Miracle-dz/UniFAR-FTUs huggingface dataset:
StickyPillars: Robust feature matching on point clouds using Graph Neural Networks

StickyPillars introduces a sparse feature matching method on point clouds. It is the first approach applying Graph Neural Networks on point clouds to stick points of interest. The feature estimation and assignment relies on the optimal transport problem, where the cost is based on the neural network itself. We utilize a Graph Neural Network for context aggregation with the aid of multihead self and cross attention. In contrast to image based feature matching methods, the architecture learns feature extraction in an end-to-end manner. Hence, the approach does not rely on handcrafted features. Our method outperforms state-of-the art matching algorithms, while providing real-time capability.
Adaptive Online Learning with Varying Norms

Given any increasing sequence of norms $\|\cdot\|_0,\dots,\|\cdot\|_{T-1}$, we provide an online convex optimization algorithm that outputs points $w_t$ in some domain $W$ in response to convex losses $\ell_t:W\to \mathbb{R}$ that guarantees regret $R_T(u)=\sum_{t=1}^T \ell_t(w_t)-\ell_t(u)\le \tilde O\left(\|u\|_{T-1}\sqrt{\sum_{t=1}^T \|g_t\|_{t-1,\star}^2}\right)$ where $g_t$ is a subgradient of $\ell_t$ at $w_t$. Our method does not require tuning to the value of $u$ and allows for arbitrary convex $W$. We apply this result to obtain new "full-matrix"-style regret bounds. Along the way, we provide a new examination of the full-matrix AdaGrad algorithm, suggesting a better learning rate value that improves significantly upon prior analysis. We use our new techniques to tune AdaGrad on-the-fly, realizing our improved bound in a concrete algorithm.
Mapping Low-Dimensional Dynamics to High-Dimensional Neural Activity: A Derivation of the Ring Model From the Neural Engineering Framework

Empirical estimates of the dimensionality of neural population activity are often much lower than the population size. Similar phenomena are also observed in trained and designed neural network models. These experimental and computational results suggest that mapping low-dimensional dynamics to high-dimensional neural space is a common feature of cortical computation. Despite the ubiquity of this observation, the constraints arising from such mapping are poorly understood. Here we consider a specific example of mapping low-dimensional dynamics to high-dimensional neural activity—the neural engineering framework. We analytically solve the framework for the classic ring model—a neural network encoding a static or dynamic angular variable. Our results provide a complete characterization of the success and failure modes for this model. Based on similarities between this and other frameworks, we speculate that these results could apply to more general scenarios.
The bright side of defects in MoS 2 and WS 2 and a generalizable chemical treatment protocol for defect passivation

Structural defects are widely regarded as detrimental to the optoelectronic properties of monolayer transition metal dichalcogenides, leading to concerted efforts to eliminate defects via improved materials growth or post-growth passivation. Here, using steady-state and ultrafast optical spectroscopy, supported by ab initio calculations, we demonstrate that sulfur vacancy defects act as exciton traps. Current chemical treatments do not passivate these sites, leading to decreased mobility and trap-limited photoluminescence. We present a generalizable treatment protocol based on the use of passivating agents such as thiols or sulfides in combination with a Lewis acid to passivate sulfur vacancies in monolayer MoS$_2$ and WS$_2$, increasing photoluminescence up to 275 fold, while maintaining mobilities. Our findings suggest a route for simple and rational defect engineering strategies, where the passivating agent varies the electronic properties, thereby allowing the design of new heterostructures.
Asymmetric Rejection Loss for Fairer Face Recognition

Face recognition performance has seen a tremendous gain in recent years, mostly due to the availability of large-scale face images dataset that can be exploited by deep neural networks to learn powerful face representations. However, recent research has shown differences in face recognition performance across different ethnic groups mostly due to the racial imbalance in the training datasets where Caucasian identities largely dominate other ethnicities. This is actually symptomatic of the under-representation of non-Caucasian ethnic groups in the celebdom from which face datasets are usually gathered, rendering the acquisition of labeled data of the under-represented groups challenging. In this paper, we propose an Asymmetric Rejection Loss, which aims at making full use of unlabeled images of those under-represented groups, to reduce the racial bias of face recognition models. We view each unlabeled image as a unique class, however as we cannot guarantee that two unlabeled samples are from a distinct class we exploit both labeled and unlabeled data in an asymmetric manner in our loss formalism. Extensive experiments show our method's strength in mitigating racial bias, outperforming state-of-the-art semi-supervision methods. Performance on the under-represented ethnicity groups increases while that on the well-represented group is nearly unchanged.
Evolution of a Complex Predator-Prey Ecosystem on Large-scale Multi-Agent Deep Reinforcement Learning

Simulation of population dynamics is a central research theme in computational biology, which contributes to understanding the interactions between predators and preys. Conventional mathematical tools of this theme, however, are incapable of accounting for several important attributes of such systems, such as the intelligent and adaptive behavior exhibited by individual agents. This unrealistic setting is often insufficient to simulate properties of population dynamics found in the real-world. In this work, we leverage multi-agent deep reinforcement learning, and we propose a new model of large-scale predator-prey ecosystems. Using different variants of our proposed environment, we show that multi-agent simulations can exhibit key real-world dynamical properties. To obtain this behavior, we firstly define a mating mechanism such that existing agents reproduce new individuals bound by the conditions of the environment. Furthermore, we incorporate a real-time evolutionary algorithm and show that reinforcement learning enhances the evolution of the agents' physical properties such as speed, attack and resilience against attacks.
Implications of Abiotic Oxygen Buildup for Earth-like Complex Life

One of the chief paradoxes of molecular oxygen (O2) is that it is an essential requirement for multicellular eukaryotes on Earth while simultaneously posing a threat to their survival via the formation of reactive oxygen species. In this paper, the constraints imposed by O2 on Earth-like complex life are invoked to explore whether worlds with abiotic O2 inventories can harbor such organisms. By taking the major O2 sources and sinks of Earth-like planets into account using a simple model, it is suggested that worlds that receive time-averaged X-ray and extreme ultraviolet fluxes that are ≳10 times higher than Earth might not be capable of hosting complex lifeforms because the photolysis of molecules such as water may lead to significant O2 buildup. Methods for testing this hypothesis by searching for anticorrelations between biosignatures and indicators of abiotic O2 atmospheres are described. In the event, however, that life successfully adapts to high-oxygen environments, these worlds could permit the evolution of large and complex organisms.
Polynomial Optimization for Bounding Lipschitz Constants of Deep Networks

The Lipschitz constant of a network plays an important role in many applications of deep learning, such as robustness certification and Wasserstein Generative Adversarial Network. We introduce a semidefinite programming hierarchy to estimate the global and local Lipschitz constant of a multiple layer deep neural network. The novelty is to combine a polynomial lifting for ReLU functions derivatives with a weak generalization of Putinar's positivity certificate. This idea could also apply to other, nearly sparse, polynomial optimization problems in machine learning. We empirically demonstrate that our method not only runs faster than state-of-the-art linear programming based method, but also provides sharper bounds.
Impact of an Active Sgr A* on the Synthesis of Water and Organic Molecules throughout the Milky Way

Sgr A*, the supermassive black hole (SMBH) in our Galaxy, is dormant today, but it should have gone through multiple gas-accretion episodes in the past billions of years to grow to its current mass of 4 × 106 M⊙. Each episode temporarily ignites the SMBH and turns the Galactic Center into an active galactic nucleus (AGN). Recently, we showed that the AGN could produce large amounts of hard X-rays that can penetrate the dense interstellar medium in the Galactic plane. Here we further study the impact of X-rays on the molecular chemistry in our Galaxy. We use a chemical-reaction network to simulate the evolution of several molecular species, including H2O, CH3OH, and H2CO, both in the gas phase and on the surface of dust grains. We find that X-ray irradiation could significantly enhance the abundances of these species. The effect is most significant in young, high-density molecular clouds and could be prominent at a Galactic distance of 8 kpc or smaller. The imprint in the chemical abundance is visible even several million years after the AGN turns off.
Two-Stream Aural-Visual Affect Analysis in the Wild

Human affect recognition is an essential part of natural human-computer interaction. However, current methods are still in their infancy, especially for in-the-wild data. In this work, we introduce our submission to the Affective Behavior Analysis in-the-wild (ABAW) 2020 competition. We propose a two-stream aural-visual analysis model to recognize affective behavior from videos. Audio and image streams are first processed separately and fed into a convolutional neural network. Instead of applying recurrent architectures for temporal analysis we only use temporal convolutions. Furthermore, the model is given access to additional features extracted during face-alignment. At training time, we exploit correlations between different emotion representations to improve performance. Our model achieves promising results on the challenging Aff-Wild2 database.The code is publicly available1.1https://github.com/kuhnkeF/ABAW2020TNT
Self-Supervised ECG Representation Learning for Emotion Recognition

We exploit a self-supervised deep multi-task learning framework for electrocardiogram (ECG) -based emotion recognition. The proposed solution consists of two stages of learning a) learning ECG representations and b) learning to classify emotions. ECG representations are learned by a signal transformation recognition network. The network learns high-level abstract representations from unlabeled ECG data. Six different signal transformations are applied to the ECG signals, and transformation recognition is performed as pretext tasks. Training the model on pretext tasks helps the network learn spatiotemporal representations that generalize well across different datasets and different emotion categories. We transfer the weights of the self-supervised network to an emotion recognition network, where the convolutional layers are kept frozen and the dense layers are trained with labelled ECG data. We show that the proposed solution considerably improves the performance compared to a network trained using fully-supervised learning. New state-of-the-art results are set in classification of arousal, valence, affective states, and stress for the four utilized datasets. Extensive experiments are performed, providing interesting insights into the impact of using a multi-task self-supervised structure instead of a single-task model, as well as the optimum level of difficulty required for the pretext self-supervised tasks.
Statistical Guarantees of Generative Adversarial Networks for Distribution Estimation

Generative Adversarial Networks (GANs) have achieved great success in unsupervised learning. Despite the remarkable empirical performance, there are limited theoretical understandings on the statistical properties of GANs. This paper provides statistical guarantees of GANs for the estimation of data distributions which have densities in a Holder space. Our main result shows that, if the generator and discriminator network architectures are properly chosen (universally for all distributions with Holder densities), GANs are consistent estimators of the data distributions under strong discrepancy metrics, such as the Wasserstein distance. To our best knowledge, this is the first statistical theory of GANs for Holder densities. In comparison with existing works, our theory requires minimum assumptions on data distributions. Our generator and discriminator networks utilize general weight matrices and the non-invertible ReLU activation function, while many existing works only apply to invertible weight matrices and invertible activation functions. In our analysis, we decompose the error into a statistical error and an approximation error by a new oracle inequality, which may be of independent interest.
Security & Privacy in IoT Using Machine Learning & Blockchain: Threats & Countermeasures

Security and privacy of the users have become significant concerns due to the involvement of the Internet of things (IoT) devices in numerous applications. Cyber threats are growing at an explosive pace making the existing security and privacy measures inadequate. Hence, everyone on the Internet is a product for hackers. Consequently, Machine Learning (ML) algorithms are used to produce accurate outputs from large complex databases, where the generated outputs can be used to predict and detect vulnerabilities in IoT-based systems. Furthermore, Blockchain (BC) techniques are becoming popular in modern IoT applications to solve security and privacy issues. Several studies have been conducted on either ML algorithms or BC techniques. However, these studies target either security or privacy issues using ML algorithms or BC techniques, thus posing a need for a combined survey on efforts made in recent years addressing both security and privacy issues using ML algorithms and BC techniques. In this paper, we provide a summary of research efforts made in the past few years, starting from 2008 to 2019, addressing security and privacy issues using ML algorithms and BCtechniques in the IoT domain. First, we discuss and categorize various security and privacy threats reported in the past twelve years in the IoT domain. Then, we classify the literature on security and privacy efforts based on ML algorithms and BC techniques in the IoT domain. Finally, we identify and illuminate several challenges and future research directions in using ML algorithms and BC techniques to address security and privacy issues in the IoT domain.
A detailed study on the reflection component for the black hole candidate MAXI J1836−194

We present a detailed spectral analysis of the black hole candidate MAXI J1836−194. The source was caught in the intermediate state during its 2011 outburst by Suzaku and RXTE. We jointly fit the X-ray data from these two missions using the relxill model to study the reflection component, and a steep inner emissivity profile indicating a compact corona as the primary source is required in order to achieve a good fit. In addition, a reflection model with a lamp-post configuration (relxilllp), which is normally invoked to explain the steep emissivity profile, gives a worse fit and is excluded at 99 per cent confidence level compared to relxill. We also explore the effect of the ionization gradient on the emissivity profile by fitting the data with two relativistic reflection components, and it is found that the inner emissivity flattens. These results may indicate that the ionization state of the disc is not constant. All the models above require a supersolar iron abundance higher than ∼4.5. However, we find that the high-density version of reflionx can describe the same spectra even with solar iron abundance well. A moderate rotating black hole (a* = 0.84–0.94) is consistently obtained by our models, which is in agreement with previously reported values.
OoDAnalyzer: Interactive Analysis of Out-of-Distribution Samples

One major cause of performance degradation in predictive models is that the test samples are not well covered by the training data. Such not well-represented samples are called OoD samples. In this article, we propose OoDAnalyzer, a visual analysis approach for interactively identifying OoD samples and explaining them in context. Our approach integrates an ensemble OoD detection method and a grid-based visualization. The detection method is improved from deep ensembles by combining more features with algorithms in the same family. To better analyze and understand the OoD samples in context, we have developed a novel <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="liu-ieq1-2973258.gif"/></alternatives></inline-formula>NN-based grid layout algorithm motivated by Hall's theorem. The algorithm approximates the optimal layout and has <inline-formula><tex-math notation="LaTeX">$O(kN^2)$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>O</mml:mi><mml:mo>(</mml:mo><mml:mi>k</mml:mi><mml:msup><mml:mi>N</mml:mi><mml:mn>2</mml:mn></mml:msup><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="liu-ieq2-2973258.gif"/></alternatives></inline-formula> time complexity, faster than the grid layout algorithm with overall best performance but <inline-formula><tex-math notation="LaTeX">$O(N^3)$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>O</mml:mi><mml:mo>(</mml:mo><mml:msup><mml:mi>N</mml:mi><mml:mn>3</mml:mn></mml:msup><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="liu-ieq3-2973258.gif"/></alternatives></inline-formula> time complexity. Quantitative evaluation and case studies were performed on several datasets to demonstrate the effectiveness and usefulness of OoDAnalyzer.
Parallel PERM

We develop and implement a parallel flatPERM algorithm (Grassberger 1997 Phys. Rev. E 56 3682–3693, Prellberg and Krawczyk 2004 Phys. Rev. Lett. 92 120602) with mutually interacting parallel flatPERM sequences and use it to sample self-avoiding walks in two and three dimensions. Our data show that the parallel implementation accelerates the convergence of the flatPERM algorithm. Moreover, increasing the number of interacting flatPERM sequences (rather than running longer simulations) improves the rate of convergence. This suggests that a more efficient implementation of flatPERM will be a massively parallel implementation, rather than long simulations of one, or a few parallel sequences. We also use the algorithm to estimate the growth constant of the self-avoiding walk in two and in three dimensions using simulations over 12 parallel sequences. Our best results are μ d = 2.638 1585 ( 1 ) , if d = 2 ; 4.684 039 ( 1 ) , if d = 3 .
Better Theory for SGD in the Nonconvex World

Large-scale nonconvex optimization problems are ubiquitous in modern machine learning, and among practitioners interested in solving them, Stochastic Gradient Descent (SGD) reigns supreme. We revisit the analysis of SGD in the nonconvex setting and propose a new variant of the recently introduced expected smoothness assumption which governs the behaviour of the second moment of the stochastic gradient. We show that our assumption is both more general and more reasonable than assumptions made in all prior work. Moreover, our results yield the optimal $\mathcal{O}(\varepsilon^{-4})$ rate for finding a stationary point of nonconvex smooth functions, and recover the optimal $\mathcal{O}(\varepsilon^{-1})$ rate for finding a global solution if the Polyak-Łojasiewicz condition is satisfied. We compare against convergence rates under convexity and prove a theorem on the convergence of SGD under Quadratic Functional Growth and convexity, which might be of independent interest. Moreover, we perform our analysis in a framework which allows for a detailed study of the effects of a wide array of sampling strategies and minibatch sizes for finite-sum optimization problems. We corroborate our theoretical results with experiments on real and synthetic data.
Segmenting High-dimensional Matrix-valued Time Series via Sequential Transformations

Modeling matrix-valued time series is an interesting and important research topic. In this paper, we extend the method of Chang et al. (2017) to matrix-valued time series. For any given $p\times q$ matrix-valued time series, we look for linear transformations to segment the matrix into many small sub-matrices for which each of them are uncorrelated with the others both contemporaneously and serially, thus they can be analyzed separately, which will greatly reduce the number of parameters to be estimated in terms of modeling. To overcome the identification issue, we propose a two-step and more structured procedure to segment the rows and columns separately. When $\max(p,q)$ is large in relation to the sample size $n$, we assume the transformation matrices are sparse and use threshold estimators for the (auto)covariance matrices. We also propose a block-wisely thresholding method to separate the columns (or rows) of the transformed matrix-valued data. The asymptotic properties are established for both fixed and diverging $\max(p,q)$. Unlike principal component analysis (PCA) for independent data, we cannot guarantee that the required linear transformation exists. When it does not, the proposed method provides an approximate segmentation, which may be useful for forecasting. The proposed method is illustrated with both simulated and real data examples. We also propose a sequential transformation algorithm to segment higher-order tensor-valued time series.
Maximizing the Total Reward via Reward Tweaking

In reinforcement learning, the discount factor $\gamma$ controls the agent's effective planning horizon. Traditionally, this parameter was considered part of the MDP; however, as deep reinforcement learning algorithms tend to become unstable when the effective planning horizon is long, recent works refer to $\gamma$ as a hyper-parameter. In this work, we focus on the finite-horizon setting and introduce \emph{reward tweaking}. Reward tweaking learns a surrogate reward function $\tilde r$ for the discounted setting, which induces an optimal (undiscounted) return in the original finite-horizon task. Theoretically, we show that there exists a surrogate reward which leads to optimality in the original task and discuss the robustness of our approach. Additionally, we perform experiments in a high-dimensional continuous control task and show that reward tweaking guides the agent towards better long-horizon returns when it plans for short horizons using the tweaked reward.