Miracle-dz/UniFAR-FTUs huggingface dataset:
The relationship between X-ray and optical absorbers in active galactic nuclei

We present a large compilation of reddening estimates from broad-line Balmer decrements for active galactic nuclei (AGNs) with measured X-ray column densities. The median reddening is E(B − V) ≈ 0.77 ± 0.10 for type-1 to type-1.9 AGNs with reported column densities. This is notably higher than the median reddening of AGNs from the SDSS. We attribute this to the selection bias of the SDSS towards blue AGNs. For other AGNs, we find evidence of a publication bias against reporting low column densities. We find a significant correlation between NH and E(B − V) but with a large scatter of ±1 dex. On average, the X-ray columns are consistent with those predicted from E(B − V) for a solar neighbourhood dust-to-gas ratio. We argue that the large scatter of column densities and reddenings can be explained by X-ray column density variability. For AGNs with detectable broad-line regions (BLRs) that have undergone significant changes of Seyfert type (‘changing-look’ AGNs), we do not find any statistically significant differences in NH or E(B − V) compared to standard type-1 to type-1.9 AGNs. There is no evidence for any type-1 AGNs being Compton thick. We also analyse type-2 AGNs and find no significant correlation between NH and narrow-line region reddening. We find no evidence for a previously claimed anticorrelation. The median column density of LINERs is 22.68 ± 0.75 compared to a column density of 22.90 ± 0.28 for type-2 AGNs. We find the majority of low column density type-2 AGNs are LINERs, but NH is probably underestimated because of scattered light.
Unsupervised Learning Methods for Visual Place Recognition in Discretely and Continuously Changing Environments

Visual place recognition in changing environments is the problem of finding matchings between two sets of observations, a query set and a reference set, despite severe appearance changes. Recently, image comparison using CNNbased descriptors showed very promising results. However, the experiments in the literature typically assume a single distinctive condition within each set (e.g., reference images are captured at daytime and the query sequence is at night). In this paper, we will demonstrate that as soon as the conditions change within one set (e.g., reference is daytime and now the query is a traversal daytime-dusk-night-dawn), different places under the same condition can suddenly look more similar than same places under different conditions. As a consequence, state-of-the-art approaches like CNN-based descriptors fail. This paper discusses this practically very important problem of in-sequence condition changes and defines a hierarchy of problem setups from (1) no in-sequence changes, (2) discrete in-sequence changes, to (3) continuous in-sequence changes. We will experimentally evaluate the effect of in-sequence condition changes on two state-of-the-art CNN-descriptors and investigate unsupervised methods to improve their performance. This includes an evaluation of the importance of statistical normalization (standardization) of descriptors, which is often omitted in existing approaches but can considerably improve results for problems up to discrete in-sequence changes. To address the practical most relevant setup of continuous changes, we investigate the application of unsupervised learning methods using two PCA-based approaches from the literature and propose a novel clustering-based extension of the statistical normalization. We experimentally demonstrate that these approaches can significantly improve place recognition performance in case of continuous in-sequence condition changes. Matlab implementations of the presented approaches are available online: www.tu-chemnitz.de/etit/proaut/cont_changing_envs
Variants of Partial Update Augmented CLMS Algorithm and Their Performance Analysis

Naturally complex-valued information or those presented in complex domain are effectively processed by an augmented complex least-mean-square (ACLMS) algorithm. In some applications, the ACLMS algorithm may be too computationally- and memory-intensive to implement. In this paper, a new algorithm, termed partial-update ACLMS (PU-ACLMS) algorithm is proposed, where only a fraction of the coefficient set is selected to update at each iteration. Doing so, two types of partial-update schemes are presented referred to as the sequential and stochastic partial-updates, to reduce computational load and power consumption in the corresponding adaptive filter. The computational cost for full-update PU-ACLMS and its partial-update implementations are discussed. Next, the steady-state mean and mean-square performance of PU-ACLMS for non-circular complex signals are analyzed and closed-form expressions of the steady-state excess mean-square error (EMSE) and mean-square deviation (MSD) are given. Then, employing the weighted energy-conservation relation, the EMSE and MSD learning curves are derived. The simulation results are verified and compared with those of theoretical predictions through numerical examples.
An Explicit Local and Global Representation Disentanglement Framework with Applications in Deep Clustering and Unsupervised Object Detection

Visual data can be understood at different levels of granularity, where global features correspond to semantic-level information and local features correspond to texture patterns. In this work, we propose a framework, called SPLIT, which allows us to disentangle local and global information into two separate sets of latent variables within the variational autoencoder (VAE) framework. Our framework adds generative assumption to the VAE by requiring a subset of the latent variables to generate an auxiliary set of observable data. This additional generative assumption primes the latent variables to local information and encourages the other latent variables to represent global information. We examine three different flavours of VAEs with different generative assumptions. We show that the framework can effectively disentangle local and global information within these models leads to improved representation, with better clustering and unsupervised object detection benchmarks. Finally, we establish connections between SPLIT and recent research in cognitive neuroscience regarding the disentanglement in human visual perception. The code for our experiments is at this https URL .
Delft University of Technology Towards a Framework for Certification of Reliable Autonomous Systems

A computational system is called autonomous if it is able to make its own decisions, or take its own actions, without human supervision or control. The capability and spread of such systems have reached the point where they are beginning to touch much of everyday life. However, regulators grapple with how to deal with autonomous systems, for example how could we certify an Unmanned Aerial System for autonomous use in civilian airspace? We here analyse what is needed in order to provide verified reliable behaviour of an autonomous system, analyse what can be done as the state-of-the-art in automated verification, and propose a roadmap towards developing regulatory guidelines, including articulating challenges to researchers, to engineers, and to regulators. Case studies in seven distinct domains illustrate the article. Michael Fisher was at the University of Liverpool when part of this work was done, and Michael Winikoff was at the University of Otago. * Neil Yorke-Smith n.yorke-smith@tudelft.nl Michael Fisher michael.fisher@manchester.ac. uk Viviana Mascardi viviana.mascardi@unige.it Kristin Yvonne Rozier kyrozier@iastate.edu Bernd-Holger Schlingloff hs@informatik.hu-berlin.de Michael Winikoff michael.winikoff@vuw.ac. nz 1 University of Manchester, Manchester, United Kingdom 2 University of Genova, Genova, Italy 3 Iowa State University, Ames, IA, USA 4 Humboldt University and Fraunhofer FOKUS, Berlin, Germany 5 Victoria University of Wellington, Wellington, New Zealand 6 Delft University of Technology, Delft, The Netherlands Autonomous Agents and Multi-Agent Systems (2021) 35:8
Refined model of the covariance/correlation matrix between securities

A new methodology has been introduced to clean the correlation matrix of single stocks returns based on a constrained principal component analysis using financial data. Portfolios were introduced, namely "Fundamental Maximum Variance Portfolios", to capture in an optimal way the risks defined by financial criteria ("Book", "Capitalization", etc.). The constrained eigenvectors of the correlation matrix, which are the linear combination of these portfolios, are then analyzed. Thanks to this methodology, several stylized patterns of the matrix were identified: i) the increase of the first eigenvalue with a time scale from 1 minute to several months seems to follow the same law for all the significant eigenvalues with 2 regimes; ii) a universal law seems to govern the weights of all the "Maximum variance" portfolios, so according to that law, the optimal weights should be proportional to the ranking based on the financial studied criteria; iii) the volatility of the volatility of the "Maximum Variance" portfolios, which are not orthogonal, could be enough to explain a large part of the diffusion of the correlation matrix; iv) the leverage effect (increase of the first eigenvalue with the decline of the stock market) occurs only for the first mode and cannot be generalized for other factors of risk. The leverage effect on the beta, which is the sensitivity of stocks with the market mode, makes variable the weights of the first eigenvector.
Approximating Surfaces in R3 by Meshes with Guaranteed Regularity

We study the problem of approximating a surface $F$ in $R^3$ by a high quality mesh, a piecewise-flat triangulated surface whose triangles are as close as possible to equilateral. The MidNormal algorithm generates a triangular mesh that is guaranteed to have angles in the interval $[49.1^o, 81.8^o]$. As the mesh size $e\rightarrow 0$, the mesh converges pointwise to $F$ through surfaces that are isotopic to $F$. The GradNormal algorithm gives a piecewise-$C^1$ approximation of $F$, with angles in the interval $[35.2^o, 101.5^o]$ as $e\rightarrow 0$. Previously achieved angle bounds were in the interval $[30^o, 120^o]$.
Small, Accurate, and Fast Vehicle Re-ID on the Edge: the SAFR Approach

We propose a Small, Accurate, and Fast Re-ID (SAFR) design for flexible vehicle re-id under a variety of compute environments such as cloud, mobile, edge, or embedded devices by only changing the re-id model backbone. Through best-fit design choices, feature extraction, training tricks, global attention, and local attention, we create a reid model design that optimizes multi-dimensionally along model size, speed, & accuracy for deployment under various memory and compute constraints. We present several variations of our flexible SAFR model: SAFR-Large for cloud-type environments with large compute resources, SAFR-Small for mobile devices with some compute constraints, and SAFR-Micro for edge devices with severe memory & compute constraints. SAFR-Large delivers state-of-the-art results with mAP 81.34 on the VeRi-776 vehicle re-id dataset (15% better than related work). SAFR-Small trades a 5.2% drop in performance (mAP 77.14 on VeRi-776) for over 60% model compression and 150% speedup. SAFR-Micro, at only 6MB and 130MFLOPS, trades 6.8% drop in accuracy (mAP 75.80 on VeRi-776) for 95% compression and 33x speedup compared to SAFR-Large.
Privacy for All: Demystify Vulnerability Disparity of Differential Privacy against Membership Inference Attack

Machine learning algorithms, when applied to sensitive data, pose a potential threat to privacy. A growing body of prior work has demonstrated that membership inference attack (MIA) can disclose specific private information in the training data to an attacker. Meanwhile, the algorithmic fairness of machine learning has increasingly caught attention from both academia and industry. Algorithmic fairness ensures that the machine learning models do not discriminate a particular demographic group of individuals (e.g., black and female people). Given that MIA is indeed a learning model, it raises a serious concern if MIA ``fairly'' treats all groups of individuals equally. In other words, whether a particular group is more vulnerable against MIA than the other groups. This paper examines the algorithmic fairness issue in the context of MIA and its defenses. First, for fairness evaluation, it formalizes the notation of vulnerability disparity (VD) to quantify the difference of MIA treatment on different demographic groups. Second, it evaluates VD on four real-world datasets, and shows that VD indeed exists in these datasets. Third, it examines the impacts of differential privacy, as a defense mechanism of MIA, on VD. The results show that although DP brings significant change on VD, it cannot eliminate VD completely. Therefore, fourth, it designs a new mitigation algorithm named FAIRPICK to reduce VD. An extensive set of experimental results demonstrate that FAIRPICK can effectively reduce VD for both with and without the DP deployment.
Deep Non-Line-of-Sight Reconstruction

The recent years have seen a surge of interest in methods for imaging beyond the direct line of sight. The most prominent techniques rely on time-resolved optical impulse responses, obtained by illuminating a diffuse wall with an ultrashort light pulse and observing multi-bounce indirect reflections with an ultrafast time-resolved imager. Reconstruction of geometry from such data, however, is a complex non-linear inverse problem that comes with substantial computational demands. In this paper, we employ convolutional feed-forward networks for solving the reconstruction problem efficiently while maintaining good reconstruction quality. Specifically, we devise a tailored autoencoder architecture, trained end-to-end, that maps transient images directly to a depth-map representation. Training is done using a recent, very efficient transient renderer for three-bounce indirect light transport that enables the quick generation of large amounts of training data for the network. We examine the performance of our method on a variety of synthetic and experimental datasets and its dependency on the choice of training data and augmentation strategies, as well as architectural features. We demonstrate that our feed-forward network, even if trained solely on synthetic data, is able to obtain results competitive with previous, model-based optimization methods, while being orders of magnitude faster.
A continuum limit for the PageRank algorithm

Semi-supervised and unsupervised machine learning methods often rely on graphs to model data, prompting research on how theoretical properties of operators on graphs are leveraged in learning problems. While most of the existing literature focuses on undirected graphs, directed graphs are very important in practice, giving models for physical, biological or transportation networks, among many other applications. In this paper, we propose a new framework for rigorously studying continuum limits of learning algorithms on directed graphs. We use the new framework to study the PageRank algorithm and show how it can be interpreted as a numerical scheme on a directed graph involving a type of normalised graph Laplacian. We show that the corresponding continuum limit problem, which is taken as the number of webpages grows to infinity, is a second-order, possibly degenerate, elliptic equation that contains reaction, diffusion and advection terms. We prove that the numerical scheme is consistent and stable and compute explicit rates of convergence of the discrete solution to the solution of the continuum limit partial differential equation. We give applications to proving stability and asymptotic regularity of the PageRank vector. Finally, we illustrate our results with numerical experiments and explore an application to data depth.
Effectively Trainable Semi-Quantum Restricted Boltzmann Machine

We propose a novel quantum model for the restricted Boltzmann machine (RBM), in which the visible units remain classical whereas the hidden units are quantized as noninteracting fermions. The free motion of the fermions is parametrically coupled to the classical signal of the visible units. This model possesses a quantum behaviour such as coherences between the hidden units. Numerical experiments show that this fact makes it more powerful than the classical RBM with the same number of hidden units. At the same time, a significant advantage of the proposed model over the other approaches to the Quantum Boltzmann Machine (QBM) is that it is exactly solvable and efficiently trainable on a classical computer: there is a closed expression for the log-likelihood gradient with respect to its parameters. This fact makes it interesting not only as a model of a hypothetical quantum simulator, but also as a quantum-inspired classical machine-learning algorithm.
Simultaneous Magnetic Polar Cap Heating during a Flaring Episode from the Magnetar 1RXS J170849.0–400910

During a pointed 2018 NuSTAR observation, we detected a flare with a 2.2 hr duration from the magnetar 1RXS J170849.0−400910. The flare, which rose in ∼25 s to a maximum flux 6 times larger than the persistent emission, is highly pulsed with an rms pulsed fraction of 53%. The pulse profile shape consists of two peaks separated by half a rotational cycle, with a peak flux ratio of ∼2. The flare spectrum is thermal with an average temperature of 2.1 keV. Phase-resolved spectroscopy shows that the two peaks possess the same temperature, but differ in size. These observational results, along with simple light curve modeling, indicate that two identical antipodal spots, likely the magnetic poles, are heated simultaneously at the onset of the flare and for its full duration. Hence, the origin of the flare has to be connected to the global dipolar structure of the magnetar. This might best be achieved externally, via twists to closed magnetospheric dipolar field lines seeding bombardment of polar footpoint locales with energetic pairs. Approximately 1.86 hr following the onset of the flare, a short burst with its own 3 minutes thermal tail occurred. The burst tail is also pulsating at the spin period of the source and phase-aligned with the flare profile, implying an intimate connection between the two phenomena. The burst may have been caused by a magnetic reconnection event in the same twisted dipolar field lines anchored to the surface hot spots, with subsequent return currents supplying extra heat to these polar caps.
A Branching and Merging Convolutional Network with Homogeneous Filter Capsules

We present a convolutional neural network design with additional branches after certain convolutions so that we can extract features with differing effective receptive fields and levels of abstraction. From each branch, we transform each of the final filters into a pair of homogeneous vector capsules. As the capsules are formed from entire filters, we refer to them as filter capsules. We then compare three methods for merging the branches--merging with equal weight and merging with learned weights, with two different weight initialization strategies. This design, in combination with a domain-specific set of randomly applied augmentation techniques, establishes a new state of the art for the MNIST dataset with an accuracy of 99.84% for an ensemble of these models, as well as establishing a new state of the art for a single model (99.79% accurate). These accuracies were achieved with a 75% reduction in both the number of parameters and the number of epochs of training relative to the previously best performing capsule network on MNIST. All training was performed using the Adam optimizer and experienced no overfitting.
SS-Auto: A Single-Shot, Automatic Structured Weight Pruning Framework of DNNs with Ultra-High Efficiency

Structured weight pruning is a representative model compression technique of DNNs for hardware efficiency and inference accelerations. Previous works in this area leave great space for improvement since sparse structures with combinations of different structured pruning schemes are not exploited fully and efficiently. To mitigate the limitations, we propose SS-Auto, a single-shot, automatic structured pruning framework that can achieve row pruning and column pruning simultaneously. We adopt soft constraint-based formulation to alleviate the strong non-convexity of l0-norm constraints used in state-of-the-art ADMM-based methods for faster convergence and fewer hyperparameters. Instead of solving the problem directly, a Primal-Proximal solution is proposed to avoid the pitfall of penalizing all weights equally, thereby enhancing the accuracy. Extensive experiments on CIFAR-10 and CIFAR-100 datasets demonstrate that the proposed framework can achieve ultra-high pruning rates while maintaining accuracy. Furthermore, significant inference speedup has been observed from the proposed framework through actual measurements on the smartphone.
Drag force in immersed granular materials

We investigate the drag forces acting on large objects moving through a granular packing immersed in water. In this aim, we conducted uplift experiments involving pulling out horizontal plates at a prescribed velocity vertically. During these tests, we observed that the drag force reaches to peak at a low displacement and then decays. Results show that the peak drag force strongly increases with the velocity and depends on the plate size and grain diameter. We identify empirical scaling laws for these properties and introduce a Darcy-flow mechanism that can explain them. Furthermore, we conducted tests involving suddenly stopping the motion of the plate, which evidenced a progressive relaxation of the drag force in time. We discuss how a visco-elasto-plastic mechanical analogue can reproduce these dynamics. These results and analyses highlight fundamental differences in drag force between dry and immersed granular materials.
Approximation by sampling‐type operators in 
 Lp ‐spaces

Approximation properties of the sampling‐type quasi‐projection operators Qj(f,φ,φ˜) for functions f from anisotropic Besov spaces are studied. Error estimates in Lp ‐norm are obtained for a large class of tempered distributions φ˜ and a large class of functions φ under the assumptions that φ has enough decay, satisfies the Strang‐Fix conditions, and has a compatibility condition with φ˜ . The estimates are given in terms of moduli of smoothness and best approximations.