Miracle-dz/UniFAR-FTUs huggingface dataset:
When Combinatorial Thompson Sampling meets Approximation Regret

We study the Combinatorial Thompson Sampling policy (CTS) for combinatorial multi-armed bandit problems (CMAB), within an approximation regret setting. Although CTS has attracted a lot of interest, it has a drawback that other usual CMAB policies do not have when considering non-exact oracles: for some oracles, CTS has a poor approximation regret (scaling linearly with the time horizon $T$) [Wang and Chen, 2018]. A study is then necessary to discriminate the oracles on which CTS could learn. This study was started by Kong et al. [2021]: they gave the first approximation regret analysis of CTS for the greedy oracle, obtaining an upper bound of order $\mathcal{O}(\log(T)/\Delta^2)$, where $\Delta$ is some minimal reward gap. In this paper, our objective is to push this study further than the simple case of the greedy oracle. We provide the first $\mathcal{O}(\log(T)/\Delta)$ approximation regret upper bound for CTS, obtained under a specific condition on the approximation oracle, allowing a reduction to the exact oracle analysis. We thus term this condition REDUCE2EXACT, and observe that it is satisfied in many concrete examples. Moreover, it can be extended to the probabilistically triggered arms setting, thus capturing even more problems, such as online influence maximization.
A Log-linear Gradient Descent Algorithm for Unbalanced Binary Classification using the All Pairs Squared Hinge Loss

Receiver Operating Characteristic (ROC) curves are plots of true positive rate versus false positive rate which are used to evaluate binary classification algorithms. Because the Area Under the Curve (AUC) is a constant function of the predicted values, learning algorithms instead optimize convex relaxations which involve a sum over all pairs of labeled positive and negative examples. Naive learning algorithms compute the gradient in quadratic time, which is too slow for learning using large batch sizes. We propose a new functional representation of the square loss and squared hinge loss, which results in algorithms that compute the gradient in either linear or log-linear time, and makes it possible to use gradient descent learning with large batch sizes. In our empirical study of supervised binary classification problems, we show that our new algorithm can achieve higher test AUC values on imbalanced data sets than previous algorithms, and make use of larger batch sizes than were previously feasible.
From stability to chaos in last‐passage percolation

We study the transition from stability to chaos in a dynamic last passage percolation model on with random weights at the vertices. Given an initial weight configuration at time 0, we perturb the model over time in such a way that the weight configuration at time is obtained by resampling each weight independently with probability . On the cube , we study geodesics, that is, weight‐maximizing up‐right paths from to , and their passage time . Under mild conditions on the weight distribution, we prove a phase transition between stability and chaos at . Indeed, as grows large, for small values of , the passage times at time 0 and time are highly correlated, while for large values of , the geodesics become almost disjoint.
A two-stage stochastic programming model for electric substation flood mitigation prior to an imminent hurricane

We present a stochastic programming model for informing the deployment of ad hoc flood mitigation measures to protect electrical substations prior to an imminent and uncertain hurricane. The first stage captures the deployment of a fixed number of mitigation resources, and the second stage captures grid operation in response to a contingency. The primary objective is to minimize expected load shed. We develop methods for simulating flooding induced by extreme rainfall and construct two geographically realistic case studies, one based on Tropical Storm Imelda and the other on Hurricane Harvey. Applying our model to those case studies, we investigate the effect of the mitigation budget on the optimal objective value and solutions. Our results highlight the sensitivity of the optimal mitigation to the budget, a consequence of those decisions being discrete. We additionally assess the value of having better mitigation options and the spatial features of the optimal mitigation.
The environmental dependence of u-band luminosity at fixed parameters or for different galaxy families

In this work, the apparent-magnitude limited Main galaxy sample of the Sloan Digital Sky Survey Data Release 8 (SDSS DR8) is applied to examine the correlation between u-band luminosity and environment at fixed parameters or for different galaxy families. Statistical results show that the abnormal environmental dependence of u-band luminosity for late-type galaxies and Low Stellar Mass (LSM) galaxies is fairly strong in the redshift range 0.03 ≤ z ≤ 0.09, while that for early-type galaxies, High Stellar Mass (HSM) galaxies, red galaxies, and blue galaxies is very weak in nearly all redshift bins. The result of this study further demonstrates that color is indeed fundamental in correlations between galaxy properties and the environment and that much of the u-band luminosity–density relation is likely due to the relation between color and density.
Edgeformers: Graph-Empowered Transformers for Representation Learning on Textual-Edge Networks

Edges in many real-world social/information networks are associated with rich text information (e.g., user-user communications or user-product reviews). However, mainstream network representation learning models focus on propagating and aggregating node attributes, lacking specific designs to utilize text semantics on edges. While there exist edge-aware graph neural networks, they directly initialize edge attributes as a feature vector, which cannot fully capture the contextualized text semantics of edges. In this paper, we propose Edgeformers, a framework built upon graph-enhanced Transformers, to perform edge and node representation learning by modeling texts on edges in a contextualized way. Specifically, in edge representation learning, we inject network information into each Transformer layer when encoding edge texts; in node representation learning, we aggregate edge representations through an attention mechanism within each node's ego-graph. On five public datasets from three different domains, Edgeformers consistently outperform state-of-the-art baselines in edge classification and link prediction, demonstrating the efficacy in learning edge and node representations, respectively.
On the Convergence of an IEQ-based first-order Numerical Scheme for the Beris-Edwards System

We present a convergence analysis of an unconditionally energy-stable first-order semi-discrete numerical scheme designed for a hydrodynamic Q-tensor model, the so-called Beris-Edwards system, based on the Invariant Energy Quadratization Method (IEQ). The model consists of the Navier-Stokes equations for the fluid flow, coupled to the Q-tensor gradient flow describing the liquid crystal molecule alignment. By using the Invariant Energy Quadratization Method, we obtain a linearly implicit scheme, accelerating the computational speed. However, this introduces an auxiliary variable to replace the bulk potential energy and it is a priori unclear whether the reformulated system is equivalent to the Beris-Edward system. In this work, we prove stability properties of the scheme and show its convergence to a weak solution of the coupled liquid crystal system. We also demonstrate the equivalence of the reformulated and original systems in the weak sense.
A Novel Vector-Field-Based Motion Planning for 3D Nonholonomic Robots

—This paper focuses on the motion planning for mobile robots in 3D, which are modelled by 6-DOF rigid body systems with nonholonomic constraints. We not only specify the target position, but also bring in the requirement of the heading direction at the terminal time, which gives rise to a new and more challenging 3D motion planning problem. The proposed planning algorithm involves a novel velocity vector ﬁeld (VF) over the workspace, and by following the VF, the robot can be navigated to the destination with the speciﬁed heading direction. In order to circumvent potential collisions with obstacles and other robots, a composite VF is presented by composing the navigation VF and an additional VF tangential to the boundary of the dangerous area. Moreover, we propose a priority-based algorithm to deal with the motion coupling among multiple robots. Finally, numerical simulations are conducted to verify the theoretical results.
Secure Antenna Selection and Beamforming in MIMO Systems

This work proposes a novel joint design for multiuser multiple-input multiple-output wiretap channels. The base station exploits a switching network to connect a subset of its antennas to the available radio frequency chains. The switching network and transmit beamformers are jointly designed to maximize the weighted secrecy sum-rate for this setting. The principal design problem reduces to an NP-hard mixed-integer non-linear programming. We invoke the fractional programming technique and the penalty dual decomposition method to develop a tractable iterative algorithm that effectively approximates the optimal design. Our numerical investigations validate the effectiveness of the proposed algorithm and its superior performance compared with the benchmark.
Learning to Generalize Provably in Learning to Optimize

Learning to optimize (L2O) has gained increasing popularity, which automates the design of optimizers by data-driven approaches. However, current L2O methods often suffer from poor generalization performance in at least two folds: (i) applying the L2O-learned optimizer to unseen optimizees, in terms of lowering their loss function values (optimizer generalization, or ``generalizable learning of optimizers"); and (ii) the test performance of an optimizee (itself as a machine learning model), trained by the optimizer, in terms of the accuracy over unseen data (optimizee generalization, or ``learning to generalize"). While the optimizer generalization has been recently studied, the optimizee generalization (or learning to generalize) has not been rigorously studied in the L2O context, which is the aim of this paper. We first theoretically establish an implicit connection between the local entropy and the Hessian, and hence unify their roles in the handcrafted design of generalizable optimizers as equivalent metrics of the landscape flatness of loss functions. We then propose to incorporate these two metrics as flatness-aware regularizers into the L2O framework in order to meta-train optimizers to learn to generalize, and theoretically show that such generalization ability can be learned during the L2O meta-training process and then transformed to the optimizee loss function. Extensive experiments consistently validate the effectiveness of our proposals with substantially improved generalization on multiple sophisticated L2O models and diverse optimizees. Our code is available at: https://github.com/VITA-Group/Open-L2O/tree/main/Model_Free_L2O/L2O-Entropy.
50 MeV electron beams accelerated by a terawatt scalable kHz laser

We show the laser-driven acceleration of unprecedented, collimated (2 mrad) and quasi-monoenergetic (dE/E = 25%) electron beams with energy up to 50 MeV at 1 kHz repetition rate. The laser driver is a multi-cycle (15 fs) 1 kHz OPCPA system, operating at 26 mJ (1.7 TW). The scalability of the driver laser technology and the electron beams reported in this work pave the way towards developing high brilliance X-ray sources for medical imaging, innovative devices for brain cancer treatment and represent a step forward to the realization of a kHz GeV electron beamline.
Recon: Reducing Conflicting Gradients from the Root for Multi-Task Learning

A fundamental challenge for multi-task learning is that different tasks may conflict with each other when they are solved jointly, and a cause of this phenomenon is conflicting gradients during optimization. Recent works attempt to mitigate the influence of conflicting gradients by directly altering the gradients based on some criteria. However, our empirical study shows that ``gradient surgery'' cannot effectively reduce the occurrence of conflicting gradients. In this paper, we take a different approach to reduce conflicting gradients from the root. In essence, we investigate the task gradients w.r. t. each shared network layer, select the layers with high conflict scores, and turn them to task-specific layers. Our experiments show that such a simple approach can greatly reduce the occurrence of conflicting gradients in the remaining shared layers and achieve better performance, with only a slight increase in model parameters in many cases. Our approach can be easily applied to improve various state-of-the-art methods including gradient manipulation methods and branched architecture search methods. Given a network architecture (e.g., ResNet18), it only needs to search for the conflict layers once, and the network can be modified to be used with different methods on the same or even different datasets to gain performance improvement. The source code is available at https://github.com/moukamisama/Recon.
A residual dense vision transformer for medical image super-resolution with segmentation-based perceptual loss fine-tuning

Super-resolution plays an essential role in medical imaging because it provides an alternative way to achieve high spatial resolutions and image quality with no extra acquisition costs. In the past few decades, the rapid development of deep neural networks has promoted super-resolution performance with novel network architectures, loss functions and evaluation metrics. Specifically, vision transformers dominate a broad range of computer vision tasks, but challenges still exist when applying them to low-level medical image processing tasks. This paper proposes an efficient vision transformer with residual dense connections and local feature fusion to achieve efficient single-image super-resolution (SISR) of medical modalities. Moreover, we implement a general-purpose perceptual loss with manual control for image quality improvements of desired aspects by incorporating prior knowledge of medical image segmentation. Compared with state-of-the-art methods on four public medical image datasets, the proposed method achieves the best PSNR scores of 6 modalities among seven modalities. It leads to an average improvement of $+0.09$ dB PSNR with only 38\% parameters of SwinIR. On the other hand, the segmentation-based perceptual loss increases $+0.14$ dB PSNR on average for SOTA methods, including CNNs and vision transformers. Additionally, we conduct comprehensive ablation studies to discuss potential factors for the superior performance of vision transformers over CNNs and the impacts of network and loss function components. The code will be released on GitHub with the paper published.
Solar Radio Spikes and Type IIIb Striae Manifestations of Subsecond Electron Acceleration Triggered by a Coronal Mass Ejection

Understanding electron acceleration associated with magnetic energy release at subsecond scales presents major challenges in solar physics. Solar radio spikes observed as subsecond, narrow-bandwidth bursts with Δf/f ∼ 10−3–10−2 are indicative of a subsecond evolution of the electron distribution. We present a statistical analysis of frequency- and time-resolved imaging of individual spikes and Type IIIb striae associated with a coronal mass ejection (CME). LOFAR imaging reveals that the cotemporal (<2 s) spike and striae intensity contours almost completely overlap. On average, both burst types have a similar source size with a fast expansion at millisecond scales. The radio source centroid velocities are often superluminal and independent of frequency over 30–45 MHz. The CME perturbs the field geometry, leading to increased spike emission likely due to frequent magnetic reconnection. As the field restores itself toward the prior configuration, the observed sky-plane emission locations drift to increased heights over tens of minutes. Combined with previous observations above 1 GHz, the average decay time and source size estimates follow a ∼1/f dependence over three decades in frequency, similar to radio-wave scattering predictions. Both time and spatial characteristics of the bursts between 30 and 70 MHz are consistent with radio-wave scattering with a strong anisotropy of the density fluctuation spectrum. Consequently, the site of the radio-wave emission does not correspond to the observed burst locations and implies acceleration and emission near the CME flank. The bandwidths suggest intrinsic emission source sizes <1″ at 30 MHz and magnetic field strengths a factor of two larger than average in events that produce decameter spikes.
BOOSTING THE PERFORMANCE OF DEEP LEARNING: A GRADIENT BOOSTING APPROACH TO TRAINING CONVOLUTIONAL AND DEEP NEURAL NETWORKS

Deep learning has revolutionized the computer vision and image classification domains. In this context Convolutional Neural Networks (CNNs) based architectures are the most widely applied models. In this article, we introduced two procedures for training Convolutional Neural Networks (CNNs) and Deep Neural Network based on Gradient Boosting (GB), namely GB-CNN and GB-DNN. These models are trained to fit the gradient of the loss function or pseudo-residuals of previous models. At each iteration, the proposed method adds one dense layer to an exact copy of the previous deep NN model. The weights of the dense layers trained on previous iterations are frozen to prevent over-fitting, permitting the model to fit the new dense as well as to fine-tune the convolutional layers (for GB-CNN) while still utilizing the information already learned. Through extensive experimentation on different 2D-image classification and tabular datasets, the presented models show superior performance in terms of classification accuracy with respect to standard CNN and Deep-NN with the same architecture.
A Reinforcement Learning Framework for Online Speaker Diarization

Speaker diarization is a task to label an audio or video recording with the identity of the speaker at each given time stamp. In this work, we propose a novel machine learning framework to conduct real-time multi-speaker diarization and recognition without prior registration and pretraining in a fully online and reinforcement learning setting. Our framework combines embedding extraction, clustering, and resegmentation into the same problem as an online decision-making problem. We discuss practical considerations and advanced techniques such as the offline reinforcement learning, semi-supervision, and domain adaptation to address the challenges of limited training data and out-of-distribution environments. Our approach considers speaker diarization as a fully online learning problem of the speaker recognition task, where the agent receives no pretraining from any training set before deployment, and learns to detect speaker identity on the fly through reward feedbacks. The paradigm of the reinforcement learning approach to speaker diarization presents an adaptive, lightweight, and generalizable system that is useful for multi-user teleconferences, where many people might come and go without extensive pre-registration ahead of time. Lastly, we provide a desktop application that uses our proposed approach as a proof of concept. To the best of our knowledge, this is the first approach to apply a reinforcement learning approach to the speaker diarization task.
Power Constrained Autotuning using Graph Neural Networks

Recent advances in multi and many-core processors have led to significant improvements in the performance of scientific computing applications. However, the addition of a large number of complex cores have also increased the overall power consumption, and power has become a first-order design constraint in modern processors. While we can limit power consumption by simply applying software-based power constraints, applying them blindly will lead to non-trivial performance degradation. To address the challenge of improving the performance, power, and energy efficiency of scientific applications on modern multi-core processors, we propose a novel Graph Neural Network based auto-tuning approach that (i) optimizes runtime performance at pre-defined power constraints, and (ii) simultaneously optimizes for runtime performance and energy efficiency by minimizing the energy-delay product. The key idea behind this approach lies in modeling parallel code regions as flow-aware code graphs to capture both semantic and structural code features. We demonstrate the efficacy of our approach by conducting an extensive evaluation on 30 benchmarks and proxy-/mini-applications with 68 OpenMP code regions. Our approach identifies OpenMP configurations at different power constraints that yield a geometric mean performance improvement of more than 25% and 13% over the default OpenMP configuration on a 32-core Skylake and a 16-core Haswell processor respectively. In addition, when we optimize for the energy-delay product, the OpenMP configurations selected by our auto-tuner demonstrate both performance improvement of 21% and 11% and energy reduction of 29% and 18% over the default OpenMP configuration at Thermal Design Power for the same Skylake and Haswell processors, respectively.
MONGOOSE: Path-wise Smooth Bayesian Optimisation via Meta-learning

In Bayesian optimisation, we often seek to minimise the black-box objective functions that arise in real-world physical systems. A primary contributor to the cost of evaluating such black-box objective functions is often the effort required to prepare the system for measurement. We consider a common scenario where preparation costs grow as the distance between successive evaluations increases. In this setting, smooth optimisation trajectories are preferred and the jumpy paths produced by the standard myopic (i.e.\ one-step-optimal) Bayesian optimisation methods are sub-optimal. Our algorithm, MONGOOSE, uses a meta-learnt parametric policy to generate smooth optimisation trajectories, achieving performance gains over existing methods when optimising functions with large movement costs.
Learning to Simulate Daily Activities via Modeling Dynamic Human Needs

Daily activity data that records individuals’ various types of activities in daily life are widely used in many applications such as activity scheduling, activity recommendation, and policymaking. Though with high value, its accessibility is limited due to high collection costs and potential privacy issues. Therefore, simulating human activities to produce massive high-quality data is of great importance to benefit practical applications. However, existing solutions, including rule-based methods with simplified assumptions of human behavior and data-driven methods directly fitting real-world data, both cannot fully qualify for matching reality. In this paper, motivated by the classic psychological theory, Maslow’s need theory describing human motivation, we propose a knowledge-driven simulation framework based on generative adversarial imitation learning. To enhance the fidelity and utility of the generated activity data, our core idea is to model the evolution of human needs as the underlying mechanism that drives activity generation in the simulation model. Specifically, this is achieved by a hierarchical model structure that disentangles different need levels, and the use of neural stochastic differential equations that successfully captures piecewise-continuous characteristics of need dynamics. Extensive experiments demonstrate that our framework outperforms the state-of-the-art baselines in terms of data fidelity and utility. Besides, we present the insightful interpretability of the need modeling. The code is available at https://github.com/tsinghua-fib-lab/Activity-Simulation-SAND.