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User feedback is crucial for the evolution of mobile apps. However, research suggests that users tend to submit uninformative, vague, or destructive feedback. Unlike recent AI4SE approaches that focus on generating code and other development artifacts, our work aims at empowering users to submit better and more constructive UI feedback with concrete suggestions on how to improve the app. We propose LikeThis!, a GenAI-based approach that takes a user comment with the corresponding screenshot to immediately generate multiple improvement alternatives, from which the user can easily choose their preferred option. To evaluate LikeThis!, we first conducted a model benchmarking study based on a public dataset of carefully critiqued UI designs. The results show that GPT-Image-1 significantly outperformed three other state-of-the-art image generation models in improving the designs to address UI issues while keeping the fidelity and without introducing new issues. An intermediate step in LikeThis! is to generate a solution specification before sketching the design as a key to achieving effective improvement. Second, we conducted a user study with 10 production apps, where 15 users used Like

The recently completed SubMIT platform is a small set of servers that provide interactive access to substantial data samples at high speeds, enabling sophisticated data analyses with very fast turnaround times. Additionally, it seamlessly integrates massive processing resources for large-scale tasks by connecting to a set of powerful batch processing systems. It serves as an ideal prototype for an Analysis Facility tailored to meet the demanding data and computational requirements anticipated during the High-Luminosity phase of the Large Hadron Collider. The key features that make this facility so powerful include highly optimized data access with a minimum of 100Gbps networking per server, a large managed NVMe storage system, and a substantial spinning-disk Ceph file system. The platform integrates a diverse set of high multicore CPU machines for tasks benefiting from the multithreading and GPU resources for example for neural network training. SubMIT also provides and supports a flexible environment for users to manage their own software needs for example by using containers. This article describes the facility, its users, and a few complementary, generic and real-life analyses t

With the dawn of AI factories ushering a new era of computing supremacy, development of strategies to effectively track and utilize the available computing resources is garnering utmost importance. These computing resources are often modeled as Markov sources, which oscillate between free and busy states, depending on their internal load and external utilization, and are commonly referred to as Markov machines (MMs). Most of the prior work solely focuses on the problem of tracking these MMs, while often assuming a rudimentary decision process that governs their utilization. Our key observation is that the ultimate goal of tracking a MM is to properly utilize it. In this work, we consider the problem of maximizing the utility of a MM, where the utility is defined as the average revenue generated by the MM. Assuming a Poisson job arrival process and a query-based sampling procedure to sample the state of the MM, we find the optimal times to submit the available jobs to the MM so as to maximize the average revenue generated per unit job. We show that, depending on the parameters of the MM, the optimal policy is in the form of either a \emph{threshold policy} or a \emph{switching polic

What is the optimal order in which a researcher should submit their papers to journals of differing quality? I analyze a sequential search model without recall where the researcher's expected value from journal submission depends on the history of past submissions. Acceptances immediately terminate the search process and deliver some payoff, while rejections carry information about the paper's quality, affecting the researcher's belief in acceptance probability over future journals. When journal feedback does not change the paper's quality, the researcher's optimal strategy is monotone in their acceptance payoff. Submission costs distort the researcher's effective acceptance payoff, but maintain monotone optimality. If journals give feedback which can affect the paper's quality, such as through \textit{referee reports}, the search order can change drastically depending on the agent's prior belief about their paper's quality. However, I identify a set of \textit{assortative matched} conditions on feedback such that monotone strategies remain optimal whenever the agent's prior is sufficiently optimistic.

Deep generative models have achieved promising results in image generation, and various generative model hubs, e.g., Hugging Face and Civitai, have been developed that enable model developers to upload models and users to download models. However, these model hubs lack advanced model management and identification mechanisms, resulting in users only searching for models through text matching, download sorting, etc., making it difficult to efficiently find the model that best meets user requirements. In this paper, we propose a novel setting called Generative Model Identification (GMI), which aims to enable the user to identify the most appropriate generative model(s) for the user's requirements from a large number of candidate models efficiently. To our best knowledge, it has not been studied yet. In this paper, we introduce a comprehensive solution consisting of three pivotal modules: a weighted Reduced Kernel Mean Embedding (RKME) framework for capturing the generated image distribution and the relationship between images and prompts, a pre-trained vision-language model aimed at addressing dimensionality challenges, and an image interrogator designed to tackle cross-modality issue

This paper presents a novel method for finding features in the analysis of variable distributions stemming from time series. We apply the methodology to the case of submitted and accepted papers in peer-reviewed journals. We provide a comparative study of editorial decisions for papers submitted to two peer-reviewed journals: the Journal of the Serbian Chemical Society (JSCS) and this MDPI Entropy journal. We cover three recent years for which the fate of submitted papers, about 600 papers to JSCS and 2500 to Entropy, is completely determined. Instead of comparing the number distributions of these papers as a function of time with respect to a uniform distribution, we analyze the relevant probabilities, from which we derive the information entropy. It is argued that such probabilities are indeed more relevant for authors than the actual number of submissions. We tie this entropy analysis to the so called diversity of the variable distributions. Furthermore, we emphasize the correspondence between the entropy and the diversity with inequality measures, like the Herfindahl-Hirschman index and the Theil index, itself being in the class of entropy measures; the Gini coefficient which a

Many species of ants forage by building up two files: an outbound one moving from the nest to the foraging area, and a nestbound one, returning from it to the nest. Those files are eventually submitted to different threats. If the danger is concentrated at one point of the file, one might expect that ants returning to the nest will pass danger information to their nestmates moving in the opposite direction towards the danger area. In this paper, we construct simple cellular automata models for foraging ants submitted to localized abduction, were danger information is transmitted using different protocols, including the possibility of no transmission. The parameters we have used in the simulations have been estimated from actual experiments under natural conditions. So, it would be easy to test our information-transmission hypothese in real experiments. Preliminary experimental results published elsewhere suggest that the behavior of foraging ants of the species Atta insularis is best described using the hypothesis of no transmission of danger information.

The Astrophysics Source Code Library (ASCL) contains 3000 metadata records about astrophysics research software and serves primarily as a registry of software, though it also can and does accept code deposit. Though the ASCL was started in 1999, many astronomers, especially those new to the field, are not very familiar with it. This hands-on virtual tutorial was geared to new users of the resource to teach them how to use the ASCL, with a focus on finding software and information about software not only in this resource, but also by using Google and NASA's Astrophysics Data System (ADS). With computational methods so important to research, finding these methods is useful for examining (for transparency) and possibly reusing the software (for reproducibility or to enable new research). Metadata about software is useful for, for example, knowing how to cite software when it is used for research and studying trends in the computational landscape. Though the tutorial was primarily aimed at new users, advanced users were also likely to learn something new.

A Charge Density Wave (CDW) submitted to an electric field displays a strong shear deformation because of pinning at the lateral surfaces of the sample. This CDW transverse pinning was recently observed but has received little attention from a theoretical point of view until now despite important consequences on electrical conductivity properties. Here, we provide a description of this phenomenon by considering a CDW submitted to an external dc electric field and constrained by boundary conditions including both longitudinal pinning due to electrical contacts and transverse surface pinning. A simple formula for the CDW phase is obtained in 3D by using the Green function and image charges method. In addition, an analytical expression of the threshold field dependence on both length and sample cross section is obtained by considering the phase slip process. We show that the experimental data are well reproduced with this model and that bulk pinning can be neglected. This study shows that the dynamical properties of CDW systems could be mainly driven by boundary effects, despite the comparatively huge sample volumes.

We present a simple and easy to apply methodology for using high-level self-submitting parallel job queues in an MPI environment. Using C++, we implemented a library of functions, MPQueue, both for testing our concepts and for use in real applications. In particular, we have applied our ideas toward solving computational combinatorics problems and for finding bifurcation diagrams of solutions of partial differential equations (PDE). Our method is general and can be applied in many situations without a lot of programming effort. The key idea is that workers themselves can easily submit new jobs to the currently running job queue. Our applications involve complicated data structures, so we employ serialization to allow data to be effortlessly passed between nodes. Using our library, one can solve large problems in parallel without being an expert in MPI. We demonstrate our methodology and the features of the library with several example programs, and give some results from our current PDE research. We show that our techniques are efficient and effective via overhead and scaling experiments.

We consider a population structured by a spacevariable and a phenotypical trait, submitted to dispersion,mutations, growth and nonlocal competition. We introduce theclimate shift due to {\it Global Warming} and discuss the dynamicsof the population by studying the long time behavior of thesolution of the Cauchy problem. We consider three sets ofassumptions on the growth function. In the so-called {\it confinedcase} we determine a critical climate change speed for theextinction or survival of the population, the latter case taking place by "strictly following the climate shift". In the so-called {\itenvironmental gradient case}, or {\it unconfined case}, we additionally determine the propagation speedof the population when it survives: thanks to a combination of migration and evolution, it can here be different from the speed of the climate shift. Finally, we consider {\it mixed scenarios}, that are complex situations, where thegrowth function satisfies the conditions of the confined case on the right, and the conditions of the unconfined case on the left.The main difficulty comes from the nonlocal competition term that prevents the use of classical methods based on comparison argum

We study the vortex formation in optical lattices submitted to artificial gauge potentials. We compute the superfluid density for Abelian and non-Abelian gauge potentials with a mean-field approach of the Bose-Hubbard model and we determine the rule describing the number of vortices as a function of the effective magnetic flux. This simple rule is represented by a remarkably rich figure that represents the superfluid density as a function of the flux. The phenomena which emanate from this work should be observed experimentally in optical lattices within which atom tunneling is laser-assisted and described by commutative or non-commutative tunneling operators.