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Although diffusion models can generate remarkably high-quality samples, they are intrinsically bottlenecked by their expensive iterative sampling procedure. Consistency models (CMs) have recently emerged as a promising diffusion model distillation method, reducing the cost of sampling by generating high-fidelity samples in just a few iterations. Consistency model distillation aims to solve the probability flow ordinary differential equation (ODE) defined by an existing diffusion model. CMs are not directly trained to minimize error against an ODE solver, rather they use a more computationally tractable objective. As a way to study how effectively CMs solve the probability flow ODE, and the effect that any induced error has on the quality of generated samples, we introduce Direct CMs, which \textit{directly} minimize this error. Intriguingly, we find that Direct CMs reduce the ODE solving error compared to CMs but also result in significantly worse sample quality, calling into question why exactly CMs work well in the first place. Full code is available at: https://github.com/layer6ai-labs/direct-cms., Comment: NeurIPS 2024 ATTRIB Workshop

We present a comprehensive set of harmonic resistance measurements of the dampinglike (DL) and fieldlike (FL) torques in Pt/CoFeB, Pt/Co, W/CoFeB, W/Co, and YIG/Pt bilayers complemented by measurements of the DL torque using the magneto-optical Kerr effect and calibrated by nitrogen vacancy magnetometry on the same devices. The magnon creation-annihilation magnetoresistances depend strongly on temperature and on the magnetic and transport properties of each bilayer, affecting the estimate of both the DL and FL torque. The DL torque, the most important parameter for applications, is overestimated by a factor of 2 in W/CoFeB and by one order of magnitude in YIG/Pt when not accounting for the magnonic contribution to the planar Hall resistance. We further show that the magnonic contribution can be quantified by combining measurements of the nonlinear longitudinal and transverse magnetoresistances, thus providing a reliable method to measure the spin-orbit torques in different material systems.

Charge-spin conversion phenomena such as the spin Hall effect allow for the excitation of magnons in a magnetic layer by passing an electric current in an adjacent nonmagnetic conductor. We demonstrate that this current-induced modification of the magnon density generates an additional nonlinear longitudinal and transverse magnetoresistance for every magnetoresistance that depends on the magnetization. Using harmonic measurements, we evidence that these magnon creation-annihilation magnetoresistances dominate the second harmonic longitudinal and transverse resistance of thin Y$_{3}$Fe$_{5}$O$_{12}$/Pt bilayers. Our results apply to both insulating and metallic magnetic layers, elucidating the dependence of the magnetoresistance on applied current and magnetic field for a broad variety of systems excited by spin currents.

A statistical framework we call CQUESST (Carbon Quantification and Uncertainty from Evolutionary Soil STochastics), which models carbon sequestration and cycling in soils, is applied to a long-running agricultural experiment that controls for crop type, tillage, and season. The experiment, known as the Millenium Tillage Trial (MTT), ran on 42 field-plots for ten years from 2000-2010; here CQUESST is used to model soil carbon dynamically in six pools, in each of the 42 agricultural plots, and on a monthly time step for a decade. We show how CQUESST can be used to estimate soil-carbon cycling rates under different treatments. Our methods provide much-needed statistical tools for quantitatively inferring the effectiveness of different experimental treatments on soil-carbon sequestration. The decade-long data are of multiple observation types, and these interacting time series are ingested into a fully Bayesian model that has a dynamic stochastic model of multiple pools of soil carbon at its core. CQUESST's stochastic model is motivated by the deterministic RothC soil-carbon model based on nonlinear difference equations. We demonstrate how CQUESST can estimate soil-carbon fluxes for different experimental treatments while acknowledging uncertainties in soil-carbon dynamics, in physical parameters, and in observations. CQUESST is implemented efficiently in the probabilistic programming language Stan using its MapReduce parallelization, and it scales well for large numbers of field-plots, using software libraries that allow for computation to be shared over multiple nodes of high-performance computing clusters.

We propose in this work a framework for synergistic positron emission tomography (PET)/computed tomography (CT) reconstruction using a joint generative model as a penalty. We use a synergistic penalty function that promotes PET/CT pairs that are likely to occur together. The synergistic penalty function is based on a generative model, namely $\beta$-variational autoencoder ($\beta$-VAE). The model generates a PET/CT image pair from the same latent variable which contains the information that is shared between the two modalities. This sharing of inter-modal information can help reduce noise during reconstruction. Our result shows that our method was able to utilize the information between two modalities. The proposed method was able to outperform individually reconstructed images of PET (i.e., by maximum likelihood expectation maximization (MLEM)) and CT (i.e., by weighted least squares (WLS)) in terms of peak signal-to-noise ratio (PSNR). Future work will focus on optimizing the parameters of the $\beta$-VAE network and further exploration of other generative network models., Comment: 17th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine (Fully3D Conference) arXiv:2310.16846

This study addresses the challenge of low dexterity in teleoperation tasks caused by limited sensory feedback and visual occlusion. We propose a novel approach that integrates haptic feedback into teleoperation using the adaptive triggers of a commercially available DualSense controller. By adjusting button stiffness based on the proximity of objects to the robot's end effector, the system provides intuitive, real-time feedback to the operator. To achieve this, the effective volume of the end effector is virtually expanded, allowing the system to predict interactions by calculating overlap with nearby objects. This predictive capability is independent of the user's intent or the robot's speed, enhancing the operator's situational awareness without requiring complex pre-programmed behaviors. The stiffness of the adaptive triggers is adjusted in proportion to this overlapping volume, effectively conveying spatial proximity and movement cues through an "one degree of freedom" haptic feedback mechanism. Compared to existing solutions, this method reduces hardware requirements and computational complexity by using a geometric simplification approach, enabling efficient operation with minimal processing demands. Simulation results demonstrate that the proposed system reduces collision risk and improves user performance, offering an intuitive, precise, and safe teleoperation experience despite real-world uncertainties and communication delays., Comment: Part of proceedings of 6th International Conference AsiaHaptics 2024

There is a growing demand for teleoperated robots. This paper presents a novel method for reducing vibration noise generated by robot's own motion, which can disrupt the quality of tactile feedback for teleoperated robots. Our approach focuses on perceived intensity, the amount of how humans experience vibration, to create a noise filter that aligns with human perceptual characteristics. This system effectively subtracts ego-noise while preserving the essential tactile signals, ensuring more accurate and reliable haptic feedback for operators. This method offers a refined solution to the challenge of maintaining high-quality tactile feedback in teleoperated systems., Comment: Part of proceedings of 6th International Conference AsiaHaptics 2024

The neutron star low-mass X-ray binary, EXO 0748--676, recently returned to outburst after a $\sim$ 16 year-long quiescence. Since its return, there has been a global effort to capture the previously unseen rise of the source and to understand its somewhat early return to outburst, as it is typical for a source to spend longer in quiescence than in outburst. Here, we report on the simultaneous optical and X-ray detection of a type I X-ray burst, captured by XMM-Newton during a DDT observation on 30th June 2024. The data show 3 X-ray eclipses consistent with the known ephemeris and one type I X-ray burst at 60492.309 MJD. The X-ray burst is reprocessed into the optical band and captured by XMM-Newton's Optical Monitor during a 4399 s exposure with the B filter in image + fast mode. We determine that the optical peak lags the X-ray peak by 4.46 $\pm$ 1.71s. The optical and X-ray rise times are similar, but the optical decay timescale is shorter than the X-ray decay timescale. The reprocessing site is likely within a few light seconds of the X-ray emitting region, so the companion star, accretion disc and ablated material are all plausible., Comment: 6 Pages, 3 Figures, Accepted for Publication in MNRAS Letters

Classical Wolf-Rayet stars are descendants of massive OB-type stars that have lost their hydrogen-rich envelopes, and are in the final stages of stellar evolution, possibly exploding as type Ib/c supernovae. It is understood that the mechanisms driving this mass-loss are either strong stellar winds and or binary interactions, so intense studies of these binaries including their evolution can tell us about the importance of the two pathways in WR formation. WR 138 (HD 193077) has a period of just over 4 years and was previously reported to be resolved through interferometry. We report on new interferometric data combined with spectroscopic radial velocities in order to provide a three-dimensional orbit of the system. The precision on our parameters tend to be about an order of magnitude better than previous spectroscopic techniques. These measurements provide masses of the stars, namely $M_{\rm WR} = 13.93\pm1.49M_{\odot}$ and $M_{\rm O} = 26.28\pm1.71M_{\odot}$. The derived orbital parallax agrees with the parallax from \textit{Gaia}, namely with a distance of 2.13 kpc. We compare the system's orbit to models from BPASS, showing that the system likely may have been formed with little interaction but could have formed through some binary interactions either following or at the start of a red supergiant phase, but with the most likely scenario occurring as the red supergiant phase starts for a $\sim 40M_\odot$ star., Comment: accepted to ApJ

Rapidly-exploring Random Tree (RRT) algorithms have been applied successfully to challenging robot motion planning and under-actuated nonlinear control problems. However a fundamental limitation of the RRT approach is the slow convergence in configuration spaces with narrow channels because of the small probability of generating test points inside narrow channels. This paper presents an improved RRT algorithm that takes advantage of narrow channels between the initial and goal states to find shorter paths by improving the exploration of narrow regions in the configuration space. The proposed algorithm detects the presence of narrow channel by checking for collision of neighborhood points with the infeasible set and attempts to add points within narrow channels with a predetermined bias. This approach is compared with the classical RRT and its variants on a variety of benchmark planning problems. Simulation results indicate that the algorithm presented in this paper computes a significantly shorter path in spaces with narrow channels.

Solving parametric partial differential equations (PDEs) presents significant challenges for data-driven methods due to the sensitivity of spatio-temporal dynamics to variations in PDE parameters. Machine learning approaches often struggle to capture this variability. To address this, data-driven approaches learn parametric PDEs by sampling a very large variety of trajectories with varying PDE parameters. We first show that incorporating conditioning mechanisms for learning parametric PDEs is essential and that among them, $\textit{adaptive conditioning}$, allows stronger generalization. As existing adaptive conditioning methods do not scale well with respect to the number of parameters to adapt in the neural solver, we propose GEPS, a simple adaptation mechanism to boost GEneralization in Pde Solvers via a first-order optimization and low-rank rapid adaptation of a small set of context parameters. We demonstrate the versatility of our approach for both fully data-driven and for physics-aware neural solvers. Validation performed on a whole range of spatio-temporal forecasting problems demonstrates excellent performance for generalizing to unseen conditions including initial conditions, PDE coefficients, forcing terms and solution domain. $\textit{Project page}$: https://geps-project.github.io

Pion-kaon ($\pi K$) final states, often appearing in heavy-particle decays at the precision frontier, are important for Standard-Model tests, to describe crossed channels with exotic states, and for spectroscopy of excited kaon resonances. We construct a representation of the $\pi K$ $S$-wave form factor using the elastic $\pi K$ scattering phase shift via dispersion relations in the elastic region and extend this model into the inelastic region using resonance exchange, while maintaining unitarity and the correct analytic structure. As a first application, we successfully described the $\tau \to K_S \pi \nu_\tau$ spectrum to not only achieve a better distinction between $S$- and $P$-wave contributions, but also to provide an improved estimate of the $CP$ asymmetry produced by a tensor operator as well as the forward-backward asymmetry, both of which can be confronted with future data at Belle II., Comment: 10 pages, 4 figures, Talk given at the 16th International Workshop on Tau Lepton Physics (Tau2021), to be published in SciPost Physics Proceedings, see https://scipost.org/submissions/scipost_202111_00029v1/

By using the squared slack variables technique, we show that a general polynomial complementarity problem can be formulated as a system of polynomial equations. Thus, the solution set of such a problem is the image of a real algebraic set under a certain projection. This paper points out that, generically, this polynomial system has finitely many complex zeros. In such a case, we use techniques from symbolic computation to compute a univariate representation of the solution set. Consequently, univariate representations of special solutions, such as least-norm and sparse solutions, are obtained. After that, enumerating solutions boils down to solving problems governed by univariate polynomials. We also provide some experiments on small-scale problems with worst-case scenarios. At the end of the paper, we propose a method for computing approximate solutions to copositive polynomial complementarity problems that may have infinitely many solutions.

Modern day audio signal classification techniques lack the ability to classify low feature audio signals in the form of spectrographic temporal frequency data representations. Additionally, currently utilized techniques rely on full diverse data sets that are often not representative of real-world distributions. This paper derives several first-of-its-kind machine learning methodologies to analyze these low feature audio spectrograms given data distributions that may have normalized, skewed, or even limited training sets. In particular, this paper proposes several novel customized convolutional architectures to extract identifying features using binary, one-class, and siamese approaches to identify the spectrographic signature of a given audio signal. Utilizing these novel convolutional architectures as well as the proposed classification methods, these experiments demonstrate state-of-the-art classification accuracy and improved efficiency than traditional audio classification methods.

Sonar based audio classification techniques are a growing area of research in the field of underwater acoustics. Usually, underwater noise picked up by passive sonar transducers contains all types of signals that travel through the ocean and is transformed into spectrographic images. As a result, the corresponding spectrograms intended to display the temporal-frequency data of a certain object often include the tonal regions of abundant extraneous noise that can effectively interfere with a 'contact'. So, a majority of spectrographic samples extracted from underwater audio signals are rendered unusable due to their clutter and lack the required indistinguishability between different objects. With limited clean true data for supervised training, creating classification models for these audio signals is severely bottlenecked. This paper derives several new techniques to combat this problem by developing a novel Score-CAM based denoiser to extract an object's signature from noisy spectrographic data without being given any ground truth data. In particular, this paper proposes a novel generative adversarial network architecture for learning and producing spectrographic training data in similar distributions to low-feature spectrogram inputs. In addition, this paper also a generalizable class activation mapping based denoiser for different distributions of acoustic data, even real-world data distributions. Utilizing these novel architectures and proposed denoising techniques, these experiments demonstrate state-of-the-art noise reduction accuracy and improved classification accuracy than current audio classification standards. As such, this approach has applications not only to audio data but for countless data distributions used all around the world for machine learning.

Large language models have drastically changed the prospects of AI by introducing technologies for more complex natural language processing. However, current methodologies to train such LLMs require extensive resources including but not limited to large amounts of data, expensive machinery, and lengthy training. To solve this problem, this paper proposes a new tokenization method inspired by universal Lempel-Ziv-Welch data compression that compresses repetitive phrases into multi-word tokens. With MultiTok as a new tokenizing tool, we show that language models are able to be trained notably more efficiently while offering a similar accuracy on more succinct and compressed training data. In fact, our results demonstrate that MultiTok achieves a comparable performance to the BERT standard as a tokenizer while also providing close to 2.5x faster training with more than 30% less training data.

GPT-4o is an autoregressive omni model that accepts as input any combination of text, audio, image, and video, and generates any combination of text, audio, and image outputs. It's trained end-to-end across text, vision, and audio, meaning all inputs and outputs are processed by the same neural network. GPT-4o can respond to audio inputs in as little as 232 milliseconds, with an average of 320 milliseconds, which is similar to human response time in conversation. It matches GPT-4 Turbo performance on text in English and code, with significant improvement on text in non-English languages, while also being much faster and 50\% cheaper in the API. GPT-4o is especially better at vision and audio understanding compared to existing models. In line with our commitment to building AI safely and consistent with our voluntary commitments to the White House, we are sharing the GPT-4o System Card, which includes our Preparedness Framework evaluations. In this System Card, we provide a detailed look at GPT-4o's capabilities, limitations, and safety evaluations across multiple categories, focusing on speech-to-speech while also evaluating text and image capabilities, and measures we've implemented to ensure the model is safe and aligned. We also include third-party assessments on dangerous capabilities, as well as discussion of potential societal impacts of GPT-4o's text and vision capabilities.

Entanglement is an essential resource for various quantum-information tasks. When a target system shares entanglement with another memory system and is stored reliably, one can use entanglement at a later time -- this is quantum memory. In practice, entanglement can be exceedingly fragile during a system's evolution. In particular, no entanglement can survive when a so-called entanglement-breaking channel acts on the target system. Are entanglement-breaking channels really useless for maintaining entanglement? As a single channel, this is certainly the case; it cannot be useful for quantum memory. However, in this work, we show that putting together two entanglement-breaking channels in a broadcasting scenario can activate their ability to maintain entanglement -- the channel's quantum memory resource can be super-activated., Comment: 4+3 pages, 2 figures

Text-to-SQL generation enables non-experts to interact with databases via natural language. Recent advances rely on large closed-source models like GPT-4 that present challenges in accessibility, privacy, and latency. To address these issues, we focus on developing small, efficient, and open-source text-to-SQL models. We demonstrate the benefits of sampling multiple candidate SQL generations and propose our method, MSc-SQL, to critique them using associated metadata. Our sample critiquing model evaluates multiple outputs simultaneously, achieving state-of-the-art performance compared to other open-source models while remaining competitive with larger models at a much lower cost. Full code can be found at github.com/layer6ai-labs/msc-sql., Comment: 3rd Table Representation Learning Workshop at NeurIPS 2024

Predicting price spikes in critical metals such as Cobalt, Copper, Magnesium, and Nickel is crucial for mitigating economic risks associated with global trends like the energy transition and reshoring of manufacturing. While traditional models have focused on regression-based approaches, our work introduces a neurosymbolic ensemble framework that integrates multiple neural models with symbolic error detection and correction rules. This framework is designed to enhance predictive accuracy by correcting individual model errors and offering interpretability through rule-based explanations. We show that our method provides up to 6.42% improvement in precision, 29.41% increase in recall at 13.24% increase in F1 over the best performing neural models. Further, our method, as it is based on logical rules, has the benefit of affording an explanation as to which combination of neural models directly contribute to a given prediction.

Finding a maximum-weight matching is a classical and well-studied problem in computer science, solvable in cubic time in general graphs. We consider the specialization called assignment problem where the input is a bipartite graph, and introduce in this work the ``discovery'' variant considering edge weights that are not provided as input but must be queried, requiring additional and costly computations. We develop here discovery algorithms aiming to minimize the number of queried weights while providing guarantees on the computed solution. We first show in this work the inherent challenges of designing discovery algorithms for general assignment problems. We then provide and analyze several efficient greedy algorithms that can make use of natural assumptions about the order in which the nodes are processed by the algorithms. Our motivations for exploring this problem stem from finding practical solutions to a variation of maximum-weight matching in bipartite hypergraphs, a problem recently emerging in the formation of peer-to-peer energy sharing communities.

Classical Wolf-Rayet stars are the descendants of massive OB stars that have lost their hydrogen envelopes and are burning helium in their cores prior to exploding as type Ib/c supernovae. The mechanisms for losing their hydrogen envelopes are either through binary interactions or through strong stellar winds potentially coupled with episodic mass-loss. Amongst the bright classical WR stars, the binary system WR\,137 (HD\,192641; WC7d + O9e) is the subject of this paper. This binary is known to have a 13-year period and produces dust near periastron. Here we report on interferometry with the CHARA Array collected over a decade of time and providing the first visual orbit for the system. We combine these astrometric measurements with archival radial velocities to measure masses of the stars of $M_{\rm WR} = 9.5\pm3.4 M_\odot$ and $M_{\rm O} = 17.3\pm 1.9 M_\odot$ when we use the most recent \textit{Gaia} distance. These results are then compared to predicted dust distribution using these orbital elements, which match the observed imaging from \textit{JWST} as discussed recently by Lau et al. Furthermore, we compare the system to the BPASS models, finding that the WR star likely formed through stellar winds and not through binary interactions. However, the companion O star did likely accrete some material from the WR's mass-loss to provide the rotation seen today that drives its status as an Oe star., Comment: Accepted to ApJ

Fej\'er monotonicity is a well-established property commonly observed in sequences generated by optimization algorithms. In this paper, we introduce an extension of this property, called Fej\'er* monotonicity, which was initially proposed in [SIAM J. Optim., 34(3), 2535-2556 (2024)]. We discuss and build upon the concept by exploring its behavior within Hilbert spaces, presenting an illustrative example and insightful results regarding weak and strong convergence. We also compare Fej\'er* monotonicity with other weak notions of Fej\'er-like monotonicity, to better establish the role of Fej\'er* monotonicity in optimization algorithms.

Homogeneous nucleation processes are important for understanding solidification and the resulting microstructure of materials. Simulating this process requires accurately describing the interactions between atoms, hich is further complicated by chemical order through cross-species interactions. The large scales needed to observe rare nucleation events are far beyond the capabilities of ab initio simulations. Machine-learning is used for overcoming these limitations in terms of both accuracy and speed, by building a high-dimensional neural network potential for binary Al-Ni alloys, which serve as a model system relevant to many industrial applications. The potential is validated against experimental diffusion, viscosity, and scattering data, and is applied to large-scale molecular dynamics simulations of homogeneous nucleation at equiatomic composition, as well as for pure Ni. Pure Ni nucleates in a single-step into an fcc crystal phase, in contrast to previous results obtained with a classical empirical potential. This highlights the sensitivity of nucleation pathways to the underlying atomic interactions. Our findings suggest that the nucleation pathway for AlNi proceeds in a single step toward a B2 structure, which is discussed in relation to the pure elements counterparts.

Entanglement is at the heart of quantum theory and is responsible for various quantum-enabling technologies. In practice, during its preparation, storage, and distribution to the intended recipients, this valuable quantum resource may suffer from noisy interactions that reduce its usefulness for the desired information-processing tasks. Conventional schemes of entanglement distillation aim to alleviate this problem by performing collective operations on multiple copies of these decohered states and sacrificing some of them to recover Bell pairs. However, for this scheme to work, the states to be distilled should already contain a large enough fraction of maximally entangled states before these collective operations. Not all entangled quantum states meet this premise. Here, by using the paradigmatic family of two-qutrit Werner states as an exemplifying example, we experimentally demonstrate how one may use single-copy local filtering operations to meet this requirement and to recover the quantumness hidden in these higher-dimensional states. Among others, our results provide the first proof-of-principle experimental certification of the Bell-nonlocal properties of these intriguing entangled states, the activation of their usefulness for quantum teleportation, dense coding, and an enhancement of their quantum steerability, and hence usefulness for certain discrimination tasks. Our theoretically established lower bounds on the steering robustness of these states, when they admit a symmetric quasiextension or a bosonic symmetric extension, and when they show hidden dense-codability may also be of independent interest., Comment: 16 pages, 15 figures

In this work, we present MedImageInsight, an open-source medical imaging embedding model. MedImageInsight is trained on medical images with associated text and labels across a diverse collection of domains, including X-Ray, CT, MRI, dermoscopy, OCT, fundus photography, ultrasound, histopathology, and mammography. Rigorous evaluations demonstrate MedImageInsight's ability to achieve state-of-the-art (SOTA) or human expert level performance across classification, image-image search, and fine-tuning tasks. Specifically, on public datasets, MedImageInsight achieves SOTA in CT 3D medical image retrieval, as well as SOTA in disease classification and search for chest X-ray, dermatology, and OCT imaging. Furthermore, MedImageInsight achieves human expert performance in bone age estimation (on both public and partner data), as well as AUC above 0.9 in most other domains. When paired with a text decoder, MedImageInsight achieves near SOTA level single image report findings generation with less than 10\% the parameters of other models. Compared to fine-tuning GPT-4o with only MIMIC-CXR data for the same task, MedImageInsight outperforms in clinical metrics, but underperforms on lexical metrics where GPT-4o sets a new SOTA. Importantly for regulatory purposes, MedImageInsight can generate ROC curves, adjust sensitivity and specificity based on clinical need, and provide evidence-based decision support through image-image search (which can also enable retrieval augmented generation). In an independent clinical evaluation of image-image search in chest X-ray, MedImageInsight outperformed every other publicly available foundation model evaluated by large margins (over 6 points AUC), and significantly outperformed other models in terms of AI fairness (across age and gender). We hope releasing MedImageInsight will help enhance collective progress in medical imaging AI research and development.

The General Data Protection Regulation (GDPR) requires an organisation that suffers a data breach to notify the competent Data Protection Authority. The organisation must also inform the relevant individuals, when a data breach threatens their rights and freedoms. This paper focuses on the following question: given the goals of the GDPR's data breach notification obligation, and we assess the obligation in the light of those goals. We refer to insights from information security and economics, and present them in a reader-friendly way for lawyers. Our main conclusion is that the GDPR's data breach rules are likely to contribute to the goals. For instance, the data breach notification obligation can nudge organisations towards better security; such an obligation enables regulators to perform their duties; and such an obligation improves transparency and accountability. However, the paper also warns that we should not have unrealistic expectations of the possibilities for people to protect their interests after a data breach notice. Likewise, we should not have high expectations of people switching to other service providers after receiving a data breach notification. Lastly, the paper calls for Data Protection Authorities to publish more information about reported data breaches. Such information can help to analyse security threats.

Quantum entanglement is a key resource for quantum technologies, including emerging ground-to-satellite quantum communication. In such a scenario, an important challenge to be overcome is to consider entanglement between two or more quantum particles in different inertial frames, potentially experiencing relativistic effects affecting quantum correlations. In this paper, we present a consistent framework that overcomes this challenge. To this end, we establish the notion of factorizable and entangled multi-time trajectories and derive a class of Euler--Lagrange equations under the constraint of a non-entangling behavior. Comparing this restricted evolution to the solutions of the unrestricted equations of motion allows one to investigate the trajectory-based entanglement of general systems. We solve our equations for interacting particles in a Klein--Gordon-type setting, thereby quantifying the dynamic and relativistic impact of entanglement in a self-consistent manner.

Probing the neutral gas distribution at the edge of magnetic confinement fusion devices is critical for divertor physics studies. In the TCV tokamak, a set of ASDEX-type hot ionization pressure gauges (APGs) has been installed for fast, in-situ measurements of the neutral pressure distribution in the TCV chamber. The APGs have been calibrated against baratron pressure gauges (BGs) for pressures ranging from less than 1 mPa to several hundred mPa. A correction to account for the residual pressure in the pumped torus is proposed to improve the measurement accuracy. APG measurements in a series of plasma discharges with varied density ramp rates are analyzed, and are compared with the pressure measurements of the existing BGs. APG measurements feature significantly higher time resolution and faster time response than the BGs, and extend the measurement range to lower pressures. APGs systematically measure higher neutral pressures than the BGs connected to the same TCV port, likely due to the BG time delay and slow time response and nonuniform neutral distribution in gauge ports during the discharge. The initial APG operations in TCV have been proven successful, which validates the APG as an adequate pressure measurement technique for the upcoming TCV divertor upgrades.

Low-dimensional embeddings are a cornerstone in the modelling and analysis of complex networks. However, most existing approaches for mining network embedding spaces rely on computationally intensive machine learning systems to facilitate downstream tasks. In the field of NLP, word embedding spaces capture semantic relationships \textit{linearly}, allowing for information retrieval using \textit{simple linear operations} on word embedding vectors. Here, we demonstrate that there are structural properties of network data that yields this linearity. We show that the more homophilic the network representation, the more linearly separable the corresponding network embedding space, yielding better downstream analysis results. Hence, we introduce novel graphlet-based methods enabling embedding of networks into more linearly separable spaces, allowing for their better mining. Our fundamental insights into the structure of network data that enable their \textit{\textbf{linear}} mining and exploitation enable the ML community to build upon, towards efficiently and explainably mining of the complex network data., Comment: 26 pages, 8 figures

We present the discovery of Aquarius III, an ultra-faint Milky Way satellite galaxy identified in the second data release of the DECam Local Volume Exploration (DELVE) survey. Based on deeper follow-up imaging with DECam, we find that Aquarius III is a low-luminosity ($M_V = -2.5^{+0.3}_{-0.5}$; $L_V = 850^{+380}_{-260} \ L_{\odot}$), extended ($r_{1/2} = 41^{+9}_{-8}$ pc) stellar system located in the outer halo ($D_{\odot} = 85 \pm 4$ kpc). From medium-resolution Keck/DEIMOS spectroscopy, we identify 11 member stars and measure a mean heliocentric radial velocity of $v_{\rm sys} = -13.1^{+1.0}_{-0.9} \ \rm km \ s^{-1}$ for the system and place an upper limit of $\sigma_v < 3.5 \rm \ km \ s^{-1}$ ($\sigma_v < 1.6 \rm \ km \ s^{-1}$) on its velocity dispersion at the 95% (68%) credible level. Based on Calcium-Triplet-based metallicities of the six brightest red giant members, we find that Aquarius III is very metal-poor ([Fe/H]$ = -2.61 \pm 0.21$) with a statistically-significant metallicity spread ($\sigma_{\rm [Fe/H]} = 0.46^{+0.26}_{-0.14}$ dex). We interpret this metallicity spread as strong evidence that the system is a dwarf galaxy as opposed to a star cluster. Combining our velocity measurement with $Gaia$ proper motions, we find that Aquarius III is currently situated near its orbital pericenter in the outer halo ($r_{\rm peri} = 78 \pm 7$ kpc) and that it is plausibly on first infall onto the Milky Way. This orbital history likely precludes significant tidal disruption from the Galactic disk, notably unlike other satellites with comparably low velocity dispersion limits in the literature. Thus, if further velocity measurements confirm that its velocity dispersion is truly below $\sigma_v \lesssim 2 \rm \ km \ s^{-1}$, Aquarius III may serve as a useful laboratory for probing galaxy formation physics in low-mass halos., Comment: 25 pages, 7 figures. Submitted to AAS Journals

Knowledge Distillation (KD) has emerged as a promising approach for transferring knowledge from a larger, more complex teacher model to a smaller student model. Traditionally, KD involves training the student to mimic the teacher's output probabilities, while more advanced techniques have explored guiding the student to adopt the teacher's internal representations. Despite its widespread success, the performance of KD in binary classification and few-class problems has been less satisfactory. This is because the information about the teacher model's generalization patterns scales directly with the number of classes. Moreover, several sophisticated distillation methods may not be universally applicable or effective for data types beyond Computer Vision. Consequently, effective distillation techniques remain elusive for a range of key real-world applications, such as sentiment analysis, search query understanding, and advertisement-query relevance assessment. Taking these observations into account, we introduce a novel method for distilling knowledge from the teacher's model representations, which we term Learning Embedding Linear Projections (LELP). Inspired by recent findings about the structure of final-layer representations, LELP works by identifying informative linear subspaces in the teacher's embedding space, and splitting them into pseudo-subclasses. The student model is then trained to replicate these pseudo-classes. Our experimental evaluation on large-scale NLP benchmarks like Amazon Reviews and Sentiment140 demonstrate the LELP is consistently competitive with, and typically superior to, existing state-of-the-art distillation algorithms for binary and few-class problems, where most KD methods suffer.

Recent contrastive multimodal vision-language models like CLIP have demonstrated robust open-world semantic understanding, becoming the standard image backbones for vision-language applications due to their aligned latent space. However, this practice has left powerful unimodal encoders for both vision and language underutilized in multimodal applications which raises a key question: Is there a plausible way to connect unimodal backbones for zero-shot vision-language tasks? To this end, we propose a novel approach that aligns vision and language modalities using only projection layers on pretrained, frozen unimodal encoders. Our method exploits the high semantic similarity between embedding spaces of well-trained vision and language models. It involves selecting semantically similar encoders in the latent space, curating a concept-rich dataset of image-caption pairs, and training simple MLP projectors. We evaluated our approach on 12 zero-shot classification datasets and 2 image-text retrieval datasets. Our best model, utilizing DINOv2 and All-Roberta-Large text encoder, achieves 76\(\%\) accuracy on ImageNet with a 20-fold reduction in data and 65 fold reduction in compute requirements. The proposed framework enhances the accessibility of model development while enabling flexible adaptation across diverse scenarios, offering an efficient approach to building multimodal models by utilizing existing unimodal architectures. Code and datasets will be released soon., Comment: Preprint, 10 pages; First two authors contributed equally

We introduce a novel family of adversarial attacks that exploit the inability of language models to interpret ASCII art. To evaluate these attacks, we propose the ToxASCII benchmark and develop two custom ASCII art fonts: one leveraging special tokens and another using text-filled letter shapes. Our attacks achieve a perfect 1.0 Attack Success Rate across ten models, including OpenAI's o1-preview and LLaMA 3.1. Warning: this paper contains examples of toxic language used for research purposes.

Bitcoin's global success has led to the rise of blockchain, but many systems labeled as "blockchain" deviate from its core principles, adding complexity to the ecosystem. This survey addresses the need for a comprehensive review and taxonomy to clarify the differences between blockchain and blockchain-like systems. We propose a reference model with four key layers: data, consensus, execution, and application, and introduce a new taxonomy for better classification. Through a qualitative and quantitative analysis of 44 DLT solutions and 26 consensus mechanisms, we highlight key challenges and offer research directions in the field.

Approximation of problems in linear elasticity having small shear modulus in a thin region is considered. Problems of this type arise when modeling ground motion due to earthquakes where rupture occurs in a thin fault. It is shown that, under appropriate scaling, solutions of these problems can be approximated by solutions of a limit problem where the fault region is represented by a surface. In a numerical context this eliminates the need to resolve the large deformations in the fault; a numerical example is presented to illustrate efficacy of this strategy.

Filtration is a crucial step in the analysis of living microparticles. In particular, the selective microfiltration of phytoplankton by size and shape remains an open problem, even though these criteria are essential for their gender and/or species identification. However, microfiltration devices necessitate physical membranes which complicate their fabrication, reduce the sample flow rate and can cause unwanted particle clogging. Recent advances in microfabrication such as electrode High Precision Capillary Printing allow to rapidly build electrode patterns over wide areas. In this study, we introduce a new concept of membrane-less dielectrophoretic (DEP) microfiltration suitable for large scale microfabrication processes. The proposed design involves two pairs of interdigitated electrodes at the top and the bottom of a microfluidic channel. We use finite-element calculations to analyse how the DEP force field throughout the channel, as well as the resulting trajectories of particles depend on the geometry of the system, on the physical properties of the particles and suspending medium and on the imposed voltage and flow rates. We numerically show that in the negative DEP regime, particles are focused in the channel mid-planes and that virtual pillars array leads either to their trapping at specific stagnation points, or to their focusing along specific lines, depending on their dielectrophoretic mobility. Simulations allow to understand how particles can be captured and to quantify the particle filtration conditions by introducing a critical dielectrophoretic mobility. We further illustrate the principle of membrane-less dielectrophoretic microfiltration using the proposed setup, by considering the separation of a binary mixture of polystyrene particles with different diameters, and validate it experimentally., Comment: 24 pages, 8 figures including supplementary information excepted experimental movies

We study the design of robust and agile controllers for hybrid underactuated systems. Our approach breaks down the task of creating a stabilizing controller into: 1) learning a mapping that is invariant under optimal control, and 2) driving the actuated coordinates to the output of that mapping. This approach, termed Zero Dynamics Policies, exploits the structure of underactuation by restricting the inputs of the target mapping to the subset of degrees of freedom that cannot be directly actuated, thereby achieving significant dimension reduction. Furthermore, we retain the stability and constraint satisfaction of optimal control while reducing the online computational overhead. We prove that controllers of this type stabilize hybrid underactuated systems and experimentally validate our approach on the 3D hopping platform, ARCHER. Over the course of 3000 hops the proposed framework demonstrates robust agility, maintaining stable hopping while rejecting disturbances on rough terrain., Comment: 8 pages, 6 figures, IROS '24

Hexagonal boron nitride encapsulation is the method of choice for protecting graphene from environmental doping and impurity scattering. It was previously demonstrated that metal-organic vapor phase epitaxy (MOVPE) grows epitaxially ordered, uniform BN layers on epigraphene (graphene grown on SiC). Due to graphene non-wetting properties, h-BN growth starts preferentially from the graphene ledges. We use this fact here to selectively promote growth of high-quality flat h-BN on epigraphene by patterning epigraphene microstructures prior to BN growth. Thin h-BN films (down to 6 nm) grown by MOVPE show smooth and pleated surface morphology on epigraphene, while crumpled BN is observed on the SiC. Cross-sectional high-resolution transmission electron microscopy images and fluorescence imaging confirm the higher BN quality grown on the epigraphene. Transport measurements reveal p-doping as expected from hydrogen intercalation of epigraphene and regions of high and low mobility. This method can be used to produce structurally uniform high-quality h-BN/epigraphene micro/nano scale heterostructure.

We show that stable, freely suspended liquid crystal films can be made from the ferroelectric nematic ($\mathrm{N_F}$) phase and from the recently discovered polar, lamellar $\mathrm{SmZ_A}$ and $\mathrm{SmA_F}$ phases. The $\mathrm{N_F}$ films display two-dimensional, smectic-like parabolic focal conic textures comprising director/polarization bend that are a manifestation of the electrostatic suppression of director splay in the film plane. In the $\mathrm{SmZ_A}$ and $\mathrm{SmA_F}$ phases, the smectic layers orient preferentially normal to the film surfaces, a condition never found in typical thermotropic or lyotropic lamellar LC phases, with the $\mathrm{SmZ_A}$ films exhibiting focal-conic fan textures mimicking the appearance of typical smectics in glass cells when the layers are oriented normal to the plates, and the $\mathrm{SmA_F}$ films showing a texture of plaquettes of uniform in-plane orientation where both bend and splay are suppressed, separated by grain boundaries. The $\mathrm{SmA_F}$ phase can also be drawn into thin filaments, in which X-ray scattering reveals that the smectic layer planes are normal to the filament axis. Remarkably, the filaments are mechanically stable even if they break, forming free-standing, fluid filaments supported only at one end. The unique architectures of these films and filaments are stabilized by the electrostatic self-interaction of the liquid crystal polarization field, which enables the formation of confined, fluid structures that are fundamentally different from those of their counterparts made using previously known liquid crystal phases., Comment: Main paper 25 pages (5 figures); Supplement: 7 pages (7 figures)

Families are now an undeniable part of prospective students' college search process. As American higher education stands at the precipice of profound demographic shifts, colleges and universities must reexamine how they engage not just prospective students, but their families as well. In collaboration with Ardeo and CampusESP, RNL collected and analyzed the findings of 11,309 families with college-bound students from across demographic and geographic backgrounds. The key findings from this 2024 Prospective Family Engagement Report highlight the considerations and inclinations that shape families' awareness and decision-making processes. Insights cover subjects such as their comfort with distance from home, type of information they value, communication channels and the frequency with which they hope to hear from institutions, and their concerns about financing their students' degree. Importantly, the 2024 research underscores how family income, generational status, and ethnicity can shape these categories. The authors' hope is that leaders in higher education institutions find the resulting recommendations to be a practical guide for developing proactive, targeted strategies to engage diverse families in the college planning process.

With so much attention to the demographic or enrollment cliff, we may have become desensitized to its implications. But as we approach 2025--the first year of contraction--new data indicate that institutions will be contending with steady contraction for most of the next 15 years. Read this eye-opening white paper from RNL to learn what the latest census data tell us and what this means for colleges and universities: (1) See data on the second demographic cliff looming after the upcoming one; (2) Look at how these changes could impact different regions of the U.S.; and (3) Learn 12 strategies that can help your institution prepare and mitigate these challenges.

When do students start the college planning process? How many plan to take the SAT or ACT? How much do they value a college degree? RNL and Halda surveyed more than 2,200 high school students to answer these and many other key questions. Read our 32-page college planning report and discover a wealth of findings on: (1) when students start to research colleges; (2) who influences their decision; (3) how they perceive the college planning process; (4) whether they view college is worth the money and effort; (5) how students perceive test-optional institutions; (6) what information they value the most; (7) how quickly they expect a response; and (8) how much they value personalization in communications. This college planning report will help campus marketing and admissions staff better understand how they can engage prospective students, meet their expectations, and keep them progressing toward enrollment. [This report was co-sponsored by Halda.]

There is mounting evidence that a second quantum revolution based on the technological capabilities to detect and manipulate single quantum particles (e.g., electrons, photons, ions, etc), a feat not achieved during the first quantum revolution, is progressing fast. It is expected that in less than 10 years, this second quantum revolution shall have a significant impact over numerous industries, including finance, medicine, energy, transportation, etc. Quantum computers threaten the status quo of cybersecurity, due to known quantum algorithms that can break asymmetric encryption, which is what gives us the ability to communicate securely using a public channel. Considering the world's dependence on digital communication through data exchange and processing, retaining the ability to communicate securely even once quantum computers come into play, cannot be stressed enough. Two solutions are available: Quantum Key Distribution (QKD) and Post-Quantum Cryptography (PQC); which, we emphasise, are not mutually exclusive. The EuroQCI initiative, of which EQUO is a part of, focuses on QKD and aims to build a network whereby EU countries can communicate securely through QKD. To this aim, the DEP (Digital Europe Programme) project aims to bring technological matureness to QKD by deploying a QKD test network and, through this exercise, understand what is lacking from an operator's point of view when the time to integrate QKD in their network comes., Comment: IEEE 2024 International Conference on Quantum Communications, Networking, and Computing (QCNC)

Materials with switchable nanostructured surfaces enable optical and electronic functionalities beyond those of natural materials. Here we report the creation of self-organized, re-writable, laser-induced surface structures in single-crystalline vanadium dioxide. We discover anisotropic features caused by canalized surface plasmon polaritons that can only propagate along one crystal axis. The nanostructures remain single-crystalline and preserve the raw material's metal-to-insulator transition, enabling femtosecond switching by temperature or light., Comment: {\dag}These authors contributed equally to this work

Stabilizing underactuated systems is an inherently challenging control task due to fundamental limitations on how the control input affects the unactuated dynamics. Decomposing the system into actuated (output) and unactuated (zero) coordinates provides useful insight as to how input enters the system dynamics. In this work, we leverage the structure of this decomposition to formalize the idea of Zero Dynamics Policies (ZDPs) -- a mapping from the unactuated coordinates to desired actuated coordinates. Specifically, we show that a ZDP exists in a neighborhood of the origin, and prove that combining output stabilization with a ZDP results in stability of the full system state. We detail a constructive method of obtaining ZDPs in a neighborhood of the origin, and propose a learning-based approach which leverages optimal control to obtain ZDPs with much larger regions of attraction. We demonstrate that such a paradigm can be used to stabilize the canonical underactuated system of the cartpole, and showcase an improvement over the nominal performance of LQR., Comment: 8 pages, 2 figures, CDC 2024