We study the symmetrical Dziobek configurations where, in $\mathbb{R}^{d}$, there are $d$ bodies with unit masses at the vertices of a regular $(d-1)$-dimensional simplex of unit edge length and two more bodies with nonzero masses $s,k$ are on the line passing through the center of the simplex and being orthogonal to it. In the case of logarithmic potential, the finiteness is proved for all $s,k eq 0, d>1$, and we obtain the bifurcation surface in the $(s,k,d)$-space through Gröbner basis computation. Using cylindrical algebraic decompositions, we find $197232$ sample points in the complement of the bifurcation surface. We propose a method to reduce the number to only $202$. By Hermite's root counting theorem, we find that, generically, there can be $0,1,2,3$ or $4$ concave, $1,2,3, $ or $4$ convex, and in totality, $1,2,3,4$ or $5$ such configurations for all dimensions $d>1$. For positive $s$ and $k$, generically, there is a unique convex configuration, while the number of concave ones can be $0,2$ or $4$. All possible combinations for the numbers described above are realized when $d=2$. We obtain a set of generalized Laura-Andoyer equations equivalent to the central config
With the rapid growth of the low-altitude economy, there is increasing demand for real-time data collection using UAV-assisted wireless sensor networks. This paper investigates the problem of minimizing the age of information (AoI) in UAV-assisted wireless sensor networks by optimizing the UAV flight routing. We formulate the AoI minimization task and propose a large language model (LLM)-assisted UAV routing algorithm (LAURA). LAURA employs an LLM as intelligent crossover operators within an evolutionary optimization framework to efficiently explore the solution space. Simulation results show that LAURA outperforms benchmark methods in reducing the maximum AoI, especially in scenarios with a large number of sensor nodes.
Code review is critical for ensuring software quality and maintainability. With the rapid growth in software scale and complexity, code review has become a bottleneck in the development process because of its time-consuming and knowledge-intensive nature and the shortage of experienced developers willing to review code. Several approaches have been proposed for automatically generating code reviews based on retrieval, neural machine translation, pre-trained models, or large language models (LLMs). These approaches mainly leverage historical code changes and review comments. However, a large amount of crucial information for code review, such as the context of code changes and prior review knowledge, has been overlooked. This paper proposes an LLM-based review knowledge-augmented, context-aware framework for code review generation, named LAURA. The framework integrates review exemplar retrieval, context augmentation, and systematic guidance to enhance the performance of ChatGPT-4o and DeepSeek v3 in generating code review comments. Besides, given the extensive low-quality reviews in existing datasets, we also constructed a high-quality dataset. Experimental results show that for bot
Online social networks allow different agencies and the public to interact and share the underlying risks and protective actions during major disasters. This study revealed such crisis communication patterns during hurricane Laura compounded by the COVID-19 pandemic. Laura was one of the strongest (Category 4) hurricanes on record to make landfall in Cameron, Louisiana. Using the Application Programming Interface (API), this study utilizes large-scale social media data obtained from Twitter through the recently released academic track that provides complete and unbiased observations. The data captured publicly available tweets shared by active Twitter users from the vulnerable areas threatened by Laura. Online social networks were based on user influence feature ( mentions or tags) that allows notifying other users while posting a tweet. Using network science theories and advanced community detection algorithms, the study split these networks into twenty-one components of various sizes, the largest of which contained eight well-defined communities. Several natural language processing techniques (i.e., word clouds, bigrams, topic modeling) were applied to the tweets shared by the us
In this paper, we propose a generalization for the class of laura algebras, which we call almost laura. We show that this new class of algebras retains most of the essential features of laura algebras, especially concerning the important role played by the non-semiregular components in their Auslander-Reiten quivers. Also, we study more intensively the left supported almost laura algebras, showing that these are characterized by the presence of a generalized standard, convex and faithful component. Finally, we prove that almost laura algebras behave well with respect to full subcategories, split-by-nilpotent extensions and skew group algebras.
In August 2020, as Texas was coming down from a large summer COVID-19 surge, forecasts suggested that Hurricane Laura was tracking towards 6M residents along the East Texas coastline, threatening to spread COVID-19 across the state and cause pandemic resurgences. To assist local authorities facing the dual-threat, we integrated survey expectations of coastal residents and observed hurricane evacuation rates in a statistical framework that combined with local pandemic conditions predicts how COVID-19 would spread in response to a hurricane. For Hurricane Laura, we estimate that 499,500 [90% Credible Interval (CI): 347,500, 624,000] people evacuated the Texan counties, that no single county accumulated more than 2.5% of hurricane evacuees, and that there were 2,900 [90% CI: 1,700, 5,800] exportations of Covid-19 across the state. In general, reception estimates were concentrated in regions with higher population densities. Nonetheless, higher importation risk is expected in small Districts, with a maximum number of importations of 10 per 10,000 residents in our case study. Overall, we present a flexible and transferable framework that captures spatial heterogeneity and incorporates g
In this paper, we study the covering theory of laura algebras. We prove that if a connected laura algebra is standard (that is, it is not quasi-tilted of canonical type and its connecting components are standard), then this algebra has nice Galois coverings associated to the coverings of the connecting component. As a consequence, we show that the first Hochschild cohomology group of a standard laura algebra vanishes if and only if it has no proper Galois coverings.
We give a simple combinatorial criterion allowing to recognize whether a string (or, more generally, a special biserial) algebra is a laura algebra or not. We also show that a special biserial algebra is laura if and only if it has a finite number of isomorphism classes of indecomposable modules which have projective dimension and injective dimension greater than or equal to two, solving a conjecture ok Skowronski for special biserial algebras.
The Dalitz plot analysis technique has become an increasingly important method in heavy flavour physics. The Laura++ fitter has been developed as a flexible tool that can be used for Dalitz plot analyses in different experimental environments. Explicitly designed for three-body decays of heavy-flavoured mesons to spinless final state particles, it is optimised in order to describe all possible resonant or nonresonant contributions, and to accommodate possible CP violation effects.
We consider the bound quiver algebras whose ordinary quiver is that of a canonical algebra. We determine which of those algebras are hereditary, tilted, quasitilted, weakly shod or laura algebras.
The Laura++ software package is designed for performing fits of amplitude models to data from decays of spin-0 particles into final states containing three spin-0 particles - so-called Dalitz-plot analysis. An overview of the amplitude formalism and its software implementation is given as well as summaries of recently added features and planned future developments.
Ultra-fast outflows (UFOs) are mildly relativistic (outflow velocity $v_{out}>0.1c$) nuclear winds detected as blueshifted absorption lines from highly ionized, dense gas in the X-ray spectra of active galactic nuclei. The AGN feedback mechanism is believed to be powered by these outflows, which can inject a large amount of energy and momentum into the surrounding interstellar medium, shaping the coevolution of the AGNs and their host galaxies. We performed a systematic search and rigorous statistical assessment of the presence of UFOs in the 7-12 keV band, in a sample of bright local AGNs. This study also aims to understand whether the presence and characteristics of UFOs depend on the state of the sources, by studying the relations between the incidence of UFOs and the accretion properties of AGNs. We collected X-ray spectroscopic flux-limited XMM data of 33 observations of local (z<0.2) type 1 AGNs. We modeled their spectra in the 2-12 keV band using a combination of direct-continuum and reflection components and searched for absorption features. This represents the first systematic search for UFOs up to 12 keV. We performed Monte Carlo simulations to assess the statistica
We present a multiparty session type (MST) framework with asynchronous mixed choice (MC). We propose a core construct for MC that allows transient inconsistencies in protocol state between distributed participants, but ensures all participants can always eventually reach a mutually consistent state. We prove the correctness of our system by establishing a progress property and an operational correspondence between global types and distributed local type projections. Based on our theory, we implement a practical toolchain for specifying and validating asynchronous MST protocols featuring MC, and programming compliant gen_statem processes in Erlang/OTP. We test our framework by using our toolchain to specify and reimplement part of the amqp_client of the RabbitMQ broker for Erlang.
The rapid adoption of complex Artificial Intelligence (AI) and Machine Learning (ML) models has led to their characterization as black boxes due to the difficulty of explaining their internal decision-making processes. This lack of transparency hinders users' ability to understand, validate and trust model behavior, particularly in high-risk applications. Although explainable AI (XAI) has made significant progress, there remains a need for versatile and effective techniques to address increasingly complex models. This work introduces Multivariate Conditional Expectation (MUCE), a model-agnostic method for local explainability designed to capture prediction changes from feature interactions. MUCE extends Individual Conditional Expectation (ICE) by exploring a multivariate grid of values in the neighborhood of a given observation at inference time, providing graphical explanations that illustrate the local evolution of model predictions. In addition, two quantitative indices, stability and uncertainty, summarize local behavior and assess model reliability. Uncertainty is further decomposed into uncertainty+ and uncertainty- to capture asymmetric effects that global measures may overl
This White Paper presents the scientific rationale and instrument concept for HRMOS (High-Resolution Multi-Object Spectrograph), a next-generation instrument proposed for the ESO Very Large Telescope within the VLT 2030 roadmap. Current and planned facilities offer either multi-object spectroscopy or ultra-high spectral resolution, but not both. HRMOS fills this gap by combining very high spectral resolution, multi-object capability, and radial-velocity stability, enabling transformative studies in Galactic and extragalactic astrophysics. The baseline design provides a resolving power of R = 80000, radial-velocity precision of 10 m s-1 (goal: 5 m s-1), simultaneous observations of 50-60 targets, and broad optical coverage down to 385 nm. These capabilities enable precise measurements of elemental abundances, isotopic ratios, line profiles, and radial velocities for large stellar samples, including crowded fields, star clusters, the Galactic bulge, and nearby dwarf galaxies. HRMOS will address key questions on the age of the oldest stellar populations through nucleocosmochronology, the formation and survival of planetary systems, the assembly history of the Milky Way and satellites,
Deficits in working memory, which includes both the ability to learn and to retain information short-term, are a hallmark of many cognitive disorders. Our study analyzes data from a neuroscience experiment on animal subjects, where performance on a working memory task was recorded as repeated binary success or failure data. We estimate continuous probability of success curves from this binary data in the context of functional data analysis, which is largely used in biological processes that are intrinsically continuous. We then register these curves to decompose each function into its amplitude, representing overall performance, and its phase, representing the speed of learning or response. Because we are able to separate speed from performance, we can address the crucial question of whether a cognitive disorder impacts not only how well subjects can learn and remember, but also how fast. This allows us to analyze the components jointly to uncover how speed and performance co-vary, and to compare them separately to pinpoint whether group differences stem from a deficit in peak performance or a change in speed.
The complex software systems developed nowadays require assessing their quality and proneness to errors. Reducing code complexity is a never-ending problem, especially in today's fast pace of software systems development. Therefore, the industry needs to find a method to determine the qualities of a software system, the degree of difficulty in developing new functionalities, or the system's proneness to errors. One way of measuring and predicting the quality attributes of a software system is to analyse the software metrics values for it and the relationships between them. More precisely, we should study the metrics that measure and determine the degree of complexity of the code. This paper aims to analyse a novel complexity metric, Hybrid Cyclomatic Complexity (HCC) and its efficiency as a feature in a defect prediction model. The main idea behind this new metric is that inherited complexity should play a role in the complexity of a class, hence the need for a metric that calculates the total complexity of a class, taking into account the complexities of its descendants. Moreover, we will present a comparative study between the HCC metric and its two components, the inherited comp
Periodically poled thin-film lithium niobate (TFLN) crystals are the fundamental building block for highly-efficient quantum light sources and frequency converters. The efficiency of these devices is strongly dependent on the interaction length between the light and the nonlinear material, scaling quadratically with this parameter. Nevertheless, the fabrication of long, continuously poled areas in TFLN remains challenging, the length of continuously poled areas rarely exceeds 10 mm. In this work, we demonstrate a significant progress in this field achieving the periodic poling of continuous poled areas of 70 mm length with a 3 microns poling period and a close to 50 % duty cycle. We compare two poling electrode design approaches to fabricate long, continuous poled areas. The first approach involves the poling of a single, continuous 70 mm long electrode. The second utilize a segmented approach including the poling of more than 20 individual sections forming together a 70 mm long poling area with no stitching errors. While the continuous electrode allows for faster fabrication, the segmented approach allows to individually optimize the poling resulting in less duty cycle variation.
Muons offer a unique opportunity to build a compact high-energy electroweak collider at the 10 TeV scale. A Muon Collider enables direct access to the underlying simplicity of the Standard Model and unparalleled reach beyond it. It will be a paradigm-shifting tool for particle physics representing the first collider to combine the high-energy reach of a proton collider and the high precision of an electron-positron collider, yielding a physics potential significantly greater than the sum of its individual parts. A high-energy muon collider is the natural next step in the exploration of fundamental physics after the HL-LHC and a natural complement to a future low-energy Higgs factory. Such a facility would significantly broaden the scope of particle colliders, engaging the many frontiers of the high energy community. The last European Strategy for Particle Physics Update and later the Particle Physics Project Prioritisation Panel in the US requested a study of the muon collider, which is being carried on by the International Muon Collider Collaboration. In this comprehensive document we present the physics case, the state of the work on accelerator design and technology, and propose