Miracle-dz/UniFAR-FTUs huggingface dataset:
Q-learning with Logarithmic Regret

This paper presents the first non-asymptotic result showing that a model-free algorithm can achieve a logarithmic cumulative regret for episodic tabular reinforcement learning if there exists a strictly positive sub-optimality gap in the optimal $Q$-function. We prove that the optimistic $Q$-learning studied in [Jin et al. 2018] enjoys a ${\mathcal{O}}\left(\frac{SA\cdot \mathrm{poly}\left(H\right)}{\mathrm{gap}_{\min}}\log\left(SAT\right)\right)$ cumulative regret bound, where $S$ is the number of states, $A$ is the number of actions, $H$ is the planning horizon, $T$ is the total number of steps, and $\mathrm{gap}_{\min}$ is the minimum sub-optimality gap. This bound matches the information theoretical lower bound in terms of $S,A,T$ up to a $\log\left(SA\right)$ factor. We further extend our analysis to the discounted setting and obtain a similar logarithmic cumulative regret bound.
Understanding and mitigating exploding inverses in invertible neural networks

Invertible neural networks (INNs) have been used to design generative models, implement memory-saving gradient computation, and solve inverse problems. In this work, we show that commonly-used INN architectures suffer from exploding inverses and are thus prone to becoming numerically non-invertible. Across a wide range of INN use-cases, we reveal failures including the non-applicability of the change-of-variables formula on in- and out-of-distribution (OOD) data, incorrect gradients for memory-saving backprop, and the inability to sample from normalizing flow models. We further derive bi-Lipschitz properties of atomic building blocks of common architectures. These insights into the stability of INNs then provide ways forward to remedy these failures. For tasks where local invertibility is sufficient, like memory-saving backprop, we propose a flexible and efficient regularizer. For problems where global invertibility is necessary, such as applying normalizing flows on OOD data, we show the importance of designing stable INN building blocks.
Dark matter from non-relativistic embedding gravity

We study the possibility to explain the mystery of the dark matter through the transition from General Relativity to embedding gravity. This modification of gravity, which was proposed by Regge and Teitelboim, is based on a simple string-inspired geometrical principle: our spacetime is considered here as a 4-dimensional surface in a flat bulk. We study the non-relativistic limit of embedding gravity - in such sense that solutions under study correspond to solutions of General Relativity with an additional contribution of non-relativistic embedding matter, which can serve as cold dark matter. This limit appears naturally as a result of the assumption that the conserved currents, which describe embedding matter, are non-relativistic in the bulk. The embedding matter turns out to have a certain self-interaction, which could be useful in the context of solving the core-cusp problem that appears in the LambdaCDM model.
Accelerated Primal-Dual Algorithms for a Class of Convex-Concave Saddle-Point Problems with Non-Bilinear Coupling Term

We develop two new primal-dual algorithms to solve a class of convex-concave saddle-point problems involving non-bilinear coupling function, which covers many existing and brand-new applications as special cases. Our approach relies on a novel combination of non-convex augmented Lagrangian and Nesterov's accelerated schemes, which is fundamentally different from existing works. Both algorithms are single-loop and only require one or at most two proximal operators of the objective function, one gradient of the coupling function per iteration. They do not require to solve any complex subproblem as in standard augmented Lagrangian or penalty approaches. When the objective function is merely convex, our first algorithm can achieve $\mathcal{O}(1/k)$ convergence rates through three different criteria (primal objective residual, dual objective residual, and primal-dual gap), on either the ergodic sequence or the non-ergodic sequence. This rate can potentially be even faster than $\mathcal{O}(1/k)$ on non-ergodic primal objective residual using a new parameter update rule. If the objective function is strongly convex, our second algorithm can boosts these convergence rates to no slower than $\mathcal{O}(1/k^2)$. To the best of our knowledge, these are the first algorithms that achieve such fast convergence rates on non-ergodic sequences for non-bilinear convex-concave saddle-point problems. As a by-product, we specify our results to handle general conic convex problems. We test our algorithms on QCQP and a convex-concave game example to verify their performance as well as to compare them with existing methods.
Hausdorff Dimension, Stochastic Differential Equations, and Generalization in Neural Networks

Despite its success in a wide range of applications, characterizing the generalization properties of stochastic gradient descent (SGD) in non-convex deep learning problems is still an important challenge. While modeling the trajectories of SGD via stochastic differential equations (SDE) under heavy-tailed gradient noise has recently shed light over several peculiar characteristics of SGD, a rigorous treatment of the generalization properties of such SDEs in a learning theoretical framework is still missing. Aiming to bridge this gap, in this paper, we prove generalization bounds for SGD under the assumption that its trajectories can be well-approximated by a Feller process, which defines a rich class of Markov processes that include several recent SDE representations (both Brownian or heavy-tailed) as its special case. We show that the generalization error can be controlled by the Hausdorff dimension of the trajectories, which is intimately linked to the tail behavior of the driving process. Our results imply that heavier-tailed processes should achieve better generalization; hence, the tail-index of the process can be used as a notion of ``capacity metric''. We support our theory with experiments on deep neural networks illustrating that the proposed capacity metric accurately estimates the generalization error, and it does not necessarily grow with the number of parameters unlike the existing capacity metrics in the literature.
FCOS: A Simple and Strong Anchor-Free Object Detector

In computer vision, object detection is one of most important tasks, which underpins a few instance-level recognition tasks and many downstream applications. Recently one-stage methods have gained much attention over two-stage approaches due to their simpler design and competitive performance. Here we propose a fully convolutional one-stage object detector (FCOS) to solve object detection in a per-pixel prediction fashion, analogue to other dense prediction problems such as semantic segmentation. Almost all state-of-the-art object detectors such as RetinaNet, SSD, YOLOv3, and Faster R-CNN rely on pre-defined anchor boxes. In contrast, our proposed detector FCOS is anchor box free, as well as proposal free. By eliminating the pre-defined set of anchor boxes, FCOS completely avoids the complicated computation related to anchor boxes such as calculating the intersection over union (IoU) scores during training. More importantly, we also avoid all hyper-parameters related to anchor boxes, which are often sensitive to the final detection performance. With the only post-processing non-maximum suppression (NMS), we demonstrate a much simpler and flexible detection framework achieving improved detection accuracy. We hope that the proposed FCOS framework can serve as a simple and strong alternative for many other instance-level tasks. Code is available at: <inline-formula><tex-math notation="LaTeX">$\tt git.io/AdelaiDet$</tex-math><alternatives><mml:math><mml:mrow><mml:mi mathvariant="monospace">git</mml:mi><mml:mo>. </mml:mo><mml:mi mathvariant="monospace">io</mml:mi><mml:mo>/</mml:mo><mml:mi mathvariant="monospace">AdelaiDet</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href="shen-ieq1-3032166.gif"/></alternatives></inline-formula>
Multiscale Deep Equilibrium Models

We propose a new class of implicit networks, the multiscale deep equilibrium model (MDEQ), suited to large-scale and highly hierarchical pattern recognition domains. An MDEQ directly solves for and backpropagates through the equilibrium points of multiple feature resolutions simultaneously, using implicit differentiation to avoid storing intermediate states (and thus requiring only O(1) memory consumption). These simultaneously-learned multi-resolution features allow us to train a single model on a diverse set of tasks and loss functions, such as using a single MDEQ to perform both image classification and semantic segmentation. We illustrate the effectiveness of this approach on two large-scale vision tasks: ImageNet classification and semantic segmentation on high-resolution images from the Cityscapes dataset. In both settings, MDEQs are able to match or exceed the performance of recent competitive computer vision models: the first time such performance and scale have been achieved by an implicit deep learning approach. The code and pre-trained models are at this https URL .
A specifically designed machine learning algorithm for GNSS position time series prediction and its applications in outlier and anomaly detection and earthquake prediction

We present a simple yet efficient supervised machine learning algorithm that is designed for the GNSS position time series prediction. This algorithm has four steps. First, the mean value of the time series is subtracted from it. Second, the trends in the time series are removed. Third, wavelets are used to separate the high and low frequencies. And fourth, a number of frequencies are derived and used for finding the weights between the hidden and the output layers, using the product of the identity and sine and cosine functions. The role of the observation precision is taken into account in this algorithm. A large-scale study of three thousand position times series of GNSS stations across the globe is presented. Seventeen different machine learning algorithms are examined. The accuracy levels of these algorithms are checked against the rigorous statistical method of Theta. It is shown that the most accurate machine learning algorithm is the method we present, in addition to being faster. Two applications of the algorithm are presented. In the first application, it is shown that the outliers and anomalies in a time series can be detected and removed by the proposed algorithm. In a large scale study, ten other methods of time series outlier detection are compared with the proposed algorithm. The study reveals that the proposed algorithm is approximately 3.22 percent more accurate in detecting outliers. In the second application, the suitability of the algorithm for earthquake prediction is investigated. A case study is presented for the Tohoku 2011 earthquake. It is shown that this earthquake could have been predicted approximately 2 hours before its happening, solely based on each of the 845 GEONET station time series. Comparison with four different studies show the improvement in prediction of the time of the earthquake.
THE CAUSAL RELATIONSHIP BETWEEN CEO TENURE AND FIRM FINANCIAL PERFORMANCE IN THE SHIPPING INDUSTRY

Shipping companies’ CEOs play a critical role in founding credibility and confidence in their firms, thereby contributing to improved firm financial performance, enhancing firm economic stability and growth prospects, and attracting investors. During their term in office CEOs are to maximize profits with short term pressures severely constraining their long term strategies. The relationship between CEO tenure and financial performance has recently been in the center of interest of academic researchers and shipping professionals, with mixed evidence and results. The purpose of this paper is to establish the relationship between CEO tenure and financial performance in the case of international listed shipping firms and examine whether there is simultaneity between those two variables. The method employed is General Method of Moments (GMM), because it corrects for the bias caused by endogenous explanatory variables. Our findings suggest a positive relationship between CEO tenure and shipping financial performance and confirm their reverse causality. a Corresponding author
Operational characterization of multipartite nonlocal correlations

Nonlocality, one of the most puzzling features of multipartite quantum correlation, has been identified as a useful resource for device-independent quantum information processing. Motivated from the resource theory of quantum entanglement, Gallego et al. in [\href{this https URL}{Phys. Rev. Lett. {\bf 109}, 070401 (2012)}] proposed an operational framework to characterize the nonlocal resource present in multipartite quantum correlations. While the bipartite no-signaling correlations allows a dichotomous classification -- local \textit{vs.} nonlocal, in multipartite scenario the authors have shown that there exist several types of nonlocality that are inequivalent under the proposed operational framework. In this work we present a finer characterization of multipartite no-signaling correlations based on the same operational framework. We also point out some erroneous conclusions in Gallego et al.'s work and make them precise here.
Ensemble-based Feature Selection and Classification Model for DNS Typo-squatting Detection

Domain Name System (DNS) plays in important role in the current IP-based Internet architecture. This is because it performs the domain name to IP resolution. However, the DNS protocol has several security vulnerabilities due to the lack of data integrity and origin authentication within it. This paper focuses on one particular security vulnerability, namely typo-squatting. Typo-squatting refers to the registration of a domain name that is extremely similar to that of an existing popular brand with the goal of redirecting users to malicious/suspicious websites. The danger of typo-squatting is that it can lead to information threat, corporate secret leakage, and can facilitate fraud. This paper builds on our previous work in [1], which only proposed majority-voting based classifier, by proposing an ensemble-based feature selection and bagging classification model to detect D NS typo-squatting attack. Experimental results show that the proposed framework achieves high accuracy and precision in identifying the malicious/suspicious typo-squatting domains (a loss of at most 1.5% in accuracy and 5% in precision when compared to the model that used the complete feature set) while having a lower computational complexity due to the smaller feature set (a reduction of more than 50 % in feature set size).
Fast Graph Attention Networks Using Effective Resistance Based Graph Sparsification

The attention mechanism has demonstrated superior performance for inference over nodes in graph neural networks (GNNs), however, they result in a high computational burden during both training and inference. We propose FastGAT, a method to make attention based GNNs lightweight by using spectral sparsification to generate an optimal pruning of the input graph. This results in a per-epoch time that is almost linear in the number of graph nodes as opposed to quadratic. Further, we provide a re-formulation of a specific attention based GNN, Graph Attention Network (GAT) that interprets it as a graph convolution method using the random walk normalized graph Laplacian. Using this framework, we theoretically prove that spectral sparsification preserves the features computed by the GAT model, thereby justifying our FastGAT algorithm. We experimentally evaluate FastGAT on several large real world graph datasets for node classification tasks, FastGAT can dramatically reduce (up to 10x) the computational time and memory requirements, allowing the usage of attention based GNNs on large graphs.
Two-photon interference: the Hong–Ou–Mandel effect

Nearly 30 years ago, two-photon interference was observed, marking the beginning of a new quantum era. Indeed, two-photon interference has no classical analogue, giving it a distinct advantage for a range of applications. The peculiarities of quantum physics may now be used to our advantage to outperform classical computations, securely communicate information, simulate highly complex physical systems and increase the sensitivity of precise measurements. This separation from classical to quantum physics has motivated physicists to study two-particle interference for both fermionic and bosonic quantum objects. So far, two-particle interference has been observed with massive particles, among others, such as electrons and atoms, in addition to plasmons, demonstrating the extent of this effect to larger and more complex quantum systems. A wide array of novel applications to this quantum effect is to be expected in the future. This review will thus cover the progress and applications of two-photon (two-particle) interference over the last three decades.
Channel Relationship Prediction with Forget-Update Module for Few-shot Classification

In this paper, we proposed a pipeline for inferring the relationship of each class in support set and a query sample using forget-update module. We first propose a novel architectural module called "channel vector sequence construction module", which boosts the performance of sequence-prediction-model-based few-shot classification methods by collecting the overall information of all support samples and a query sample. The channel vector sequence generated by this module is organized in a way that each time step of the sequence contains the information from the corresponding channel of all support samples and the query sample to be inferred. Channel vector sequence is obtained by a convolutional neural network and a fully connected network, and the spliced channel vector sequence is spliced of the corresponding channel vectors of support samples and a query sample in the original channel order. Also, we propose a forget-update module consisting of stacked forget-update blocks. The forget block modify the original information with the learned weights and the update block establishes a dense connection for the model. The proposed pipeline, which consists of channel vector sequence construction module and forget-update module, can infer the relationship between the query sample and support samples in few-shot classification scenario. Experimental results show that the pipeline can achieve state-of-the-art results on miniImagenet, CUB dataset, and cross-domain scenario.
Two Steps at a Time - Taking GAN Training in Stride with Tseng's Method

Motivated by the training of Generative Adversarial Networks (GANs), we study methods for solving minimax problems with additional nonsmooth regularizers. We do so by employing \emph{monotone operator} theory, in particular the \emph{Forward-Backward-Forward (FBF)} method, which avoids the known issue of limit cycling by correcting each update by a second gradient evaluation. Furthermore, we propose a seemingly new scheme which recycles old gradients to mitigate the additional computational cost. In doing so we rediscover a known method, related to \emph{Optimistic Gradient Descent Ascent (OGDA)}. For both schemes we prove novel convergence rates for convex-concave minimax problems via a unifying approach. The derived error bounds are in terms of the gap function for the ergodic iterates. For the deterministic and the stochastic problem we show a convergence rate of $\mathcal{O}(1/k)$ and $\mathcal{O}(1/\sqrt{k})$, respectively. We complement our theoretical results with empirical improvements in the training of Wasserstein GANs on the CIFAR10 dataset.
AlphaGAN: Fully Differentiable Architecture Search for Generative Adversarial Networks

Generative Adversarial Networks (GANs) are formulated as minimax game problems that generative networks attempt to approach real data distributions by adversarial learning against discriminators which learn to distinguish generated samples from real ones, of which the intrinsic problem complexity poses challenges to performance and robustness. In this work, we aim to boost model learning from the perspective of network architectures, by incorporating recent progress on automated architecture search into GANs. Specially we propose a fully differentiable search framework, dubbed <italic>alphaGAN</italic>, where the searching process is formalized as solving a bi-level minimax optimization problem. The outer-level objective aims for seeking an optimal network architecture towards pure Nash Equilibrium conditioned on the network parameters of generators and discriminators optimized with a traditional adversarial loss within inner level. The entire optimization performs a first-order approach by alternately minimizing the two-level objective in a fully differentiable manner that enables obtaining a suitable architecture efficiently from an enormous search space. Extensive experiments on CIFAR-10 and STL-10 datasets show that our algorithm can obtain high-performing architectures only with 3-GPU hours on a single GPU in the search space comprised of approximate <inline-formula><tex-math notation="LaTeX">$2\times 10^{11}$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>2</mml:mn><mml:mo>×</mml:mo><mml:msup><mml:mn>10</mml:mn><mml:mn>11</mml:mn></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href="tian-ieq1-3099829.gif"/></alternatives></inline-formula> possible configurations. We further validate the method on the state-of-the-art network StyleGAN2, and push the score of Fréchet Inception Distance (FID) further, i.e., achieving 1.94 on CelebA, 2.86 on LSUN-church and 2.75 on FFHQ, with relative improvements <inline-formula><tex-math notation="LaTeX">$3\%{\sim} 26\%$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>3</mml:mn><mml:mo>%</mml:mo><mml:mo>∼</mml:mo><mml:mn>26</mml:mn><mml:mo>%</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="tian-ieq2-3099829.gif"/></alternatives></inline-formula> over the baseline architecture. We also provide a comprehensive analysis of the behavior of the searching process and the properties of searched architectures, which would benefit further research on architectures for generative models. Codes and models are available at <uri>https://github.com/yuesongtian/AlphaGAN</uri>.
First detection of ozone in the mid-infrared at Mars: implications for methane detection

Aims. The ExoMars Trace Gas Orbiter was sent to Mars in March 2016 to search for trace gases diagnostic of active geological or biogenic processes. Methods. We report the first observation of the spectral features of Martian ozone (O3) in the mid-infrared range using the Atmospheric Chemistry Suite Mid-InfaRed (MIR) channel, a cross-dispersion spectrometer operating in solar occultation mode with the finest spectral resolution of any remote sensing mission to Mars. Results. Observations of ozone were made at high northern latitudes (>65°N) prior to the onset of the 2018 global dust storm (Ls = 163–193°). During this fast transition phase between summer and winter ozone distribution, the O3 volume mixing ratio observed is 100–200 ppbv near 20 km. These amounts are consistent with past observations made at the edge of the southern polar vortex in the ultraviolet range. The observed spectral signature of ozone at 3000–3060 cm−1 directly overlaps with the spectral range of the methane (CH4) ν3 vibration-rotation band, and it, along with a newly discovered CO2 band in the same region, may interfere with measurements of methane abundance.