Miracle-dz/UniFAR-FTUs huggingface dataset:
Batch Stationary Distribution Estimation

We consider the problem of approximating the stationary distribution of an ergodic Markov chain given a set of sampled transitions. Classical simulation-based approaches assume access to the underlying process so that trajectories of sufficient length can be gathered to approximate stationary sampling. Instead, we consider an alternative setting where a fixed set of transitions has been collected beforehand, by a separate, possibly unknown procedure. The goal is still to estimate properties of the stationary distribution, but without additional access to the underlying system. We propose a consistent estimator that is based on recovering a correction ratio function over the given data. In particular, we develop a variational power method (VPM) that provides provably consistent estimates under general conditions. In addition to unifying a number of existing approaches from different subfields, we also find that VPM yields significantly better estimates across a range of problems, including queueing, stochastic differential equations, post-processing MCMC, and off-policy evaluation.
Structure–Adaptive Sequential Testing for Online False Discovery Rate Control

Abstract Consider the online testing of a stream of hypotheses where a real-time decision must be made before the next data point arrives. The error rate is required to be controlled at all decision points. Conventional simultaneous testing rules are no longer applicable due to the more stringent error constraints and absence of future data. Moreover, the online decision-making process may come to a halt when the total error budget, or alpha-wealth, is exhausted. This work develops a new class of structure-adaptive sequential testing (SAST) rules for online false discovery rate (FDR) control. A key element in our proposal is a new alpha-investing algorithm that precisely characterizes the gains and losses in sequential decision making. SAST captures time varying structures of the data stream, learns the optimal threshold adaptively in an ongoing manner and optimizes the alpha-wealth allocation across different time periods. We present theory and numerical results to show that SAST is asymptotically valid for online FDR control and achieves substantial power gain over existing online testing rules.
Style Example-Guided Text Generation using Generative Adversarial Transformers

We introduce a language generative model framework for generating a styled paragraph based on a context sentence and a style reference example. The framework consists of a style encoder and a texts decoder. The style encoder extracts a style code from the reference example, and the text decoder generates texts based on the style code and the context. We propose a novel objective function to train our framework. We also investigate different network design choices. We conduct extensive experimental validation with comparison to strong baselines to validate the effectiveness of the proposed framework using a newly collected dataset with diverse text styles. Both code and dataset will be released upon publication.
Triangle-Net: Towards Robustness in Point Cloud Classification

3D object recognition is becoming a key desired capability for many computer vision systems such as autonomous vehicles, service robots and surveillance drones to operate more effectively in unstructured environments. These real-time systems require effective classification methods that are robust to sampling resolution, measurement noise, and pose configuration of the objects. Previous research has shown that sparsity, rotation and positional variance of points can lead to a significant drop in the performance of point cloud based classification techniques. In this regard, we propose a novel approach for 3D classification that takes sparse point clouds as input and learns a model that is robust to rotational and positional variance as well as point sparsity. To this end, we introduce new feature descriptors which are fed as an input to our proposed neural network in order to learn a robust latent representation of the 3D object. We show that such latent representations can significantly improve the performance of object classification and retrieval. Further, we show that our approach outperforms PointNet and 3DmFV by 34.4% and 27.4% respectively in classification tasks using sparse point clouds of only 16 points under arbitrary SO(3) rotation.
Microstructure and continuous phase transition of a regular Hayward black hole in anti-de Sitter spacetime

In this article we study the thermodynamic phase transition of a regular Hayward-AdS black hole, by introducing a new order parameter, which is the potential conjugate to the magnetic charge arising from a non-linearly coupled electromagnetic field. We use Landau continuous phase transition theory to discuss the van der Waals-like critical phenomena of the black hole. The well-known interpretation of the phase transition of an AdS black hole as being a large and small black hole transition is re-interpreted as being a transition between a high-potential phase and a low-potential phase. The microstructure associated with this phase transition is studied using the Ruppeiner geometry. By investigating the behaviour of the Ruppeiner scalar curvature, we find that charged and uncharged (effective) molecules of the black hole have distinct microstructure, which is analogous to that of fermion and boson gas.
The exploration of the Adjacent Possible explains the emergence and evolution of social networks

The interactions among human beings represent the backbone of our societies. How people interact, establish new connections, and allocate their activities among these links can reveal a lot of our social organization. Despite focused attention by very diverse scientific communities, we still lack a first-principles modeling framework able to account for the birth and evolution of social networks. Here, we tackle this problem by looking at social interactions as a way to explore a very peculiar space, namely the adjacent possible space, i.e., the set of individuals we can meet at any given point in time during our lifetime. We leverage on a recent mathematical formalization of the adjacent possible space to propose a first-principles theory of social exploration based on simple microscopic rules defining how people get in touch and interact. The new theory predicts both microscopic and macroscopic features of social networks. The most striking feature captured on the microscopic side is the probability for an individual, with already $k$ connections, to acquire a new acquaintance. On the macroscopic side, the model reproduces the main static and dynamic features of social networks: the broad distribution of degree and activities, the average clustering coefficient and the innovation rate at the global and local level. The theory is born out in three diverse real-world social networks: the network of mentions between Twitter users, the network of co-authorship of the American Physical Society and a mobile-phone-call network.
StructSum: Incorporating Latent and Explicit Sentence Dependencies for Single Document Summarization

Traditional preneural approaches to single document summarization relied on modeling the intermediate structure of a document before generating the summary. In contrast, the current state of the art neural summarization models do not preserve any intermediate structure, resorting to encoding the document as a sequence of tokens. The goal of this work is two-fold: to improve the quality of generated summaries and to learn interpretable document representations for summarization. To this end, we propose incorporating latent and explicit sentence dependencies into single-document summarization models. We use structure-aware encoders to induce latent sentence relations, and inject explicit coreferring mention graph across sentences to incorporate explicit structure. On the CNN/DM dataset, our model outperforms standard baselines and provides intermediate latent structures for analysis. We present an extensive analysis of our summaries and show that modeling document structure reduces copying long sequences and incorporates richer content from the source document while maintaining comparable summary lengths and an increased degree of abstraction.
Geometry-based circulation of local photonic transport in a triangular metastructure

A geometry-based mechanism for inducing circulation of photons is illustrated by a metastructure consisting of quantum dots arranged in a triangle coupled to photonic structures. The coupling between the photons and the excitons in the quantum dots leads to a photon blockade and limits the number of photons participating in the transport. In the steady state described by the quantum master equation of photons, the local photonic currents exhibit distinct circulation patterns, which originate from the wave nature in a multi-path geometry. The geometry-based mechanism does not require an artificial gauge field from light-matter interactions. The phase diagrams showing where different patterns of circulation can be found saturate as the number of photons allowed on each site increases. By using the third-quantization formalism, we show the circulation survives without any photon blockade in the noninteracting case. Moreover, we demonstrate the decoupling of the direction of the local current from the density difference and propose possible applications of the local photonic transport.
Finding Interesting Frames in Deep Video Analytics: a Top-K Approach

Recently, the impressive accuracy of deep neural networks (DNNs) has created great demands on practical analytics over video data. Although efficient and accurate, the latest video analytic systems have not supported analytics beyond simple queries like selection. In data analytics, Top-K is a very important analytical operation that enables analysts to focus on the most important entities. In this paper, we present Everest, the first system that supports efficient and accurate Top-K video analytics. Everest ranks and identifies the most interesting frames/moments from videos with probabilistic guarantees. Everest is built with a careful synthesis of deep computer vision, machine learning, uncertain data management, and Top-K query processing. Evaluations on five real-world videos and the latest Visual Road benchmark show that Everest achieves between 16.3x to 20.6x higher efficiency than baseline approaches with high result accuracy.
Exposing Backdoors in Robust Machine Learning Models

The introduction of robust optimisation has pushed the state-of-the-art in defending against adversarial attacks. However, the behaviour of such optimisation has not been studied in the light of a fundamentally different class of attacks called backdoors. In this paper, we demonstrate that adversarially robust models are susceptible to backdoor attacks. Subsequently, we observe that backdoors are reflected in the feature representation of such models. Then, this observation is leveraged to detect backdoor-infected models via a detection technique called AEGIS. Specifically, AEGIS uses feature clustering to effectively detect backdoor-infected robust Deep Neural Networks (DNNs). In our evaluation of major classification tasks using CIFAR-10, MNIST and FMNIST datasets, AEGIS effectively detects robust DNNs infected with backdoors. Overall, AEGIS has 97% (70/72) detection accuracy and 0.3% (2/648) false positive rate, for all configurations. Our investigation reveals that salient features of adversarially robust DNNs break the stealthy nature of backdoor attacks.
Analogue gravity on a superconducting chip

We describe how analogues of a Hawking evaporating black hole as well as the Unruh effect for an oscillatory, accelerating photodetector in vacuum may be realized using superconducting, microwave circuits that are fashioned out of Josephson tunnel junction and film bulk acoustic resonator elements. This article is part of a discussion meeting issue ‘The next generation of analogue gravity experiments’.
Modularity-Deltas: a Semi-Global Centrality Measure for Complex Networks

The phenomena of community structure in real-world networks has been central to network science's success. While much attention has been paid to community detection algorithms and evaluation criteria, a related question has been overlooked: what would the change in modularity be if a node were to be deleted from a network? In this work, we first show that the answer to this question can be calculated for all nodes in the network in $O(m)$ time. Then, we show that this quantity has implications in three research areas within network science: community analysis, network robustness, and community deception. Modularity-deltas are useful in community analysis as they are a semi-global measure, scalable like local measures but using global information through the network's this http URL robustness, modularity maximizing attack strategies are more efficient than betweenness-based ones, and are shown to damage the US Powergrid and European Road networks more effectively than degree based attacks. Finally, we show that inverting the robustness method to select modularity minimizing nodes solves the community deception problem in a scalable manner.
MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships

Monocular 3D object detection is an essential component in autonomous driving while challenging to solve, especially for those occluded samples which are only partially visible. Most detectors consider each 3D object as an independent training target, inevitably resulting in a lack of useful information for occluded samples. To this end, we propose a novel method to improve the monocular 3D object detection by considering the relationship of paired samples. This allows us to encode spatial constraints for partially-occluded objects from their adjacent neighbors. Specifically, the proposed detector computes uncertainty-aware predictions for object locations and 3D distances for the adjacent object pairs, which are subsequently jointly optimized by nonlinear least squares. Finally, the one-stage uncertainty-aware prediction structure and the post-optimization module are dedicatedly integrated for ensuring the run-time efficiency. Experiments demonstrate that our method yields the best performance on KITTI 3D detection benchmark, by outperforming state-of-the-art competitors by wide margins, especially for the hard samples.
Dust-depleted Inner Disks in a Large Sample of Transition Disks through Long-baseline ALMA Observations

Transition disks with large inner dust cavities are thought to host massive companions. However, the disk structure inside the companion orbit and how material flows toward an actively accreting star remain unclear. We present a high-resolution continuum study of inner disks in the cavities of 38 transition disks. Measurements of the dust mass from archival Atacama Large Millimeter/Submillimeter Array observations are combined with stellar properties and spectral energy distributions to assemble a detailed picture of the inner disk. An inner dust disk is detected in 18 of 38 disks in our sample. Of the 14 resolved disks, 8 are significantly misaligned with the outer disk. The near-infrared excess is uncorrelated with the mm-dust mass of the inner disk. The size–luminosity correlation known for protoplanetary disks is recovered for the inner disks as well, consistent with radial drift. The inner disks are depleted in dust relative to the outer disk, and their dust mass is uncorrelated with the accretion rates. This is interpreted as the result of radial drift and trapping by planets in a low α (∼10−3) disk, or a failure of the α-disk model to describe angular momentum transport and accretion. The only disk in our sample with confirmed planets in the gap, PDS 70, has an inner disk with a significantly larger radius and lower inferred gas-to-dust ratio than other disks in the sample. We hypothesize that these inner disk properties and the detection of planets are due to the gap having only been opened recently by young, actively accreting planets.
Identification of Random Coefficient Latent Utility Models

This paper provides nonparametric identification results for random coefficient distributions in perturbed utility models. We cover discrete and continuous choice models. We establish identification using variation in mean quantities, and the results apply when an analyst observes aggregate demands but not whether goods are chosen together. We require exclusion restrictions and independence between random slope coefficients and random intercepts. We do not require regressors to have large supports or parametric assumptions.
NeuroTac: A Neuromorphic Optical Tactile Sensor applied to Texture Recognition

Developing artificial tactile sensing capabilities that rival human touch is a long-term goal in robotics and prosthetics. Gradually more elaborate biomimetic tactile sensors are being developed and applied to grasping and manipulation tasks to help achieve this goal. Here we present the neuroTac, a novel neuromorphic optical tactile sensor. The neuroTac combines the biomimetic hardware design from the TacTip sensor which mimicks the layered papillae structure of human glabrous skin, with an event-based camera (DAVIS240, iniVation) and algorithms which transduce contact information in the form of spike trains. The performance of the sensor is evaluated on a texture classification task, with four spike coding methods being implemented and compared: Intensive, Spatial, Temporal and Spatiotemporal. We found timing-based coding methods performed with the highest accuracy over both artificial and natural textures. The spike-based output of the neuroTac could enable the development of biomimetic tactile perception algorithms in robotics as well as non-invasive and invasive haptic feedback methods in prosthetics.
PointASNL: Robust Point Clouds Processing Using Nonlocal Neural Networks With Adaptive Sampling

Raw point clouds data inevitably contains outliers or noise through acquisition from 3D sensors or reconstruction algorithms. In this paper, we present a novel end-to-end network for robust point clouds processing, named PointASNL, which can deal with point clouds with noise effectively. The key component in our approach is the adaptive sampling (AS) module. It first re-weights the neighbors around the initial sampled points from farthest point sampling (FPS), and then adaptively adjusts the sampled points beyond the entire point cloud. Our AS module can not only benefit the feature learning of point clouds, but also ease the biased effect of outliers. To further capture the neighbor and long-range dependencies of the sampled point, we proposed a local-nonlocal (L-NL) module inspired by the nonlocal operation. Such L-NL module enables the learning process insensitive to noise. Extensive experiments verify the robustness and superiority of our approach in point clouds processing tasks regardless of synthesis data, indoor data, and outdoor data with or without noise. Specifically, PointASNL achieves state-of-the-art robust performance for classification and segmentation tasks on all datasets, and significantly outperforms previous methods on real-world outdoor SemanticKITTI dataset with considerate noise.
Analysis of Intelligent Reflecting Surface-Assisted mmWave Doubly Massive-MIMO Communications

The emerging novel intelligent reflecting surface (IRS) is envisioned to be an important technique for future wireless networks in terms of enhancing both spectrum efficiency and energy efficiency. This paper is concerned with a millimeter-wave (mmWave) single-user system aided by an IRS which consists of several subsurfaces, each having the same number of passive reflecting elements. The achievable sum rate of such an IRS-aided system is derived under the assumption that both transmit and receive terminals are equipped with very large antenna arrays. Furthermore, with the objective of maximizing the sum rate, optimal solutions of precoding/combining, IRS's phase shifts, and power allocation are presented. Then it is shown that the multiplexing gain of the IRS-aided system increases with the number of subsurfaces while the power gain increases quadratically as the number of reflecting elements at each subsurface increases. Finally, numerical results are presented to corroborate analytical results.
Deep Learning for Musculoskeletal Image Analysis

The diagnosis, prognosis, and treatment of patients with musculoskeletal (MSK) disorders require radiology imaging (using computed tomography, magnetic resonance imaging (MRI), and ultrasound) and their precise analysis by expert radiologists. Radiology scans can also help assessment of metabolic health, aging, and diabetes. This study presents how machine learning, specifically deep learning methods, can be used for rapid and accurate image analysis of MRI scans, an unmet clinical need in MSK radiology. As a challenging example, we focus on automatic analysis of knee images from MRI scans and study machine learning classification of various abnormalities including meniscus and anterior cruciate ligament tears. Using widely used convolutional neural network (CNN) based architectures, we comparatively evaluated the knee abnormality classification performances of different neural network architectures under limited imaging data regime and compared single and multi-view imaging when classifying the abnormalities. Promising results indicated the potential use of multi-view deep learning based classification of MSK abnormalities in routine clinical assessment.
Vision-based Robot Manipulation Learning via Human Demonstrations

Vision-based learning methods provide promise for robots to learn complex manipulation tasks. However, how to generalize the learned manipulation skills to real-world interactions remains an open question. In this work, we study robotic manipulation skill learning from a single third-person view demonstration by using activity recognition and object detection in computer vision. To facilitate generalization across objects and environments, we propose to use a prior knowledge base in the form of a text corpus to infer the object to be interacted with in the context of a robot. We evaluate our approach in a real-world robot, using several simple and complex manipulation tasks commonly performed in daily life. The experimental results show that our approach achieves good generalization performance even from small amounts of training data.
Better Depth-Width Trade-offs for Neural Networks through the lens of Dynamical Systems

The expressivity of neural networks as a function of their depth, width and type of activation units has been an important question in deep learning theory. Recently, depth separation results for ReLU networks were obtained via a new connection with dynamical systems, using a generalized notion of fixed points of a continuous map $f$, called periodic points. In this work, we strengthen the connection with dynamical systems and we improve the existing width lower bounds along several aspects. Our first main result is period-specific width lower bounds that hold under the stronger notion of $L^1$-approximation error, instead of the weaker classification error. Our second contribution is that we provide sharper width lower bounds, still yielding meaningful exponential depth-width separations, in regimes where previous results wouldn't apply. A byproduct of our results is that there exists a universal constant characterizing the depth-width trade-offs, as long as $f$ has odd periods. Technically, our results follow by unveiling a tighter connection between the following three quantities of a given function: its period, its Lipschitz constant and the growth rate of the number of oscillations arising under compositions of the function $f$ with itself.