Miracle-dz/UniFAR-FTUs huggingface dataset:
A Calibration Scheme for Non-Line-of-Sight Imaging Setups

The recent years have given rise to a large number of techniques for "looking around corners", i.e., for reconstructing or tracking occluded objects from indirect light reflections off a wall. While the direct view of cameras is routinely calibrated in computer vision applications, the calibration of non-line-of-sight setups has so far relied on manual measurement of the most important dimensions (device positions, wall position and orientation, etc.). In this paper, we propose a method for calibrating time-of-flight-based non-line-of-sight imaging systems that relies on mirrors as known targets. A roughly determined initialization is refined in order to optimize for spatio-temporal consistency. Our system is general enough to be applicable to a variety of sensing scenarios ranging from single sources/detectors via scanning arrangements to large-scale arrays. It is robust towards bad initialization and the achieved accuracy is proportional to the depth resolution of the camera system.
Circ-Tree: A B+-Tree Variant With Circular Design for Persistent Memory

Several B+-tree variants have been developed to exploit the byte-addressable non-volatile memory (NVM). We attentively investigate the properties of B+-tree and find that, a conventional B+-tree node is a linear structure in which key-value (KV) pairs are maintained from the zero offset of a node. These KV pairs are shifted in a unidirectional fashion for insertions and deletions. Inserting and deleting one KV pair may inflict a large amount of write amplifications due to shifting existing KV pairs. This badly impairs the performance of in-NVM B+-tree. In this article, we propose a novel circular design for B+-tree. With regard to NVM's byte-addressability, our Circ-Tree embraces tree nodes in a circular structure without a fixed base address, and bidirectionally shifts KV pairs for insertions and deletions to minimize write amplifications. We have implemented a prototype for Circ-Tree and conducted extensive experiments. Experimental results show that Circ-Tree significantly outperforms two state-of-the-art in-NVM B+-tree variants, i.e., NV-tree and FAST+FAIR, by up to 1.6× and 8.6×, respectively, in terms of write performance. The end-to-end comparison by running YCSB to KV stores built on NV-tree, FAST+FAIR, and Circ-Tree reveals that Circ-Tree yields up to 29.3 and 47.4 percent higher write performance, respectively, than NV-tree and FAST+FAIR.
Online Analysis of Distributed Dataflows with Timely Dataflow

We present ST2, an end-to-end solution to analyze distributed dataflows in an online setting. It is powered by Timely Dataflow, a low-latency, distributed data-parallel dataflow computational framework, and expands on its predecessor SnailTrail 1, a system to run online critical path analysis on program activity graphs derived from dataflow execution traces. ST2 connects to a running Timely computation, creates the program activity graph representation, and runs multiple analyses on top of it. Analyses include aggregate metrics, progress and temporal invariant checking, and graph pattern matching. Through a command-line interface and a real-time dashboard, users are able to interact with and visualize ST2's analysis results. For ST2's implementation, we discuss Differential Dataflow, a framework that uses differential computation to incrementalize even complex relational dataflow operators, as an alternative to Timely Dataflow, but ultimately settle on using Timely. In our performance evaluations, we are able to show that ST2 is able to comfortably keep up with common streaming computations in offline and online settings, even exceeding SnailTrail 1's performance. We also showcase and evaluate ST2 from a functional standpoint in a case study. Using the dashboard to profile a faulty source computation, we manage to successfully detect the issues' root cause. We argue that ST2 is an extendable system that paves the way for users to debug, monitor, and optimize online distributed dataflows.
When Explanations Lie: Why Modified BP Attribution Fails

Modiﬁed backpropagation methods are a popular group of attribution methods. We analyse the most prominent methods: Deep Taylor Decomposition, Layer-wise Relevance Propagation, Excitation BP, PatternAttribution, Deconv, and Guided BP. We found empirically that the explanations of the mentioned modiﬁed BP methods are independent of the parameters of later layers and show that the z + rule used by multiple methods converges to a rank-1 matrix. This can explain well why the actual network’s decision is ignored. We also develop a new metric cosine similarity convergence (CSC) to directly quantify the convergence of the modiﬁed BP methods to a rank-1 matrix. Our conclusion is that many modiﬁed BP methods do not explain the predictions of deep neural networks faithfully.
Second-order Information in First-order Optimization Methods

In this paper, we try to uncover the second-order essence of several first-order optimization methods. For Nesterov Accelerated Gradient, we rigorously prove that the algorithm makes use of the difference between past and current gradients, thus approximates the Hessian and accelerates the training. For adaptive methods, we related Adam and Adagrad to a powerful technique in computation statistics---Natural Gradient Descent. These adaptive methods can in fact be treated as relaxations of NGD with only a slight difference lying in the square root of the denominator in the update rules. Skeptical about the effect of such difference, we design a new algorithm---AdaSqrt, which removes the square root in the denominator and scales the learning rate by sqrt(T). Surprisingly, our new algorithm is comparable to various first-order methods(such as SGD and Adam) on MNIST and even beats Adam on CIFAR-10! This phenomenon casts doubt on the convention view that the square root is crucial and training without it will lead to terrible performance. As far as we have concerned, so long as the algorithm tries to explore second or even higher information of the loss surface, then proper scaling of the learning rate alone will guarantee fast training and good generalization performance. To the best of our knowledge, this is the first paper that seriously considers the necessity of square root among all adaptive methods. We believe that our work can shed light on the importance of higher-order information and inspire the design of more powerful algorithms in the future.
Statistical Properties of Transmissions Subject to Rayleigh Fading and Ornstein-Uhlenbeck Mobility

In this paper, we derive closed-form expressions for significant statistical properties of the link signal-to-noise ratio (SNR) and the separation distance in mobile ad hoc networks subject to Ornstein-Uhlenbeck (OU) mobility and Rayleigh fading. In these systems, the SNR is a critical parameter as it directly influences link performance. In the absence of signal fading, the distribution of the link SNR depends exclusively on the squared distance between nodes, which is governed by the mobility model. In our analysis, nodes move randomly according to an Ornstein-Uhlenbeck process, using one tuning parameter to control the temporal dependency in the mobility pattern. We derive a complete statistical description of the squared distance and show that it forms a stationary Markov process. Then, we compute closed-form expressions for the probability density function (pdf), the cumulative distribution function (cdf), the bivariate pdf, and the bivariate cdf of the link SNR. Next, we introduce small-scale fading, modeled by a Rayleigh random variable, and evaluate the pdf of the link SNR for rational path loss exponents. The validity of our theoretical analysis is verified by extensive simulation studies. The results presented in this work can be used to quantify link uncertainty and evaluate stability in mobile ad hoc wireless systems.
OGLE-2013-BLG-0911Lb: A Secondary on the Brown-dwarf Planet Boundary around an M Dwarf

We present the analysis of the binary-lens microlensing event OGLE-2013-BLG-0911. The best-fit solutions indicate the binary mass ratio of q ≃ 0.03, which differs from that reported in Shvartzvald et al. The event suffers from the well-known close/wide degeneracy, resulting in two groups of solutions for the projected separation normalized by the Einstein radius of s ∼ 0.15 or s ∼ 7. The finite source and the parallax observations allow us to measure the lens physical parameters. The lens system is an M dwarf orbited by a massive Jupiter companion at very close ( Mhost=0.30−0.06+0.08M⊙ , Mcomp=10.1−2.2+2.9MJup , aexp=0.40−0.04+0.05au ) or wide ( Mhost=0.28−0.08+0.10M⊙ , Mcomp=9.9−3.5+3.8MJup , aexp=18.0−3.2+3.2au ) separation. Although the mass ratio is slightly above the planet-brown dwarf (BD) mass-ratio boundary of q = 0.03, which is generally used, the median physical mass of the companion is slightly below the planet-BD mass boundary of 13M Jup. It is likely that the formation mechanisms for BDs and planets are different and the objects near the boundaries could have been formed by either mechanism. It is important to probe the distribution of such companions with masses of ∼13M Jup in order to statistically constrain the formation theories for both BDs and massive planets. In particular, the microlensing method is able to probe the distribution around low-mass M dwarfs and even BDs, which is challenging for other exoplanet detection methods.
Robust estimation and variable selection for the accelerated failure time model

This article concerns robust modeling of the survival time for cancer patients. Accurate prediction of patient survival time is crucial to the development of effective therapeutic strategies. To this goal, we propose a unified Expectation‐Maximization approach combined with the L1‐norm penalty to perform variable selection and parameter estimation simultaneously in the accelerated failure time model with right‐censored survival data of moderate sizes. Our approach accommodates general loss functions, and reduces to the well‐known Buckley‐James method when the squared‐error loss is used without regularization. To mitigate the effects of outliers and heavy‐tailed noise in real applications, we recommend the use of robust loss functions under the general framework. Furthermore, our approach can be extended to incorporate group structure among covariates. We conduct extensive simulation studies to assess the performance of the proposed methods with different loss functions and apply them to an ovarian carcinoma study as an illustration.
Stubborn Set Reduction for Two-Player Reachability Games

Partial order reductions have been successfully applied to model checking of concurrent systems and practical applications of the technique show nontrivial reduction in the size of the explored state space. We present a theory of partial order reduction based on stubborn sets in the game-theoretical setting of 2-player games with reachability objectives. Our stubborn reduction allows us to prune the interleaving behaviour of both players in the game, and we formally prove its correctness on the class of games played on general labelled transition systems. We then instantiate the framework to the class of weighted Petri net games with inhibitor arcs and provide its efficient implementation in the model checker TAPAAL. Finally, we evaluate our stubborn reduction on several case studies and demonstrate its efficiency.
Identity Document to Selfie Face Matching Across Adolescence

Matching live images (“selfies”) to images from ID documents is a problem that can arise in various applications. A challenging instance of the problem arises when the face image on the ID document is from early adolescence and the live image is from later adolescence. We explore this problem using a private dataset called Chilean Young Adult (CHIYA) dataset, where we match live face images taken at age 18–19 to face images on scanned ID documents created at ages 9 to 18. State-of-the-art deep learning face matchers (e.g., ArcFace) have relatively poor accuracy for document-to-selfie face matching. To achieve higher accuracy, we fine-tune the best available open-source model with triplet loss for a few-shot learning. Experiments show that our approach achieves higher accuracy than the DocFace+ model recently developed for this problem. Our fine-tuned model was able to improve the true acceptance rate for the most difficult (largest age span) subset from 62.92% to 96.67% at a false acceptance rate of 0.01%. Our fine-tuned model is available for use by other researchers.
The $e$-positivity of two families of $(claw, 2K_2)$-free graphs

Motivated by Stanley's conjecture about the $e$-positivity of claw-free incomparability graphs, Hamel and her collaborators studied the $e$-positivity of $(claw, H)$-free graphs, where $H$ is a four-vertex graph. In this paper we establish the $e$-positivity of generalized pyramid graphs and $2K_2$-free unit interval graphs, which are two important families of $(claw, 2K_2)$-free graphs. Hence we affirmatively solve one problem proposed by Hamel, Hoang and Tuero, and another problem considered by Foley, Hoang and Merkel.
HiLLoC: Lossless Image Compression with Hierarchical Latent Variable Models

We make the following striking observation: fully convolutional VAE models trained on 32x32 ImageNet can generalize well, not just to 64x64 but also to far larger photographs, with no changes to the model. We use this property, applying fully convolutional models to lossless compression, demonstrating a method to scale the VAE-based 'Bits-Back with ANS' algorithm for lossless compression to large color photographs, and achieving state of the art for compression of full size ImageNet images. We release Craystack, an open source library for convenient prototyping of lossless compression using probabilistic models, along with full implementations of all of our compression results.
Invertible Gaussian Reparameterization: Revisiting the Gumbel-Softmax

The Gumbel-Softmax is a continuous distribution over the simplex that is often used as a relaxation of discrete distributions. Because it can be readily interpreted and easily reparameterized, it enjoys widespread use. We propose a conceptually simpler and more flexible alternative family of reparameterizable distributions where Gaussian noise is transformed into a one-hot approximation through an invertible function. This invertible function is composed of a modified softmax and can incorporate diverse transformations that serve different specific purposes. For example, the stick-breaking procedure allows us to extend the reparameterization trick to distributions with countably infinite support, or normalizing flows let us increase the flexibility of the distribution. Our construction enjoys theoretical advantages over the Gumbel-Softmax, such as closed form KL, and significantly outperforms it in a variety of experiments.
Zeroth-order Stochastic Compositional Algorithms for Risk-Aware Learning

We present Free-MESSAGEp, the first zeroth-order algorithm for convex mean-semideviation-based risk-aware learning, which is also the first three-level zeroth-order compositional stochastic optimization algorithm, whatsoever. Using a non-trivial extension of Nesterov's classical results on Gaussian smoothing, we develop the Free-MESSAGEp algorithm from first principles, and show that it essentially solves a smoothed surrogate to the original problem, the former being a uniform approximation of the latter, in a useful, convenient sense. We then present a complete analysis of the Free-MESSAGEp algorithm, which establishes convergence in a user-tunable neighborhood of the optimal solutions of the original problem, as well as explicit convergence rates for both convex and strongly convex costs. Orderwise, and for fixed problem parameters, our results demonstrate no sacrifice in convergence speed compared to existing first-order methods, while striking a certain balance among the condition of the problem, its dimensionality, as well as the accuracy of the obtained results, naturally extending previous results in zeroth-order risk-neutral learning.
Data-driven distributionally robust LQR with multiplicative noise

We present a data-driven method for solving the linear quadratic regulator problem for systems with multiplicative disturbances, the distribution of which is only known through sample estimates. We adopt a distributionally robust approach to cast the controller synthesis problem as semidefinite programs. Using results from high dimensional statistics, the proposed methodology ensures that their solution provides mean-square stabilizing controllers with high probability even for low sample sizes. As sample size increases the closed-loop cost approaches that of the optimal controller produced when the distribution is known. We demonstrate the practical applicability and performance of the method through a numerical experiment.
MFPN: A Novel Mixture Feature Pyramid Network of Multiple Architectures for Object Detection

Feature pyramids are widely exploited in many detectors to solve the scale variation problem for object detection. In this paper, we first investigate the Feature Pyramid Network (FPN) architectures and briefly categorize them into three typical fashions: top-down, bottom-up and fusing-splitting, which have their own merits for detecting small objects, large objects, and medium-sized objects, respectively. Further, we design three FPNs of different architectures and propose a novel Mixture Feature Pyramid Network (MFPN) which inherits the merits of all these three kinds of FPNs, by assembling the three kinds of FPNs in a parallel multi-branch architecture and mixing the features. MFPN can significantly enhance both one-stage and two-stage FPN-based detectors with about 2 percent Average Precision(AP) increment on the MS-COCO benchmark, at little sacrifice in running time latency. By simply assembling MFPN with the one-stage and two-stage baseline detectors, we achieve competitive single-model detection results on the COCO detection benchmark without bells and whistles.
Radio Sources Associated with Optical Galaxies and Having Unresolved or Extended Morphologies (ROGUE). I. A Catalog of SDSS Galaxies with FIRST Core Identifications

We present the catalog of Radio sources associated with Optical Galaxies and having Unresolved or Extended morphologies I (ROGUE I), consisting of 32,616 spectroscopically selected galaxies. It is the largest handmade catalog of this kind, obtained by cross-matching galaxies from the Sloan Digital Sky Survey (SDSS) Data Release 7 and radio sources from both the First Images of Radio Sky at Twenty Centimetre (FIRST) survey and the NRAO Very Large Array Sky Survey, without imposing a limit on the radio flux densities. The catalog provides a visual classification of radio and optical morphologies of galaxies presenting a FIRST core within 3″ of the optical position. The radio morphological classification is performed by examining the radio–optical overlays of linear sizes equal to 1 Mpc at the source distance, while the 120″ image snapshots from the SDSS database are used for optical classification. The results of our search are as follows: (i) single-component unresolved and elongated radio sources constitute the major group in the ROGUE I catalog (∼90%), and ∼8% exhibit extended morphologies; (ii) samples of 269, 730, and 115 Fanaroff–Riley (FR) type I, II, and hybrid galaxies, respectively, are presented; (iii) we report 55 newly discovered giant/possible giant, 16 double–double, 9 X-shaped, and 25 Z-shaped radio sources; (iv) on the optical front, most galaxies have elliptical morphologies (∼62%) while spirals form the second major category (∼17%) followed by distorted (∼12%) and lenticular (∼7%) morphologies; and (v) division between the FR I and the FR II sources in the radio–optical luminosity plane is blurred, in tune with recent studies.
secml: A Python Library for Secure and Explainable Machine Learning

We present secml, an open-source Python library for secure and explainable machine learning. It implements the most popular attacks against machine learning, including not only test-time evasion attacks to generate adversarial examples against deep neural networks, but also training-time poisoning attacks against support vector machines and many other algorithms. These attacks enable evaluating the security of learning algorithms and of the corresponding defenses under both white-box and black-box threat models. To this end, secml provides built-in functions to compute security evaluation curves, showing how quickly classification performance decreases against increasing adversarial perturbations of the input data. secml also includes explainability methods to help understand why adversarial attacks succeed against a given model, by visualizing the most influential features and training prototypes contributing to each decision. It is distributed under the Apache License 2.0, and hosted at this https URL.
DeepMUSIC: Multiple Signal Classification via Deep Learning

This letter introduces a deep learning (DL) framework for the classification of multiple signals in direction finding (DF) scenario via sensor arrays. Previous works in DL context mostly consider a single or two target scenario, which is a strong limitation in practice. Hence, in this letter, we propose a DL framework called DeepMUSIC for multiple signal classification. We design multiple deep convolutional neural networks (CNNs), each of which is dedicated to a subregion of the angular spectrum. Each CNN learns the MUltiple SIgnal Classification (MUSIC) spectra of the corresponding angular subregion. Hence, it constructs a nonlinear relationship between the received sensor data and the angular spectrum. We have shown, through simulations, that the proposed DeepMUSIC framework has superior estimation accuracy and exhibits less computational complexity in comparison with both DL- and non-DL-based techniques.
Measurement of the top quark forward-backward production asymmetry and the anomalous chromoelectric and chromomagnetic moments in pp collisions at $\sqrt{s}=$ 13 TeV

The parton-level top quark (t) forward-backward asymmetry and the anomalous chromoelectric (d̂t) and chromomagnetic (μ̂t) moments have been measured using LHC pp collisions at a center-of-mass energy of 13 TeV, collected in the CMS detector in a data sample corresponding to an integrated luminosity of 35.9 fb−1. The linearized variable A FB is used to approximate the asymmetry. Candidate tt events decaying to a muon or electron and jets in final states with low and high Lorentz boosts are selected and reconstructed using a fit of the kinematic distributions of the decay products to those expected for tt final states. The values found for the parameters are A FB = 0.048 +0.095 −0.087 (stat) +0.020 −0.029 (syst), μ̂t = −0.024+0.013 −0.009 (stat) +0.016 −0.011 (syst), and a limit is placed on the magnitude of |d̂t | < 0.03 at 95% confidence level. Submitted to the Journal of High Energy Physics c © 2019 CERN for the benefit of the CMS Collaboration. CC-BY-4.0 license ∗See Appendix C for the list of collaboration members ar X iv :1 91 2. 09 54 0v 1 [ he pex ] 1 9 D ec 2 01 9