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Participatory design in digital civics aims to foster mutual learning and co-creation between public services and citizens. However, rarely do we collectively explore the challenges and failures we experience within PD and digital civics, to enable us to grow as a community. This workshop will explore real-world experiences that had to adapt to unforeseen circumstances. Through case presentations and thematic group discussions, participants will reflect on the challenges faced, the causes that led to these challenges, and collaboratively problem-solve effective solutions. Furthermore, we aim to discuss well-being impact on researchers and communities when faced with these obstacles, the strategies participants use to overcome them and how this can be fed back into the digital civics community. By that, the workshop seeks to foster dialogue, reflection, and collective learning, empowering participants with insights to navigate complexities effectively and promote resilient design practices in digital civics.

Participatory design initiatives, especially within the realm of digital civics, are often integrated and co-developed with the very citizens and communities they intend to assist. Digital civics research aims to create positive social change using a variety of digital technologies. These research projects commonly adopt various embedded processes, such as commissioning models \cite{dcitizensproj22}. Despite the adoption of this process within a range of domains, there isn't currently a framework for best practices and accountability procedures to ensure we engage with citizens ethically and ensure the sustainability of our projects. This workshop aims to provide a space to start collaboratively constructing a dynamic framework of best practices, laying the groundwork for the future of sustainable embedded research processes. The overarching goal is to foster discussions and share insights that contribute to developing effective practices, ensuring the longevity and impact of participatory digital civics projects.

Unsupervised object detection using deep neural networks is typically a difficult problem with few to no guarantees about the learned representation. In this work we present the first unsupervised object detection method that is theoretically guaranteed to recover the true object positions up to quantifiable small shifts. We develop an unsupervised object detection architecture and prove that the learned variables correspond to the true object positions up to small shifts related to the encoder and decoder receptive field sizes, the object sizes, and the widths of the Gaussians used in the rendering process. We perform detailed analysis of how the error depends on each of these variables and perform synthetic experiments validating our theoretical predictions up to a precision of individual pixels. We also perform experiments on CLEVR-based data and show that, unlike current SOTA object detection methods (SAM, CutLER), our method's prediction errors always lie within our theoretical bounds. We hope that this work helps open up an avenue of research into object detection methods with theoretical guarantees.

In this paper, we propose Flash3D, a method for scene reconstruction and novel view synthesis from a single image which is both very generalisable and efficient. For generalisability, we start from a "foundation" model for monocular depth estimation and extend it to a full 3D shape and appearance reconstructor. For efficiency, we base this extension on feed-forward Gaussian Splatting. Specifically, we predict a first layer of 3D Gaussians at the predicted depth, and then add additional layers of Gaussians that are offset in space, allowing the model to complete the reconstruction behind occlusions and truncations. Flash3D is very efficient, trainable on a single GPU in a day, and thus accessible to most researchers. It achieves state-of-the-art results when trained and tested on RealEstate10k. When transferred to unseen datasets like NYU it outperforms competitors by a large margin. More impressively, when transferred to KITTI, Flash3D achieves better PSNR than methods trained specifically on that dataset. In some instances, it even outperforms recent methods that use multiple views as input. Code, models, demo, and more results are available at https://www.robots.ox.ac.uk/~vgg/research/flash3d/., Comment: Project page: https://www.robots.ox.ac.uk/~vgg/research/flash3d/

Benchmarks have been essential for driving progress in machine learning. A better understanding of LLM capabilities on real world tasks is vital for safe development. Designing adequate LLM benchmarks is challenging: Data from real-world tasks is hard to collect, public availability of static evaluation data results in test data contamination and benchmark overfitting, and periodically generating new evaluation data is tedious and may result in temporally inconsistent results. We introduce HelloFresh, based on continuous streams of real-world data generated by intrinsically motivated human labelers. It covers recent events from X (formerly Twitter) community notes and edits of Wikipedia pages, mitigating the risk of test data contamination and benchmark overfitting. Any X user can propose an X note to add additional context to a misleading post (formerly tweet); if the community classifies it as helpful, it is shown with the post. Similarly, Wikipedia relies on community-based consensus, allowing users to edit articles or revert edits made by other users. Verifying whether an X note is helpful or whether a Wikipedia edit should be accepted are hard tasks that require grounding by querying the web. We backtest state-of-the-art LLMs supplemented with simple web search access and find that HelloFresh yields a temporally consistent ranking. To enable continuous evaluation on HelloFresh, we host a public leaderboard and periodically updated evaluation data at https://tinyurl.com/hello-fresh-LLM., Comment: ACL 2024 Findings

The NEXT-White detector, a high-pressure gaseous xenon time projection chamber, demonstrated the excellence of this technology for future neutrinoless double beta decay searches using photomultiplier tubes (PMTs) to measure energy and silicon photomultipliers (SiPMs) to extract topology information. This analysis uses $^{83m}\text{Kr}$ data from the NEXT-White detector to measure and understand the energy resolution that can be obtained with the SiPMs, rather than with PMTs. The energy resolution obtained of (10.9 $\pm$ 0.6) $\%$, full-width half-maximum, is slightly larger than predicted based on the photon statistics resulting from very low light detection coverage of the SiPM plane in the NEXT-White detector. The difference in the predicted and measured resolution is attributed to poor corrections, which are expected to be improved with larger statistics. Furthermore, the noise of the SiPMs is shown to not be a dominant factor in the energy resolution and may be negligible when noise subtraction is applied appropriately, for high-energy events or larger SiPM coverage detectors. These results, which are extrapolated to estimate the response of large coverage SiPM planes, are promising for the development of future, SiPM-only, readout planes that can offer imaging and achieve similar energy resolution to that previously demonstrated with PMTs.

Reliable and profound studies of actual magnetic domain structure in rare-earth A2Ir2O7 pyrochlore iridates are frequently limited by insufficient sample quality or lack of single crystals. We report the magnetic properties of the for-the-first-time synthesised Lu2Ir2O7 and Er2Ir2O7 single-crystals. The paper is focused on the robust ferromagnetic component of magnetisation present in the material with the antiferromagnetically ordered state of the all-in-all-out (AIAO) type, and is discussed in the framework of AIAO and AOAI domains and interfaces on the geometrically frustrated lattice., Comment: 6 pages, 3 figures in main text, supplementary material attached

Altermagnets are a new class of magnetic materials with zero net magnetization (like antiferromagnets) but spin-split electronic bands (like ferromagnets) over a fraction of reciprocal space. As in antiferromagnets, magnons in altermagnets come in two flavours, that either add one or remove one unit of spin to the $S=0$ ground state. However, in altermagnets these two magnon modes are non-degenerate along some directions in reciprocal space. Here we show that the lifetime of altermagnetic magnons has a very strong dependence on both flavour and direction. Strikingly, coupling to Stoner modes leads to a complete suppression of magnon propagation along selected spatial directions. This giant anisotropy will impact electronic, spin, and energy transport properties and may be exploited in spintronic applications., Comment: 9 pages, 11 figures (including the appendices)

The imaging of individual Ba$^{2+}$ ions in high pressure xenon gas is one possible way to attain background-free sensitivity to neutrinoless double beta decay and hence establish the Majorana nature of the neutrino. In this paper we demonstrate selective single Ba$^{2+}$ ion imaging inside a high-pressure xenon gas environment. Ba$^{2+}$ ions chelated with molecular chemosensors are resolved at the gas-solid interface using a diffraction-limited imaging system with scan area of 1$\times$1~cm$^2$ located inside 10~bar of xenon gas. This new form of microscopy represents an important enabling step in the development of barium tagging for neutrinoless double beta decay searches in $^{136}$Xe, as well as a new tool for studying the photophysics of fluorescent molecules and chemosensors at the solid-gas interface.

AI agents are commonly trained with large datasets of demonstrations of human behavior. However, not all behaviors are equally safe or desirable. Desired characteristics for an AI agent can be expressed by assigning desirability scores, which we assume are not assigned to individual behaviors but to collective trajectories. For example, in a dataset of vehicle interactions, these scores might relate to the number of incidents that occurred. We first assess the effect of each individual agent's behavior on the collective desirability score, e.g., assessing how likely an agent is to cause incidents. This allows us to selectively imitate agents with a positive effect, e.g., only imitating agents that are unlikely to cause incidents. To enable this, we propose the concept of an agent's Exchange Value, which quantifies an individual agent's contribution to the collective desirability score. The Exchange Value is the expected change in desirability score when substituting the agent for a randomly selected agent. We propose additional methods for estimating Exchange Values from real-world datasets, enabling us to learn desired imitation policies that outperform relevant baselines. The project website can be found at https://tinyurl.com/select-to-perfect., Comment: ICLR 2024

This paper introduces a relational perspective on ethics within the context of Feminist Digital Civics and community-led design. Ethics work in HCI has primarily focused on prescriptive machine ethics and bioethics principles rather than people. In response, we advocate for a community-led, processual approach to ethics, acknowledging power dynamics and local contexts. We thus propose a multidimensional adaptive model for ethics in HCI design, integrating an intersectional feminist ethical lens. This framework embraces feminist epistemologies, methods, and methodologies, fostering a reflexive practice. By weaving together situated knowledges, standpoint theory, intersectionality, participatory methods, and care ethics, our approach offers a holistic foundation for ethics in HCI, aiming to advance community-led practices and enrich the discourse surrounding ethics within this field.

Accessibility research has gained traction, yet ethical gaps persist in the inclusion of individuals with disabilities, especially children. Inclusive research practices are essential to ensure research and design solutions cater to the needs of all individuals, regardless of their abilities. Working with children with disabilities in Human-Computer Interaction and Human-Robot Interaction presents a unique set of ethical dilemmas. These young participants often require additional care, support, and accommodations, which can fall off researchers' resources or expertise. The lack of clear guidance on navigating these challenges further aggravates the problem. To provide a base and address this issue, we adopt a critical reflective approach, evaluating our impact by analyzing two case studies involving children with disabilities in HCI/HRI research.

Two-dimensional (2D) freehand ultrasonography is one of the most commonly used medical imaging modalities, particularly in obstetrics and gynaecology. However, it only captures 2D cross-sectional views of inherently 3D anatomies, losing valuable contextual information. As an alternative to requiring costly and complex 3D ultrasound scanners, 3D volumes can be constructed from 2D scans using machine learning. However this usually requires long computational time. Here, we propose RapidVol: a neural representation framework to speed up slice-to-volume ultrasound reconstruction. We use tensor-rank decomposition, to decompose the typical 3D volume into sets of tri-planes, and store those instead, as well as a small neural network. A set of 2D ultrasound scans, with their ground truth (or estimated) 3D position and orientation (pose) is all that is required to form a complete 3D reconstruction. Reconstructions are formed from real fetal brain scans, and then evaluated by requesting novel cross-sectional views. When compared to prior approaches based on fully implicit representation (e.g. neural radiance fields), our method is over 3x quicker, 46% more accurate, and if given inaccurate poses is more robust. Further speed-up is also possible by reconstructing from a structural prior rather than from scratch.

Two years ago, Stable Diffusion achieved super-human performance at generating images with super-human numbers of fingers. Following the steady decline of its technical novelty, we propose Stale Diffusion, a method that solidifies and ossifies Stable Diffusion in a maximum-entropy state. Stable Diffusion works analogously to a barn (the Stable) from which an infinite set of horses have escaped (the Diffusion). As the horses have long left the barn, our proposal may be seen as antiquated and irrelevant. Nevertheless, we vigorously defend our claim of novelty by identifying as early adopters of the Slow Science Movement, which will produce extremely important pearls of wisdom in the future. Our speed of contributions can also be seen as a quasi-static implementation of the recent call to pause AI experiments, which we wholeheartedly support. As a result of a careful archaeological expedition to 18-months-old Git commit histories, we found that naturally-accumulating errors have produced a novel entropy-maximising Stale Diffusion method, that can produce sleep-inducing hyper-realistic 5D video that is as good as one's imagination., Comment: SIGBOVIK 2024

In this work we prove that the non-negative functions $u \in L^s_{loc}(\Omega)$, for some $s>0$, belonging to the De Giorgi classes \begin{equation}\label{eq0.1} \fint\limits_{B_{r(1-\sigma)}(x_{0})} \big|\nabla \big(u-k\big)_{-}\big|^{p}\, dx \leqslant \frac{c}{\sigma^{q}} \,\Lambda\big(x_{0}, r, k\big)\bigg(\frac{k}{r}\bigg)^{p}\bigg(\frac{\big|B_{r}(x_{0})\cap\big\{u\leqslant k\big\}\big|}{|B_{r}(x_{0})|}\bigg)^{1-\delta}, \end{equation} under proper assumptions on $\Lambda$, satisfy a weak Harnack inequality with a constant depending on the $L^s$-norm of $u$. Under suitable assumptions on $\Lambda$, the minimizers of elliptic functionals with generalized Orlicz growth belong to De Giorgi classes satisfying \eqref{eq0.1}; thus this study gives a wider interpretation of Harnack-type estimates derived to double-phase, degenerate double-phase functionals and functionals with variable exponents.

Advancements in instrumentation have revealed a multitude of small-scale EUV events in the solar atmosphere. Our aim is to employ high-resolution magnetograms to gain a detailed understanding of the magnetic origin of such phenomena. We have used coordinated observations from SST, IRIS, and SDO to analyze an ephemeral magnetic flux emergence episode and the following chain of small-scale energetic events. These unique observations clearly link these phenomena together. The high-resolution (0."057/pixel) magnetograms obtained with SST/CRISP allows us to reliably measure the magnetic field at the photosphere and detect the emerging bipole that causes the subsequent eruptive atmospheric events. Notably, this small-scale emergence episode remains indiscernible in the lower resolution SDO/HMI magnetograms (0."5/pixel). We report the appearance of a dark bubble in Ca II K related to the emerging bipole, a sign of the canonical expanding magnetic dome predicted in flux emergence simulations. Evidences of reconnection are also found: first through an Ellerman bomb, and later by the launch of a surge next to a UV burst. The UV burst exhibits a weak EUV counterpart in the coronal SDO/AIA channels. By calculating DEM, its plasma is shown to reach a temperature beyond 1 MK and have densities between the upper chromosphere and transition region. Our study showcases the importance of high-resolution magnetograms to unveil the mechanisms triggering phenomena such as EBs, UV bursts, and surges. This could hold implications for small-scale events akin to those recently reported in EUV using Solar Orbiter. The finding of temperatures beyond 1 MK in the UV burst plasma strongly suggests that we are examining analogous features. Therefore, we signal caution regarding drawing conclusions from full-disk magnetograms that lack the necessary resolution to reveal their true magnetic origin., Comment: Accepted in A&A, 11 pages, 7 figures, 5 movies

Understanding complex scenes at multiple levels of abstraction remains a formidable challenge in computer vision. To address this, we introduce Nested Neural Feature Fields (N2F2), a novel approach that employs hierarchical supervision to learn a single feature field, wherein different dimensions within the same high-dimensional feature encode scene properties at varying granularities. Our method allows for a flexible definition of hierarchies, tailored to either the physical dimensions or semantics or both, thereby enabling a comprehensive and nuanced understanding of scenes. We leverage a 2D class-agnostic segmentation model to provide semantically meaningful pixel groupings at arbitrary scales in the image space, and query the CLIP vision-encoder to obtain language-aligned embeddings for each of these segments. Our proposed hierarchical supervision method then assigns different nested dimensions of the feature field to distill the CLIP embeddings using deferred volumetric rendering at varying physical scales, creating a coarse-to-fine representation. Extensive experiments show that our approach outperforms the state-of-the-art feature field distillation methods on tasks such as open-vocabulary 3D segmentation and localization, demonstrating the effectiveness of the learned nested feature field.

We study the boundary behavior of solutions to parabolic double-phase equations through the celebrated Wiener's sufficiency criterion. The analysis is conducted for cylindrical domains and the regularity up to the lateral boundary is shown in terms of either its $p$ or $q$ capacity, depending on whether the phase vanishes at the boundary or not. Eventually we obtain a fine boundary estimate that, when considering uniform geometric conditions as density or fatness, leads us to the boundary H\"older continuity of solutions. In particular, the double-phase elicits new questions on the definition of an adapted capacity., Comment: 33 pages, 2 figures, appendix at the end of the paper

This article investigates applying advanced machine learning models, specifically LSTM and BERT, for text classification to predict multiple categories in the retail sector. The study demonstrates how applying data augmentation techniques and the focal loss function can significantly enhance accuracy in classifying products into multiple categories using a robust Brazilian retail dataset. The LSTM model, enriched with Brazilian word embedding, and BERT, known for its effectiveness in understanding complex contexts, were adapted and optimized for this specific task. The results showed that the BERT model, with an F1 Macro Score of up to $99\%$ for segments, $96\%$ for categories and subcategories and $93\%$ for name products, outperformed LSTM in more detailed categories. However, LSTM also achieved high performance, especially after applying data augmentation and focal loss techniques. These results underscore the effectiveness of NLP techniques in retail and highlight the importance of the careful selection of modelling and preprocessing strategies. This work contributes significantly to the field of NLP in retail, providing valuable insights for future research and practical applications.

Video databases from the internet are a valuable source of text-audio retrieval datasets. However, given that sound and vision streams represent different "views" of the data, treating visual descriptions as audio descriptions is far from optimal. Even if audio class labels are present, they commonly are not very detailed, making them unsuited for text-audio retrieval. To exploit relevant audio information from video-text datasets, we introduce a methodology for generating audio-centric descriptions using Large Language Models (LLMs). In this work, we consider the egocentric video setting and propose three new text-audio retrieval benchmarks based on the EpicMIR and EgoMCQ tasks, and on the EpicSounds dataset. Our approach for obtaining audio-centric descriptions gives significantly higher zero-shot performance than using the original visual-centric descriptions. Furthermore, we show that using the same prompts, we can successfully employ LLMs to improve the retrieval on EpicSounds, compared to using the original audio class labels of the dataset. Finally, we confirm that LLMs can be used to determine the difficulty of identifying the action associated with a sound., Comment: 9 pages, 2 figures, 9 tables, Accepted at ICASSP 2024

With the growing demand for immersive digital applications, the need to understand and reconstruct 3D scenes has significantly increased. In this context, inpainting indoor environments from a single image plays a crucial role in modeling the internal structure of interior spaces as it enables the creation of textured and clutter-free reconstructions. While recent methods have shown significant progress in room modeling, they rely on constraining layout estimators to guide the reconstruction process. These methods are highly dependent on the performance of the structure estimator and its generative ability in heavily occluded environments. In response to these issues, we propose an innovative approach based on a U-Former architecture and a new Windowed-FourierMixer block, resulting in a unified, single-phase network capable of effectively handle human-made periodic structures such as indoor spaces. This new architecture proves advantageous for tasks involving indoor scenes where symmetry is prevalent, allowing the model to effectively capture features such as horizon/ceiling height lines and cuboid-shaped rooms. Experiments show the proposed approach outperforms current state-of-the-art methods on the Structured3D dataset demonstrating superior performance in both quantitative metrics and qualitative results. Code and models will be made publicly available.

Unlocking the potential of topological order within many-body spin systems has long been a central pursuit in the realm of quantum materials. Despite extensive efforts, the quest for a versatile platform enabling site-selective spin manipulation, essential for tuning and probing diverse topological phases, has persisted. Here, we utilize on-surface synthesis to construct spin-1/2 alternating-exchange Heisenberg (AH) chains[1] with antiferromagnetic couplings $J_1$ and $J_2$ by covalently linking Clar's goblets -- nanographenes each hosting two antiferromagnetically-coupled unpaired electrons[2]. Utilizing scanning tunneling microscopy, we exert atomic-scale control over the spin chain lengths, parities and exchange-coupling terminations, and probe their magnetic response by means of inelastic tunneling spectroscopy. Our investigation confirms the gapped nature of bulk excitations in the chains, known as triplons[3]. Besides, the triplon dispersion relation is successfully extracted from the spatial variation of tunneling spectral amplitudes. Furthermore, depending on the parity and termination of chains, we observe varying numbers of in-gap $S=1/2$ edge spins, enabling the determination of the degeneracy of distinct topological ground states in the thermodynamic limit-either 1, 2, or 4. By monitoring interactions between these edge spins, we identify the exponential decay of spin correlations. Our experimental findings, corroborated by theoretical calculations, present a phase-controlled many-body platform, opening promising avenues toward the development of spin-based quantum devices.

In the evolving field of machine learning, ensuring fairness has become a critical concern, prompting the development of algorithms designed to mitigate discriminatory outcomes in decision-making processes. However, achieving fairness in the presence of group-specific concept drift remains an unexplored frontier, and our research represents pioneering efforts in this regard. Group-specific concept drift refers to situations where one group experiences concept drift over time while another does not, leading to a decrease in fairness even if accuracy remains fairly stable. Within the framework of federated learning, where clients collaboratively train models, its distributed nature further amplifies these challenges since each client can experience group-specific concept drift independently while still sharing the same underlying concept, creating a complex and dynamic environment for maintaining fairness. One of the significant contributions of our research is the formalization and introduction of the problem of group-specific concept drift and its distributed counterpart, shedding light on its critical importance in the realm of fairness. In addition, leveraging insights from prior research, we adapt an existing distributed concept drift adaptation algorithm to tackle group-specific distributed concept drift which utilizes a multi-model approach, a local group-specific drift detection mechanism, and continuous clustering of models over time. The findings from our experiments highlight the importance of addressing group-specific concept drift and its distributed counterpart to advance fairness in machine learning.

Pattern discovery plays a central role in both descriptive and predictive tasks across multiple domains. Actionable patterns must meet rigorous statistical significance criteria and, in the presence of target variables, further uphold discriminative power. Our work addresses the underexplored area of guiding pattern discovery by integrating statistical significance and discriminative power criteria into state-of-the-art algorithms while preserving pattern quality. We also address how pattern quality thresholds, imposed by some algorithms, can be rectified to accommodate these additional criteria. To test the proposed methodology, we select the triclustering task as the guiding pattern discovery case and extend well-known greedy and multi-objective optimization triclustering algorithms, $\delta$-Trimax and TriGen, that use various pattern quality criteria, such as Mean Squared Residual (MSR), Least Squared Lines (LSL), and Multi Slope Measure (MSL). Results from three case studies show the role of the proposed methodology in discovering patterns with pronounced improvements of discriminative power and statistical significance without quality deterioration, highlighting its importance in supervisedly guiding the search. Although the proposed methodology is motivated over multivariate time series data, it can be straightforwardly extended to pattern discovery tasks involving multivariate, N-way (N>3), transactional, and sequential data structures. Availability: The code is freely available at https://github.com/JupitersMight/MOF_Triclustering under the MIT license.

Extracting point correspondences from two or more views of a scene is a fundamental computer vision problem with particular importance for relative camera pose estimation and structure-from-motion. Existing local feature matching approaches, trained with correspondence supervision on large-scale datasets, obtain highly-accurate matches on the test sets. However, they do not generalise well to new datasets with different characteristics to those they were trained on, unlike classic feature extractors. Instead, they require finetuning, which assumes that ground-truth correspondences or ground-truth camera poses and 3D structure are available. We relax this assumption by removing the requirement of 3D structure, e.g., depth maps or point clouds, and only require camera pose information, which can be obtained from odometry. We do so by replacing correspondence losses with epipolar losses, which encourage putative matches to lie on the associated epipolar line. While weaker than correspondence supervision, we observe that this cue is sufficient for finetuning existing models on new data. We then further relax the assumption of known camera poses by using pose estimates in a novel bootstrapping approach. We evaluate on highly challenging datasets, including an indoor drone dataset and an outdoor smartphone camera dataset, and obtain state-of-the-art results without strong supervision.

We propose LangProp, a framework for iteratively optimizing code generated by large language models (LLMs), in both supervised and reinforcement learning settings. While LLMs can generate sensible coding solutions zero-shot, they are often sub-optimal. Especially for code generation tasks, it is likely that the initial code will fail on certain edge cases. LangProp automatically evaluates the code performance on a dataset of input-output pairs, catches any exceptions, and feeds the results back to the LLM in the training loop, so that the LLM can iteratively improve the code it generates. By adopting a metric- and data-driven training paradigm for this code optimization procedure, one could easily adapt findings from traditional machine learning techniques such as imitation learning, DAgger, and reinforcement learning. We show LangProp's applicability to general domains such as Sudoku and CartPole, as well as demonstrate the first proof of concept of automated code optimization for autonomous driving in CARLA. We show that LangProp can generate interpretable and transparent policies that can be verified and improved in a metric- and data-driven way. Our code is available at https://github.com/shuishida/LangProp.

Optical microscopy is an indispensable tool in life sciences research, but conventional techniques require compromises between imaging parameters like speed, resolution, field-of-view, and phototoxicity. To overcome these limitations, data-driven microscopes incorporate feedback loops between data acquisition and analysis. This review overviews how machine learning enables automated image analysis to optimise microscopy in real-time. We first introduce key data-driven microscopy concepts and machine learning methods relevant to microscopy image analysis. Subsequently, we highlight pioneering works and recent advances in integrating machine learning into microscopy acquisition workflows, including optimising illumination, switching modalities and acquisition rates, and triggering targeted experiments. We then discuss the remaining challenges and future outlook. Overall, intelligent microscopes that can sense, analyse, and adapt promise to transform optical imaging by opening new experimental possibilities., Comment: 7 pages, 4 figures, review

This paper shortly summarises the experimental results obtained since 2011 by a large European academic consortium for the scientific conceptualisation, the definition of the technical requirements, the generation of experimental data, and the validation of a numerical code, for the Pulsating Heat Pipes (PHP) experiment on the International Space Station (ISS). The PHP is a passive, wickless thermal device, whereby a two-phase fluid, forming liquid plugs and vapour slugs, moves with a pulsating or circulating motion inside a meandering tube or channel. The PHP may have a very broad range of geometries (flat, tubular, 3D structured), it can dissipate heat from large areas, and it can be suitable for high power applications with low/medium heat fluxes. PHP functioning is based on the capillary effect, which provides the existence of liquid plugs completely filling the channel cross-section, in a way that any expansion or contraction of the vapour slugs will naturally generate a movement of the fluid along the channel axis. For this, it is important that the channel has a cross-section size below a given threshold, which depends on the liquid surface tension and (for a static fluid) on the gravity acceleration. In space, when only residual accelerations are acting, such a static size threshold is virtually infinite, while a finite dynamic threshold exists even in the absence of gravity. The concept of a ''Space PHP'' was originally developed in 2014 by the team, and from then 17 Parabolic Flight Campaigns (PFC) and 3 Sounding Rocket (SR) experiments have been carried out to generate the data for the preparation of an experiment targeting a Low Earth Orbit (LEO) mission. Both a tubular and a flat plate PHP have been successfully tested in reduced gravity and on ground, by using different combinations of fluids and building materials. The need for having an experiment on a LEO environment is mainly because, during a PFC, only 22sec of reduced gravity are possible, which is a period below the characteristic time for reaching a steady state condition for almost all of the tested devices. Instead, a steady state was reached using the SR campaigns: in this case however, only one experimental condition was achievable, and long-duration data of the PHP performance still remains beyond reach. Several measurement methodologies have been used to characterise the Space PHP, like infrared analysis, high-speed camera visualisation techniques, with data processed with different techniques, from wavelets to inverse heat transfer problem solution. The results clearly showed that PHPs are very interesting for space applications due to their simplicity of construction, the capacity to transfer heat up to several hundred watts, a high power/weight ratio, their geometrical adaptability, and, in particular, the Space PHP will be a breakthrough technology for space thermal management.

The study of open-shell nanographenes has relied on a paradigm where spins are the only low-energy degrees of freedom. Here we show that some nanographenes can host low-energy excitations that include strongly coupled spin and orbital degrees of freedom. The key ingredient is the existence of orbital degeneracy, as a consequence of leaving the benzenoid/half-filling scenario. We analyze the case of nitrogen-doped triangulenes, using both density-functional theory and Hubbard model multiconfigurational and random-phase approximation calculations. We find a rich interplay between orbital and spin degrees of freedom that confirms the need to go beyond the spin-only paradigm, opening a new venue in this field of research., Comment: 6 pages, 4 figures

Developing generalizable manipulation skills is a core challenge in embodied AI. This includes generalization across diverse task configurations, encompassing variations in object shape, density, friction coefficient, and external disturbances such as forces applied to the robot. Rapid Motor Adaptation (RMA) offers a promising solution to this challenge. It posits that essential hidden variables influencing an agent's task performance, such as object mass and shape, can be effectively inferred from the agent's action and proprioceptive history. Drawing inspiration from RMA in locomotion and in-hand rotation, we use depth perception to develop agents tailored for rapid motor adaptation in a variety of manipulation tasks. We evaluated our agents on four challenging tasks from the Maniskill2 benchmark, namely pick-and-place operations with hundreds of objects from the YCB and EGAD datasets, peg insertion with precise position and orientation, and operating a variety of faucets and handles, with customized environment variations. Empirical results demonstrate that our agents surpass state-of-the-art methods like automatic domain randomization and vision-based policies, obtaining better generalization performance and sample efficiency., Comment: Accepted at CVPR 2024. 12 pages

Motivated by recent experimental breakthroughs, we propose a strategy to design two-dimensional spin lattices with competing interactions that lead to non-trivial emergent quantum states. We consider $S=1/2$ nanographenes with $C_3$ symmetry as building blocks, and we leverage the potential to control both the sign and the strength of exchange with first neighbours to build a family of spin models. Specifically, we consider the case of a Heisenberg model in a triangle-decorated honeycomb lattice with competing ferromagnetic and antiferromagnetic interactions whose ratio can be varied in a wide range. Based on exact diagonalization of both fermionic and spin models we predict a quantum phase transition between a valence bond crystal of spin singlets with triplon excitations living in a Kagom\'e lattice and a N\'eel phase of effective $S=3/2$ in the limit of dominant ferromagnetic interactions., Comment: 6 pages, 4 figures

We tackle the problem of 3D point cloud localization based on a few natural linguistic descriptions and introduce a novel neural network, Text2Loc, that fully interprets the semantic relationship between points and text. Text2Loc follows a coarse-to-fine localization pipeline: text-submap global place recognition, followed by fine localization. In global place recognition, relational dynamics among each textual hint are captured in a hierarchical transformer with max-pooling (HTM), whereas a balance between positive and negative pairs is maintained using text-submap contrastive learning. Moreover, we propose a novel matching-free fine localization method to further refine the location predictions, which completely removes the need for complicated text-instance matching and is lighter, faster, and more accurate than previous methods. Extensive experiments show that Text2Loc improves the localization accuracy by up to $2\times$ over the state-of-the-art on the KITTI360Pose dataset. Our project page is publicly available at \url{https://yan-xia.github.io/projects/text2loc/}., Comment: Accepted by CVPR 2024

NEXT-100 is currently being constructed at the Laboratorio Subterr\'aneo de Canfranc in the Spanish Pyrenees and will search for neutrinoless double beta decay using a high-pressure gaseous time projection chamber (TPC) with 100 kg of xenon. Charge amplification is carried out via electroluminescence (EL) which is the process of accelerating electrons in a high electric field region causing secondary scintillation of the medium proportional to the initial charge. The NEXT-100 EL and cathode regions are made from tensioned hexagonal meshes of 1 m diameter. This paper describes the design, characterization, and installation of these parts for NEXT-100. Simulations of the electric field are performed to model the drift and amplification of ionization electrons produced in the detector under various EL region alignments and rotations. Measurements of the electrostatic breakdown voltage in air characterize performance under high voltage conditions and identify breakdown points. The electrostatic deflection of the mesh is quantified and fit to a first-principles mechanical model. Measurements were performed with both a standalone test EL region and with the NEXT-100 EL region before its installation in the detector. Finally, we describe the parts as installed in NEXT-100, following their deployment in Summer 2023., Comment: 35 pages, 25 Figures, update includes accepted version in JINST

Noble element time projection chambers are a leading technology for rare event detection in physics, such as for dark matter and neutrinoless double beta decay searches. Time projection chambers typically assign event position in the drift direction using the relative timing of prompt scintillation and delayed charge collection signals, allowing for reconstruction of an absolute position in the drift direction. In this paper, alternate methods for assigning event drift distance via quantification of electron diffusion in a pure high pressure xenon gas time projection chamber are explored. Data from the NEXT-White detector demonstrate the ability to achieve good position assignment accuracy for both high- and low-energy events. Using point-like energy deposits from $^{83\mathrm{m}}$Kr calibration electron captures ($E\sim45$keV), the position of origin of low-energy events is determined to $2~$cm precision with bias $< 1$mm. A convolutional neural network approach is then used to quantify diffusion for longer tracks (E$\geq$1.5MeV), yielding a precision of 3cm on the event barycenter. The precision achieved with these methods indicates the feasibility energy calibrations of better than 1% FWHM at Q$_{\beta\beta}$ in pure xenon, as well as the potential for event fiducialization in large future detectors using an alternate method that does not rely on primary scintillation., Comment: 18 pages, 16 figures