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Continual Federated Learning (CFL) is essential for enabling real-world applications where multiple decentralized clients adaptively learn from continuous data streams. A significant challenge in CFL is mitigating catastrophic forgetting, where models lose previously acquired knowledge when learning new information. Existing approaches often face difficulties due to the constraints of device storage capacities and the heterogeneous nature of data distributions among clients. While some CFL algorithms have addressed these challenges, they frequently rely on unrealistic assumptions about the availability of task boundaries (i.e., knowing when new tasks begin). To address these limitations, we introduce Fed-A-GEM, a federated adaptation of the A-GEM method (Chaudhry et al., 2019), which employs a buffer-based gradient projection approach. Fed-A-GEM alleviates catastrophic forgetting by leveraging local buffer samples and aggregated buffer gradients, thus preserving knowledge across multiple clients. Our method is combined with existing CFL techniques, enhancing their performance in the CFL context. Our experiments on standard benchmarks show consistent performance improvements across diverse scenarios. For example, in a task-incremental learning scenario using the CIFAR-100 dataset, our method can increase the accuracy by up to 27%. Our code is available at https://github.com/shenghongdai/Fed-A-GEM., Comment: A preliminary version of this work was presented at the Federated Learning Systems (FLSys) Workshop @ Sixth Conference on Machine Learning and Systems, June 2023

Let $E$ be an elliptic curve over $\mathbb{Q}$ with Mordell--Weil rank $2$ and $p$ be an odd prime of good ordinary reduction. For every imaginary quadratic field $K$ satisfying the Heegner hypothesis, there is (subject to the Shafarevich--Tate conjecture) a line, i.e., a free $\mathbb{Z}_p$-submodule of rank $1$, in $ E(K)\otimes \mathbb{Z}_p$ given by universal norms coming from the Mordell--Weil groups of subfields of the anticyclotomic $\mathbb{Z}_p$-extension of $K$; we call it the {\it shadow line}. When the twist of $E$ by $K$ has analytic rank $1$, the shadow line is conjectured to lie in $E(\mathbb{Q})\otimes\mathbb{Z}_p$; we verify this computationally in all our examples. We study the distribution of shadow lines in $E(\mathbb{Q})\otimes\mathbb{Z}_p$ as $K$ varies, framing conjectures based on the computations we have made.

Computed Tomography (CT) scans are the standard-of-care for the visualization and diagnosis of many clinical ailments, and are needed for the treatment planning of external beam radiotherapy. Unfortunately, the availability of CT scanners in low- and mid-resource settings is highly variable. Planar x-ray radiography units, in comparison, are far more prevalent, but can only provide limited 2D observations of the 3D anatomy. In this work we propose DIFR3CT, a 3D latent diffusion model, that can generate a distribution of plausible CT volumes from one or few (<10) planar x-ray observations. DIFR3CT works by fusing 2D features from each x-ray into a joint 3D space, and performing diffusion conditioned on these fused features in a low-dimensional latent space. We conduct extensive experiments demonstrating that DIFR3CT is better than recent sparse CT reconstruction baselines in terms of standard pixel-level (PSNR, SSIM) on both the public LIDC and in-house post-mastectomy CT datasets. We also show that DIFR3CT supports uncertainty quantification via Monte Carlo sampling, which provides an opportunity to measure reconstruction reliability. Finally, we perform a preliminary pilot study evaluating DIFR3CT for automated breast radiotherapy contouring and planning -- and demonstrate promising feasibility. Our code is available at https://github.com/yransun/DIFR3CT., Comment: 11 pages, 9 figures

Current observational evidence reveals that fast radio bursts (FRBs) exhibit bandwidths ranging from a few dozen MHz to several GHz. Traditional FRB searches primarily employ matched filter methods on time series collapsed across the entire observational bandwidth. However, with modern ultra-wideband receivers featuring GHz-scale observational bandwidths, this approach may overlook a significant number of events. We investigate the efficacy of sub-banded searches for FRBs, a technique seeking bursts within limited portions of the bandwidth. These searches aim to enhance the significance of FRB detections by mitigating the impact of noise outside the targeted frequency range, thereby improving signal-to-noise ratios. We conducted a series of Monte Carlo simulations, for the $400$-MHz bandwidth Parkes 21-cm multi-beam (PMB) receiver system and the Parkes Ultra-Wideband Low (UWL) receiver, simulating bursts down to frequency widths of about $100$\,MHz. Additionally, we performed a complete reprocessing of the high-latitude segment of the High Time Resolution Universe South survey (HTRU-S) of the Parkes-Murriyang telescope using sub-banded search techniques. Simulations reveal that a sub-banded search can enhance the burst search efficiency by $67_{-42}^{+133}$ % for the PMB system and $1433_{-126}^{+143}$ % for the UWL receiver. Furthermore, the reprocessing of HTRU led to the confident detection of eighteen new bursts, nearly tripling the count of FRBs found in this survey. These results underscore the importance of employing sub-banded search methodologies to effectively address the often modest spectral occupancy of these signals., Comment: Accepted for publication (A&A)

Aggregating the preferences of multiple agents into a collective decision is a common step in many important problems across areas of computer science including information retrieval, reinforcement learning, and recommender systems. As Social Choice Theory has shown, the problem of designing algorithms for aggregation rules with specific properties (axioms) can be difficult, or provably impossible in some cases. Instead of designing algorithms by hand, one can learn aggregation rules, particularly voting rules, from data. However, the prior work in this area has required extremely large models, or been limited by the choice of preference representation, i.e., embedding. We recast the problem of designing a good voting rule into one of learning probabilistic versions of voting rules that output distributions over a set of candidates. Specifically, we use neural networks to learn probabilistic social choice functions from the literature. We show that embeddings of preference profiles derived from the social choice literature allows us to learn existing voting rules more efficiently and scale to larger populations of voters more easily than other work if the embedding is tailored to the learning objective. Moreover, we show that rules learned using embeddings can be tweaked to create novel voting rules with improved axiomatic properties. Namely, we show that existing voting rules require only minor modification to combat a probabilistic version of the No Show Paradox.

The median $M$ of a graph $G$ is the set of vertices with a minimum total distance to all other vertices in the graph. In this paper, we determine the median of Sierpi\'{n}ski triangle graphs. Sierpi\'{n}ski triangle graphs, also known as Sierpi\'{n}ski gasket graphs of order $n$ are graphs formed by contracting all non-clique edges from the Sierpi\'{n}ski graphs of order ($n+1$).

Computer vision systems have witnessed rapid progress over the past two decades due to multiple advances in the field. As these systems are increasingly being deployed in high-stakes real-world applications, there is a dire need to ensure that they do not propagate or amplify any discriminatory tendencies in historical or human-curated data or inadvertently learn biases from spurious correlations. This paper presents a comprehensive survey on fairness that summarizes and sheds light on ongoing trends and successes in the context of computer vision. The topics we discuss include 1) The origin and technical definitions of fairness drawn from the wider fair machine learning literature and adjacent disciplines. 2) Work that sought to discover and analyze biases in computer vision systems. 3) A summary of methods proposed to mitigate bias in computer vision systems in recent years. 4) A comprehensive summary of resources and datasets produced by researchers to measure, analyze, and mitigate bias and enhance fairness. 5) Discussion of the field's success, continuing trends in the context of multimodal foundation and generative models, and gaps that still need to be addressed. The presented characterization should help researchers understand the importance of identifying and mitigating bias in computer vision and the state of the field and identify potential directions for future research., Comment: 20 pages, 4 figures

Analysis of matrix-variate data is becoming increasingly common in the literature, particularly in the field of clustering and classification. It is well-known that real data, including real matrix-variate data, often exhibit high levels of asymmetry. To address this issue, one common approach is to introduce a tail or skewness parameter to a symmetric distribution. In this regard, we introduced here a new distribution called the matrix-variate skew t distribution (MVST), which provides flexibility in terms of heavy tail and skewness. We then conduct a thorough investigation of various characterizations and probabilistic properties of the MVST distribution. We also explore extensions of this distribution to a finite mixture model. To estimate the parameters of the MVST distribution, we develop an efficient EM-type algorithm that computes maximum likelihood (ML) estimates of the model parameters. To validate the effectiveness and usefulness of the developed models and associated methods, we perform empirical experiments using simulated data as well as three real data examples. Our results demonstrate the efficacy of the developed approach in handling asymmetric matrix-variate data.

This paper addresses the challenge of obtaining precise demographic information at a fine-grained spatial level, a necessity for planning localized public services such as water distribution networks, or understanding local human impacts on the ecosystem. While population sizes are commonly available for large administrative areas, such as wards in India, practical applications often demand knowledge of population density at smaller spatial scales. We explore the integration of alternative data sources, specifically satellite-derived products, including land cover, land use, street density, building heights, vegetation coverage, and drainage density. Using a case study focused on Bangalore City, India, with a ward-level population dataset for 198 wards and satellite-derived sources covering 786,702 pixels at a resolution of 30mX30m, we propose a semiparametric Bayesian spatial regression model for obtaining pixel-level population estimates. Given the high dimensionality of the problem, exact Bayesian inference is deemed impractical; we discuss an approximate Bayesian inference scheme based on the recently proposed max-and-smooth approach, a combination of Laplace approximation and Markov chain Monte Carlo. A simulation study validates the reasonable performance of our inferential approach. Mapping pixel-level estimates to the ward level demonstrates the effectiveness of our method in capturing the spatial distribution of population sizes. While our case study focuses on a demographic application, the methodology developed here readily applies to count-type spatial datasets from various scientific disciplines, where high-resolution alternative data sources are available., Comment: 30 pages, 7 figures, 3 tables

The ability of Language Models (LMs) to understand natural language makes them a powerful tool for parsing human instructions into task plans for autonomous robots. Unlike traditional planning methods that rely on domain-specific knowledge and handcrafted rules, LMs generalize from diverse data and adapt to various tasks with minimal tuning, acting as a compressed knowledge base. However, LMs in their standard form face challenges with long-horizon tasks, particularly in partially observable multi-agent settings. We propose an LM-based Long-Horizon Planner for Multi-Agent Robotics (LLaMAR), a cognitive architecture for planning that achieves state-of-the-art results in long-horizon tasks within partially observable environments. LLaMAR employs a plan-act-correct-verify framework, allowing self-correction from action execution feedback without relying on oracles or simulators. Additionally, we present MAP-THOR, a comprehensive test suite encompassing household tasks of varying complexity within the AI2-THOR environment. Experiments show that LLaMAR achieves a 30% higher success rate compared to other state-of-the-art LM-based multi-agent planners., Comment: 27 pages, 4 figures, 5 tables

We present the results of muon-spin relaxation ($\mu^{+}$SR) measurements of the van der Waals magnet NiI$_2$, which probe magnetic phase transitions at $T_{\mathrm{N1}}=73$K and $T_{\mathrm{N2}}=60$K. Supporting density functional theory (DFT) calculations allow the determination of a single muon stopping site whose magnetic environment is consistent with the proposed ground-state magnetic structure. $\mu^{+}$SR measurements of the dynamics reveal behavior consistent with spin-wave excitations below $T_{\mathrm{N2}}$, with an additional contribution from a nonmagnetic component of the material. In the region $T_{\mathrm{N1}}

Pre-fault tolerant quantum computers have already demonstrated the ability to estimate observable values accurately, at a scale beyond brute-force classical computation. This has been enabled by error mitigation techniques that often rely on a representative model on the device noise. However, learning and maintaining these models is complicated by fluctuations in the noise over unpredictable time scales, for instance, arising from resonant interactions between superconducting qubits and defect two-level systems (TLS). Such interactions affect the stability and uniformity of device performance as a whole, but also affect the noise model accuracy, leading to incorrect observable estimation. Here, we experimentally demonstrate that tuning of the qubit-TLS interactions helps reduce noise instabilities and consequently enables more reliable error-mitigation performance. These experiments provide a controlled platform for studying the performance of error mitigation in the presence of quasi-static noise. We anticipate that the capabilities introduced here will be crucial for the exploration of quantum applications on solid-state processors at non-trivial scales., Comment: 8 pages, 4 figures (13 pages, 8 figures for supplementary material), reference numbering has been fixed at v2

3D building reconstruction from imaging data is an important task for many applications ranging from urban planning to reconnaissance. Modern Novel View synthesis (NVS) methods like NeRF and Gaussian Splatting offer powerful techniques for developing 3D models from natural 2D imagery in an unsupervised fashion. These algorithms generally require input training views surrounding the scene of interest, which, in the case of large buildings, is typically not available across all camera elevations. In particular, the most readily available camera viewpoints at scale across most buildings are at near-ground (e.g., with mobile phones) and aerial (drones) elevations. However, due to the significant difference in viewpoint between drone and ground image sets, camera registration - a necessary step for NVS algorithms - fails. In this work we propose a method, DRAGON, that can take drone and ground building imagery as input and produce a 3D NVS model. The key insight of DRAGON is that intermediate elevation imagery may be extrapolated by an NVS algorithm itself in an iterative procedure with perceptual regularization, thereby bridging the visual feature gap between the two elevations and enabling registration. We compiled a semi-synthetic dataset of 9 large building scenes using Google Earth Studio, and quantitatively and qualitatively demonstrate that DRAGON can generate compelling renderings on this dataset compared to baseline strategies., Comment: 12 pages, 9 figures, accepted to ICCP 2024

The list of interesting electrical properties exhibited by transition metal dichalcogenides has grown with the discovery of extremely large magnetoresistance (MR) and type-II Weyl semimetal behaviour in WTe$_2$ and MoTe$_2$. The extremely large MR in WTe$_2$ is still not adequately understood. Here, we systematically study the effect of Mo substitution on the quantum oscillations in the MR in WTe$_2$. The MR decreases with Mo substitution, however, the carrier concentrations extracted from the quantum oscillations show that the charge compensation improves. We believe that earlier interpretations based on the two-band theory, which attribute the decrease in MR to charge imbalance, could be incorrect due to over-parametrization. We attribute the decrease in MR in the presence of charge compensation to a fall in transport mobility, which is evident from the residual resistivity ratio data. The quantum scattering time and the effective masses do not change within experimental errors upon substitution., Comment: This revised version shows the main and supplementary material in the correct order

In a world where elections touch every aspect of society, the need for secure voting is paramount. Traditional safeguards, based on classical cryptography, rely on complex math problems like factoring large numbers. However, quantum computing is changing the game. Recent advances in quantum technology suggest that classical cryptographic methods may not be as secure as we thought. This paper introduces a quantum voting protocol, a blend of quantum principles (entanglement and superposition), blockchain technology, and digital signatures, all powered by $\log_2{n}$ qubits, and designed for approval voting with n candidates. The result is a symphony of security features - binding, anonymity, non-reusability, verifiability, eligibility, and fairness - that chart a new course for voting security. The real world beckons, as we tested this protocol on IBM quantum hardware, achieving impressively low error rates of just 1.17% in a four-candidate election.

Generating ambient sounds and effects is a challenging problem due to data scarcity and often insufficient caption quality, making it difficult to employ large-scale generative models for the task. In this work, we tackle the problem by introducing two new models. First, we propose AutoCap, a high-quality and efficient automatic audio captioning model. We show that by leveraging metadata available with the audio modality, we can substantially improve the quality of captions. AutoCap reaches CIDEr score of 83.2, marking a 3.2% improvement from the best available captioning model at four times faster inference speed. We then use AutoCap to caption clips from existing datasets, obtaining 761,000 audio clips with high-quality captions, forming the largest available audio-text dataset. Second, we propose GenAu, a scalable transformer-based audio generation architecture that we scale up to 1.25B parameters and train with our new dataset. When compared to state-of-the-art audio generators, GenAu obtains significant improvements of 15.7% in FAD score, 22.7% in IS, and 13.5% in CLAP score, indicating significantly improved quality of generated audio compared to previous works. This shows that the quality of data is often as important as its quantity. Besides, since AutoCap is fully automatic, new audio samples can be added to the training dataset, unlocking the training of even larger generative models for audio synthesis., Comment: Project Webpage: https://snap-research.github.io/GenAU/

Hydrodynamic simulations of the stellar winds from Wolf-Rayet stars within the Galactic Center can provide predictions for the X-ray spectrum of supermassive black hole Sgr A*. Herein, we present results from updated smooth particle hydrodynamics simulations, building on the architecture of Cuadra et al. (2015); Russell et al. (2017), finding that a cold gas disk forms around Sgr A* with a simulation runtime of 3500 years. This result is consistent with previous grid-based simulations, demonstrating that a cold disk can form regardless of numerical method. We examine the plasma scenarios arising from an environment with and without this cold disk, by generating synthetic spectra for comparison to the quiescent Fe K alpha Sgr A* spectrum from Chandra HETG-S, taken through the Chandra X-ray Visionary Program. We find that current and future X-ray missions are unlikely to distinguish between the kinematic signatures in the plasma in these two scenarios. Nonetheless, the stellar wind plasma model presents a good fit to the dispersed Chandra spectra within 1.5" of Sgr A*. We compare our results to the Radiatively Inefficient Accretion Flow (RIAF) model fit to the HETG-S spectrum presented in Paper I and find that the Bayesian model evidence does not strongly favor either model. With 9" angular resolution and high spectral resolution of the X-IFU, NewAthena will offer a clearer differentiation between the RIAF plasma model and hydrodynamic simulations, but only a future X-ray mission with arcsecond resolution will significantly advance our understanding of Sgr A*'s accretion flow in X-rays., Comment: 14 pages, 6 figures. Accepted for publication in ApJ

The extreme low-luminosity supermassive black hole Sgr A* provides a unique laboratory in which to test radiatively inefficient accretion flow (RIAF) models. Previous fits to the quiescent Chandra ACIS-S spectrum found a RIAF model with an equal inflow-outflow balance works well. In this work, we apply the RIAF model to the Chandra HETG-S spectrum obtained through the Chandra X-ray Visionary Program, which displays features suggestive of temperature and velocity structures within the plasma. A comprehensive forward model analysis accounting for the accretion flow geometry and HETG-S instrumental effects is required for a full interpretation of the quiescent Chandra HETG-S spectrum. We present a RIAF model that takes these effects into account. Our fits to the high-resolution gratings spectrum indicate an inflow balanced by an outflow ($s \sim 1$) alongside a temperature profile that appears shallower than what would be expected from a gravitational potential following $1/r$. The data require that the abundance of Iron relative to solar is $Z_{Fe} < 0.32 Z_\odot$ (90\% credible interval), much lower than the $2~Z_\odot$ metallicity measured in nearby late-type giants. While future missions like NewAthena will provide higher spectral resolution, source separation will continue to be a problem. Leveraging Chandra's unparalleled spatial resolution, which is not expected to be surpassed for decades, remains essential for detailed investigations of the densely populated Galactic Center in X-rays., Comment: 11 pages, 5 figures, 1 table. Accepted for publication in ApJ

Energy-efficient spintronic devices require a large spin-orbit torque (SOT) and low damping to excite magnetic precession. In conventional devices with heavy-metal/ferromagnet bilayers, reducing the ferromagnet thickness to $\sim$1 nm enhances the SOT but dramatically increases damping. Here, we investigate an alternative approach based on a 10 nm thick single-layer ferromagnet to attain both low damping and a sizable SOT. Instead of relying on a single interface, we continuously break the bulk inversion symmetry with a vertical compositional gradient of two ferromagnetic elements: Fe with low intrinsic damping and Ni with sizable spin-orbit coupling. We find low effective damping parameters of $\alpha_\mathrm{eff} < 5\times10^{-3}$ in the FeNi alloy films, despite the steep compositional gradients. Moreover, we reveal a sizable anti-damping SOT efficiency of $\theta_\mathrm{AD} \approx 0.05$, even without an intentional compositional gradient. Through depth-resolved x-ray diffraction, we identify a lattice strain gradient as crucial symmetry breaking that underpins the SOT. Our findings provide fresh insights into damping and SOTs in single-layer ferromagnets for power-efficient spintronic devices.

In this brief report, we discuss the characteristics of B$^{\alpha}$ gates. We provide the conditions for the two-qubit gates generated by two applications of a B$^{\alpha}$ gate. We propose an experimental scheme to implement B$^{\alpha}$ gates in ion-trap system. In this scheme, we assume that only a single vibrational mode contributes to spin-spin coupling. This scheme is an extension of a recently proposed scheme to realize XY-type interaction in ion-trap system. With the successful implementation of this scheme, B$^{\alpha}$ gates can be used for doing quantum computation in ion-trap quantum computers., Comment: 4 pages, Research in progress, Comments are welcome

We study the problem of constructing an estimator of the average treatment effect (ATE) that exhibits doubly-robust asymptotic linearity (DRAL). This is a stronger requirement than doubly-robust consistency. A DRAL estimator can yield asymptotically valid Wald-type confidence intervals even when the propensity score or the outcome model is inconsistently estimated. On the contrary, the celebrated doubly-robust, augmented-IPW (AIPW) estimator generally requires consistent estimation of both nuisance functions for standard root-n inference. We make three main contributions. First, we propose a new hybrid class of distributions that consists of the structure-agnostic class introduced in Balakrishnan et al (2023) with additional smoothness constraints. While DRAL is generally not possible in the pure structure-agnostic class, we show that it can be attained in the new hybrid one. Second, we calculate minimax lower bounds for estimating the ATE in the new class, as well as in the pure structure-agnostic one. Third, building upon the literature on doubly-robust inference (van der Laan, 2014, Benkeser et al, 2017, Dukes et al 2021), we propose a new estimator of the ATE that enjoys DRAL. Under certain conditions, we show that its rate of convergence in the new class can be much faster than that achieved by the AIPW estimator and, in particular, matches the minimax lower bound rate, thereby establishing its optimality. Finally, we clarify the connection between DRAL estimators and those based on higher-order influence functions (Robins et al, 2017) and complement our theoretical findings with simulations., Comment: 54 pages, 2 figures

Object detection algorithms particularly those based on YOLO have demonstrated remarkable efficiency in balancing speed and accuracy. However, their application in brain tumour detection remains underexplored. This study proposes RepVGG-GELAN, a novel YOLO architecture enhanced with RepVGG, a reparameterized convolutional approach for object detection tasks particularly focusing on brain tumour detection within medical images. RepVGG-GELAN leverages the RepVGG architecture to improve both speed and accuracy in detecting brain tumours. Integrating RepVGG into the YOLO framework aims to achieve a balance between computational efficiency and detection performance. This study includes a spatial pyramid pooling-based Generalized Efficient Layer Aggregation Network (GELAN) architecture which further enhances the capability of RepVGG. Experimental evaluation conducted on a brain tumour dataset demonstrates the effectiveness of RepVGG-GELAN surpassing existing RCS-YOLO in terms of precision and speed. Specifically, RepVGG-GELAN achieves an increased precision of 4.91% and an increased AP50 of 2.54% over the latest existing approach while operating at 240.7 GFLOPs. The proposed RepVGG-GELAN with GELAN architecture presents promising results establishing itself as a state-of-the-art solution for accurate and efficient brain tumour detection in medical images. The implementation code is publicly available at https://github.com/ThensiB/RepVGG-GELAN.

Adversarial machine learning, focused on studying various attacks and defenses on machine learning (ML) models, is rapidly gaining importance as ML is increasingly being adopted for optimizing wireless systems such as Open Radio Access Networks (O-RAN). A comprehensive modeling of the security threats and the demonstration of adversarial attacks and defenses on practical AI based O-RAN systems is still in its nascent stages. We begin by conducting threat modeling to pinpoint attack surfaces in O-RAN using an ML-based Connection management application (xApp) as an example. The xApp uses a Graph Neural Network trained using Deep Reinforcement Learning and achieves on average 54% improvement in the coverage rate measured as the 5th percentile user data rates. We then formulate and demonstrate evasion attacks that degrade the coverage rates by as much as 50% through injecting bounded noise at different threat surfaces including the open wireless medium itself. Crucially, we also compare and contrast the effectiveness of such attacks on the ML-based xApp and a non-ML based heuristic. We finally develop and demonstrate robust training-based defenses against the challenging physical/jamming-based attacks and show a 15% improvement in the coverage rates when compared to employing no defense over a range of noise budgets, Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

When estimating causal effects from observational studies, researchers often need to adjust for many covariates to deconfound the non-causal relationship between exposure and outcome, among which many covariates are discrete. The behavior of commonly used estimators in the presence of many discrete covariates is not well understood since their properties are often analyzed under structural assumptions including sparsity and smoothness, which do not apply in discrete settings. In this work, we study the estimation of causal effects in a model where the covariates required for confounding adjustment are discrete but high-dimensional, meaning the number of categories $d$ is comparable with or even larger than sample size $n$. Specifically, we show the mean squared error of commonly used regression, weighting and doubly robust estimators is bounded by $\frac{d^2}{n^2}+\frac{1}{n}$. We then prove the minimax lower bound for the average treatment effect is of order $\frac{d^2}{n^2 \log^2 n}+\frac{1}{n}$, which characterizes the fundamental difficulty of causal effect estimation in the high-dimensional discrete setting, and shows the estimators mentioned above are rate-optimal up to log-factors. We further consider additional structures that can be exploited, namely effect homogeneity and prior knowledge of the covariate distribution, and propose new estimators that enjoy faster convergence rates of order $\frac{d}{n^2} + \frac{1}{n}$, which achieve consistency in a broader regime. The results are illustrated empirically via simulation studies., Comment: 66 pages, 5 figures

Recent advancements in machine learning have led to the development of novel medical imaging systems and algorithms that address ill-posed problems. Assessing their trustworthiness and understanding how to deploy them safely at test time remains an important and open problem. In this work, we propose using conformal prediction to compute valid and distribution-free bounds on downstream metrics given reconstructions generated by one algorithm, and retrieve upper/lower bounds and inlier/outlier reconstructions according to the adjusted bounds. Our work offers 1) test time image reconstruction evaluation without ground truth, 2) downstream performance guarantees, 3) meaningful upper/lower bound reconstructions, and 4) meaningful statistical inliers/outlier reconstructions. We demonstrate our method on post-mastectomy radiotherapy planning using 3D breast CT reconstructions, and show 1) that metric-guided bounds have valid coverage for downstream metrics while conventional pixel-wise bounds do not and 2) anatomical differences of upper/lower bounds between metric-guided and pixel-wise methods. Our work paves way for more meaningful and trustworthy test-time evaluation of medical image reconstructions. Code available at https://github.com/matthewyccheung/conformal-metric

This paper considers the best- and worst-case of a general class of distortion risk measures when only partial information regarding the underlying distributions is available. Specifically, explicit sharp lower and upper bounds for a general class of distortion risk measures are derived based on the first two moments along with some shape information, such as symmetry/unimodality property of the underlying distributions. The proposed approach provides a unified framework for extremal problems of distortion risk measures., Comment: 36 pages

Featurizing microscopy images for use in biological research remains a significant challenge, especially for large-scale experiments spanning millions of images. This work explores the scaling properties of weakly supervised classifiers and self-supervised masked autoencoders (MAEs) when training with increasingly larger model backbones and microscopy datasets. Our results show that ViT-based MAEs outperform weakly supervised classifiers on a variety of tasks, achieving as much as a 11.5% relative improvement when recalling known biological relationships curated from public databases. Additionally, we develop a new channel-agnostic MAE architecture (CA-MAE) that allows for inputting images of different numbers and orders of channels at inference time. We demonstrate that CA-MAEs effectively generalize by inferring and evaluating on a microscopy image dataset (JUMP-CP) generated under different experimental conditions with a different channel structure than our pretraining data (RPI-93M). Our findings motivate continued research into scaling self-supervised learning on microscopy data in order to create powerful foundation models of cellular biology that have the potential to catalyze advancements in drug discovery and beyond., Comment: CVPR 2024 Highlight. arXiv admin note: text overlap with arXiv:2309.16064

Broadband low-resolution near-infrared spectrographs in a compact form are crucial for ground- and space-based astronomy and other fields of sensing. Astronomical spectroscopy poses stringent requirements including high efficiency, broad band operation ($>$ 300 nm), and in some cases, polarization insensitivity. We present and compare experimental results from the design, fabrication, and characterization of broadband (1200 - 1650 nm) arrayed waveguide grating (AWG) spectrographs built using the two most promising low-loss platforms - Si$_3$N$_4$ (rectangular waveguides) and doped-SiO$_2$ (square waveguides). These AWGs have a resolving power ($\lambda/\Delta\lambda$) of ~200, a free spectral range of ~ 200-350 nm, and a small footprint of ~ 50-100 mm$^2$. The peak overall (fiber-chip-fiber) efficiency of the doped-SiO$_2$ AWG was ~ 79\% (1 dB), and it exhibited a negligible polarization-dependent shift compared to the channel spacing. For Si$_3$N$_4$ AWGs, the peak overall efficiency in TE mode was ~ 50\% (3 dB), and the main loss component was found to be fiber-to-chip coupling losses. These broadband AWGs are key to enabling compact integrations such as multi-object spectrographs or dispersion back-ends for other astrophotonic devices such as photonic lanterns or nulling interferometers., Comment: 16 pages, 8 Figures, accepted for publication in Optics Express

Doubly robust estimators with cross-fitting have gained popularity in causal inference due to their favorable structure-agnostic error guarantees. However, when additional structure, such as H\"{o}lder smoothness, is available then more accurate "double cross-fit doubly robust" (DCDR) estimators can be constructed by splitting the training data and undersmoothing nuisance function estimators on independent samples. We study a DCDR estimator of the Expected Conditional Covariance, a functional of interest in causal inference and conditional independence testing, and derive a series of increasingly powerful results with progressively stronger assumptions. We first provide a structure-agnostic error analysis for the DCDR estimator with no assumptions on the nuisance functions or their estimators. Then, assuming the nuisance functions are H\"{o}lder smooth, but without assuming knowledge of the true smoothness level or the covariate density, we establish that DCDR estimators with several linear smoothers are semiparametric efficient under minimal conditions and achieve fast convergence rates in the non-$\sqrt{n}$ regime. When the covariate density and smoothnesses are known, we propose a minimax rate-optimal DCDR estimator based on undersmoothed kernel regression. Moreover, we show an undersmoothed DCDR estimator satisfies a slower-than-$\sqrt{n}$ central limit theorem, and that inference is possible even in the non-$\sqrt{n}$ regime. Finally, we support our theoretical results with simulations, providing intuition for double cross-fitting and undersmoothing, demonstrating where our estimator achieves semiparametric efficiency while the usual "single cross-fit" estimator fails, and illustrating asymptotic normality for the undersmoothed DCDR estimator.

Using MeerKAT, we have discovered three new millisecond pulsars (MSPs) in the bulge globular cluster M62: M62H, M62I, and M62J. All three are in binary systems, which means all ten known pulsars in the cluster are in binaries. M62H has a planetary-mass companion with a median mass $M_{\rm c,med} \sim 3$ M$_{\rm J}$ and a mean density of $\rho \sim 11$ g cm$^{-3}$. M62I has an orbital period of 0.51 days and a $M_{\rm c,med} \sim 0.15$ M$_{\odot}$. Neither of these low-mass systems exhibit eclipses. M62J has only been detected in the two UHF band (816 MHz) observations with a flux density $S_{816} = 0.08$ mJy. The non-detection in the L-band (1284 MHz) indicates it has a relatively steep spectrum ($\beta < -3.1$). We also present 23-yr-long timing solutions obtained using data from the Parkes "Murriyang", Effelsberg and MeerKAT telescopes for the six previously known pulsars. For all these pulsars, we measured the second spin-period derivatives and the rate of change of orbital period caused by the gravitational field of the cluster, and their proper motions. From these measurements, we conclude that the pulsars' maximum accelerations are consistent with the maximum cluster acceleration assuming a core-collapsed mass distribution. Studies of the eclipses of the redback M62B and the black widow M62E at four and two different frequency bands, respectively, reveal a frequency dependence with longer and asymmetric eclipses at lower frequencies. The presence of only binary MSPs in this cluster challenges models which suggest that the MSP population of core-collapsed clusters should be dominated by isolated MSPs., Comment: 22 pages, 8 figures, 6 tables. Accepted for publication in MNRAS

Centrosymmetric GdRu$_2$Si$_2$ exhibits a variety of multi-Q magnetic states as a function of temperature and applied magnetic field, including a square skyrmion-lattice phase. The material's behavior is strongly dependent on the direction of the applied field, with different phase diagrams resulting for fields applied parallel or perpendicular to the crystallographic $c$ axis. Here, we present the results of muon-spin relaxation ($\mu^+$SR) measurements on single crystals of GdRu$_2$Si$_2$. Our analysis is based on the computation of muon stopping sites and consideration of zero-point motion effects, allowing direct comparison with the underlying spin textures in the material. Using transverse-field $\mu^+$SR with fields applied along either the [001] or [100] crystallographic directions, we distinguish between the magnetic phases in this system via their distinct muon response, providing additional evidence for the skyrmion and meron-lattice phases, while also suggesting the existence of RKKY-driven muon hyperfine coupling. Zero-field $\mu^+$SR provides clear evidence for a transition between two distinct magnetically-ordered phases at 39 K., Comment: 16 pages, 8 figures

We introduce a large-scale dataset named MultiGripperGrasp for robotic grasping. Our dataset contains 30.4M grasps from 11 grippers for 345 objects. These grippers range from two-finger grippers to five-finger grippers, including a human hand. All grasps in the dataset are verified in the robot simulator Isaac Sim to classify them as successful and unsuccessful grasps. Additionally, the object fall-off time for each grasp is recorded as a grasp quality measurement. Furthermore, the grippers in our dataset are aligned according to the orientation and position of their palms, allowing us to transfer grasps from one gripper to another. The grasp transfer significantly increases the number of successful grasps for each gripper in the dataset. Our dataset is useful to study generalized grasp planning and grasp transfer across different grippers. Data, code and videos for the project are available at https://irvlutd.github.io/MultiGripperGrasp, Comment: Published in IROS 2024

Series-parallel network topologies generally exhibit simplified dynamical behavior and avoid high combinatorial complexity. A comprehensive analysis of how flow complexity emerges with a graph's deviation from series-parallel topology is therefore of fundamental interest. We introduce the notion of a robust $k$-path on a directed acycylic graph, with increasing values of the length $k$ reflecting increasing deviations. We propose a graph homology with robust $k$-paths as the bases of its chain spaces. In this framework, the topological simplicity of series-parallel graphs translates into a triviality of higher-order chain spaces. We discuss a correspondence between the space of order-three chains and sites within the network that are susceptible to the Braess paradox, a well-known phenomenon in transportation networks. In this manner, we illustrate the utility of the proposed graph homology in sytematically studying the complexity of flow networks., Comment: 7 pages, 3 figures, letter

We present local and nonlocal electrical transport measurements of the correlated insulator FeSb$_2$. By employing wiring configurations that delineate between bulk- and surface-dominated conduction, we reveal the formation of a metallic surface state in FeSb$_2$ for temperatures $\lessapprox 5$~K. This result is corroborated by an angular rotation study of this material's magnetotransport, which also shows signatures of the transition from bulk- to surface-dominated conduction over the same temperature interval as the local/nonlocal transport divergence. Notable similarities with the topological Kondo insulator candidate SmB$_6$ are discussed.

Query performance (e.g., execution time) prediction is a critical component of modern DBMSes. As a pioneering cloud data warehouse, Amazon Redshift relies on an accurate execution time prediction for many downstream tasks, ranging from high-level optimizations, such as automatically creating materialized views, to low-level tasks on the critical path of query execution, such as admission, scheduling, and execution resource control. Unfortunately, many existing execution time prediction techniques, including those used in Redshift, suffer from cold start issues, inaccurate estimation, and are not robust against workload/data changes. In this paper, we propose a novel hierarchical execution time predictor: the Stage predictor. The Stage predictor is designed to leverage the unique characteristics and challenges faced by Redshift. The Stage predictor consists of three model states: an execution time cache, a lightweight local model optimized for a specific DB instance with uncertainty measurement, and a complex global model that is transferable across all instances in Redshift. We design a systematic approach to use these models that best leverages optimality (cache), instance-optimization (local model), and transferable knowledge about Redshift (global model). Experimentally, we show that the Stage predictor makes more accurate and robust predictions while maintaining a practical inference latency and memory overhead. Overall, the Stage predictor can improve the average query execution latency by $20\%$ on these instances compared to the prior query performance predictor in Redshift., Comment: 15 pages

A key open question in the study of layered superconducting nickelate films is the role that hydrogen incorporation into the lattice plays in the appearance of the superconducting state. Due to the challenges of stabilizing highly crystalline square planar nickelate films, films are prepared by the deposition of a more stable parent compound which is then transformed into the target phase via a topotactic reaction with a strongly reducing agent such as CaH$_2$. Recent studies, both experimental and theoretical, have introduced the possibility that the incorporation of hydrogen from the reducing agent into the nickelate lattice may be critical for the superconductivity. In this work, we use secondary ion mass spectrometry to examine superconducting La$_{1-x}$X$_x$NiO$_2$ / SrTiO$_3$ (X = Ca and Sr) and Nd$_6$Ni$_5$O$_{12}$ / NdGaO$_3$ films, along with non-superconducting NdNiO$_2$ / SrTiO$_3$ and (Nd,Sr)NiO$_2$ / SrTiO$_3$. We find no evidence for extensive hydrogen incorporation across a broad range of samples, including both superconducting and non-superconducting films. Theoretical calculations indicate that hydrogen incorporation is broadly energetically unfavorable in these systems, supporting our conclusion that hydrogen incorporation is not generally required to achieve a superconducting state in layered square-planar nickelates., Comment: 23 pages, 21 figures (including supplemental information)

Semi-supervised datasets are ubiquitous across diverse domains where obtaining fully labeled data is costly or time-consuming. The prevalence of such datasets has consistently driven the demand for new tools and methods that exploit the potential of unlabeled data. Responding to this demand, we introduce semi-supervised U-statistics enhanced by the abundance of unlabeled data, and investigate their statistical properties. We show that the proposed approach is asymptotically Normal and exhibits notable efficiency gains over classical U-statistics by effectively integrating various powerful prediction tools into the framework. To understand the fundamental difficulty of the problem, we derive minimax lower bounds in semi-supervised settings and showcase that our procedure is semi-parametrically efficient under regularity conditions. Moreover, tailored to bivariate kernels, we propose a refined approach that outperforms the classical U-statistic across all degeneracy regimes, and demonstrate its optimality properties. Simulation studies are conducted to corroborate our findings and to further demonstrate our framework.

BiTeI is a polar semiconductor with strong spin-orbit coupling (SOC) that produces large Rashba spin splitting. Due to its potential utility in spintronics and magnetoelectrics, it is essential to understand how defects impact the spin transport in this material. Using scanning tunneling microscopy and spectroscopy, we image ring-like charging states of single-atom defects on the iodine surface of BiTeI. We observe nanoscale variations in the Rashba energy around each defect, which we correlate with the local electric field extracted from the bias dependence of each ring radius. Our data demonstrate the local impact of atomic defects on the Rashba effect, which is both a challenge and an opportunity for the development of future nanoscale spintronic devices.

The interface of two materials can harbor unexpected emergent phenomena. One example is interface-induced superconductivity. In this work, we employ molecular beam epitaxy to grow a series of heterostructures formed by stacking together two non-superconducting antiferromagnetic materials, an intrinsic antiferromagnetic topological insulator MnBi2Te4 and an antiferromagnetic iron chalcogenide FeTe. Our electrical transport measurements reveal interface-induced superconductivity in these heterostructures. By performing scanning tunneling microscopy and spectroscopy measurements, we observe a proximity-induced superconducting gap on the top surface of the MnBi2Te4 layer, confirming the interaction between superconductivity and antiferromagnetism in the MnBi2Te4 layer. Our findings will advance the fundamental inquiries into the topological superconducting phase in hybrid devices and provide a promising platform for the exploration of chiral Majorana physics in MnBi2Te4-based heterostructures., Comment: 24 pages, 4 figures, comments are welcome

Many products in engineering are highly reliable with large mean lifetimes to failure. Performing lifetests under normal operations conditions would thus require long experimentation times and high experimentation costs. Alternatively, accelerated lifetests shorten the experimentation time by running the tests at higher than normal stress conditions, thus inducing more failures. Additionally, a log-linear regression model can be used to relate the lifetime distribution of the product to the level of stress it experiences. After estimating the parameters of this relationship, results can be extrapolated to normal operating conditions. On the other hand, censored data is common in reliability analysis. Interval-censored data arise when continuous inspection is difficult or infeasible due to technical or budgetary constraints. In this paper, we develop robust restricted estimators based on the density power divergence for step-stress accelerated life-tests under Weibull distributions with interval-censored data. We present theoretical asymptotic properties of the estimators and develop robust Rao-type test statistics based on the proposed robust estimators for testing composite null hypothesis on the model parameters.

Accelerated life tests (ALTs) play a crucial role in reliability analyses, providing lifetime estimates of highly reliable products. Among ALTs, step-stress design increases the stress level at predefined times, while maintaining a constant stress level between successive changes. This approach accelerates the occurrence of failures, reducing experimental duration and cost. While many studies assume a specific form for the lifetime distribution, in certain applications instead a general form satisfying certain properties should be preferred. Proportional hazard model assumes that applied stresses act multiplicatively on the hazard rate, so the hazards function may be divided into two factors, with one representing the effect of the stress, and the other representing the baseline hazard. In this work we examine two particular forms of baseline hazards, namely, linear and quadratic. Moreover, certain experiments may face practical constraints making continuous monitoring of devices infeasible. Instead, devices under test are inspected at predetermined intervals, leading to interval-censoring data. On the other hand, recent works have shown an appealing trade-off between the efficiency and robustness of divergence-based estimators. This paper introduces the step-stress ALT model under proportional hazards and presents a robust family of minimum density power divergence estimators (MDPDEs) for estimating device reliability and related lifetime characteristics such as mean lifetime and distributional quantiles. The asymptotic distributions of these estimates are derived, providing approximate confidence intervals. Empirical evaluations through Monte Carlo simulations demonstrate their performance in terms of robustness and efficiency. Finally, an illustrative example is provided to demonstrate the usefulness of the model and associated methods developed.

We improve the worst-case information theoretic lower bound of Munro and Wu (ISAAC 2018) for $n-$vertex unlabeled chordal graphs when vertex leafage is bounded and leafage is unbounded. The class of unlabeled $k-$vertex leafage chordal graphs that consists of all chordal graphs with vertex leafage at most $k$ and unbounded leafage, denoted $\mathcal{G}_k$, is introduced for the first time. For $k>0$ in $o(n/\log n)$, we obtain a lower bound of $((k-1)n \log n -kn \log k - O(\log n))-$bits on the size of any data structure that encodes a graph in $\mathcal{G}_k$. Further, for every $k-$vertex leafage chordal graph $G$ such that for $k>1$ in $o(n^c), c >0$, we present a $((k-1)n \log n + o(kn \log n))-$bit succinct data structure, constructed using the succinct data structure for path graphs with $kn/2$ vertices. Our data structure supports adjacency query in $O(k \log n)$ time and using additional $2n \log n$ bits, an $O(k^2 d_v \log n + \log^2 n)$ time neighbourhood query where $d_v$ is degree of $v \in V$., Comment: 19 pages, 2 figure

We assess the extent of similarity between pairs of probability distributions that arise naturally in quantum optics. We employ the Wasserstein distance, the Kullback-Leibler divergence and the Bhattacharyya distance to compare different states of light. We investigate the effectiveness of these quantifiers in identifying nonclassical effects such as wave packet revival phenomena and sudden changes in entanglement in generic bipartite systems. The calculations are carried out on relevant optical tomograms, circumventing state reconstruction., Comment: 11 pages, 11 figures

Electrical manipulation of magnetic order by current-induced spin torques lays the foundation for spintronics. One promising approach is encoding information in the N\'eel vector of antiferromagnetic (AFM) materials, particularly to collinear antiferromagnets with the perpendicular magnetic anisotropy (PMA), as the negligible stray fields and terahertz spin dynamics can enable memory devices with higher integration density and ultrafast speed. Here we demonstrate that the N\'eel order information in a prototypical collinear AFM insulator with PMA, Cr2O3, can be reliably readout via the anomalous Hall effect and efficiently switched by the spin-orbit torque (SOT) effect with a low current density of 5.8*106 A/cm2. Moreover, using Cr2O3 as a mediator, we electrically switch the magnetization of a Y3Fe5O12 film exchange-coupled to the Cr2O3 layer, unambiguously confirming the N\'eel order switching of the Cr2O3 layer. This work provides a significant basis for developing AFM memory devices based on collinear AFM materials with PMA.

Electrically driven insulator-metal transitions in prototypical quantum materials such as VO2 offer a foundational platform for designing novel solid-state devices. Tuning the V: O stoichiometry offers a vast electronic phase space with non-trivial collective properties. Here, we report the discovery of discrete threshold switching voltages with constant threshold voltage difference between cycles in vanadium oxide crystals. The observed threshold fields over 10000 cycles are ~100X lower than that noted for stoichiometric VO2 and show unique discrete behaviour. We correlate the observed discrete memristor behaviour with the valence change mechanism and fluctuations in the chemical composition of spatially distributed VO2-VnO2n-1 complex oxide phases. Design of chemical heterogeneity in Mott insulators, therefore, offers an intriguing path to realizing low-energy neuromorphic devices., Comment: 21 pages, 4 figures

In this paper, we propose R\'enyi information generating function (RIGF) and discuss its various properties. The relation between the RIGF and Shannon entropy of order $q>0$ is established. Several bounds are obtained. The RIGF of escort distribution is also derived. Furthermore, we introduce R\'enyi divergence information generating function (RDIGF) and discuss its effect under monotone transformations. Next, we propose Jensen-R\'enyi information generating function (JRIGF) and establish its properties. In addition, we present non-parametric and parametric estimators of the RIGF. For illustrative purpose, a simulation study is carried out and a real data relating to the failure times of electronic components is analyzed. Finally, a comparison study between the non-parametric and parametric estimators is made in terms of absolute bias and mean square error (MSE).