Search

AI algorithms have become valuable in aiding professionals in healthcare. The increasing confidence obtained by these models is helpful in critical decision demands. In clinical dermatology, classification models can detect malignant lesions on patients' skin using only RGB images as input. However, most learning-based methods employ data acquired from dermoscopic datasets on training, which are large and validated by a gold standard. Clinical models aim to deal with classification on users' smartphone cameras that do not contain the corresponding resolution provided by dermoscopy. Also, clinical applications bring new challenges. It can contain captures from uncontrolled environments, skin tone variations, viewpoint changes, noises in data and labels, and unbalanced classes. A possible alternative would be to use transfer learning to deal with the clinical images. However, as the number of samples is low, it can cause degradations on the model's performance; the source distribution used in training differs from the test set. This work aims to evaluate the gap between dermoscopic and clinical samples and understand how the dataset variations impact training. It assesses the main differences between distributions that disturb the model's prediction. Finally, from experiments on different architectures, we argue how to combine the data from divergent distributions, decreasing the impact on the model's final accuracy., Comment: 10 pages, 1 figure

Coronary CT angiography (CCTA) and intravascular ultrasound (IVUS) provide complementary information for coronary artery disease assessment, making their registration valuable for comprehensive analysis. However, existing registration methods require manual interaction or extensive segmentations, limiting their practical application. In this work, we present a fully automatic framework for CCTA-IVUS registration using deep learning-based feature detection and a differentiable image registration module. Our approach leverages a convolutional neural network trained to identify key anatomical features from polar-transformed multiplanar reformatted CCTA or IVUS data. These detected anatomical featuers subsequently guide a differentiable registration module to optimize transformation parameters of an automatically extracted coronary artery centerline. The method does not require landmark selection or segmentations as input, while accounting for the presence of IVUS guidewire artifacts. Evaluated on 48 clinical cases with reference CCTA centerlines corresponding to IVUS pullback, our method achieved successful registration in 83.3\% of cases, with a median centerline overlap F$_1$-score of 0.982 and median cosine similarities of 0.940 and 0.944 for cross-sectional plane orientation. Our results demonstrate that automatically detected anatomical features can be leveraged for accurate registration. The fully automatic nature of the approach represents a significant step toward streamlined multimodal coronary analysis, potentially facilitating large-scale studies of coronary plaque characteristics across modalities., Comment: Submitted to IEEE Transactions on Medical Imaging

This paper presents a study of the radiation hardness of the hadronic Tile Calorimeter of the ATLAS experiment in the LHC Run 2. Both the plastic scintillators constituting the detector active media and the wavelength-shifting optical fibres collecting the scintillation light into the photodetector readout are elements susceptible to radiation damage. The dedicated calibration and monitoring systems of the detector (caesium radioactive sources, laser and minimum bias integrator) allow to assess the response of these optical components. Data collected with these systems between 2015 and 2018 are analysed to measure the degradation of the optical instrumentation across Run 2. Moreover, a simulation of the total ionising dose in the calorimeter is employed to study and model the degradation profile as a function of the exposure conditions, both integrated dose and dose rate. The measurement of the relative light output loss in Run 2 is presented and extrapolations to future scenarios are drawn based on current data. The impact of radiation damage on the cell response uniformity is also analysed., Comment: 31 pages in total, 18 figures, 2 tables, submitted to JINST

To date, most place recognition methods focus on single-modality retrieval. While they perform well in specific environments, cross-modal methods offer greater flexibility by allowing seamless switching between map and query sources. It also promises to reduce computation requirements by having a unified model, and achieving greater sample efficiency by sharing parameters. In this work, we develop a universal solution to place recognition, UniLoc, that works with any single query modality (natural language, image, or point cloud). UniLoc leverages recent advances in large-scale contrastive learning, and learns by matching hierarchically at two levels: instance-level matching and scene-level matching. Specifically, we propose a novel Self-Attention based Pooling (SAP) module to evaluate the importance of instance descriptors when aggregated into a place-level descriptor. Experiments on the KITTI-360 dataset demonstrate the benefits of cross-modality for place recognition, achieving superior performance in cross-modal settings and competitive results also for uni-modal scenarios. Our project page is publicly available at https://yan-xia.github.io/projects/UniLoc/., Comment: 14 pages, 10 figures

We tackle the problem of localizing the traffic surveillance cameras in cooperative perception. To overcome the lack of large-scale real-world intersection datasets, we introduce Carla Intersection, a new simulated dataset with 75 urban and rural intersections in Carla. Moreover, we introduce a novel neural network, TrafficLoc, localizing traffic cameras within a 3D reference map. TrafficLoc employs a coarse-to-fine matching pipeline. For image-point cloud feature fusion, we propose a novel Geometry-guided Attention Loss to address cross-modal viewpoint inconsistencies. During coarse matching, we propose an Inter-Intra Contrastive Learning to achieve precise alignment while preserving distinctiveness among local intra-features within image patch-point group pairs. Besides, we introduce Dense Training Alignment with a soft-argmax operator to consider additional features when regressing the final position. Extensive experiments show that our TrafficLoc improves the localization accuracy over the state-of-the-art Image-to-point cloud registration methods by a large margin (up to 86%) on Carla Intersection and generalizes well to real-world data. TrafficLoc also achieves new SOTA performance on KITTI and NuScenes datasets, demonstrating strong localization ability across both in-vehicle and traffic cameras. Our project page is publicly available at https://tum-luk.github.io/projects/trafficloc/., Comment: 17 pages, 12 figures

Collective excitations of charged particles under the influence of an electromagnetic field give rise to a rich variety of hybrid light-matter quasiparticles with unique properties. In metals, intraband collective response manifested by negative permittivity leads to plasmon-polaritons with extreme field confinement, wavelength "squeezing", and potentially low propagation losses. In contrast, photons in semiconductors commonly couple to interband collective response in the form of exciton-polaritons, which give rise to completely different polaritonic properties, described by a superposition of the photon and exciton and an anti-crossing of the eigenstates. In this work, we identify the existence of plasmon-like collective excitations originating from the interband excitonic response of biased bilayer and trilayer graphene, in the form of graphene-exciton-polaritons (GEPs). We find that GEPs possess electrically tunable polaritonic properties and discover that such excitations follow a universal dispersion law for all surface polaritons in 2D excitonic systems. Accounting for nonlocal corrections to the excitonic response, we find that the GEPs exhibit confinement factors that can exceed those of graphene plasmons, and with moderate losses that would enable their observation in cryo-SNOM experiments. Furthermore, by electrically tuning the excitons energy into the Reststrahlen band of the surrounding hexagonal-boron-nitride, we demonstrate that strong coupling between the excitons and hyperbolic phonon polaritons can be achieved, and well-described by an electromagnetic transmission line model. These predictions of plasmon-like interband collective excitations in biased graphene systems open up new research avenues for tunable plasmonic phenomena based on excitonic systems, and the ability to control and manipulate such phenomena at the atomic scale.

We study $\mathrm{W}^*$-categories, and explain the ways in which complete $\mathrm{W}^*$-categories behave like categorified Hilbert spaces. Every $\mathrm{W}^*$-category $C$ admits a canonical categorified inner product $\langle\,\,,\,\rangle_{\mathrm{Hilb}}\,:\,\overline C\times C\,\to\, \mathrm{Hilb}$. Moreover, if $C$ and $D$ are complete $\mathrm{W}^*$-categories there is an antilinear equivalence $$\dagger:\mathrm{Func}(C,D) \leftrightarrow \mathrm{Func}(D,C)$$ characterised by $\langle c,F^\dagger(d)\rangle_{\mathrm{Hilb}} \simeq \langle F(c),d\rangle_{\mathrm{Hilb}}$, for $c\in C$ and $d \in D$., Comment: 41 pages; 1 very long table of analogies between Hilbert spaces and complete W*-categories

Broadly intelligent agents should form task-specific abstractions that selectively expose the essential elements of a task, while abstracting away the complexity of the raw sensorimotor space. In this work, we present Neuro-Symbolic Predicates, a first-order abstraction language that combines the strengths of symbolic and neural knowledge representations. We outline an online algorithm for inventing such predicates and learning abstract world models. We compare our approach to hierarchical reinforcement learning, vision-language model planning, and symbolic predicate invention approaches, on both in- and out-of-distribution tasks across five simulated robotic domains. Results show that our approach offers better sample complexity, stronger out-of-distribution generalization, and improved interpretability., Comment: In submission

Noble gas radiation detectors with optical readout are gaining popularity in fields like astrophysics and particle physics due to their ability to produce both ionization and scintillation signals in response to ionizing radiation interaction. In addition, the amplification of primary ionization signals can be achieved by promoting secondary scintillation in the gas. Noble gas purity, especially concerning impurities like H$_{2}$O, N$_{2}$, O$_{2}$, CO$_{2}$, and hydrocarbons, greatly influences its performance. These impurities can cause the loss of primary electrons and quench the scintillation signal. A very high purity level of the gas is required. In the early 90's, a simple method was developed for noble gas purification in sealed, small volume (up to few litters) gas radiation detectors. Gas purification is achieved promoting gas circulation through Zr-based hot getters, simply maintaining the gas circulation by convection. The effectiveness of this method has been only confirmed by the energy resolutions achieved in those detectors, which were similar to that achieved in other high-performance noble gas detectors. In this work, we used waveform analysis of the primary and secondary scintillation signals and we were able to evaluate the impact of the attachment and quenching caused by impurities in one of our detectors filled with pure Xe, and estimate upper values for the impurity content in the gas. The maximum overall impurity concentration was estimated to be below 4 ppm, considering nearly all the impurities, and below 82 ppm if N$_{2}$ is considered. The electron lifetime was measured to be 2.1 $\pm$ 0.1 ms, in line with those achieved in other high-performance optical detectors.

Learning interpretable representations of visual data is an important challenge, to make machines' decisions understandable to humans and to improve generalisation outside of the training distribution. To this end, we propose a deep learning framework where one can specify nonlinear priors for videos (e.g. of Newtonian physics) that allow the model to learn interpretable latent variables and use these to generate videos of hypothetical scenarios not observed at training time. We do this by extending the Variational Auto-Encoder (VAE) prior from a simple isotropic Gaussian to an arbitrary nonlinear temporal Additive Noise Model (ANM), which can describe a large number of processes (e.g. Newtonian physics). We propose a novel linearization method that constructs a Gaussian Mixture Model (GMM) approximating the prior, and derive a numerically stable Monte Carlo estimate of the KL divergence between the posterior and prior GMMs. We validate the method on different real-world physics videos including a pendulum, a mass on a spring, a falling object and a pulsar (rotating neutron star). We specify a physical prior for each experiment and show that the correct variables are learned. Once a model is trained, we intervene on it to change different physical variables (such as oscillation amplitude or adding air drag) to generate physically correct videos of hypothetical scenarios that were not observed previously., Comment: Accepted to BMVC 2024 (Oral)

The Lie algebra of vector fields on $S^1$ integrates to the Lie group of diffeomorphisms of $S^1$. It is well known since the work of Segal and Neretin that there is no Lie group whose Lie algebra is the complexification of vector fields on $S^1$. A substitute for that non-existent group is provided by the complex semigroup whose elements are annuli: genus zero Riemann surfaces with two boundary circles parametrized by $S^1$. The group $\mathrm{Diff}(S^1)$ sits at the boundary of that semigroup, and can be thought of as annuli which are completely thin, i.e. with empty interior. In this paper, we consider an enlargement of the semigroup of annuli, denoted $\mathrm{Ann}$, where the annuli are allowed to be partially thin: their two boundary circles are allowed to touch each other along an arbitrary closed subset. We prove that every (partially thin) annulus $A\in \mathrm{Ann}$ is the time-ordered exponential of a path with values in the cone of inward pointing complexified vector fields on $S^1$, and use that fact to construct a central extension \[ 0\to \mathbb{C} \times \mathbb{Z} \to \tilde{\mathrm{Ann}} \to \mathrm{Ann} \to 0 \] that integrates the universal (Virasoro) central extension of the Lie algebra of vector fields on $S^1$. In later work, we will prove that every unitary positive energy representations of the Virasoro algebra integrates to a holomorphic representation of $\tilde{\mathrm{Ann}}$ by bounded operators on a Hilbert space., Comment: 40 pages

Group fairness in machine learning is a critical area of research focused on achieving equitable outcomes across different groups defined by sensitive attributes such as race or gender. Federated learning, a decentralized approach to training machine learning models across multiple devices or organizations without sharing raw data, amplifies the need for fairness due to the heterogeneous data distributions across clients, which can exacerbate biases. The intersection of federated learning and group fairness has attracted significant interest, with 47 research works specifically dedicated to addressing this issue. However, no dedicated survey has focused comprehensively on group fairness in federated learning. In this work, we present an in-depth survey on this topic, addressing the critical challenges and reviewing related works in the field. We create a novel taxonomy of these approaches based on key criteria such as data partitioning, location, and applied strategies. Additionally, we explore broader concerns related to this problem and investigate how different approaches handle the complexities of various sensitive groups and their intersections. Finally, we review the datasets and applications commonly used in current research. We conclude by highlighting key areas for future research, emphasizing the need for more methods to address the complexities of achieving group fairness in federated systems.

Deep learning-based medical image segmentation and surface mesh generation typically involve a sequential pipeline from image to segmentation to meshes, often requiring large training datasets while making limited use of prior geometric knowledge. This may lead to topological inconsistencies and suboptimal performance in low-data regimes. To address these challenges, we propose a data-efficient deep learning method for direct 3D anatomical object surface meshing using geometric priors. Our approach employs a multi-resolution graph neural network that operates on a prior geometric template which is deformed to fit object boundaries of interest. We show how different templates may be used for the different surface meshing targets, and introduce a novel masked autoencoder pretraining strategy for 3D spherical data. The proposed method outperforms nnUNet in a one-shot setting for segmentation of the pericardium, left ventricle (LV) cavity and the LV myocardium. Similarly, the method outperforms other lumen segmentation operating on multi-planar reformatted images. Results further indicate that mesh quality is on par with or improves upon marching cubes post-processing of voxel mask predictions, while remaining flexible in the choice of mesh triangulation prior, thus paving the way for more accurate and topologically consistent 3D medical object surface meshing., Comment: Submitted to Medical Image Analysis

Reconstructing realistic 3D human models from monocular images has significant applications in creative industries, human-computer interfaces, and healthcare. We base our work on 3D Gaussian Splatting (3DGS), a scene representation composed of a mixture of Gaussians. Predicting such mixtures for a human from a single input image is challenging, as it is a non-uniform density (with a many-to-one relationship with input pixels) with strict physical constraints. At the same time, it needs to be flexible to accommodate a variety of clothes and poses. Our key observation is that the vertices of standardized human meshes (such as SMPL) can provide an adequate density and approximate initial position for Gaussians. We can then train a transformer model to jointly predict comparatively small adjustments to these positions, as well as the other Gaussians' attributes and the SMPL parameters. We show empirically that this combination (using only multi-view supervision) can achieve fast inference of 3D human models from a single image without test-time optimization, expensive diffusion models, or 3D points supervision. We also show that it can improve 3D pose estimation by better fitting human models that account for clothes and other variations. The code is available on the project website https://abdullahamdi.com/gst/ ., Comment: preprint

Recent advancements in machine learning have fueled research on multimodal tasks, such as for instance text-to-video and text-to-audio retrieval. These tasks require models to understand the semantic content of video and audio data, including objects, and characters. The models also need to learn spatial arrangements and temporal relationships. In this work, we analyse the temporal ordering of sounds, which is an understudied problem in the context of text-to-audio retrieval. In particular, we dissect the temporal understanding capabilities of a state-of-the-art model for text-to-audio retrieval on the AudioCaps and Clotho datasets. Additionally, we introduce a synthetic text-audio dataset that provides a controlled setting for evaluating temporal capabilities of recent models. Lastly, we present a loss function that encourages text-audio models to focus on the temporal ordering of events. Code and data are available at https://www.robots.ox.ac.uk/~vgg/research/audio-retrieval/dtu/., Comment: 9 pages, 5 figures, ACM Multimedia 2024, https://www.robots.ox.ac.uk/~vgg/research/audio-retrieval/dtu/

The Atacama Large Millimeter/submillimeter Array (ALMA) offers new diagnostic capabilities for studying the Sun, providing complementary insights through high spatial and temporal resolution at millimeter wavelengths. ALMA acts as a linear thermometer for atmospheric gas, aiding in understanding the solar atmosphere's structure, dynamics, and energy balance. Given the Sun's complex emission patterns and rapid evolution, high-cadence imaging is essential for solar observations. Snapshot imaging is required, though it limits available visibility data, making full exploitation of ALMA's capabilities non-trivial. Challenges in processing solar ALMA data highlight the need for revising and enhancing the solar observing mode. The ALMA development study High-Cadence Imaging of the Sun demonstrated the potential benefits of high cadence observations through a forward modelling approach. The resulting report provides initial recommendations for improved post-processing solar ALMA data and explores increasing the observing cadence to sub-second intervals to improve image reliability., Comment: ALMA Memo 628 summarising the results of the ESO-funded ALMA development study "High-cadence Imaging of the Sun", concluded in 2023 (74 pages, 53 figures)

Haldane's seminal work established two fundamentally different types of excitation spectra for antiferromagnetic Heisenberg quantum spin chains: gapped excitations in integer-spin chains and gapless excitations in half-integer-spin chains. In finite-length half-integer spin chains, quantization, however, induces a gap in the excitation spectrum, with the upper bound given by the Lieb-Schulz-Mattis (LSM) theorem. Here, we investigate the length-dependent excitations in spin-1/2 Heisenberg chains obtained by covalently linking olympicenes--Olympic rings shaped nanographenes carrying spin-1/2--into one-dimensional chains. The large exchange interaction (J~38 mV) between olympicenes and the negligible magnetic anisotropy in these nanographenes make them an ideal platform for studying quantum spin excitations, which we directly measure using inelastic electron tunneling spectroscopy. We observe a power-law decay of the lowest excitation energy with increasing chain length L, remaining below the LSM boundary. In a long chain with L = 50, a nearly V-shaped excitation continuum is observed, reinforcing the system's gapless nature in the thermodynamic limit. Finally, we visualize the standing wave of a single spinon confined in odd-numbered chains using low-bias current maps. Our results provide compelling evidence for the realization of a one-dimensional analog of a gapless spin liquid., Comment: 5 figures. arXiv admin note: text overlap with arXiv:2402.13590

Egocentric videos present unique challenges for 3D scene understanding due to rapid camera motion, frequent object occlusions, and limited object visibility. This paper introduces a novel approach to instance segmentation and tracking in first-person video that leverages 3D awareness to overcome these obstacles. Our method integrates scene geometry, 3D object centroid tracking, and instance segmentation to create a robust framework for analyzing dynamic egocentric scenes. By incorporating spatial and temporal cues, we achieve superior performance compared to state-of-the-art 2D approaches. Extensive evaluations on the challenging EPIC Fields dataset demonstrate significant improvements across a range of tracking and segmentation consistency metrics. Specifically, our method outperforms the next best performing approach by $7$ points in Association Accuracy (AssA) and $4.5$ points in IDF1 score, while reducing the number of ID switches by $73\%$ to $80\%$ across various object categories. Leveraging our tracked instance segmentations, we showcase downstream applications in 3D object reconstruction and amodal video object segmentation in these egocentric settings., Comment: Camera-ready for ACCV 2024. More experiments added

Understanding and engineering the coupling of spins in nanomaterials is of central importance for designing novel devices. Graphene nanostructures with {\pi}-magnetism offer a chemically tunable platform to explore quantum magnetic interactions. However, realizing spin chains bearing controlled odd-even effects with suitable nanographene systems is challenging. Here, we demonstrate the successful on-surface synthesis of spin-1/2 antiferromagnetic Heisenberg chains with parity-dependent magnetization based on antiaromatic diaza-hexa-peri-hexabenzocoronene (diaza-HBC) units. Using distinct synthetic strategies, two types of spin chains with different terminals were synthesized, both exhibiting a robust odd-even effect on the spin coupling along the chain. Combined investigations using scanning tunneling microscopy, non-contact atomic force microscopy, density functional theory calculations, and quantum spin models confirmed the structures of the diaza-HBC chains and revealed their magnetic properties, which has an S = 1/2 spin per unit through electron donation from the diaza-HBC core to the Au(111) substrate. Gapped excitations were observed in even-numbered chains, while enhanced Kondo resonance emerged in odd-numbered units of odd-numbered chains due to the redistribution of the unpaired spin along the chain. Our findings provide an effective strategy to construct nanographene spin chains and unveil the odd-even effect in their magnetic properties, offering potential applications in nanoscale spintronics.

This work compares ways of extending Reinforcement Learning algorithms to Partially Observed Markov Decision Processes (POMDPs) with options. One view of options is as temporally extended action, which can be realized as a memory that allows the agent to retain historical information beyond the policy's context window. While option assignment could be handled using heuristics and hand-crafted objectives, learning temporally consistent options and associated sub-policies without explicit supervision is a challenge. Two algorithms, PPOEM and SOAP, are proposed and studied in depth to address this problem. PPOEM applies the forward-backward algorithm (for Hidden Markov Models) to optimize the expected returns for an option-augmented policy. However, this learning approach is unstable during on-policy rollouts. It is also unsuited for learning causal policies without the knowledge of future trajectories, since option assignments are optimized for offline sequences where the entire episode is available. As an alternative approach, SOAP evaluates the policy gradient for an optimal option assignment. It extends the concept of the generalized advantage estimation (GAE) to propagate option advantages through time, which is an analytical equivalent to performing temporal back-propagation of option policy gradients. This option policy is only conditional on the history of the agent, not future actions. Evaluated against competing baselines, SOAP exhibited the most robust performance, correctly discovering options for POMDP corridor environments, as well as on standard benchmarks including Atari and MuJoCo, outperforming PPOEM, as well as LSTM and Option-Critic baselines. The open-sourced code is available at https://github.com/shuishida/SoapRL.

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., Comment: Accepted to NeurIPS 2024

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)