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<p>Universality and scaling laws are hallmarks of equilibrium phase transitions and critical phenomena. However, extending these concepts to non-equilibrium systems is an outstanding challenge. Despite recent progress in the study of dynamical phases, the universality classes and scaling laws for non-equilibrium phenomena are far less understood than those in equilibrium. In this work, using a trapped-ion quantum simulator with single-ion resolution, we investigate the non-equilibrium nature of critical fluctuations following a quantum quench to the critical point. We probe the scaling of spin fluctuations after a series of quenches to the cri…
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Latest: Sept. 21, 2023, 7:33 a.m.
<p>3D human reconstruction from RGB images achieves decent results in good weather conditions but degrades dramatically in rough weather. Complementary, mmWave radars have been employed to reconstruct 3D human joints and meshes in rough weather. However, combining RGB and mmWave signals for robust all-weather 3D human reconstruction is still an open challenge, given the sparse nature of mmWave and the vulnerability of RGB images. In this paper, we present ImmFusion, the first mmWave-RGB fusion solution to reconstruct 3D human bodies in all weather conditions robustly. Specifically, our ImmFusion consists of image and point backbones for token …
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Latest: Sept. 21, 2023, 7:31 a.m.
<p>To mitigate climate change, the share of renewable energies in power production needs to be increased. Renewables introduce new challenges to power grids regarding the dynamic stability due to decentralization, reduced inertia, and volatility in production. Since dynamic stability simulations are intractable and exceedingly expensive for large grids, graph neural networks (GNNs) are a promising method to reduce the computational effort of analyzing the dynamic stability of power grids. As a testbed for GNN models, we generate new, large datasets of dynamic stability of synthetic power grids, and provide them as an open-source resource to th…
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Latest: Sept. 21, 2023, 7:31 a.m.
<p>The large-scale simulation of dynamical systems is critical in numerous scientific and engineering disciplines. However, traditional numerical solvers are limited by the choice of step sizes when estimating integration, resulting in a trade-off between accuracy and computational efficiency. To address this challenge, we introduce a deep learning-based corrector called Neural Vector (NeurVec), which can compensate for integration errors and enable larger time step sizes in simulations. Our extensive experiments on a variety of complex dynamical system benchmarks demonstrate that NeurVec exhibits remarkable generalization capability on a cont…
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Latest: Sept. 21, 2023, 7:31 a.m.
<p>We analyze several variations of a single-photon optical switch operating in the continuous wave regime, as presented in the accompanying paper [Tsiamis et al., Continuous wave single photon switch based on a Rydberg atom ensemble]. The devices are based on ensembles of Rydberg atoms that interact through van der Waals interaction. Continuously probing the atomic cloud with a weak coherent probe field, under the conditions of electromagnetically induced transparency (EIT) leads to total reflection/transmission of the probe in the absence of control photons. Exciting a Rydberg state with a single control photon breaks the EIT conditions, dra…
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Latest: Sept. 21, 2023, 7:33 a.m.
<p>We revisit the problem of characterising the complexity of Quantum PAC learning, as introduced by Bshouty and Jackson [SIAM J. Comput. 1998, 28, 1136-1153]. Several quantum advantages have been demonstrated in this setting, however, none are generic: they apply to particular concept classes and typically only work when the distribution that generates the data is known. In the general case, it was recently shown by Arunachalam and de Wolf [JMLR, 19 (2018) 1-36] that quantum PAC learners can only achieve constant factor advantages over classical PAC learners. </p> <p>We show that with a natural extension of the def…
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Latest: Sept. 21, 2023, 7:33 a.m.
<p>We propose a method for analyzing the distributed random coordinate descent algorithm for solving separable resource allocation problems in the context of an open multiagent system, where agents can be replaced during the process. In particular, we characterize the evolution of the distance to the minimizer in expectation by following a time-varying optimization approach which builds on two components. First, we establish the linear convergence of the algorithm in closed systems, in terms of the estimate towards the minimizer, for general graphs and appropriate step-size. Second, we estimate the change of the optimal solution after a replac…
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Latest: Sept. 21, 2023, 7:31 a.m.
<p>The trade-off between computation time and path optimality is a key consideration in motion planning algorithms. While classical sampling based algorithms fall short of computational efficiency in high dimensional planning, learning based methods have shown great potential in achieving time efficient and optimal motion planning. The SOTA learning based motion planning algorithms utilize paths generated by sampling based methods as expert supervision data and train networks via regression techniques. However, these methods often overlook the important multimodal property of the optimal paths in the training set, making them incapable of find…
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Latest: Sept. 21, 2023, 7:31 a.m.
<p>Sparse index tracking is one of the prominent passive portfolio management strategies that construct a sparse portfolio to track a financial index. A sparse portfolio is desirable over a full portfolio in terms of transaction cost reduction and avoiding illiquid assets. To enforce the sparsity of the portfolio, conventional studies have proposed formulations based on $\ell_p$-norm regularizations as a continuous surrogate of the $\ell_0$-norm regularization. Although such formulations can be used to construct sparse portfolios, they are not easy to use in actual investments because parameter tuning to specify the exact upper bound on the nu…
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Latest: Sept. 21, 2023, 7:33 a.m.
<p>Homomorphic encryption, which enables the execution of arithmetic operations directly on ciphertexts, is a promising solution for protecting privacy of cloud-delegated computations on sensitive data. However, the correctness of the computation result is not ensured. We propose two error detection encodings and build authenticators that enable practical client-verification of cloud-based homomorphic computations under different trade-offs and without compromising on the features of the encryption algorithm. Our authenticators operate on top of trending ring learning with errors based fully homomorphic encryption schemes over the integers. We…
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Latest: Sept. 21, 2023, 7:31 a.m.
<p>We investigate the parameterized complexity of Binary CSP parameterized by the vertex cover number and the treedepth of the constraint graph, as well as by a selection of related modulator-based parameters. The main findings are as follows: </p> <p>Binary CSP parameterized by the vertex cover number is $\mathrm{W}[3]$-complete. More generally, for every positive integer $d$, Binary CSP parameterized by the size of a modulator to a treedepth-d graph is $\mathrm{W}[2d+1]$-complete. This provides a new family of natural problems that are complete for odd levels of the W-hierarchy. </p> <p&gt…
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Latest: Sept. 21, 2023, 7:31 a.m.
<p>Quantum machine learning (QML) networks promise to have some computational (or quantum) advantage for classifying supervised datasets (e.g., satellite images) over some conventional deep learning (DL) techniques due to their expressive power via their local effective dimension. There are, however, two main challenges regardless of the promised quantum advantage: 1) Currently available quantum bits (qubits) are very small in number, while real-world datasets are characterized by hundreds of high-dimensional elements (i.e., features). Additionally, there is not a single unified approach for embedding real-world high-dimensional datasets in a …
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Latest: Sept. 21, 2023, 7:31 a.m.
<p>3D point clouds are discrete samples of continuous surfaces which can be used for various applications. However, the lack of true connectivity information, i.e., edge information, makes point cloud recognition challenging. Recent edge-aware methods incorporate edge modeling into network designs to better describe local structures. Although these methods show that incorporating edge information is beneficial, how edge information helps remains unclear, making it difficult for users to analyze its usefulness. To shed light on this issue, in this study, we propose a new algorithm called Diffusion Unit (DU) that handles edge information in a pr…
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Latest: Sept. 21, 2023, 7:31 a.m.
<p>We study the robustness of conformal prediction, a powerful tool for uncertainty quantification, to label noise. Our analysis tackles both regression and classification problems, characterizing when and how it is possible to construct uncertainty sets that correctly cover the unobserved noiseless ground truth labels. We further extend our theory and formulate the requirements for correctly controlling a general loss function, such as the false negative proportion, with noisy labels. Our theory and experiments suggest that conformal prediction and risk-controlling techniques with noisy labels attain conservative risk over the clean ground tr…
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Latest: Sept. 21, 2023, 7:31 a.m.
<p>In existing joint detection and tracking methods, pairwise relational features are used to match previous tracklets to current detections. However, the features may not be discriminative enough for a tracker to identify a target from a large number of detections. Selecting only high-scored detections for tracking may lead to missed detections whose confidence score is low. Consequently, in the online setting, this results in disconnections of tracklets which cannot be recovered. In this regard, we present Sparse Graph Tracker (SGT), a novel online graph tracker using higher-order relational features which are more discriminative by aggregat…
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Latest: Sept. 21, 2023, 7:31 a.m.
<p>How should a robot speak in a formal, quiet and dark, or a bright, lively and noisy environment? By designing robots to speak in a more social and ambient-appropriate manner we can improve perceived awareness and intelligence for these agents. We describe a process and results toward selecting robot voice styles for perceived social appropriateness and ambiance awareness. Understanding how humans adapt their voices in different acoustic settings can be challenging due to difficulties in voice capture in the wild. Our approach includes 3 steps: (a) Collecting and validating voice data interactions in virtual Zoom ambiances, (b) Exploration a…
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Latest: Sept. 21, 2023, 7:31 a.m.
<p>The growth of mega-constellations is rapidly increasing the number of rocket launches required to place new satellites in space. While Low Earth Orbit (LEO) broadband satellites help to connect unconnected communities and achieve the Sustainable Development Goals, there are also a range of negative environmental externalities, from the burning of rocket fuels and resulting environmental emissions. We present sustainability analytics for phase 1 of the three main LEO constellations including Amazon Kuiper (3,236 satellites), OneWeb (648 satellites), and SpaceX Starlink (4,425 satellites). In baseline scenarios over five years, we find a per …
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Latest: Sept. 21, 2023, 7:33 a.m.
<p>For systems that are not observable at the very equilibrium of interest to be stabilized, output-feedback stabilization is considerably challenging. In this paper we solve this control problem for the case-study of a second-order system that is bilinear and affine, both in the input and the output, but it is unobservable at the target equilibrium. The case-study is representative of a well-studied class of non-uniformly observable systems and stems from automotive control. Our main contribution is a novel certainty-equivalence hybrid controller that achieves asymptotic stabilization semiglobally. The controller relies on a switched observer…
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Latest: Sept. 21, 2023, 7:31 a.m.
<p>Hypothesis-pruning maximizes the hypothesis updates for active learning to find those desired unlabeled data. An inherent assumption is that this learning manner can derive those updates into the optimal hypothesis. However, its convergence may not be guaranteed well if those incremental updates are negative and disordered. In this paper, we introduce a black-box teaching hypothesis $h^\mathcal{T}$ employing a tighter slack term $\left(1+\mathcal{F}^{\mathcal{T}}(\widehat{h}_t)\right)\Delta_t$ to replace the typical $2\Delta_t$ for pruning. Theoretically, we prove that, under the guidance of this teaching hypothesis, the learner can converg…
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Latest: Sept. 21, 2023, 7:31 a.m.
<p>Generalization techniques have many applications, such as template construction, argument generalization, and indexing. Modern interactive provers can exploit advancement in generalization methods over expressive-type theories to further develop proof generalization techniques and other transformations. So far, investigations concerned with anti-unification (AU) over lambda terms and similar type theories have focused on developing algorithms for well-studied variants. These variants forbid the nesting of generalization variables, restrict the structure of their arguments, and are unitary. Extending these methods to more expressive variants…
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Latest: Sept. 21, 2023, 7:31 a.m.
<p>This paper is concerned with the inverse problem of reconstructing an inhomogeneous medium from the acoustic far-field data at a fixed frequency in two dimensions. This inverse problem is severely ill-posed (and also strongly nonlinear), and certain regularization strategy is thus needed. However, it is difficult to select an appropriate regularization strategy which should enforce some a priori information of the unknown scatterer. To address this issue, we plan to use a deep learning approach to learn some a priori information of the unknown scatterer from certain ground truth data, which is then combined with a traditional iteration meth…
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Latest: Sept. 21, 2023, 7:31 a.m.
<p>Understanding the interactions between elementary particles and mapping out the internal structure of the hadrons are of fundamental importance in high energy nuclear and particle physics. This thesis concentrates on the strong interaction, described by Quantum Chromodynamics (QCD). We introduce a novel concept called "polarized jet fragmentation functions" and develop the associated theory framework known as QCD factorization which allows us to utilize jet substructure to probe spin dynamics of hadrons, especially nucleon's three-dimensional imaging. Furthermore, non-perturbative QCD studies, particularly of the QCD phase di…
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Latest: Sept. 21, 2023, 7:33 a.m.
<p>In recent years, image and video manipulations with Deepfake have become a severe concern for security and society. Many detection models and datasets have been proposed to detect Deepfake data reliably. However, there is an increased concern that these models and training databases might be biased and, thus, cause Deepfake detectors to fail. In this work, we investigate the bias issue caused by public Deepfake datasets by (a) providing large-scale demographic and non-demographic attribute annotations of 47 different attributes for five popular Deepfake datasets and (b) comprehensively analysing AI-bias of three state-of-the-art Deepfake de…
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Latest: Sept. 21, 2023, 7:31 a.m.
<p>Apptainer (formerly known as Singularity) since its beginning implemented many of its container features with the assistance of a setuid-root program. It still supports that mode, but as of version 1.1.0 it no longer uses setuid by default. This is feasible because it now can mount squashfs filesystems, ext3 filesystems, and overlay filesystems using unprivileged user namespaces and FUSE. It also now enables unprivileged users to build containers, even without requiring system administrators to configure /etc/subuid and /etc/subgid unlike other "rootless" container systems. As a result, all the unprivileged functions can be used n…
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Latest: Sept. 21, 2023, 7:31 a.m.
<p>We initiate the algorithmic study of the Quantum Max-$d$-Cut problem, a quantum generalization of the well-known Max-$d$-Cut problem. The Quantum Max-$d$-Cut problem involves finding a quantum state that maximizes the expected energy associated with the projector onto the antisymmetric subspace of two, $d$-dimensional qudits over all local interactions. Equivalently, this problem is physically motivated by the $SU(d)$-Heisenberg model, a spin glass model that generalized the well-known Heisenberg model over qudits. We develop a polynomial-time randomized approximation algorithm that finds product-state solutions of mixed states with bounded…
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Latest: Sept. 21, 2023, 7:33 a.m.
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