Miracle-dz/UniFAR-FTUs huggingface dataset:
MVFuseNet: Improving End-to-End Object Detection and Motion Forecasting through Multi-View Fusion of LiDAR Data

In this work, we propose MVFuseNet, a novel end-to-end method for joint object detection and motion forecasting from a temporal sequence of LiDAR data. Most existing methods operate in a single view by projecting data in either range view (RV) or bird’s eye view (BEV). In contrast, we propose a method that effectively utilizes both RV and BEV for spatio-temporal feature learning as part of a temporal fusion network as well as for multi-scale feature learning in the backbone network. Further, we propose a novel sequential fusion approach that effectively utilizes multiple views in the temporal fusion network. We show the benefits of our multi-view approach for the tasks of detection and motion forecasting on two large-scale self-driving data sets, achieving state-of-the-art results. Furthermore, we show that MVFusenet scales well to large operating ranges while maintaining real-time performance.
Flow visualization and PIV of wind-waves (2019 Edition)

This article presents a flow visualizationmethod for wind-waves, as well as a technique to measure flow field on two sides of interface by stereoscopic particle image velocimetry (PIV) simultaneously. The new flow visualization method applied a special illumination setup to enhance reflection and weaken refraction on interface, which improves contrast and detail performance of photos. The concatenation of flow visualization photo along flow direction is able to demonstrate the scenario from capillary waves to gravity-capillary waves at the early stage of windwaves. After that, time-resolved stereoscopic PIV is employed in vertical planes on both sides of interface with two camera pairs. Two kinds of tracking particles with different scattering performance are spread in air or water separately. Then interface detection algorithm is designed to separate different particles and determine interface position. Adaptive min-max normalization and near-wall image preprocessing is used to improve near-wall and main stream PIV result. Finally, clip arts of results show the flow field in the same time in air and water near interface.
Probing low-energy Lorentz violation from high-energy modified dispersion in dipolar Bose-Einstein condensates

Zehua Tian 2, 3, ∗ and Jiangfeng Du 2, 3, † Hefei National Laboratory for Physical Sciences at the Microscale and Department of Modern Physics, University of Science and Technology of China, Hefei 230026, China CAS Key Laboratory of Microscale Magnetic Resonance, University of Science and Technology of China, Hefei 230026, China Synergetic Innovation Center of Quantum Information and Quantum Physics, University of Science and Technology of China, Hefei 230026, China
Framing Unpacked: A Semi-Supervised Interpretable Multi-View Model of Media Frames

Understanding how news media frame political issues is important due to its impact on public attitudes, yet hard to automate. Computational approaches have largely focused on classifying the frame of a full news article while framing signals are often subtle and local. Furthermore, automatic news analysis is a sensitive domain, and existing classifiers lack transparency in their predictions. This paper addresses both issues with a novel semi-supervised model, which jointly learns to embed local information about the events and related actors in a news article through an auto-encoding framework, and to leverage this signal for document-level frame classification. Our experiments show that: our model outperforms previous models of frame prediction; we can further improve performance with unlabeled training data leveraging the semi-supervised nature of our model; and the learnt event and actor embeddings intuitively corroborate the document-level predictions, providing a nuanced and interpretable article frame representation.
Probing the minimal $U(1)_X$ model at future electron-positron colliders via the fermion pair-production channel

The minimal U(1)X extension of the Standard Model (SM) is a well-motivated new physics scenario, where anomaly cancellation dictates new neutral gauge boson (Z ′) couplings with the SM fermions in terms of the U(1)X charges of the new scalar fields. We investigate the SM charged fermion pair-production process for different values of these U(1)X charges at future e −e+ colliders: e+e− → ff̄ . Apart from the standard γ and Z-mediated processes, this model features additional s-channel (or both s and t-channel when f = e−) Z ′ exchange which interferes with the SM processes. We first estimate the bounds on the U(1)X coupling (g ′) and the Z ′ mass (MZ′) considering the latest dilepton and dijet constraints from the heavy resonance searches at the LHC. Then using the allowed values of g′, we study the angular distributions, forward-backward (AFB), left-right (ALR) and left-right forward-backward (ALR,FB) asymmetries of the ff̄ final states. We find that these observables can show substantial deviations from the SM results in the U(1)X model, thus providing a powerful probe of the multi-TeV Z ′ bosons at future e+e− colliders. ∗ arindamdas@oia.hokudai. ac.jp † bdev@wustl.edu ‡ hosotani@het.phys.sci.osaka-u.ac.jp § smandal@ific.uv. es 1 ar X iv :2 10 4. 10 90 2v 3 [ he pph ] 2 1 Ju n 20 22
On Doped SC-LDPC Codes for Streaming

In streaming applications, doping improves the performance of spatially-coupled low-density parity-check (SC-LDPC) codes by creating reduced-degree check nodes in the coupled chain. We formulate a scaling law to predict the bit and block error rate of periodically-doped semi-infinite SC-LDPC code ensembles streamed over the binary erasure channel under sliding window decoding for a given finite component block length. The scaling law assumes that with some probability doping is equivalent to full termination and triggers two decoding waves; otherwise, decoding performs as if the coupled chain had not been doped at all. We approximate that probability and use the derived scaling laws to predict the error rates of SC-LDPC code ensembles in the presence of doping. The proposed scaling law provides accurate error rate predictions. We further use it to show that in streaming applications periodic doping can yield higher rates than periodic full termination for the same error-correcting performance.
Sharp Global Guarantees for Nonconvex Low-Rank Matrix Recovery in the Overparameterized Regime

We prove that it is possible for nonconvex low-rank matrix recovery to contain no spurious local minima when the rank of the unknown ground truth $r^{\star}<r$ is strictly less than the search rank $r$, and yet for the claim to be false when $r^{\star}=r$. Under the restricted isometry property (RIP), we prove, for the general overparameterized regime with $r^{\star}\le r$, that an RIP constant of $\delta<1/(1+\sqrt{r^{\star}/r})$ is sufficient for the inexistence of spurious local minima, and that $\delta<1/(1+1/\sqrt{r-r^{\star}+1})$ is necessary due to existence of counterexamples. Without an explicit control over $r^{\star}\le r$, an RIP constant of $\delta<1/2$ is both necessary and sufficient for the exact recovery of a rank-$r$ ground truth. But if the ground truth is known a priori to have $r^{\star}=1$, then the sharp RIP threshold for exact recovery is improved to $\delta<1/(1+1/\sqrt{r})$.
Automatic Qubit Characterization and Gate Optimization with QubiC

As the size and complexity of a quantum computer increases, quantum bit (qubit) characterization and gate optimization become complex and time-consuming tasks. Current calibration techniques require complicated and verbose measurements to tune up qubits and gates, which cannot easily expand to the large-scale quantum systems. We develop a concise and automatic calibration protocol to characterize qubits and optimize gates using QubiC, which is an open source FPGA (field-programmable gate array)-based control and measurement system for superconducting quantum information processors. We propose multi-dimensional loss-based optimization of single-qubit gates and full XY-plane measurement method for the two-qubit CNOT gate calibration. We demonstrate the QubiC automatic calibration protocols are capable of delivering high-fidelity gates on the state-of-the-art transmon-type processor operating at the Advanced Quantum Testbed at Lawrence Berkeley National Laboratory. The single-qubit and two-qubit Clifford gate infidelities measured by randomized benchmarking are of 4.9(1.1) × 10-4 and 1.4(3) × 10-2, respectively.
A simple way to distinguish the hydrodynamic and (quasi) ballistic phonon transport

Many studies have predicted the failure of Fourier’s law of thermal conduction due to the existence of wave like propagation of heat. This non-Fourier thermal transport phenomenon can appear in both the hydrodynamic and (quasi) ballistic regimes. Hence, it is not easy to clearly distinguish these two nonFourier regimes only by the wave like propagation of heat. In this work, the transient heat propagation in homogeneous thermal system is studied based on the phonon Boltzmann transport equation (BTE) under the Callaway model. Given a quasi-one or quasi-two dimensional thermal system with homogeneous environment temperature, at initial moment, a heat source is added suddenly at the center of the system with high temperature, then the heat propagates from the center to the outer. Numerical results show that in quasitwo dimensional thermal system, the transient temperature will be lower than the lowest value of initial temperature in the hydrodynamic regime within a certain range of time and space. This phenomenon appears only when the normal scattering dominates heat conduction. Besides, it disappears in quasi-one dimensional thermal system. Similar phenomenon is also observed in thermal system with time varying heat source. This peculiar heat propagation phenomenon of hydrodynamic phonon transport distinguishes it well from (quasi) ballistic phonon transport.
Analyzing Monotonic Linear Interpolation in Neural Network Loss Landscapes

Linear interpolation between initial neural network parameters and converged parameters after training with stochastic gradient descent (SGD) typically leads to a monotonic decrease in the training objective. This Monotonic Linear Interpolation (MLI) property, first observed by Goodfellow et al. (2014) persists in spite of the non-convex objectives and highly non-linear training dynamics of neural networks. Extending this work, we evaluate several hypotheses for this property that, to our knowledge, have not yet been explored. Using tools from differential geometry, we draw connections between the interpolated paths in function space and the monotonicity of the network - providing sufficient conditions for the MLI property under mean squared error. While the MLI property holds under various settings (e.g. network architectures and learning problems), we show in practice that networks violating the MLI property can be produced systematically, by encouraging the weights to move far from initialization. The MLI property raises important questions about the loss landscape geometry of neural networks and highlights the need to further study their global properties.
Train Once and Use Forever: Solving Boundary Value Problems in Unseen Domains with Pre-trained Deep Learning Models

Physics-informed neural networks (PINNs) are increasingly employed to replace/augment traditional numerical methods in solving partial differential equations (PDEs). While having many attractive features, state-of-the-art PINNs surrogate a specific realization of a PDE system and hence are problem-specific. That is, each time the boundary conditions and domain shape change, the model needs to be re-trained. This limitation prohibits the application of PINNs in realistic or large-scale engineering problems especially since the costs and efforts associated with their training are considerable. This paper introduces a transferable framework for solving boundary value problems (BVPs) via deep neural networks which can be trained once and used forever for various domains of un-seen sizes, shapes, and boundary conditions. First, we introduce genomic flow network (GFNet), a neural network that can infer the solution of a BVP across arbitrary boundary conditions on a small square domain called genome . Then, we propose mosaic flow (MF) predictor, a novel iterative algorithm that assembles or stitches the GFNet’s inferences to obtain the solution of BVPs on unseen, large domains while preserving the spatial regularity of the solution. We demonstrate that our framework can estimate the solution of Laplace and Navier-Stokes equations in domains of unseen shapes and boundary conditions that are, respectively, 1200 and 12 times larger than the domains where training is performed. Since our framework eliminates the need to re-train, it demonstrates up to 3 orders of magnitude speedups compared to the state-of-the-art.
A Historical Perspective on the Diversity of Explanations for New Classes of Transient and VariableStars

As new classes of transients and variable stars are discovered, and theoretical models are established to work or not to work for a few members of the class, it is often the case that some researchers will make arguments on the basis of Occam’s razor that all members of the class must be produced by whichever mechanism first successfully explained one of the objects. It is also frequent that this assumption will be more more implicitly. Retrospective analysis shows rather clearly that this argument fails a large fraction of the time, and in many cases, this search for false consistency has led to more fundamental astrophysical errors, a few of which are quite prominent in the history of astronomy. A corollary of this is that on numerous occasions, theoretical models to explain transients have turned out to be models that describe real (but often not yet discovered) phenomenona other than the ones to which they have first been applied, albeit with minor errors that caused the model to appear to fit to a known phenomenon it did not describe. A set of examples of such events is presented here (some of which will be quite familiar to most astronomers), along with a discussion of why this phenomenon occurs, how it may be manifesting itself at the present time. Some discussion will also be made of why and when survey designs have led to immediate separation of various transient mechanisms, generally by being overpowered in some way relative to what is needed to detect a new class of objects.
A Unifying Bayesian Formulation of Measures of Interpretability in Human-AI

Existing approaches for generating human-aware agent behaviors have considered different measures of interpretability in isolation. Further, these measures have been studied under differing assumptions, thus precluding the possibility of designing a single framework that captures these measures under the same assumptions. In this paper, we present a unifying Bayesian framework that models a human observer's evolving beliefs about an agent and thereby define the problem of Generalized Human-Aware Planning. We will show that the definitions of interpretability measures like explicability, legibility and predictability from the prior literature fall out as special cases of our general framework. Through this framework, we also bring a previously ignored fact to light that the human-robot interactions are in effect open-world problems, particularly as a result of modeling the human's beliefs over the agent. Since the human may not only hold beliefs unknown to the agent but may also form new hypotheses about the agent when presented with novel or unexpected behaviors.
Ultralow Percolation Threshold in Poly(l-lactide)/Poly(ε-caprolactone)/Multiwall Carbon Nanotubes Composites with a Segregated Electrically Conductive Network

Improvement on the electrical property of conductive polymer composites is dependent on the controllable dispersion of conductive additives in polymer matrices to form a conductive network. Here we show a segregated electrically conductive network is assembled in poly(l-lactide)/poly(e-caprolactone)/multiwall carbon nanotubes (PLLA/PCL/MWCNTs) composites. First, the MWCNTs were dispersed in PCL to obtain the PCL/MWCNTs phase. Second, the PLLA particles were well coated with PCL/MWCNTs phase at 100 °C, which is between melting temperature of PLLA and PCL. Finally, the coated PLLA particles were compressed above the melting temperature of PLLA to form PCL/MWCNTs segregated structures. The morphological observation showed MWCNTs successful location in the continuous PCL phase, resulting in an ultralow percolation threshold of 0.0085 vol % MWCNTs. To our best knowledge, it is the lowest percolation threshold in PLLA- or PCL-based conductive composites at present. The composites with the segregated structure w.
Reinforcement Learning using Guided Observability

Due to recent breakthroughs, reinforcement learning (RL) has demonstrated impressive performance in challenging sequential decision-making problems. However, an open question is how to make RL cope with partial observability which is prevalent in many real-world problems. Contrary to contemporary RL approaches, which focus mostly on improved memory representations or strong assumptions about the type of partial observability, we propose a simple but efficient approach that can be applied together with a wide variety of RL methods. Our main insight is that smoothly transitioning from full observability to partial observability during the training process yields a high performance policy. The approach, called partially observable guided reinforcement learning (PO-GRL), allows to utilize full state information during policy optimization without compromising the optimality of the final policy. A comprehensive evaluation in discrete partially observableMarkov decision process (POMDP) benchmark problems and continuous partially observable MuJoCo and OpenAI gym tasks shows that PO-GRL improves performance. Finally, we demonstrate PO-GRL in the ball-in-the-cup task on a real Barrett WAM robot under partial observability.
Accented Speech Recognition: A Survey

Automatic Speech Recognition (ASR) systems generalize poorly on accented speech. The phonetic and linguistic variability of accents present hard challenges for ASR systems today in both data collection and modeling strategies. The resulting bias in ASR performance across accents comes at a cost to both users and providers of ASR. We present a survey of current promising approaches to accented speech recognition and highlight the key challenges in the space. Approaches mostly focus on single model generalization and accent feature engineering. Among the challenges, lack of a standard benchmark makes research and comparison especially difficult.
Properties of the Rovelli–Smolin–DePietri volume operator in the spaces of monochromatic intertwiners

We study some properties of the Rovelli–Smolin–DePietri volume operator in loop quantum gravity, which significantly simplify the diagonalization problem and shed some light on the pattern of degeneracy of the eigenstates. The operator is defined by its action in the spaces of tensor products Hj1⊗⋯⊗HjN of the irreducible SU(2) representation spaces Hji,i=1,…,N , labelled with spins ji∈12N . We restrict to spaces of SU(2) invariant tensors (intertwiners) with all spins equal j 1 =⋯= j N = j. We call them spin j monochromatic intertwiners. Such spaces are important in the study of SU(2) gauge invariant states that are isotropic and can be applied to extract the cosmological sector of the theory. In the case of spin 1/2 we solve the eigenvalue problem completely: we show that the volume operator is proportional to identity and calculate the proportionality factor.
Robust 360-8PA: Redesigning The Normalized 8-point Algorithm for 360-FoV Images

In this paper, we present a novel preconditioning strategy for the classic 8-point algorithm (8-PA) for estimating an essential matrix from 360-FoV images (i.e., equirectangular images) in spherical projection. To alleviate the effect of uneven key-feature distributions and outlier correspondences, which can potentially decrease the accuracy of an essential matrix, our method optimizes a non-rigid transformation to deform a spherical camera into a new spatial domain, defining a new constraint and a more robust and accurate solution for an essential matrix. Through several experiments using random synthetic points, 360-FoV, and fish-eye images, we demonstrate that our normalization can increase the camera pose accuracy about 20% without significantly overhead the computation time. In addition, we present further benefits of our method through both a constant weighted least-square optimization that improves further the well known Gold Standard Method (GSM) (i.e., the non-linear optimization by using epipolar errors); and a relaxation of the number of RANSAC iterations, both showing that our normalization outcomes a more reliable, robust, and accurate solution.
Assessing the effectiveness of regional physical distancing measures of COVID-19 in rural regions of British Columbia

We study the effects of physical distancing measures for the spread of COVID-19 in regional areas within British Columbia, using the reported cases of the five provincial Health Authorities. Building on the Bayesian epidemiological model of Anderson et al. (2020), we propose a hierarchical Bayesian model with time-varying regional parameters to account for the relative reduction in contact due to physical distancing and increased testing from March to December of 2020. In the absence of COVID-19 variants and vaccinations during this period, we examine the regionalized basic reproduction number, modelled prevalence, fraction of normal contacts, proportion of anticipated cases, and we observed significant differences between the provincial-wide and regional models.
Exceptional points and domains of unitarity for a class of strongly non-Hermitian real-matrix Hamiltonians

A family of non-Hermitian real and tridiagonal-matrix candidates H(λ) = H (N) 0 + λW (λ) for a hiddenly Hermitian (a.k.a. quasi-Hermitian) quantum Hamiltonian is proposed and studied. Fairly weak assumptions are imposed upon the unperturbed matrix (the square-well-simulating spectrum of H (N) 0 is not assumed equidistant) as well as upon its maximally non-Hermitian N−parametric antisymmetric-matrix perturbations (matrix W (λ) is not even required to be PT −symmetric). In spite of that, the “physical” parametric domain D[N ] is (constructively! ) shown to exist, guaranteeing that in its interior the spectrum remains real and non-degenerate, rendering the quantum evolution unitary. Among the non-Hermitian degeneracies occurring at the boundary ∂D[N ] of the domain of stability our main attention is paid to their extreme version corresponding to the Kato’s exceptional point of order N (EPN). The localization of the EPNs and, in their vicinity, of the quantum-phase-transition boundaries ∂D[N ] is found feasible, at the not too large N , using computer-assisted symbolic manipulations including, in particular, the Gröbner basis elimination and the high-precision arithmetics.