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Resetting processes, where a system is regularly returned to a given state after a fixed or random duration, offer a simple strategy to generate non-equilibrium properties and optimize search operations. While earlier theoretical frameworks focused on instantaneous resetting, wherein the system is directly teleported to a resetting state, there is a growing interest in physical resetting mechanisms that involve a finite return time. In this paper, we present an exact thermodynamic analysis of a diffusing particle whose position is intermittently reset to a specific site by employing a stochastic return protocol with external confining trap. We derive a renewal formula for the joint distribution of the first-passage time and the work done in terms of the characteristic measures of the underlying process. Using this formula, we compute general expressions for the averages of the work done and the first-passage time for an arbitrary confining potential. We next focus on a family of potentials $U_R(x) \sim |x|^{m}$ with $m>0$, and optimise, with respect to the potential shape, the expected first-passage time for a given value of the work done. The Pareto front associated with this optimisation is exactly obtained, along with the optimal resetting potentials. Finally, we use this to demonstrate a trade-off relation between the first-passage time and the work done., Comment: 16+9 pages, 3 Figures

Enterprises grapple with the significant challenge of managing proprietary unstructured data, hindering efficient information retrieval. This has led to the emergence of AI-driven information retrieval solutions, designed to adeptly extract relevant insights to address employee inquiries. These solutions often leverage pre-trained embedding models and generative models as foundational components. While pre-trained embeddings may exhibit proximity or disparity based on their original training objectives, they might not fully align with the unique characteristics of enterprise-specific data, leading to suboptimal alignment with the retrieval goals of enterprise environments. In this paper, we propose a methodology to fine-tune pre-trained embedding models specifically for enterprise environments. By adapting the embeddings to better suit the retrieval tasks prevalent in enterprises, we aim to enhance the performance of information retrieval solutions. We discuss the process of fine-tuning, its effect on retrieval accuracy, and the potential benefits for enterprise information management. Our findings demonstrate the efficacy of fine-tuned embedding models in improving the precision and relevance of search results in enterprise settings.

We present a comprehensive investigation of the electronic structure, magnetization, specific heat, and crystallography of TbSi (FeB structure type) and TbSi$_{0.6}$Ge$_{0.4}$ (CrB structure type) compounds. Both TbSi and TbSi$_{0.6}$Ge$_{0.4}$ exhibit two antiferromagnetic (AFM) transitions at T$_{\rm N1}\approx$ 58~K and 57~K, and T$_{\rm N2}\approx$ 36~K and 44~K, respectively, along with an onset of weak metamagnetic-like transition around 6~T between T$_{\rm N1}$ and T$_{\rm N2}$. High-resolution specific heat (C$_{\rm P}$) measurements show the second- and first-order nature of the magnetic transition at T$_{\rm N1}$ and T$_{\rm N2}$, respectively, for both samples. However, in the case of TbSi, the low-temperature (LT) AFM to high-temperature (HT) AFM transition takes place via an additional AFM phase at the intermediate temperature (IT), where both LT to IT AFM and IT to HT AFM phase transitions exhibit a first-order nature. Both TbSi and TbSi$_{0.6}$Ge$_{0.4}$ manifest significant magnetic entropy changes ($\Delta S_{\rm M}$) of 9.6 and 11.6~J/kg-K, respectively, for $\Delta \mu_0H$=7~T, at T$_{\rm N2}$. The HT AFM phase of TbSi$_{0.6}$Ge$_{0.4}$ is found to be more susceptible to the external magnetic field, causing a significant broadening in the peaks of $\Delta S_{\rm M}$ curves at higher magnetic fields. Temperature and field-dependent specific heat data have been utilized to construct the complex H-T phase diagram of these compounds. Furthermore, temperature-dependent x-ray diffraction measurements demonstrate substantial magnetostriction and anisotropic thermal expansion of the unit cell in both samples., Comment: Submitted on 10 May 2024

Chemical gradients can be used by a particle to determine its position. This \textit{positional information} is of crucial importance, for example in developmental biology in the formation of patterns in an embryo. The central goal of this paper is to study the fundamental physical limits on how much positional information can be stored inside a system. To achieve this, we study positional information for general boundary-driven systems, and derive, in the near-equilibrium regime, a universal expression involving only the chemical potential and density gradients of the system. We also conjecture that this expression serves as an upper bound on the positional information of boundary driven systems beyond linear response. To support this claim, we test it on a broad range of solvable boundary-driven systems.

The metallurgy and materials communities have long known and exploited fundamental links between chemical and structural ordering in metallic solids and their mechanical properties. The highest reported strength achievable through the combination of multiple metals (alloying) has rapidly climbed and given rise to new classifications of materials with extraordinary properties. Metallic glasses and high-entropy alloys are two limiting examples of how tailored order can be used to manipulate mechanical behavior. Here, we show that the complex electronic-structure mechanisms governing the peak strength of alloys and pure metals can be reduced to a few physically-meaningful parameters based on their atomic arrangements and used (with no fitting parameters) to predict the maximum strength of any metallic solid, regardless of degree of structural or chemical ordering. Predictions of maximum strength based on the activation energy for a stress-driven phase transition to an amorphous state is shown to accurately describe the breakdown in Hall-Petch behavior at the smallest crystallite sizes for pure metals, intermetallic compounds, metallic glasses, and high-entropy alloys. This activation energy is also shown to be directly proportional to interstitial (electronic) charge density, which is a good predictor of ductility, stiffness (moduli), and phase stability in high-entropy alloys, and in solid metals generally. The proposed framework suggests the possibility of coupling ordering and intrinsic strength to mechanisms like dislocation nucleation, hydrogen embrittlement, and transport properties. It additionally opens the prospect for greatly accelerated structural materials design and development to address materials challenges limiting more sustainable and efficient use of energy., Comment: 16 pages, four figures

In the past few decades, the rapid development of information and internet technologies has spawned massive amounts of data and information. The information explosion drives many enterprises or individuals to seek to rent cloud computing infrastructure to put their applications in the cloud. However, the agreements reached between cloud computing providers and clients are often not efficient. Many factors affect the efficiency, such as the idleness of the providers' cloud computing infrastructure, and the additional cost to the clients. One possible solution is to introduce a comprehensive, bargaining game (a type of negotiation), and schedule resources according to the negotiation results. We propose an agent-based auto-negotiation system for resource scheduling based on fuzzy logic. The proposed method can complete a one-to-one auto-negotiation process and generate optimal offers for the provider and client. We compare the impact of different member functions, fuzzy rule sets, and negotiation scenario cases on the offers to optimize the system. It can be concluded that our proposed method can utilize resources more efficiently and is interpretable, highly flexible, and customizable. We successfully train machine learning models to replace the fuzzy negotiation system to improve processing speed. The article also highlights possible future improvements to the proposed system and machine learning models. All the codes and data are available in the open-source repository., Comment: Accepted in IEEE CLOUD 2023. 13 pages, 5 figures

We investigate the work fluctuations in an overdamped non-equilibrium process that is stopped at a stochastic time. The latter is characterized by a first passage event that marks the completion of the non-equilibrium process. In particular, we consider a particle diffusing in one dimension in the presence of a time-dependent potential $U(x,t) = k |x-vt|^n/n$, where $k>0$ is the stiffness and $n>0$ is the order of the potential. Moreover, the particle is confined between two absorbing walls, located at $L_{\pm}(t) $, that move with a constant velocity $v$ and are initially located at $L_{\pm}(0) = \pm L$. As soon as the particle reaches any of the boundaries, the process is said to be completed and here, we compute the work done $W$ by the particle in the modulated trap upto this random time. Employing the Feynman-Kac path integral approach, we find that the typical values of the work scale with $L$ with a crucial dependence on the order $n$. While for $n>1$, we show that $\mom{W} \sim L^{1-n}~\exp \left[ \left( {k L^{n}}/{n}-v L \right)/D \right] $ for large $L$, we get an algebraic scaling of the form $\mom{W} \sim L^n$ for the $n<1$ case. The marginal case of $n=1$ is exactly solvable and our analysis unravels three distinct scaling behaviours: (i) $\mom{W} \sim L$ for $v>k$, (ii) $\mom{W} \sim L^2$ for $v=k$ and (iii) $\mom{W} \sim \exp\left[{-(v-k)L}\right]$ for $v

The simulation of nanophotonic structures relies on electromagnetic solvers, which play a crucial role in understanding their behavior. However, these solvers often come with a significant computational cost, making their application in design tasks, such as optimization, impractical. To address this challenge, machine learning techniques have been explored for accurate and efficient modeling and design of photonic devices. Deep neural networks, in particular, have gained considerable attention in this field. They can be used to create both forward and inverse models. An inverse modeling approach avoids the need for coupling a forward model with an optimizer and directly performs the prediction of the optimal design parameters values. In this paper, we propose an inverse modeling method for nanophotonic structures, based on a mixture density network model enhanced by transfer learning. Mixture density networks can predict multiple possible solutions at a time including their respective importance as Gaussian distributions. However, multiple challenges exist for mixture density network models. An important challenge is that an upper bound on the number of possible simultaneous solutions needs to be specified in advance. Also, another challenge is that the model parameters must be jointly optimized, which can result computationally expensive. Moreover, optimizing all parameters simultaneously can be numerically unstable and can lead to degenerate predictions. The proposed approach allows overcoming these limitations using transfer learning-based techniques, while preserving a high accuracy in the prediction capability of the design solutions given an optical response as an input. A dimensionality reduction step is also explored. Numerical results validate the proposed method.

We present an adaptive approach for robust learning from corrupted training sets. We identify corrupted and non-corrupted samples with latent Bernoulli variables and thus formulate the learning problem as maximization of the likelihood where latent variables are marginalized. The resulting problem is solved via variational inference, using an efficient Expectation-Maximization based method. The proposed approach improves over the state-of-the-art by automatically inferring the corruption level, while adding minimal computational overhead. We demonstrate our robust learning method and its parameter-free nature on a wide variety of machine learning tasks including online learning and deep learning where it adapts to different levels of noise and maintains high prediction accuracy., Comment: Accepted at ICML 2024

Resetting is a renewal mechanism in which a process is intermittently repeated after a random or fixed time. This simple act of stop and repeat profoundly influences the behaviour of a system as exemplified by the emergence of non-equilibrium properties and expedition of search processes. Herein, we explore the ramifications of stochastic resetting in the context of a single-file system called random average process (RAP) in one dimension. In particular, we focus on the dynamics of tracer particles and analytically compute the variance, equal time correlation, autocorrelation and unequal time correlation between the positions of different tracer particles. Our study unveils that resetting gives rise to rather different behaviours depending on whether the particles move symmetrically or asymmetrically. For the asymmetric case, the system for instance exhibits a long-range correlation which is not seen in absence of the resetting. Similarly, in contrast to the reset-free RAP, the variance shows distinct scalings for symmetric and asymmetric cases. While for the symmetric case, it decays (towards its steady value) as $\sim e^{-r t} / \sqrt{t}$, we find $\sim t e^{-r t}$ decay for the asymmetric case ($r$ being the resetting rate). Finally, we examine the autocorrelation and unequal time correlation in the steady state and demonstrate that they obey interesting scaling forms at late times. All our analytical results are substantiated by extensive numerical simulations., Comment: 16 pages, 7 figures