Miracle-dz/UniFAR-FTUs huggingface dataset:
On Ranking-based Tests of Independence

In this paper we develop a novel nonparametric framework to test the independence of two random variables $\mathbf{X}$ and $\mathbf{Y}$ with unknown respective marginals $H(dx)$ and $G(dy)$ and joint distribution $F(dx dy)$, based on {\it Receiver Operating Characteristic} (ROC) analysis and bipartite ranking. The rationale behind our approach relies on the fact that, the independence hypothesis $\mathcal{H}_0$ is necessarily false as soon as the optimal scoring function related to the pair of distributions $(H\otimes G,\; F)$, obtained from a bipartite ranking algorithm, has a ROC curve that deviates from the main diagonal of the unit square.We consider a wide class of rank statistics encompassing many ways of deviating from the diagonal in the ROC space to build tests of independence. Beyond its great flexibility, this new method has theoretical properties that far surpass those of its competitors. Nonasymptotic bounds for the two types of testing errors are established. From an empirical perspective, the novel procedure we promote in this paper exhibits a remarkable ability to detect small departures, of various types, from the null assumption $\mathcal{H}_0$, even in high dimension, as supported by the numerical experiments presented here.
Comments on characterizing demand flexibility to provide power grid services

Many loads have flexibility in demand that can be used to provide ancillary services to power grids. A large body of literature exists on designing algorithms to coordinate actions of many loads to provide such a service. The topic of characterizing the flexibility of one or a collection of loads - to determine what kinds of demand deviation from the baseline is feasible - has also been studied. However, there is a large diversity in definitions of flexibility and methods proposed to characterize flexibility. As a result there are several gaps in the literature on flexibility characterization. Some approaches on flexibility characterization are based on ad-hoc approximations that lead to highly conservative estimates. In this paper we point out some of these issues and their implications, with the hope to encourage additional research to address them.
Probabilistic Easy Variational Causal Effect

Let $X$ and $Z$ be random vectors, and $Y=g(X,Z)$. In this paper, on the one hand, for the case that $X$ and $Z$ are continuous, by using the ideas from the total variation and the flux of $g$, we develop a point of view in causal inference capable of dealing with a broad domain of causal problems. Indeed, we focus on a function, called Probabilistic Easy Variational Causal Effect (PEACE), which can measure the direct causal effect of $X$ on $Y$ with respect to continuously and interventionally changing the values of $X$ while keeping the value of $Z$ constant. PEACE is a function of $d\ge 0$, which is a degree managing the strengths of probability density values $f(x|z)$. On the other hand, we generalize the above idea for the discrete case and show its compatibility with the continuous case. Further, we investigate some properties of PEACE using measure theoretical concepts. Furthermore, we provide some identifiability criteria and several examples showing the generic capability of PEACE. We note that PEACE can deal with the causal problems for which micro-level or just macro-level changes in the value of the input variables are important. Finally, PEACE is stable under small changes in $\partial g_{in}/\partial x$ and the joint distribution of $X$ and $Z$, where $g_{in}$ is obtained from $g$ by removing all functional relationships defining $X$ and $Z$.
Joint Selection: Adaptively Incorporating Public Information for Private Synthetic Data

Mechanisms for generating differentially private synthetic data based on marginals and graphical models have been successful in a wide range of settings. However, one limitation of these methods is their inability to incorporate public data. Initializing a data generating model by pre-training on public data has shown to improve the quality of synthetic data, but this technique is not applicable when model structure is not determined a priori. We develop the mechanism jam-pgm, which expands the adaptive measurements framework to jointly select between measuring public data and private data. This technique allows for public data to be included in a graphical-model-based mechanism. We show that jam-pgm is able to outperform both publicly assisted and non publicly assisted synthetic data generation mechanisms even when the public data distribution is biased.
Intra-video Positive Pairs in Self-Supervised Learning for Ultrasound (preprint)

Self-supervised learning (SSL) is one strategy for addressing the paucity of labelled data in medical imaging by learning representations from unlabelled images. Contrastive and non-contrastive SSL methods produce learned representations that are similar for pairs of related images. Such pairs are commonly constructed by randomly distorting the same image twice. The videographic nature of ultrasound offers flexibility for defining the similarity relationship between pairs of images. In this study, we investigated the effect of utilizing proximal, distinct images from the same B-mode ultrasound video as pairs for SSL. Additionally, we introduced a sample weighting scheme that increases the weight of closer image pairs and demonstrated how it can be integrated into SSL objectives. Named Intra-Video Positive Pairs (IVPP), the method surpassed previous ultrasound-specific contrastive learning methods' average test accuracy on COVID-19 classification with the POCUS dataset by $\ge 1.3\%$. Detailed investigations of IVPP's hyperparameters revealed that some combinations of IVPP hyperparameters can lead to improved or worsened performance, depending on the downstream task. Guidelines for practitioners were synthesized based on the results, such as the merit of IVPP with task-specific hyperparameters, and the improved performance of contrastive methods for ultrasound compared to non-contrastive counterparts.
SHRIMP U–Pb zircon ages of the Hida metamorphic and plutonic rocks, Japan: Implications for late Paleozoic to Mesozoic tectonics around the Korean Peninsula

A new U–Pb zircon geochronological study for the Hida metamorphic and plutonic rocks from the Tateyama area in the Hida Mountains of north central Japan is presented. The U–Pb ages of metamorphic zircon grains with inherited/detrital cores in paragneisses suggest that a metamorphic event took place at around 235–250 Ma; the cores yield ages around 275 Ma, 300 Ma, 330 Ma, 1 850 Ma, and 2 650 Ma. New age data, together with geochronological and geological context of the Hida Belt, indicate that a sedimentary protolith of the paragneisses is younger than 275 Ma and was crystallized at around 235–250 Ma. Detrital ages support a model that the Hida Belt was located in the eastern margin of the North China Craton, which provided zircon grains from Paleoproterozoic to Paleozoic rocks and also from Archean and rare Neoproterozoic rocks. Triassic regional metamorphism possibly reflects collision between the North and South China Cratons.
Taming Pre-trained LLMs for Generalised Time Series Forecasting via Cross-modal Knowledge Distillation

Multivariate time series forecasting has recently gained great success with the rapid growth of deep learning models. However, existing approaches usually train models from scratch using limited temporal data, preventing their generalization. Recently, with the surge of the Large Language Models (LLMs), several works have attempted to introduce LLMs into time series forecasting. Despite promising results, these methods directly take time series as the input to LLMs, ignoring the inherent modality gap between temporal and text data. In this work, we propose a novel Large Language Models and time series alignment framework, dubbed LLaTA, to fully unleash the potentials of LLMs in the time series forecasting challenge. Based on cross-modal knowledge distillation, the proposed method exploits both input-agnostic static knowledge and input-dependent dynamic knowledge in pre-trained LLMs. In this way, it empowers the forecasting model with favorable performance as well as strong generalization abilities. Extensive experiments demonstrate the proposed method establishes a new state of the art for both long- and short-term forecasting. Code is available at \url{https://github.com/Hank0626/LLaTA}.
Real-time observation of picosecond-timescale optical quantum entanglement toward ultrafast quantum information processing

Entanglement is a fundamental resource of various optical quantum-information-processing (QIP) applications. Towards high-speed QIP system, entanglement should be encoded in short wavepackets. We report real-time observation of ultrafast optical Einstein-Podolsky-Rosen (EPR) correlation at a picosecond timescale in a continuous-wave (CW) system. Optical phase-sensitive amplification using 6-THz-bandwidth waveguide-optical-parametric amplifier enhances the effective efficiency of 70-GHz-bandwidth homodyne detectors, mainly used in 5th-generation telecommunication, enabling its use in real-time quantum-state measurement. While power measurement using frequency scanning, i.e., optical spectrum analyzer, is not performed in real-time, our observation is demonstrated through real-time amplitude measurement and can be directly employed in QIP applications. Observed EPR states show quantum correlation of 4.5 dB below shotnoise level encoded in wavepackets with 40-ps period, equivalent to 25-GHz repetition -- ${10^3}$ times faster than previous entanglement observation in CW system. The quantum correlation of 4.5 dB is already sufficient for several QIP applications, and our system can be readily extended to large-scale entanglement. Moreover, our scheme has high compatibility with optical communication technology such as wavelength-division multiplexing, and femtosecond-timescale observation is also feasible. Our demonstration is paradigm shift in accelerating accessible quantum correlation, the foundational resource of all quantum applications, from the nanosecond to picosecond timescale, enabling ultra-fast optical QIP.
A Converting Autoencoder Toward Low-latency and Energy-efficient DNN Inference at the Edge

Reducing inference time and energy usage while maintaining prediction accuracy has become a significant concern for deep neural networks (DNN) inference on resource-constrained edge devices. To address this problem, we propose a novel approach based on"converting"autoencoder and lightweight DNNs. This improves upon recent work such as early-exiting framework and DNN partitioning. Early-exiting frameworks spend different amounts of computation power for different input data depending upon their complexity. However, they can be inefficient in real-world scenarios that deal with many hard image samples. On the other hand, DNN partitioning algorithms that utilize the computation power of both the cloud and edge devices can be affected by network delays and intermittent connections between the cloud and the edge. We present CBNet, a low-latency and energy-efficient DNN inference framework tailored for edge devices. It utilizes a"converting"autoencoder to efficiently transform hard images into easy ones, which are subsequently processed by a lightweight DNN for inference. To the best of our knowledge, such autoencoder has not been proposed earlier. Our experimental results using three popular image-classification datasets on a Raspberry Pi 4, a Google Cloud instance, and an instance with Nvidia Tesla K80 GPU show that CBNet achieves up to 4.8x speedup in inference latency and 79% reduction in energy usage compared to competing techniques while maintaining similar or higher accuracy.
OPEN TEACH: A Versatile Teleoperation System for Robotic Manipulation

Open-sourced, user-friendly tools form the bedrock of scientific advancement across disciplines. The widespread adoption of data-driven learning has led to remarkable progress in multi-fingered dexterity, bimanual manipulation, and applications ranging from logistics to home robotics. However, existing data collection platforms are often proprietary, costly, or tailored to specific robotic morphologies. We present OPEN TEACH, a new teleoperation system leveraging VR headsets to immerse users in mixed reality for intuitive robot control. Built on the affordable Meta Quest 3, which costs $500, OPEN TEACH enables real-time control of various robots, including multi-fingered hands and bimanual arms, through an easy-to-use app. Using natural hand gestures and movements, users can manipulate robots at up to 90Hz with smooth visual feedback and interface widgets offering closeup environment views. We demonstrate the versatility of OPEN TEACH across 38 tasks on different robots. A comprehensive user study indicates significant improvement in teleoperation capability over the AnyTeleop framework. Further experiments exhibit that the collected data is compatible with policy learning on 10 dexterous and contact-rich manipulation tasks. Currently supporting Franka, xArm, Jaco, and Allegro platforms, OPEN TEACH is fully open-sourced to promote broader adoption. Videos are available at https://open-teach.github. io/.
Synthesis of epitaxial magnetic pyrochlore heterojunctions

The synthesis of stoichiometric and epitaxial pyrochlore iridate thin films presents significant challenges yet is critical for unlocking experimental access to novel topological and magnetic states. Towards this goal, we unveil an in-situ two-stage growth mechanism that facilitates the synthesis of high-quality oriented pyrochlore iridate thin films. The growth starts with the deposition of a pyrochlore titanate as an active iso-structural template, followed by the application of an in-situ solid phase epitaxy technique in the second stage to accomplish the formation of single crystalline, large-area films. This novel protocol ensures the preservation of stoichiometry and structural homogeneity, leading to a marked improvement in surface and interface qualities over previously reported methods. The success of this synthesis approach is attributed to the application of directional laser-heat annealing, which effectively reorganizes the continuous random network of ions into a crystalline structure, as evidenced by our comprehensive analysis of the growth kinetics. This new synthesis approach advances our understanding of pyrochlore iridate film fabrication and opens a new perspective for investigating their unique physical properties.
On the Global Convergence of Policy Gradient in Average Reward Markov Decision Processes

We present the first finite time global convergence analysis of policy gradient in the context of infinite horizon average reward Markov decision processes (MDPs). Specifically, we focus on ergodic tabular MDPs with finite state and action spaces. Our analysis shows that the policy gradient iterates converge to the optimal policy at a sublinear rate of $O\left({\frac{1}{T}}\right),$ which translates to $O\left({\log(T)}\right)$ regret, where $T$ represents the number of iterations. Prior work on performance bounds for discounted reward MDPs cannot be extended to average reward MDPs because the bounds grow proportional to the fifth power of the effective horizon. Thus, our primary contribution is in proving that the policy gradient algorithm converges for average-reward MDPs and in obtaining finite-time performance guarantees. In contrast to the existing discounted reward performance bounds, our performance bounds have an explicit dependence on constants that capture the complexity of the underlying MDP. Motivated by this observation, we reexamine and improve the existing performance bounds for discounted reward MDPs. We also present simulations to empirically evaluate the performance of average reward policy gradient algorithm.
Benign overfitting in leaky ReLU networks with moderate input dimension

The problem of benign overfitting asks whether it is possible for a model to perfectly fit noisy training data and still generalize well. We study benign overfitting in two-layer leaky ReLU networks trained with the hinge loss on a binary classification task. We consider input data which can be decomposed into the sum of a common signal and a random noise component, which lie on subspaces orthogonal to one another. We characterize conditions on the signal to noise ratio (SNR) of the model parameters giving rise to benign versus non-benign, or harmful, overfitting: in particular, if the SNR is high then benign overfitting occurs, conversely if the SNR is low then harmful overfitting occurs. We attribute both benign and non-benign overfitting to an approximate margin maximization property and show that leaky ReLU networks trained on hinge loss with Gradient Descent (GD) satisfy this property. In contrast to prior work we do not require near orthogonality conditions on the training data: notably, for input dimension $d$ and training sample size $n$, while prior work shows asymptotically optimal error when $d = \Omega(n^2 \log n)$, here we require only $d = \Omega\left(n \log \frac{1}{\epsilon}\right)$ to obtain error within $\epsilon$ of optimal.
An Early Carboniferous (Late Visean) Brachiopod Fauna from Tairagai in the Yokota Area, South Kitakami Belt, Japan

Abstract. In this paper, a brachiopod fauna (the Tairagai fauna), consisting of 11 species in 11 genera, is described from the uppermost part of the Odaira Formation at Tairagai in the Yokota area, South Kitakami Belt, northeastern Japan. The age of the fauna is identified as the late Visean (early Carboniferous). Palaeobiogeographically, the Tairagai fauna has a close affinity with those of western Europe (the UK, Germany and Belgium), central Russia (southern Urals and Kuznetsk Basin), Kazakhstan, Kyrgyzstan and northwestern China (Xinjiang and Gansu). The South Kitakami region probably was located near the North China block in the late Palaeozoic, and tectonically belongs to the CAOB.
Self-phoretic oscillatory motion in a harmonic trap

We consider the motion of a harmonically trapped overdamped particle, which is submitted to a self-phoretic force, that is proportional to the gradient of a diffusive field for which the particle itself is the source. In agreement with existing results for free particles or particles in a bounded domain, we find that the system exhibits a transition between an immobile phase, where the particle stays at the center of the trap, and an oscillatory state. We perform an exact analysis giving access to the bifurcation threshold, as well as the frequency of oscillations and their amplitude near the threshold. Our analysis also characterizes the shape of two-dimensional oscillations, that take place along a circle or a straight line. Our results are confirmed by numerical simulations.
On the Robustness of Lexicase Selection to Contradictory Objectives

Lexicase and epsilon-lexicase selection are state of the art parent selection techniques for problems featuring multiple selection criteria. Originally, lexicase selection was developed for cases where these selection criteria are unlikely to be in conflict with each other, but preliminary work suggests it is also a highly effective many-objective optimization algorithm. However, to predict whether these results generalize, we must understand lexicase selection's performance on contradictory objectives. Prior work has shown mixed results on this question. Here, we develop theory identifying circumstances under which lexicase selection will succeed or fail to find a Pareto-optimal solution. To make this analysis tractable, we restrict our investigation to a theoretical problem with maximally contradictory objectives. Ultimately, we find that lexicase and epsilon-lexicase selection each have a region of parameter space where they are incapable of optimizing contradictory objectives. Outside of this region, however, they perform well despite the presence of contradictory objectives. Based on these findings, we propose theoretically-backed guidelines for parameter choice. Additionally, we identify other properties that may affect whether a many-objective optimization problem is a good fit for lexicase or epsilon-lexicase selection.
On classical de Sitter solutions and parametric control

Finding string backgrounds with de Sitter spacetime, where all approximations and corrections are controlled, is an open problem. We revisit the search for de Sitter solutions in the classical regime for specific type IIB supergravity compactifications on group manifolds, an under-explored corner of the landscape that offers an interesting testing ground for swampland conjectures. While the supergravity de Sitter solutions we obtain numerically are ambiguous in terms of their classicality, we find an analytic scaling that makes four out of six compactification radii, as well as the overall volume, arbitrarily large. This potentially provides parametric control over corrections. If we could show that these solutions, or others to be found, are fully classical, they would constitute a counterexample to conjectures stating that asymptotic de Sitter solutions do not exist. We discuss this point in great detail.
QED Effects on Kerr-Newman Black Hole Shadows

Incorporating first-order QED effects, we explore the shadows of Kerr-Newman black holes with a magnetic charge through the numerical backward ray-tracing method. Our investigation accounts for both the direct influence of the electromagnetic field on light rays and the distortion of the background spacetime metric due to QED corrections. We notice that the area of the shadow increases with the QED effect, mainly due to the fact that the photons move more slowly in the effective medium and become easier to be trapped by the black hole.
Accurate Spatial Gene Expression Prediction by integrating Multi-resolution features

Recent advancements in Spatial Transcriptomics (ST) technology have facilitated detailed gene expression analysis within tissue contexts. However, the high costs and methodological limitations of ST necessitate a more robust predictive model. In response, this paper introduces TRIPLEX, a novel deep learning framework designed to predict spatial gene expression from Whole Slide Images (WSIs). TRIPLEX uniquely harnesses multi-resolution features, capturing cellular morphology at individual spots, the local context around these spots, and the global tissue organization. By integrating these features through an effective fusion strategy, TRIPLEX achieves accurate gene expression prediction. Our comprehensive benchmark study, conducted on three public ST datasets and supplemented with Visium data from 10X Genomics, demonstrates that TRIPLEX outperforms current state-of-the-art models in Mean Squared Error (MSE), Mean Absolute Error (MAE), and Pearson Correlation Coefficient (PCC). The model's predictions align closely with ground truth gene expression profiles and tumor annotations, underscoring TRIPLEX's potential in advancing cancer diagnosis and treatment.