing the trapped air to escape. The morphology of the craters as a function of impact energy in submerged beds exhibits different scaling laws than what is known for dry beds. This phenomenon is attributed to the contact line motion on the hydrophobic fractal-like surface of submerged grains. The volume of the gas released is a function of multiple factors, chiefly the velocity of the projectile, depth of the bed and depth of the water column., Comment: 15 pages, 10 figures

The Super-Kamiokande and T2K collaborations present a joint measurement of neutrino oscillation parameters from their atmospheric and beam neutrino data. It uses a common interaction model for events overlapping in neutrino energy and correlated detector systematic uncertainties between the two datasets, which are found to be compatible. Using 3244.4 days of atmospheric data and a beam exposure of $19.7(16.3) \times 10^{20}$ protons on target in (anti)neutrino mode, the analysis finds a 1.9$\sigma$ exclusion of CP-conservation (defined as $J_{CP}=0$) and a preference for the normal mass ordering., Comment: 12 pages, 4 figures

Probabilistic graphical models that encode an underlying Markov random field are fundamental building blocks of generative modeling to learn latent representations in modern multivariate data sets with complex dependency structures. Among these, the exponential family graphical models are especially popular, given their fairly well-understood statistical properties and computational scalability to high-dimensional data based on pseudo-likelihood methods. These models have been successfully applied in many fields, such as the Ising model in statistical physics and count graphical models in genomics. Another strand of models allows some nodes to be latent, so as to allow the marginal distribution of the observable nodes to depart from exponential family to capture more complex dependence. These approaches form the basis of generative models in artificial intelligence, such as the Boltzmann machines and their restricted versions. A fundamental barrier to likelihood-based (i.e., both maximum likelihood and fully Bayesian) inference in both fully and partially observed cases is the intractability of the likelihood. The usual workaround is via adopting pseudo-likelihood based approaches, following the pioneering work of Besag (1974). The goal of this paper is to demonstrate that full likelihood based analysis of these models is feasible in a computationally efficient manner. The chief innovation lies in using a technique of Geyer (1991) to estimate the intractable normalizing constant, as well as its gradient, for intractable graphical models. Extensive numerical results, supporting theory and comparisons with pseudo-likelihood based approaches demonstrate the applicability of the proposed method.

In nonparameteric Bayesian approaches, Gaussian stochastic processes can serve as priors on real-valued function spaces. Existing literature on the posterior convergence rates under Gaussian process priors shows that it is possible to achieve optimal or near-optimal posterior contraction rates if the smoothness of the Gaussian process matches that of the target function. Among those priors, Gaussian processes with a parametric Mat\'ern covariance function is particularly notable in that its degree of smoothness can be determined by a dedicated smoothness parameter. \citet{ma2022beyond} recently introduced a new family of covariance functions called the Confluent Hypergeometric (CH) class that simultaneously possess two parameters: one controls the tail index of the polynomially decaying covariance function, and the other parameter controls the degree of mean-squared smoothness analogous to the Mat\'ern class. In this paper, we show that with proper choice of rescaling parameters in the Mat\'ern and CH covariance functions, it is possible to obtain the minimax optimal posterior contraction rate for $\eta$-regular functions for nonparametric regression model with fixed design. Unlike the previous results for unrescaled cases, the smoothness parameter of the covariance function need not equal $\eta$ for achieving the optimal minimax rate, for either rescaled Mat\'ern or rescaled CH covariances, illustrating a key benefit for rescaling. We also consider a fully Bayesian treatment of the rescaling parameters and show the resulting posterior distributions still contract at the minimax-optimal rate. The resultant hierarchical Bayesian procedure is fully adaptive to the unknown true smoothness., Comment: 38 pages, 7 figures

We show that massive young star clusters may be possible candidates that can accelerate Galactic cosmic rays (CRs) in the range of $10^7\hbox{--}10^9$ GeV (between the `knee' and `ankle'). Various plausible scenarios such as acceleration at the wind termination shock (WTS), supernova shocks inside these young star clusters, etc. have been proposed,since it is difficult to accelerate particles up to the $10^7\hbox{--}10^9$ GeV range in the standard paradigm of CR acceleration in supernova remnants. We consider a model for the production of different nuclei in CRs from massive stellar winds using the observed distribution of young star clusters in the Galactic plane. We present a detailed calculation of CR transport in the Galaxy, taking into account the effect of diffusion, interaction losses during propagation, and particle re-acceleration by old supernova remnants to determine the all-particle CR spectrum. Using the maximum energy estimate from the Hillas criterion, we argue that a young massive star cluster can accelerate protons up to a few tens of PeV. Upon comparison with the observed data, our model requires a CR source spectrum with an exponential cutoff of $5\times 10^7 Z$ GeV ($50\,Z$~PeV) from these clusters together with a cosmic-ray injection fraction of $\sim 5\%$ of the wind kinetic energy. We discuss the possibility of achieving these requirements in star clusters, and the associated uncertainties, in the context of considering star clusters as the natural accelerator of the `second component' of Galactic cosmic rays., Comment: 18 pages, 6 figures, accepted for publication in ApJ

Modeling of multivariate random fields through Gaussian processes calls for the construction of valid cross-covariance functions describing the dependence between any two component processes at different spatial locations. The required validity conditions often present challenges that lead to complicated restrictions on the parameter space. The purpose of this work is to present techniques using multivariate mixtures for establishing validity that are simultaneously simplified and comprehensive. This is accomplished using results on conditionally negative semidefinite matrices and the Schur product theorem. For illustration, we use the recently-introduced Confluent Hypergeometric (CH) class of covariance functions. In addition, we establish the spectral density of the Confluent Hypergeometric covariance and use this to construct valid multivariate models as well as propose new cross-covariances. Our approach leads to valid multivariate cross-covariance models that inherit the desired marginal properties of the Confluent Hypergeometric model and outperform the multivariate Mat\'ern model in out-of-sample prediction under slowly-decaying correlation of the underlying multivariate random field. We also establish properties of the new models, including results on equivalence of Gaussian measures. We demonstrate the new model's use for multivariate oceanography dataset consisting of temperature, salinity and oxygen, as measured by autonomous floats in the Southern Ocean.

Graphical models are powerful tools to investigate complex dependency structures in high-throughput datasets. However, most existing graphical models make one of the two canonical assumptions: (i) a homogeneous graph with a common network for all subjects; or (ii) an assumption of normality especially in the context of Gaussian graphical models. Both assumptions are restrictive and can fail to hold in certain applications such as proteomic networks in cancer. To this end, we propose an approach termed robust Bayesian graphical regression (rBGR) to estimate heterogeneous graphs for non-normally distributed data. rBGR is a flexible framework that accommodates non-normality through random marginal transformations and constructs covariate-dependent graphs to accommodate heterogeneity through graphical regression techniques. We formulate a new characterization of edge dependencies in such models called conditional sign independence with covariates along with an efficient posterior sampling algorithm. In simulation studies, we demonstrate that rBGR outperforms existing graphical regression models for data generated under various levels of non-normality in both edge and covariate selection. We use rBGR to assess proteomic networks across two cancers: lung and ovarian, to systematically investigate the effects of immunogenic heterogeneity within tumors. Our analyses reveal several important protein-protein interactions that are differentially impacted by the immune cell abundance; some corroborate existing biological knowledge whereas others are novel findings.

Free-ranging dogs (FRDs) in human-dominated areas encounter obstacles such as noise, pollution, limited food sources, and anthropogenic disturbance while resting. Since FRDs have survived as a population in India, as in many other parts of the Global South for centuries, they provide a unique opportunity to study adaptation of animals to the human-dominated urban landscape. We documented factors impacting resting behaviour and site preferences in three states of India, for 284 dogs, leading to 6047 observations over 3 years. 7 physical parameters of the resting sites, along with the biological factors like mating and pup-rearing and time of day affected their choice of resting sites. The frequency-rank distribution of the unique combinations in which the parameters were selected followed a Power law distribution, which suggests underlying biological reasons for the observed preferences. Further, 3 of these parameters showed maximum consistency of choice in terms of the sub-parameters selected, explaining 30% of the observations. FRDs prefer to rest close to their resource sites within the territory, at a place that enabled maximum visibility of the surroundings. They chose such sites in the core of the territory for sleeping. At other times, they chose such sites away from the core, and were less restive, thus allowing for immediate response in case of intrusion or threat. They generally avoided anthropogenic disturbance for sleeping, and preferred areas with shade.Incorporating these aspects into urban management plans can promote human-dog cooperation and reduce situations of conflict. We envisage more inclusive urban areas in the future, that can allow for co-existence of the humans and their oldest companions in the commensal relationship that has been maintained for hundreds of generations of dogs in this part of the world., Comment: 2 figures, 2 tables, ESM

We report an updated measurement of the $\nu_{\mu}$-induced, and the first measurement of the $\bar{\nu}_{\mu}$-induced coherent charged pion production cross section on $^{12}C$ nuclei in the T2K experiment. This is measured in a restricted region of the final-state phase space for which $p_{\mu,\pi} > 0.2$ GeV, $\cos(\theta_{\mu}) > 0.8$ and $\cos(\theta_{\pi}) > 0.6$, and at a mean (anti)neutrino energy of 0.85 GeV using the T2K near detector. The measured $\nu_{\mu}$ CC coherent pion production flux-averaged cross section on $^{12}C$ is $(2.98 \pm 0.37 (stat.) \pm 0.31 (syst.) \substack{ +0.49 \\ -0.00 } \mathrm{ (Q^2\,model)}) \times 10^{-40}~\mathrm{cm}^{2}$. The new measurement of the $\bar{\nu}_{\mu}$-induced cross section on $^{12}{C}$ is $(3.05 \pm 0.71 (stat.) \pm 0.39 (syst.) \substack{ +0.74 \\ -0.00 } \mathrm{(Q^2\,model)}) \times 10^{-40}~\mathrm{cm}^{2}$. The results are compatible with both the NEUT 5.4.0 Berger-Sehgal (2009) and GENIE 2.8.0 Rein-Sehgal (2007) model predictions.

Weyl's conformal gravity theory, which is considered as a compelling alternative to general relativity theory, has been claimed to describe the observed flat rotation curve feature of spiral galaxies without the need of invoking dark matter. However, it is important to examine whether the Weyl theory can also explain the relevant gravitational lensing observations correctly without considering any dark matter. In this regard, the gravitational bending angle in static spherically space-time (Mannheim-Kazanas metric) in Weyl theory has been calculated by several authors over the last two decades, but the results are found largely divergent. In this work, we have revisited the problem and obtain the correct and consistent expression of the deflection angle in conformal gravity. Subsequently we perform the gravitational lensing analysis. We compare the prediction of Weyl gravity with the gravitational lensing observations of the rich galaxy clusters Abell 370 and Abell 2390 and is found that Weyl theory cannot describe the stated lensing observations without considering dark matter., Comment: 9 pages