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Accurately describing strongly correlated systems with affordable quantum resources remains a central challenge for quantum chemistry applications on near and intermediate-term quantum computers. The localized active space self-consistent field (LASSCF) approximates the complete active space self-consistent field (CASSCF) by generating active space-based wave functions within specific fragments while treating interfragment correlation with mean-field approach, hence is computationally less expensive. Hardware-efficient ansatzes (HEA) offer affordable and shallower circuits, yet they often fail to capture the necessary correlation. Previously, Jastrow-factor-inspired non-unitary qubit operators were proposed to use with HEA for variational quantum eigensolver (VQE) calculations (nuVQE), as they do not increase circuit depths and recover correlation beyond the mean-field level for Hartree-Fock initial states. Here, we explore running nuVQE with LASSCF as the initial state. The method, named LAS-nuVQE, is shown to recover interfragment correlations, reach chemical accuracy with a small number of gates (<70) in both H4 and square cyclobutadiene. To further address the inherent symmetry-breaking in HEA, we implemented spin-constrained LAS-nuVQE to extend the capabilities of HEA further and show spin-pure results for square cyclobutadiene. We mitigate the increased measurement overhead of nuVQE via Pauli grouping and shot-frugal sampling, reducing measurement costs by up to two orders of magnitude compared to ungrouped operator, and show that one can achieve better accuracy with a small number of shots (10^3-4) per one expectation value calculation compared to noiseless simulations with one or two orders of magnitude more shots. Finally, wall clock time estimates show that, with our measurement mitigation protocols, nuVQE becomes a cheaper and more accurate alternative than VQE with HEA.

The audio visual benefit in speech perception, where congruent visual input enhances auditory processing, is well documented across age groups, particularly in challenging listening conditions and among individuals with varying hearing abilities. However, most studies rely on highly controlled laboratory environments with scripted stimuli. Here, we examine the audio visual benefit using unscripted, natural speech from untrained speakers within a virtual acoustic environment. Using electroencephalography (EEG) and cortical speech tracking, we assessed neural responses across audio visual, audio only, visual only, and masked lip conditions to isolate the role of lip movements. Additionally, we analysed individual differences in acoustic and visual features of the speakers, including pitch, jitter, and lip openness, to explore their influence on the audio visual speech tracking benefit. Results showed a significant audio visual enhancement in speech tracking with background noise, with the masked lip condition performing similarly to the audio-only condition, emphasizing the importance of lip movements in adverse listening situations. Our findings reveal the feasibility of cortical speech tracking with naturalistic stimuli and underscore the impact of individual speaker characteristics on audio-visual integration in real world listening contexts.

This paper explores ideas and provides a potential roadmap for the development and evaluation of physics-specific large-scale AI models, which we call Large Physics Models (LPMs). These models, based on foundation models such as Large Language Models (LLMs) - trained on broad data - are tailored to address the demands of physics research. LPMs can function independently or as part of an integrated framework. This framework can incorporate specialized tools, including symbolic reasoning modules for mathematical manipulations, frameworks to analyse specific experimental and simulated data, and mechanisms for synthesizing theories and scientific literature. We begin by examining whether the physics community should actively develop and refine dedicated models, rather than relying solely on commercial LLMs. We then outline how LPMs can be realized through interdisciplinary collaboration among experts in physics, computer science, and philosophy of science. To integrate these models effectively, we identify three key pillars: Development, Evaluation, and Philosophical Reflection. Development focuses on constructing models capable of processing physics texts, mathematical formulations, and diverse physical data. Evaluation assesses accuracy and reliability by testing and benchmarking. Finally, Philosophical Reflection encompasses the analysis of broader implications of LLMs in physics, including their potential to generate new scientific understanding and what novel collaboration dynamics might arise in research. Inspired by the organizational structure of experimental collaborations in particle physics, we propose a similarly interdisciplinary and collaborative approach to building and refining Large Physics Models. This roadmap provides specific objectives, defines pathways to achieve them, and identifies challenges that must be addressed to realise physics-specific large scale AI models.

Machine learning (ML) will likely play a large role in many processes in the future, also for insurance companies. However, ML models are at risk of being attacked and manipulated. In this work, the robustness of Gradient Boosted Decision Tree (GBDT) models and Deep Neural Networks (DNN) within an insurance context will be evaluated. Therefore, two GBDT models and two DNNs are trained on two different tabular datasets from an insurance context. Past research in this domain mainly used homogenous data and there are comparably few insights regarding heterogenous tabular data. The ML tasks performed on the datasets are claim prediction (regression) and fraud detection (binary classification). For the backdoor attacks different samples containing a specific pattern were crafted and added to the training data. It is shown, that this type of attack can be highly successful, even with a few added samples. The backdoor attacks worked well on the models trained on one dataset but poorly on the models trained on the other. In real-world scenarios the attacker will have to face several obstacles but as attacks can work with very few added samples this risk should be evaluated., Comment: 40 pages, 14 figures

In the span of four decades, quantum computation has evolved from an intellectual curiosity to a potentially realizable technology. Today, small-scale demonstrations have become possible for quantum algorithmic primitives on hundreds of physical qubits and proof-of-principle error-correction on a single logical qubit. Nevertheless, despite significant progress and excitement, the path toward a full-stack scalable technology is largely unknown. There are significant outstanding quantum hardware, fabrication, software architecture, and algorithmic challenges that are either unresolved or overlooked. These issues could seriously undermine the arrival of utility-scale quantum computers for the foreseeable future. Here, we provide a comprehensive review of these scaling challenges. We show how the road to scaling could be paved by adopting existing semiconductor technology to build much higher-quality qubits, employing system engineering approaches, and performing distributed quantum computation within heterogeneous high-performance computing infrastructures. These opportunities for research and development could unlock certain promising applications, in particular, efficient quantum simulation/learning of quantum data generated by natural or engineered quantum systems. To estimate the true cost of such promises, we provide a detailed resource and sensitivity analysis for classically hard quantum chemistry calculations on surface-code error-corrected quantum computers given current, target, and desired hardware specifications based on superconducting qubits, accounting for a realistic distribution of errors. Furthermore, we argue that, to tackle industry-scale classical optimization and machine learning problems in a cost-effective manner, distributed quantum-assisted probabilistic computing with custom-designed accelerators should be considered as a complementary path toward scalability., Comment: 50 pages, 42 figures

This document is comprised of a collection of updated preliminary parameters for the key parts of the muon collider. The updated preliminary parameters follow on from the October 2023 Tentative Parameters Report. Particular attention has been given to regions of the facility that are believed to hold greater technical uncertainty in their design and that have a strong impact on the cost and power consumption of the facility. The data is collected from a collaborative spreadsheet and transferred to overleaf.

Owing to their similarities with giant exoplanets, brown dwarf companions of stars provide insights into the fundamental processes of planet formation and evolution. From their orbits, several brown dwarf companions are found to be more massive than theoretical predictions given their luminosities and the ages of their host stars (e.g. Brandt et al. 2021, Cheetham et al. 2018, Li et al. 2023). Either the theory is incomplete or these objects are not single entities. For example, they could be two brown dwarfs each with a lower mass and intrinsic luminosity (Brandt et al. 2021, Howe et al. 2024). The most problematic example is Gliese 229 B (Nakajima et al. 1995, Oppenheimer et al. 1995), which is at least 2-6 times less luminous than model predictions given its dynamical mass of $71.4\pm0.6$ Jupiter masses ($M_{\rm Jup}$) (Brandt et al. 2021). We observed Gliese 229 B with the GRAVITY interferometer and, separately, the CRIRES+ spectrograph at the Very Large Telescope. Both sets of observations independently resolve Gliese 229 B into two components, Gliese 229 Ba and Bb, settling the conflict between theory and observations. The two objects have a flux ratio of $0.47\pm0.03$ at a wavelength of 2 $\mu$m and masses of $38.1\pm1.0$ and $34.4\pm1.5$ $M_{\rm Jup}$, respectively. They orbit each other every 12.1 days with a semimajor axis of 0.042 astronomical units (AU). The discovery of Gliese 229 BaBb, each only a few times more massive than the most massive planets, and separated by 16 times the Earth-moon distance, raises new questions about the formation and prevalence of tight binary brown dwarfs around stars., Comment: Published in Nature. The Version of Record of this article is located at https://www.nature.com/articles/s41586-024-08064-x

Label noise, which refers to the mislabeling of instances in a dataset, can significantly impair classifier performance, increase model complexity, and affect feature selection. While most research has concentrated on deep neural networks for image and text data, this study explores the impact of label noise on gradient-boosted decision trees (GBDTs), the leading algorithm for tabular data. This research fills a gap by examining the robustness of GBDTs to label noise, focusing on adapting two noise detection methods from deep learning for use with GBDTs and introducing a new detection method called Gradients. Additionally, we extend a method initially designed for GBDTs to incorporate relabeling. By using diverse datasets such as Covertype and Breast Cancer, we systematically introduce varying levels of label noise and evaluate the effectiveness of early stopping and noise detection methods in maintaining model performance. Our noise detection methods achieve state-of-the-art results, with a noise detection accuracy above 99% on the Adult dataset across all noise levels. This work enhances the understanding of label noise in GBDTs and provides a foundation for future research in noise detection and correction methods.

This study reports the successful fabrication of high-positive refractive index change waveguides, exceeding 0.02 in fluoride glasses, marking a significant advancement in integrated optical components for visible to mid-infrared applications. This research overcomes longstanding challenges in direct-write photonics and therefore enables the realization of true 3D geometries in optical elements, access to novel visible lasing wavelengths typically suppressed in high phonon hosts, and the miniaturization of mid-infrared optical devices. The investigation into the waveguides' origin attributes the exceptionally high index change to material densification driven by the migration of specific elements, mainly barium, within the glass composition. These waveguides, characterized by low insertion losses, and highly customizable V-numbers evidenced by multimode operation at 3.5 um, offers substantial potential for chip laser technology and the creation of advanced optical devices for sensing and spectroscopy., Comment: 12 pages, 9 figures

Cyclic ozone additives have the potential to significantly increase the specific impulse of rocket fuel, which would lead to greater efficiency and reduced costs for space launches, allowing up to one third more payload per rocket. Although practical attempts to capture this isomer have not been successful, cyclic ozone might be stabilized within confined geometries. However, the required synthetic methods are challenging to design and need theory-driven inputs that exceed the capabilities of classical methods. Quantum computation could enable these calculations, but the hardware requirements for many practical applications are still unclear. We provide a comprehensive analysis of how quantum methods could aid efforts to isolate cyclic ozone using fullerene encapsulation. Our discussion goes beyond formal complexity analysis, offering both logical and physical overhead estimates for determining ground state energies based on quantum phase estimation (QPE). Together, these data outline a plausible scale for realistic, computationally-assisted molecular design efforts using fault-tolerant quantum computation.

Lack of low phonon energy glass waveguides delays the progress of integrated photonics in the mid-infrared wavelength. This perspective provides insights on the history of waveguide production in the past and what to expect in the near future., Comment: 4 Pages, 3 figures

The International Muon Collider Collaboration (IMCC) [1] was established in 2020 following the recommendations of the European Strategy for Particle Physics (ESPP) and the implementation of the European Strategy for Particle Physics-Accelerator R&D Roadmap by the Laboratory Directors Group [2], hereinafter referred to as the the European LDG roadmap. The Muon Collider Study (MuC) covers the accelerator complex, detectors and physics for a future muon collider. In 2023, European Commission support was obtained for a design study of a muon collider (MuCol) [3]. This project started on 1st March 2023, with work-packages aligned with the overall muon collider studies. In preparation of and during the 2021-22 U.S. Snowmass process, the muon collider project parameters, technical studies and physics performance studies were performed and presented in great detail. Recently, the P5 panel [4] in the U.S. recommended a muon collider R&D, proposed to join the IMCC and envisages that the U.S. should prepare to host a muon collider, calling this their "muon shot". In the past, the U.S. Muon Accelerator Programme (MAP) [5] has been instrumental in studies of concepts and technologies for a muon collider., Comment: This document summarises the International Muon Collider Collaboration (IMCC) progress and status of the Muon Collider R&D programme

Recent estimates indicate that the U.S. Department of Defense spends over \$20 billion USD annually on corrosion-related maintenance. This expenditure is accompanied by a substantial loss in asset readiness, ranging from 10% to 30%. Moreover, the global costs associated with corrosion damage have been estimated at an astonishing \$2.5 trillion USD per year, or approximately 3.4% of global GDP in 2016. This project aims to describe how quantum computers might be leveraged to fundamentally change the way material-environment interactions are modeled for material discovery, selection, and design. This project also seeks to understand the plausibility and utility of replacing portions of classical computing workflows with algorithms optimized for quantum computing hardware. The utility of quantum computers is explored through the lens of two industrially relevant problems: (1) characterizing magnesium alloy corrosion properties in aqueous environments and (2) identifying stable niobium-rich alloys with corrosion resistance at temperatures above 1500K. This paper presents an end-to-end analysis of the complexity of both classical and quantum algorithms used in application workflows. Resource estimates are produced using a custom software package, pyLIQTR, based on the qubitized Quantum Phase Estimation (QPE) algorithm. Estimates for the two aforementioned applications show that industrially-relevant computational models that have the potential to deliver commercial utility require quantum computers with thousands to hundreds of thousands of logical qubits and the ability to execute $10^{13}$ to $10^{19}$ T-gates. These estimates represent an upper bound and motivate continued research into improved quantum algorithms and resource reduction techniques.

Amyloid beta, an intrinsically disordered protein, plays a seemingly important but not well-understood role in neurodegenerative diseases like Alzheimer's disease. A key feature of amyloid beta, which could lead to potential therapeutic intervention pathways, is its binding affinity to certain metal centers, like iron and copper. Numerically calculating such binding affinities is a computationally challenging task, involving strongly correlated metal centers. A key bottleneck in understanding the binding affinity is obtaining estimates of the ground state energy. Quantum computers have the potential to accelerate such calculations but it is important to understand the quantum resources required. In this work, we detail a computational workflow for binding affinity calculations for amyloid beta utilizing quantum algorithms, providing estimated quantum resources required, at both the logical and hardware level.

Language identification is used as the first step in many data collection and crawling efforts because it allows us to sort online text into language-specific buckets. However, many modern languages, such as Konkani, Kashmiri, Punjabi etc., are synchronically written in several scripts. Moreover, languages with different writing systems do not share significant lexical, semantic, and syntactic properties in neural representation spaces, which is a disadvantage for closely related languages and low-resource languages, especially those from the Indian Subcontinent. To counter this, we propose learning script-agnostic representations using several different experimental strategies (upscaling, flattening, and script mixing) focusing on four major Dravidian languages (Tamil, Telugu, Kannada, and Malayalam). We find that word-level script randomization and exposure to a language written in multiple scripts is extremely valuable for downstream script-agnostic language identification, while also maintaining competitive performance on naturally occurring text., Comment: Under Review in ACL Rolling Review

Since 2019, GRAVITY has provided direct observations of giant planets and brown dwarfs at separations of down to 95 mas from the host star. Some of these observations have provided the first direct confirmation of companions previously detected by indirect techniques (astrometry and radial velocities). We want to improve the observing strategy and data reduction in order to lower the inner working angle of GRAVITY in dual-field on-axis mode. We also want to determine the current limitations of the instrument when observing faint companions with separations in the 30-150 mas range. To improve the inner working angle, we propose a fiber off-pointing strategy during the observations to maximize the ratio of companion-light-to-star-light coupling in the science fiber. We also tested a lower-order model for speckles to decouple the companion light from the star light. We then evaluated the detection limits of GRAVITY using planet injection and retrieval in representative archival data. We compare our results to theoretical expectations. We validate our observing and data-reduction strategy with on-sky observations; first in the context of brown dwarf follow-up on the auxiliary telescopes with HD 984 B, and second with the first confirmation of a substellar candidate around the star Gaia DR3 2728129004119806464. With synthetic companion injection, we demonstrate that the instrument can detect companions down to a contrast of $8\times 10^{-4}$ ($\Delta \mathrm{K}= 7.7$ mag) at a separation of 35 mas, and a contrast of $3\times 10^{-5}$ ($\Delta \mathrm{K}= 11$ mag) at 100 mas from a bright primary (K<6.5), for 30 min exposure time. With its inner working angle and astrometric precision, GRAVITY has a unique reach in direct observation parameter space. This study demonstrates the promising synergies between GRAVITY and Gaia for the confirmation and characterization of substellar companions., Comment: 16 pages, 14 figures. Submitted to A&A

We propose an implementation of bivariate bicycle codes (Nature {\bf 627}, 778 (2024)) based on long-range Rydberg gates between stationary neutral atom qubits. An optimized layout of data and ancilla qubits reduces the maximum Euclidean communication distance needed for non-local parity check operators. An optimized Rydberg gate pulse design enables $\sf CZ$ entangling operations with fidelity ${\mathcal F}>0.999$ at a distance greater than $12~\mu\rm m$. The combination of optimized layout and gate design leads to a quantum error correction cycle time of $\sim 1.28~\rm ms$ for a $[[144,12,12]]$ code, nearly a factor of two improvement over previous designs., Comment: revised with additional analysis of long range gate fidelity. 5 figures

As quantum hardware continues to improve, more and more application scientists have entered the field of quantum computing. However, even with the rapid improvements in the last few years, quantum devices, especially for quantum chemistry applications, still struggle to perform calculations that classical computers could not calculate. In lieu of being able to perform specific calculations, it is important have a systematic way of estimating the resources necessary to tackle specific problems. Standard arguments about computational complexity provide hope that quantum computers will be useful for problems in quantum chemistry but obscure the true impact of many algorithmic overheads. These overheads will ultimately determine the precise point when quantum computers will perform better than classical computers. We have developed QREChem to provide logical resource estimates for ground state energy estimation in quantum chemistry through a Trotter-based quantum phase estimation approach. QREChem provides resource estimates which include the specific overheads inherent to problems in quantum chemistry by including heuristic estimates of the number of Trotter steps and number of necessary ancilla, allowing for more accurate estimates of the total number of gates. We utilize QREChem to provide logical resource estimates for a variety of small molecules in various basis sets, obtaining estimates in the range of $10^7-10^{15}$ for total number of T gates. We also determine estimates for the FeMoco molecule and compare all estimates to other resource estimation tools.

We introduce a hybrid quantum-classical algorithm, the localized active space unitary selective coupled cluster singles and doubles (LAS-USCCSD) method. Derived from the localized active space unitary coupled cluster (LAS-UCCSD) method, LAS-USCCSD first performs a classical LASSCF calculation, then selectively identifies the most important parameters (cluster amplitudes used to build the multireference UCC ansatz) for restoring inter-fragment interaction energy using this reduced set of parameters with the variational quantum eigensolver method. We benchmark LAS-USCCSD against LAS-UCCSD by calculating the total energies of $(\mathrm{H}_2)_2$, $(\mathrm{H}_2)_4$ and \textit{trans}-butadiene, and the magnetic coupling constant for a bimetallic compound [Cr$_2$(OH)$_3$(NH$_3$)$_6$]$^{3+}$. For these systems, we find that LAS-USCCSD reduces the number of required parameters and thus the circuit depth by at least one order of magnitude, an aspect which is important for the practical implementation of multireference hybrid quantum-classical algorithms like LAS-UCCSD on near-term quantum computers.

With four companions at separations from 16 to 71 au, HR 8799 is a unique target for direct imaging, presenting an opportunity for the comparative study of exoplanets with a shared formation history. Combining new VLTI/GRAVITY observations obtained within the ExoGRAVITY program with archival data, we perform a systematic atmospheric characterisation of all four planets. We explore different levels of model flexibility to understand the temperature structure, chemistry and clouds of each planet using both petitRADTRANS atmospheric retrievals and fits to self-consistent radiative-convective equilibrium models. Using Bayesian Model Averaging to combine multiple retrievals, we find that the HR 8799 planets are highly enriched in metals, with [M/H] $\gtrsim$1, and have stellar to super-stellar C/O ratios. The C/O ratio increases with increasing separation from $0.55^{+0.12}_{-0.10}$ for d to $0.78^{+0.03}_{-0.04}$ for b, with the exception of the innermost planet which has a C/O ratio of $0.87\pm0.03$. By retrieving a quench pressure and using a disequilibrium chemistry model we derive vertical mixing strengths compatible with predictions for high-metallicity, self-luminous atmospheres. Bayesian evidence comparisons strongly favour the presence of HCN in HR 8799 c and e, as well as CH$_{4}$ in HR 8799 c, with detections at $>5\sigma$ confidence. All of the planets are cloudy, with no evidence for patchiness. The clouds of c, d and e are best fit by silicate clouds lying above a deep iron cloud layer, while the clouds of the cooler HR 8799 b are more likely composed of Na$_{2}$S. With well defined atmospheric properties, future exploration of this system is well positioned to unveil further detail in these planets, extending our understanding of the composition, structure, and formation history of these siblings., Comment: 45 pages, 25 figures, accepted by Astronomy & Astrophysics

Language model-based code completion models have quickly grown in use, helping thousands of developers write code in many different programming languages. However, research on code completion models typically focuses on imperative languages such as Python and JavaScript, which results in a lack of representation for functional programming languages. Consequently, these models often perform poorly on functional languages such as Haskell. To investigate whether this can be alleviated, we evaluate the performance of two language models for code, CodeGPT and UniXcoder, on the functional programming language Haskell. We fine-tune and evaluate the models on Haskell functions sourced from a publicly accessible Haskell dataset on HuggingFace. Additionally, we manually evaluate the models using our novel translated HumanEval dataset. Our automatic evaluation shows that knowledge of imperative programming languages in the pre-training of LLMs may not transfer well to functional languages, but that code completion on functional languages is feasible. Consequently, this shows the need for more high-quality Haskell datasets. A manual evaluation on HumanEval-Haskell indicates CodeGPT frequently generates empty predictions and extra comments, while UniXcoder more often produces incomplete or incorrect predictions. Finally, we release HumanEval-Haskell, along with the fine-tuned models and all code required to reproduce our experiments on GitHub (https://github.com/AISE-TUDelft/HaskellCCEval)., Comment: To appear in the First Special Event on AI Foundation Models and Software Engineering (FORGE 2024)

Precise mass constraints are vital for the characterisation of brown dwarfs and exoplanets. Here we present how the combination of data obtained by Gaia and GRAVITY can help enlarge the sample of substellar companions with measured dynamical masses. We show how the Non-Single-Star (NSS) two-body orbit catalogue contained in Gaia DR3 can be used to inform high-angular-resolution follow-up observations with GRAVITY. Applying the method presented in this work to eight Gaia candidate systems, we detect all eight predicted companions, seven of which were previously unknown and five are of a substellar nature. Among the sample is Gaia DR3 2728129004119806464 B, which - detected at an angular separation of (34.01 $\pm$ 0.15) mas from the host - is the closest substellar companion ever imaged. This translates to a semi-major axis of (0.938 $\pm$ 0.023) AU. WT 766 B, detected at a greater angular separation, was confirmed to be on an orbit exhibiting an even smaller semi-major axis of (0.676 $\pm$ 0.008) AU. The GRAVITY data were then used to break the host-companion mass degeneracy inherent to the Gaia NSS orbit solutions as well as to constrain the orbital solutions of the respective target systems. Knowledge of the companion masses enabled us to further characterise them in terms of their ages, effective temperatures, and radii via the application of evolutionary models. The inferred ages exhibit a distinct bias towards values younger than what is to be expected based on the literature. The results serve as an independent validation of the orbital solutions published in the NSS two-body orbit catalogue and show that the combination of astrometric survey missions and high-angular-resolution direct imaging holds great promise for efficiently increasing the sample of directly imaged companions in the future, especially in the light of Gaia's upcoming DR4 and the advent of GRAVITY+., Comment: Accepted for publication in Astronomy and Astrophysics

In the era of digital commerce, the surge in online shopping and the expectation for rapid delivery have placed unprecedented demands on warehouse operations. The traditional method of order fulfilment, where human order pickers traverse large storage areas to pick items, has become a bottleneck, consuming valuable time and resources. Robotic Mobile Fulfilment Systems (RMFS) offer a solution by using robots to transport storage racks directly to human-operated picking stations, eliminating the need for pickers to travel. This paper introduces combi-stations, a novel type of station that enables both item picking and replenishment, as opposed to traditional separate stations. We analyse the efficiency of combi-stations using queueing theory and demonstrate their potential to streamline warehouse operations. Our results suggest that combi-stations can reduce the number of robots required for stability and significantly reduce order turnover time, indicating a promising direction for future warehouse automation., Comment: 15 pages, 7 figures. arXiv admin note: text overlap with arXiv:1912.01782

This paper proposes an approach for a tool-agnostic and heterogeneous static code analysis toolchain in combination with an exchange format. This approach enhances both traceability and comparability of analysis results. State of the art toolchains support features for either test execution and build automation or traceability between tests, requirements and design information. Our approach combines all those features and extends traceability to the source code level, incorporating static code analysis. As part of our approach we introduce the "ASSUME Static Code Analysis tool exchange format" that facilitates the comparability of different static code analysis results. We demonstrate how this approach enhances the usability and efficiency of static code analysis in a development process. On the one hand, our approach enables the exchange of results and evaluations between static code analysis tools. On the other hand, it enables a complete traceability between requirements, designs, implementation, and the results of static code analysis. Within our approach we also propose an OSLC specification for static code analysis tools and an OSLC communication framework., Comment: Published in IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC)

Transformer-based language models for automatic code completion have shown great promise so far, yet the evaluation of these models rarely uses real data. This study provides both quantitative and qualitative assessments of three public code language models when completing real-world code. We first developed an open-source IDE extension, Code4Me, for the online evaluation of the models. We collected real auto-completion usage data for over a year from more than 1200 users, resulting in over 600K valid completions. These models were then evaluated using six standard metrics across twelve programming languages. Next, we conducted a qualitative study of 1690 real-world completion requests to identify the reasons behind the poor model performance. A comparative analysis of the models' performance in online and offline settings was also performed, using benchmark synthetic datasets and two masking strategies. Our findings suggest that while developers utilize code completion across various languages, the best results are achieved for mainstream languages such as Python and Java. InCoder outperformed the other models across all programming languages, highlighting the significance of training data and objectives. Our study also revealed that offline evaluations do not accurately reflect real-world scenarios. Upon qualitative analysis of the model's predictions, we found that 66.3% of failures were due to the models' limitations, 24.4% occurred due to inappropriate model usage in a development context, and 9.3% were valid requests that developers overwrote. Given these findings, we propose several strategies to overcome the current limitations. These include refining training objectives, improving resilience to typographical errors, adopting hybrid approaches, and enhancing implementations and usability., Comment: To be published in the proceedings of the 46th IEEE/ACM International Conference on Software Engineering (ICSE 2024)

Dual-field interferometric observations with VLTI/GRAVITY sometimes require the use of a "binary calibrator", a binary star whose individual components remain unresolved by the interferometer, with a separation between 400 and 2000 mas for observations with the Units Telescopes (UTs), or 1200 to 3000 mas for the Auxiliary Telescopes (ATs). The separation vector also needs to be predictable to within 10 mas for proper pointing of the instrument. Up until now, no list of properly vetted calibrators was available for dual-field observations with VLTI/GRAVITY on the UTs. Our objective is to compile such a list, and make it available to the community. We identify a list of candidates from the Washington Double Star (WDS) catalogue, all with appropriate separations and brightness, scattered over the Southern sky. We observe them as part of a dedicated calibration programme, and determine whether these objects are true binaries (excluding higher multiplicities resolved interferometrically but unseen by imaging), and extract measurements of the separation vectors. We combine these new measurements with those available in the WDS to determine updated orbital parameters for all our vetted calibrators. We compile a list of 13 vetted binary calibrators for observations with VLTI/GRAVITY on the UTs, and provide orbital estimates and astrometric predictions for each of them. We show that our list guarantees that there are always at least two binary calibrators at airmass < 2 in the sky over the Paranal observatory, at any point in time. Any Principal Investigator wishing to use the dual-field mode of VLTI/GRAVITY with the UTs can now refer to this list to select an appropriate calibrator. We encourage the use of "whereistheplanet" to predict the astrometry of these calibrators, which seamlessly integrates with "p2Gravity" for VLTI/GRAVITY dual-field observing material preparation., Comment: 16 pages, 6 figures. Submitted to A&A

Computational models are an essential tool for the design, characterization, and discovery of novel materials. Hard computational tasks in materials science stretch the limits of existing high-performance supercomputing centers, consuming much of their simulation, analysis, and data resources. Quantum computing, on the other hand, is an emerging technology with the potential to accelerate many of the computational tasks needed for materials science. In order to do that, the quantum technology must interact with conventional high-performance computing in several ways: approximate results validation, identification of hard problems, and synergies in quantum-centric supercomputing. In this paper, we provide a perspective on how quantum-centric supercomputing can help address critical computational problems in materials science, the challenges to face in order to solve representative use cases, and new suggested directions., Comment: 65 pages, 15 figures; comments welcome

Young, low-mass Brown Dwarfs orbiting early-type stars, with low mass ratios ($q\lesssim0.01$), appear intrinsically rare and present a formation dilemma: could a handful of these objects be the highest mass outcomes of ``planetary" formation channels (bottom up within a protoplanetary disk), or are they more representative of the lowest mass ``failed binaries" (formed via disk fragmentation, or core fragmentation)? Additionally, their orbits can yield model-independent dynamical masses, and when paired with wide wavelength coverage and accurate system age estimates, can constrain evolutionary models in a regime where the models have a wide dispersion depending on initial conditions. We present new interferometric observations of the $16\,\mathrm{Myr}$ substellar companion HD~136164~Ab (HIP~75056~Ab) with VLTI/GRAVITY and an updated orbit fit including proper motion measurements from the Hipparcos-Gaia Catalogue of Accelerations. We estimate a dynamical mass of $35\pm10\,\mathrm{M_J}$ ($q\sim0.02$), making HD~136164~Ab the youngest substellar companion with a dynamical mass estimate. The new mass and newly constrained orbital eccentricity ($e=0.44\pm0.03$) and separation ($22.5\pm1\,\mathrm{au}$) could indicate that the companion formed via the low-mass tail of the Initial Mass Function. Our atmospheric fit to the \texttt{SPHINX} M-dwarf model grid suggests a sub-solar C/O ratio of $0.45$, and $3\times$ solar metallicity, which could indicate formation in the circumstellar disk via disk fragmentation. Either way, the revised mass estimate likely excludes ``bottom-up" formation via core accretion in the circumstellar disk. HD~136164~Ab joins a select group of young substellar objects with dynamical mass estimates; epoch astrometry from future \textit{Gaia} data releases will constrain the dynamical mass of this crucial object further., Comment: Accepted for publication in The Astronomical Journal. 9 figures, 3 tables

Gait analysis holds significant importance in monitoring daily health, particularly among older adults. Advancements in sensor technology enable the capture of movement in real-life environments and generate big data. Machine learning, notably deep learning (DL), shows promise to use these big data in gait analysis. However, the inherent black-box nature of these models poses challenges for their clinical application. This study aims to enhance transparency in DL-based gait classification for aged-related gait patterns using Explainable Artificial Intelligence, such as SHAP. A total of 244 subjects, comprising 129 adults and 115 older adults (age>65), were included. They performed a 3-minute walking task while accelerometers were affixed to the lumbar segment L3. DL models, convolutional neural network (CNN) and gated recurrent unit (GRU), were trained using 1-stride and 8-stride accelerations, respectively, to classify adult and older adult groups. SHAP was employed to explain the models' predictions. CNN achieved a satisfactory performance with an accuracy of 81.4% and an AUC of 0.89, and GRU demonstrated promising results with an accuracy of 84.5% and an AUC of 0.94. SHAP analysis revealed that both CNN and GRU assigned higher SHAP values to the data from vertical and walking directions, particularly emphasizing data around heel contact, spanning from the terminal swing to loading response phases. Furthermore, SHAP values indicated that GRU did not treat every stride equally. CNN accurately distinguished between adults and older adults based on the characteristics of a single stride's data. GRU achieved accurate classification by considering the relationships and subtle differences between strides. In both models, data around heel contact emerged as most critical, suggesting differences in acceleration and deceleration patterns during walking between different age groups.

Human Assumed Central Sensitization is involved in the development and maintenance of chronic low back pain (CLBP). The Central Sensitization Inventory (CSI) was developed to evaluate the presence of HACS, with a cut-off value of 40/100 based on patients with chronic pain. However, various factors including pain conditions (e.g., CLBP), and gender may influence this cut-off value. For chronic pain condition such as CLBP, unsupervised clustering approaches can take these factors into consideration and automatically learn the HACS-related patterns. Therefore, this study aimed to determine the cut-off values for a Dutch-speaking population with CLBP, considering the total group and stratified by gender based on unsupervised machine learning. In this study, questionnaire data covering pain, physical, and psychological aspects were collected from patients with CLBP and aged-matched pain-free adults (referred to as healthy controls, HC). Four clustering approaches were applied to identify HACS-related clusters based on the questionnaire data and gender. The clustering performance was assessed using internal and external indicators. Subsequently, receiver operating characteristic analysis was conducted on the best clustering results to determine the optimal cut-off values. The study included 151 subjects, consisting of 63 HCs and 88 patients with CLBP. Hierarchical clustering yielded the best results, identifying three clusters: healthy group, CLBP with low HACS level, and CLBP with high HACS level groups. Based on the low HACS levels group (including HC and CLBP with low HACS level) and high HACS level group, the cut-off value for the overall groups were 35, 34 for females, and 35 for. The findings suggest that the optimal cut-off values for CLBP is 35. The gender-related cut-off values should be interpreted with caution due to the unbalanced gender distribution in the sample., Comment: 31 pages, 5 tables, 3 figures