Miracle-dz/UniFAR-FTUs huggingface dataset:
Slow exponential growth representations of Sp(n, 1) at the edge of Cowling's strip

We obtain a slow exponential growth estimate for the spherical principal series representation rho_s of Lie group Sp(n, 1) at the edge (Re(s)=1) of Cowling's strip (|Re(s)|<1) on the Sobolev space H^alpha(G/P) when alpha is the critical value Q/2=2n+1. As a corollary, we obtain a slow exponential growth estimate for the homotopy rho_s (s in [0, 1]) of the spherical principal series which is required for the first author's program for proving the Baum--Connes conjecture with coefficients for Sp(n,1).
FP-Age: Leveraging Face Parsing Attention for Facial Age Estimation in the Wild

Image-based age estimation aims to predict a person's age from facial images. It is used in a variety of real-world applications. Although end-to-end deep models have achieved impressive results for age estimation on benchmark datasets, their performance in-the-wild still leaves much room for improvement due to the challenges caused by large variations in head pose, facial expressions, and occlusions. To address this issue, we propose a simple yet effective method to explicitly incorporate facial semantics into age estimation, so that the model would learn to correctly focus on the most informative facial components from unaligned facial images regardless of head pose and non-rigid deformation. To this end, we design a face parsing-based network to learn semantic information at different scales and a novel face parsing attention module to leverage these semantic features for age estimation. To evaluate our method on in-the-wild data, we also introduce a new challenging large-scale benchmark called IMDB-Clean. This dataset is created by semi-automatically cleaning the noisy IMDB-WIKI dataset using a constrained clustering method. Through comprehensive experiment on IMDB-Clean and other benchmark datasets, under both intra-dataset and cross-dataset evaluation protocols, we show that our method consistently outperforms all existing age estimation methods and achieves a new state-of-the-art performance. To the best of our knowledge, our work presents the first attempt of leveraging face parsing attention to achieve semantic-aware age estimation, which may be inspiring to other high level facial analysis tasks.
Affect-driven Engagement Measurement from Videos

—In education and intervention programs, person’s engagement has been identiﬁed as a major factor in successful program completion. Automatic measurement of person’s engagement provides useful information for instructors to meet program objectives and individualize program delivery. In this paper, we present a novel approach for video-based engagement measurement in virtual learning programs. We propose to use affect states, continuous values of valence and arousal extracted from consecutive video frames, along with a new latent affective feature vector and behavioral features for engagement measurement. Deep learning-based temporal, and traditional machine-learning-based non-temporal models are trained and validated on frame-level, and video-level features, respectively. In addition to the conventional centralized learning, we also implement the proposed method in a decentralized federated learning setting and study the effect of model personalization in engagement measurement. We evaluated the performance of the proposed method on the only two publicly available video engagement measurement datasets, DAiSEE and EmotiW, containing videos of students in online learning programs. Our experiments show a state-of-the-art engagement level classiﬁcation accuracy of 63.3% and correctly classifying disengagement videos in the DAiSEE dataset and a regression mean squared error of 0.0673 on the EmotiW dataset. Our ablation study shows the effectiveness of incorporating affect states in engagement measurement. We interpret the ﬁndings from the experimental results based on psychology concepts in the ﬁeld of engagement.
Deep Learning-Based Active User Detection for Grant-free SCMA Systems

Grant-free random access and uplink non- orthogonal multiple access (NOMA) have been introduced to reduce transmission latency and signaling overhead in massive machine-type communication (mMTC). In this paper, we propose two novel group-based deep neural network active user detection (AUD) schemes for the grant-free sparse code multiple access (SCMA) system in mMTC uplink framework. The proposed AUD schemes learn the nonlinear mapping, i.e., multi-dimensional codebook structure and the channel characteristic. This is accomplished through the received signal which incorporates the sparse structure of device activity with the training dataset. Moreover, the offline pre-trained model is able to detect the active devices without any channel state information and prior knowledge of the device sparsity level. Simulation results show that with several active devices, the proposed schemes obtain more than twice the probability of detection compared to the conventional AUD schemes over the signal to noise ratio range of interest.
Strong decays of the low-lying doubly bottom baryons

In this work, we adopt the P0 model to investigate the strong decays of the low-lying doubly bottom baryons in the j− j coupling scheme systematically. In this scheme, we construct the formulism of P0 model under the spectator assumption, and then the heavy diquark symmetry is preserved automatically. Our results show that some of the λ-mode Ξbb(1P) andΩbb(1P) states are narrow, which have good potentials to be observed by future experiments. For the low-lying ρ-mode and ρ-λ hybrid states, the Okubo-Zweig-Iizuka-allowed strong decays are highly suppressed and they should be extremely narrow. Future experiments can test our phenomenological predictions at the quark level.
OptiDICE: Offline Policy Optimization via Stationary Distribution Correction Estimation

We consider the offline reinforcement learning (RL) setting where the agent aims to optimize the policy solely from the data without further environment interactions. In offline RL, the distributional shift becomes the primary source of difficulty, which arises from the deviation of the target policy being optimized from the behavior policy used for data collection. This typically causes overestimation of action values, which poses severe problems for model-free algorithms that use bootstrapping. To mitigate the problem, prior offline RL algorithms often used sophisticated techniques that encourage underestimation of action values, which introduces an additional set of hyperparameters that need to be tuned properly. In this paper, we present an offline RL algorithm that prevents overestimation in a more principled way. Our algorithm, OptiDICE, directly estimates the stationary distribution corrections of the optimal policy and does not rely on policy-gradients, unlike previous offline RL algorithms. Using an extensive set of benchmark datasets for offline RL, we show that OptiDICE performs competitively with the state-of-the-art methods.
Moving in a 360 World: Synthesizing Panoramic Parallaxes from a Single Panorama

We present Omnidirectional Neural Radiance Fields (OmniNeRF), the first method to the application of parallax-enabled novel panoramic view synthesis. Recent works for novel view synthesis focus on perspective images with limited field-of-view and require sufficient pictures captured in a specific condition. Conversely, OmniNeRF can generate panorama images for unknown viewpoints given a single equirectangular image as training data. To this end, we propose to augment the single RGB-D panorama by projecting back and forth between a 3D world and different 2D panoramic coordinates at different virtual camera positions. By doing so, we are able to optimize an Omnidirectional Neural Radiance Field with visible pixels collecting from omnidirectional viewing angles at a fixed center for the estimation of new viewing angles from varying camera positions. As a result, the proposed OmniNeRF achieves convincing renderings of novel panoramic views that exhibit the parallax effect. We showcase the effectiveness of each of our proposals on both synthetic and real-world datasets.
Principal Decomposition of Velocity Gradient Tensor in the Cartesian Coordinates

Traditional Cauchy-Stokes decomposition of velocity gradient tensor gives a symmetric and an anti-symmetric subtensors which are called the strain-rate and vorticity tensors. There are two problems with Cauchy-Stokes decomposition. The first one is that the anti-symmetric or vorticity tensor cannot represent the fluid rotation or vortex. The second one is that the symmetric (strain-rate) tensor cannot distinguish the stretching (compression) and shear. The stretching and shear are dependent on the coordinate or are not Galilean invariant. Since vorticity cannot distinguish between the non-rotational shear and the rigid rotation, vorticity has been decomposed to a rigid rotation called Liutex and anti-symmetric shear in our previous work. A Liutex-based principal coordinate was developed and the velocity gradient tensor was decomposed in the principal coordinate as a rigid rotation (Liutex tensor), a pure shear tensor and a stretching (compression) tensor, which is called the principal decomposition. However, the principal decomposition is made at each point which has own principal coordinate different from other points. This paper derives the principal decomposition in the original xyz coordinate system, and, therefore, provides a new tool for fluid kinematics to conduct the velocity gradient tensor decomposition to rigid rotation, pure shear, and stretching (compression) which is Galilean invariant and has clear physical meanings. The new velocity gradient tensor decomposition could become a foundation for new fluid kinematics.
Low-Rank Characteristic Tensor Density Estimation Part II: Compression and Latent Density Estimation

Learning generative probabilistic models is a core problem in machine learning, which presents significant challenges due to the curse of dimensionality. This paper proposes a joint dimensionality reduction and non-parametric density estimation framework, using a novel estimator that can explicitly capture the underlying distribution of appropriate reduced-dimension representations of the input data. The idea is to jointly design a nonlinear dimensionality reducing auto-encoder to model the training data in terms of a parsimonious set of latent random variables, and learn a canonical low-rank tensor model of the joint distribution of the latent variables in the Fourier domain. The proposed latent density model is non-parametric and “universal,” as opposed to the predefined prior that is assumed in variational auto-encoders. Joint optimization of the auto-encoder and the latent density estimator is pursued via a formulation which learns both by minimizing a combination of the negative log-likelihood in the latent domain and the auto-encoder reconstruction loss. We demonstrate that the proposed model achieves very promising results on toy, tabular, and image datasets on regression tasks, sampling, and anomaly detection.
Local Acceleration of Protons to 100 keV in a Quasi‐Parallel Bow Shock

Recent observations in the quasi‐parallel bow shock by the Magnetospheric Multiscale spacecraft show rapid heating and acceleration of ions up to an energy of about 100 keV. It is demonstrated that a prominent acceleration mechanism is the nonlinear interaction with a spectrum of waves produced by gradient‐driven instabilities, including the lower hybrid drift (LHD) instability, modified two‐stream (MTS) instability, and electron cyclotron drift (ECD) instability. Test‐particle simulations show that the observed spectrum of waves can rapidly accelerate protons up to a few hundreds of keV by the ExB mechanism. The ExB wave mechanism is related to the surfatron mechanism at shocks, but through the coupling with the stochastic heating condition, it produces significant acceleration on much shorter temporal and spatial scales by the interaction with bursts of waves within a cyclotron period. The results of this study are built on the heritage of four‐point measurement techniques developed for the Cluster mission and imply that the concepts of Fermi acceleration, diffusive shock acceleration, and shock drift acceleration are not needed to explain proton acceleration to hundreds of keV at the Earth’s bow shock.
PIANO: A Parametric Hand Bone Model from Magnetic Resonance Imaging

Hand modeling is critical for immersive VR/AR, action understanding, or human healthcare. Existing parametric models account only for hand shape, pose, or texture, without modeling the anatomical attributes like bone, which is essential for realistic hand biomechanics analysis. In this paper, we present PIANO, the first parametric bone model of human hands from MRI data. Our PIANO model is biologically correct, simple to animate, and differentiable, achieving more anatomically precise modeling of the inner hand kinematic structure in a data-driven manner than the traditional hand models based on the outer surface only. Furthermore, our PIANO model can be applied in neural network layers to enable training with a fine-grained semantic loss, which opens up the new task of data-driven fine-grained hand bone anatomic and semantic understanding from MRI or even RGB images. We make our model publicly available.
EvoGrad: Efficient Gradient-Based Meta-Learning and Hyperparameter Optimization

Gradient-based meta-learning and hyperparameter optimization have seen significant progress recently, enabling practical end-to-end training of neural networks together with many hyperparameters. Nevertheless, existing approaches are relatively expensive as they need to compute second-order derivatives and store a longer computational graph. This cost prevents scaling them to larger network architectures. We present EvoGrad, a new approach to meta-learning that draws upon evolutionary techniques to more efficiently compute hypergradients. EvoGrad estimates hypergradient with respect to hyperparameters without calculating second-order gradients, or storing a longer computational graph, leading to significant improvements in efficiency. We evaluate EvoGrad on three substantial recent meta-learning applications, namely cross-domain few-shot learning with feature-wise transformations, noisy label learning with Meta-Weight-Net and low-resource cross-lingual learning with meta representation transformation. The results show that EvoGrad significantly improves efficiency and enables scaling meta-learning to bigger architectures such as from ResNet10 to ResNet34.
Constrained randomization and statistical inference for multi‐arm parallel cluster randomized controlled trials

A practical limitation of cluster randomized controlled trials (cRCTs) is that the number of available clusters may be small, resulting in an increased risk of baseline imbalance under simple randomization. Constrained randomization overcomes this issue by restricting the allocation to a subset of randomization schemes where sufficient overall covariate balance across comparison arms is achieved. However, for multi‐arm cRCTs, several design and analysis issues pertaining to constrained randomization have not been fully investigated. Motivated by an ongoing multi‐arm cRCT, we elaborate the method of constrained randomization and provide a comprehensive evaluation of the statistical properties of model‐based and randomization‐based tests under both simple and constrained randomization designs in multi‐arm cRCTs, with varying combinations of design and analysis‐based covariate adjustment strategies. In particular, as randomization‐based tests have not been extensively studied in multi‐arm cRCTs, we additionally develop most‐powerful randomization tests under the linear mixed model framework for our comparisons. Our results indicate that under constrained randomization, both model‐based and randomization‐based analyses could gain power while preserving nominal type I error rate, given proper analysis‐based adjustment for the baseline covariates. Randomization‐based analyses, however, are more robust against violations of distributional assumptions. The choice of balance metrics and candidate set sizes and their implications on the testing of the pairwise and global hypotheses are also discussed. Finally, we caution against the design and analysis of multi‐arm cRCTs with an extremely small number of clusters, due to insufficient degrees of freedom and the tendency to obtain an overly restricted randomization space.
Teacher's pet: understanding and mitigating biases in distillation

Knowledge distillation is widely used as a means of improving the performance of a relatively simple student model using the predictions from a complex teacher model. Several works have shown that distillation significantly boosts the student's overall performance; however, are these gains uniform across all data subgroups? In this paper, we show that distillation can harm performance on certain subgroups, e.g., classes with few associated samples. We trace this behaviour to errors made by the teacher distribution being transferred to and amplified by the student model. To mitigate this problem, we present techniques which soften the teacher influence for subgroups where it is less reliable. Experiments on several image classification benchmarks show that these modifications of distillation maintain boost in overall accuracy, while additionally ensuring improvement in subgroup performance.
Bayesian inference of ODEs with Gaussian processes

Recent machine learning advances have proposed black-box estimation of unknown continuous-time system dynamics directly from data. However, earlier works are based on approximative ODE solutions or point estimates. We propose a novel Bayesian nonparametric model that uses Gaussian processes to infer posteriors of unknown ODE systems directly from data. We derive sparse variational inference with decoupled functional sampling to represent vector ﬁeld posteriors. We also introduce a probabilistic shooting augmentation to enable efﬁcient inference from arbitrarily long trajectories. The method demonstrates the beneﬁt of computing vector ﬁeld posteriors, with predictive uncertainty scores outperforming alternative methods on multiple ODE learning tasks
Cycles of paleomagnetic activity in the phanerozoic

Quasi-periodic changes of the paleointensity and geomagnetic polarity in the intervals of 170 Ma to the present time and of 550 Ma to the present time were studied, respectively. It is revealed that the spectrum of the basic variations in the paleointensity and of the duration of the polar intervals is discrete and includes quasi-periodic oscillations with characteristic times of 15 Ma, 8 Ma, 5 Ma, and 3 Ma. The characteristic time of these quasi-periodic changes of the geomagnetic field at the beginning and at the end of the Phanerozoic differed by no more than 10%. The spectral density of quasi-periodic variations of the geomagnetic field changed cyclically over geological time. The relation between the behaviors of the amplitude of paleointensity variations, the duration of the polar intervals, and their spectral density was shown. Quasi-periodic variations of the paleointensity (geomagnetic activity) had a relatively high spectral density in the interval of (150 40) Ma (in the Cretaceous Early Paleogene). In this interval, both the amplitude of paleointensity variations and the duration of polar intervals increased. In the intervals of (170 150) Ma and of 30 Ma to the present, a quasi-periodic variation in the paleointensity practically did not detect against the background of its noise variations. At the same time, the amplitude of the paleointensity variations and duration of polar intervals decreased. An alternation of time intervals in which the paleointensity variations acquired either a quasi-periodic or noise character took place during the geomagnetic history.
On fine-tuning of Autoencoders for Fuzzy rule classifiers

Recent discoveries in Deep Neural Networks are allowing researchers to tackle some very complex problems such as image classification and audio classification, with improved theoretical and empirical justifications. This paper presents a novel scheme to incorporate the use of autoencoders in Fuzzy rule classifiers (FRC). Autoencoders when stacked can learn the complex non-linear relationships amongst data, and the proposed framework built towards FRC can allow users to input expert knowledge to the system. This paper further introduces four novel fine-tuning strategies for autoencoders to improve the FRC's classification and rule reduction performance. The proposed framework has been tested across five real-world benchmark datasets. Elaborate comparisons with over 15 previous studies, and across 10-fold cross validation performance, suggest that the proposed methods are capable of building FRCs which can provide state of the art accuracies.
Tumor Radiogenomics with Bayesian Layered Variable Selection

We propose a statistical framework to integrate radiological magnetic resonance imaging (MRI) and genomic data to identify the underlying radiogenomic associations in lower grade gliomas (LGG). We devise a novel imaging phenotype by dividing the tumor region into concentric spherical layers that mimics the tumor evolution process. MRI data within each layer is represented by voxel–intensity-based probability density functions which capture the complete information about tumor heterogeneity. Under a Riemannian-geometric framework these densities are mapped to a vector of principal component scores which act as imaging phenotypes. Subsequently, we build Bayesian variable selection models for each layer with the imaging phenotypes as the response and the genomic markers as predictors. Our novel hierarchical prior formulation incorporates the interior-to-exterior structure of the layers, and the correlation between the genomic markers. We employ a computationally-efficient Expectation–Maximization-based strategy for estimation. Simulation studies demonstrate the superior performance of our approach compared to other approaches. With a focus on the cancer driver genes in LGG, we discuss some biologically relevant findings. Genes implicated with survival and oncogenesis are identified as being associated with the spherical layers, which could potentially serve as early-stage diagnostic markers for disease monitoring, prior to routine invasive approaches.
Learning to Rank Question Answer Pairs with Bilateral Contrastive Data Augmentation

In this work, we propose a novel and easy-to-apply data augmentation strategy, namely Bilateral Generation (BiG), with a contrastive training objective for improving the performance of ranking question answer pairs with existing labeled data. In specific, we synthesize pseudo-positive QA pairs in contrast to the original negative QA pairs with two pre-trained generation models, one for question generation, the other for answer generation, which are fine-tuned on the limited positive QA pairs from the original dataset. With the augmented dataset, we design a contrastive training objective for learning to rank question answer pairs. Experimental results on three benchmark datasets show that our method significantly improves the performance of ranking models by making full use of existing labeled data and can be easily applied to different ranking models.