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A popular approach of automated mechanism design is to formulate a linear program (LP) whose solution gives a mechanism with desired properties. We analytically derive a class of optimal solutions for such an LP that gives mechanisms achieving standard properties of efficiency, incentive compatibility, strong budget balance (SBB), and individual rationality (IR), where SBB and IR are satisfied in expectation. Notably, our solutions are represented by an exponentially smaller number of essential variables than the original variables of LP. Our solutions, however, involve a term whose exact evaluation requires solving a certain optimization problem exponentially many times as the number of players, $N$, grows. We thus evaluate this term by modeling it as the problem of estimating the mean reward of the best arm in multi-armed bandit (MAB), propose a Probably and Approximately Correct estimator, and prove its asymptotic optimality by establishing a lower bound on its sample complexity. This MAB approach reduces the number of times the optimization problem is solved from exponential to $O(N\,\log N)$. Numerical experiments show that the proposed approach finds mechanisms that are guaranteed to achieve desired properties with high probability for environments with up to 128 players, which substantially improves upon the prior work., Comment: 35 pages

The primary contribution of this paper resides in devising constant-factor approximation guarantees for revenue maximization in two-sided matching markets, under general pairwise rewards. A major distinction between our work and state-of-the-art results in this context (Ashlagi et al., 2022; Torrico et al., 2023) is that, for the first time, we are able to address reward maximization, reflected by assigning each customer-supplier pair an arbitrarily-valued reward. The specific type of performance guarantees we attain depends on whether one considers the customized model or the inclusive model. The fundamental difference between these settings lies in whether the platform should display to each supplier all selecting customers, as in the inclusive model, or whether the platform can further personalize this set, as in the customized model. Technically speaking, our algorithmic approach and its analysis revolve around presenting novel linear relaxations, leveraging convex stochastic orders, employing approximate dynamic programming, and developing tailor-made analytical ideas. In both models considered, these ingredients allow us to overcome the lack of submodularity and subadditivity that stems from pairwise rewards, plaguing the applicability of existing methods.

We study the nonlinear process of second harmonic generation in photonic time-crystals, materials with refractive index that varies abruptly and periodically in time, and obtain the phase matching condition for this process. We find conditions for which the second harmonic generation is highly enhanced even in the absence of phase matching, governed by the exponential growth of the modes residing in the momentum gap of the photonic time crystal. Additionally, under these conditions, a cascade of higher order harmonics is generated at growing exponential rates. The process is robust, with no requirement for phase-matching, the presence of a resonance or a threshold, drawing energy from the modulation.

Energy efficient, safe and reliable Li-ion batteries (LIBs) are required for a wide range of applications. Charging capabilities of thick electrodes still holding their stored high-energy is a most desirable characteristic in future advanced LIBs. The introduction of ultra-thick graphite anode meets limitations in internal electrode transport properties, leading to Li-ion gradients with detrimental consequences for battery cell performance and lifetime. Yet, there is a lack of experimental tools capable of providing a complete view of local processes and evolving gradients within such thick electrodes. Here, we introduce a multi-modal operando measurement approach, enabling quantitative spatio-temporal observations of Li concentrations and intercalation phases in ultra-thick, graphite electrodes. Neutron imaging and diffraction concurrently provide correlated information from the macroscopic scale of the cell and electrode down to the crystallographic scale portraying the intercalation and deintercalation processes. In particular, the evolving formation of the solid electrolyte interphase (SEI), observation of gradients in total lithium content, as well as in the formation of ordered LixC6 phases and trapped lithium have been mapped throughout the first charge-discharge cycle of the cell. Different lithiation stages co-exist during charging and discharging of an ultra-thick composite graphite-based electrode; delayed lithiation and delithiation processes are observed at the central region of the electrode, while the SEI formation, potential plating and dead lithium are predominantly found closer to the interface with the separator. The study furthermore emphasizes the potential of the method to study Li ion diffusion and the kinetics of lithiation phase formation in advanced ultra-thick electrodes.

We present three separate void catalogs created using a volume-limited sample of the DESI Year 1 Bright Galaxy Survey. We use the algorithms VoidFinder and V2 to construct void catalogs out to a redshift of z=0.24. We obtain 1,461 interior voids with VoidFinder, 420 with V2 using REVOLVER pruning, and 295 with V2 using VIDE pruning. Comparing our catalog with an overlapping SDSS void catalog, we find generally consistent void properties but significant differences in the void volume overlap, which we attribute to differences in the galaxy selection and survey masks. These catalogs are suitable for studying the variation in galaxy properties with cosmic environment and for cosmological studies., Comment: 17 pages, 6 figures

A Galactic core-collapse supernova (CCSN) is likely to be observed in neutrino detectors around the world minutes to hours before the electromagnetic radiation arrives. The SNEWS2.0 network of neutrino and dark matter detectors aims to use the relative arrival times of the neutrinos at the different experiments to point back to the supernova. One of the simplest methods to calculate the CCSN direction is to use the first neutrino events detected through the inverse beta decay (IBD) process, $\overline{\nu}_e p\rightarrow e^+n$. We will consider neutrino detectors sensitive to IBD interactions with low backgrounds. The difference in signal arrival times between a large and a small detector will be biased, however, with the first event at the smaller detector, on average, arriving later than that at the larger detector. This bias can be mitigated by using these first events in a data-driven approach without recourse to simulations or models. In this article, we demonstrate this method and its uncertainty estimate using pairs of detectors of different sizes and with different supernova distances. Finally, we use this method to calculate probability skymaps using four detectors (Super-Kamiokande, JUNO, LVD, and SNO+) and show that the calculated probabilities yield appropriate confidence intervals. The resulting skymaps should be useful for the multi-messenger community to follow up on the SNEWS2.0 Galactic CCSN neutrino alert., Comment: 9 pages, 9 figures

Recent advancements in Web agents have introduced novel architectures and benchmarks showcasing progress in autonomous web navigation and interaction. However, most existing benchmarks prioritize effectiveness and accuracy, overlooking factors like safety and trustworthiness which are essential for deploying web agents in enterprise settings. We present STWebAgentBench, a benchmark designed to evaluate web agents safety and trustworthiness across six critical dimensions, essential for reliability in enterprise applications. This benchmark is grounded in a detailed framework that defines safe and trustworthy (ST) agent behavior. Our work extends WebArena with safety templates and evaluation functions to assess safety policy compliance rigorously. We introduce the Completion Under Policy to measure task success while adhering to policies, alongside the Risk Ratio, which quantifies policy violations across dimensions, providing actionable insights to address safety gaps. Our evaluation reveals that current SOTA agents struggle with policy adherence and cannot yet be relied upon for critical business applications. We open-source this benchmark and invite the community to contribute, with the goal of fostering a new generation of safer, more trustworthy AI agents. All code, data, environment reproduction resources, and video demonstrations are available at https://sites.google.com/view/st-webagentbench/home.

This study investigates the impact of increased debt servicing costs on household consumption resulting from monetary policy tightening. It utilizes observational panel microdata on all mortgage holders in Israel and leverages quasi-exogenous variation in exposure to adjustable-rate mortgages (ARMs) due to a regulatory shift. Our analysis indicates that when monetary policy became more restrictive, consumers with a higher ratio of ARMs experienced a more marked reduction in their consumption patterns. This effect is predominantly observed in mid- to lower-income households and those with a higher ratio of mortgage payments to total spending. These findings highlight the substantial role of the mortgage cash-flow channel in monetary policy transmission, emphasizing its implications for economic stability and inequality.

For a given region, and specified boundary flux and density rate of an extensive property, the optimal flux field that satisfies the balance conditions is considered. The optimization criteria are the $L^{p}$-norm and a Sobolev-like norm of the flux field. Finally, the capacity of the region to accommodate various boundary fluxes and density rates is defined and analyzed.

General web-based agents are increasingly essential for interacting with complex web environments, yet their performance in real-world web applications remains poor, yielding extremely low accuracy even with state-of-the-art frontier models. We observe that these agents can be decomposed into two primary components: Planning and Grounding. Yet, most existing research treats these agents as black boxes, focusing on end-to-end evaluations which hinder meaningful improvements. We sharpen the distinction between the planning and grounding components and conduct a novel analysis by refining experiments on the Mind2Web dataset. Our work proposes a new benchmark for each of the components separately, identifying the bottlenecks and pain points that limit agent performance. Contrary to prevalent assumptions, our findings suggest that grounding is not a significant bottleneck and can be effectively addressed with current techniques. Instead, the primary challenge lies in the planning component, which is the main source of performance degradation. Through this analysis, we offer new insights and demonstrate practical suggestions for improving the capabilities of web agents, paving the way for more reliable agents.

The Dark Energy Spectroscopic Instrument (DESI) Peculiar Velocity Survey aims to measure the peculiar velocities of early and late type galaxies within the DESI footprint using both the Fundamental Plane and Tully-Fisher relations. Direct measurements of peculiar velocities can significantly improve constraints on the growth rate of structure, reducing uncertainty by a factor of approximately 2.5 at redshift 0.1 compared to the DESI Bright Galaxy Survey's redshift space distortion measurements alone. We assess the quality of stellar velocity dispersion measurements from DESI spectroscopic data. These measurements, along with photometric data from the Legacy Survey, establish the Fundamental Plane relation and determine distances and peculiar velocities of early-type galaxies. During Survey Validation, we obtain spectra for 6698 unique early-type galaxies, up to a photometric redshift of 0.15. 64\% of observed galaxies (4267) have relative velocity dispersion errors below 10\%. This percentage increases to 75\% if we restrict our sample to galaxies with spectroscopic redshifts below 0.1. We use the measured central velocity dispersion, along with photometry from the DESI Legacy Imaging Surveys, to fit the Fundamental Plane parameters using a 3D Gaussian maximum likelihood algorithm that accounts for measurement uncertainties and selection cuts. In addition, we conduct zero-point calibration using the absolute distance measurements to the Coma cluster, leading to a value of the Hubble constant, $H_0 = 76.05 \pm 0.35$(statistical) $\pm 0.49$(systematic FP) $\pm 4.86$(statistical due to calibration) $\mathrm{km \ s^{-1} Mpc^{-1}}$. This $H_0$ value is within $2\sigma$ of Planck Cosmic Microwave Background results and within $1\sigma$, of other low redshift distance indicator-based measurements., Comment: 18 pages, 9 figures, 2 tables. Submitted for publication in MNRAS

Automatic reviewing helps handle a large volume of papers, provides early feedback and quality control, reduces bias, and allows the analysis of trends. We evaluate the alignment of automatic paper reviews with human reviews using an arena of human preferences by pairwise comparisons. Gathering human preference may be time-consuming; therefore, we also use an LLM to automatically evaluate reviews to increase sample efficiency while reducing bias. In addition to evaluating human and LLM preferences among LLM reviews, we fine-tune an LLM to predict human preferences, predicting which reviews humans will prefer in a head-to-head battle between LLMs. We artificially introduce errors into papers and analyze the LLM's responses to identify limitations, use adaptive review questions, meta prompting, role-playing, integrate visual and textual analysis, use venue-specific reviewing materials, and predict human preferences, improving upon the limitations of the traditional review processes. We make the reviews of publicly available arXiv and open-access Nature journal papers available online, along with a free service which helps authors review and revise their research papers and improve their quality. This work develops proof-of-concept LLM reviewing systems that quickly deliver consistent, high-quality reviews and evaluate their quality. We mitigate the risks of misuse, inflated review scores, overconfident ratings, and skewed score distributions by augmenting the LLM with multiple documents, including the review form, reviewer guide, code of ethics and conduct, area chair guidelines, and previous year statistics, by finding which errors and shortcomings of the paper may be detected by automated reviews, and evaluating pairwise reviewer preferences. This work identifies and addresses the limitations of using LLMs as reviewers and evaluators and enhances the quality of the reviewing process., Comment: 42 pages

In earlier work we studied the structure of primitive axial algebras of Jordan type (PAJ's), not necessarily commutative, in terms of their primitive axes. In this paper we weaken primitivity and permit several pairs of (left and right) eigenvalues satisfying a more general fusion rule, bringing in interesting new examples such as the band semigroup algebras and other commutative and noncommutative examples. Also we broaden our investigation and describe 2-generated algebras in which only one of the generating axes is weakly primitive and satisfies the fusion rules, on condition that its zero-eigenspace is one dimensional. We also characterize when both axes satisfy the fusion rules (weak PAJ's), and describe precisely the 2-dimensional axial algebras. In contrast to the previous situation, there are weak PAJ's of dimension~$> 3$ generated by two axes., Comment: 61 pages. Some typos were corrected

We present the concept of time-domain bound states in continuum. We show that a rapid judiciously-designed temporal modulation of the refractive index in a spatially homogenous medium gives rise to a bound state in time embedded in a continuum of wavenumbers. Mathematically, these bound states in the continuum (BIC) are analytic solutions of the Maxwell equations in time and one-dimensional space. Our results show the potential to extend known wave phenomena in space to the temporal domain, providing new avenues for light-matter interactions in time-varying media.

The primary objective of this work is to revisit and revitalize one of the most fundamental models in deterministic inventory management, the continuous-time joint replenishment problem. Our main contribution consists of resolving several long-standing open questions in this context. For most of these questions, we obtain the first quantitative improvement over power-of-$2$ policies and their nearby derivatives, which have been state-of-the-art in terms of provable performance guarantees since the mid-80's.

Business users dedicate significant amounts of time to repetitive tasks within enterprise digital platforms, highlighting a critical need for automation. Despite advancements in low-code tools for UI automation, their complexity remains a significant barrier to adoption among non-technical business users. However, recent advancements in large language models (LLMs) have created new opportunities to overcome this barrier by offering more powerful, yet simpler and more human-centric programming environments. This paper presents IDA (Intelligent Digital Apprentice), a novel no-code Web UI automation tool designed specifically to empower business users with no technical background. IDA incorporates human-centric design principles, including guided programming by demonstration, semantic programming model, and teacher-student learning metaphor which is tailored to the skill set of business users. By leveraging LLMs, IDA overcomes some of the key technical barriers that have traditionally limited the possibility of no-code solutions. We have developed a prototype of IDA and conducted a user study involving real world business users and enterprise applications. The promising results indicate that users could effectively utilize IDA to create automation. The qualitative feedback indicates that IDA is perceived as user-friendly and trustworthy. This study contributes to unlocking the potential of AI assistants to enhance the productivity of business users through no-code user interface automation.

Despite the unprecedented success of text-to-image diffusion models, controlling the number of depicted objects using text is surprisingly hard. This is important for various applications from technical documents, to children's books to illustrating cooking recipes. Generating object-correct counts is fundamentally challenging because the generative model needs to keep a sense of separate identity for every instance of the object, even if several objects look identical or overlap, and then carry out a global computation implicitly during generation. It is still unknown if such representations exist. To address count-correct generation, we first identify features within the diffusion model that can carry the object identity information. We then use them to separate and count instances of objects during the denoising process and detect over-generation and under-generation. We fix the latter by training a model that predicts both the shape and location of a missing object, based on the layout of existing ones, and show how it can be used to guide denoising with correct object count. Our approach, CountGen, does not depend on external source to determine object layout, but rather uses the prior from the diffusion model itself, creating prompt-dependent and seed-dependent layouts. Evaluated on two benchmark datasets, we find that CountGen strongly outperforms the count-accuracy of existing baselines., Comment: Project page is at https://make-it-count-paper.github.io/

Generating 3D visual scenes is at the forefront of visual generative AI, but current 3D generation techniques struggle with generating scenes with multiple high-resolution objects. Here we introduce Lay-A-Scene, which solves the task of Open-set 3D Object Arrangement, effectively arranging unseen objects. Given a set of 3D objects, the task is to find a plausible arrangement of these objects in a scene. We address this task by leveraging pre-trained text-to-image models. We personalize the model and explain how to generate images of a scene that contains multiple predefined objects without neglecting any of them. Then, we describe how to infer the 3D poses and arrangement of objects from a 2D generated image by finding a consistent projection of objects onto the 2D scene. We evaluate the quality of Lay-A-Scene using 3D objects from Objaverse and human raters and find that it often generates coherent and feasible 3D object arrangements.

Nonlinear optical phenomena are at the heart of various technological domains such as high-speed data transfer, optical logic applications, and emerging fields such as non-reciprocal optics and photonic time crystal design. However, conventional nonlinear materials exhibit inherent limitations in the post-fabrication tailoring of their nonlinear optical properties. Achieving real-time control over optical nonlinearities remains a challenge. In this work, we demonstrate a method to switch third harmonic generation (THG), a commonly occurring nonlinear optical response. Third harmonic generation enhancements up to 50 times are demonstrated in zinc oxide films via the photoexcited state generation and tunable electric field enhancement. More interestingly, the enhanced third harmonic generation follows a quadratic scaling with incident power, as opposed to the conventional cubic scaling, which demonstrates a previously unreported mechanism of third harmonic generation. The THG can also be suppressed by modulating the optical losses in the film. This work shows that the photoexcitation of states can not only enhance nonlinearities, but can create new processes for third harmonic generation. Importantly, the proposed method enables real-time manipulation of the nonlinear response of a medium. The process is switchable and reversible, with the modulations occurring at picosecond timescale. Our study paves the way to boost or suppress the nonlinearities of solid-state media, enabling robust, switchable sources for nonlinear optical applications.

We present the MOST Hosts survey (Multi-Object Spectroscopy of Transient Hosts). The survey is planned to run throughout the five years of operation of the Dark Energy Spectroscopic Instrument (DESI) and will generate a spectroscopic catalog of the hosts of most transients observed to date, in particular all the supernovae observed by most public, untargeted, wide-field, optical surveys (PTF/iPTF, SDSS II, ZTF, DECAT, DESIRT). Scientific questions for which the MOST Hosts survey will be useful include Type Ia supernova cosmology, fundamental plane and peculiar velocity measurements, and the understanding of the correlations between transients and their host galaxy properties. Here, we present the first release of the MOST Hosts survey: 21,931 hosts of 20,235 transients. These numbers represent 36% of the final MOST Hosts sample, consisting of 60,212 potential host galaxies of 38,603 transients (a transient can be assigned multiple potential hosts). Of these galaxies, 40% do not appear in the DESI primary target list and therefore require a specific program like MOST Hosts. Of all the transients in the MOST Hosts list, only 26.7% have existing classifications, and so the survey will provide redshifts (and luminosities) for nearly 30,000 transients. A preliminary Hubble diagram and a transient luminosity-duration diagram are shown as examples of future potential uses of the MOST Hosts survey. The survey will also provide a training sample of spectroscopically observed transients for photometry-only classifiers, as we enter an era when most newly observed transients will lack spectroscopic classification. The MOST Hosts DESI survey data will be released through the Wiserep platform on a rolling cadence and updated to match the DESI releases. Dates of future releases and updates are available through the https://mosthosts.desi.lbl.gov website., Comment: Submitted to ApJS

We explore how the definition of a void influences the conclusions drawn about the impact of the void environment on galactic properties using two void-finding algorithms in the Void Analysis Software Toolkit: V2, a Python implementation of ZOBOV, and VoidFinder, an algorithm which grows and merges spherical void regions. Using the Sloan Digital Sky Survey Data Release 7, we find that galaxies found in VoidFinder voids tend to be bluer, fainter, and have higher (specific) star formation rates than galaxies in denser regions. Conversely, galaxies found in V2 voids show no significant differences when compared to galaxies in denser regions, inconsistent with the large-scale environmental effects on galaxy properties expected from both simulations and previous observations. These results align with previous simulation results that show V2-identified voids "leaking" into the dense walls between voids because their boundaries extend up to the density maxima in the walls. As a result, when using ZOBOV-based void finders, galaxies likely to be part of wall regions are instead classified as void galaxies, a misclassification that can be critical to our understanding of galaxy evolution., Comment: 13 pages, 9 figures, 1 table, submitted to ApJ

High-ionization iron coronal lines (CLs) are a rare phenomenon observed in galaxy and quasi-stellar object spectra that are thought to be created by high-energy emission from active galactic nuclei and certain types of transients. In cases known as extreme coronal line emitting galaxies (ECLEs), these CLs are strong and fade away on a timescale of years. The most likely progenitors of these variable CLs are tidal disruption events (TDEs), which produce sufficient high-energy emission to create and sustain the CLs over these timescales. To test the possible connection between ECLEs and TDEs, we present the most complete variable ECLE rate calculation to date and compare the results to TDE rates from the literature. To achieve this, we search for ECLEs in the Sloan Digital Sky Survey (SDSS). We detect sufficiently strong CLs in 16 galaxies, more than doubling the number previously found in SDSS. We find that none of the nine new ECLEs evolve in a manner consistent with that of the five previously discovered variable ECLEs. Using this sample of five variable ECLEs, we calculate the galaxy-normalized rate of variable ECLEs in SDSS to be $R_\mathrm{G}=3.6~^{+2.6}_{-1.8}~(\mathrm{statistical})~^{+5.1}_{-0.0} (\mathrm{systematic})\times10^{-6}~\mathrm{galaxy}^{-1}~\mathrm{yr}^{-1}$. The mass-normalised rate is $R_\mathrm{M}=3.1~^{+2.3}_{-1.5}~(\mathrm{statistical})~^{+4.4}_{-0.0}~(\mathrm{systematic})\times10^{-17}~\mathrm{M_\odot^{-1}}~\mathrm{yr}^{-1}$ and the volumetric rate is $R_\mathrm{V}=7~^{+20}_{-5}~(\mathrm{statistical})~^{+10}_{-0.0}~(\mathrm{systematic})\times10^{-9}~\mathrm{Mpc}^{-3}~\mathrm{yr}^{-1}$. Our rates are one to two orders of magnitude lower than TDE rates from the literature, which suggests that only 10 to 40 per cent of all TDEs produce variable ECLEs. Additional uncertainties in the rates arising from the structure of the interstellar medium have yet to be included., Comment: 29 pages, 14 figures. Accepted by MNRAS

Significantly simplifying the creation of optimization models for real-world business problems has long been a major goal in applying mathematical optimization more widely to important business and societal decisions. The recent capabilities of Large Language Models (LLMs) present a timely opportunity to achieve this goal. Therefore, we propose research at the intersection of LLMs and optimization to create a Decision Optimization CoPilot (DOCP) - an AI tool designed to assist any decision maker, interacting in natural language to grasp the business problem, subsequently formulating and solving the corresponding optimization model. This paper outlines our DOCP vision and identifies several fundamental requirements for its implementation. We describe the state of the art through a literature survey and experiments using ChatGPT. We show that a) LLMs already provide substantial novel capabilities relevant to a DOCP, and b) major research challenges remain to be addressed. We also propose possible research directions to overcome these gaps. We also see this work as a call to action to bring together the LLM and optimization communities to pursue our vision, thereby enabling much more widespread improved decision-making.

In recent years there has been significant progress in the development of artificial ion pumping membranes. Ion pumps based on asymmetric nano-pores have been shown to operate as ionic current rectifiers, thus pumping a net ion flux against a concentration gradient even when driven with unbiased ac signals. However, since ion transport relies on charged nano-pores, it is selective to either cations or anions, and thus cannot pump both cations and anions simultaneously. In this paper, we present a model for an electronically active membrane which is based on a flashing ratchet mechanism. The model includes adjacent electrolyte reservoirs and ion-ion interactions, which were not accounted for in prior similar models, and thus provides a better understanding of the driving mechanism and potential capabilities and limitations. It is shown that unlike most other proposed ion pumps, the ratchet-based ion pump (RBIP) drives both cations and anions in the same direction and up a concentration gradient. This process, referred to as ambipolar ion pumping, is shown to be highly robust for many electrolyte compositions and input signals. The membrane is composed of alternating conductive thin layers (electrodes), separated by insulating layers in an asymmetric design. With insulating layers thickness of 70 and 30 nm and an input signal amplitude of 0.5 V, the device drives a salt flux of 0.03 mol/m^2 s in a mildly saline solution (10 mM). Thus, RBIPs may pave the way for many exciting future applications involving ambipolar ion pumping, most notably for desalination., Comment: 35 pages, 11 figures

Predicting the next activity in an ongoing process is one of the most common classification tasks in the business process management (BPM) domain. It allows businesses to optimize resource allocation, enhance operational efficiency, and aids in risk mitigation and strategic decision-making. This provides a competitive edge in the rapidly evolving confluence of BPM and AI. Existing state-of-the-art AI models for business process prediction do not fully capitalize on available semantic information within process event logs. As current advanced AI-BPM systems provide semantically-richer textual data, the need for novel adequate models grows. To address this gap, we propose the novel SNAP method that leverages language foundation models by constructing semantic contextual stories from the process historical event logs and using them for the next activity prediction. We compared the SNAP algorithm with nine state-of-the-art models on six benchmark datasets and show that SNAP significantly outperforms them, especially for datasets with high levels of semantic content.

Generalizing smooth volumetric growth to the singular case, using de Rham currents and flat chains, we demonstrate how regular boundaries of bodies may evolve to fractals.

This paper offers an informal instructive introduction to some of the main notions of geometric continuum mechanics for the case of smooth fields. We use a metric invariant stress theory of continuum mechanics to formulate a simple generalization of the fields of electrodynamics and Maxwell's equations to general differentiable manifolds of any dimension, thus viewing generalized electrodynamics as a special case of continuum mechanics. The basic kinematic variable is the potential, which is represented as a $p$-form in an $n$-dimensional spacetime. The stress for the case of generalized electrodynamics is assumed to be represented by an $(n-p-1)$-form, a generalization of the Maxwell $2$-form.

We study the time-reflection and time-refraction of waves caused by a spatial interface with a medium undergoing a sudden temporal change in permittivity. We show that monochromatic waves are transformed into a pulse by the permittivity change, and that time-reflection is enhanced at the vicinity of the critical angle for total internal reflection. In this regime, we find that the evanescent field is transformed into a propagating pulse by the sudden change in permittivity. These effects display enhancement of the time-reflection and high sensitivity near the critical angle, paving the way to experiments on time-reflection and photonic time-crystals at optical frequencies

The material structure of bodies undergoing growth is considered. In the geometric framework of a general differential manifold modeling the physical space and a fiber bundle modeling spacetime, body points may be defined for any extensive property for which a smooth flux field exists, even if the property is not conserved. Singular flux fields are considered using the notion of a de Rham current. Writing a generalized balance law using the boundary of the current corresponding to a singular flux field, surface growth is unified with volumetric growth., Comment: 5 figures

In enterprise cloud computing, there is a big and increasing investment to move to multi-cloud computing, which allows enterprises to seamlessly utilize IT resources from multiple cloud providers, so as to take advantage of different cloud providers' capabilities and costs. This investment raises several key questions: Will multi-cloud always be more beneficial to the cloud users? How will this impact the cloud providers? Is it possible to create a multi-cloud market that is beneficial to all participants? In this work, we begin addressing these questions by using the game theoretic model of trading networks and formally compare between the single and multi-cloud markets. This comparson a) provides a sufficient condition under which the multi-cloud network can be considered more efficient than the single cloud one in the sense that a centralized coordinator having full information can impose an outcome that is strongly Pareto-dominant for all players and b) shows a surprising result that without centralized coordination, settings are possible in which even the cloud buyers' utilities may decrease when moving from a single cloud to a multi-cloud network. As these two results emphasize the need for centralized coordination to ensure a Pareto-dominant outcome and as the aforementioned Pareto-dominant result requires truthful revelation of participant's private information, we provide an automated mechanism design (AMD) approach, which, in the Bayesian setting, finds mechanisms which result in expectation in such Pareto-dominant outcomes, and in which truthful revelation of the parties' private information is the dominant strategy. We also provide empirical analysis to show the validity of our AMD approach.

The Large Array Survey Telescope (LAST) is a wide-field telescope designed to explore the variable and transient sky with a high cadence and to be a test-bed for cost-effective telescope design. A LAST node is composed of 48 (32 already deployed), 28-cm f/2.2 telescopes. A single telescope has a 7.4 deg^2 field of view and reaches a 5-sigma limiting magnitude of 19.6 (21.0) in 20s (20x20s) (filter-less), while the entire system provides a 355 deg^2 field of view. The basic strategy of LAST is to obtain multiple 20-s consecutive exposures of each field (a visit). Each telescope carries a 61 Mpix camera, and the system produces, on average, about 2.2 Gbit/s. This high data rate is analyzed in near real-time at the observatory site, using limited computing resources (about 700 cores). Given this high data rate, we have developed a new, efficient data reduction and analysis pipeline. The data pipeline includes two major parts: (i) Processing and calibration of single images, followed by a coaddition of the visit's exposures. (ii) Building the reference images and performing image subtraction and transient detection. Here we describe in detail the first part of the pipeline. Among the products of this pipeline are photometrically and astrometrically calibrated single and coadded images, 32-bit mask images marking a wide variety of problems and states of each pixel, source catalogs built from individual and coadded images, Point Spread Function (PSF) photometry, merged source catalogs, proper motion and variability indicators, minor planets detection, calibrated light curves, and matching with external catalogs. The entire pipeline code is made public. Finally, we demonstrate the pipeline performance on real data taken by LAST., Comment: Accepted to PASP, 15 pages, 10 figures

Motor skills, especially fine motor skills like handwriting, play an essential role in academic pursuits and everyday life. Traditional methods to teach these skills, although effective, can be time-consuming and inconsistent. With the rise of advanced technologies like robotics and artificial intelligence, there is increasing interest in automating such teaching processes using these technologies, via human-robot and human-computer interactions. In this study, we examine the potential of a virtual AI teacher in emulating the techniques of human educators for motor skill acquisition. We introduce an AI teacher model that captures the distinct characteristics of human instructors. Using a Reinforcement Learning environment tailored to mimic teacher-learner interactions, we tested our AI model against four guiding hypotheses, emphasizing improved learner performance, enhanced rate of skill acquisition, and reduced variability in learning outcomes. Our findings, validated on synthetic learners, revealed significant improvements across all tested hypotheses. Notably, our model showcased robustness across different learners and settings and demonstrated adaptability to handwriting. This research underscores the potential of integrating Reinforcement Learning and Imitation Learning models with robotics in revolutionizing the teaching of critical motor skills., Comment: arXiv admin note: The first version of this paper has been removed by arXiv administrators as the submitter did not have the right to agree to the license at the time of submission. This version resolves the rights issue, includes two additional authors, and is cleared to go public