Miracle-dz/UniFAR-FTUs huggingface dataset:
FRIDA: Fisheye Re-Identification Dataset with Annotations

Person re-identification (PRID) from side-mounted rectilinear-lens cameras is a well-studied problem. On the other hand, PRID from overhead fisheye cameras is new and largely unstudied, primarily due to the lack of suitable image datasets. To fill this void, we introduce the “Fisheye Re-IDentification Dataset with Annotations” (FRIDA)1, with 240k+ bounding-box annotations of people, captured by 3 time-synchronized, ceiling-mounted fisheye cameras in a large indoor space. Due to a field-of-view overlap, PRID in this case differs from a typical PRID problem, which we discuss in depth. We also evaluate the performance of 10 state-of-the-art PRID algorithms on FRIDA. We show that for 6 CNN-based algorithms, training on FRIDA boosts the performance by up to 11.64% points in mAP compared to training on a common rectilinear-camera PRID dataset.1vip. bu.edu/frida
THE SYNERGY BETWEEN THE DARK ENERGY SURVEY AND THE SOUTH POLE TELESCOPE

The Dark Energy Survey (DES) has recently completed its science verification (SV) phase, collecting data over 150 deg2 of sky. In this work we analyze to what extent it is beneficial to supplement the analysis of DES data with cosmic microwave background (CMB) lensing data. We provide forecasts for both DES-SV and for the full survey covering 5000 deg2. We show that data presently available from DES-SV and SPT-SZ would allow a ∼8% measurement of the linear galaxy bias in three out of four redshift bins. We further show that a joint analysis of cosmic shear, galaxy density, and CMB lensing data allows to break the degeneracy between the shear multiplicative bias, the linear galaxy bias, and the normalization of the matter power spectrum. We show that these observables can thus be self-calibrated to the percent or sub-percent level, depending on the quality of available data and the fraction of overlap of the footprints and priors included in the analysis.
Selection by Prediction with Conformal p-values

Decision making or scientific discovery pipelines such as job hiring and drug discovery often involve multiple stages: before any resource-intensive step, there is often an initial screening that uses predictions from a machine learning model to shortlist a few candidates from a large pool. We study screening procedures that aim to select candidates whose unobserved outcomes exceed user-specified values. We develop a method that wraps around any prediction model to produce a subset of candidates while controlling the proportion of falsely selected units. Building upon the conformal inference framework, our method first constructs p-values that quantify the statistical evidence for large outcomes; it then determines the shortlist by comparing the p-values to a threshold introduced in the multiple testing literature. In many cases, the procedure selects candidates whose predictions are above a data-dependent threshold. Our theoretical guarantee holds under mild exchangeability conditions on the samples, generalizing existing results on multiple conformal p-values. We demonstrate the empirical performance of our method via simulations, and apply it to job hiring and drug discovery datasets.
Learning Depth Vision-Based Personalized Robot Navigation From Dynamic Demonstrations in Virtual Reality

For the best human-robot interaction experience, the robot's navigation policy should take into account personal preferences of the user. In this paper, we present a learning framework complemented by a perception pipeline to train a depth vision-based, personalized navigation controller from user demonstrations. Our virtual reality interface enables the demonstration of robot navigation trajectories under motion of the user for dynamic interaction scenarios. The novel perception pipeline enrolls a variational autoencoder in combination with a motion predictor. It compresses the perceived depth images to a latent state representation to enable efficient reasoning of the learning agent about the robot's dynamic environment. In a detailed analysis and ablation study, we evaluate different configurations of the perception pipeline. To further quantify the navigation controller's quality of personalization, we develop and apply a novel metric to measure preference reflection based on the Frechet Distance. We discuss the robot's navigation performance in various virtual scenes and demonstrate the first personalized robot navigation controller that solely relies on depth images. A supplemental video highlighting our approach is available online11Full video: hrl.uni-bonn.de/publications/deheuve123iros_learning. mp4.
Mining Duplicate Questions of Stack Overflow

There has a been a significant rise in the use of Community Question Answering sites (CQAs) over the last decade owing primarily to their ability to leverage the wisdom of the crowd. Duplicate questions have a crippling effect on the quality of these sites. Tackling duplicate questions is therefore an important step towards improving quality of CQAs. In this regard, we propose two neural network based architectures for duplicate question detection on Stack Overflow. We also propose explicitly modeling the code present in questions to achieve results that surpass the state of the art.
Absorption of hybrid fibre modes by Cs atoms in quadrupole transitions

We evaluate the rate of the absorption of an optical nanofibre mode by a Cs atom in an electric quadrupole transition. With the Cs atom localised near the outer surface of the optical nano-fibre, an interaction occurs between the atomic quadrupole tensor components and the gradients of the vector components of the electric field of a hybrid fibre mode. The absorption rate is evaluated as a function of the radial position of the atom from the fibre axis, assuming a specific value of the laser power and we use experimentally accessible parameters. We find that the absorption of the hybrid modes by the Cs atom decreases as the atom recedes away from the fibre axis and it formally vanishes at sufficiently large radial distances. Close to the fibre, however, the absorption rate for the input power chosen can be two orders of magnitude larger than the quadrupole de-excitation rate despite the moderate power used.
Lepton flavor violation in the supersymmetric seesaw model after the LHC 8 TeV run

We study the lepton flavor violation in the supersymmetric seesaw model taking into account recent experimental improvements, especially for the Higgs boson mass measurement, direct searches of superpartners and the rare decay of B_s -> mu+ mu- at the LHC, the neutrino mixing angle of theta_{13} at the neutrino experiments, and the search of mu -> e gamma at the MEG experiment. We obtain the latest constraints on the parameters in the supersymmetry breaking terms and study the effect on the lepton flavor violating decays of tau -> mu gamma and mu -> e gamma. In particular, we consider two kinds of assumption on the structures in the Majorana mass matrix and the neutrino Yukawa matrix. In the case of the Majorana mass matrix proportional to the unit matrix, allowing non-vanishing CP violating parameters in the neutrino Yukawa matrix, we find that the branching ratio of tau -> mu gamma can be larger than 10^{-9} within the improved experimental limit of mu -> e gamma. We also consider the neutrino Yukawa matrix that includes the mixing only in the second and third generations, and find that a larger branching ratio of tau -> mu gamma than 10^{-9} is possible with satisfying the recent constraints.
Higher-Order Asymptotic Properties of Kernel Density Estimator with Global Plug-In and Its Accompanying Pilot Bandwidth

This study investigates the effect of bandwidth selection via a plug-in method on the asymptotic structure of the nonparametric kernel density estimator. We generalise the result of Hall and Kang (2001) and find that the plug-in method has no effect on the asymptotic structure of the estimator up to the order of O{(nh0) + h0} = O(n−L/(2L+1)) for a bandwidth h0 and any kernel order L when the kernel order for pilot estimation Lp is high enough. We also provide the valid Edgeworth expansion up to the order of O{(nh0) + h2L 0 } and find that, as long as the Lp is high enough , the plug-in method has an effect from on the term whose convergence rate is O{(nh0)h0 + hL+1 0 } = O(n −(L+1)/(2L+1)). In other words, we derive the exact achievable convergence rate of the deviation between the distribution functions of the estimator with a deterministic bandwidth and with the plug-in bandwidth. In addition, we weaken the conditions on kernel order Lp for pilot estimation by considering the effect of pilot bandwidth associated with the plug-in bandwidth. We also show that the bandwidth selection via the global plug-in method possibly has an effect on the asymptotic structure even up to the order of O{(nh0) +h0}. Finally, Monte Carlo experiments are conducted to see whether our approximation improves previous results.
RAP: Risk-Aware Prediction for Robust Planning

Robust planning in interactive scenarios requires predicting the uncertain future to make risk-aware decisions. Unfortunately, due to long-tail safety-critical events, the risk is often under-estimated by finite-sampling approximations of probabilistic motion forecasts. This can lead to overconfident and unsafe robot behavior, even with robust planners. Instead of assuming full prediction coverage that robust planners require, we propose to make prediction itself risk-aware. We introduce a new prediction objective to learn a risk-biased distribution over trajectories, so that risk evaluation simplifies to an expected cost estimation under this biased distribution. This reduces the sample complexity of the risk estimation during online planning, which is needed for safe real-time performance. Evaluation results in a didactic simulation environment and on a real-world dataset demonstrate the effectiveness of our approach. The code and a demo are available.
How deep convolutional neural networks lose spatial information with training

A central question of machine learning is how deep nets manage to learn tasks in high dimensions. An appealing hypothesis is that they achieve this feat by building a representation of the data where information irrelevant to the task is lost. For image datasets, this view is supported by the observation that after (and not before) training, the neural representation becomes less and less sensitive to diffeomorphisms acting on images as the signal propagates through the network. This loss of sensitivity correlates with performance and surprisingly correlates with a gain of sensitivity to white noise acquired during training. Which are the mechanisms learned by convolutional neural networks (CNNs) responsible for the these phenomena? In particular, why is the sensitivity to noise heightened with training? Our approach consists of two steps. (1) Analyzing the layer-wise representations of trained CNNs, we disentangle the role of spatial pooling in contrast to channel pooling in decreasing their sensitivity to image diffeomorphisms while increasing their sensitivity to noise. (2) We introduce model scale-detection tasks, which qualitatively reproduce the phenomena reported in our empirical analysis. In these models we can assess quantitatively how spatial pooling affects these sensitivities. We find that the increased sensitivity to noise observed in deep ReLU networks is a mechanistic consequence of the perturbing noise piling up during spatial pooling, after being rectified by ReLU units. Using odd activation functions like tanh drastically reduces the CNNs’ sensitivity to noise.
A Conditional-Probability-Distribution Model for Bandwidth Estimation with Application in Live Video Streaming

Experience of live video streaming can be improved if the video uploader has more accurate knowledge about the future available bandwidth. Because with such knowledge, one is able to know what sizes should he encode the frames to be in an ever-changing network. Researchers have developed some algorithms to predict throughputs in the literature, from where some are simple hence practical. However, limitation remains as most current bandwidth prediction methods are predicting a value, or a point estimate, of future bandwidth. Because in many practical scenarios, it is desirable to control the performance to some targets, e.g., video delivery rate over a given target percentage, which cannot be easily achieved via most current methods. In this work, we propose the use of probability distribution to model future bandwidth. Specifically, we model future bandwidth using past data transfer measurements and then derive a probability model for use in the application. This changes the selection of parameters in application into a probabilistic manner such that given target performance can be achieved in the long run. Inside our model, we use the conditional-probability method to correlate past and future bandwidth and hence further improve the estimating performance.
Extraneousness-Aware Imitation Learning

Visual imitation learning provides an effective framework to learn skills from demonstrations. However, the quality of the provided demonstrations usually significantly affects the ability of an agent to acquire desired skills. Therefore, the standard visual imitation learning assumes near-optimal demonstrations, which are expensive or sometimes prohibitive to collect. Previous works propose to learn from noisy demonstrations; however, the noise is usually assumed to follow a context-independent distribution such as a uniform or gaussian distribution. In this paper, we consider another crucial yet underexplored setting - imitation learning with task-irrelevant yet locally consistent segments in the demonstrations (e.g., wiping sweat while cutting potatoes in a cooking tutorial). We argue that such noise is common in real world data and term them as “extraneous” segments. To tackle this problem, we introduce Extraneousness-Aware Imitation Learning (EIL), a self-supervised approach that learns visuomotor policies from third-person demonstrations with extraneous subsequences. EIL learns action-conditioned observation embeddings in a self-supervised manner and retrieves task-relevant observations across visual demonstrations while excluding the extraneous ones. Experimental results show that EIL outperforms strong baselines and achieves comparable policies to those trained with perfect demonstration on both simulated and real-world robot control tasks. The project page can be found here: https://sites.google. com/view/eil-website.
Max-Quantile Grouped Infinite-Arm Bandits

In this paper, we consider a bandit problem in which there are a number of groups each consisting of infinitely many arms. Whenever a new arm is requested from a given group, its mean reward is drawn from an unknown reservoir distribution (different for each group), and the uncertainty in the arm's mean reward can only be reduced via subsequent pulls of the arm. The goal is to identify the infinite-arm group whose reservoir distribution has the highest $(1-\alpha)$-quantile (e.g., median if $\alpha = \frac{1}{2}$), using as few total arm pulls as possible. We introduce a two-step algorithm that first requests a fixed number of arms from each group and then runs a finite-arm grouped max-quantile bandit algorithm. We characterize both the instance-dependent and worst-case regret, and provide a matching lower bound for the latter, while discussing various strengths, weaknesses, algorithmic improvements, and potential lower bounds associated with our instance-dependent upper bounds.
The Surface of Electrolyte Solutions is Stratified

The distribution of ions at the air/water interface plays a decisive role in many natural processes. It is generally understood that polarizable ions with low charge density are surface-active, implying they sit on top of the water surface. Here, we revise this established hypothesis by combining surface-specific heterodyne-detected vibrational sum-frequency generation with neural network-assisted ab initio molecular dynamics simulations. Our results directly demonstrate that ions in typical electrolyte solutions are, in fact, located in a subsurface region leading to a stratification of such interfaces into two distinctive water layers. The outermost surface is ion-depleted, and the sub-surface layer is ion-enriched. As a result, an effective liquid/liquid interface buried a few Å inside the solution emerges, creating a second water/electrolyte interface, in addition to the outermost air/water interface.
Centroid Distance Keypoint Detector for Colored Point Clouds

Keypoint detection serves as the basis for many computer vision and robotics applications. Despite the fact that colored point clouds can be readily obtained, most existing keypoint detectors extract only geometry-salient keypoints, which can impede the overall performance of systems that intend to (or have the potential to) leverage color information. To promote advances in such systems, we propose an efficient multi-modal keypoint detector that can extract both geometry-salient and color-salient keypoints in colored point clouds. The proposed CEntroid Distance (CED) key- point detector comprises an intuitive and effective saliency measure, the centroid distance, that can be used in both 3D space and color space, and a multi-modal non-maximum suppression algorithm that can select keypoints with high saliency in two or more modalities. The proposed saliency measure leverages directly the distribution of points in a local neighborhood and does not require normal estimation or eigenvalue decomposition. We evaluate the proposed method in terms of repeatability and computational efficiency (i.e. running time) against state-of-the-art key- point detectors on both synthetic and real-world datasets. Results demonstrate that our proposed CED keypoint detector requires minimal computational time while attaining high repeatability. To showcase one of the potential applications of the proposed method, we further investigate the task of colored point cloud registration. Results suggest that our proposed CED detector outperforms state-of- the-art handcrafted and learning-based keypoint detectors in the evaluated scenes. The C++ implementation of the proposed method is made publicly available at https://github.com/UCR-Robotics/CED_Detector.
Compact object mergers: observations of supermassive binary black holes and stellar tidal disruption events

Abstract The capture and disruption of stars by supermassive black holes (SMBHs), and the formation and coalescence of binaries, are inevitable consequences of the presence of SMBHs at the cores of galaxies. Pairs of active galactic nuclei (AGN) and binary SMBHs are important stages in the evolution of galaxy mergers, and an intense search for these systems is currently ongoing. In the early and advanced stages of galaxy merging, observations of the triggering of accretion onto one or both BHs inform us about feedback processes and BH growth. Identification of the compact binary SMBHs at parsec and sub-parsec scales provides us with important constraints on the interaction processes that govern the shrinkage of the binary beyond the “final parsec”. Coalescing binary SMBHs are among the most powerful sources of gravitational waves (GWs) in the universe. Stellar tidal disruption events (TDEs) appear as luminous, transient, accretion flares when part of the stellar material is accreted by the SMBH. About 30 events have been identified by multi-wavelength observations by now, and they will be detected in the thousands in future ground-based or space-based transient surveys. The study of TDEs provides us with a variety of new astrophysical tools and applications, related to fundamental physics or astrophysics. Here, we provide a review of the current status of observations of SMBH pairs and binaries, and TDEs, and discuss astrophysical implications.
Infinitesimal deformations of Killing spinors on nearly parallel $\mathrm{G}_2$-manifolds

Manifolds admitting Killing spinors are Einstein manifolds. Thus, a deformation of a Killing spinor entails a deformation of Einstein metrics. In this paper, we study inﬁnitesimal deformations of Killing spinors on nearly parallel G 2 -manifolds. Since there is a one-to-one correspondence between nearly parallel G 2 -structures and Killing spinors on 7-dimensional spin manifolds, our results imply that inﬁnitesimal deformations of nearly parallel G 2 -structures are examined in terms of Killing spinors. Applying the same technique, we identify that the space of the Rarita-Schwinger ﬁelds coincides with a subspace of the eigenspace of the Laplacian.
Isolating Chemical Reaction Mechanism as a Variable with Reactive Coarse-Grained Molecular Dynamics: Step-Growth versus Chain-Growth Polymerization

We present a general approach to isolate chemical reaction mechanism as an independently controllable variable across chemically distinct systems. Modern approaches to reduce the computational expense of molecular dynamics simulations often group multiple atoms into a single"coarse-grained"interaction site, which leads to a loss of chemical resolution. In this work we convert this shortcoming into a feature and use identical coarse-grained models to represent molecules that share non-reactive characteristics but react by different mechanisms. As a proof of concept we use this approach to simulate and investigate distinct, yet similar, trifunctional isocyanurate resin formulations that polymerize by either chain- or step-growth. Since the underlying molecular mechanics of these models are identical, all emergent differences are a function of the reaction mechanism only. We find that the microscopic morphologies resemble related all-atom simulations and that simulated mechanical testing reasonably agrees with experiment.
A dynamical approach to sampling and interpolation in unimodular groups

We introduce a notion of covolume for point sets in locally compact groups that simultaneously generalizes the covolume of a lattice and the reciprocal of the Beurling density for amenable, unimodular groups. This notion of covolume arises naturally from transverse measure theory applied to the hull dynamical system associated to a point set. Using groupoid techniques, we prove necessary conditions for sampling and interpolation in reproducing kernel Hilbert spaces of functions on unimodular groups in terms of this new notion of covolume. These conditions generalize previously known density theorems for compactly generated groups of polynomial growth, while also covering important new examples, in particular model sets arising from cut-and-project schemes.
Experimental comparison of Raman and rf outcouplers for high-flux atom lasers

We study the properties of an atom laser beam derived from a Bose-Einstein condensate using three different outcouplers, one based on multistate radio-frequency transitions and two others based on Raman transitions capable of imparting momentum to the beam. We first summarize the differences that arise in such systems, and how they may impact on the use of an atom laser in interferometry. Experimentally, we examine the formation of a bound state in all three outcouplers, a phenomenon which limits the atom laser flux, and find that a two-state Raman outcoupler is the preferred option for high-flux, low-divergence atom laser beams.