Miracle-dz/UniFAR-FTUs huggingface dataset:
Out-of-distribution detection in satellite image classification

In satellite image analysis, distributional mismatch between the training and test data may arise due to several reasons, including unseen classes in the test data and differences in the geographic area. Deep learning based models may behave in unexpected manner when subjected to test data that has such distributional shifts from the training data, also called out-of-distribution (OOD) examples. Predictive uncertainly analysis is an emerging research topic which has not been explored much in context of satellite image analysis. Towards this, we adopt a Dirichlet Prior Network based model to quantify distributional uncertainty of deep learning models for remote sensing. The approach seeks to maximize the representation gap between the in-domain and OOD examples for a better identification of unknown examples at test time. Experimental results on three exemplary test scenarios show the efficacy of the model in satellite image analysis.
Dynamic Matching Markets in Power Grid: Concepts and Solution using Deep Reinforcement Learning

Traditional bulk load flexibility options, such as load shifting and load curtailment, for managing uncertainty in power markets limit the diversity of options and ignore the preferences of the individual loads, thus reducing efficiency and welfare. This paper proposes an alternative to bulk load flexibility options for managing uncertainty in power markets: a reinforcement learning based dynamic matching market. We propose a novel hybrid learning-based model for maximizing social welfare in the dynamic matching market. The key features of our model is a fixed rule-based function and a learnable component that can be trained by data gathered online with no prior knowledge or expert supervision. The output of the learnable component is a probability distribution over the matching decisions for the individual customers. The proposed hybrid model enables the learning algorithm to find an effective matching policy that simultaneously satisfies the customers' servicing preferences. The simulations show that the learning algorithm learns an effective matching policy for different generation-consumption profiles and exhibits better performance compared to standard online matching heuristics such as Match on Arrival, Match to the Highest, and Match to the Earliest Deadline policies.
View-Guided Point Cloud Completion

This paper presents a view-guided solution for the task of point cloud completion. Unlike most existing methods directly inferring the missing points using shape priors, we address this task by introducing ViPC (view-guided point cloud completion) that takes the missing crucial global structure information from an extra single-view image. By leveraging a framework that sequentially performs effective cross-modality and cross-level fusions, our method achieves significantly superior results over typical existing solutions on a new large-scale dataset we collect for the view-guided point cloud completion task.
AutoGL: A Library for Automated Graph Learning

Recent years have witnessed an upsurge in research interests and applications of machine learning on graphs. However, manually designing the optimal machine learning algorithms for different graph datasets and tasks is inflexible, labor-intensive, and requires expert knowledge, limiting its adaptivity and applicability. Automated machine learning (AutoML) on graphs, aiming to automatically design the optimal machine learning algorithm for a given graph dataset and task, has received considerable attention. However, none of the existing libraries can fully support AutoML on graphs. To fill this gap, we present Automated Graph Learning (AutoGL), the first dedicated library for automated machine learning on graphs. AutoGL is open-source, easy to use, and flexible to be extended. Specifically, we propose a three-layer architecture, consisting of backends to interface with devices, a complete automated graph learning pipeline, and supported graph applications. The automated machine learning pipeline further contains five functional modules: auto feature engineering, neural architecture search, hyper-parameter optimization, model training, and auto ensemble, covering the majority of existing AutoML methods on graphs. For each module, we provide numerous state-of-the-art methods and flexible base classes and APIs, which allow easy usage and customization. We further provide experimental results to showcase the usage of our AutoGL library. We also present AutoGL-light, a lightweight version of AutoGL to facilitate customizing pipelines and enriching applications, as well as benchmarks for graph neural architecture search. The codes of AutoGL are publicly available at https://github.com/THUMNLab/AutoGL.
Adaptive Learning on the Grids for Elliptic Hemivariational Inequalities

This paper introduces a deep learning method for solving an elliptic hemivariational inequality (HVI). In this method, an expectation minimization problem is first formulated based on the variational principle of underlying HVI, which is solved by stochastic optimization algorithms using three different training strategies for updating network parameters. The method is applied to solve two practical problems in contact mechanics, one of which is a frictional bilateral contact problem and the other of which is a frictionless normal compliance contact problem. Numerical results show that the deep learning method is efficient in solving HVIs and the adaptive mesh-free multigrid algorithm can provide the most accurate solution among the three learning methods.
Statistical inference of finite-rank tensors

We consider a general statistical inference model of finite-rank tensor products. For any interaction structure and any order of tensor products, we identify the limit free energy of the model in terms of a variational formula. Our approach consists of showing first that the limit free energy must be the viscosity solution to a certain Hamilton-Jacobi equation.
Effectiveness of Social Virtual Reality

A lot of work in social virtual reality, including our own group’s, has focused on effectiveness of specific social behaviours such as eye-gaze, turn taking, gestures and other verbal and non-verbal cues. We have built upon these to look at emergent phenomena such as co-presence, leadership and trust. These give us good information about the usability issues of specific social VR systems, but they don’t give us much information about the requirements for such systems going forward. In this short paper we discuss how we are broadening the scope of our work on social systems, to move out of the laboratory to more ecologically valid situations and to study groups using social VR for longer periods of time.
Concentration inequalities for ultra log-concave distributions

We establish concentration inequalities in the class of ultra log-concave distributions. In particular, we show that ultra log-concave distributions satisfy Poisson concentration bounds. As an application, we derive concentration bounds for the intrinsic volumes of a convex body, which generalizes and improves a result of Lotz, McCoy, Nourdin, Peccati, and Tropp (2019).
DuRIN: A Deep-unfolded Sparse Seismic Reflectivity Inversion Network

We consider the reflection seismology problem of recovering the locations of interfaces and the amplitudes of reflection coefficients from seismic data, which are vital for estimating the subsurface structure. The reflectivity inversion problem is typically solved using greedy algorithms and iterative techniques. Sparse Bayesian learning framework, and more recently, deep learning techniques have shown the potential of data-driven approaches to solve the problem. In this paper, we propose a weighted minimax-concave penalty-regularized reflectivity inversion formulation and solve it through a model-based neural network. The network is referred to as deep-unfolded reflectivity inversion network (DuRIN). We demonstrate the efficacy of the proposed approach over the benchmark techniques by testing on synthetic 1-D seismic traces and 2-D wedge models and validation with the simulated 2-D Marmousi2 model and real data from the Penobscot 3D survey off the coast of Nova Scotia, Canada.
Preprocessing Methods of Lane Detection and Tracking for Autonomous Driving

In the past few years, researches on advanced driver assistance systems (ADASs) have been carried out and deployed in intelligent vehicles. Systems that have been developed can perform different tasks, such as lane keeping assistance (LKA), lane departure warning (LDW), lane change warning (LCW) and adaptive cruise control (ACC). Real time lane detection and tracking (LDT) is one of the most consequential parts to performing the above tasks. Images which are extracted from the video, contain noise and other unwanted factors such as variation in lightening, shadow from nearby objects and etc. that requires robust preprocessing methods for lane marking detection and tracking. Preprocessing is critical for the subsequent steps and real time performance because its main function is to remove the irrelevant image parts and enhance the feature of interest. In this paper, we survey preprocessing methods for detecting lane marking as well as tracking lane boundaries in real time focusing on vision-based system.
Deep learning using Havrda-Charvat entropy for classification of pulmonary endomicroscopy

Pulmonary optical endomicroscopy (POE) is an imaging technology in real time. It allows to examine pulmonary alveoli at a microscopic level. Acquired in clinical settings, a POE image sequence can have as much as 25% of the sequence being uninformative frames (i.e. pure-noise and motion artefacts). For future data analysis, these uninformative frames must be first removed from the sequence. Therefore, the objective of our work is to develop an automatic detection method of uninformative images in endomicroscopy images. We propose to take the detection problem as a classification one. Considering advantages of deep learning methods, a classifier based on CNN (Convolutional Neural Network) is designed with a new loss function based on Havrda-Charvat entropy which is a parametrical generalization of the Shannon entropy. We propose to use this formula to get a better hold on all sorts of data since it provides a model more stable than the Shannon entropy. Our method is tested on one POE dataset including 2947 distinct images, is showing better results than using Shannon entropy and behaves better with regard to the problem of overfitting. Keywords: Deep Learning, CNN, Shannon entropy, Havrda-Charvat entropy, Pulmonary optical endomicroscopy.
Representation Learning for Networks in Biology and Medicine: Advancements, Challenges, and Opportunities

With the remarkable success of representation learning in providing powerful predictions and data insights, we have witnessed a rapid expansion of representation learning techniques into modeling, analysis, and learning with networks. Biomedical networks are universal descriptors of systems of interacting elements, from protein interactions to disease networks, all the way to healthcare systems and scientiﬁc knowledge. In this review, we put forward an observation that long-standing principles of network biology and medicine—while often unspoken in machine learning research—can provide the conceptual grounding for representation learning, explain its current successes and limitations, and inform future advances. We synthesize a spectrum of algorithmic approaches that, at their core, leverage topological features to embed networks into compact vector spaces. We also provide a taxonomy of biomedical areas that are likely to beneﬁt most from algorithmic innovation. Representation learning techniques are becoming essential for identifying causal variants underlying complex traits, disentangling behaviors of single cells and their impact on health, and diagnosing and treating diseases with safe and eﬀective medicines.
Boltzmann Tuning of Generative Models

The paper focuses on the a posteriori tuning of a generative model in order to favor the generation of good instances in the sense of some external differentiable criterion. The proposed approach, called Boltzmann Tuning of Generative Models (BTGM), applies to a wide range of applications. It covers conditional generative modelling as a particular case, and offers an affordable alternative to rejection sampling. The contribution of the paper is twofold. Firstly, the objective is formalized and tackled as a well-posed optimization problem; a practical methodology is proposed to choose among the candidate criteria representing the same goal, the one best suited to efficiently learn a tuned generative model. Secondly, the merits of the approach are demonstrated on a real-world application, in the context of robust design for energy policies, showing the ability of BTGM to sample the extreme regions of the considered criteria.
3D Convection-resolving Model of Temperate, Tidally Locked Exoplanets

A large fraction of known terrestrial-size exoplanets located in the habitable zone of M-dwarfs are expected to be tidally locked. Numerous efforts have been conducted to study the climate of such planets, using in particular 3D global climate models (GCMs). One of the biggest challenges in simulating such an extreme environment is to properly represent the effects of sub-grid convection. Most GCMs use either a simplistic convective-adjustment parameterization or sophisticated (e.g., mass flux scheme) Earth-tuned parameterizations. One way to improve the representation of convection is to study convection using numerical convection-resolving models (CRMs), with a fine spatial resolution. In this study, we developed a CRM coupling the non-hydrostatic dynamical core Advanced Research Weather-Weather Research and Forecast model with the radiative transfer and cloud/precipitation models of the Laboratoire de Météorologie Dynamique generic climate model to study convection and clouds on tidally locked planets, with a focus on Proxima b. Simulations were performed for a set of three surface temperatures (corresponding to three different incident fluxes) and two rotation rates, assuming an Earth-like atmosphere. The main result of our study is that while we recover the prediction of GCMs that (low-altitude) cloud albedo increases with increasing stellar flux, the cloud feedback is much weaker due to transient aggregation of convection leading to low partial cloud cover.
Learning Chebyshev Basis in Graph Convolutional Networks for Skeleton-based Action Recognition

Spectral graph convolutional networks (GCNs) are particular deep models which aim at extending neural networks to arbitrary irregular domains. The principle of these networks consists in projecting graph signals using the eigen-decomposition of their Laplacians, then achieving filtering in the spectral domain prior to back-project the resulting filtered signals onto the input graph domain. However, the success of these operations is highly dependent on the relevance of the used Laplacians which are mostly handcrafted and this makes GCNs clearly sub-optimal. In this paper, we introduce a novel spectral GCN that learns not only the usual convolutional parameters but also the Laplacian operators. The latter are designed"end-to-end"as a part of a recursive Chebyshev decomposition with the particularity of conveying both the differential and the non-differential properties of the learned representations -- with increasing order and discrimination power -- without overparametrizing the trained GCNs. Extensive experiments, conducted on the challenging task of skeleton-based action recognition, show the generalization ability and the outperformance of our proposed Laplacian design w.r. t. different baselines (built upon handcrafted and other learned Laplacians) as well as the related work.
Velocity Skinning for Real‐time Stylized Skeletal Animation

Secondary animation effects are essential for liveliness. We propose a simple, real‐time solution for adding them on top of standard skinning, enabling artist‐driven stylization of skeletal motion. Our method takes a standard skeleton animation as input, along with a skin mesh and rig weights. It then derives per‐vertex deformations from the different linear and angular velocities along the skeletal hierarchy. We highlight two specific applications of this general framework, namely the cartoon‐like “squashy” and “floppy” effects, achieved from specific combinations of velocity terms. As our results show, combining these effects enables to mimic, enhance and stylize physical‐looking behaviours within a standard animation pipeline, for arbitrary skinned characters. Interactive on CPU, our method allows for GPU implementation, yielding real‐time performances even on large meshes. Animator control is supported through a simple interface toolkit, enabling to refine the desired type and magnitude of deformation at relevant vertices by simply painting weights. The resulting rigged character automatically responds to new skeletal animation, without further input.
On the Prediction of the Subharmonic Threshold of an Ultrasound Contrast Agent

Subharmonics emitted from microbubble-based ultrasound contrast agents (UCA) are considered as a noninvasive indicator for diagnostic applications in medical fields. Contrast ultrasound imaging using the modality of subharmonics could be also better than that of using harmonics. Unlike the harmonics of an UCA that increase with increasing the amplitude of excitation pressure, subharmonics occur only when the excitation pressure exceeds a threshold value. The excitation pressure inducing the onset of subharmonic components from the UCA during nonlinear oscillations is known as the subharmonic threshold. Although numerous studies on the subharmonics of free bubbles or UCAs have been carried out for decades, several experimental observations—such as the shift of the subharmonic resonance and subharmonic threshold at low excitation pressures—cannot be well characterized by existing theories. An analytical method for resolving these inconsistencies remains incomplete. The present article theoretically investigates the subharmonic threshold of an UCA through the techniques of energy balancing in the phase plane and instability analysis of subharmonic-free oscillations. The influence of shell parameters of the UCA on the subharmonic threshold is studied. Those experimental observations are explained by the amplitudedependent nonlinear resonance and damping. The effect of nonspherical oscillations on subharmonic emissions is also discussed.
WEC: Deriving a Large-scale Cross-document Event Coreference dataset from Wikipedia

Cross-document event coreference resolution is a foundational task for NLP applications involving multi-text processing. However, existing corpora for this task are scarce and relatively small, while annotating only modest-size clusters of documents belonging to the same topic. To complement these resources and enhance future research, we present Wikipedia Event Coreference (WEC), an efficient methodology for gathering a large-scale dataset for cross-document event coreference from Wikipedia, where coreference links are not restricted within predefined topics. We apply this methodology to the English Wikipedia and extract our large-scale WEC-Eng dataset. Notably, our dataset creation method is generic and can be applied with relatively little effort to other Wikipedia languages. To set baseline results, we develop an algorithm that adapts components of state-of-the-art models for within-document coreference resolution to the cross-document setting. Our model is suitably efficient and outperforms previously published state-of-the-art results for the task.
One-shot learning for solution operators of partial differential equations

Discovering governing equations of a physical system, represented by partial differential equations (PDEs), from data is a central challenge in a variety of areas of science and engineering. Current methods require either some prior knowledge (e.g., candidate PDE terms) to discover the PDE form, or a large dataset to learn a surrogate model of the PDE solution operator. Here, we propose the first solution operator learning method that only needs one PDE solution, i.e., one-shot learning. We first decompose the entire computational domain into small domains, where we learn a local solution operator, and then we find the coupled solution via either mesh-based fixed-point iteration or meshfree local-solution-operator informed neural networks. We demonstrate the effectiveness of our method on different PDEs, and our method exhibits a strong generalization property.
Deep Recursive Embedding for High-Dimensional Data

Embedding high-dimensional data onto a low-dimensional manifold is of both theoretical and practical value. In this paper, we propose to combine deep neural networks (DNN) with mathematics-guided embedding rules for high-dimensional data embedding. We introduce a generic deep embedding network (DEN) framework, which is able to learn a parametric mapping from high-dimensional space to low-dimensional space, guided by well-established objectives such as Kullback-Leibler (KL) divergence minimization. We further propose a recursive strategy, called deep recursive embedding (DRE), to make use of the latent data representations for boosted embedding performance. We exemplify the flexibility of DRE by different architectures and loss functions, and benchmarked our method against the two most popular embedding methods, namely, t-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP). The proposed DRE method can map out-of-sample data and scale to extremely large datasets. Experiments on a range of public datasets demonstrated improved embedding performance in terms of local and global structure preservation, compared with other state-of-the-art embedding methods.
Sublinear Time Nearest Neighbor Search over Generalized Weighted Manhattan Distance

Nearest Neighbor Search (NNS) over generalized weighted distances is fundamental to a wide range of applications. The problem of NNS over the generalized weighted square Euclidean distance has been studied in previous work. However, numerous studies have shown that the Manhattan distance could be more effective than the Euclidean distance for high-dimensional NNS, which indicates that the generalized weighted Manhattan distance is possibly more practical than the generalized weighted square Euclidean distance in high dimensions. To the best of our knowledge, no prior work solves the problem of NNS over the generalized weighted Manhattan distance in sublinear time. This paper achieves the goal by proposing two novel hashing schemes ($d_w^{l_1},l_2$)-ALSH and ($d_w^{l_1},\theta$)-ALSH.
Synthesis of Frame Field-Aligned Multi-Laminar Structures

In the field of topology optimization, the homogenization approach has been revived as an important alternative to the established, density-based methods. Homogenization can represent microstructures at length scales decoupled from the resolution of the computational grid. The optimal microstructure for a single load case is an orthogonal rank-3 laminate. Initially, we investigate where singularities occur in orthogonal rank-3 laminates and show that the laminar parts of the structures we seek are unaffected by the singularities. Based on this observation, we propose a method for generating multi-laminar structures from frame fields that describe rank-3 laminates. Rather than establishing a parametrization of the domain, we compute stream surfaces that align with the frame fields and solve an optimization problem to find a well-spaced collection of such stream surfaces. Since our method does not rely on a parametrization, we also do not need a combing of the frame fields to generate this collection. Finally, we provide a method for synthesizing multi-laminar structures from a stream surface collection. This method produces a volumetric solid for each surface and combines these to form the output. We demonstrate our method on several frame fields produced by the homogenization approach to topology optimization.