Role of chemical potential at kinetic freeze-out using Tsallis non-extensive statistics in proton-proton collisions at the Large Hadron Collider

The charged-particle transverse momentum spectra ($p_{\rm T}$-spectra) measured by the ALICE collaboration for $pp$ collisions at $\sqrt {s} =$ 7 and 13 TeV have been studied using a thermodynamically consistent form of Tsallis non-extensive statistics. The Tsallis distribution function is fitted to the $p_{\rm T}$-spectra and the results are analyzed as a function of final state charged-particle multiplicity for various light flavor and strange particles, such as $\pi^{\pm}, K^{\pm}, p+\bar{p}, \phi, \Lambda+\bar{\Lambda}, \Xi+\bar{\Xi}, \Omega+\bar{\Omega}$. At the LHC energies, particles and antiparticles are produced in equal numbers. However, the equality of particle and antiparticle yields at the kinetic freeze-out may imply that they have the same but opposite chemical potential which is not necessarily zero. We use an alternative procedure that makes use of parameter redundancy, by introducing a finite chemical potential at the kinetic freeze-out stage. This article emphasizes the importance of the chemical potential of the system produced in $pp$ collisions at the LHC energies using the Tsallis distribution function which brings the system to a single freeze-out scenario.Comment: Same as the published version in EPJ

Misspecification-robust Sequential Neural Likelihood for Simulation-based Inference

Simulation-based inference techniques are indispensable for parameter estimation of mechanistic and simulable models with intractable likelihoods. While traditional statistical approaches like approximate Bayesian computation and Bayesian synthetic likelihood have been studied under well-specified and misspecified settings, they often suffer from inefficiencies due to wasted model simulations. Neural approaches, such as sequential neural likelihood (SNL) avoid this wastage by utilising all model simulations to train a neural surrogate for the likelihood function. However, the performance of SNL under model misspecification is unreliable and can result in overconfident posteriors centred around an inaccurate parameter estimate. In this paper, we propose a novel SNL method, which through the incorporation of additional adjustment parameters, is robust to model misspecification and capable of identifying features of the data that the model is not able to recover. We demonstrate the efficacy of our approach through several illustrative examples, where our method gives more accurate point estimates and uncertainty quantification than SNL

Decentralized Multi-Robot Navigation for Autonomous Surface Vehicles with Distributional Reinforcement Learning

Collision avoidance algorithms for Autonomous Surface Vehicles (ASV) that follow the Convention on the International Regulations for Preventing Collisions at Sea (COLREGs) have been proposed in recent years. However, it may be difficult and unsafe to follow COLREGs in congested waters, where multiple ASVs are navigating in the presence of static obstacles and strong currents, due to the complex interactions. To address this problem, we propose a decentralized multi-ASV collision avoidance policy based on Distributional Reinforcement Learning, which considers the interactions among ASVs as well as with static obstacles and current flows. We evaluate the performance of the proposed Distributional RL based policy against a traditional RL-based policy and two classical methods, Artificial Potential Fields (APF) and Reciprocal Velocity Obstacles (RVO), in simulation experiments, which show that the proposed policy achieves superior performance in navigation safety, while requiring minimal travel time and energy. A variant of our framework that automatically adapts its risk sensitivity is also demonstrated to improve ASV safety in highly congested environments.Comment: The 2024 IEEE International Conference on Robotics and Automation (ICRA 2024

Active Information Gathering for Long-Horizon Navigation Under Uncertainty by Learning the Value of Information

We address the task of long-horizon navigation in partially mapped environments for which active gathering of information about faraway unseen space is essential for good behavior. We present a novel planning strategy that, at training time, affords tractable computation of the value of information associated with revealing potentially informative regions of unseen space, data used to train a graph neural network to predict the goodness of temporally-extended exploratory actions. Our learning-augmented model-based planning approach predicts the expected value of information of revealing unseen space and is capable of using these predictions to actively seek information and so improve long-horizon navigation. Across two simulated office-like environments, our planner outperforms competitive learned and non-learned baseline navigation strategies, achieving improvements of up to 63.76% and 36.68%, demonstrating its capacity to actively seek performance-critical information.Comment: Submitted at IROS'24. arXiv admin note: text overlap with arXiv:2307.1450

Quark Counting, Drell-Yan West, and the Pion Wave Function

The relation between the pion's quark distribution function, $q(x)$, its light-front wave function, and the elastic charge form factor, $F(\Delta^2)$ is explored. The square of the leading-twist pion wave function at a special probe scale, $\zeta_H$, is determined using models and Poincare covariance from realistic results for $q(x)$. This wave function is then used to compute form factors with the result that the Drell-Yan-West and quark counting relationships are not satisfied. A new relationship between $q(x)$ and $F(\Delta^2)$ is proposed.Comment: 6 pages, 2 figure

Bergman spaces for the bicomplex Vekua equation with bounded coefficients

We develop the theory for the Bergman spaces of generalized $L_p$-solutions of the bicomplex-Vekua equation $\overline{\boldsymbol{\partial}}W=aW+b\overline{W}$ on bounded domains, where the coefficients $a$ and $b$ are bounded bicomplex-valued functions. We study the completeness of the Bergman space, the regularity of the solutions, and the boundedness of the evaluation functional. For the case $p=2$, the existence of a reproducing kernel is established, along with a representation of the orthogonal projection onto the Bergman space in terms of the obtained reproducing kernel, and an explicit expression for the orthogonal complement. Additionally, we analyze the main Vekua equation ($a=0$, $b = \frac{\overline{\boldsymbol{\partial}}f}{f}$ with $f$ being a non-vanishing complex-valued function). Results concerning its relationship with a pair of conductivity equations, the construction of metaharmonic conjugates, and the Runge property are presented

A two-line representation of stationary measure for open TASEP

We show that the stationary measure for the totally asymmetric simple exclusion process on a segment with open boundaries is given by a marginal of a two-line measure with a simple and explicit description. We use this representation to analyze asymptotic fluctuations of the height function near the triple point for a larger set of parameters than was previously studied. As a second application, we determine a single expression for the rate function in the large deviation principle for the height function in the fan and in the shock region. We then discuss how this expression relates to the expressions available in the literature

Emotional Tandem Robots: How Different Robot Behaviors Affect Human Perception While Controlling a Mobile Robot

In human-robot interaction (HRI), we study how humans interact with robots, but also the effects of robot behavior on human perception and well-being. Especially, the influence on humans by tandem robots with one human controlled and one autonomous robot or even semi-autonomous multi-robot systems is not yet fully understood. Here, we focus on a leader-follower scenario and study how emotionally expressive motion patterns of a small, mobile follower robot affect the perception of a human operator controlling the leading robot. We examined three distinct emotional behaviors for the follower compared to a neutral condition: angry, happy and sad. We analyzed how participants maneuvered the leader robot along a set path while experiencing each follower behavior in a randomized order. We identified a significant shift in attention toward the follower with emotionally expressive behaviors compared to the neutral condition. For example, the angry behavior significantly heightened participant stress levels and was considered the least preferred behavior. The happy behavior was the most preferred and associated with increased excitement by the participants. Integrating the proposed behaviors in robots can profoundly influence the human operator's attention, emotional state, and overall experience. These insights are valuable for future HRI tandem robot designs.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Gravitational waves from first-order phase transitions in LISA: reconstruction pipeline and physics interpretation

We develop a tool for the analysis of stochastic gravitational wave backgrounds from cosmological first-order phase transitions with LISA: we initiate a template databank for these signals, prototype their searches, and forecast their reconstruction. The templates encompass the gravitational wave signals sourced by bubble collisions, sound waves and turbulence. Accounting for Galactic and extra-Galactic foregrounds, we forecast the region of the parameter space that LISA will reconstruct with better than $\sim 10\,\%$ accuracy, if certain experimental and theoretical uncertainties are solved by the time LISA flies. We illustrate the accuracy with which LISA can reconstruct the parameters on a few benchmark signals, both in terms of the template parameters and the phase transition ones. To show the impact of the forecasts on physics beyond the Standard Model, we map the reconstructed benchmark measurements into the parameter spaces of the singlet extension of the Standard Model and of the classically conformal invariant $U(1)_{B-L}$ model.Comment: 54 pages, 18 figure

A&B BNN: Add&Bit-Operation-Only Hardware-Friendly Binary Neural Network

Binary neural networks utilize 1-bit quantized weights and activations to reduce both the model's storage demands and computational burden. However, advanced binary architectures still incorporate millions of inefficient and nonhardware-friendly full-precision multiplication operations. A&B BNN is proposed to directly remove part of the multiplication operations in a traditional BNN and replace the rest with an equal number of bit operations, introducing the mask layer and the quantized RPReLU structure based on the normalizer-free network architecture. The mask layer can be removed during inference by leveraging the intrinsic characteristics of BNN with straightforward mathematical transformations to avoid the associated multiplication operations. The quantized RPReLU structure enables more efficient bit operations by constraining its slope to be integer powers of 2. Experimental results achieved 92.30%, 69.35%, and 66.89% on the CIFAR-10, CIFAR-100, and ImageNet datasets, respectively, which are competitive with the state-of-the-art. Ablation studies have verified the efficacy of the quantized RPReLU structure, leading to a 1.14% enhancement on the ImageNet compared to using a fixed slope RLeakyReLU. The proposed add&bit-operation-only BNN offers an innovative approach for hardware-friendly network architecture.Comment: CVPR 2024 Accepte