Miracle-dz/UniFAR-FTUs huggingface dataset:
Predicting Responses to a Robot's Future Motion using Generative Recurrent Neural Networks

Robotic navigation through crowds or herds requires the ability to both predict the future motion of nearby individuals and understand how these predictions might change in response to a robot's future action. State of the art trajectory prediction models using Recurrent Neural Networks (RNNs) do not currently account for a planned future action of a robot, and so cannot predict how an individual will move in response to a robot's planned path. We propose an approach that adapts RNNs to use a robot's next planned action as an input alongside the current position of nearby individuals. This allows the model to learn the response of individuals with regards to a robot's motion from real world observations. By linking a robot's actions to the response of those around it in training, we show that we are able to not only improve prediction accuracy in close range interactions, but also to predict the likely response of surrounding individuals to simulated actions. This allows the use of the model to simulate state transitions, without requiring any assumptions on agent interaction. We apply this model to varied datasets, including crowds of pedestrians interacting with vehicles and bicycles, and livestock interacting with a robotic vehicle.
Deeply Matting-based Dual Generative Adversarial Network for Image and Document Label Supervision

Although many methods have been proposed to deal with nature image super-resolution (SR) and get impressive performance, the text images SR is not good due to their ignorance of document images. In this paper, we propose a matting-based dual generative adversarial network (mdGAN) for document image SR. Firstly, the input image is decomposed into document text, foreground and background layers using deep image matting. Then two parallel branches are constructed to recover text boundary information and color information respectively. Furthermore, in order to improve the restoration accuracy of characters in output image, we use the input image's corresponding ground truth text label as extra supervise information to refine the two-branch networks during training. Experiments on real text images demonstrate that our method outperforms several state-of-the-art methods quantitatively and qualitatively.
Celeb-DF: A New Dataset for DeepFake Forensics

AI-synthesized face swapping videos, commonly known as the DeepFakes, have become an emerging problem recently. Correspondingly, there is an increasing interest in developing algorithms that can detect them. However, existing dataset of DeepFake videos suffer from low visual quality and abundant artifacts that do not reﬂect the reality of DeepFake videos circulated on the Internet. In this work, we present a new DeepFake dataset, Celeb-DF, for the development and evaluation of DeepFake detection algorithms. The Celeb-DF dataset is generated using a reﬁned synthesis algorithm that reduces the visual artifacts observed in existing datasets. Based on the Celeb-DF dataset, we also benchmark existing DeepFake detection algorithms.
Integrated Triaging for Fast Reading Comprehension

Although according to several benchmarks automatic machine reading comprehension (MRC) systems have recently reached super-human performance, less attention has been paid to their computational efficiency. However, efficiency is of crucial importance for training and deployment in real world applications. This paper introduces Integrated Triaging, a framework that prunes almost all context in early layers of a network, leaving the remaining (deep) layers to scan only a tiny fraction of the full corpus. This pruning drastically increases the efficiency of MRC models and further prevents the later layers from overfitting to prevalent short paragraphs in the training set. Our framework is extremely flexible and naturally applicable to a wide variety of models. Our experiment on doc-SQuAD and TriviaQA tasks demonstrates its effectiveness in consistently improving both speed and quality of several diverse MRC models.
On Generalizing Detection Models for Unconstrained Environments

Object detection has seen tremendous progress in recent years. However, current algorithms don't generalize well when tested on diverse data distributions. We address the problem of incremental learning in object detection on the India Driving Dataset (IDD). Our approach involves using multiple domain-specific classifiers and effective transfer learning techniques focussed on avoiding catastrophic forgetting. We evaluate our approach on the IDD and BDD100K dataset. Results show the effectiveness of our domain adaptive approach in the case of domain shifts in environments.
Transition from inspiral to plunge: A complete near-extremal trajectory and associated waveform

We extend the Ori and Thorne (OT) procedure to compute the transition from an adiabatic inspiral into a geodesic plunge for any spin, with emphasis on near-extremal ones. Our analysis revisits the validity of the approximations made in OT. In particular, we discuss possible effects coming from eccentricity and non-geodesic past-history of the orbital evolution. We find three different scaling regimes according to whether the mass ratio is much smaller, of the same order or much larger than the near extremal parameter describing how fast the primary black hole rotates. Eccentricity and non-geodesic past-history corrections are always sub-leading, indicating that the quasi-circular approximation applies throughout the transition regime. However, we show that the OT assumption that the energy and angular momentum evolve linearly with proper time must be modified in the near-extremal regime. Using our transition equations, we describe an algorithm to compute the full worldline in proper time for an extreme mass ratio inspiral (EMRI) and the resultant gravitational waveform in the high spin limit.
Efficient meta reinforcement learning via meta goal generation

Meta reinforcement learning (meta-RL) is able to accelerate the acquisition of new tasks by learning from past experience. Current meta-RL methods usually learn to adapt to new tasks by directly optimizing the parameters of policies over primitive actions. However, for complex tasks which requires sophisticated control strategies, it would be quite inefficient to to directly learn such a meta-policy. Moreover, this problem can become more severe and even fail in spare reward settings, which is quite common in practice. To this end, we propose a new meta-RL algorithm called meta goal-generation for hierarchical RL (MGHRL) by leveraging hierarchical actor-critic framework. Instead of directly generate policies over primitive actions for new tasks, MGHRL learns to generate high-level meta strategies over subgoals given past experience and leaves the rest of how to achieve subgoals as independent RL subtasks. Our empirical results on several challenging simulated robotics environments show that our method enables more efficient and effective meta-learning from past experience and outperforms state-of-the-art meta-RL and Hierarchical-RL methods in sparse reward settings.
Capacity Preserving Mapping for High-dimensional Data Visualization

We provide a rigorous mathematical treatment to the crowding issue in data visualization when high dimensional data sets are projected down to low dimensions for visualization. By properly adjusting the capacity of high dimensional balls, our method makes right enough room to prepare for the embedding. A key component of the proposed method is an estimation of the correlation dimension at various scales which reflects the data density variation. The proposed adjustment to the capacity applies to any distance (Euclidean, geodesic, diffusion) and can potentially be used in many existing methods to mitigate the crowding during the dimension reduction. We demonstrate the effectiveness of the new method using synthetic and real datasets.
A MUSE study of the fast bar in the weakly-interacting galaxy NGC 4264

Abstract We present surface photometry and stellar kinematics of NGC 4264, a lenticular galaxy in the region of the Virgo Cluster undergoing a tidal interaction with its neighbour, NGC 4261. We measured the bar radius and strength from SDSS imaging and the bar pattern speed from MUSE integral-field spectroscopy. We find that NGC 4264 hosts a strong and large bar, which is rotating fast. The accurate measurement of the bar rotation rate allows us to exclude that the formation of the bar was triggered by the ongoing interaction.
“Quantum Weirdness” in Exploitation by the International Gravitational‐Wave Observatory Network

The detectors of the international gravitational‐wave (GW) observatory network are currently taking data with sensitivities improved via squeezing the photon counting noise of the laser light used. Several GW candidate events, such as black‐hole mergers, are already in the pipeline to be analyzed in detail. While the brand‐new field of GW astronomy relies on squeezed light for reaching higher sensitivities, the physical understanding of such light, although being well described by quantum theory, is still under discussion. Herein, a physical description of why squeezed light, as now being exploited by GW observatories, constitutes rather remarkable physics is presented. Consideration of the Fourier transform seems to provide an argument why the measurement of squeezed photon statistics from quasi‐monochromatic light is “weird. ” It is further argued that the measurement of squeezed photon statistics is related to the famous gedanken experiment formulated by Einstein, Podolsky, and Rosen in 1935. This work illuminates “quantum weirdness” in a clear way and might be the starting point for finding the physics of quantum correlations in general, which scientists have been seeking for decades.
RandAugment: Practical data augmentation with no separate search

Recent work has shown that data augmentation has the potential to significantly improve the generalization of deep learning models. Recently, learned augmentation strategies have led to state-of-the-art results in image classification and object detection. While these strategies were optimized for improving validation accuracy, they also led to state-of-the-art results in semi-supervised learning and improved robustness to common corruptions of images. One obstacle to a large-scale adoption of these methods is a separate search phase which significantly increases the training complexity and may substantially increase the computational cost. Additionally, due to the separate search phase, these learned augmentation approaches are unable to adjust the regularization strength based on model or dataset size. Learned augmentation policies are often found by training small models on small datasets and subsequently applied to train larger models. In this work, we remove both of these obstacles. RandAugment may be trained on the model and dataset of interest with no need for a separate proxy task. Furthermore, due to the parameterization, the regularization strength may be tailored to different model and dataset sizes. RandAugment can be used uniformly across different tasks and datasets and works out of the box, matching or surpassing all previous learned augmentation approaches on CIFAR-10, CIFAR-100, SVHN, and ImageNet. On the ImageNet dataset we achieve 85.0% accuracy, a 0.6% increase over the previous state-of-the-art and 1.0% increase over baseline augmentation. On object detection, RandAugment leads to 1.0-1.3% improvement over baseline augmentation, and is within 0.3% mAP of AutoAugment on COCO. Finally, due to its interpretable hyperparameter, RandAugment may be used to investigate the role of data augmentation with varying model and dataset size.
FaSNet: Low-Latency Adaptive Beamforming for Multi-Microphone Audio Processing

1. ABSTRACT Beamforming has been extensively investigated for multi-channel audio processing tasks. Recently, learning-based beamforming methods, sometimes called neural beamformers, have achieved significant improvements in both signal quality (e.g. signal-to-noise ratio (SNR)) and speech recognition (e.g. word error rate (WER)). Such systems are generally non-causal and require a large context for robust estimation of inter-channel features, which is impractical in applications requiring low-latency responses. In this paper, we propose filter-and-sum network (FaSNet), a time-domain, filter-based beamforming approach suitable for low-latency scenarios. FaSNet has a two-stage system design that first learns frame-level time-domain adaptive beamforming filters for a selected reference channel, and then calculate the filters for all remaining channels. The filtered outputs at all channels are summed to generate the final output. Experiments show that despite its small model size, FaSNet is able to outperform several traditional oracle beamformers with respect to scale-invariant signal-to-noise ratio (SI-SNR) in reverberant speech enhancement and separation tasks. Moreover, when trained with a frequency-domain objective function on the CHiME-3 dataset, FaSNet achieves 14.3% relative word error rate reduction (RWERR) compared with the baseline model. These results show the efficacy of FaSNet particularly in reverberant and noisy signal conditions.
Deep Multiple Instance Learning for Taxonomic Classification of Metagenomic read sets

Metagenomic studies have increasingly utilized sequencing technologies in order to analyze DNA fragments found in environmental samples. It can provide useful insights for studying the interactions between hosts and microbes, infectious disease proliferation, and novel species discovery. One important step in this analysis is the taxonomic classification of those DNA fragments. Of particular interest is the determination of the distribution of the taxa of microbes in metagenomic samples. Recent attempts using deep learning focus on architectures that classify single DNA reads independently from each other. In this work, we attempt to solve the task of directly predicting the distribution over the taxa of whole metagenomic read sets. We formulate this task as a Multiple Instance Learning (MIL) problem. We extend architectures used in single-read taxonomic classification with two different types of permutation-invariant MIL pooling layers: a) deepsets and b) attention-based pooling. We illustrate that our architecture can exploit the co-occurrence of species in metagenomic read sets and outperforms the single-read architectures in predicting the distribution over the taxa at higher taxonomic ranks.
Characterization of Yb-doped ZBLAN fiber as a platform for radiation-balanced lasers

Recent advances in power scaling of fiber lasers are hindered by the thermal issues, which deteriorate the beam quality. Anti-Stokes fluorescence cooling has been suggested as a viable method to balance the heat generated by the quantum defect and background absorption. Such radiation-balanced configurations rely on the availability of cooling-grade rare-earth-doped gain materials. Herein, we perform a series of tests on a ytterbium-doped ZBLAN optical fiber to extract its laser cooling-related parameters and show that it is a viable laser cooling medium for radiation-balancing. In particular, a detailed Laser Induced Modulation Spectrum (LITMoS) test is performed to highlight the transition of this fiber to the cooling regime as a function of the pump laser wavelength. Numerical simulations support the feasibility of a radiation-balanced laser, but highlight that practical radiation-balanced designs are more demanding on the fiber material properties, especially on the background absorption, than are solid-state laser cooling experiments.
Unsupervised Pose Flow Learning for Pose Guided Synthesis

Pose guided synthesis aims to generate a new image in an arbitrary target pose while preserving the appearance details from the source image. Existing approaches rely on either hard-coded spatial transformations or 3D body modeling. They often overlook complex non-rigid pose deformation or unmatched occluded regions, thus fail to effectively preserve appearance information. In this paper, we propose an unsupervised pose flow learning scheme that learns to transfer the appearance details from the source image. Based on such learned pose flow, we proposed GarmentNet and SynthesisNet, both of which use multi-scale feature-domain alignment for coarse-to-fine synthesis. Experiments on the DeepFashion, MVC dataset and additional real-world datasets demonstrate that our approach compares favorably with the state-of-the-art methods and generalizes to unseen poses and clothing styles.
Matching Observations to Distributions: Efficient Estimation via Sparsified Hungarian Algorithm

Suppose we are given observations, where each observation is drawn independently from one of k known distributions. The goal is to match each observation to the distribution from which it was drawn. We observe that the maximum likelihood estimator (MLE) for this problem can be computed using weighted bipartite matching, even when n, the number of observations per distribution, exceeds one. This is achieved by instantiating n duplicates of each distribution node. However, in the regime where the number of observations per distribution is much larger than the number of distributions, the Hungarian matching algorithm for computing the weighted bipartite matching requires $O(n^{3})$ time. We introduce a novel randomized matching algorithm that reduces the runtime to $\tilde{O}(n^{2})$ by sparsifying the original graph, returning the exact MLE with high probability. Next, we give statistical justification for using the MLE by bounding the excess risk of the MLE, where the loss is defined as the negative $\log$-likelihood. We test these bounds for the case of isotropic Gaussians with equal covariances and whose means are separated by a distance $\eta$, and find (1) that $\gg \log k$ separation suffices to drive the proportion of mismatches of the MLE to 0, and (2) that the expected fraction of mismatched observations goes to zero at rate $O((\log k)^{2}/\eta^{2})$.
Stein Bridging: Enabling Mutual Reinforcement between Explicit and Implicit Generative Models

Deep generative models are generally categorized into explicit models and implicit models. The former defines an explicit density form, whose normalizing constant is often unknown; while the latter, including generative adversarial networks (GANs), generates samples without explicitly defining a density function. In spite of substantial recent advances demonstrating the power of the two classes of generative models in many applications, both of them, when used alone, suffer from respective limitations and drawbacks. To mitigate these issues, we propose Stein Bridging, a novel joint training framework that connects an explicit density estimator and an implicit sample generator with Stein discrepancy. We show that the Stein Bridge induces new regularization schemes for both explicit and implicit models. Convergence analysis and extensive experiments demonstrate that the Stein Bridging i) improves the stability and sample quality of the GAN training, and ii) facilitates the density estimator to seek more modes in data and alleviate the mode-collapse issue. Additionally, we discuss several applications of Stein Bridging and useful tricks in practical implementation used in our experiments.
K-metamodes: frequency-and ensemble-based distributed k-modes clustering for security analytics

Nowadays processing of Big Security Data, such as log messages, is commonly used for intrusion detection purposes. Its heterogeneous nature, as well as a combination of numerical and categorical attributes, do not allow to apply the existing data mining methods directly on the data without feature preprocessing. Therefore, a rather computationally expensive conversion of categorical attributes into vector space should be utilised for analysis of such data. However, a well-known k-modes algorithm allows to cluster the categorical data directly and avoid conversion into the vector space. The existing implementations of k-modes for Big Data processing are ensemble-based and utilise two-step clustering, where data subsets are first clustered independently, whereas the resulting cluster modes are clustered again in order to calculate metamodes (or "modes of modes") valid for all data subsets. In this paper, the novel frequency-based distance function is proposed for the second step of ensemble-based k-modes clustering. Besides this, the existing feature discretisation method from the previous work is utilised in order to adapt k-modes for processing of mixed data sets. The resulting k-metamodes algorithm was tested on two public security data sets and reached higher effectiveness in comparison with the previous work.
Neighborhood Inclusions for Minimal Dominating Sets Enumeration: Linear and Polynomial Delay Algorithms in P7-Free and P8-Free Chordal Graphs

In [M. M. Kante, V. Limouzy, A. Mary, and L. Nourine. On the enumeration of minimal dominating sets and related notions. SIAM Journal on Discrete Mathematics, 28(4):1916-1929, 2014] the authors give an $O(n+m)$ delay algorithm based on neighborhood inclusions for the enumeration of minimal dominating sets in split and $P_6$-free chordal graphs. In this paper, we investigate generalizations of this technique to $P_k$-free chordal graphs for larger integers $k$. In particular, we give $O(n+m)$ and $O(n^3\cdot m)$ delays algorithms in the classes of $P_7$-free and $P_8$-free chordal graphs. As for $P_k$-free chordal graphs for $k\geq 9$, we give evidence that such a technique is inefficient as a key step of the algorithm, namely the irredundant extension problem, becomes NP-complete.
Residual Attention Graph Convolutional Network for Geometric 3D Scene Classification

Geometric 3D scene classification is a very challenging task. Current methodologies extract the geometric information using only a depth channel provided by an RGB-D sensor. These kinds of methodologies introduce possible errors due to missing local geometric context in the depth channel. This work proposes a novel Residual Attention Graph Convolutional Network that exploits the intrinsic geometric context inside a 3D space without using any kind of point features, allowing the use of organized or unorganized 3D data. Experiments are done in NYU Depth v1 and SUN-RGBD datasets to study the different configurations and to demonstrate the effectiveness of the proposed method. Experimental results show that the proposed method outperforms current state-of-the-art in geometric 3D scene classification tasks.
Nonlinear Dipole Inversion (NDI) enables Quantitative Susceptibility Mapping (QSM) without parameter tuning

We propose Nonlinear Dipole Inversion (NDI) for high-quality Quantitative Susceptibility Mapping (QSM) without regularization tuning, while matching the image quality of state-of-the-art reconstruction techniques. In addition to avoiding over-smoothing that these techniques often suffer from, we also obviate the need for parameter selection. NDI is flexible enough to allow for reconstruction from an arbitrary number of head orientations, and outperforms COSMOS even when using as few as 1-direction data. This is made possible by a nonlinear forward-model that uses the magnitude as an effective prior, for which we derived a simple gradient descent update rule. We synergistically combine this physics-model with a Variational Network (VN) to leverage the power of deep learning in the VaNDI algorithm. This technique adopts the simple gradient descent rule from NDI and learns the network parameters during training, hence requires no additional parameter tuning. Further, we evaluate NDI at 7T using highly accelerated Wave-CAIPI acquisitions at 0.5 mm isotropic resolution and demonstrate high-quality QSM from as few as 2-direction data.