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We introduce a novel optomechanical microgear cavity for both optical and mechanical isotropic materials, featuring a single etch configuration. The design leverages a conjunction of phononic and photonic crystal-like structures to achieve remarkable confinement of both optical and mechanical fields. The microgear cavity we designed in amorphous silicon nitride exhibits a mechanical resonance at 4.8 GHz, and whispering gallery modes in the near-infrared, with scattering-limited quality factors above the reported material limit of up $10^7$. Notably, the optomechanical photoelastic overlap contribution reaches 75% of the ideal configuration seen in a floating ring structure., Comment: 4 Figures, 1 Supplemental document

Run-time steering strategies like Medprompt are valuable for guiding large language models (LLMs) to top performance on challenging tasks. Medprompt demonstrates that a general LLM can be focused to deliver state-of-the-art performance on specialized domains like medicine by using a prompt to elicit a run-time strategy involving chain of thought reasoning and ensembling. OpenAI's o1-preview model represents a new paradigm, where a model is designed to do run-time reasoning before generating final responses. We seek to understand the behavior of o1-preview on a diverse set of medical challenge problem benchmarks. Following on the Medprompt study with GPT-4, we systematically evaluate the o1-preview model across various medical benchmarks. Notably, even without prompting techniques, o1-preview largely outperforms the GPT-4 series with Medprompt. We further systematically study the efficacy of classic prompt engineering strategies, as represented by Medprompt, within the new paradigm of reasoning models. We found that few-shot prompting hinders o1's performance, suggesting that in-context learning may no longer be an effective steering approach for reasoning-native models. While ensembling remains viable, it is resource-intensive and requires careful cost-performance optimization. Our cost and accuracy analysis across run-time strategies reveals a Pareto frontier, with GPT-4o representing a more affordable option and o1-preview achieving state-of-the-art performance at higher cost. Although o1-preview offers top performance, GPT-4o with steering strategies like Medprompt retains value in specific contexts. Moreover, we note that the o1-preview model has reached near-saturation on many existing medical benchmarks, underscoring the need for new, challenging benchmarks. We close with reflections on general directions for inference-time computation with LLMs., Comment: 25 pages

We investigate the nature of star formation in gas-rich galaxies at $z > 7$ forming in a markedly overdense region, in the whereabouts of a massive virialized halo already exceeding $10^{12}$ M$_{\odot}$. We find that not only the primary galaxy, but also the lower-mass companion galaxies rapidly develop massive self-gravitating compact gas disks, less than 500~pc in size, which undergo fragmentation by gravitational instability into very massive bound clumps. Star formation proceeds fast in the clumps, which quickly turn into compact star clusters with masses in the range $10^5$-$10^8$ M$_{\odot}$ and typical half-mass radii of a few pc, reaching characteristic densities above $10^5$ M$_{\odot}$ pc$^{-2}$. The properties of the clusters in the lowest-mass galaxy bear a striking resemblance to those recently discovered by the James Webb Space Telescope (JWST) in the lensed Cosmic Gems arc system at $z = 10.2$. We argue that, due to their extremely high stellar densities, intermediate-mass black holes (IMBHs) would form rapidly inside the clusters, which would then swiftly sink and merge on their way to the galactic nucleus, easily growing a $10^7$~M$_{\odot}$ supermassive black hole (SMBH). Due to the high fractional mass contribution of clusters to the stellar mass of the galaxies, in the range $20$-$40\%$, the central SMBH would comprise more than $10\%$ of the mass of its host galaxy, naturally explaining the overmassive SMBHs discovered by JWST at $z > 6$., Comment: 9 pages, 5 figures, to be submitted to ApJ Letters

The SRG/eROSITA All-Sky Surveys (eRASSs) combine the advantages of complete sky coverage and the energy resolution provided by the charge couple device and offer the most holistic and detailed view of the diffuse soft X-ray background (SXRB) to date. The first eRASS (eRASS1) was completed at solar minimum, when solar wind charge exchange emission was minimal, providing the clearest view of the SXRB. We aim to extract spatial and spectral information from each constituent of the SXRB in the western Galactic hemisphere, focusing on the local hot bubble (LHB). We extracted and analysed eRASS1 spectra from almost all directions in the western Galactic hemisphere by dividing the sky into equal signal-to-noise bins. We fitted all bins with fixed spectral templates of known background constituents. We find the temperature of the LHB exhibits a north-south dichotomy at high latitudes ($|b|>30^{\circ}$), with the south being hotter, with a mean temperature at $kT=121.8\pm0.6\,$eV and the north at $kT=100.8\pm0.5\,$eV. At low latitudes, the LHB temperature increases towards the Galactic plane, especially towards the inner Galaxy. The LHB emission measure (${\rm EM_{LHB}}$) enhances approximately towards the Galactic poles. The ${\rm EM_{LHB}}$ map shows clear anti-correlation with the local dust column density. In particular, we found tunnels of dust cavities filled with hot plasma, potentially forming a wider network of hot interstellar medium. We also constructed a three-dimensional LHB model from ${\rm EM_{LHB}}$, assuming constant density. The average thermal pressure of the LHB is $P_{\rm thermal}/k=10100^{+1200}_{-1500}\,{\rm cm^{-3}\,K}$, a lower value than typical supernova remnants and wind-blown bubbles. This could be an indication of the LHB being open towards high Galactic latitudes., Comment: 31 pages, 37 figures, published in A&A

The European Union's Artificial Intelligence Act (AI Act) introduces comprehensive guidelines for the development and oversight of Artificial Intelligence (AI) and Machine Learning (ML) systems, with significant implications for Graph Neural Networks (GNNs). This paper addresses the unique challenges posed by the AI Act for GNNs, which operate on complex graph-structured data. The legislation's requirements for data management, data governance, robustness, human oversight, and privacy necessitate tailored strategies for GNNs. Our study explores the impact of these requirements on GNN training and proposes methods to ensure compliance. We provide an in-depth analysis of bias, robustness, explainability, and privacy in the context of GNNs, highlighting the need for fair sampling strategies and effective interpretability techniques. Our contributions fill the research gap by offering specific guidance for GNNs under the new legislative framework and identifying open questions and future research directions.

This paper offers a contemporary and comprehensive perspective on the classical algorithms utilized for the solution of minimum-time problem for linear systems (MTPLS). The use of unified notations supported by visual geometric representations serves to highlight the differences between the Neustadt-Eaton and Barr-Gilbert algorithms. Furthermore, these notations assist in the interpretation of the distance-finding algorithms utilized in the Barr-Gilbert algorithm. Additionally, we present a novel algorithm for solving MTPLS and provide a constructive proof of its convergence. Similar to the Barr-Gilbert algorithm, the novel algorithm employs distance search algorithms. The design of the novel algorithm is oriented towards solving such MTPLS for which the analytic description of the reachable set is available. To illustrate the advantages of the novel algorithm, we utilize the isotropic rocket benchmark. Numerical experiments demonstrate that, for high-precision computations, the novel algorithm outperforms others by factors of tens or hundreds and exhibits the lowest failure rate., Comment: 23 pages, 10 figures

Cooperative manipulation tasks impose various structure-, task-, and robot-specific constraints on mobile manipulators. However, current methods struggle to model and solve these myriad constraints simultaneously. We propose a twofold solution: first, we model constraints as a family of manifolds amenable to simultaneous solving. Second, we introduce the constrained nonlinear Kaczmarz (cNKZ) projection technique to produce constraint-satisfying solutions. Experiments show that cNKZ dramatically outperforms baseline approaches, which cannot find solutions at all. We integrate cNKZ with a sampling-based motion planning algorithm to generate complex, coordinated motions for 3 to 6 mobile manipulators (18--36 DoF), with cNKZ solving up to 80 nonlinear constraints simultaneously and achieving up to a 92% success rate in cluttered environments. We also demonstrate our approach on hardware using three Turtlebot3 Waffle Pi robots with OpenMANIPULATOR-X arms.

The origin of planetary mass objects (PMOs) wandering in young star clusters remains enigmatic, especially when they come in pairs. They could represent the lowest-mass object formed via molecular cloud collapse or high-mass planets ejected from their host stars. However, neither theory fully accounts for their abundance and multiplicity. Here, we show via hydrodynamic simulations that free-floating PMOs have a unique formation channel via the fragmentation of tidal bridge between encountering circumstellar disks. This process can be highly productive in density clusters like Trapezium forming metal-poor PMOs with disks. Free-floating multiple PMOs also naturally emerge when neighboring PMOs are caught by mutual gravity. PMOs may thus form a distinct population different from stars and planets., Comment: submitted version, comments are welcome

Virtualized environments offer a flexible and scalable platform for evaluating network performance, but they can introduce significant variability that complicates accurate measurement. This paper presents PASTRAMI, a methodology designed to assess the stability of software routers, which is critical to accurately evaluate performance metrics such as the Partial Drop Rate at 0.5% (PDR@0.5%). While PDR@0.5% is a key metric to assess packet processing capabilities of a software router, its reliable evaluation depends on consistent router performance with minimal measurement variability. Our research reveals that different Linux versions exhibit distinct behaviors, with some demonstrating non-negligible packet loss even at low loads and high variability in loss measurements, rendering them unsuitable for accurate performance assessments. This paper proposes a systematic approach to differentiate between stable and unstable environments, offering practical guidance on selecting suitable configurations for robust networking performance evaluations in virtualized environments., Comment: Extended version of paper submitted to conference - version 01

We investigate the effect of dynamically coupling gas torques with gravitational wave (GW) emission during the orbital evolution of an equal-mass massive black hole binary (MBHB). We perform hydrodynamical simulations of eccentric MBHBs with total mass $M=10^6~{\rm M}_\odot$ embedded in a prograde locally isothermal circumbinary disk (CBD). We evolve the binary from $53$ to $30$ Schwarzschild radii separations using up to 2.5 post-Newtonian (PN) corrections to the binary dynamics, which allow us to follow the GW-driven in-spiral. For the first time, we report the measurement of gas torques onto a live binary a few years before the merger, with and without concurrent GW radiation. We also identify and measure a novel GW-gas coupling term in the in-spiral rate that makes gas effects an order of magnitude stronger than the gas-only contribution. We show that the evolution rate ($\dot a$) of the MBHB can be neatly expressed as the sum of the GW rate ($\dot a_{\rm GW}$), the pure gas-driven rate ($\dot a_{\rm gas}$), and their cross-term $\propto\dot a_{\rm GW}\dot a_{\rm gas}$. The source-frame gas-induced dephasing in the GW waveform is equivalent to losing $\sim0.5$ GW cycles over the expected $\sim1700$ cycles in a vacuum, which LISA should detect at redshift $z=1$. We also propose a phenomenological model that captures the essence of simulations and can be used to perform Bayesian inference. Our results show how GWs alone can be used to probe the astrophysical properties of CBDs and have important implications on multi-messenger strategies aimed at studying the environments of MBHBs., Comment: 8 pages, 4 figures. To be submitted to ApJ Letters

We present parametric breadth-first search (PBFS), a new algorithm for solving the parametric minimum cut problem in a network with source-sink-monotone capacities. The objective is to find the set of breakpoints, i.e., the points at which the minimum cut changes. It is well known that this problem can be solved in the same asymptotic runtime as the static minimum cut problem. However, existing algorithms that achieve this runtime bound involve fairly complicated steps that are inefficient in practice. PBFS uses a simpler approach that discovers the breakpoints in ascending order, which allows it to achieve the desired runtime bound while still performing well in practice. We evaluate our algorithm on benchmark instances from polygon aggregation and computer vision. Polygon aggregation was recently proposed as an application for parametric minimum cut, but the monotonicity property has not been exploited fully. PBFS outperforms the state of the art on most benchmark instances, usually by a factor of 2-3. It is particularly strong on instances with many breakpoints, which is the case for polygon aggregation. Compared to the existing min-cut-based approach for polygon aggregation, PBFS scales much better with the instance size. On large instances with millions of vertices, it is able to compute all breakpoints in a matter of seconds.

Monolayer transition metal dichalcogenides have emerged as prominent candidates to explore the complex interplay between the spin and the valleys degrees of freedom. The strong spin-orbit interaction and broken inversion symmetry within these materials lead to the spin-valley locking effect, in which carriers occupying the K and K' valleys of the reciprocal space must have opposite spin depending on which valley they reside. This effect is particularly strong for holes due to a larger spin-orbit gap in the valence band. By reducing the dimensionality of a monolayer of tungsten diselenide to 1D via electrostatic confinement, we demonstrate that spin-valley locking in combination with strong hole-hole interactions lead to a ferromagnetic state in which hole transport through the 1D system is spin-valley polarized, even without an applied magnetic field, and that the persistence of this spin-valley polarized configuration can be tuned by a global back-gate. This observation opens the possibility of implementing a robust and stable valley polarized system, essential for valleytronic applications.

Single-photon sources are of fundamental importance to emergent quantum technologies. Nano-structured optomechanical crystals provide an attractive platform for single photon generation due to their unique engineering freedom and compatibility with on-chip silicon fabrication. However, optical absorption heating has thus far prevented these systems from being widely used in practical applications. Here, we overcome this limitation through the use of a quasi-two-dimensional optomechanical crystal structure and demonstrate an on-chip source of single photons natively at telecom wavelength. We verify the low thermal noise and resulting high purity of the generated single photons through a Hanbury Brown-Twiss experiment with $g^{(2)}(0)=0.35^{+0.10}_{-0.08}$. Furthermore, we perform Hong-Ou-Mandel interference of the emitted photons showcasing the indistinguishability and coherence of photons generated from our source with visibility $V=0.52 \pm 0.15$ after 1.43 km of fiber delay line. With the possibility of using the mechanical mode as a quantum memory, we can retrieve the single photons on-demand. Crucial for applications, the optomechanical interaction at the heart of our device allows the bandwidth of emitted single photons to be tuned over a large range from 100 kHz to several hundreds of MHz, which makes them directly compatible with leading quantum memory platforms.

Magnetic fields have been shown both observationally and through theoretical work to be an important factor in the formation of protostars and their accretion disks. Accurate modelling of the evolution of the magnetic field in low-ionization molecular cloud cores requires the inclusion of non-ideal magnetohydrodynamics (MHD) processes, specifically Ohmic and ambipolar diffusion and the Hall effect. These have a profound influence on the efficiency of magnetic removal of angular momentum from protostellar disks and simulations that include them can avoid the `magnetic-braking catastrophe' in which disks are not able to form. However, the impact of the Hall effect, in particular, is complex and remains poorly studied. In this work, we perform a large suite of simulations of the collapse of cloud cores to protostars with several non-ideal MHD chemistry models and initial core geometries using the moving-mesh code {\small AREPO}. We find that the efficiency of angular momentum removal is significantly reduced with respect to ideal MHD, in line with previous results. The Hall effect has a varied influence on the evolution of the disk which depends on the initial orientation of the magnetic field. This extends to the outflows seen in a subset of the models, where this effect can act to enhance or suppress them and open up new outflow channels. We conclude, in agreement with a subset of the previous literature, that the Hall effect is the dominant non-ideal MHD process in some collapse scenarios and thus should be included in simulations of protostellar disk formation., Comment: 23 pages, 20 Figures

Understanding the intentions of robots is essential for natural and seamless human-robot collaboration. Ensuring that robots have means for non-verbal communication is a basis for intuitive and implicit interaction. For this, we contribute an approach to elicit and design human-understandable robot expressions. We outline the approach in the context of non-humanoid robots. We paired human mimicking and enactment with research from gesture elicitation in two phases: first, to elicit expressions, and second, to ensure they are understandable. We present an example application through two studies (N=16 \& N=260) of our approach to elicit expressions for a simple 6-DoF robotic arm. We show that it enabled us to design robot expressions that signal curiosity and interest in getting attention. Our main contribution is an approach to generate and validate understandable expressions for robots, enabling more natural human-robot interaction.

Effective field theory-based searches for new physics at colliders are relatively insensitive to interactions involving only right-handed down-type quarks. These interactions can hide amongst jet backgrounds at the LHC, and their indirect effects in electroweak and Higgs processes are small. Identifying scenarios in which these interactions dominate, we can naturally pick out just two tree-level mediators, both scalar diquarks. Over the full parameter space of these states, we analyse exotics searches at current and future hadron colliders, Higgs signal strength constraints, and indirect constraints from flavour physics, finding genuine complementarity between the data sets. In particular, while flavour constraints can exclude diquarks in the hundreds of TeV mass range, these can be evaded once a flavour structure is imposed on the couplings, as we illustrate by embedding the diquarks within a composite Higgs model. In combination, however, we show that flavour and collider constraints exclude down-type diquarks to multi-TeV scales, thus narrowing the remaining hiding places for new interactions amongst LHC data., Comment: 27 pages, 11 figures

The opening of the gravitational wave window has significantly enhanced our capacity to explore the universe's most extreme and dynamic sector. In the mHz frequency range, a diverse range of compact objects, from the most massive black holes at the farthest reaches of the Universe to the lightest white dwarfs in our cosmic backyard, generate a complex and dynamic symphony of gravitational wave signals. Once recorded by gravitational wave detectors, these unique fingerprints have the potential to decipher the birth and growth of cosmic structures over a wide range of scales, from stellar binaries and stellar clusters to galaxies and large-scale structures. The TianQin space-borne gravitational wave mission is scheduled for launch in the 2030s, with an operational lifespan of five years. It will facilitate pivotal insights into the history of our universe. This document presents a concise overview of the detectable sources of TianQin, outlining their characteristics, the challenges they present, and the expected impact of the TianQin observatory on our understanding of them., Comment: TianQin Gravitational Wave Whitepaper, 72 pages, 30 figures

Exoplanets form from circumstellar protoplanetary discs whose fundamental properties (notably their extent, composition, mass, temperature and lifetime) depend on the host star properties, such as their mass and luminosity. B-stars are among the most massive stars and their protoplanetary discs test extreme conditions for exoplanet formation. This paper investigates the frequency of giant planet companions around young B-stars (median age of 16 Myr) in the Scorpius-Centaurus association, the closest association containing a large population of B-stars. We systematically search for massive exoplanets with the high-contrast direct imaging instrument SPHERE using the data from the BEAST survey, that targets an homogeneous sample of young B-stars from the wide Sco-Cen association. We derive accurate detection limits in case of non-detections. We found evidence in previous papers for two substellar companions around 42 stars. The masses of these companions are straddling the ~13 Jupiter mass deuterium burning limit but their mass ratio with respect to their host star is close to that of Jupiter. We derive a frequency of such massive planetary mass companions around B stars of 11-5+7%, accounting for the survey sensitivity. The discoveries of substellar companions bcen b and mu2sco B happened after only few stars in the survey had been observed, raising the possibility that massive Jovian planets might be common around B-stars. However our statistical analysis show that the occurrence rate of such planets is similar around B-stars and around solar-type stars of similar age, while B-star companions exhibit low mass ratios and larger semi-major axis., Comment: Accepted in A&A

In this article we obtain certain inequalities involving the entropy of a positive integer and divergence of two positive integers, respectively the entropy of an ideal and divergence of two ideals of a ring of algebraic integers., Comment: 16 pages, 1 figure. Key words and phrases. entropy, divergence, numbers, ideals, ramification theory in algebraic number fields

Medical imaging is spearheading the AI transformation of healthcare. Performance reporting is key to determine which methods should be translated into clinical practice. Frequently, broad conclusions are simply derived from mean performance values. In this paper, we argue that this common practice is often a misleading simplification as it ignores performance variability. Our contribution is threefold. (1) Analyzing all MICCAI segmentation papers (n = 221) published in 2023, we first observe that more than 50% of papers do not assess performance variability at all. Moreover, only one (0.5%) paper reported confidence intervals (CIs) for model performance. (2) To address the reporting bottleneck, we show that the unreported standard deviation (SD) in segmentation papers can be approximated by a second-order polynomial function of the mean Dice similarity coefficient (DSC). Based on external validation data from 56 previous MICCAI challenges, we demonstrate that this approximation can accurately reconstruct the CI of a method using information provided in publications. (3) Finally, we reconstructed 95% CIs around the mean DSC of MICCAI 2023 segmentation papers. The median CI width was 0.03 which is three times larger than the median performance gap between the first and second ranked method. For more than 60% of papers, the mean performance of the second-ranked method was within the CI of the first-ranked method. We conclude that current publications typically do not provide sufficient evidence to support which models could potentially be translated into clinical practice., Comment: Paper accepted at MICCAI 2024 conference

Consistency, in a narrow sense, denotes the alignment between the forecast-optimization strategy and the verification directive. The current recommended deterministic solar forecast verification practice is to report the skill score based on root mean square error (RMSE), which would violate the notion of consistency if the forecasts are optimized under another strategy such as minimizing the mean absolute error (MAE). This paper overcomes such difficulty by proposing a so-called "potential RMSE skill score," which depends only on: (1) the crosscorrelation between forecasts and observations, and (2) the autocorrelation of observations. While greatly simplifying the calculation, the new skill score does not discriminate inconsistent forecasts as much, e.g., even MAE-optimized forecasts can attain a high RMSE skill score., Comment: 10 pages, 5 figures

Recent developments in large language models (LLMs) have led to significant improvements in intelligent dialogue systems'ability to handle complex inquiries. However, current LLMs still exhibit limitations in specialized domain knowledge, particularly in technical fields such as agriculture. To address this problem, we propose ShizishanGPT, an intelligent question answering system for agriculture based on the Retrieval Augmented Generation (RAG) framework and agent architecture. ShizishanGPT consists of five key modules: including a generic GPT-4 based module for answering general questions; a search engine module that compensates for the problem that the large language model's own knowledge cannot be updated in a timely manner; an agricultural knowledge graph module for providing domain facts; a retrieval module which uses RAG to supplement domain knowledge; and an agricultural agent module, which invokes specialized models for crop phenotype prediction, gene expression analysis, and so on. We evaluated the ShizishanGPT using a dataset containing 100 agricultural questions specially designed for this study. The experimental results show that the tool significantly outperforms general LLMs as it provides more accurate and detailed answers due to its modular design and integration of different domain knowledge sources. Our source code, dataset, and model weights are publicly available at https://github.com/Zaiwen/CropGPT., Comment: 15 pages,3 figures, WISE2024

Feeding the growing human population sustainably amidst climate change is one of the most important challenges in the 21st century. Current practices often lead to the overuse of agronomic inputs, such as synthetic fertilizers and water, resulting in environmental contamination and diminishing returns on crop productivity. The complexity of agricultural systems, involving plant-environment interactions and human management, presents significant scientific and technical challenges for developing sustainable practices. Addressing these challenges necessitates transdisciplinary research, involving intense collaboration among fields such as plant science, engineering, computer science, and social sciences. Here, we present five case studies from two research centers demonstrating successful transdisciplinary approaches toward more sustainable water and fertilizer use. These case studies span multiple scales. Starting from whole-plant signaling, we explore how reporter plants can transform our understanding of plant communication and enable efficient application of water and fertilizers. We then show how new fertilizer technologies could increase the availability of phosphorus in the soil. To accelerate advancements in breeding new cultivars, we discuss robotic technologies for high-throughput plant screening in different environments at a population scale. At the ecosystem scale, we investigate phosphorus recovery from aquatic systems and methods to minimize phosphorus leaching. Finally, as agricultural outputs affect all people, we show how to integrate stakeholder perspectives and needs into the research. With these case studies, we hope to encourage the scientific community to adopt transdisciplinary research and promote cross-training among biologists, engineers, and social scientists to drive discovery and innovation in advancing sustainable agricultural systems.

Graph neural networks (GNNs) are a type of neural network capable of learning on graph-structured data. However, training GNNs on large-scale graphs is challenging due to iterative aggregations of high-dimensional features from neighboring vertices within sparse graph structures combined with neural network operations. The sparsity of graphs frequently results in suboptimal memory access patterns and longer training time. Graph reordering is an optimization strategy aiming to improve the graph data layout. It has shown to be effective to speed up graph analytics workloads, but its effect on the performance of GNN training has not been investigated yet. The generalization of reordering to GNN performance is nontrivial, as multiple aspects must be considered: GNN hyper-parameters such as the number of layers, the number of hidden dimensions, and the feature size used in the GNN model, neural network operations, large intermediate vertex states, and GPU acceleration. In our work, we close this gap by performing an empirical evaluation of 12 reordering strategies in two state-of-the-art GNN systems, PyTorch Geometric and Deep Graph Library. Our results show that graph reordering is effective in reducing training time for CPU- and GPU-based training, respectively. Further, we find that GNN hyper-parameters influence the effectiveness of reordering, that reordering metrics play an important role in selecting a reordering strategy, that lightweight reordering performs better for GPU-based than for CPU-based training, and that invested reordering time can in many cases be amortized., Comment: To be published in proceedings of the 51st International Conference on Very Large Data Bases (VLDB), September 1-5, 2025

Previous research has demonstrated that specific states of the climate system can lead to enhanced subseasonal predictability (i.e., state-dependent predictability). However, biases in Earth system models can affect the representation of these states and their subsequent evolution. Here, we present a machine learning framework to identify state-dependent biases in Earth system models. In particular, we investigate the utility of transfer learning with explainable neural networks to identify tropical state-dependent biases in historical simulations of the Energy Exascale Earth System Model version 2 (E3SMv2) relevant for midlatitude subseasonal predictability. Using a perfect model framework, we find transfer learning may require substantially more data than provided by present-day reanalysis datasets to update neural network weights, imparting a cautionary tale for future transfer learning approaches focused on subseasonal modes of variability., Comment: This work has been submitted for publication in Artificial Intelligence for the Earth Systems (AIES). Copyright in this work may be transferred without further notice

Dynamic topic modeling is useful at discovering the development and change in latent topics over time. However, present methodology relies on algorithms that separate document and word representations. This prevents the creation of a meaningful embedding space where changes in word usage and documents can be directly analyzed in a temporal context. This paper proposes an expansion of the compass-aligned temporal Word2Vec methodology into dynamic topic modeling. Such a method allows for the direct comparison of word and document embeddings across time in dynamic topics. This enables the creation of visualizations that incorporate temporal word embeddings within the context of documents into topic visualizations. In experiments against the current state-of-the-art, our proposed method demonstrates overall competitive performance in topic relevancy and diversity across temporal datasets of varying size. Simultaneously, it provides insightful visualizations focused on temporal word embeddings while maintaining the insights provided by global topic evolution, advancing our understanding of how topics evolve over time., Comment: 11 pages, 9 figures, conference paper

In the realm of industrial manufacturing, Artificial Intelligence (AI) is playing an increasing role, from automating existing processes to aiding in the development of new materials and techniques. However, a significant challenge arises in smaller, experimental processes characterized by limited training data availability, questioning the possibility to train AI models in such small data contexts. In this work, we explore the potential of Transfer Learning to address this challenge, specifically investigating the minimum amount of data required to develop a functional AI model. For this purpose, we consider the use case of quality control of Carbon Fiber Reinforced Polymer (CFRP) tape laying in aerospace manufacturing using optical sensors. We investigate the behavior of different open-source computer vision models with a continuous reduction of the training data. Our results show that the amount of data required to successfully train an AI model can be drastically reduced, and the use of smaller models does not necessarily lead to a loss of performance.

Industrial robots become increasingly prevalent, resulting in a growing need for intuitive, comforting human-robot collaboration. We present a user-aware robotic system that adapts to operator behavior in real time while non-intrusively monitoring physiological signals to create a more responsive and empathetic environment. Our prototype dynamically adjusts robot speed and movement patterns while measuring operator pupil dilation and proximity. Our user study compares this adaptive system to a non-adaptive counterpart, and demonstrates that the adaptive system significantly reduces both perceived and physiologically measured cognitive load while enhancing usability. Participants reported increased feelings of comfort, safety, trust, and a stronger sense of collaboration when working with the adaptive robot. This highlights the potential of integrating real-time physiological data into human-robot interaction paradigms. This novel approach creates more intuitive and collaborative industrial environments where robots effectively 'read' and respond to human cognitive states, and we feature all data and code for future use., Comment: 15 pages, 4 figures

Across the technology industry, many companies have expressed their commitments to AI ethics and created dedicated roles responsible for translating high-level ethics principles into product. Yet it is unclear how effective this has been in leading to meaningful product changes. Through semi-structured interviews with 26 professionals working on AI ethics in industry, we uncover challenges and strategies of institutionalizing ethics work along with translation into product impact. We ultimately find that AI ethics professionals are highly agile and opportunistic, as they attempt to create standardized and reusable processes and tools in a corporate environment in which they have little traditional power. In negotiations with product teams, they face challenges rooted in their lack of authority and ownership over product, but can push forward ethics work by leveraging narratives of regulatory response and ethics as product quality assurance. However, this strategy leaves us with a minimum viable ethics, a narrowly scoped industry AI ethics that is limited in its capacity to address normative issues separate from compliance or product quality. Potential future regulation may help bridge this gap.

In recent years, large language models (LLMs) have emerged as powerful tools with potential applications in various fields, including software engineering. Within the scope of this research, we evaluate five different state-of-the-art LLMs - Bard, BingChat, ChatGPT, Llama2, and Code Llama - concerning their capabilities for text-to-code generation. In an empirical study, we feed prompts with textual descriptions of coding problems sourced from the programming website LeetCode to the models with the task of creating solutions in Python. Subsequently, the quality of the generated outputs is assessed using the testing functionalities of LeetCode. The results indicate large differences in performance between the investigated models. ChatGPT can handle these typical programming challenges by far the most effectively, surpassing even code-specialized models like Code Llama. To gain further insights, we measure the runtime as well as the memory usage of the generated outputs and compared them to the other code submissions on Leetcode. A detailed error analysis, encompassing a comparison of the differences concerning correct indentation and form of the generated code as well as an assignment of the incorrectly solved tasks to certain error categories allows us to obtain a more nuanced picture of the results and potential for improvement. The results also show a clear pattern of increasingly incorrect produced code when the models are facing a lot of context in the form of longer prompts., Comment: Conference Paper accepted at the 9th SwissText Conference (2024)

In this study, we explored the fundamental contrast limit of NIRCam coronagraphy observations, representing the achievable performance with post-processing techniques. This limit is influenced by photon noise and readout noise, with complex noise propagation through post-processing methods like principal component analysis. We employed two approaches: developing a formula based on simplified scenarios and using Markov Chain Monte Carlo (MCMC) methods, assuming Gaussian noise properties and uncorrelated pixel noise. Tested on datasets HIP\,65426, AF\,Lep, and HD\,114174, the MCMC method provided accurate but computationally intensive estimates. The analytical approach offered quick, reliable estimates closely matching MCMC results in simpler scenarios. Our findings showed the fundamental contrast curve is significantly deeper than the current achievable contrast limit obtained with post-processing techniques at shorter separations, being 10 times deeper at $0.5''$ and 4 times deeper at $1''$. At greater separations, increased exposure time improves sensitivity, with the transition between photon and readout noise dominance occurring between $2''$ and $3''$. We conclude the analytical approach is a reliable estimate of the fundamental contrast limit, offering a faster alternative to MCMC. These results emphasize the potential for greater sensitivity at shorter separations, highlighting the need for improved or new post-processing techniques to enhance JWST NIRCam sensitivity or contrast curve., Comment: Proceeding of the SPIE July 2024

Red teams simulate adversaries and conduct sophisticated attacks against defenders without informing them about used tactics in advance. These interactive cyber exercises are highly beneficial to assess and improve the security posture of organizations, detect vulnerabilities, and train employees. Unfortunately, they are also time-consuming and expensive, which often limits their scale or prevents them entirely. To address this situation, adversary emulation tools partially automate attacker behavior and enable fast, continuous, and repeatable security testing even when involved personnel lacks red teaming experience. Currently, a wide range of tools designed for specific use-cases and requirements exist. To obtain an overview of these solutions, we conduct a review and structured comparison of nine open-source adversary emulation tools. To this end, we assemble a questionnaire with 80 questions addressing relevant aspects, including setup, support, documentation, usability, and technical features. In addition, we conduct a user study with domain experts to investigate the importance of these aspects for distinct user roles. Based on the evaluation and user feedback, we rank the tools and find MITRE Caldera, Metasploit, and Atomic Red Team on top.

Emergent language research has made significant progress in recent years, but still largely fails to explore how communication emerges in more complex and situated multi-agent systems. Existing setups often employ a reference game, which limits the range of language emergence phenomena that can be studied, as the game consists of a single, purely language-based interaction between the agents. In this paper, we address these limitations and explore the emergence and utility of token-based communication in open-ended multi-agent environments, where situated agents interact with the environment through movement and communication over multiple time-steps. Specifically, we introduce two novel cooperative environments: Multi-Agent Pong and Collectors. These environments are interesting because optimal performance requires the emergence of a communication protocol, but moderate success can be achieved without one. By employing various methods from explainable AI research, such as saliency maps, perturbation, and diagnostic classifiers, we are able to track and interpret the agents' language channel use over time. We find that the emerging communication is sparse, with the agents only generating meaningful messages and acting upon incoming messages in states where they cannot succeed without coordination., Comment: 10 pages, 4 figures, 4 tables, preprint

Intermediate Mass Black Hole (IMBH) mergers with masses $10^4 - 10^6$ $M_{\odot}$ are expected to produce gravitational waves (GWs) detectable by the Laser Interferometer Space Antenna (LISA) with high signal to noise ratios out to redshift 20. IMBH mergers are expected to take place within dwarf galaxies, however, the dynamics, timescales, and effect on their hosts are largely unexplored. In a previous study, we examined how IMBHs would pair and merge within nucleated dwarf galaxies. IMBHs in nucleated hosts evolve very efficiently, forming a binary system and coalescing within a few hundred million years. Although the fraction of dwarf galaxies ($10^7$ M$_{\odot} \leq$ $M_{\star} \leq 10^{10}$ M$_{\odot}$) hosting nuclear star clusters is between 60-100\%, this fraction drops to 20-70\% for lower-mass dwarfs ($M_{\star}\approx 10^7$ M$_{\odot}$), with the largest drop in low-density environments. Here, we extend our previous study by performing direct $N-$body simulations to explore the dynamics and evolution of IMBHs within non-nucleated dwarf galaxies, under the assumption that IMBHs exist within these dwarfs. To our surprise, none of IMBHs in our simulation suite merge within a Hubble time, despite many attaining high eccentricities $e \sim 0.7-0.95$. We conclude that extremely low stellar density environments in the centers of non-nucleated dwarfs do not provide an ample supply of stars to interact with IMBHs binary resulting in its stalling, in spite of triaxiality and high eccentricity, common means to drive a binary to coalescence. Our findings underline the importance of considering all detailed host properties to predict IMBH merger rates for LISA., Comment: Submitted to ApJ

In this tutorial, we present a compact and holistic discussion of Deep Learning with a focus on Convolutional Neural Networks (CNNs) and supervised regression. While there are numerous books and articles on the individual topics we cover, comprehensive and detailed tutorials that address Deep Learning from a foundational yet rigorous and accessible perspective are rare. Most resources on CNNs are either too advanced, focusing on cutting-edge architectures, or too narrow, addressing only specific applications like image classification.This tutorial not only summarizes the most relevant concepts but also provides an in-depth exploration of each, offering a complete yet agile set of ideas. Moreover, we highlight the powerful synergy between learning theory, statistic, and machine learning, which together underpin the Deep Learning and CNN frameworks. We aim for this tutorial to serve as an optimal resource for students, professors, and anyone interested in understanding the foundations of Deep Learning. Upon acceptance we will provide an accompanying repository under \href{https://github.com/neoglez/deep-learning-tutorial}{https://github.com/neoglez/deep-learning-tutorial} Keywords: Tutorial, Deep Learning, Convolutional Neural Networks, Machine Learning., Comment: Submitted to the journal Machine Learning and Knowledge Extraction

Particle-particle and particle-gas processes significantly impact planetary precursors such as dust aggregates and planetesimals. We investigate gas permeability ($\kappa$) in 12 granular samples, mimicking planetesimal dust regoliths. Using parabolic flights, this study assesses how gravitational compression -- and lack thereof -- influences gas permeation, impacting the equilibrium state of low-gravity objects. Transitioning between micro- and hyper-gravity induces granular sedimentation dynamics, revealing collective dust-grain aerodynamics. Our experiments measure $\kappa$ across Knudsen number (Kn) ranges, reflecting transitional flow. Using mass and momentum conservation, we derive $\kappa$ and calculate pressure gradients within the granular matrix. Key findings: 1. As confinement pressure increases with gravitational load and mass flow, $\kappa$ and average pore space decrease. This implies that a planetesimal's unique dust-compaction history limits sub-surface volatile outflows. 2. The derived pressure gradient enables tensile strength determination for asteroid regolith simulants with cohesion. This offers a unique approach to studying dust-layer properties when suspended in confinement pressures comparable to the equilibrium state on planetesimals surfaces, which will be valuable for modelling their collisional evolution. 3. We observe a dynamical flow symmetry breaking when granular material moves against the pressure gradient. This occurs even at low Reynolds numbers, suggesting that Stokes numbers for drifting dust aggregates near the Stokes-Epstein transition require a drag force modification based on permeability.

Layered two-dimensional (2D) semiconductors have shown enhanced ion migration capabilities along their van der Waals (vdW) gaps and on their surfaces. This effect can be employed for resistive switching (RS) in devices for emerging memories, selectors, and neuromorphic computing. To date, all lateral molybdenum disulfide (MoS${_2}$)-based volatile RS devices with silver (Ag) ion migration have been demonstrated using exfoliated, single-crystal MoS${_2}$ flakes requiring a forming step to enable RS. Here, we present volatile RS with multilayer MoS${_2}$ grown by metal-organic chemical vapor deposition (MOCVD) with repeatable forming-free operation. The devices show highly reproducible volatile RS with low operating voltages of approximately 2 V and fast switching times down to 130 ns considering their micrometer scale dimensions. We investigate the switching mechanism based on Ag ion surface migration through transmission electron microscopy, electronic transport modeling, and density functional theory. Finally, we develop a physics-based compact model and explore the implementation of our volatile memristors as artificial neurons in neuromorphic systems., Comment: 43 pages

In the ever-evolving field of artificial neural networks and learning systems, complex-valued neural networks (CVNNs) have become a cornerstone, achieving exceptional performance in image processing and telecommunications. More precisely, in digital communication systems, CVNNs have been delivering significant results in tasks like equalization, channel estimation, beamforming, and decoding. Among the CVNN architectures, the complex-valued radial basis function neural network (C-RBF) stands out, especially when operating in noisy environments such as 5G multiple-input multiple-output (MIMO) systems. In such a context, this paper extends the classical shallow C-RBF to deep architectures, increasing its flexibility for a wider range of applications. Also, based on the parameter selection of the phase transmittance radial basis function (PT-RBF) neural network, we propose an initialization scheme for the deep C-RBF. Via rigorous simulations conforming to 3GPP TS 38 standards for digital communications, our method not only outperforms conventional initialization strategies like random, $K$-means, and constellation-based methods but it also seems to be the only approach to achieve successful convergence for deep C-RBF architectures. These findings pave the way to more robust and efficient neural network deployments in complex-valued digital communication systems., Comment: 19th International Symposium on Wireless Communication Systems (ISWCS'24). arXiv admin note: substantial text overlap with arXiv:2408.07692

This paper presents an online method for joint channel estimation and decoding in massive MIMO-OFDM systems using complex-valued neural networks (CVNNs). The study evaluates the performance of various CVNNs, such as the complex-valued feedforward neural network (CVFNN), split-complex feedforward neural network (SCFNN), complex radial basis function (C-RBF), fully-complex radial basis function (FC-RBF) and phase-transmittance radial basis function (PT-RBF), in realistic 5G communication scenarios. Results demonstrate improvements in mean squared error (MSE), convergence, and bit error rate (BER) accuracy. The C-RBF and PT-RBF architectures show the most promising outcomes, suggesting that RBF-based CVNNs provide a reliable and efficient solution for complex and noisy communication environments. These findings have potential implications for applying advanced neural network techniques in next-generation wireless systems., Comment: XLII Simp\'osio Brasileiro de Telecomunica\c{c}\~oes e Processamento de Sinais (SBrT 2024)

In the ever-evolving field of digital communication systems, complex-valued neural networks (CVNNs) have become a cornerstone, delivering exceptional performance in tasks like equalization, channel estimation, beamforming, and decoding. Among the myriad of CVNN architectures, the phase-transmittance radial basis function neural network (PT-RBF) stands out, especially when operating in noisy environments such as 5G MIMO systems. Despite its capabilities, achieving convergence in multi-layered, multi-input, and multi-output PT-RBFs remains a daunting challenge. Addressing this gap, this paper presents a novel Deep PT-RBF parameter initialization technique. Through rigorous simulations conforming to 3GPP TS 38 standards, our method not only outperforms conventional initialization strategies like random, $K$-means, and constellation-based methods but is also the only approach to achieve successful convergence in deep PT-RBF architectures. These findings pave the way to more robust and efficient neural network deployments in complex digital communication systems., Comment: IEEE International Conference on Machine Learning for Communication and Networking (ICMLCN 2024)

Two-dimensional materials (2DMs) have been widely investigated because of their potential for heterogeneous integration with modern electronics. However, several major challenges remain, such as the deposition of high-quality dielectrics on 2DMs and the tuning of the 2DM doping levels. Here, we report a scalable plasma-enhanced atomic layer deposition (PEALD) process for direct deposition of a nonstoichiometric aluminum oxide (AlOX) dielectric, overcoming the damage issues associated with conventional methods. Furthermore, we control the thickness of the dielectric layer to systematically tune the doping level of 2DMs. The experimental results demonstrate successful deposition without detectable damage, as confirmed by Raman spectroscopy and electrical measurements. Our method enables tuning of the Dirac and threshold voltages of back-gated graphene and MoS${_2}$ field-effect transistors (FETs), respectively, while also increasing the charge carrier mobility in both device types. We further demonstrate the method in top-gated MoS${_2}$ FETs with double-stack dielectric layers (AlOX+Al${_2}$O${_3}$), achieving critical breakdown field strengths of 7 MV/cm and improved mobility compared with the back gate configuration. In summary, we present a PEALD process that offers a scalable and low-damage solution for dielectric deposition on 2DMs, opening new possibilities for precise tuning of device characteristics in heterogeneous electronic circuits., Comment: 28 pages

For binary outcome models, an endogeneity correction based on nonlinear rank-based transformations is proposed. Identification without external instruments is achieved under one of two assumptions: either the endogenous regressor is a nonlinear function of one component of the error term, conditional on the exogenous regressors, or the dependence between the endogenous and exogenous regressors is nonlinear. Under these conditions, we prove consistency and asymptotic normality. Monte Carlo simulations and an application on German insolvency data illustrate the usefulness of the method.

The CO(1-0) line has been carefully calibrated as a tracer of molecular gas mass. However, recent studies often favor higher J transitions of the CO molecule which are brighter and accessible for redshift ranges where CO(1-0) is not. These lines are not perfect analogues for CO(1-0), owing to their more stringent excitation conditions, and must be calibrated for use as molecular gas tracers. Here we introduce the Arizona Molecular ISM Survey with the SMT (AMISS), a multi-CO line survey of z~0 galaxies conducted to calibrate the CO(2-1) and CO(3-2) lines. The final survey includes CO(2-1) spectra of 176 galaxies and CO(3-2) spectra for a subset of 45. We supplement these with archival CO(1-0) spectra from xCOLD GASS for all sources and additional CO(1-0) observations with the Kitt Peak 12m Telescope. Targets were selected to be representative of the galaxy population in the stellar mass range of $10^9$ to $10^{11.5}$ M$_\odot$. Our project emphasized careful characterization of statistical and systematic uncertainties to enable studies of trends in CO line ratios. We show that optical and CO disk sizes are on average equal, for both the CO(1-0) and CO(2-1) line. We measure the distribution of CO line luminosity ratios, finding medians (16th-84th percentile) of 0.71 (0.51-0.96) for the CO(2-1)-to-CO(1-0) ratio, 0.39 (0.24-0.53) for the CO(3-2)-to-CO(1-0) ratio, and 0.53 (0.41-0.74) for the CO(3-2)-to-CO(2-1) ratio. A companion paper presents our study of CO(2-1)'s applicability as a molecular gas mass tracer and search for trends in the CO(2-1)-to-CO(1-0) ratio. Our catalog of CO line luminosities will be publicly available with the published version of this article., Comment: Resubmitted to ApJ after incorporating reviewer feedback

The novel COVID-19 disease has been declared a pandemic event. Early detection of infection symptoms and contact tracing are playing a vital role in containing COVID-19 spread. As demonstrated by recent literature, multi-sensor and connected wearable devices might enable symptom detection and help tracing contacts, while also acquiring useful epidemiological information. This paper presents the design and implementation of a fully open-source wearable platform called H-Watch. It has been designed to include several sensors for COVID-19 early detection, multi-radio for wireless transmission and tracking, a microcontroller for processing data on-board, and finally, an energy harvester to extend the battery lifetime. Experimental results demonstrated only 5.9 mW of average power consumption, leading to a lifetime of 9 days on a small watch battery. Finally, all the hardware and the software, including a machine learning on MCU toolkit, are provided open-source, allowing the research community to build and use the H-Watch.

Radio Frequency (RF) wireless power transfer is a promising technology that has the potential to constantly power small Internet of Things (IoT) devices, enabling even battery-less systems and reducing their maintenance requirements. However, to achieve this ambitious goal, carefully designed RF energy harvesting (EH) systems are needed to minimize the conversion losses and the conversion efficiency of the limited power. For intelligent internet of things sensors and devices, which often have non-constant power requirements, an additional power management stage with energy storage is needed to temporarily provide a higher power output than the power being harvested. This paper proposes an RF wireless power energy conversion system for miniaturized IoT composed of an impedance matching network, a rectifier, and power management with energy storage. The proposed sub-system has been experimentally validated and achieved an overall power conversion efficiency (PCE) of over 30 % for an input power of -10 dBm and a peak efficiency of 57 % at 3 dBm.

The neutron-rich unbound fluorine isotope $^{30}$F$_{21}$ has been observed for the first time by measuring its neutron decay at the SAMURAI spectrometer (RIBF, RIKEN) in the quasi-free proton knockout reaction of $^{31}$Ne nuclei at 235 MeV/nucleon. The mass and thus one-neutron-separation energy of $^{30}$F has been determined to be $S_n = -472\pm 58 \mathrm{(stat.)} \pm 33 \mathrm{(sys.)}$ keV from the measurement of its invariant-mass spectrum. The absence of a sharp drop in $S_n$($^{30}$F) shows that the ``magic'' $N=20$ shell gap is not restored close to $^{28}$O, which is in agreement with our shell-model calculations that predict a near degeneracy between the neutron $d$ and $fp$ orbitals, with the $1p_{3/2}$ and $1p_{1/2}$ orbitals becoming more bound than the $0f_{7/2}$ one. This degeneracy and reordering of orbitals has two potential consequences: $^{28}$O behaves like a strongly superfluid nucleus with neutron pairs scattering across shells, and both $^{29,31}$F appear to be good two-neutron halo-nucleus candidates., Comment: 9 pages, 2 figures, accepted for publication in Physical Review Letters