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Aiming for more effective experiment design, such as in video content advertising where different content options compete for user engagement, these scenarios can be modeled as multi-arm bandit problems. In cases where limited interactions are available due to external factors, such as the cost of conducting experiments, recommenders often face constraints due to the small number of user interactions. In addition, there is a trade-off between selecting the best treatment and the ability to personalize and contextualize based on individual factors. A popular solution to this dilemma is the Contextual Bandit framework. It aims to maximize outcomes while incorporating personalization (contextual) factors, customizing treatments such as a user's profile to individual preferences. Despite their advantages, Contextual Bandit algorithms face challenges like measurement bias and the 'curse of dimensionality.' These issues complicate the management of numerous interventions and often lead to data sparsity through participant segmentation. To address these problems, we introduce the Weighted Allocation Probability Adjusted Thompson Sampling (WAPTS) algorithm. WAPTS builds on the contextual Thompson Sampling method by using a dynamic weighting parameter. This improves the allocation process for interventions and enables rapid optimization in data-sparse environments. We demonstrate the performance of our approach on different numbers of arms and effect sizes.

Pressure is a powerful thermodynamic parameter for tuning the magnetic properties of van der Waals magnets owing to their weak interlayer bonding. However, local magnetometry measurements under high pressure still remain elusive for this important class of emerging materials. Here we introduce a method enabling in situ magnetic imaging of van der Waals magnets under high pressure with sub-micron spatial resolution. Our approach relies on a quantum sensing platform based on boron-vacancy (V$_\text{B}^-$) centers in hexagonal boron nitride (hBN), which can be placed in atomic contact of any type of two-dimensional (2D) material within a van der Waals heterostructure. We first show that the V$_\text{B}^-$ center can be used as a magnetic field sensor up to pressures of a few GPa, a pressure range for which the properties of a wide variety of van der Waals magnets are efficiently altered. We then use V$_\text{B}^-$ centers in a thin hBN layer to perform magnetic imaging of a van der Waals magnet under pressure. As a proof of concept, we study the pressure-dependent magnetization in micrometer-sized flakes of $1T$-CrTe$_2$, whose evolution is explained by a shift of the Curie temperature. Besides providing a new path for studying pressure-induced phase transitions in van der Waals magnets, this work also opens up interesting perspectives for exploring the physics of 2D superconductors under pressure via local measurements of the Meissner effect., Comment: 10 pages, 5 figures

Physarum polycephalum, a biological transport network, has emerged as a model for the study of self-organisation and coordination at large spatial scales. This self-organisation is largely due to cytoplasmic fluid flows driven by propagating contractile waves that are delocalised over the entire organism. Furthermore, complex modulation of these contractile waves on long time scales has also been observed, but is scarcely studied. In this study, we comprehensively characterise this long-time scale modulation by confining the organism within ring geometries.Our findings reveal a clear correlation between contractile wave direction, amplitude modulation, and the moving mean vein diameter. We observe simultaneous spatio-temporal travelling and standing wave patterns that bear a resemblance to the ring oscillating fundamental modes. Furthermore, we ascertain that the time periods of these modes scale with the system size and correspond to multiples of the statistically observed intrinsic modulation time scale. This suggests the presence of a common mechanism. These findings serve to reinforce the hypothesis that the flows patterns modulation is the result of the transport of a chemical agent responsible for modifying the local rigidity of the membrane, which would act as a signaling mechanism for the organism.

Current video diffusion models achieve impressive generation quality but struggle in interactive applications due to bidirectional attention dependencies. The generation of a single frame requires the model to process the entire sequence, including the future. We address this limitation by adapting a pretrained bidirectional diffusion transformer to an autoregressive transformer that generates frames on-the-fly. To further reduce latency, we extend distribution matching distillation (DMD) to videos, distilling 50-step diffusion model into a 4-step generator. To enable stable and high-quality distillation, we introduce a student initialization scheme based on teacher's ODE trajectories, as well as an asymmetric distillation strategy that supervises a causal student model with a bidirectional teacher. This approach effectively mitigates error accumulation in autoregressive generation, allowing long-duration video synthesis despite training on short clips. Our model achieves a total score of 84.27 on the VBench-Long benchmark, surpassing all previous video generation models. It enables fast streaming generation of high-quality videos at 9.4 FPS on a single GPU thanks to KV caching. Our approach also enables streaming video-to-video translation, image-to-video, and dynamic prompting in a zero-shot manner. We will release the code based on an open-source model in the future., Comment: Project Page: https://causvid.github.io/

This document is comprised of a collection of updated preliminary parameters for the key parts of the muon collider. The updated preliminary parameters follow on from the October 2023 Tentative Parameters Report. Particular attention has been given to regions of the facility that are believed to hold greater technical uncertainty in their design and that have a strong impact on the cost and power consumption of the facility. The data is collected from a collaborative spreadsheet and transferred to overleaf.

We report the observation of spin-dependent force induced by Nitrogen Vacancy (NV) centers embedded in a diamond crystal attached to a tethered oscillator. This result was obtained using a spin-dependent torque generated by a micro-diamond containing billions of NV centers, placed at the end of a commercially available silicon cantilever. We demonstrate that the fundamental flexural mode of the cantilever is excited by the spins of the NV centers. These spins are uniformly coupled to an externally applied homogeneous magnetic field, which enables the NV centers to exert a measurable spin-dependent torque on the cantilever, driving its center of mass motion. Our experiment highlights the intricate interplay between the spin properties of NV centers and the mechanical behavior of the oscillator in the presence of a pure torque, providing a set of tools for exploring quantum effects in state of the art mechanical systems.

Characterization of the participant-zone (PZ) in the $^{129,124}$Xe + $^{112,124}$Sn reaction at the energy range 65-150 MeV/nucleon reveals copious cluster production. A detailed study of the chemical composition as a function of the impact parameter shows that heavier clusters ($^6$He, $^{6-8}$Li, $^{7-10}$Be...) are most likely produced for central collisions. A hierarchy of the cluster production with the neutron-richness of the total system is observed, suggesting a full mixing of the projectile and target in the PZ. An estimate of the maximum density in central collisions has been deduced from the kinetic energy of the emitted fragments, reaching almost 2 time the normal density (2$\rho_0$) at 150 MeV/nucleon., Comment: 5 pages, 3 figures, nn2024 conference

Adversarial training is arguably the most popular way to provide empirical robustness against specific adversarial examples. While variants based on multi-step attacks incur significant computational overhead, single-step variants are vulnerable to a failure mode known as catastrophic overfitting, which hinders their practical utility for large perturbations. A parallel line of work, certified training, has focused on producing networks amenable to formal guarantees of robustness against any possible attack. However, the wide gap between the best-performing empirical and certified defenses has severely limited the applicability of the latter. Inspired by recent developments in certified training, which rely on a combination of adversarial attacks with network over-approximations, and by the connections between local linearity and catastrophic overfitting, we present experimental evidence on the practical utility and limitations of using certified training towards empirical robustness. We show that, when tuned for the purpose, a recent certified training algorithm can prevent catastrophic overfitting on single-step attacks, and that it can bridge the gap to multi-step baselines under appropriate experimental settings. Finally, we present a novel regularizer for network over-approximations that can achieve similar effects while markedly reducing runtime.

Generating realistic and diverse layouts of furnished indoor 3D scenes unlocks multiple interactive applications impacting a wide range of industries. The inherent complexity of object interactions, the limited amount of available data and the requirement to fulfill spatial constraints all make generative modeling for 3D scene synthesis and arrangement challenging. Current methods address these challenges autoregressively or by using off-the-shelf diffusion objectives by simultaneously predicting all attributes without 3D reasoning considerations. In this paper, we introduce DeBaRA, a score-based model specifically tailored for precise, controllable and flexible arrangement generation in a bounded environment. We argue that the most critical component of a scene synthesis system is to accurately establish the size and position of various objects within a restricted area. Based on this insight, we propose a lightweight conditional score-based model designed with 3D spatial awareness at its core. We demonstrate that by focusing on spatial attributes of objects, a single trained DeBaRA model can be leveraged at test time to perform several downstream applications such as scene synthesis, completion and re-arrangement. Further, we introduce a novel Self Score Evaluation procedure so it can be optimally employed alongside external LLM models. We evaluate our approach through extensive experiments and demonstrate significant improvement upon state-of-the-art approaches in a range of scenarios., Comment: NeurIPS 2024

We point out a very simple method for calculating the mixed Coulomb-nuclear corrections to the $pp$ and $\bar pp$ scattering amplitudes that has been missed in the extensive past work on this problem. The method expresses the correction in terms of a rapidly convergent integral involving the inverse Fourier-Bessel transform of the nuclear amplitude and a known factor containing the Coulomb phase shift with form-factor corrections. The transform can be calculated analytically for the exponential-type model nuclear amplitudes commonly used in fits to the high-energy data at small momentum transfers, and gives very accurate results for the corrections. We examine the possible effects of the Martin zero in the real part of the nuclear amplitude, and the accuracy of the Bethe-West-Yennie phase approximation for the Coulomb-nuclear corrections. We then apply the method to a redetermination of the ratio $\rho$ of the real to the imaginary parts of the forward scattering amplitude in fits to high-energy ISR data previously analyzed using an approximate version of the correction. The only significant changes relative the accuracy of those fits are at 52.8 GeV. Our method is applicable more generally, and can be used also at lower energies and for proton-nucleus scattering., Comment: 14 Pages, 6 figures. arXiv admin note: substantial text overlap with arXiv:2401.06746

We investigate the ability to discover data assimilation (DA) schemes meant for chaotic dynamics with deep learning. The focus is on learning the analysis step of sequential DA, from state trajectories and their observations, using a simple residual convolutional neural network, while assuming the dynamics to be known. Experiments are performed with the Lorenz 96 dynamics, which display spatiotemporal chaos and for which solid benchmarks for DA performance exist. The accuracy of the states obtained from the learned analysis approaches that of the best possibly tuned ensemble Kalman filter, and is far better than that of variational DA alternatives. Critically, this can be achieved while propagating even just a single state in the forecast step. We investigate the reason for achieving ensemble filtering accuracy without an ensemble. We diagnose that the analysis scheme actually identifies key dynamical perturbations, mildly aligned with the unstable subspace, from the forecast state alone, without any ensemble-based covariances representation. This reveals that the analysis scheme has learned some multiplicative ergodic theorem associated to the DA process seen as a non-autonomous random dynamical system.

Nuclear Magnetic Resonance (NMR) spans diverse fields from biology to quantum science. Employing NMR on a floating object could unveil novel possibilities beyond conventional operational paradigms. Here, we observe Nuclear Magnetic Resonance (NMR) within a levitating micro-diamond using the nuclear spins of nitrogen-14 atoms. By tightly confining the angular degrees of freedom of the diamond in a Paul trap, we achieve efficient hyperfine interaction between optically polarized electronic spins of nitrogen-vacancy centers and the $^{14}$N nuclear spin, enabling nuclear spin polarization and quantum state read-out revealing coherence times up to hundreds of microseconds. This represents the longest recorded spin coherence time in a levitated system, surpassing previous records by three orders of magnitude. Our results offer promise for various applications, including cooling macroscopic particles to their motional ground state and exploring geometric phases for gyroscopy.

We study the fundamental properties of pointwise semi-Lipschitz functions between asymmetric spaces, which are the natural asymmetric counterpart of pointwise Lipschitz functions. We also study the influence that partial symmetries of a given space may have on the behavior of pointwise semi-Lipschitz functions defined on it. Furthermore, we are interested in characterizing the pointwise semi-Lipschitz structure of an asymmetric space in terms of real-valued pointwise semi-Lipschitz functions defined on it. By using two algebras of functions naturally associated to our spaces of pointwise real-valued semi-Lipschitz functions, we are able to provide two Banach-Stone type results in this context. In fact, these results are obtained as consequences of a general Banach-Stone type theorem of topological nature, stated for abstract functional spaces, which is quite flexible and can be applied to many spaces of continuous functions over metric and asymmetric spaces., Comment: No additional comments

Formulating a real-world problem under the Reinforcement Learning framework involves non-trivial design choices, such as selecting a discount factor for the learning objective (discounted cumulative rewards), which articulates the planning horizon of the agent. This work investigates the impact of the discount factor on the biasvariance trade-off given structural parameters of the underlying Markov Decision Process. Our results support the idea that a shorter planning horizon might be beneficial, especially under partial observability., Comment: Presented at deployable RL (RLC conference 2024)

In this letter we report the first scientific result based on antineutrinos emitted from the BR2 reactor at SCK CEN. The SoLid experiment uses a novel type of highly granular detector whose basic detection unit combines two scintillators, PVT and 6LiF:ZnS(Ag), to measure antineutrinos via their inverse-beta-decay products. An advantage of PVT is its highly linear response as a function of deposited particle energy. The full-scale detector comprises 12800 voxels and operates over a very short 6.3--8.9 m baseline from the reactor core. The detector segmentation and its 3D imaging capabilities facilitate the extraction of the positron energy from the rest of the visible energy, allowing the latter to be utilised for signal-background discrimination. We present a result based on 280 reactor-on days (55 MW mean power) and 172 reactor-off days, respectively, of live data-taking. A total of 29479 $\pm$ 603 (stat.) antineutrino candidates have been selected, corresponding to an average rate of 105 events per day and a signal-to-background ratio of 0.27. A search for disappearance of antineutrinos to a sterile state has been conducted using complementary model-dependent frequentist and Bayesian fits, providing constraints on the allowed region of the Reactor Antineutrino Anomaly., Comment: 8 pages, 6 figures

The International Muon Collider Collaboration (IMCC) [1] was established in 2020 following the recommendations of the European Strategy for Particle Physics (ESPP) and the implementation of the European Strategy for Particle Physics-Accelerator R&D Roadmap by the Laboratory Directors Group [2], hereinafter referred to as the the European LDG roadmap. The Muon Collider Study (MuC) covers the accelerator complex, detectors and physics for a future muon collider. In 2023, European Commission support was obtained for a design study of a muon collider (MuCol) [3]. This project started on 1st March 2023, with work-packages aligned with the overall muon collider studies. In preparation of and during the 2021-22 U.S. Snowmass process, the muon collider project parameters, technical studies and physics performance studies were performed and presented in great detail. Recently, the P5 panel [4] in the U.S. recommended a muon collider R&D, proposed to join the IMCC and envisages that the U.S. should prepare to host a muon collider, calling this their "muon shot". In the past, the U.S. Muon Accelerator Programme (MAP) [5] has been instrumental in studies of concepts and technologies for a muon collider., Comment: This document summarises the International Muon Collider Collaboration (IMCC) progress and status of the Muon Collider R&D programme

We make the first steps towards diffusion models for unconditional generation of multivariate and Arctic-wide sea-ice states. While targeting to reduce the computational costs by diffusion in latent space, latent diffusion models also offer the possibility to integrate physical knowledge into the generation process. We tailor latent diffusion models to sea-ice physics with a censored Gaussian distribution in data space to generate data that follows the physical bounds of the modelled variables. Our latent diffusion models reach similar scores as the diffusion model trained in data space, but they smooth the generated fields as caused by the latent mapping. While enforcing physical bounds cannot reduce the smoothing, it improves the representation of the marginal ice zone. Therefore, for large-scale Earth system modelling, latent diffusion models can have many advantages compared to diffusion in data space if the significant barrier of smoothing can be resolved., Comment: 21 pages, 5 Figures, Camera-ready version for the ICML 2024 Machine Learning for Earth System Modeling workshop

This article presents two novel algorithms for generating random increasing trees. The first algorithm efficiently generates strictly increasing binary trees using an ad hoc method. The second algorithm improves the recursive method for weighted strictly increasing unary-binary increasing trees, optimizing randomness usage., Comment: In Proceedings GASCom 2024, arXiv:2406.14588

We report the first detection of coherent elastic neutrino-nucleus scattering (CEvNS) on germanium, measured at the Spallation Neutron Source at Oak Ridge National Laboratory. The Ge-Mini detector of the COHERENT collaboration employs large-mass, low-noise, high-purity germanium spectrometers, enabling excellent energy resolution, and an analysis threshold of 1.5 keV electron-equivalent ionization energy. We observe a on-beam excess of 20.6$_{+7.1}^{-6.3}$ counts with a total exposure of 10.22 GWhkg and we reject the no-CEvNS hypothesis with 3.9 sigma significance. The result agrees with the predicted standard model of particle physics signal rate within 2 sigma., Comment: 7 pages, 5 figures

When a solid metal is struck, its free surface can eject fast and fine particles. Despite the many diagnostics that have been implemented to measure the mass, size, velocity or temperature of ejecta, these efforts provide only a partial picture of this phenomenon. Ejecta characterization, especially in constrained geometries, is an inherently ill-posed problem. In this context, Photon Doppler Velocimetry (PDV) has been a valuable diagnostic, measuring reliably particles and free surface velocities in the single scattering regime. Here we present ejecta experiments in gas and how, in this context, PDV allows one to retrieve additional information on the ejecta, i.e. information on the particles' size. We explain what governs ejecta transport in gas and how it can be simulated. To account for the multiple scattering of light in these ejecta, we use the Radiative Transfer Equation (RTE) that quantitatively describes PDV spectrograms, and their dependence on the velocity but also on the size distribution of the ejecta. We remind how spectrograms can be simulated by solving numerically this RTE and we show how to do so on hydrodynamic ejecta simulation results. Finally, we use this complex machinery in different ejecta transport scenarios to simulate the corresponding spectrograms. Comparing these to experimental results, we iteratively constrain the ejecta description at an unprecedented level. This work demonstrates our ability to recover particle size information from what is initially a velocity diagnostic, but more importantly it shows how, using existing simulation of ejecta, we capture through simulation the complexity of experimental spectrograms.

Prethermalization occurs as an important phase in the dynamics of many-body systems when strong coupling drives a quasi-equilibrium in a subspace separated from the thermodynamic equilibrium by the restriction of a gap in energy or other conserved quantity. Here, we report the signature of an enduring prethermal regime of arrested relaxation in the molecular ultracold plasma that forms following the avalanche of a state-selected Rydberg gas of nitric oxide. Electron collisions mix orbital angular momentum, scattering Rydberg molecules to states of very high-$\ell$. Spontaneous predissociation purifies this non-penetrating character, creating an extraordinary gap between the plasma states of $n \approx \ell$, with measured $n>200$ and penetrating states of $\ell = 0, ~1$ and 2. Evolution to a statistically equilibrated state of N and O atoms cannot occur without Rydberg electron penetration, and this gap blocks relaxation for a millisecond or more. Evolving through the critical phase, electrons that balance the NO$^+$ charge behave as though localized in the prethermal phase and play an ineffective role in bridging this gap. However, the application of a weak radiofrequency (RF) field promotes a dramatic degree of relaxation owing to electron collisions. On an entirely different scale, exciting a quantum-state transition in an exceedingly small fraction of the molecules in the prethermalized ensemble acts with even greater effect to drive the entire system toward equilibrium. We ascribe this to dissipative character added to a small fraction of the states in the prethermally localized ensemble. Using the Lindblad master equation, we illustrate qualitatively similar dynamics for a toy model of an open quantum system that consists of a localized set of spins on which dissipation acts locally at a single site.

Recent approaches have shown promises distilling diffusion models into efficient one-step generators. Among them, Distribution Matching Distillation (DMD) produces one-step generators that match their teacher in distribution, without enforcing a one-to-one correspondence with the sampling trajectories of their teachers. However, to ensure stable training, DMD requires an additional regression loss computed using a large set of noise-image pairs generated by the teacher with many steps of a deterministic sampler. This is costly for large-scale text-to-image synthesis and limits the student's quality, tying it too closely to the teacher's original sampling paths. We introduce DMD2, a set of techniques that lift this limitation and improve DMD training. First, we eliminate the regression loss and the need for expensive dataset construction. We show that the resulting instability is due to the fake critic not estimating the distribution of generated samples accurately and propose a two time-scale update rule as a remedy. Second, we integrate a GAN loss into the distillation procedure, discriminating between generated samples and real images. This lets us train the student model on real data, mitigating the imperfect real score estimation from the teacher model, and enhancing quality. Lastly, we modify the training procedure to enable multi-step sampling. We identify and address the training-inference input mismatch problem in this setting, by simulating inference-time generator samples during training time. Taken together, our improvements set new benchmarks in one-step image generation, with FID scores of 1.28 on ImageNet-64x64 and 8.35 on zero-shot COCO 2014, surpassing the original teacher despite a 500X reduction in inference cost. Further, we show our approach can generate megapixel images by distilling SDXL, demonstrating exceptional visual quality among few-step methods., Comment: Code, model, and dataset are available at https://tianweiy.github.io/dmd2

Topological insulators (TI) are promising materials for future spintronics applications and their epitaxial integration would allow the realization of new hybrid interfaces. As the first materials studied, Bismuth Antimony alloys (Bi1-xSbx) show great potential due to their tuneable electronic band structure and efficient charge-to-spin conversion. Here, we report the growth of Bi1-xSbx thin films on GaAs (111)A substrates following two different protocols. For the conventional epitaxy process, the grown films show excellent crystallinity and twin domains corresponding to an in-plane 180{\textdegree} rotation of the crystalline structure. Domain walls are found to be composition-dependent and have a lower density for Antimony-rich films. For the optimized process, depositing an Antimony bilayer prior to BiSb growth allows achieving single crystallinity of the TI films. The topologically protected surface states are evidenced by ex-situ ARPES measurements for domains-free and conventional films. To the best of our knowledge, this work presents the first large-scale epitaxial integration of single crystalline Bi1-xSbx thin films on industrial substrates., Comment: ACS Applied Electronic Materials, 2024

We focus on the online-based active learning (OAL) setting where an agent operates over a stream of observations and trades-off between the costly acquisition of information (labelled observations) and the cost of prediction errors. We propose a novel foundation for OAL tasks based on partial monitoring, a theoretical framework specialized in online learning from partially informative actions. We show that previously studied binary and multi-class OAL tasks are instances of partial monitoring. We expand the real-world potential of OAL by introducing a new class of cost-sensitive OAL tasks. We propose NeuralCBP, the first PM strategy that accounts for predictive uncertainty with deep neural networks. Our extensive empirical evaluation on open source datasets shows that NeuralCBP has favorable performance against state-of-the-art baselines on multiple binary, multi-class and cost-sensitive OAL tasks.

This paper addresses the anytime sorting problem, aiming to develop algorithms providing tentative estimates of the sorted list at each execution step. Comparisons are treated as steps, and the Spearman's footrule metric evaluates estimation accuracy. We propose a general approach for making any sorting algorithm anytime and introduce two new algorithms: multizip sort and Corsort. Simulations showcase the superior performance of both algorithms compared to existing methods. Multizip sort keeps a low global complexity, while Corsort produces intermediate estimates surpassing previous algorithms., Comment: 33rd International Joint Conference on Artificial Intelligence (IJCAI 2024), Aug 2024, Jeju City, Jeju Island, South Korea