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Hypothesis Understanding the scission of rod-like micelles under mechanical forces is crucial for optimizing their stability and behavior in industrial applications. This study investigates how micelle length, flexibility, and external forces interact, offering insights into the design of micellar systems in processes influenced by mechanical stress. Although significant, direct experimental observations of flow-induced micellar scission using scattering techniques remain scarce. Experiments and Simulations Small angle neutron scattering (SANS) is used to explore the shear response of aqueous cetyltrimethylammonium bromide (CTAB) solutions with sodium nitrate. Rheological tests show shear thinning with no shear banding, ensuring a uniform flow field for reliable interpretation of scattering data. As shear rate increases, the scattering spectra show angular distortion, which is analyzed using spherical harmonic decomposition to characterize flow-induced scission and micelle orientation under shear. Findings Two analysis steps are used: a model-independent spectral eigendecomposition reveals a decrease in micellar length, while regression analysis quantifies the evolution of the length distribution and mean length with shear rate. Additionally, micelle alignment increases with shear, quantified by the orientational distribution function. These findings provide experimental evidence for flow-induced alignment and scission, offering a new framework for understanding shear-induced phenomena in micellar systems

The masses of Population III stars are largely unconstrained since no simulations exist that take all relevant primordial star formation physics into account. We perform the first suite of radiation magnetohydrodynamics (RMHD) simulations of Population III star formation, with the POPSICLE project. Compared to control simulations that only include magnetic fields (MHD), protostellar ionizing and dissociating feedback, or neither, the RMHD simulation best resembles the MHD simulation during the earliest stages of collapse and star formation. In $5000\,\rm{yrs}$, the mass of the most massive star is $65\,\rm{M_{\odot}}$ in the RMHD simulation, compared to $120\,\rm{M_{\odot}}$ in simulations without magnetic fields. This difference arises because magnetic fields act against gravity, suppress mass transport, and reduce compressional heating. The maximum stellar mass of Population III stars is thus already limited by magnetic fields, even before accretion rates drop to allow significant protostellar radiative feedback. Following classical main sequence stellar evolution with MESA reveals that it is difficult to create Population III stars with masses larger than $600\,\rm{M_{\odot}}$ in typical dark matter minihaloes at $z \gtrsim 20$, with maximum stellar masses $\sim 100\,\rm{M_{\odot}}$ more likely due to expected negative feedback from both magnetic fields and stellar radiation. This work lays the first step in building a full physics-informed mass function of Population III stars., Comment: 5 pages + appendix, submitted to MNRAS

We introduce MAGI, a hybrid video generation framework that combines masked modeling for intra-frame generation with causal modeling for next-frame generation. Our key innovation, Complete Teacher Forcing (CTF), conditions masked frames on complete observation frames rather than masked ones (namely Masked Teacher Forcing, MTF), enabling a smooth transition from token-level (patch-level) to frame-level autoregressive generation. CTF significantly outperforms MTF, achieving a +23% improvement in FVD scores on first-frame conditioned video prediction. To address issues like exposure bias, we employ targeted training strategies, setting a new benchmark in autoregressive video generation. Experiments show that MAGI can generate long, coherent video sequences exceeding 100 frames, even when trained on as few as 16 frames, highlighting its potential for scalable, high-quality video generation., Comment: 12 pages, 9 figures

Sparse optimization seeks an optimal solution with few nonzero entries. To achieve this, it is common to add to the criterion a penalty term proportional to the $\ell_1$-norm, which is recognized as the archetype of sparsity-inducing norms. In this approach, the number of nonzero entries is not controlled a priori. By contrast, in this paper, we focus on finding an optimal solution with at most~$k$ nonzero coordinates (or for short, $k$-sparse vectors), where $k$ is a given sparsity level (or ``sparsity budget''). For this purpose, we study the class of generalized $k$-support norms that arise from a given source norm. When added as a penalty term, we provide conditions under which such generalized $k$-support norms promote $k$-sparse solutions. The result follows from an analysis of the exposed faces of closed convex sets generated by $k$-sparse vectors, and of how primal support identification can be deduced from dual information. Finally, we study some of the geometric properties of the unit balls for the $k$-support norms and their dual norms when the source norm belongs to the family of $\ell_p$-norms.

Precision agriculture in general, and precision weeding in particular, have greatly benefited from the major advancements in deep learning and computer vision. A large variety of commercial robotic solutions are already available and deployed. However, the adoption by farmers of such solutions is still low for many reasons, an important one being the lack of trust in these systems. This is in great part due to the opaqueness and complexity of deep neural networks and the manufacturers' inability to provide valid guarantees on their performance. Conformal prediction, a well-established methodology in the machine learning community, is an efficient and reliable strategy for providing trustworthy guarantees on the predictions of any black-box model under very minimal constraints. Bridging the gap between the safe machine learning and precision agriculture communities, this article showcases conformal prediction in action on the task of precision weeding through deep learning-based image classification. After a detailed presentation of the conformal prediction methodology and the development of a precision spraying pipeline based on a ''conformalized'' neural network and well-defined spraying decision rules, the article evaluates this pipeline on two real-world scenarios: one under in-distribution conditions, the other reflecting a near out-of-distribution setting. The results show that we are able to provide formal, i.e. certifiable, guarantees on spraying at least 90% of the weeds.

Many robotic tasks in real-world environments require physical interactions with an object such as pick up or push. For successful interactions, the robot needs to know the object's affordances, which are defined as the potential actions the robot can perform with the object. In order to learn a robot-specific affordance predictor, we propose an interactive exploration pipeline which allows the robot to collect interaction experiences while exploring an unknown environment. We integrate an object-level map in the exploration pipeline such that the robot can identify different object instances and track objects across diverse viewpoints. This results in denser and more accurate affordance annotations compared to state-of-the-art methods, which do not incorporate a map. We show that our affordance exploration approach makes exploration more efficient and results in more accurate affordance prediction models compared to baseline methods., Comment: International Symposium of Robotics Research (ISRR) 2024

This work thoroughly examines several analytical tools, each possessing a different level of mathematical intricacy, for the purpose of characterizing the orientation distribution function of elongated objects under flow. Our investigation places an emphasis on connecting the orientation distribution to the small angle scattering spectra measured experimentally. The diverse range of mathematical approaches investigated herein provides insights into the flow behavior of elongated particles from different perspectives and serves as powerful tools for elucidating the complex interplay between flow dynamics and the orientation distribution function.

We examine a generalized KPP equation with a ``$q$-diffusion", which is a framework that unifies various standard linear diffusion regimes: Fickian diffusion ($q = 0$), Stratonovich diffusion ($q = 1/2$), Fokker-Planck diffusion ($q = 1$), and nonstandard diffusion regimes for general $q\in\mathbb{R}$. Using both analytical methods and numerical simulations, we explore how the ability of persistence (measured by some principal eigenvalue) and how the asymptotic spreading speed depend on the parameter $q$ and on the phase shift between the growth rate $r(x)$ and the diffusion coefficient $D(x)$. Our results demonstrate that persistence and spreading properties generally depend on $q$: for example, appropriate configurations of $r(x)$ and $D(x)$ can be constructed such that $q$-diffusion either enhances or diminishes the ability of persistence and the spreading speed with respect to the traditional Fickian diffusion. We show that the spatial arrangement of $r(x)$ with respect to $D(x)$ has markedly different effects depending on whether $q > 0$, $q = 0$, or $q < 0$. The case where $r$ is constant is an exception: persistence becomes independent of $q$, while the spreading speed displays a symmetry around $q = 1/2$. This work underscores the importance of carefully selecting diffusion models in ecological and epidemiological contexts, highlighting their potential implications for persistence, spreading, and control strategies.

This paper considers the problem of analyzing the timing side-channel security of binary programs through decompilation and source-level analysis. We focus on two popular policies, namely constant-time and speculative constant-time, (S)CT for short, used to protect cryptographic libraries. First, we observe that popular decompilers remove (S)CT violations, i.e., transform non-(S)CT programs into (S)CT programs; it follows that analyzing decompiled programs is not sound. Second, we develop techniques to prove that decompilers are transparent, i.e., neither introduce nor remove (S)CT violations. Third, we apply our techniques to \refleCT{}, a core but non-trivial decompiler. As a contribution of independent interest, we find that constant-time verification tools may not be sound, due to their use of preprocessors (e.g.\, binary lifters or IR converters) that eliminate CT violations.

Having a sufficient quantity of quality data is a critical enabler of training effective machine learning models. Being able to effectively determine the adequacy of a dataset prior to training and evaluating a model's performance would be an essential tool for anyone engaged in experimental design or data collection. However, despite the need for it, the ability to prospectively assess data sufficiency remains an elusive capability. We report here on two experiments undertaken in an attempt to better ascertain whether or not basic descriptive statistical measures can be indicative of how effective a dataset will be at training a resulting model. Leveraging the effect size of our features, this work first explores whether or not a correlation exists between effect size, and resulting model performance (theorizing that the magnitude of the distinction between classes could correlate to a classifier's resulting success). We then explore whether or not the magnitude of the effect size will impact the rate of convergence of our learning rate, (theorizing again that a greater effect size may indicate that the model will converge more rapidly, and with a smaller sample size needed). Our results appear to indicate that this is not an effective heuristic for determining adequate sample size or projecting model performance, and therefore that additional work is still needed to better prospectively assess adequacy of data.

Small-angle scattering (SAS) techniques are indispensable tools for probing the structure of soft materials. However, traditional analytical models often face limitations in structural inversion for complex systems, primarily due to the absence of closed-form expressions of scattering functions. To address these challenges, we present a machine learning framework based on the Kolmogorov-Arnold Network (KAN) for directly extracting real-space structural information from scattering spectra in reciprocal space. This model-independent, data-driven approach provides a versatile solution for analyzing intricate configurations in soft matter. By applying the KAN to lyotropic lamellar phases and colloidal suspensions -- two representative soft matter systems -- we demonstrate its ability to accurately and efficiently resolve structural collectivity and complexity. Our findings highlight the transformative potential of machine learning in enhancing the quantitative analysis of soft materials, paving the way for robust structural inversion across diverse systems., Comment: 9 pages, 8 figures

Compliant walls are widespread in biological and engineering systems. Because of their singular nature, adapted tools are required to accurately study their rheological properties as well as the consequences of the latter within a given mechanical setting. Because of their slender nature, membranes can be considered as prototypical examples of highly compliant boundaries. In this study, we describe a modified Surface Forces Apparatus (SFA) developed to measure the forces acting on a compliant membrane by measuring its deformation field. We discuss how such a device can be used to characterize the rheology of suspended membranes and accurately measure the electrostatic interactions between a polarized membrane and a spherical electrode, without the need of an external measurement spring.

Locally Markov walks are natural generalizations of classical Markov chains, where instead of a particle moving independently of the past, it decides where to move next depending on the last action performed at the current location. We introduce the concept of locally Markov walks on finite graphs and investigate several basic properties. For a particular locally Markov walk - the uniform unicycle walk on the complete graph - we investigate the mixing time and we prove that it exhibits cutoff., Comment: preliminary version; comments are welcome

Autonomous Driving Systems (ADSs) rely on Deep Neural Networks, allowing vehicles to navigate complex, open environments. However, the unpredictability of these scenarios highlights the need for rigorous system-level testing to ensure safety, a task usually performed with a simulator in the loop. Though one important goal of such testing is to detect safety violations, there are many undesirable system behaviors, that may not immediately lead to violations, that testing should also be focusing on, thus detecting more subtle problems and enabling a finer-grained analysis. This paper introduces Cooperative Co-evolutionary MEtamorphic test Generator for Autonomous systems (CoCoMEGA), a novel automated testing framework aimed at advancing system-level safety assessments of ADSs. CoCoMEGA combines Metamorphic Testing (MT) with a search-based approach utilizing Cooperative Co-Evolutionary Algorithms (CCEA) to efficiently generate a diverse set of test cases. CoCoMEGA emphasizes the identification of test scenarios that present undesirable system behavior, that may eventually lead to safety violations, captured by Metamorphic Relations (MRs). When evaluated within the CARLA simulation environment on the Interfuser ADS, CoCoMEGA consistently outperforms baseline methods, demonstrating enhanced effectiveness and efficiency in generating severe, diverse MR violations and achieving broader exploration of the test space. These results underscore CoCoMEGA as a promising, more scalable solution to the inherent challenges in ADS testing with a simulator in the loop. Future research directions may include extending the approach to additional simulation platforms, applying it to other complex systems, and exploring methods for further improving testing efficiency such as surrogate modeling., Comment: 24 pages, 6 figures, to be published in IEEE Transactions on Software Engineering

A lattice $(d,k)$-polytope is the convex hull of a set of points in $\mathbb{R}^d$ whose coordinates are integers ranging between $0$ and $k$. We consider the smallest possible distance $\varepsilon(d,k)$ between two disjoint lattice $(d,k)$-polytopes. We propose an algebraic model for this distance and derive from it an explicit formula for $\varepsilon(2,k)$. Our model also allows for the computation of previously intractable values of $\varepsilon(d,k)$. In particular, we compute $\varepsilon(3,k)$ when $4\leq{k}\leq8$, $\varepsilon(4,k)$ when $2\leq{k}\leq3$, and $\varepsilon(6,1)$., Comment: 17 pages, 2 figures

Cyber-Physical Systems (CPSs) software is used to enforce desired behaviours on physical systems. To test the interaction between the CPS software and the system's physics, engineers provide traces of desired physical states and observe traces of the actual physical states. CPS requirements describe how closely the actual physical traces should track the desired traces. These requirements are typically defined for specific, simple input traces such as step or ramp sequences, and thus are not applicable to arbitrary inputs. This limits the availability of oracles for CPSs. Our recent work proposes an approach to testing CPS using control-theoretical design assumptions instead of requirements. This approach circumvents the oracle problem by leveraging the control-theoretical guarantees that are provided when the design assumptions are satisfied. To address the test case generation and oracle problems, researchers have proposed metamorphic testing, which is based on the study of relations across tests, i.e., metamorphic relations (MRs). In this work, we define MRs based on the design assumptions and explore combinations of these MRs using genetic programming to generate CPS test cases. This enables the generation of CPS input traces with potentially arbitrary shapes, together with associated expected output traces. We use the deviation from the expected output traces to guide the generation of input traces that falsify the MRs. Our experiment results show that the MR-falsification provides engineers with new information, helping them identify passed and failed test cases. Furthermore, we show that the generation of traces that falsify the MRs is a non-trivial problem, which is successfully addressed by our genetic search.

Given conflicting probability estimates for a set of events, how can we quantify how much they conflict? How can we find a single probability distribution that best encapsulates the given estimates? One approach is to minimize a loss function such as binary KL-divergence that quantifies the dissimilarity between the given estimates and the candidate probability distribution. Given a set of events, we characterize the facets of the polytope of coherent probability estimates about those events. We explore two applications of these ideas: eliciting the beliefs of large language models, and merging expert forecasts into a single coherent forecast., Comment: 33 pages, 3 figures, and 4 tables

An efficient source of hydrated electrons generated by visible light has the potential to have a major impact on solar homogeneous catalysis. Diamond has potentially a high capability of emitting hydrated electrons, but only using ultraviolet light (lambda<225 nm). In this work, we demonstrate the efficient absorption of visible light by nanocomposites consisting of detonation nanodiamonds and gold nanoparticles (AuNP@DNDs), which subsequently emit electrons into the aqueous environment in which they are suspended. This has been done by exciting the AuNP@DND with visible laser light and monitoring the appearance and intensity of the transient absorption of hydrated electrons centered at around 720 nm. We suggest that this mechanism is made possible by the plasmonic enhancement of visible absorption by sp2-hybridized islands on the DND surface. Optimization of this process could lead to important breakthroughs in solar photocatalysis of energy-intensive reactions such as N2 and CO2 reduction as well as providing a non-toxic source of hydrated electrons for applications in wastewater management and nanomedicine.

Controllers for software-defined networks (SDNs) are centralised software components that enable advanced network functionalities, such as dynamic traffic engineering and network virtualisation. However, these functionalities increase the complexity of SDN controllers, making thorough testing crucial. SDN controllers are stateful, interacting with multiple network devices through sequences of control messages. Identifying stateful failures in an SDN controller is challenging due to the infinite possible sequences of control messages, which result in an unbounded number of stateful interactions between the controller and network devices. In this article, we propose SeqFuzzSDN, a learning-guided fuzzing method for testing stateful SDN controllers. SeqFuzzSDN aims to (1) efficiently explore the state space of the SDN controller under test, (2) generate effective and diverse tests (i.e., control message sequences) to uncover failures, and (3) infer accurate failure-inducing models that characterise the message sequences leading to failures. In addition, we compare SeqFuzzSDN with three extensions of state-of-the-art (SOTA) methods for fuzzing SDNs. Our findings show that, compared to the extended SOTA methods, SeqFuzzSDN (1) generates more diverse message sequences that lead to failures within the same time budget, and (2) produces more accurate failure-inducing models, significantly outperforming the other extended SOTA methods in terms of sensitivity.

We introduce FrontierMath, a benchmark of hundreds of original, exceptionally challenging mathematics problems crafted and vetted by expert mathematicians. The questions cover most major branches of modern mathematics -- from computationally intensive problems in number theory and real analysis to abstract questions in algebraic geometry and category theory. Solving a typical problem requires multiple hours of effort from a researcher in the relevant branch of mathematics, and for the upper end questions, multiple days. FrontierMath uses new, unpublished problems and automated verification to reliably evaluate models while minimizing risk of data contamination. Current state-of-the-art AI models solve under 2% of problems, revealing a vast gap between AI capabilities and the prowess of the mathematical community. As AI systems advance toward expert-level mathematical abilities, FrontierMath offers a rigorous testbed that quantifies their progress.

Accurate classification of sleep stages from less obtrusive sensor measurements such as the electrocardiogram (ECG) or photoplethysmogram (PPG) could enable important applications in sleep medicine. Existing approaches to this problem have typically used deep learning models designed and trained to operate on one or more specific input signals. However, the datasets used to develop these models often do not contain the same sets of input signals. Some signals, particularly PPG, are much less prevalent than others, and this has previously been addressed with techniques such as transfer learning. Additionally, only training on one or more fixed modalities precludes cross-modal information transfer from other sources, which has proved valuable in other problem domains. To address this, we introduce wav2sleep, a unified model designed to operate on variable sets of input signals during training and inference. After jointly training on over 10,000 overnight recordings from six publicly available polysomnography datasets, including SHHS and MESA, wav2sleep outperforms existing sleep stage classification models across test-time input combinations including ECG, PPG, and respiratory signals., Comment: Accepted to Machine Learning for Health (ML4H) 2024

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.

We present the photometric performance of SPIRIT, a ground-based near-infrared InGaAs CMOS-based instrument (1280 by 1024 pixels, 12 micron pitch), using on-sky results from the SPECULOOS-Southern Observatory during 2022 - 2023. SPIRIT was specifically designed to optimise time-series photometric precision for observing late M and L type stars. To achieve this, a custom wide-pass filter (0.81 - 1.33 microns, zYJ ) was used, which was also designed to minimise the effects of atmospheric precipitable water vapour (PWV) variability on differential photometry. Additionally, SPIRIT was designed to be maintenance-free by eliminating the need for liquid nitrogen for cooling. We compared SPIRIT's performance with a deeply-depleted (2048 by 2048 pixels, 13.5 micron pitch) CCD-based instrument (using an I+z' filter, 0.7 - 1.1 microns) through simultaneous observations. For L type stars and cooler, SPIRIT exhibited better photometric noise performance compared to the CCD-based instrument. The custom filter also significantly minimised red noise in the observed light curves typically introduced by atmospheric PWV variability. In SPIRIT observations, the detector's read noise was the dominant limitation, although in some cases, we were limited by the lack of comparison stars., Comment: SPIE Astronomical Telescopes + Instrumentation 2024

The use of eXtended Reality (XR) technologies in the space domain has increased significantly over the past few years as it can offer many advantages when simulating complex and challenging environments. Space agencies are currently using these disruptive tools to train astronauts for Extravehicular Activities (EVAs), to test equipment and procedures, and to assess spacecraft and hardware designs. With the Moon being the current focus of the next generation of space exploration missions, simulating its harsh environment is one of the key areas where XR can be applied, particularly for astronaut training. Peculiar lunar lighting conditions in combination with reduced gravity levels will highly impact human locomotion especially for movements such as walking, jumping, and running. In order to execute operations on the lunar surface and to safely live on the Moon for an extended period of time, innovative training methodologies and tools such as XR are becoming paramount to perform pre-mission validation and certification. This research work presents the findings of the experiments aimed at exploring the integration of XR technology and parabolic flight activities for astronaut training. In addition, the study aims to consolidate these findings into a set of guidelines that can assist future researchers who wish to incorporate XR technology into lunar training and preparation activities, including the use of such XR tools during long duration missions., Comment: 75th International Astronautical Congress (IAC), Milan, Italy, 14-18 October 2024

Deep Neural Networks (DNNs) are increasingly deployed across applications. However, ensuring their reliability remains a challenge, and in many situations, alternative models with similar functionality and accuracy are available. Traditional accuracy-based evaluations often fail to capture behavioral differences between models, especially with limited test datasets, making it difficult to select or combine models effectively. Differential testing addresses this by generating test inputs that expose discrepancies in DNN model behavior. However, existing approaches face significant limitations: many rely on model internals or are constrained by available seed inputs. To address these challenges, we propose DiffGAN, a black-box test image generation approach for differential testing of DNN models. DiffGAN leverages a Generative Adversarial Network (GAN) and the Non-dominated Sorting Genetic Algorithm II to generate diverse and valid triggering inputs that reveal behavioral discrepancies between models. DiffGAN employs two custom fitness functions, focusing on diversity and divergence, to guide the exploration of the GAN input space and identify discrepancies between models' outputs. By strategically searching this space, DiffGAN generates inputs with specific features that trigger differences in model behavior. DiffGAN is black-box, making it applicable in more situations. We evaluate DiffGAN on eight DNN model pairs trained on widely used image datasets. Our results show DiffGAN significantly outperforms a SOTA baseline, generating four times more triggering inputs, with greater diversity and validity, within the same budget. Additionally, the generated inputs improve the accuracy of a machine learning-based model selection mechanism, which selects the best-performing model based on input characteristics and can serve as a smart output voting mechanism when using alternative models.

In this technical report, we introduce OpenR, an open-source framework designed to integrate key components for enhancing the reasoning capabilities of large language models (LLMs). OpenR unifies data acquisition, reinforcement learning training (both online and offline), and non-autoregressive decoding into a cohesive software platform. Our goal is to establish an open-source platform and community to accelerate the development of LLM reasoning. Inspired by the success of OpenAI's o1 model, which demonstrated improved reasoning abilities through step-by-step reasoning and reinforcement learning, OpenR integrates test-time compute, reinforcement learning, and process supervision to improve reasoning in LLMs. Our work is the first to provide an open-source framework that explores the core techniques of OpenAI's o1 model with reinforcement learning, achieving advanced reasoning capabilities beyond traditional autoregressive methods. We demonstrate the efficacy of OpenR by evaluating it on the MATH dataset, utilising publicly available data and search methods. Our initial experiments confirm substantial gains, with relative improvements in reasoning and performance driven by test-time computation and reinforcement learning through process reward models. The OpenR framework, including code, models, and datasets, is accessible at https://openreasoner.github.io.

Mangroves are dynamic coastal ecosystems that are crucial to environmental health, economic stability, and climate resilience. The monitoring and preservation of mangroves are of global importance, with remote sensing technologies playing a pivotal role in these efforts. The integration of cutting-edge artificial intelligence with satellite data opens new avenues for ecological monitoring, potentially revolutionizing conservation strategies at a time when the protection of natural resources is more crucial than ever. The objective of this work is to provide a comprehensive evaluation of recent deep-learning models on the task of mangrove segmentation. We first introduce and make available a novel open-source dataset, MagSet-2, incorporating mangrove annotations from the Global Mangrove Watch and satellite images from Sentinel-2, from mangrove positions all over the world. We then benchmark three architectural groups, namely convolutional, transformer, and mamba models, using the created dataset. The experimental outcomes further validate the deep learning community's interest in the Mamba model, which surpasses other architectures in all metrics., Comment: 12 pages, accepted to the MACLEAN workshop of ECML/PKDD 2024

The problem of uncertainty is a feature of real world robotics problems and any control framework must contend with it in order to succeed in real applications tasks. Reinforcement Learning is no different, and epistemic uncertainty arising from model uncertainty or misspecification is a challenge well captured by the sim-to-real gap. A simple solution to this issue is domain randomization (DR), which unfortunately can result in conservative agents. As a remedy to this conservativeness, the use of universal policies that take additional information about the randomized domain has risen as an alternative solution, along with recurrent neural network-based controllers. Uncertainty-aware universal policies present a particularly compelling solution able to account for system identification uncertainties during deployment. In this paper, we reveal that the challenge of efficiently optimizing uncertainty-aware policies can be fundamentally reframed as solving the convex coverage set (CCS) problem within a multi-objective reinforcement learning (MORL) context. By introducing a novel Markov decision process (MDP) framework where each domain's performance is treated as an independent objective, we unify the training of uncertainty-aware policies with MORL approaches. This connection enables the application of MORL algorithms for domain randomization (DR), allowing for more efficient policy optimization. To illustrate this, we focus on the linear utility function, which aligns with the expectation in DR formulations, and propose a series of algorithms adapted from the MORL literature to solve the CCS, demonstrating their ability to enhance the performance of uncertainty-aware policies., Comment: 27 pages, 9 figures, 12 tables, under review by IJRR

The response of a graphene Hall-Effect sensor to the inhomogeneous magnetic field generated by a dipole located above it, is investigated numerically at room temperature as a function of the dipole position, its orientation and the conduction regime of the sensor, i.e., diffusive or ballistic. By means of dedicated models, we highlight that the correction factor {\alpha} frequently used to relate the Hall voltage to the magnetic field averaged over the sensor area can be greatly improved in the high proximity situation enabled by the use of graphene, particularly in the ballistic regime. In addition, it is demonstrated that by fine-tuning the dipole position in the sensor plane, the Hall response becomes highly selective with respect to the dipole orientation. These analyses show that diffusive graphene Hall sensors may be preferred for particle detection, while ballistic ones used as close as possible to a nanomagnet would be preferred for magnetometry. With the support of micromagnetic simulations, the principle of the magnetic hysteresis loop measurement of an isolated nanomagnet is quantitively demonstrated with large signal-to-noise ratio to probe incoherent magnetization reversal. Devices based on specially designed graphene Hall crosses operating in the ballistic regime promise to outperform state-of-the-art ballistic Hall sensors based on semiconductor quantum wells especially for nano-magnetometry.

Many successful methods to learn dynamical systems from data have recently been introduced. Such methods often rely on the availability of the system's full state. However, this underlying hypothesis is rather restrictive as it is typically not confirmed in practice, leaving us with partially observed systems. Utilizing the Mori-Zwanzig (MZ) formalism from statistical physics, we demonstrate that Constant Lag Neural Delay Differential Equations (NDDEs) naturally serve as suitable models for partially observed states. In empirical evaluation, we show that such models outperform existing methods on both synthetic and experimental data.

Deforestation is a compiler optimization that removes intermediate data structure allocations from functional programs to improve their efficiency. This is an old idea, but previous approaches have proved limited or impractical: they either only worked on compositions of predefined combinators (shortcut fusion), or involved the aggressive unfolding of recursive definitions until a depth limit was reached or a reoccurring pattern was found to tie the recursive knot, resulting in impractical algorithmic complexity and large amounts of code duplication. We present Lumberhack, a general-purpose deforestation approach for purely functional call-by-value programs. Lumberhack uses subtype inference to reason about data structure production and consumption and uses an elaboration pass to fuse the corresponding recursive definitions. It fuses large classes of mutually recursive definitions while avoiding much of the unproductive (and sometimes counter-productive) code duplication inherent in previous approaches. We prove the soundness of Lumberhack using logical relations and experimentally demonstrate significant speedups in the standard nofib benchmark suite., Comment: This is the technical report version of the paper published at ICFP 2024

We present a novel approach for benchmarking and validating quantitative phase tomography (QPT) systems using three-dimensional microphantoms. These microphantoms, crafted from biological and imaging data, replicate the optical and structural properties of multicellular biological samples. Their fabrication featuring refractive index modulation at sub-micrometer details is enabled by two-photon polymerization. We showcase the effectiveness of our technique via a round-robin test of healthy and tumoral liver organoid phantoms across three different QPT systems. This test reveals sample- and system-dependent errors in measuring dry mass and morphology. Tailored microphantoms establish the gold standard for the optimization of hardware setups, reconstruction strategies and error assessment, paving the way for novel non-invasive, label-free measurements of 3D biological samples.

The BICEP3 and BICEP Array polarimeters are small-aperture refracting telescopes located at the South Pole designed to measure primordial gravitational wave signatures in the Cosmic Microwave Background (CMB) polarization, predicted by inflation. Constraining the inflationary signal requires not only excellent sensitivity, but also careful control of instrumental systematics. Both instruments use antenna-coupled orthogonally polarized detector pairs, and the polarized sky signal is reconstructed by taking the difference in each detector pair. As a result, the differential response between detectors within a pair becomes an important systematic effect we must control. Additionally, mapping the intensity and polarization response in regions away from the main beam can inform how sidelobe levels affect CMB measurements. Extensive calibration measurements are taken in situ every austral summer for control of instrumental systematics and instrument characterisation. In this work, we detail the set of beam calibration measurements that we conduct on the BICEP receivers, from deep measurements of main beam response to polarized beam response and sidelobe mapping. We discuss the impact of these measurements for instrumental systematics studies and design choices for future CMB receivers., Comment: 13 pages, 7 figures, 1 table, Proceedings paper SPIE 2024

The exponential growth in data sizes and storage costs has brought considerable challenges to the data science community, requiring solutions to run learning methods on such data. While machine learning has scaled to achieve predictive accuracy in big data settings, statistical inference and uncertainty quantification tools are still lagging. Priority scientific fields collect vast data to understand phenomena typically studied with statistical methods like regression. In this setting, regression parameter estimation can benefit from efficient computational procedures, but the main challenge lies in computing error process parameters with complex covariance structures. Identifying and estimating these structures is essential for inference and often used for uncertainty quantification in machine learning with Gaussian Processes. However, estimating these structures becomes burdensome as data scales, requiring approximations that compromise the reliability of outputs. These approximations are even more unreliable when complexities like long-range dependencies or missing data are present. This work defines and proves the statistical properties of the Generalized Method of Wavelet Moments with Exogenous variables (GMWMX), a highly scalable, stable, and statistically valid method for estimating and delivering inference for linear models using stochastic processes in the presence of data complexities like latent dependence structures and missing data. Applied examples from Earth Sciences and extensive simulations highlight the advantages of the GMWMX.

In this study, we propose a novel model describing the coevolution between hosts and pathogens, based on a non-local partial differential equation formalism for populations structured by phenotypic traits. Our objective with this model is to illustrate scenarios corresponding to the evolutionary concept of ''Chase Red Queen scenario'', characterized by perpetual evolutionary chases between hosts and pathogens. First, numerical simulations show the emergence of such scenarios, depicting the escape of the host (in phenotypic space) pursued by the pathogen. We observe two types of behaviors, depending on the assumption about the presence of a phenotypic optimum for the host: either the formation of traveling pulses moving along a straight line with constant speed and constant profiles, or stable phenotypic distributions that periodically rotate along a circle in the phenotypic space. Through rigorous perturbation techniques and careful application of the implicit function theorem in rather intricate function spaces, we demonstrate the existence of the first type of behavior, namely traveling pulses moving with constant speed along a straight line. Just as the Lotka-Volterra models have revealed periodic dynamics without the need for environmental forcing, our work shows that, from the pathogen's point of view, various trajectories of mobile optima can emerge from coevolution with a host species.

Asymmetric 3D structures are observed in the outflows of evolved low- and intermediate-mass stars, and are believed to be shaped through the interaction of companions that are hidden within the dense wind. We investigate how triple systems can shape the outflow of AGB stars. We focus on coplanar systems in a hierarchical, stable orbit, consisting of an AGB star with one relatively close companion, and one at large orbital separation. We model a grid of hierarchical triple systems including a wind-launching AGB star, with the smoothed-particle-hydrodynamic Phantom code. We vary the outer companion mass, the AGB wind velocity and the orbital eccentricities to study the impact of these parameters on the wind morphology. Further, we investigate if the outflow of the AGB star R Aql could be shaped by a triple system, by post-processing one of our triple models with a radiative transfer routine, and comparing this to data of the ALMA ATOMIUM programme. The characteristic wind structures resulting from a hierarchical triple system are the following. A large two-edged spiral wake results behind the outer companion star. This structure lies on top of the spiral structure formed by the close binary, which is affected by the orbital motion around the system centre-of-mass. This dense inner spiral pattern interacts with, and strongly impacts, the spiral wake of the outer companion, resulting in a waved-pattern on the outer edge of this spiral wake. From the comparison of our models to the observations of R Aql, we conclude that this circumstellar environment might be shaped by a similar system as the ones modelled in this work, but an elaborate study of the observational data is needed to determine better the orbital parameters and characteristics of the central system., Comment: 20 pages, 19 figures

Taylor's power law (TL) or fluctuation scaling has been verified empirically for the abundances of many species, human and non-human, and in many other fields including physics, meteorology, computer science, and finance. TL asserts that the variance is directly proportional to a power of the mean, exactly for population moments and, whether or not population moments exist, approximately for sample moments. In many papers, linear regression of log variance as a function of log mean is used to estimate TL's parameters. We provide some statistical guarantees with large-sample asymptotics for this kind of inference under general conditions, and we derive confidence intervals for the parameters. In many ecological applications, the means and variances are estimated over time or across space from arrays of abundance data collected at different locations and time points. When the ratio between the time-series length and the number of spatial points converges to a constant as both become large, the usual normalized statistics are asymptotically biased. We provide a bias correction to get correct confidence intervals. TL, widely studied in multiple sciences, is a source of challenging new statistical problems in a nonstationary spatiotemporal framework. We illustrate our results with both simulated and real data sets.

High-resolution observations reveal that the outflows of evolved low- and intermediate-mass stars harbour complex morphological structures that are linked to the presence of one or multiple companions. Hydrodynamical simulations provide a way to study the impact of a companion on the shaping of the AGB outflow. Using smoothed particle hydrodynamic (SPH) simulations of a mass losing AGB star with a binary companion, we study the impact of including HI atomic line cooling on the flow morphology and on the properties of accretion disks that form around the companion. We perform high-resolution 3D SPH simulations of the interaction of a solar-mass companion with the outflow of an AGB star using different wind velocities and eccentricities. We compare the models properties computed with and without the inclusion of HI cooling. The inclusion of HI cooling produces a large decrease in the temperature up to one order of magnitude in the region closely surrounding the companion star. As a consequence, morphological irregularities and relatively energetic outflows that were obtained without HI cooling no longer appear. In case of an eccentric orbit and low wind velocity, the morphologies are still highly asymmetric, but the same structures recur at every orbital period, making the morphology more regular. Flared accretion disks with a (sub)-Keplerian velocity profile form around the companion in all models, provided the accretion radius is small enough. The disks have radial sizes ranging from about 0.4 to 0.9 au. For the considered wind velocities, mass accretion onto the companion is up to 2 times higher than predicted by the BHL rate, and ranges between 0.04-0.21 times the AGB wind mass loss rate. The lower the wind velocity at the location of the companion, the larger and the more massive the disk, and the higher the mass accretion efficiency., Comment: 18 pages (32 including appendices), 18 figures (33 including apendices) For associated input files, final output dumps and 3 movies, see https://zenodo.org/records/13224218

How do people understand and evaluate claims about others' beliefs, even though these beliefs cannot be directly observed? In this paper, we introduce a cognitive model of epistemic language interpretation, grounded in Bayesian inferences about other agents' goals, beliefs, and intentions: a language-augmented Bayesian theory-of-mind (LaBToM). By translating natural language into an epistemic ``language-of-thought'', then evaluating these translations against the inferences produced by inverting a probabilistic generative model of rational action and perception, LaBToM captures graded plausibility judgments about epistemic claims. We validate our model in an experiment where participants watch an agent navigate a maze to find keys hidden in boxes needed to reach their goal, then rate sentences about the agent's beliefs. In contrast with multimodal LLMs (GPT-4o, Gemini Pro) and ablated models, our model correlates highly with human judgments for a wide range of expressions, including modal language, uncertainty expressions, knowledge claims, likelihood comparisons, and attributions of false belief., Comment: 21 pages

The advent of large language models (LLMs) has revolutionized online content creation, making it much easier to generate high-quality fake news. This misuse threatens the integrity of our digital environment and ethical standards. Therefore, understanding the motivations and mechanisms behind LLM-generated fake news is crucial. In this study, we analyze the creation of fake news from a social psychology perspective and develop a comprehensive LLM-based theoretical framework, LLM-Fake Theory. We introduce a novel pipeline that automates the generation of fake news using LLMs, thereby eliminating the need for manual annotation. Utilizing this pipeline, we create a theoretically informed Machine-generated Fake news dataset, MegaFake, derived from the GossipCop dataset. We conduct comprehensive analyses to evaluate our MegaFake dataset. We believe that our dataset and insights will provide valuable contributions to future research focused on the detection and governance of fake news in the era of LLMs.

As Artificial Intelligence (AI) models are increasingly integrated into critical systems, the need for a robust framework to establish the trustworthiness of AI is increasingly paramount. While collaborative efforts have established conceptual foundations for such a framework, there remains a significant gap in developing concrete, technically robust methods for assessing AI model quality and performance. A critical drawback in the traditional methods for assessing the validity and generalizability of models is their dependence on internal developer datasets, rendering it challenging to independently assess and verify their performance claims. This paper introduces a novel approach for assessing a newly trained model's performance based on another known model by calculating correlation between neural networks. The proposed method evaluates correlations by determining if, for each neuron in one network, there exists a neuron in the other network that produces similar output. This approach has implications for memory efficiency, allowing for the use of smaller networks when high correlation exists between networks of different sizes. Additionally, the method provides insights into robustness, suggesting that if two highly correlated networks are compared and one demonstrates robustness when operating in production environments, the other is likely to exhibit similar robustness. This contribution advances the technical toolkit for responsible AI, supporting more comprehensive and nuanced evaluations of AI models to ensure their safe and effective deployment. Code is available at https://github.com/aheldis/Cross-model-correlation.git.

We propose models to quantify the distortion the credit portfolio (expected and unexpected) losses, when the obligor companies as well as their guarantees belong to an economy subject to the climate transition. The economy's productivity is modeled as a multidimensional Ornstein-Uhlenbeck (O.-U.) process while the climate transition is represented by a continuous deterministic carbon price process. We define each loan's loss at default as the difference between Exposure at Default (EAD) and the liquidated collateral, which will help us to define the Loss Given Default (LGD). We consider two types of collateral: financial asset (such as invoices, cash, or investments) or physical asset (such as real estate, business equipment, or inventory). For financial assets, we model them by the continuous time version of the discounted cash flows methodology, where the cash flows SDE is driven by the instantaneous output growth, the instantaneous growth of a carbon price function, and an arithmetic Brownian motion. For physical assets, we focus on property in the housing market. We define, as Sopgoui (2024), their value as the difference between the price of an equivalent efficient building following an exponential O.-U. as well as the actualized renovation costs and the actualized sum of the future additional energy costs due to the inefficiency of the building, before an optimal renovation date which depends on the carbon price process. Finally, we obtain how the loss' risk measures of a credit portfolio are skewed in the context of climate transition through carbon price and/or energy performance of buildings when both the obligors and their guarantees are affected. This work provides a methodology to calculate the (statistics of the) loss of a portfolio of secured loans, starting from a given climate transition scenario described by a carbon price., Comment: 52 pages

Artificial Intelligence (AI) is revolutionizing various fields, including public health surveillance. In Africa, where health systems frequently encounter challenges such as limited resources, inadequate infrastructure, failed health information systems and a shortage of skilled health professionals, AI offers a transformative opportunity. This paper investigates the applications of AI in public health surveillance across the continent, presenting successful case studies and examining the benefits, opportunities, and challenges of implementing AI technologies in African healthcare settings. Our paper highlights AI's potential to enhance disease monitoring and health outcomes, and support effective public health interventions. The findings presented in the paper demonstrate that AI can significantly improve the accuracy and timeliness of disease detection and prediction, optimize resource allocation, and facilitate targeted public health strategies. Additionally, our paper identified key barriers to the widespread adoption of AI in African public health systems and proposed actionable recommendations to overcome these challenges.