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Topological crystalline insulators (TCIs) host topological phases of matter protected by crystal symmetries. Topological surface states in three-dimensional TCIs have been predicted and observed in IV-VI SnTe-class semiconductors. Despite the prediction of a two-dimensional (2D) TCI characterized by two pairs of edge states inside the bulk gap, materials challenges have thus far prevented its experimental realization. Here we report the growth and characterization of bilayer SnTe on the 2$H$-NbSe$_2$ substrate by molecular beam epitaxy and scanning tunneling microscopy. We experimentally observe two anticorrelated, periodically modulated pairs of conducting edge states along the perimeters of the sample with a large band gap exceeding $0.2$ eV. We identify these states with a 2D TCI through first principles calculations. Finally, we probe the coupling of adjacent topological edge states and demonstrate the resulting energy shift driven by a combination of electrostatic interactions and tunneling coupling. Our work opens the door to investigations of tunable topological states in 2D TCIs, of potential impact for spintronics and nanoelectronics applications at room temperature.

This proposal outlines the future plans of the Brazilian High-Energy Physics (HEP) community for upcoming collider experiments. With the construction of new particle colliders on the horizon and the ongoing operation of the High-Luminosity LHC, several research groups in Brazil have put forward technical proposals, covering both hardware and software contributions, as part of the Brazilian contribution to the global effort. The primary goal remains to foster a unified effort within the Brazilian HEP community, optimizing resources and expertise to deliver a high-impact contribution to the international HEP community., Comment: 24 pages, submitted to the Latin American Strategy Forum for Research Infrastructure

We study a geometric facility location problem under imprecision. Given $n$ unit intervals in the real line, each with one of $k$ colors, the goal is to place one point in each interval such that the resulting \emph{minimum color-spanning interval} is as large as possible. A minimum color-spanning interval is an interval of minimum size that contains at least one point from a given interval of each color. We prove that if the input intervals are pairwise disjoint, the problem can be solved in $O(n)$ time, even for intervals of arbitrary length. For overlapping intervals, the problem becomes much more difficult. Nevertheless, we show that it can be solved in $O(n \log^2 n)$ time when $k=2$, by exploiting several structural properties of candidate solutions, combined with a number of advanced algorithmic techniques. Interestingly, this shows a sharp contrast with the 2-dimensional version of the problem, recently shown to be NP-hard.

The NIFFTE fission Time Projection Chamber (fissionTPC) has been used to measure the $^{239}$Pu(n,f)/$^{235}$U(n,f) cross-section ratio for neutron-induced fission in the range of 0.1 - 100 MeV, with high precision. A white neutron source was provided by the Los Alamos Neutron Science Center, where the experiment was conducted as a remeasurement to evaluate a roughly 2% discrepancy of the previous fissionTPC results with ENDF/B-VIII.0. A detailed accounting of measurement uncertainties was performed, based on the fissionTPC's novel ability to provide three-dimensional reconstruction of fission-fragment ionization profiles. Current results obtained using a vapor-deposited, highly uniform $^{239}$Pu target, in comparison to the measurement published in 2021, where a $^{239}$Pu electroplated target was used, are presented and discussed. The remeasurement presented here is in agreement with the previous fissionTPC result within measurement uncertainties.

In recent years, Data Science has become increasingly relevant as a support tool for industry, significantly enhancing decision-making in a way never seen before. In this context, the MLOps discipline emerges as a solution to automate the life cycle of Machine Learning models, ranging from experimentation to monitoring in productive environments. Research results shows MLOps is a constantly evolving discipline, with challenges and solutions for integrating development and production environments, publishing models in production environments, and monitoring models throughout the end to end development lifecycle. This paper contributes to the understanding of MLOps techniques and their most diverse applications.

Modern artificial intelligence (AI) systems are powered by foundation models. This paper presents a new set of foundation models, called Llama 3. It is a herd of language models that natively support multilinguality, coding, reasoning, and tool usage. Our largest model is a dense Transformer with 405B parameters and a context window of up to 128K tokens. This paper presents an extensive empirical evaluation of Llama 3. We find that Llama 3 delivers comparable quality to leading language models such as GPT-4 on a plethora of tasks. We publicly release Llama 3, including pre-trained and post-trained versions of the 405B parameter language model and our Llama Guard 3 model for input and output safety. The paper also presents the results of experiments in which we integrate image, video, and speech capabilities into Llama 3 via a compositional approach. We observe this approach performs competitively with the state-of-the-art on image, video, and speech recognition tasks. The resulting models are not yet being broadly released as they are still under development.

Languages continually evolve in response to societal events, resulting in new terms and shifts in meanings. These changes have significant implications for computer applications, including automatic translation and chatbots, making it essential to characterize them accurately. The recent development of LLMs has notably advanced natural language understanding, particularly in sense inference and reasoning. In this paper, we investigate the potential of LLMs in characterizing three types of semantic change: dimension, relation, and orientation. We achieve this by combining LLMs' Chain-of-Thought with rhetorical devices and conducting an experimental assessment of our approach using newly created datasets. Our results highlight the effectiveness of LLMs in capturing and analyzing semantic changes, providing valuable insights to improve computational linguistic applications.

This document focuses on databases disseminating data on (hazardous) substances found on the North American and the European (EU) market. The goal is to analyse the FAIRness (Findability, Accessibility, Interoperability and Reusability) of published open data on these substances and to qualitatively evaluate to what extend the selected databases already fulfil the criteria set out in the commission draft regulation on a common data chemicals platform. We implemented two complementary approaches: Manual, and Automatic. The manual approach is based on online questionnaires. These questionnaires provide a structured approach to evaluating FAIRness by guiding users through a series of questions related to the FAIR principles. They are particularly useful for initiating discussions on FAIR implementation within research teams and for identifying areas that require further attention. Automated tools for FAIRness assessment, such as F-UJI and FAIR Checker, are gaining prominence and are continuously under development. Unlike manual tools, automated tools perform a series of tests automatically starting from a dereferenceable URL to the data resource to be evaluated. We analysed ten widely adopted datasets managed in Europe and North America. The highest score from automatic analysis was 54/100. The manual analysis shows that several FAIR metrics were satisfied, but not detectable by automatic tools because there is no metadata, or the format of the information was not a standard one. Thus, it was not interpretable by the tool. We present the details of the analysis and tables summarizing the outcomes, the issues, and the suggestions to address these issues.

In May 2024, the scientific community observed intense solar eruptions that resulted in an extreme geomagnetic storm and auroral extension, highlighting the need to document and quantify these events. This study mainly focuses on their quantification. The source active region (AR 13664) evolved from 113 to 2761 millionths of the solar hemisphere between 4 May and 14 May 2024. AR 13664's magnetic free energy surpassed 1033 erg on 7 May 2024, triggering 12 X-class flares. Multiple interplanetary coronal mass ejections (ICMEs) came out from this AR, accelerating solar energetic particles toward Earth. At least four ICMEs seemingly piled up to disturb the interplanetary space, according to the satellite data and interplanetary scintillation data. The shock arrival at 17:05 UT on 10 May 2024 significantly compressed the magnetosphere down to ~ 5.04 R_E, and triggered a deep Forbush decrease. GOES satellite data and ground-based neutron monitors confirmed a ground-level enhancement from 2 UT to 10 UT on 11 May 2024. The ICMEs induced extreme geomagnetic storms, peaking at a Dst index of -412 nT at 2 UT on 11 May 2024, marking the sixth-largest storm since 1957. The AE and AL indices showed extreme auroral extensions that located the AE/AL stations into the polar cap. We gathered auroral records at that time and reconstructed the equatorward boundary of the visual auroral oval to 29.8{\deg} invariant latitude. We compared naked-eye and camera auroral visibility, providing critical caveats on their difference. We also confirmed global enhancements of storm-enhanced density of the ionosphere., Comment: 39 pages, 20 figures, and 2 tables. Figures 15 and 17 will appear on the record version. Under Review

Carbon-based nanostructures possessing {\pi}-electron magnetism have attracted tremendous interest due to their great potential for nano spintronics. In particular, quantum chains with magnetic molecular units synthesized by on-surface reactions provide an ideal playground for investigating magnetic exchange interactions between localized spin components. Here, we present an extensive study of antiferromagnetic nanographene chains with the diazahexabenzocoronene molecule as the repeating unit. A combination of bond-resolved scanning tunneling microscopy, density functional theory and quantum spin models revealed their detailed structures and electronic and magnetic properties. We found that the antiferromagnetic chains host a collective state featuring gapped excitations for an even number of repeating units and one featuring a Kondo excitation for an odd number. Comparing with exact many-body quantum spin models, our molecular chains provide the realization of an entangled quantum Heisenberg model. Coupled with the tunability of the molecular building blocks, these systems can act as an ideal platform for the experimental realization of topological spin lattices.

Turnover consists of moving into and out of professional employees in the company in a given period. Such a phenomenon significantly impacts the software industry since it generates knowledge loss, delays in the schedule, and increased costs in the final project. Despite the efforts made by researchers and professionals to minimize the turnover, more studies are needed to understand the motivation that drives Software Engineers to leave their jobs and the main strategies CEOs adopt to retain these professionals in software development companies. In this paper, we contribute a mixed methods study involving semi-structured interviews with Software Engineers and CEOs to obtain a wider opinion of these professionals about turnover and a subsequent validation survey with additional software engineers to check and review the insights from interviews. In studying such aspects, we identified 19 different reasons for software engineers' turnover and 18 more efficient strategies used in the software development industry to reduce it. Our findings provide several implications for industry and academia, which can drive future research.

The recent success of Large Language Models (LLMs) has sparked concerns about their potential to spread misinformation. As a result, there is a pressing need for tools to identify ``fake arguments'' generated by such models. To create these tools, examples of texts generated by LLMs are needed. This paper introduces a methodology to obtain good, bad and ugly arguments from argumentative essays produced by ChatGPT, OpenAI's LLM. We then describe a novel dataset containing a set of diverse arguments, ArGPT. We assess the effectiveness of our dataset and establish baselines for several argumentation-related tasks. Finally, we show that the artificially generated data relates well to human argumentation and thus is useful as a tool to train and test systems for the defined tasks.

We consider the simultaneous control of the relative phase and populations of two-level quantum systems by an external field. We apply a reverse engineering approach, which allows obtaining an analytical expression for the control field depending upon two user-defined functions that dictate the population and the relative phase dynamics. We show that, in general, the prescribed functions for the dynamics cannot be chosen arbitrarily. We implement the reverse engineering technique to reach several target states using different kinds of functions to specify the system dynamics. We show that by adjusting these dynamical functions, we can produce different kinds of control fields. These controls can be easily build, needing, apart from the dynamical function themselves, only their derivatives. The methodology presented here will certainly find many applications that go beyond simple two-level systems.

In response to the prevailing challenges in contemporary software development, this article introduces an innovative approach to code augmentation centered around Impermanent Identifiers. The primary goal is to enhance the software development experience by introducing dynamic identifiers that adapt to changing contexts, facilitating more efficient interactions between developers and source code, ultimately advancing comprehension, maintenance, and collaboration in software development. Additionally, this study rigorously evaluates the adoption and acceptance of Impermanent Identifiers within the software development landscape. Through a comprehensive empirical examination, we investigate how developers perceive and integrate this approach into their daily programming practices, exploring perceived benefits, potential barriers, and factors influencing its adoption. In summary, this article charts a new course for code augmentation, proposing Impermanent Identifiers as its cornerstone while assessing their feasibility and acceptance among developers. This interdisciplinary research seeks to contribute to the continuous improvement of software development practices and the progress of code augmentation technology., Comment: to be published in The Journal of Systems & Software

Van der Waals heterostructures have emerged as an ideal platform for creating engineered artificial electronic states. While vertical heterostructures have been extensively studied, realizing high-quality lateral heterostructures with atomically sharp interfaces remains a major experimental challenge. Here, we advance a one-pot two-step molecular beam lateral epitaxy approach and successfully synthesize atomically well-defined 1T-VSe$_2$ -- 1H-NbSe$_2$ lateral heterostructures. We demonstrate the formation of defect-free lateral heterostructures and characterize their electronic structure using scanning tunnelling microscopy and spectroscopy together with density functional theory calculations. We find additional electronic states at the one-dimensional interface as well as signatures of Kondo resonances in a side-coupled geometry. Our experiments explore the full potential of lateral heterostructures for realizing exotic electronic states in low-dimensional systems for further studies of artificial designer quantum materials.

Continuous 2-dimensional space is often discretized by considering a mesh of weighted cells. In this work we study how well a weighted mesh approximates the space, with respect to shortest paths. We consider a shortest path $ \mathit{SP_w}(s,t) $ from $ s $ to $ t $ in the continuous 2-dimensional space, a shortest vertex path $ \mathit{SVP_w}(s,t) $ (or any-angle path), which is a shortest path where the vertices of the path are vertices of the mesh, and a shortest grid path $ \mathit{SGP_w}(s,t) $, which is a shortest path in a graph associated to the weighted mesh. We provide upper and lower bounds on the ratios $ \frac{\lVert \mathit{SGP_w}(s,t)\rVert}{\lVert \mathit{SP_w}(s,t)\rVert} $, $ \frac{\lVert \mathit{SVP_w}(s,t)\rVert}{\lVert \mathit{SP_w}(s,t)\rVert} $, $ \frac{\lVert \mathit{SGP_w}(s,t)\rVert}{\lVert \mathit{SVP_w}(s,t)\rVert} $ in square and hexagonal meshes, extending previous results for triangular grids. These ratios determine the effectiveness of existing algorithms that compute shortest paths on the graphs obtained from the grids. Our main results are that the ratio $ \frac{\lVert \mathit{SGP_w}(s,t)\rVert}{\lVert \mathit{SP_w}(s,t)\rVert} $ is at most $ \frac{2}{\sqrt{2+\sqrt{2}}} \approx 1.08 $ and $ \frac{2}{\sqrt{2+\sqrt{3}}} \approx 1.04 $ in a square and a hexagonal mesh, respectively.

The CMS detector will be upgraded for the HL-LHC to include a MIP Timing Detector (MTD). The MTD will consist of barrel and endcap timing layers, BTL and ETL respectively, providing precision timing of charged particles. The BTL sensors are based on LYSO:Ce scintillation crystals coupled to SiPMs with TOFHIR2 ASICs for the front-end readout. A resolution of 30-60 ps for MIP signals at a rate of 2.5 Mhit/s per channel is expected along the HL-LHC lifetime. We present an overview of the TOFHIR2 requirements and design, simulation results and measurements with TOFHIR2 ASICs. The measurements of TOFHIR2 associated to sensor modules were performed in different test setups using internal test pulses or blue and UV laser pulses emulating the signals expected in the experiment. The measurements show a time resolution of 24 ps initially during Beginning of Operation (BoO) and 58 ps at End of Operation (EoO) conditions, matching well the BTL requirements. We also showed that the time resolution is stable up to the highest expected MIP rate. Extensive radiation tests were performed, both with x-rays and heavy ions, showing that TOFHIR2 is not affected by the radiation environment during the experiment lifetime., Comment: Editor: J. Varela

Starting from the perspective of reports published in Brazilian newspapers at the time, as well as letters exchanged between scientists who worked in Brazil and North American colleagues and documents from the symposium on cosmic rays, a chronological sequence of how the so-called Compton mission in Brazil took place and was perceived by the literate public will be presented., Comment: 20 pages, 8 figures

The cosmological principle posits that the universe is statistically homogeneous and isotropic on large scales, implying all matter shares the same rest frame. This principle suggests that velocity estimates of our motion from various sources should agree with the cosmic microwave background (CMB) dipole's inferred velocity of 370 km/s. Yet, for over two decades, analyses of radio galaxy and quasar catalogs have found velocities at odds with the CMB dipole, with tensions up to 5$\sigma$. In a blind analysis of BOSS and eBOSS spectroscopic data from galaxies and quasars across $0.2

About 40 transneptunian binaries (TNBs) have fully determined orbits with about 10 others being solved except for breaking the mirror ambiguity. Despite decades of study almost all TNBs have only ever been analyzed with a model that assumes perfect Keplerian motion (e.g., two point masses). In reality, all TNB systems are non-Keplerian due to non-spherical shapes, possible presence of undetected system components, and/or solar perturbations. In this work, we focus on identifying candidates for detectable non-Keplerian motion based on sample of 45 well-characterized binaries. We use MultiMoon, a non-Keplerian Bayesian inference tool, to analyze published relative astrometry allowing for non-spherical shapes of each TNB system's primary. We first reproduce the results of previous Keplerian fitting efforts with MultiMoon, which serves as a comparison for the non-Keplerian fits and confirms that these fits are not biased by the assumption of a Keplerian orbit. We unambiguously detect non-Keplerian motion in 8 TNB systems across a range of primary radii, mutual orbit separations, and system masses. As a proof of concept for non-Keplerian fitting, we perform detailed fits for (66652) Borasisi-Pabu, possibly revealing a $J_2 \approx 0.44$, implying Borasisi (and/or Pabu) may be a contact binary or an unresolved compact binary. However, full confirmation of this result will require new observations. This work begins the next generation of TNB analyses that go beyond the point mass assumption to provide unique and valuable information on the physical properties of TNBs with implications for their formation and evolution., Comment: Accepted to AJ

Gastric and oesophageal (OG) cancers are the leading causes of cancer mortality worldwide. In OG cancers, recent studies have showed that PDL1 immune checkpoint inhibitors (ICI) in combination with chemotherapy improves patient survival. However, our understanding of the tumour immune microenvironment in OG cancers remains limited. In this study, we interrogate multiplex immunofluorescence (mIF) images taken from patients with advanced Oesophagogastric Adenocarcinoma (OGA) who received first-line fluoropyrimidine and platinum-based chemotherapy in the PLATFORM trial (NCT02678182) to predict the efficacy of the treatment and to explore the biological basis of patients responding to maintenance durvalumab (PDL1 inhibitor). Our proposed Artificial Intelligence (AI) based marker successfully identified responder from non-responder (p < 0.05) as well as those who could potentially benefit from ICI with statistical significance (p < 0.05) for both progression free and overall survival. Our findings suggest that T cells that express FOXP3 seem to heavily influence the patient treatment response and survival outcome. We also observed that higher levels of CD8+PD1+ cells are consistently linked to poor prognosis for both OS and PFS, regardless of ICI.

Live languages continuously evolve to integrate the cultural change of human societies. This evolution manifests through neologisms (new words) or \textbf{semantic changes} of words (new meaning to existing words). Understanding the meaning of words is vital for interpreting texts coming from different cultures (regionalism or slang), domains (e.g., technical terms), or periods. In computer science, these words are relevant to computational linguistics algorithms such as translation, information retrieval, question answering, etc. Semantic changes can potentially impact the quality of the outcomes of these algorithms. Therefore, it is important to understand and characterize these changes formally. The study of this impact is a recent problem that has attracted the attention of the computational linguistics community. Several approaches propose methods to detect semantic changes with good precision, but more effort is needed to characterize how the meaning of words changes and to reason about how to reduce the impact of semantic change. This survey provides an understandable overview of existing approaches to the \textit{characterization of semantic changes} and also formally defines three classes of characterizations: if the meaning of a word becomes more general or narrow (change in dimension) if the word is used in a more pejorative or positive/ameliorated sense (change in orientation), and if there is a trend to use the word in a, for instance, metaphoric or metonymic context (change in relation). We summarized the main aspects of the selected publications in a table and discussed the needs and trends in the research activities on semantic change characterization.

In this paper, we consider the Weighted Region Problem. In the Weighted Region Problem, the length of a path is defined as the sum of the weights of the subpaths within each region, where the weight of a subpath is its Euclidean length multiplied by a weight $ \alpha \geq 0 $ depending on the region. We study a restricted version of the problem of determining shortest paths through a single weighted rectangular region. We prove that even this very restricted version of the problem is unsolvable within the Algebraic Computation Model over the Rational Numbers (ACMQ). On the positive side, we provide the equations for the shortest paths that are computable within the ACMQ. Additionally, we provide equations for the bisectors between regions of the Shortest Path Map for a source point on the boundary of (or inside) the rectangular region.

Synchronization is one of the most striking instances of collective behavior, occurring in many natural phenomena. For example, in some ant species, ants are inactive within the nest most of the time, but their bursts of activity are highly synchronized and involve the entire nest population. Here we revisit a simulation model that generates this synchronized rhythmic activity through autocatalytic behavior, i.e., active ants can activate inactive ants, followed by a period of rest. We derive a set of delay differential equations that provide an accurate description of the short- and medium-time simulations for large ant colonies. Analysis of the fixed-point solutions, complemented by numerical integration of the equations, indicates the existence of stable limit-cycle solutions when the rest period is greater than a threshold and the event of spontaneous activation of inactive ants is very unlikely, so that most of the arousal of ants is done by active ants. Furthermore, we argue that the persistent oscillations observed in the simulations for colonies of finite size are due to resonant amplification of demographic noise.