Miracle-dz/UniFAR-FTUs huggingface dataset:
Choice Bandits

There has been much interest in recent years in the problem of dueling bandits, where on each round the learner plays a pair of arms and receives as feedback the outcome of a relative pairwise comparison between them. Here we study a natural generalization, that we term choice bandits, where the learner plays a set of up to k ≥ 2 arms, and receives limited relative feedback in the form of a single multiway choice among the pulled arms, drawn from an underlying multiway choice model. We study choice bandits under a very general class of choice models that is characterized by the existence of a unique ‘best’ arm (which we term generalized Condorcet winner), and includes as special cases the well-studied multinomial logit (MNL) and multinomial probit (MNP) choice models, and more generally, the class of random utility models with i.i. d. noise (IID-RUMs). We propose an algorithm for choice bandits, termed Winner Beats All (WBA), with a distribution dependent O(log T ) regret bound under all these choice models. The challenge in our setting is that the decision space is Θ(n), which is large for even moderate k. Our algorithm addresses this challenge by extracting just O(n) statistics from multiway choices and exploiting the existence of a unique ‘best’ arm to find arms that are competitive to this arm in order to construct sets with low regret. Since these statistics are extracted from the same choice observations, one needs a careful martingale analysis in order to show that these statistics are concentrated. We complement our upper bound result with a lower bound result, which shows that our upper bound is order-wise optimal. Our experiments demonstrate that for the special case of k = 2, our algorithm is competitive with previous dueling bandit algorithms, and for the more general case k > 2, outperforms the recently proposed MaxMinUCB algorithm designed for the MNL model.
Ratio Trace Formulation of Wasserstein Discriminant Analysis

We reformulate the Wasserstein Discriminant Analysis (WDA) as a ratio trace problem and present an eigensolver-based algorithm to compute the discriminative subspace of WDA. This new formulation, along with the proposed algorithm, can be served as an efﬁcient and more stable alternative to the original trace ratio formulation and its gradient-based algorithm. We provide a rigorous convergence analysis for the proposed algorithm under the self-consistent ﬁeld framework, which is crucial but missing in the literature. As an application, we combine WDA with low-dimensional clustering techniques, such as K-means, to perform sub-space clustering. Numerical experiments on real datasets show promising results of the ratio trace formulation of WDA in both classiﬁcation and clustering tasks.
Nonconvex Sparse Graph Learning under Laplacian Constrained Graphical Model

In this paper, we consider the problem of learning a sparse graph from the Laplacian constrained Gaussian graphical model. This problem can be formulated as a penalized maximum likelihood estimation of the precision matrix under Laplacian structural constraints. Like in the classical graphical lasso problem, recent works made use of the `1-norm with the goal of promoting sparsity in the Laplacian constrained precision matrix estimation. However, through empirical evidence, we observe that the `1-norm is not effective in imposing a sparse solution in this problem. From a theoretical perspective, we prove that a large regularization parameter will surprisingly lead to a solution representing a complete graph, i.e., every pair of vertices is connected by an edge. To address this issue, we propose a nonconvex penalized maximum likelihood estimation method, and establish the order of the statistical error. Numerical experiments involving synthetic and realworld data sets demonstrate the effectiveness of the proposed method. An open source R package is available at https://github.com/mirca/sparseGraph.
Adaptive Importance Sampling for Finite-Sum Optimization and Sampling with Decreasing Step-Sizes

Reducing the variance of the gradient estimator is known to improve the convergence rate of stochastic gradient-based optimization and sampling algorithms. One way of achieving variance reduction is to design importance sampling strategies. Recently, the problem of designing such schemes was formulated as an online learning problem with bandit feedback, and algorithms with sub-linear static regret were designed. In this work, we build on this framework and propose Avare, a simple and efficient algorithm for adaptive importance sampling for finite-sum optimization and sampling with decreasing step-sizes. Under standard technical conditions, we show that Avare achieves $\mathcal{O}(T^{2/3})$ and $\mathcal{O}(T^{5/6})$ dynamic regret for SGD and SGLD respectively when run with $\mathcal{O}(1/t)$ step sizes. We achieve this dynamic regret bound by leveraging our knowledge of the dynamics defined by the algorithm, and combining ideas from online learning and variance-reduced stochastic optimization. We validate empirically the performance of our algorithm and identify settings in which it leads to significant improvements.
Mitigating Forgetting in Online Continual Learning via Instance-Aware Parameterization

Online continual learning is a challenging scenario where a model needs to learn from a continuous stream of data without revisiting any previously encountered data instances. The phenomenon of catastrophic forgetting is worsened since the model should not only address the forgetting at the task-level but also at the data instance-level within the same task. To mitigate this, we leverage the concept of "instance awareness" in the neural network, where each data instance is classiﬁed by a path in the network searched by the controller from a meta-graph. To preserve the knowledge we learn from previous instances, we proposed a method to protect the path by restricting the gradient updates of one instance from overriding past updates calculated from previous instances if these instances are not similar. On the other hand, it also encourages ﬁne-tuning the path if the incoming instance shares the similarity with previous instances. The mechanism of selecting paths according to instances similarity is naturally determined by the controller, which is compact and online updated. Experimental results show that the proposed method outperforms state-of-the-arts in online continual learning. Furthermore, the proposed method is evaluated against a realistic setting where the boundaries between tasks are blurred. Experimental results conﬁrm that the proposed method outperforms the state-of-the-arts on CIFAR-10, CIFAR-100, and Tiny-ImageNet.
Graduated Assignment for Joint Multi-Graph Matching and Clustering with Application to Unsupervised Graph Matching Network Learning

This paper considers the setting of jointly matching and clustering multiple graphs belonging to different groups, which naturally rises in many realistic problems. Both graph matching and clustering are challenging (NP-hard) and a joint solution is appealing due to the natural connection of the two tasks. In this paper, we resort to a graduated assignment procedure for soft matching and clustering over iterations, whereby the two-way constraint and clustering conﬁdence are modulated by two separate annealing parameters, respectively. Our technique can be further utilized for end-to-end learning whose loss refers to the cross-entropy between two lines of matching pipelines, as such the keypoint feature extraction CNNs can be learned without ground-truth supervision. Experimental results on real-world benchmarks show our method outperforms learning-free algorithms and performs comparatively against two-graph based supervised graph matching approaches. Source code is publicly available as a module of ThinkMatch .
An introduction to physics-based animation

Physics-based animation has emerged as a core area of computer graphics finding widespread application in the film and video game industries as well as in areas such as virtual surgery, virtual reality, and training simulations. This course introduces students and practitioners to fundamental concepts in physics-based animation, placing an emphasis on breadth of coverage and providing a foundation for pursuing more advanced topics and current research in the area. The course focuses on imparting practical knowledge and intuitive understanding rather than providing detailed derivations of the underlying mathematics. The course is suitable for someone with no background in physics-based animation---the only prerequisites are basic calculus, linear algebra, and introductory physics. We begin with a simple, and complete, example of a mass-spring system, introducing the principles behind physics-based animation: mathematical modeling and numerical integration. From there, we systematically present the mathematical models commonly used in physics-based animation beginning with Newton's laws of motion and conservation of mass, momentum, and energy. We then describe the underlying physical and mathematical models for animating rigid bodies, soft bodies, and fluids. Then we describe how these continuous models are discretized in space and time, covering Lagrangian and Eulerian formulations, spatial discretizations and interpolation, and explicit and implicit time integration. In the final section, we discuss commonly used constraint formulations and solution methods.
Determinants of We-intention for Continue Playing FPS Game: Cooperation and Competition

This research explores factors that influence players' we-intention in first-person shooting (FPS) games. We-intention reflects one's perception of a task in the perspective of groups. Specifically, FPS as a multiplayer online games requires team efforts and interdependence between two or among more players. Therefore, the intention for continue playing is no longer one's personal decision. In order to understand the causes and consequences of we-intention in FPS games, this research draws on the perspectives of cooperation and competition, with spatial presence as the moderating variable. A research model was developed and empirically examined with 165 contributors from popular Facebook gaming communities in Taiwan. Data from the online survey was analyzed and results show that group cohesion is positively associated with we-intention for continue playing FPS game, and that social identity is positively associated with we-intention. In contrast, influences of team goal commitment and situational competitiveness on we-intention, and the moderating effects of social identity was not supported.
Counterfactual Prediction for Bundle Treatment

Estimating counterfactual outcome of different treatments from observational data is an important problem to assist decision making in a variety of ﬁelds. Among the various forms of treatment speciﬁcation, bundle treatment has been widely adopted in many scenarios, such as recommendation systems and online marketing. The bundle treatment usually can be abstracted as a high dimensional binary vector, which makes it more challenging for researchers to remove the confounding bias in observational data. In this work, we assume the existence of low dimensional latent structure underlying bundle treatment. Via the learned latent representations of treatments, we propose a novel variational sample re-weighting (VSR) method to eliminate confounding bias by decorrelating the treatments and confounders. Finally, we conduct extensive experiments to demonstrate that the predictive model trained on this re-weighted dataset can achieve more accurate counterfactual outcome prediction.
Profile Entropy: A Fundamental Measure for the Learnability and Compressibility of Distributions

The proﬁle of a sample is the multiset of its symbol frequencies. We show that for samples of discrete distributions, proﬁle entropy is a fundamental measure unifying the concepts of estimation, inference, and compression. Speciﬁcally, proﬁle entropy: a) determines the speed of estimating the distribution relative to the best natural estimator; b) characterizes the rate of inferring all symmetric properties compared with the best estimator over any label-invariant distribution collection; c) serves as the limit of proﬁle compression, for which we derive optimal near-linear-time block and sequential algorithms. To further our understanding of proﬁle entropy, we investigate its attributes, provide algorithms for approximating its value, and determine its magnitude for numerous structural distribution families.
Preparation and Characterization of (1 — x) Pb(Mg1/3Nb2/3) O3 — x PbTiO3 Electrocaloric Ceramics

Lead magnesium niobate Pb(Mg1/3Nb2/3)O3 — lead titanate PbTiO3 [abbr. as (1 — x) PMN — x PT] ferroelectric ceramics with different excesses of MgO and PbO were systematically studied under different processing conditions for ferroelectric refrigeration application. It was found that the excess amount of MgO and PbO, and the sintering temperature have great effect on the crystallographic properties of the ceramics. In our experiments, (1 — x) PMN —x PT (x = 0.08, x = 0.10, and x = 0.25 respectively) with the excesses of 2 mol% MgO and 2 mol% PbO ceramics sintered at 1250 °C/1 hour possess the desired perovskite structures and large electrocaloric temperature change (ΔT = 1 K and more) in the vicinity of room temperature under a dc electric field of 1.5 kV/mm. It is expected that (1 — x) PMN — x PT electrocaloric ceramics could be applied for cascade refrigeration near room temperature.
FracTrain: Fractionally Squeezing Bit Savings Both Temporally and Spatially for Efficient DNN Training

Recent breakthroughs in deep neural networks (DNNs) have fueled a tremendous demand for intelligent edge devices featuring on-site learning, while the practical realization of such systems remains a challenge due to the limited resources available at the edge and the required massive training costs for state-of-the-art (SOTA) DNNs. As reducing precision is one of the most effective knobs for boosting training time/energy efficiency, there has been a growing interest in low-precision DNN training. In this paper, we explore from an orthogonal direction: how to fractionally squeeze out more training cost savings from the most redundant bit level, progressively along the training trajectory and dynamically per input. Specifically, we propose FracTrain that integrates (i) progressive fractional quantization which gradually increases the precision of activations, weights, and gradients that will not reach the precision of SOTA static quantized DNN training until the final training stage, and (ii) dynamic fractional quantization which assigns precisions to both the activations and gradients of each layer in an input-adaptive manner, for only"fractionally"updating layer parameters. Extensive simulations and ablation studies (six models, four datasets, and three training settings including standard, adaptation, and fine-tuning) validate the effectiveness of FracTrain in reducing computational cost and hardware-quantified energy/latency of DNN training while achieving a comparable or better (-0.12%~+1.87%) accuracy. For example, when training ResNet-74 on CIFAR-10, FracTrain achieves 77.6% and 53.5% computational cost and training latency savings, respectively, compared with the best SOTA baseline, while achieving a comparable (-0.07%) accuracy. Our codes are available at: https://github.com/RICE-EIC/FracTrain.
Interaction Patterns for Staggered Assembly of Fibrils from Semiflexible Chains

The design of colloidal interactions to achieve target self-assembled structures has especially been done for compact objects such as spheres with isotropic interaction potentials, patchy spheres and other compact objects with patchy interactions. Inspired by the self-assembly of collagen-I fibrils and intermediate filaments, we here consider the design of interaction patterns on semiflexible chains that could drive their staggered assembly into regular (para)crystalline fibrils. We consider semiflexible chains composed of a finite number of types of interaction beads (uncharged hydrophilic, hydrophobic, positively charged and negatively charged) and optimize the sequence of these interaction beads with respect to the interaction energy of the semiflexible chains in a number of target-staggered crystalline packings. We find that structures with the lowest interaction energies, that form simple lattices, also have low values of L/D (where L is chain length and D is stagger). In the low interaction energy sequences, similar types of interaction beads cluster together to form stretches. Langevin Dynamics simulations confirm that semiflexible chains with optimal sequences self-assemble into the designed staggered (para)crystalline fibrils. We conclude that very simple interaction patterns should suffice to drive the assembly of long semiflexible chains into staggered (para)crystalline fibrils.
In-column electrochemical detection for liquid chromatography microdevice

Liquid chromatography is a pressure driven separation method for chemical mixture: different affinities of the analytes for the mobile phase and the stationary phase under pressure will cause separation of analytes as diverse as proteins (Oates and Jorgenson 1990) to neurotransmitters (McKenzie et al. , 2002)
GPS-Net: Graph-based Photometric Stereo Network

Learning-based photometric stereo methods predict the surface normal either in a per-pixel or an all-pixel manner. Per-pixel methods explore the inter-image intensity variation of each pixel but ignore features from the intra-image spatial domain. All-pixel methods explore the intra-image intensity variation of each input image but pay less attention to the inter-image lighting variation. In this paper, we present a Graph-based Photometric Stereo Network, which uniﬁes per-pixel and all-pixel processings to explore both inter-image and intra-image information. For per-pixel operation, we propose the Unstructured Feature Extraction Layer to connect an arbitrary number of input image-light pairs into graph structures, and introduce Structure-aware Graph Convolution ﬁlters to balance the input data by appropriately weighting shadows and specular highlights. For all-pixel operation, we propose the Normal Regression Network to make efﬁcient use of the intra-image spatial information for predicting a surface normal map with rich details. Experimental results on the real-world benchmark show that our method achieves excellent performance under both sparse and dense lighting distributions.
Nanostructured T1 MRI contrast agents

Recent advances on new T1 MRI contrast agents using nanostructured materials will be presented. Paramagnetic Gd3+ ions have been immobilized in nanostructured matrices such as liposomes, perfluorocarbons, silica-based nanoparticles, emulsions, and carbon nanotube to generate nanostructured ionic T1 MRI contrast agents with high relaxivity. Inorganic nanoparticles of gadolinium compounds and manganese oxides have been recently developed as new types of T1 MRI contrast agents using the large surface concentration of metal ions with high magnetic moments. Functionalization of these nanostructured T1 contrast agents enabled targeted imaging, multimodal imaging, and simultaneous imaging and drug delivery.
UnModNet: Learning to Unwrap a Modulo Image for High Dynamic Range Imaging

A conventional camera often suffers from over-or under-exposure when recording a real-world scene with a very high dynamic range (HDR). In contrast, a modulo camera with a Markov random ﬁeld (MRF) based unwrapping algorithm can theoretically accomplish unbounded dynamic range but shows degenerate performances when there are modulus-intensity ambiguity, strong local contrast, and color misalignment. In this paper, we reformulate the modulo image unwrapping problem into a series of binary labeling problems and propose a modulo edge-aware model, named as UnModNet, to iteratively estimate the binary rollover masks of the modulo image for unwrapping. Experimental results show that our approach can generate 12 -bit HDR images from 8 -bit modulo images reliably, and runs much faster than the previous MRF-based algorithm thanks to the GPU acceleration.
Neural Message Passing for Multi-Relational Ordered and Recursive Hypergraphs

Message passing neural network (MPNN) has recently emerged as a successful framework by achieving state-of-the-art performances on many graph-based learning tasks. MPNN has also recently been extended to multi-relational graphs (each edge is labelled), and hypergraphs (each edge can connect any number of vertices). However, in real-world datasets involving text and knowledge, relationships are much more complex in which hyperedges can be multi-relational, recursive, and ordered. Such structures present several unique challenges because it is not clear how to adapt MPNN to variable -sized hyperedges in them. In this work, we ﬁrst unify exisiting MPNNs on different structures into G-MPNN (Generalised-MPNN) framework. Motivated by real-world datasets, we then propose a novel extension of the framework, MPNN-R (MPNN-Recursive) to handle recursively-structured data. Experimental results demonstrate the effectiveness of proposed instances of G-MPNN and MPNN-R. The code is available. 1
Domain Generalization via Entropy Regularization

Domain generalization aims to learn from multiple source domains a predictive model that can generalize to unseen target domains. One essential problem in domain generalization is to learn discriminative domain-invariant features. To arrive at this, some methods introduce a domain discriminator through adversarial learning to match the feature distributions in multiple source domains. However, adversarial training can only guarantee that the learned features have invariant marginal distributions, while the invariance of conditional distributions is more important for prediction in new domains. To ensure the conditional invariance of learned features, we propose an entropy regularization term that measures the dependency between the learned features and the class labels. Combined with the typical task-related loss, e.g., cross-entropy loss for classiﬁcation, and adversarial loss for domain discrimination, our overall objective is guaranteed to learn conditional-invariant features across all source domains and thus can learn classiﬁers with better generalization capabilities. We demonstrate the effectiveness of our method through comparison with state-of-the-art methods on both simulated and real-world datasets. Code is available at: https://github.com/sshan-zhao/DG_via_ER .
A New Hyperchaotic Map for a Secure Communication Scheme with an Experimental Realization

Using different chaotic systems in secure communication, nonlinear control, and many other applications has revealed that these systems have several drawbacks in different aspects. This can cause unfavorable effects to chaos-based applications. Therefore, presenting a chaotic map with complex behaviors is considered important. In this paper, we introduce a new 2D chaotic map, namely, the 2D infinite-collapse-Sine model (2D-ICSM). Various metrics including Lyapunov exponents and bifurcation diagrams are used to demonstrate the complex dynamics and robust hyperchaotic behavior of the 2D-ICSM. Furthermore, the cross-correlation coefficient, phase space diagram, and Sample Entropy algorithm prove that the 2D-ICSM has a high sensitivity to initial values and parameters, extreme complexity performance, and a much larger hyperchaotic range than existing maps. To empirically verify the efficiency and simplicity of the 2D-ICSM in practical applications, we propose a symmetric secure communication system using the 2D-ICSM. Experimental results are presented to demonstrate the validity of the proposed system.