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The first very long baseline interferometry (VLBI) detections at 870$\mu$m wavelength (345$\,$GHz frequency) are reported, achieving the highest diffraction-limited angular resolution yet obtained from the surface of the Earth, and the highest-frequency example of the VLBI technique to date. These include strong detections for multiple sources observed on inter-continental baselines between telescopes in Chile, Hawaii, and Spain, obtained during observations in October 2018. The longest-baseline detections approach 11$\,$G$\lambda$ corresponding to an angular resolution, or fringe spacing, of 19$\mu$as. The Allan deviation of the visibility phase at 870$\mu$m is comparable to that at 1.3$\,$mm on the relevant integration time scales between 2 and 100$\,$s. The detections confirm that the sensitivity and signal chain stability of stations in the Event Horizon Telescope (EHT) array are suitable for VLBI observations at 870$\mu$m. Operation at this short wavelength, combined with anticipated enhancements of the EHT, will lead to a unique high angular resolution instrument for black hole studies, capable of resolving the event horizons of supermassive black holes in both space and time., Comment: Corresponding author: S. Doeleman

Skeleton-based Temporal Action Segmentation involves the dense action classification of variable-length skeleton sequences. Current approaches primarily apply graph-based networks to extract framewise, whole-body-level motion representations, and use one-hot encoded labels for model optimization. However, whole-body motion representations do not capture fine-grained part-level motion representations and the one-hot encoded labels neglect the intrinsic semantic relationships within the language-based action definitions. To address these limitations, we propose a novel method named Language-assisted Human Part Motion Representation Learning (LPL), which contains a Disentangled Part Motion Encoder (DPE) to extract dual-level (i.e., part and whole-body) motion representations and a Language-assisted Distribution Alignment (LDA) strategy for optimizing spatial relations within representations. Specifically, after part-aware skeleton encoding via DPE, LDA generates dual-level action descriptions to construct a textual embedding space with the help of a large-scale language model. Then, LDA motivates the alignment of the embedding space between text descriptions and motions. This alignment allows LDA not only to enhance intra-class compactness but also to transfer the language-encoded semantic correlations among actions to skeleton-based motion learning. Moreover, we propose a simple yet efficient Semantic Offset Adapter to smooth the cross-domain misalignment. Our experiments indicate that LPL achieves state-of-the-art performance across various datasets (e.g., +4.4\% Accuracy, +5.6\% F1 on the PKU-MMD dataset). Moreover, LDA is compatible with existing methods and improves their performance (e.g., +4.8\% Accuracy, +4.3\% F1 on the LARa dataset) without additional inference costs., Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

Controlling greenhouse crop production systems is a complex task due to uncertain and non-linear dynamics between crops, indoor and outdoor climate, and economics. The declining number of skilled growers necessitates the development of autonomous greenhouse control systems. Reinforcement Learning (RL) is a promising approach that can learn a control policy to automate greenhouse management. RL optimises a control policy through interactions with a model of the greenhouse while guided by an economic-based reward function. However, its application to real-world systems is limited due to discrepancies between models and real-world dynamics. Moreover, RL controllers may struggle to maintain state constraints while optimising the primary objective, especially when models inadequately capture the adverse effects of constraint violations on crop growth. Also, the generalisation to novel states, for example, due to unseen weather trajectories, is underexplored in RL-based greenhouse control. This work addresses these challenges through three key contributions. First, we present GreenLight-Gym, the first open-source environment designed for training and evaluating RL algorithms on the state-of-the-art greenhouse model GreenLight. GreenLight-Gym enables the community to benchmark RL-based control methodologies. Second, we compare two reward-shaping approaches, using either a multiplicative or additive penalty, to enforce state boundaries. The additive penalty achieves more stable training while better adhering to state constraints, while the multiplicative penalty yields marginally higher profits. Finally, we evaluate RL performance on a disjoint training and testing weather dataset, demonstrating improved generalisation to unseen conditions. Our environment and experiment scripts are open-sourced, facilitating innovative research on learning-based greenhouse control.

The Core Data Ontology (CDO) and the Informatics Domain Model represent a transformative approach to computational systems, shifting from traditional node-centric designs to a data-centric paradigm. This paper introduces a framework where data is categorized into four modalities: objects, events, concepts, and actions. This quadrimodal structure enhances data security, semantic interoperability, and scalability across distributed data ecosystems. The CDO offers a comprehensive ontology that supports AI development, role-based access control, and multimodal data management. By focusing on the intrinsic value of data, the Informatics Domain Model redefines system architectures to prioritize data security, provenance, and auditability, addressing vulnerabilities in current models. The paper outlines the methodology for developing the CDO, explores its practical applications in fields such as AI, robotics, and legal compliance, and discusses future directions for scalable, decentralized, and interoperable data ecosystems., Comment: Proceedings of the 9th International Conference on Data Mining & Knowledge Management (DaKM 2024)

In the past decade, chiral materials have drawn significant attention because it is widely claimed that they can act as spin injectors/detectors due to the chirality-induced spin selectivity (CISS) effect. Nevertheless, the microscopic origin of this effect is not understood, and there is an intensive discussion about the manifestation of the magnetoresistance that is generated between a chiral system and a ferromagnet. Hanle spin precession measurements can unambiguously prove the injection and detection of a spin accumulation in a non-magnetic material, as was shown with traditional ferromagnetic injectors/detectors. Here, we analyze in detail the Hanle spin precession induced magnetoresistance and find that it is inverted as compared to the ferromagnetic case. We explicitly model the spin injection and detection by both a chiral system and a ferromagnetic system, as well as the spin transport in a semiconductor, for a general set of (spin) transport parameters that cover the relevant experimental regime. For all sets of parameters, we find that the Hanle signals for a chiral system and ferromagnet are each others opposites.

We introduce VascX models, a comprehensive set of model ensembles for analyzing retinal vasculature from color fundus images (CFIs). Annotated CFIs were aggregated from public datasets for vessel, artery-vein, and disc segmentation; and fovea localization. Additional CFIs from the population-based Rotterdam Study were, with arteries and veins annotated by graders at pixel level. Our models achieved robust performance across devices from different vendors, varying levels of image quality levels, and diverse pathologies. Our models demonstrated superior segmentation performance compared to existing systems under a variety of conditions. Significant enhancements were observed in artery-vein and disc segmentation performance, particularly in segmentations of these structures on CFIs of intermediate quality, a common characteristic of large cohorts and clinical datasets. Our model outperformed human graders in segmenting vessels with greater precision. With VascX models we provide a robust, ready-to-use set of model ensembles and inference code aimed at simplifying the implementation and enhancing the quality of automated retinal vasculature analyses. The precise vessel parameters generated by the model can serve as starting points for the identification of disease patterns in and outside of the eye.

Memristors are nonlinear two-terminal circuit elements whose resistance at a given time depends on past electrical stimuli. Recently, networks of memristors have received attention in neuromorphic computing since they can be used as a tool to perform linear algebraic operations, like matrix-vector multiplication, directly in hardware. In this paper, the aim is to resolve two fundamental questions pertaining to a specific, but relevant, class of memristive circuits called crossbar arrays. In particular, we show (1) how the resistance values of the memristors at a given time can be determined from external (voltage and current) measurements, and (2) how the resistances can be steered to desired values by applying suitable external voltages to the network. The results will be applied to solve a prototypical learning problem, namely linear least squares, by applying and measuring voltages and currents in a suitable memristive circuit., Comment: Accepted for CDC 2024

The significance of Mendeleev's periodic table extends beyond the classification of elements; it lies in its remarkable predictive power for discovering new elements and properties, revealing the underlying symmetrical patterns of nature that were only fully understood with the advent of quantum mechanics. Fundamental material properties, such as electron transport and magnetism, are also governed by crystal symmetry. In particular, spin transport depends on the spin polarization of electronic states, and recently discovered materials where the electron spin polarization is independent of momentum - a property known as a persistent spin texture (PST) - promise extended spin lifetime and efficient spin accumulation. In this paper, we establish the general conditions for the existence of symmetry-protected PST in bulk crystals. By systematically analyzing all 230 crystallographic space groups, similar to elements in the periodic table, we demonstrate that PST is universally present in all nonmagnetic solids lacking inversion symmetry. Using group theory, we identify the regions within the Brillouin zone that host PST and determine the corresponding directions of spin polarization. Our findings, supported by first-principles calculations of representative materials, open the route for discovering robust spintronic materials based on PST.

Greenhouse climate control is concerned with maximizing performance in terms of crop yield and resource efficiency. One promising approach is model predictive control (MPC), which leverages a model of the system to optimize the control inputs, while enforcing physical constraints. However, prediction models for greenhouse systems are inherently inaccurate due to the complexity of the real system and the uncertainty in predicted weather profiles. For model-based control approaches such as MPC, this can degrade performance and lead to constraint violations. Existing approaches address uncertainty in the prediction model with robust or stochastic MPC methodology; however, these necessarily reduce crop yield due to conservatism and often bear higher computational loads. In contrast, learning-based control approaches, such as reinforcement learning (RL), can handle uncertainty naturally by leveraging data to improve performance. This work proposes an MPC-based RL control framework to optimize the climate control performance in the presence of prediction uncertainty. The approach employs a parametrized MPC scheme that learns directly from data, in an online fashion, the parametrization of the constraints, prediction model, and optimization cost that minimizes constraint violations and maximizes climate control performance. Simulations show that the approach can learn an MPC controller that significantly outperforms the current state-of-the-art in terms of constraint violations and efficient crop growth., Comment: 12 pages, 6 figures, code available at https://github.com/SamuelMallick/mpcrl-greenhouse, submitted to Computers and Electronics in Agriculture

Object manipulation capabilities are essential skills that set apart embodied agents engaging with the world, especially in the realm of robotics. The ability to predict outcomes of interactions with objects is paramount in this setting. While model-based control methods have started to be employed for tackling manipulation tasks, they have faced challenges in accurately manipulating objects. As we analyze the causes of this limitation, we identify the cause of underperformance in the way current world models represent crucial positional information, especially about the target's goal specification for object positioning tasks. We introduce a general approach that empowers world model-based agents to effectively solve object-positioning tasks. We propose two declinations of this approach for generative world models: position-conditioned (PCP) and latent-conditioned (LCP) policy learning. In particular, LCP employs object-centric latent representations that explicitly capture object positional information for goal specification. This naturally leads to the emergence of multimodal capabilities, enabling the specification of goals through spatial coordinates or a visual goal. Our methods are rigorously evaluated across several manipulation environments, showing favorable performance compared to current model-based control approaches.

This work proposes an approach that integrates reinforcement learning and model predictive control (MPC) to efficiently solve finite-horizon optimal control problems in mixed-logical dynamical systems. Optimization-based control of such systems with discrete and continuous decision variables entails the online solution of mixed-integer quadratic or linear programs, which suffer from the curse of dimensionality. Our approach aims at mitigating this issue by effectively decoupling the decision on the discrete variables and the decision on the continuous variables. Moreover, to mitigate the combinatorial growth in the number of possible actions due to the prediction horizon, we conceive the definition of decoupled Q-functions to make the learning problem more tractable. The use of reinforcement learning reduces the online optimization problem of the MPC controller from a mixed-integer linear (quadratic) program to a linear (quadratic) program, greatly reducing the computational time. Simulation experiments for a microgrid, based on real-world data, demonstrate that the proposed method significantly reduces the online computation time of the MPC approach and that it generates policies with small optimality gaps and high feasibility rates.

The key parameter for describing frictional strength at the onset of sliding is the static friction coefficient. Yet, how the static friction coefficient at the macroscale emerges from contacting asperities at the microscale is still an open problem. Here, we present friction experiments in which the normal load was varied over more than three orders of magnitude, so that a transition from a single asperity contact at low loads to multi-asperity contacts at high loads was achieved. We find a remarkable drop in static friction coefficient with increasing normal load. Using a simple stick-slip transition model we identify the presence of pre-sliding and subcritical contact points as the cause of smaller static friction coefficient at increased normal loads. Our measurements and model bridge the gap between friction behavior commonly observed in atomic force microscopy (AFM) experiments at microscopic forces, and industrially relevant multi-asperity contact interfaces loaded with macroscopic forces., Comment: 9 pages, 7 figures

Magnon based spintronic devices require the modulation of magnon spin transport for their operations. We provide a proof-of-principle of two unconventional non-local magnon transport devices in which we modulate the diffusive magnon transport of incoherent magnons in the van der Waals antiferromagnet Chromium thiophosphate, CrPS4. The non-local signals generated electrically by spin injection via the spin Hall effect (SHE) and thermally via the spin Seebeck effect (SSE) are altered by a modulator electrode. The current through the modulator increases or decreases the magnon chemical potential via the SHE and changes the magnon temperature through Joule heating. We achieve up to 25%/mA and 16%/mA modulation efficiencies for the electrically and thermally generated magnon spin transport, respectively, for CrPS4 in the collinear state at in plane fields >7T at a temperature of 25K.

We investigate the injection, transport and detection of magnon spins in the van der Waals antiferromagnet chromium thiophosphate (CrPS4). We electrically and thermally inject magnon spins by platinum contacts and examine the non-local resistance as a function of in-plane magnetic field up to 12 Tesla. We observe a large non-local resistance from both the electrically and thermally excited magnon modes above the spin-flip field where CrPS4 is in the collinear state. At 25 K for an in-plane field of ranging 5 - 12 T, we extract the magnon relaxation length ranging $200-800$ nm and a typical magnon conductivity of 1e4 Sm-1, which is one order of magnitude smaller than in yttrium iron garnet (YIG) films at room temperature. Moreover, we find that the magnon conductivity is almost zero for CrPS4 in the non-collinear state. Our results open up the way to understanding the role of the antiferromagnetic magnon modes on spin injection into antiferromagnets and implementation of two-dimensional magnets for scalable magnonic circuits., Comment: 7 pages, 5 figures

We build on the interpretation of the Economic Complexity method as Correspondence Analysis (CA), and propose that the Canonical form of CA (CCA), which originated in the ecology literature, can be used to calculate multi-dimensional economic complexity. The traditional (CA) way of calculating economic complexity includes no "external" information such as countries' development characteristics to facilitate interpretation of "complexity". This has led to a wide range of fairly ad hoc interpretations of economic complexity on the basis of ex-post correlation to a long list of other variables. By the ex-ante inclusion of a number of country variables in the construction of the complexity indicators, CCA enables better interpretation, also in the case of multi-dimensional indicators. The analysis is further facilitated by another element of the ecologists' toolbox, the so-called biplots, which are CCA-based graph embeddings that represent a lower-dimensional product-space in which products and countries are positioned together, in mutual correspondence to each other. We show that in this way, CCA provides a richer account of development in many of its aspects, especially economic growth.

Though control algorithms for multirotor Unmanned Air Vehicle (UAV) are well understood, the configuration, parameter estimation, and tuning of flight control algorithms takes quite some time and resources. In previous work, we have shown that it is possible to identify the control effectiveness and motor dynamics of a multirotor fast enough for it to recover to a stable hover after being thrown 4 meters in the air. In this paper, we extend this to include estimation of the position of the Inertial Measurement Unit (IMU) relative to the Center of Gravity (CoG), estimation of the IMU rotation, the thrust direction of all motors and the optimal combined thrust direction. In order to guarantee a correct IMU position estimation, two prior throw-and-catches of the vehicle with spin around different axes are required. For these throws, a height as low as 1 meter is sufficient. Quadrotor flight experimentation confirms the efficacy of the approach, and a simulation shows its applicability to fully-actuated crafts with multiple possible hover orientations., Comment: This work has been submitted to IMAV 2024 for possible publication

The NP-hard Odd Cycle Transversal problem asks for a minimum vertex set whose removal from an undirected input graph $G$ breaks all odd cycles, and thereby yields a bipartite graph. The problem is well-known to be fixed-parameter tractable when parameterized by the size $k$ of the desired solution. It also admits a randomized kernelization of polynomial size, using the celebrated matroid toolkit by Kratsch and Wahlstr\"{o}m. The kernelization guarantees a reduction in the total $\textit{size}$ of an input graph, but does not guarantee any decrease in the size of the solution to be sought; the latter governs the size of the search space for FPT algorithms parameterized by $k$. We investigate under which conditions an efficient algorithm can detect one or more vertices that belong to an optimal solution to Odd Cycle Transversal. By drawing inspiration from the popular $\textit{crown reduction}$ rule for Vertex Cover, and the notion of $\textit{antler decompositions}$ that was recently proposed for Feedback Vertex Set, we introduce a graph decomposition called $\textit{tight odd cycle cut}$ that can be used to certify that a vertex set is part of an optimal odd cycle transversal. While it is NP-hard to compute such a graph decomposition, we develop parameterized algorithms to find a set of at least $k$ vertices that belong to an optimal odd cycle transversal when the input contains a tight odd cycle cut certifying the membership of $k$ vertices in an optimal solution. The resulting algorithm formalizes when the search space for the solution-size parameterization of Odd Cycle Transversal can be reduced by preprocessing. To obtain our results, we develop a graph reduction step that can be used to simplify the graph to the point that the odd cycle cut can be detected via color coding.

Mid-infrared imaging of exoplanets and disks is now possible with the coronagraphs of the MIRI on the JWST. This wavelength range unveils new features of young directly imaged systems and allows us to obtain new constraints for characterizing the atmosphere of young giant exoplanets and associated disks. These observations aim to characterize the atmosphere of the planet HD 95086 b by adding mid-infrared information so that the various hypotheses about its atmospheric parameters values can be unraveled. Improved images of circumstellar disks are provided. We present the MIRI coronagraphic imaging of the system HD 95086 obtained with the F1065C, F1140, and F2300C filters at central wavelengths of 10.575, 11.3, and 23 microns, respectively. We explored the method for subtracting the stellar diffraction pattern in the particular case when bright dust emitting at short separation is present. Furthermore, we compared different methods for extracting the photometry of the planet. Using the atmospheric models Exo-REM and ATMO, we measured the atmospheric parameters of HD 95086 b. The planet HD 95086 b and the contribution from the inner disk are detected at the two shortest MIRI wavelengths F1065C and F1140C. The outer colder belt is imaged at 23 microns. The mid-infrared photometry provides better constraints on the atmospheric parameters. We evaluate a temperature of 850-1020 K, consistent with one previous hypothesis that only used NIR data. The radius measurement of 1.0-1.13 RJup is better aligned with evolutionary models, but still smaller than predicted. These observations allow us to refute the hypothesis of a warm circumplanetary disk. HD 95086 is one of the first exoplanetary systems to be revealed at mid-infrared wavelengths. This highlights the interests and challenges of observations at these wavelengths., Comment: Accepted for publication in A&A

Images are increasingly becoming the currency for documenting biodiversity on the planet, providing novel opportunities for accelerating scientific discoveries in the field of organismal biology, especially with the advent of large vision-language models (VLMs). We ask if pre-trained VLMs can aid scientists in answering a range of biologically relevant questions without any additional fine-tuning. In this paper, we evaluate the effectiveness of 12 state-of-the-art (SOTA) VLMs in the field of organismal biology using a novel dataset, VLM4Bio, consisting of 469K question-answer pairs involving 30K images from three groups of organisms: fishes, birds, and butterflies, covering five biologically relevant tasks. We also explore the effects of applying prompting techniques and tests for reasoning hallucination on the performance of VLMs, shedding new light on the capabilities of current SOTA VLMs in answering biologically relevant questions using images. The code and datasets for running all the analyses reported in this paper can be found at https://github.com/sammarfy/VLM4Bio., Comment: 36 pages, 37 figures, 7 tables

Thin low-frequency acoustic absorbers that are economical to produce in large quantities are scarce, and their efficiency is often limited to a narrow frequency range. In this paper, we present opportunities to use highly porous mineral foams, in particular optimally designed gypsum foams, to achieve high absorption levels for layers of less than 1/10 of a wavelength thick. To reach this goal, we perforate a fraction of the initially closed pores using thin needles. Finite element simulations of the fluid flow in a representative volume element show how the combination of foam properties (cell size and wall thickness) and perforation pattern (hole diameter and perforation distance) can be chosen such that sub-wavelength absorption is obtained. In particular two transport parameters used in the approximate but robust Johnson-Champoux-Allard model for porous media have to be optimized: the flow resistivity and high-frequency tortuosity. The fluid flow modeling results are successfully compared with sound absorption measurements, showing indeed that the proposed material, once appropriately perforated, yields a remarkable low-frequency sound absorption peak. On a more fundamental level, this paper shows how the multiporosity, the presence of microcracks, and the material's surface roughness can be exploited to enhance its acoustic absorption at very low frequencies.

Materials with chiral electronic structures are of great interest. We report a triazatruxene, TAT, molecular semiconductor with chiral alkyl side chains that crystallises from solution to form chirally-stacked columns with a helical pitch of 6 TATs (2.3 nm). These crystals show strong circularly polarised, CP, green photoluminescence, with dissymmetry of 24%. Electronic structure calculations using the full crystal structure, show that this chiral stacking associates angular momentum to the valence and conduction states and thus gives rise to the observed CP luminescence. Free-standing crystals are not useful for active semiconductor devices, but we have discovered that co-sublimation of TAT as the guest in a structurally mismatched host enables the fabrication of thin films where the chiral crystallization is achieved in-situ by thermally-triggered nano-phase segregation of dopant and host whilst preserving the integrity of the film. This enables fabrication of bright (green) organic light-emitting diodes with unexpectedly high external quantum efficiencies of up to 16% and electroluminescence dissymmetries above 10%. These materials and this process method offer significant application potential in spintronics, optical displays and multidimensional optoelectronics.

We introduce Smooth InfoMax (SIM), a novel method for self-supervised representation learning that incorporates an interpretability constraint into the learned representations at various depths of the neural network. SIM's architecture is split up into probabilistic modules, each locally optimized using the InfoNCE bound. Inspired by VAEs, the representations from these modules are designed to be samples from Gaussian distributions and are further constrained to be close to the standard normal distribution. This results in a smooth and predictable space, enabling traversal of the latent space through a decoder for easier post-hoc analysis of the learned representations. We evaluate SIM's performance on sequential speech data, showing that it performs competitively with its less interpretable counterpart, Greedy InfoMax (GIM). Moreover, we provide insights into SIM's internal representations, demonstrating that the contained information is less entangled throughout the representation and more concentrated in a smaller subset of the dimensions. This further highlights the improved interpretability of SIM.

Approximation Fixpoint Theory (AFT) is an algebraic framework designed to study the semantics of non-monotonic logics. Despite its success, AFT is not readily applicable to higher-order definitions. To solve such an issue, we devise a formal mathematical framework employing concepts drawn from Category Theory. In particular, we make use of the notion of Cartesian closed category to inductively construct higher-order approximation spaces while preserving the structures necessary for the correct application of AFT. We show that this novel theoretical approach extends standard AFT to a higher-order environment, and generalizes the AFT setting of arXiv:1804.08335 ., Comment: 24 pages, 0 figures, extended version of the paper "A Category-Theoretic Perspective on Higher-Order Approximation Fixpoint Theory" accepted for LPNMR 2024

We propose a stable model semantics for higher-order logic programs. Our semantics is developed using Approximation Fixpoint Theory (AFT), a powerful formalism that has successfully been used to give meaning to diverse non-monotonic formalisms. The proposed semantics generalizes the classical two-valued stable model semantics of (Gelfond and Lifschitz 1988) as-well-as the three-valued one of (Przymusinski 1990), retaining their desirable properties. Due to the use of AFT, we also get for free alternative semantics for higher-order logic programs, namely supported model, Kripke-Kleene, and well-founded. Additionally, we define a broad class of stratified higher-order logic programs and demonstrate that they have a unique two-valued higher-order stable model which coincides with the well-founded semantics of such programs. We provide a number of examples in different application domains, which demonstrate that higher-order logic programming under the stable model semantics is a powerful and versatile formalism, which can potentially form the basis of novel ASP systems.

Uncertainty is inherent to most data, including vector field data, yet it is often omitted in visualizations and representations. Effective uncertainty visualization can enhance the understanding and interpretability of vector field data. For instance, in the context of severe weather events such as hurricanes and wildfires, effective uncertainty visualization can provide crucial insights about fire spread or hurricane behavior and aid in resource management and risk mitigation. Glyphs are commonly used for representing vector uncertainty but are often limited to 2D. In this work, we present a glyph-based technique for accurately representing 3D vector uncertainty and a comprehensive framework for visualization, exploration, and analysis using our new glyphs. We employ hurricane and wildfire examples to demonstrate the efficacy of our glyph design and visualization tool in conveying vector field uncertainty.

Reliable communication is a fundamental distributed communication abstraction that allows any two nodes of a network to communicate with each other. It is necessary for more powerful communication primitives, such as broadcast and consensus. Using different authentication models, two classical protocols implement reliable communication in unknown and sufficiently connected networks. In the first one, network links are authenticated, and processes rely on dissemination paths to authenticate messages. In the second one, processes generate digital signatures that are flooded in the network. This work considers the hybrid system model that combines authenticated links and authenticated processes. We additionally aim to leverage the possible presence of trusted nodes and trusted components in networks, which have been assumed in the scientific literature and in practice. We first extend the two classical reliable communication protocols to leverage trusted nodes. We then propose DualRC, a novel algorithm that enables reliable communication in the hybrid authentication model by manipulating both dissemination paths and digital signatures, and leverages the possible presence of trusted nodes (e.g., network gateways) and trusted components (e.g., Intel SGX enclaves). We provide correctness verification algorithms to assess whether our algorithms implement reliable communication for all nodes on a given network.

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

Drawing inspiration from animal navigation strategies, we introduce a novel computational model for navigation and mapping, rooted in biologically inspired principles. Animals exhibit remarkable navigation abilities by efficiently using memory, imagination, and strategic decision-making to navigate complex and aliased environments. Building on these insights, we integrate traditional cognitive mapping approaches with an Active Inference Framework (AIF) to learn an environment structure in a few steps. Through the incorporation of topological mapping for long-term memory and AIF for navigation planning and structure learning, our model can dynamically apprehend environmental structures and expand its internal map with predicted beliefs during exploration. Comparative experiments with the Clone-Structured Graph (CSCG) model highlight our model's ability to rapidly learn environmental structures in a single episode, with minimal navigation overlap. this is achieved without prior knowledge of the dimensions of the environment or the type of observations, showcasing its robustness and effectiveness in navigating ambiguous environments., Comment: IWAI workshop 2024

A social interaction (so-called higher-order event/interaction) can be regarded as the activation of the hyperlink among the corresponding individuals. Social interactions can be, thus, represented as higher-order temporal networks, that record the higher-order events occurring at each time step over time. The prediction of higher-order interactions is usually overlooked in traditional temporal network prediction methods, where a higher-order interaction is regarded as a set of pairwise interactions. The prediction of future higher-order interactions is crucial to forecast and mitigate the spread the information, epidemics and opinion on higher-order social contact networks. In this paper, we propose novel memory-based models for higher-order temporal network prediction. By using these models, we aim to predict the higher-order temporal network one time step ahead, based on the network observed in the past. Importantly, we also intent to understand what network properties and which types of previous interactions enable the prediction. The design and performance analysis of these models are supported by our analysis of the memory property of networks, e.g., similarity of the network and activity of a hyperlink over time respectively. Our models assume that a target hyperlink's future activity (active or not) depends the past activity of the target link and of all or selected types of hyperlinks that overlap with the target. We then compare the performance of both models with a baseline utilizing a pairwise temporal network prediction method. In eight real-world networks, we find that both models consistently outperform the baseline and the refined model tends to perform the best. Our models also reveal how past interactions of the target hyperlink and different types of hyperlinks that overlap with the target contribute to the prediction of the target's future activity., Comment: arXiv admin note: text overlap with arXiv:2309.04376

A central problem in biology is to understand how organisms evolve and adapt to their environment by acquiring variations in the observable characteristics or traits of species across the tree of life. With the growing availability of large-scale image repositories in biology and recent advances in generative modeling, there is an opportunity to accelerate the discovery of evolutionary traits automatically from images. Toward this goal, we introduce Phylo-Diffusion, a novel framework for conditioning diffusion models with phylogenetic knowledge represented in the form of HIERarchical Embeddings (HIER-Embeds). We also propose two new experiments for perturbing the embedding space of Phylo-Diffusion: trait masking and trait swapping, inspired by counterpart experiments of gene knockout and gene editing/swapping. Our work represents a novel methodological advance in generative modeling to structure the embedding space of diffusion models using tree-based knowledge. Our work also opens a new chapter of research in evolutionary biology by using generative models to visualize evolutionary changes directly from images. We empirically demonstrate the usefulness of Phylo-Diffusion in capturing meaningful trait variations for fishes and birds, revealing novel insights about the biological mechanisms of their evolution.

The foundational capabilities of humanoid robots should include robustly standing, walking, and mimicry of whole and partial-body motions. This work introduces the Masked Humanoid Controller (MHC), which supports all of these capabilities by tracking target trajectories over selected subsets of humanoid state variables while ensuring balance and robustness against disturbances. The MHC is trained in simulation using a carefully designed curriculum that imitates partially masked motions from a library of behaviors spanning standing, walking, optimized reference trajectories, re-targeted video clips, and human motion capture data. It also allows for combining joystick-based control with partial-body motion mimicry. We showcase simulation experiments validating the MHC's ability to execute a wide variety of behaviors from partially-specified target motions. Moreover, we demonstrate sim-to-real transfer on the real-world Digit V3 humanoid robot. To our knowledge, this is the first instance of a learned controller that can realize whole-body control of a real-world humanoid for such diverse multi-modal targets., Comment: Website: https://masked-humanoid.github.io/mhc/

Carbon black (CB)-elastomers can serve as low-cost, highly deformable sensor materials, but hardly any work exists on their structure-property relationships. We report on flow-induced anisotropy, considering CB-silicone films generated via doctor blade coating. Cured films showed slight electrical anisotropy, with conductivity parallel to the coating direction being lower than perpendicular to it. Furthermore, piezoresistive sensitivity was much larger for stretch perpendicular to the coating direction than for parallel stretch. Structural analysis for length scales up to the CB agglomerate level yielded only weak evidence of anisotropy. Based on this evidence and insight from CB network simulations, we hypothesize that shear flow during coating fragments the CB network and then induces a preferential aggregate alignment, as well as increased inter-particle distances, parallel to the coating direction. As a practical conclusion, already weak anisotropic structuration suffices to cause significant electric anisotropy.

Validators in permissionless, large-scale blockchains (e.g., Ethereum) are typically payoff-maximizing, rational actors. Ethereum relies on in-protocol incentives, like rewards for validators delivering correct and timely votes, to induce honest behavior and secure the blockchain. However, external incentives, such as the block proposer's opportunity to capture maximal extractable value (MEV), may tempt validators to deviate from honest protocol participation. We show a series of commitment attacks on LMD GHOST, a core part of Ethereum's consensus mechanism. We demonstrate how a single adversarial block proposer can orchestrate long-range chain reorganizations by manipulating Ethereum's reward system for timely votes. These attacks disrupt the intended balance of power between proposers and voters: by leveraging credible threats, the adversarial proposer can coerce voters from previous slots into supporting blocks that conflict with the honest chain, enabling a chain reorganization at no cost to the adversary. In response, we introduce a novel reward mechanism that restores the voters' role as a check against proposer power. Our proposed mitigation is fairer and more decentralized -- not only in the context of these attacks -- but also practical for implementation in Ethereum.

The goal of future quantum networks is to enable new internet applications that are impossible to achieve using solely classical communication. Up to now, demonstrations of quantum network applications and functionalities on quantum processors have been performed in ad-hoc software that was specific to the experimental setup, programmed to perform one single task (the application experiment) directly into low-level control devices using expertise in experimental physics. Here, we report on the design and implementation of the first architecture capable of executing quantum network applications on quantum processors in platform-independent high-level software. We demonstrate the architecture's capability to execute applications in high-level software, by implementing it as a quantum network operating system -- QNodeOS -- and executing test programs including a delegated computation from a client to a server on two quantum network nodes based on nitrogen-vacancy (NV) centers in diamond. We show how our architecture allows us to maximize the use of quantum network hardware, by multitasking different applications on a quantum network for the first time. Our architecture can be used to execute programs on any quantum processor platform corresponding to our system model, which we illustrate by demonstrating an additional driver for QNodeOS for a trapped-ion quantum network node based on a single $^{40}\text{Ca}^+$ atom. Our architecture lays the groundwork for computer science research in the domain of quantum network programming, and paves the way for the development of software that can bring quantum network technology to society., Comment: 12 pages, 5 figures, supplementary materials (48 pages, 24 figures, 11 tables)

Genetic variants (GVs) are defined as differences in the DNA sequences among individuals and play a crucial role in diagnosing and treating genetic diseases. The rapid decrease in next generation sequencing cost has led to an exponential increase in patient-level GV data. This growth poses a challenge for clinicians who must efficiently prioritize patient-specific GVs and integrate them with existing genomic databases to inform patient management. To addressing the interpretation of GVs, genomic foundation models (GFMs) have emerged. However, these models lack standardized performance assessments, leading to considerable variability in model evaluations. This poses the question: How effectively do deep learning methods classify unknown GVs and align them with clinically-verified GVs? We argue that representation learning, which transforms raw data into meaningful feature spaces, is an effective approach for addressing both indexing and classification challenges. We introduce a large-scale Genetic Variant dataset, named GV-Rep, featuring variable-length contexts and detailed annotations, designed for deep learning models to learn GV representations across various traits, diseases, tissue types, and experimental contexts. Our contributions are three-fold: (i) Construction of a comprehensive dataset with 7 million records, each labeled with characteristics of the corresponding variants, alongside additional data from 17,548 gene knockout tests across 1,107 cell types, 1,808 variant combinations, and 156 unique clinically verified GVs from real-world patients. (ii) Analysis of the structure and properties of the dataset. (iii) Experimentation of the dataset with pre-trained GFMs. The results show a significant gap between GFMs current capabilities and accurate GV representation. We hope this dataset will help advance genomic deep learning to bridge this gap., Comment: Preprint

With the advent of JWST, we acquire unprecedented insights into the physical and chemical structure of the inner regions of planet-forming disks where terrestrial planet formation occurs. The very low-mass stars (VLMS) are known to have a high occurrence rate of the terrestrial planets around them. Exploring the chemical composition of the gas in these inner regions of the disks can aid a better understanding of the connection between planet-forming disks and planets. The MIRI mid-Infrared Disk Survey (MINDS) project is a large JWST Guaranteed Time program to characterize the chemistry and physical state of planet-forming and debris disks. We use the JWST-MIRI/MRS spectrum to investigate the gas and dust composition of the planet-forming disk around the very low-mass star Sz28 (M5.5, 0.12\,M$_{\odot}$). We use the dust-fitting tool (DuCK) to determine the dust continuum and to get constraints on the dust composition and grain sizes. We use 0D slab models to identify and fit the molecular spectral features, yielding estimates on the temperature, column density and the emitting area. To test our understanding of the chemistry in the disks around VLMS, we employ the thermo-chemical disk model {P{\tiny RO}D{\tiny I}M{\tiny O}} and investigate the reservoirs of the detected hydrocarbons. We explore how the C/O ratio affects the inner disk chemistry. JWST reveals a plethora of hydrocarbons, including \ce{CH3}, \ce{CH4}, \ce{C2H2}, \ce{^{13}CCH2}, \ce{C2H6}, \ce{C3H4}, \ce{C4H2} and \ce{C6H6} suggesting a disk with a gaseous C/O\,>\,1. Additionally, we detect \ce{CO2}, \ce{^{13}CO2}, \ce{HCN}, and \ce{HC3N}. \ce{H2O} and OH are absent in the spectrum. We do not detect PAHs. Photospheric stellar absorption lines of \ce{H2O} and \ce{CO} are identified. Notably, our radiation thermo-chemical disk models are able to produce these detected hydrocarbons in the surface layers of the disk when the ..., Comment: accepted for publication in A&A

Hybrid modeling aims to augment traditional theory-driven models with machine learning components that learn unknown parameters, sub-models or correction terms from data. In this work, we build on FluxRGNN, a recently developed hybrid model of continental-scale bird migration, which combines a movement model inspired by fluid dynamics with recurrent neural networks that capture the complex decision-making processes of birds. While FluxRGNN has been shown to successfully predict key migration patterns, its spatial resolution is constrained by the typically sparse observations obtained from weather radars. Additionally, its trainable components lack explicit incentives to adequately predict take-off and landing events. Both aspects limit our ability to interpret model results ecologically. To address this, we propose two major modifications that allow for more detailed predictions on any desired tessellation while providing control over the interpretability of model components. In experiments on the U.S. weather radar network, the enhanced model effectively leverages the underlying movement model, resulting in strong extrapolation capabilities to unobserved locations., Comment: AI for Science workshop at ICML 2024

NOTT (formerly Hi-5) is the L'-band (3.5-4.0~microns) nulling interferometer of Asgard, an instrument suite in preparation for the VLTI visitor focus. The primary scientific objectives of NOTT include characterizing (i) young planetary systems near the snow line, a critical region for giant planet formation, and (ii) nearby main-sequence stars close to the habitable zone, with a focus on detecting exozodiacal dust that could obscure Earth-like planets. In 2023-2024, the final warm optics have been procured and assembled in a new laboratory at KU Leuven. First fringes and null measurements were obtained using a Gallium Lanthanum Sulfide (GLS) photonic chip that was also tested at cryogenic temperatures. In this paper, we present an overall update of the NOTT project with a particular focus on the cold mechanical design, the first results in the laboratory with the final NOTT warm optics, and the ongoing Asgard integration activities. We also report on other ongoing activities such as the characterization of the photonic chip (GLS, LiNbO3, SiO), the development of the exoplanet science case, the design of the dispersion control module, and the progress with the self-calibration data reduction software., Comment: 11 pages (incl. 5 figures); Proc. SPIE Astronomical Telescopes + Instrumentation 2024 (Yokohama; Japan), Optical and Infrared Interferometry and Imaging VI

Fishes are integral to both ecological systems and economic sectors, and studying fish traits is crucial for understanding biodiversity patterns and macro-evolution trends. To enable the analysis of visual traits from fish images, we introduce the Fish-Visual Trait Analysis (Fish-Vista) dataset - a large, annotated collection of about 60K fish images spanning 1900 different species, supporting several challenging and biologically relevant tasks including species classification, trait identification, and trait segmentation. These images have been curated through a sophisticated data processing pipeline applied to a cumulative set of images obtained from various museum collections. Fish-Vista provides fine-grained labels of various visual traits present in each image. It also offers pixel-level annotations of 9 different traits for 2427 fish images, facilitating additional trait segmentation and localization tasks. The ultimate goal of Fish-Vista is to provide a clean, carefully curated, high-resolution dataset that can serve as a foundation for accelerating biological discoveries using advances in AI. Finally, we provide a comprehensive analysis of state-of-the-art deep learning techniques on Fish-Vista.

Accessing the terahertz (THz) spectral domain through surface-enhanced Raman spectroscopy (SERS) is challenging and opens up the study of low-frequency molecular and electronic excitations. Compared to direct THz probing of heterogenous ensembles, the extreme plasmonic confinement of visible light to deep sub-wavelength scales allows the study of hundreds or even single molecules. We show that self-assembled molecular monolayers of a set of simple aromatic thiols confined inside single-particle plasmonic nanocavities can be distinguished by their low-wavenumber spectral peaks below 200 cm-1, after removal of a bosonic inelastic contribution and an exponential background from the spectrum Developing environment-dependent density-functional-theory simulations of the metal-molecule configuration enables the assignment and classification of their THz vibrations as well as the identification of intermolecular coupling effects and of the influence of the gold surface configuration Furthermore, we show dramatically narrower THz SERS spectra from individual molecules at picocavities, which indicates the possibility to study intrinsic vibrational properties beyond inhomogeneous broadening further supporting the key role of local environment.

Efficient conversion between charge currents and spin signals is crucial for realizing magnet-free spintronic devices. However, the strong spin-orbit coupling that enables such a conversion, causes rapid relaxation of spins, making them difficult to transport over large length scales. Here, we show that spin-momentum entanglement at the Fermi surface of chiral tellurium crystals leads to the appearance of slow collective relaxation modes. These modes, called relaxons, resemble the persistent spin helix, a collective spin-wave excitation with extended lifetime observed in quantum wells. The slow relaxons dominate the electrically generated spin and orbital angular momentum accumulation in tellurium and make it possible to combine a very high 50% conversion efficiency with a long spin lifetime. These results, obtained from the exact solution of the Boltzmann transport equation, show that chiral crystals can be used for highly efficient generation and transmission of spin signals over long distances in spintronic devices., Comment: 9 pages, 4 figures

In the Steiner Tree problem we are given an undirected edge-weighted graph as input, along with a set $K$ of vertices called terminals. The task is to output a minimum-weight connected subgraph that spans all the terminals. The famous Dreyfus-Wagner algorithm running in $3^{|K|} \mathsf{poly}(n)$ time shows that the problem is fixed-parameter tractable parameterized by the number of terminals. We present fixed-parameter tractable algorithms for Steiner Tree using structurally smaller parameterizations. Our first result concerns the parameterization by a multiway cut $S$ of the terminals, which is a vertex set $S$ (possibly containing terminals) such that each connected component of $G-S$ contains at most one terminal. We show that Steiner Tree can be solved in $2^{O(|S|\log|S|)}\mathsf{poly}(n)$ time and polynomial space, where $S$ is a minimum multiway cut for $K$. The algorithm is based on the insight that, after guessing how an optimal Steiner tree interacts with a multiway cut $S$, computing a minimum-cost solution of this type can be formulated as minimum-cost bipartite matching. Our second result concerns a new hybrid parameterization called $K$-free treewidth that simultaneously refines the number of terminals $|K|$ and the treewidth of the input graph. By utilizing recent work on $\mathcal{H}$-Treewidth in order to find a corresponding decomposition of the graph, we give an algorithm that solves Steiner Tree in time $2^{O(k)} \mathsf{poly}(n)$, where $k$ denotes the $K$-free treewidth of the input graph. To obtain this running time, we show how the rank-based approach for solving Steiner Tree parameterized by treewidth can be extended to work in the setting of $K$-free treewidth, by exploiting existing algorithms parameterized by $|K|$ to compute the table entries of leaf bags of a tree $K$-free decomposition., Comment: Full version of a paper that will appear at ESA 2024

This paper presents a comprehensive systematic review of generative models (GANs, VAEs, DMs, and LLMs) used to synthesize various medical data types, including imaging (dermoscopic, mammographic, ultrasound, CT, MRI, and X-ray), text, time-series, and tabular data (EHR). Unlike previous narrowly focused reviews, our study encompasses a broad array of medical data modalities and explores various generative models. Our search strategy queries databases such as Scopus, PubMed, and ArXiv, focusing on recent works from January 2021 to November 2023, excluding reviews and perspectives. This period emphasizes recent advancements beyond GANs, which have been extensively covered previously. The survey reveals insights from three key aspects: (1) Synthesis applications and purpose of synthesis, (2) generation techniques, and (3) evaluation methods. It highlights clinically valid synthesis applications, demonstrating the potential of synthetic data to tackle diverse clinical requirements. While conditional models incorporating class labels, segmentation masks and image translations are prevalent, there is a gap in utilizing prior clinical knowledge and patient-specific context, suggesting a need for more personalized synthesis approaches and emphasizing the importance of tailoring generative approaches to the unique characteristics of medical data. Additionally, there is a significant gap in using synthetic data beyond augmentation, such as for validation and evaluation of downstream medical AI models. The survey uncovers that the lack of standardized evaluation methodologies tailored to medical images is a barrier to clinical application, underscoring the need for in-depth evaluation approaches, benchmarking, and comparative studies to promote openness and collaboration.