<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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<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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<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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<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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<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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<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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<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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<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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<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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<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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<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>…

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<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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<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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<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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<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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<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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<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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<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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<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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<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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<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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<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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<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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<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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<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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