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arxiv_dataset-179002302.05066 | The Critical Beta-splitting Random Tree: Heights and Related Results
math.PR
In the critical beta-splitting model of a random $n$-leaf binary tree,
leaf-sets are recursively split into subsets, and a set of $m$ leaves is split
into subsets containing $i$ and $m-i$ leaves with probabilities proportional to
$1/{i(m-i)}$. We study the continuous-time model in which the holding time
before that split is exponential with rate $h_{m-1}$, the harmonic number. We
(sharply) evaluate the first two moments of the time-height $D_n$ and of the
edge-height $L_n$ of a uniform random leaf (that is, the length of the path
from the root to the leaf), and prove the corresponding CLTs. We find the
limiting value of the correlation between the heights of two random leaves of
the same tree realization, and analyze the expected number of splits necessary
for a set of $t$ leaves to partially or completely break away from each other.
We give tail bounds for the time-height and the edge-height of the {\em tree},
that is the maximal leaf heights. We show that there is a limit distribution
for the size of a uniform random subtree, and derive the asymptotics of the
mean size. Our proofs are based on asymptotic analysis of the attendant
(sum-type) recurrences. The essential idea is to replace such a recursive
equality by a pair of recursive inequalities for which matching asymptotic
solutions can be found, allowing one to bound, both ways, the elusive explicit
solution of the recursive equality. This reliance on recursive inequalities
necessitates usage of Laplace transforms rather than Fourier characteristic
functions.
| arxiv topic:math.PR |
arxiv_dataset-179012302.05166 | An Assessment Methodology and Instrument for Cybersecurity: The Ireland
Use Case
cs.CR
Governments around the world are required to strengthen their national
cybersecurity capabilities to respond effectively to the growing, changing, and
sophisticated cyber threats and attacks, thus protecting society and the way of
life as a whole. Responsible government institutions need to revise, evaluate,
and bolster their national cybersecurity capabilities to fulfill the new
requirements, for example regarding new trends affecting cybersecurity, key
supporting laws and regulations, and implementations risk and challenges. This
report presents a comprehensive assessment instrument for cybersecurity at the
national level in order to help countries to ensure optimum response capability
and more effective use of critical resources of each state. More precisely, the
report - builds a common understanding of the critical cybersecurity
capabilities and competence to be assessed at the national level, - adds value
to national strategic planning and implementation which impact the development
and adaptation of national cybersecurity strategies, - provides an overview of
the assessment approaches at the national level, including capabilities,
frameworks, and controls, - introduces a comprehensive cybersecurity instrument
for countries to determine areas of improvement and develop enduring national
capabilities, - describes how to apply the proposed national cybersecurity
assessment framework in a real-world case, and - presents the results and
lessons learned of the application of the assessment framework at the national
level to assist governments in further building cybersecurity capabilities.
| arxiv topic:cs.CR |
arxiv_dataset-179022302.05266 | On the Applicability of Explainable Artificial Intelligence for Software
Requirement Analysis
cs.SE
The applications of Artificial Intelligence (AI) methods especially machine
learning techniques have increased in recent years. Classification algorithms
have been successfully applied to different problems such as requirement
classification. Although these algorithms have good performance, most of them
cannot explain how they make a decision. Explainable Artificial Intelligence
(XAI) is a set of new techniques that explain the predictions of machine
learning algorithms. In this work, the applicability of XAI for software
requirement classification is studied. An explainable software requirement
classifier is presented using the LIME algorithm. The explainability of the
proposed method is studied by applying it to the PROMISE software requirement
dataset. The results show that XAI can help the analyst or requirement
specifier to better understand why a specific requirement is classified as
functional or non-functional. The important keywords for such decisions are
identified and analyzed in detail. The experimental study shows that the XAI
can be used to help analysts and requirement specifiers to better understand
the predictions of the classifiers for categorizing software requirements.
Also, the effect of the XAI on feature reduction is analyzed. The results
showed that the XAI model has a positive role in feature analysis.
| arxiv topic:cs.SE |
arxiv_dataset-179032302.05366 | Online Algorithms with Randomly Infused Advice
cs.DS
We introduce a novel method for the rigorous quantitative evaluation of
online algorithms that relaxes the "radical worst-case" perspective of classic
competitive analysis. In contrast to prior work, our method, referred to as
randomly infused advice (RIA), does not make any probabilistic assumptions
about the input sequence and does not rely on the development of designated
online algorithms. Rather, it can be applied to existing online randomized
algorithms, introducing a means to evaluate their performance in scenarios that
lie outside the radical worst-case regime. More concretely, an online algorithm
ALG with RIA benefits from pieces of advice generated by an omniscient but not
entirely reliable oracle. The crux of the new method is that the advice is
provided to ALG by writing it into the buffer B from which ALG normally reads
its random bits, hence allowing us to augment it through a very simple and
non-intrusive interface. The (un)reliability of the oracle is captured via a
parameter 0 {\le} {\alpha} {\le} 1 that determines the probability (per round)
that the advice is successfully infused by the oracle; if the advice is not
infused, which occurs with probability 1 - {\alpha}, then the buffer B contains
fresh random bits (as in the classic online setting).
The applicability of the new RIA method is demonstrated by applying it to
three extensively studied online problems: paging, uniform metrical task
systems, and online set cover. For these problems, we establish new upper
bounds on the competitive ratio of classic online algorithms that improve as
the infusion parameter {\alpha} increases. These are complemented with (often
tight) lower bounds on the competitive ratio of online algorithms with RIA for
the three problems.
| arxiv topic:cs.DS |
arxiv_dataset-179042302.05466 | The Next Generation Deep Extragalactic Exploratory Public (NGDEEP)
Survey
astro-ph.GA
We present the Next Generation Deep Extragalactic Exploratory Public (NGDEEP)
Survey, a deep slitless spectroscopic and imaging Cycle 1 JWST treasury survey
designed to constrain feedback mechanisms in low-mass galaxies across cosmic
time. NGDEEP targets the Hubble Ultra Deep Field (HUDF) with NIRISS slitless
spectroscopy (f~1.2e-18 erg/s/cm^2, 5sigma) to measure metallicities and
star-formation rates (SFRs) for low-mass galaxies through the peak of the
cosmic SFR density (0.5<z<4). In parallel, NGDEEP targets the HUDF-Par2
parallel field with NIRCam (m=30.6-30.9, 5sigma) to discover galaxies to z>12,
constraining the slope of the faint-end of the rest-ultraviolet luminosity
function. NGDEEP overlaps with the deepest HST ACS optical imaging in the sky:
F435W in the HUDF (m=29.6), and F814W in HUDF-Par2 (m=30), making this a
premier HST+JWST Deep Field. As a treasury survey, NGDEEP data is public
immediately, and we will rapidly release data products and catalogs in the
spirit of previous deep field initiatives. In this paper we present the NGDEEP
survey design, summarize the science goals, and detail plans for the public
release of NGDEEP reduced data products.
| arxiv topic:astro-ph.GA |
arxiv_dataset-179052302.05566 | Updated analyses of gluon distribution functions for the pion and kaon
from the gauge-invariant nonlocal chiral quark model
hep-ph nucl-th
In this work, we investigate the gluon distribution functions for the pion
and kaon, in addition to the improved result of the valence-quark ones, in the
gauge-invariant nonlocal chiral-quark model (NL$\chi$QM), in which the momentum
dependence of the quark interactions is properly taken into account. We then
analyze the gluon distribution functions, generated dynamically through the
splitting functions in the DGLAP QCD evolution. By comparing with the recent
lattice QCD results and JAM global analyses, it is found that the present
numerical results for the gluon parton distribution functions for the pion
exhibit a remarkable agreement, followed by the valence up-quark distribution
results for the pion by reproducing the reanalyzed experimental data. Our
prediction on the gluon distribution functions for the kaon is also consistent
with the recent lattice data for the kaon within the errors.
| arxiv topic:hep-ph nucl-th |
arxiv_dataset-179062302.05666 | Jaccard Metric Losses: Optimizing the Jaccard Index with Soft Labels
cs.CV cs.AI cs.LG
Intersection over Union (IoU) losses are surrogates that directly optimize
the Jaccard index. Leveraging IoU losses as part of the loss function have
demonstrated superior performance in semantic segmentation tasks compared to
optimizing pixel-wise losses such as the cross-entropy loss alone. However, we
identify a lack of flexibility in these losses to support vital training
techniques like label smoothing, knowledge distillation, and semi-supervised
learning, mainly due to their inability to process soft labels. To address
this, we introduce Jaccard Metric Losses (JMLs), which are identical to the
soft Jaccard loss in standard settings with hard labels but are fully
compatible with soft labels. We apply JMLs to three prominent use cases of soft
labels: label smoothing, knowledge distillation and semi-supervised learning,
and demonstrate their potential to enhance model accuracy and calibration. Our
experiments show consistent improvements over the cross-entropy loss across 4
semantic segmentation datasets (Cityscapes, PASCAL VOC, ADE20K, DeepGlobe Land)
and 13 architectures, including classic CNNs and recent vision transformers.
Remarkably, our straightforward approach significantly outperforms
state-of-the-art knowledge distillation and semi-supervised learning methods.
The code is available at
\href{https://github.com/zifuwanggg/JDTLosses}{https://github.com/zifuwanggg/JDTLosses}.
| arxiv topic:cs.CV cs.AI cs.LG |
arxiv_dataset-179072302.05766 | Versatile Millikelvin Hybrid Cooling Platform for Superconductivity
Research
cond-mat.mes-hall quant-ph
Closed cycle $He^{3}-He^{4}$ dilution cryostats became the platform of choice
in quantum sciences in the era of helium shortage. However, in many
experiments, the mechanical vibrations induced by the pulsed cryocoolers
present a significant drawback reflected both in electronic and mechanical
noises. Here, we present a hybrid dilution cryostat platform; we have automated
a commercial closed-cycle system to operate on a cryocooler or on a liquid
helium battery. We implemented a scanning SQUID microscope in the hybrid
dilution refrigerator. In this work we show the design of the hybrid setup and
how its operation eliminates vibration artefacts in magnetic imaging.
| arxiv topic:cond-mat.mes-hall quant-ph |
arxiv_dataset-179082302.05866 | Nonminimally Assisted Inflation: A General Analysis
astro-ph.CO gr-qc
The effects of a scalar field, known as the "assistant field," which
nonminimally couples to gravity, on single-field inflationary models are
studied. The analysis provides analytical expressions for inflationary
observables such as the spectral index ($n_s$), the tensor-to-scalar ratio
($r$), and the local-type nonlinearity parameter ($f_{\rm NL}^{(\rm local)}$).
The presence of the assistant field leads to a lowering of $n_s$ and $r$ in
most of the parameter space, compared to the original predictions. In some
cases, $n_s$ may increase due to the assistant field. This revives
compatibility between ruled-out single-field models and the latest observations
by Planck-BICEP/Keck. The results are demonstrated using three example models:
loop inflation, power-law inflation, and hybrid inflation.
| arxiv topic:astro-ph.CO gr-qc |
arxiv_dataset-179092302.05966 | Infinite Lewis Weights in Spectral Graph Theory
cs.DS
We study the spectral implications of re-weighting a graph by the
$\ell_\infty$-Lewis weights of its edges. Our main motivation is the
ER-Minimization problem (Saberi et al., SIAM'08): Given an undirected graph
$G$, the goal is to find positive normalized edge-weights $w\in \mathbb{R}_+^m$
which minimize the sum of pairwise \emph{effective-resistances} of $G_w$
(Kirchhoff's index). By contrast, $\ell_\infty$-Lewis weights minimize the
\emph{maximum} effective-resistance of \emph{edges}, but are much cheaper to
approximate, especially for Laplacians. With this algorithmic motivation, we
study the ER-approximation ratio obtained by Lewis weights.
Our first main result is that $\ell_\infty$-Lewis weights provide a constant
($\approx 3.12$) approximation for ER-minimization on \emph{trees}. The proof
introduces a new technique, a local polarization process for
effective-resistances ($\ell_2$-congestion) on trees, which is of independent
interest in electrical network analysis. For general graphs, we prove an upper
bound $\alpha(G)$ on the approximation ratio obtained by Lewis weights, which
is always $\leq \min\{ \text{diam}(G), \kappa(L_{w_\infty})\}$, where $\kappa$
is the condition number of the weighted Laplacian. All our approximation
algorithms run in \emph{input-sparsity} time $\tilde{O}(m)$, a major
improvement over Saberi et al.'s $O(m^{3.5})$ SDP for exact ER-minimization.
Finally, we demonstrate the favorable effects of $\ell_\infty$-LW reweighting
on the \emph{spectral-gap} of graphs and on their \emph{spectral-thinness}
(Anari and Gharan, 2015). En-route to our results, we prove a weighted analogue
of Mohar's classical bound on $\lambda_2(G)$, and provide a new
characterization of leverage-scores of a matrix, as the gradient (w.r.t
weights) of the volume of the enclosing ellipsoid.
| arxiv topic:cs.DS |
arxiv_dataset-179102302.06066 | Universal Online Optimization in Dynamic Environments via Uniclass
Prediction
cs.LG stat.ML
Recently, several universal methods have been proposed for online convex
optimization which can handle convex, strongly convex and exponentially concave
cost functions simultaneously. However, most of these algorithms have been
designed with static regret minimization in mind, but this notion of regret may
not be suitable for changing environments. To address this shortcoming, we
propose a novel and intuitive framework for universal online optimization in
dynamic environments. Unlike existing universal algorithms, our strategy does
not rely on the construction of a set of experts and an accompanying
meta-algorithm. Instead, we show that the problem of dynamic online
optimization can be reduced to a uniclass prediction problem. By leaving the
choice of uniclass loss function in the user's hands, they are able to control
and optimize dynamic regret bounds, which in turn carry over into the original
problem. To the best of our knowledge, this is the first paper proposing a
universal approach with state-of-the-art dynamic regret guarantees even for
general convex cost functions.
| arxiv topic:cs.LG stat.ML |
arxiv_dataset-179112302.06166 | Electronic Janus lattice and kagome-like bands in coloring-triangular
MoTe2 monolayers
cond-mat.mtrl-sci
Polymorphic structures of transition-metal dichalcogenides (TMDs) host exotic
electronic states, like charge density wave and superconductivity. However, the
number of these structures is limited by crystal symmetries, which poses a
challenge to achieve tailored lattices and properties both theoretically and
experimentally. Here, we report a coloring-triangle (CT) latticed MoTe2
monolayer, termed CT-MoTe2, constructed by controllably introducing uniform and
ordered mirror-twin-boundaries into a pristine monolayer in molecular beam
epitaxy. Low-temperature scanning tunneling microscopy and spectroscopy
(STM/STS) together with theoretical calculations reveal that the monolayer has
an electronic Janus lattice, i.e., an energy-dependent atomic-lattice and a
pseudo-Te sublattice, and shares the identical geometry with the Mo5Te8 layer.
Dirac-like and flat electronic bands inherently existing in the CT lattice are
identified by two broad and two prominent peaks in STS spectra, respectively,
and verified with density-functional-theory calculations. Two types of
intrinsic domain boundaries were observed, in one of which the
electronic-Janus-lattice feature maintains, implying potential applications as
an energy-tunable electron-tunneling barrier in future functional devices.
| arxiv topic:cond-mat.mtrl-sci |
arxiv_dataset-179122302.06266 | Exploring the mass and redshift dependence of the cluster pressure
profile with stacks on thermal SZ maps
astro-ph.CO
We provide novel constraints on the parameters defining the universal
pressure profile (UPP) within clusters of galaxies, and explore their
dependence on the cluster mass and redshift, from measurements of
Sunyaev-Zel'dovich Compton-$y$ profiles. We employ both the $\textit{Planck}$
2015 MILCA and the ACT-DR4 $y$ maps over the common $\sim 2,100\,\text{deg}^2$
footprint. We combine existing cluster catalogs based on KiDS, SDSS and DESI
observations, for a total of 23,820 clusters spanning the mass range
$10^{14.0}\,\text{M}_{\odot}<M_{500}<10^{15.1}\,\text{M}_{\odot}$ and the
redshift range $0.02<z<0.98$. We split the clusters into three independent bins
in mass and redshift; for each combination we detect the stacked SZ cluster
signal and extract the mean $y$ angular profile. The latter is predicted
theoretically adopting a halo model framework, and MCMCs are employed to
estimate the UPP parameters, the hydrostatic mass bias $b_{\rm h}$ and possible
cluster miscentering effects. We constrain $[P_0,c_{500},\alpha,\beta]$ to
$[5.9,2.0,1.8,4.9]$ with $\textit{Planck}$ and to $[3.8,1.3,1.0,4.4]$ with ACT
using the full cluster sample, in agreement with previous findings. We do not
find any compelling evidence for a residual mass or redshift dependence, thus
expanding the validity of the cluster pressure profile over much larger
$M_{500}$ and $z$ ranges; this is the first time the model has been tested on
such a large (complete and representative) cluster sample. Finally, we obtain
loose constraints on the hydrostatic mass bias in the range 0.2-0.3, again in
broad agreement with previous works.
| arxiv topic:astro-ph.CO |
arxiv_dataset-179132302.06366 | Right-Adjoints for Datalog Programs, and Homomorphism Dualities over
Restricted Classes
cs.LO cs.DB
A Datalog program can be viewed as a syntactic specification of a functor
from database instances over some schema to database instances over another
schema. The same holds more generally for $\exists$Datalog. We establish large
classes of Datalog and $\exists$Datalog programs for which the corresponding
functor admits a generalized right-adjoint. We employ these results to obtain
new insights into the existence of, and methods for constructing, homomorphism
dualities within restricted classes of instances. We also derive new results
regarding the existence of uniquely characterizing data examples for database
queries.
| arxiv topic:cs.LO cs.DB |
arxiv_dataset-179142302.06466 | ChatGPT versus Traditional Question Answering for Knowledge Graphs:
Current Status and Future Directions Towards Knowledge Graph Chatbots
cs.CL cs.AI cs.IR
Conversational AI and Question-Answering systems (QASs) for knowledge graphs
(KGs) are both emerging research areas: they empower users with natural
language interfaces for extracting information easily and effectively.
Conversational AI simulates conversations with humans; however, it is limited
by the data captured in the training datasets. In contrast, QASs retrieve the
most recent information from a KG by understanding and translating the natural
language question into a formal query supported by the database engine.
In this paper, we present a comprehensive study of the characteristics of the
existing alternatives towards combining both worlds into novel KG chatbots. Our
framework compares two representative conversational models, ChatGPT and
Galactica, against KGQAN, the current state-of-the-art QAS. We conduct a
thorough evaluation using four real KGs across various application domains to
identify the current limitations of each category of systems. Based on our
findings, we propose open research opportunities to empower QASs with chatbot
capabilities for KGs. All benchmarks and all raw results are available1 for
further analysis.
| arxiv topic:cs.CL cs.AI cs.IR |
arxiv_dataset-179152302.06566 | Characterizing the VPN Ecosystem in the Wild
cs.NI
With the shift to working remotely after the COVID-19 pandemic, the use of
Virtual Private Networks (VPNs) around the world has nearly doubled. Therefore,
measuring the traffic and security aspects of the VPN ecosystem is more
important now than ever. It is, however, challenging to detect and characterize
VPN traffic since some VPN protocols use the same port number as web traffic
and port-based traffic classification will not help. VPN users are also
concerned about the vulnerabilities of their VPN connections due to privacy
issues. In this paper, we aim at detecting and characterizing VPN servers in
the wild, which facilitates detecting the VPN traffic. To this end, we perform
Internet-wide active measurements to find VPN servers in the wild, and
characterize them based on their vulnerabilities, certificates, locations, and
fingerprinting. We find 9.8M VPN servers distributed around the world using
OpenVPN, SSTP, PPTP, and IPsec, and analyze their vulnerability. We find SSTP
to be the most vulnerable protocol with more than 90% of detected servers being
vulnerable to TLS downgrade attacks. Of all the servers that respond to our VPN
probes, 2% also respond to HTTP probes and therefore are classified as Web
servers. We apply our list of VPN servers to the traffic from a large European
ISP and observe that 2.6% of all traffic is related to these VPN servers.
| arxiv topic:cs.NI |
arxiv_dataset-179162302.06666 | Decay of solutions of the wave equation in cosmological spacetimes -- a
numerical analysis
gr-qc
We numerically evolve spherically symmetric solutions to the linear wave
equation on some expanding Friedmann-Lema\^itre-Robertson-Walker (FLRW)
spacetimes and study the respective asymptotics for large times. We find a
quantitative relation between the expansion rate of the underlying background
universe and the decay rate of linear waves, also in the context of
spatially-hyperbolic spacetimes, for which rigorous proofs of decay rates are
not generally known. A prominent role in the decay mechanism is shown to be
played by tails, i.e. scattered waves propagating in the interior of the
lightcone.
| arxiv topic:gr-qc |
arxiv_dataset-179172302.06766 | Creation of super-high-flux photo-neutrons and gamma-rays > 8 MeV using
a petawatt laser to irradiate high-Z solid targets
physics.app-ph nucl-ex
We report the creation of super-high-flux gamma-rays with energy >8 MeV and
photo-neutrons via the (g,n) reaction near giant dipole resonance energies (8 -
20 MeV), using the ~130 J Texas Petawatt laser to irradiate high-Z (Au, Pt, Re,
W) targets of mm - cm thickness, at laser intensities up to ~5x1021W/cm2. We
detected up to ~ several x 1012 gamma-rays > 8 MeV (~3% of incident laser
energy) and ~ 1010 photo-neutrons per shot. Due to the short pulse and narrow
gamma-ray cone (~17o half-width) around laser forward, the peak emergent
gamma-ray flux >8 MeV reached ~1027 gammas/cm2/sec, and the peak emergent
neutron flux reached ~1020 neutrons/cm2/sec. Such intense gamma-ray and neutron
fluxes are among the highest achieved for short-pulse laser experiments. They
will facilitate the study of nuclear reactions requiring super-high-flux of
gamma-rays or neutrons, such as the creation of r-process elements. These
results may also have far-reaching applications for nuclear energy, such as the
transmutation of nuclear waste, isotope production and inertial fusion.
| arxiv topic:physics.app-ph nucl-ex |
arxiv_dataset-179182302.06866 | Quantum state resolved molecular dipolar collisions over four decades of
energy
physics.atom-ph physics.chem-ph
Collisions between cold polar molecules represent a fascinating research
frontier, but have proven hard to probe experimentally. We report measurements
of inelastic cross sections for collisions between NO and ND 3 molecules at
energies between 0.1 and 580 cm-1 , with full quantum state resolution. At
energies below the 100 cm-1 well depth of the interaction potential, we
observed backward glories originating from peculiar U-turn trajectories. At
energies below 0.2 cm-1, we observed a breakdown of the Langevin capture model,
which we interpreted in terms of a suppressed mutual polarization during the
collision, effectively switching off the molecular dipole moments. Scattering
calculations based on an ab initio NO-ND3 potential energy surface revealed the
crucial role of near-degenerate rotational levels with opposite parity in
low-energy dipolar collisions.
| arxiv topic:physics.atom-ph physics.chem-ph |
arxiv_dataset-179192302.06966 | Proof of Reputation
cs.CR
We present the new mining protocol Proof-of-Reputation (PoR) for
decentralized Proof-of-Work (PoW) blockchains, in particular for Bitcoin. PoR
combines the classical PoW with the new ingredient of cryptographic reputation.
The same level of security compared to pure PoW can be achieved with a
significant energy consumption reduction (of the order of 30\%) for the same
security level. The proper implementation of a decentralized reputation
protocol is suitable with an extra layer of mining security: Certified Mining.
| arxiv topic:cs.CR |
arxiv_dataset-179202302.07066 | The non-normal abyss in Kleene's computability theory
math.LO cs.LO
Kleene's computability theory based on his S1-S9 computation schemes
constitutes a model for computing with objects of any finite type and extends
Turing's `machine model' which formalises computing with real numbers. A
fundamental distinction in Kleene's framework is between normal and non-normal
functionals where the former compute the associated Kleene quantifier
$\exists^{n}$ and the latter do not. Historically, the focus was on normal
functionals, but recently new non-normal functionals have been studied, based
on well-known theorems like the uncountability of the reals. These new
non-normal functionals are fundamentally different from historical examples
like Tait's fan functional: the latter is computable from $\exists^{2}$ while
the former are only computable in $\exists^{3}$. While there is a great divide
separating $\exists^{2}$ and $\exists^{3}$, we identify certain closely related
non-normal functionals that fall on different sides of this abyss. Our examples
are based on mainstream mathematical notions, like quasi-continuity, Baire
classes, and semi-continuity.
| arxiv topic:math.LO cs.LO |
arxiv_dataset-179212302.07166 | Noisy quantum batteries
quant-ph
In realistic situations, physical systems can not be completely isolated from
its environment. Its inevitable interaction with the environment can influence
the working process of the device. In this paper, we consider two-qubit quantum
batteries where one qubit of the battery is successively interacting with the
spins present in the surrounding environment. We examine the effect of the
interaction on the maximum amount of energy that can be extracted from the
battery using unitaries. In this context, we use the notion of locally passive
states. In particular, we examine the behavior of the amount of extractable
work from the noisy battery, initially prepared in a locally passive or
ordinary pure state, having a fixed initial entanglement, with the number of
interactions the qubit has gone through. We also examine the amount of locally
extractable work from the noisy battery. We realize though the amount of
extractable energy, be it global or local, as a whole will decrease with the
number of spins of environment it interacted with, but if we increase the time
interval of the interaction with each spin, after a cut off value of the
interval, the small time behavior shows a peculiarity, i.e., the extractable
energy within a single interaction starts to increase with time. The cut-off
time indicates the Markovian-to-non-Markovian transition of the interaction. We
also observe a non-Markovian increase in extractable energy from the Markovian
scenario.
| arxiv topic:quant-ph |
arxiv_dataset-179222302.07266 | Identifying and characterising the population of hot sub-luminous stars
with multi-colour MeerLICHT data
astro-ph.SR astro-ph.GA stat.ME
Colour-magnitude diagrams reveal a population of blue (hot) sub-luminous
objects with respect to the main sequence. These hot sub-luminous stars are the
result of evolutionary processes that require stars to expel their obscuring,
hydrogen-rich envelopes to reveal the hot helium core. As such, these objects
offer a direct window into the hearts of stars that are otherwise inaccessible
to direct observation. We showcase MeerLICHT's capabilities of detecting faint
hot subdwarfs and identifying the dominant frequency in the photometric
variability of these compact hot stars, in comparison to their $Gaia$ DR3 data.
We hunt for oscillations, which will be an essential ingredient for accurately
probing stellar interiors in future asteroseismology. Comparative MeerLICHT and
$Gaia$ colour-magnitude diagrams are presented as a way to select hot subdwarfs
from our sample. A dedicated frequency determination technique is developed and
applied to the selected candidates to determine their dominant variability
using time-series data from MeerLICHT and $Gaia$ DR3. We explore the power of
both datasets in determining the dominant frequency. Using the $g-i$ colour,
MeerLICHT offers a colour-magnitude diagram that is comparable in quality to
that of $Gaia$ DR3. The MeerLICHT colour-colour diagrams allow for the study of
different stellar populations. The frequency analysis of MeerLICHT and $Gaia$
DR3 data demonstrates the superiority of our MeerLICHT multi-colour photometry
in estimating the dominant frequency compared to the sparse $Gaia$ DR3 data.
MeerLICHT's multi-band photometry leads to the discovery of high-frequency
faint subdwarfs. Our MeerLICHT results are a proof-of-concept of the capacity
of the BlackGEM instrument currently in the commissioning stage at ESO's La
Silla Observatory in Chile.
| arxiv topic:astro-ph.SR astro-ph.GA stat.ME |
arxiv_dataset-179232302.07366 | WEAVE-StePS. A stellar population survey using WEAVE at WHT
astro-ph.GA
The upcoming new generation of optical spectrographs on four-meter-class
telescopes will provide valuable opportunities for forthcoming galaxy surveys
through their huge multiplexing capabilities, excellent spectral resolution,
and unprecedented wavelength coverage. WEAVE is a new wide-field spectroscopic
facility mounted on the 4.2 m William Herschel Telescope in La Palma.
WEAVE-StePS is one of the five extragalactic surveys that will use WEAVE during
its first five years of operations. It will observe galaxies using WEAVE MOS
(~950 fibres across a field of view of ~3 deg2 on the sky) in low-resolution
mode (R~5000, spanning the wavelength range 3660-9590 AA). WEAVE-StePS will
obtain high-quality spectra (S/N ~ 10 per AA at R~5000) for a magnitude-limited
(I_AB = 20.5) sample of ~25,000 galaxies, the majority selected at z>=0.3. The
survey goal is to provide precise spectral measurements in the crucial interval
that bridges the gap between LEGA-C and SDSS data. The wide area coverage of
~25 deg2 will enable us to observe galaxies in a variety of environments. The
ancillary data available in each observed field (including X-ray coverage,
multi-narrow-band photometry and spectroscopic redshift information) will
provide an environmental characterisation for each observed galaxy. This paper
presents the science case of WEAVE-StePS, the fields to be observed, the parent
catalogues used to define the target sample, and the observing strategy chosen
after a forecast of the expected performance of the instrument for our typical
targets. WEAVE-StePS will go back further in cosmic time than SDSS, extending
its reach to encompass more than ~6 Gyr, nearly half of the age of the
Universe. The spectral and redshift range covered by WEAVE-StePS will open a
new observational window by continuously tracing the evolutionary path of
galaxies in the largely unexplored intermediate-redshift range.
| arxiv topic:astro-ph.GA |
arxiv_dataset-179242302.07466 | Randomized Orthogonal Projection Methods for Krylov Subspace Solvers
math.NA cs.NA
Randomized orthogonal projection methods (ROPMs) can be used to speed up the
computation of Krylov subspace methods in various contexts. Through a
theoretical and numerical investigation, we establish that these methods
produce quasi-optimal approximations over the Krylov subspace. Our numerical
experiments outline the convergence of ROPMs for all matrices in our test set,
with occasional spikes, but overall with a convergence rate similar to that of
standard OPMs.
| arxiv topic:math.NA cs.NA |
arxiv_dataset-179252302.07566 | Qualitative Data Augmentation for Performance Prediction in VLSI
circuits
cs.LG
Various studies have shown the advantages of using Machine Learning (ML)
techniques for analog and digital IC design automation and optimization. Data
scarcity is still an issue for electronic designs, while training highly
accurate ML models. This work proposes generating and evaluating artificial
data using generative adversarial networks (GANs) for circuit data to aid and
improve the accuracy of ML models trained with a small training data set. The
training data is obtained by various simulations in the Cadence Virtuoso,
HSPICE, and Microcap design environment with TSMC 180nm and 22nm CMOS
technology nodes. The artificial data is generated and tested for an
appropriate set of analog and digital circuits. The experimental results show
that the proposed artificial data generation significantly improves ML models
and reduces the percentage error by more than 50\% of the original percentage
error, which were previously trained with insufficient data. Furthermore, this
research aims to contribute to the extensive application of AI/ML in the field
of VLSI design and technology by relieving the training data
availability-related challenges.
| arxiv topic:cs.LG |
arxiv_dataset-179262302.07666 | Forbidden Patterns in Temporal Graphs Resulting from Encounters in a
Corridor
cs.DS
In this paper, we study temporal graphs arising from mobility models, where
vertices correspond to agents moving in space and edges appear each time two
agents meet. We propose a rather natural one-dimensional model.
If each pair of agents meets exactly once, we get a simple temporal clique
where the edges are ordered according to meeting times. In order to
characterize which temporal cliques can be obtained as such `mobility graphs',
we introduce the notion of forbidden patterns in temporal graphs. Furthermore,
using a classical result in combinatorics, we count the number of such mobility
cliques for a given number of agents, and show that not every temporal clique
resulting from the 1D model can be realized with agents moving with different
constant speeds. For the analogous circular problem, where agents are moving
along a circle, we provide a characterization via circular forbidden patterns.
Our characterization in terms of forbidden patterns can be extended to the
case where each edge appears at most once. We also study the problem where
pairs of agents are allowed to cross each other several times, using an
approach from automata theory. We observe that in this case, there is no finite
set of forbidden patterns that characterize such temporal graphs and
nevertheless give a linear-time algorithm to recognize temporal graphs arising
from this model.
| arxiv topic:cs.DS |
arxiv_dataset-179272302.07766 | An optimal control problem subject to strong solutions of
chemotaxis-consumption models
math.OC
We consider a bilinear optimal control problem associated to the following
chemotaxis-consumption model in a bounded domain $\Omega \subset \mathbb{R}^3$
during a time interval $(0,T)$: $$\partial_t u - \Delta u = - \nabla \cdot (u
\nabla v), \quad \partial_t v - \Delta v = - u^s v + f v 1_{\Omega_c},$$ with
$s \geq 1$, endowed with isolated boundary conditions and initial conditions
for $(u,v)$, $u$ being the cell density, $v$ the chemical concentration and $f$
the bilinear control acting in a subdomain $\Omega_c \subset \Omega$. The
existence of weak solutions $(u,v)$ to this model given $f \in L^q((0,T) \times
\Omega)$, for some $q > 5/2$, has been proved in [F. Guill\'en-Gonz\'alez and
A. L. Corr\^ea Vianna Filho, Optimal Control Related to Weak Solutions of a
Chemotaxis-Consumption Model, arXiv:2211.14612, 2022]. In this paper, we study
a related optimal control problem in the strong solution setting. First,
imposing the regularity criterion $u ^s \in L^q((0,T) \times \Omega)$ ($q >
5/2$) for a given weak solution, we prove existence and uniqueness of
global-in-time strong solutions. Then, the existence of a global optimal
solution can be deduced. Finally, using a Lagrange multipliers theorem, we
establish first order optimality conditions for any local optimal solution,
proving existence, uniqueness and regularity of the associated Lagrange
multipliers.
| arxiv topic:math.OC |
arxiv_dataset-179282302.07866 | Do Deep Neural Networks Capture Compositionality in Arithmetic
Reasoning?
cs.CL cs.AI
Compositionality is a pivotal property of symbolic reasoning. However, how
well recent neural models capture compositionality remains underexplored in the
symbolic reasoning tasks. This study empirically addresses this question by
systematically examining recently published pre-trained seq2seq models with a
carefully controlled dataset of multi-hop arithmetic symbolic reasoning. We
introduce a skill tree on compositionality in arithmetic symbolic reasoning
that defines the hierarchical levels of complexity along with three
compositionality dimensions: systematicity, productivity, and substitutivity.
Our experiments revealed that among the three types of composition, the models
struggled most with systematicity, performing poorly even with relatively
simple compositions. That difficulty was not resolved even after training the
models with intermediate reasoning steps.
| arxiv topic:cs.CL cs.AI |
arxiv_dataset-179292302.07966 | The qudit Pauli group: non-commuting pairs, non-commuting sets, and
structure theorems
quant-ph math-ph math.AC math.MP
Qudits with local dimension $d>2$ can have unique structure and uses that
qubits ($d=2$) cannot. Qudit Pauli operators provide a very useful basis of the
space of qudit states and operators. We study the structure of the qudit Pauli
group for any, including composite, $d$ in several ways. To cover composite
values of $d$, we work with modules over commutative rings, which generalize
the notion of vector spaces over fields. For any specified set of commutation
relations, we construct a set of qudit Paulis satisfying those relations. We
also study the maximum size of sets of Paulis that mutually non-commute and
sets that non-commute in pairs. Finally, we give methods to find near minimal
generating sets of Pauli subgroups, calculate the sizes of Pauli subgroups, and
find bases of logical operators for qudit stabilizer codes. Useful tools in
this study are normal forms from linear algebra over commutative rings,
including the Smith normal form, alternating Smith normal form, and Howell
normal form of matrices. Possible applications of this work include the
construction and analysis of qudit stabilizer codes, entanglement assisted
codes, parafermion codes, and fermionic Hamiltonian simulation.
| arxiv topic:quant-ph math-ph math.AC math.MP |
arxiv_dataset-179302302.08066 | Masking and Mixing Adversarial Training
cs.CV cs.AI
While convolutional neural networks (CNNs) have achieved excellent
performances in various computer vision tasks, they often misclassify with
malicious samples, a.k.a. adversarial examples. Adversarial training is a
popular and straightforward technique to defend against the threat of
adversarial examples. Unfortunately, CNNs must sacrifice the accuracy of
standard samples to improve robustness against adversarial examples when
adversarial training is used. In this work, we propose Masking and Mixing
Adversarial Training (M2AT) to mitigate the trade-off between accuracy and
robustness. We focus on creating diverse adversarial examples during training.
Specifically, our approach consists of two processes: 1) masking a perturbation
with a binary mask and 2) mixing two partially perturbed images. Experimental
results on CIFAR-10 dataset demonstrate that our method achieves better
robustness against several adversarial attacks than previous methods.
| arxiv topic:cs.CV cs.AI |
arxiv_dataset-179312302.08166 | Learning Neural Operators on Riemannian Manifolds
math.NA cs.NA
In Artificial Intelligence (AI) and computational science, learning the
mappings between functions (called operators) defined on complex computational
domains is a common theoretical challenge. Recently, Neural Operator emerged as
a promising framework with a discretisation-independent model structure to
break the fixed-dimension limitation of classical deep learning models.
However, existing operator learning methods mainly focus on regular
computational domains, and many components of these methods rely on Euclidean
structural data. In real-life applications, many operator learning problems are
related to complex computational domains such as complex surfaces and solids,
which are non-Euclidean and widely referred to as Riemannian manifolds. Here,
we report a new concept, Neural Operator on Riemannian Manifolds (NORM), which
generalises Neural Operator from being limited to Euclidean spaces to being
applicable to Riemannian manifolds, and can learn the mapping between functions
defined on any real-life complex geometries, while preserving the
discretisation-independent model structure. NORM shifts the
function-to-function mapping to finite-dimensional mapping in the Laplacian
eigenfunctions' subspace of geometry, and holds universal approximation
property in learning operators on Riemannian manifolds even with only one
fundamental block. The theoretical and experimental analysis prove that NORM is
a significant step forward in operator learning and has the potential to solve
complex problems in many fields of applications sharing the same nature and
theoretical principle.
| arxiv topic:math.NA cs.NA |
arxiv_dataset-179322302.08266 | Fairly Adaptive Negative Sampling for Recommendations
cs.IR
Pairwise learning strategies are prevalent for optimizing recommendation
models on implicit feedback data, which usually learns user preference by
discriminating between positive (i.e., clicked by a user) and negative items
(i.e., obtained by negative sampling). However, the size of different item
groups (specified by item attribute) is usually unevenly distributed. We
empirically find that the commonly used uniform negative sampling strategy for
pairwise algorithms (e.g., BPR) can inherit such data bias and oversample the
majority item group as negative instances, severely countering group fairness
on the item side. In this paper, we propose a Fairly adaptive Negative sampling
approach (FairNeg), which improves item group fairness via adaptively adjusting
the group-level negative sampling distribution in the training process. In
particular, it first perceives the model's unfairness status at each step and
then adjusts the group-wise sampling distribution with an adaptive momentum
update strategy for better facilitating fairness optimization. Moreover, a
negative sampling distribution Mixup mechanism is proposed, which gracefully
incorporates existing importance-aware sampling techniques intended for mining
informative negative samples, thus allowing for achieving multiple optimization
purposes. Extensive experiments on four public datasets show our proposed
method's superiority in group fairness enhancement and fairness-utility
tradeoff.
| arxiv topic:cs.IR |
arxiv_dataset-179332302.08366 | Defect Transfer GAN: Diverse Defect Synthesis for Data Augmentation
cs.CV
Data-hunger and data-imbalance are two major pitfalls in many deep learning
approaches. For example, on highly optimized production lines, defective
samples are hardly acquired while non-defective samples come almost for free.
The defects however often seem to resemble each other, e.g., scratches on
different products may only differ in a few characteristics. In this work, we
introduce a framework, Defect Transfer GAN (DT-GAN), which learns to represent
defect types independent of and across various background products and yet can
apply defect-specific styles to generate realistic defective images. An
empirical study on the MVTec AD and two additional datasets showcase DT-GAN
outperforms state-of-the-art image synthesis methods w.r.t. sample fidelity and
diversity in defect generation. We further demonstrate benefits for a critical
downstream task in manufacturing -- defect classification. Results show that
the augmented data from DT-GAN provides consistent gains even in the few
samples regime and reduces the error rate up to 51% compared to both
traditional and advanced data augmentation methods.
| arxiv topic:cs.CV |
arxiv_dataset-179342302.08466 | Marich: A Query-efficient Distributionally Equivalent Model Extraction
Attack using Public Data
cs.LG cs.CR stat.ML
We study design of black-box model extraction attacks that can send minimal
number of queries from a publicly available dataset to a target ML model
through a predictive API with an aim to create an informative and
distributionally equivalent replica of the target. First, we define
distributionally equivalent and Max-Information model extraction attacks, and
reduce them into a variational optimisation problem. The attacker sequentially
solves this optimisation problem to select the most informative queries that
simultaneously maximise the entropy and reduce the mismatch between the target
and the stolen models. This leads to an active sampling-based query selection
algorithm, Marich, which is model-oblivious. Then, we evaluate Marich on
different text and image data sets, and different models, including CNNs and
BERT. Marich extracts models that achieve $\sim 60-95\%$ of true model's
accuracy and uses $\sim 1,000 - 8,500$ queries from the publicly available
datasets, which are different from the private training datasets. Models
extracted by Marich yield prediction distributions, which are $\sim 2-4\times$
closer to the target's distribution in comparison to the existing active
sampling-based attacks. The extracted models also lead to $84-96\%$ accuracy
under membership inference attacks. Experimental results validate that Marich
is query-efficient, and capable of performing task-accurate, high-fidelity, and
informative model extraction.
| arxiv topic:cs.LG cs.CR stat.ML |
arxiv_dataset-179352302.08566 | Local versus global stability in dynamical systems with consecutive
Hopf-Bifurcations
nlin.AO
Quantifying the stability of an equilibrium is central in the theory of
dynamical systems as well as in engineering and control. A comprehensive
picture must include the response to both small and large perturbations,
leading to the concepts of local (linear) and global stability. Here, we show
how systems displaying Hopf bifurcations show contrarian results on these two
aspects of stability: Global stability is large close to the point where the
system loses its local stability altogether. We demonstrate this effect for an
elementary model system, an anharmonic oscillator and a realistic model of
power system dynamics with delayed control. Detailed investigations of the
bifurcation explain the seeming paradox in terms of the location of the
attractors relative to the equilibrium.
| arxiv topic:nlin.AO |
arxiv_dataset-179362302.08666 | Quantum computing for data science
quant-ph
I provide a perspective on the development of quantum computing for data
science, including a dive into state-of-the-art for both hardware and
algorithms and the potential for quantum machine learning
| arxiv topic:quant-ph |
arxiv_dataset-179372302.08766 | A Lower Bound and a Near-Optimal Algorithm for Bilevel Empirical Risk
Minimization
stat.ML cs.LG math.OC
Bilevel optimization problems, which are problems where two optimization
problems are nested, have more and more applications in machine learning. In
many practical cases, the upper and the lower objectives correspond to
empirical risk minimization problems and therefore have a sum structure. In
this context, we propose a bilevel extension of the celebrated SARAH algorithm.
We demonstrate that the algorithm requires
$\mathcal{O}((n+m)^{\frac12}\varepsilon^{-1})$ oracle calls to achieve
$\varepsilon$-stationarity with $n+m$ the total number of samples, which
improves over all previous bilevel algorithms. Moreover, we provide a lower
bound on the number of oracle calls required to get an approximate stationary
point of the objective function of the bilevel problem. This lower bound is
attained by our algorithm, making it optimal in terms of sample complexity.
| arxiv topic:stat.ML cs.LG math.OC |
arxiv_dataset-179382302.08866 | Geometric Phase in Quantum Synchronization
quant-ph cond-mat.mes-hall nlin.AO
We consider a quantum limit-cycle oscillator implemented in a spin system
whose quantization axis is slowly rotated. Using a kinematic approach to define
geometric phases in nonunitary evolution, we show that the quantum limit-cycle
oscillator attains a geometric phase when the rotation is sufficiently slow. In
the presence of an external signal, the geometric phase as a function of the
signal strength and the detuning between the signal and the natural frequency
of oscillation shows a structure that is strikingly similar to the Arnold
tongue of synchronization. Surprisingly, this structure vanishes together with
the Arnold tongue when the system is in a parameter regime of synchronization
blockade. We derive an analytic expression for the geometric phase of this
system, valid in the limit of slow rotation of the quantization axis and weak
external signal strength, and we provide an intuitive interpretation for this
surprising effect.
| arxiv topic:quant-ph cond-mat.mes-hall nlin.AO |
arxiv_dataset-179392302.08966 | Photon pumping, photodissociation and dissipation at interplay for the
fluorescence of a molecule in a cavity
quant-ph cond-mat.mes-hall physics.atom-ph physics.optics
We introduce a model description of a diatomic molecule in an optical cavity,
with pump and fluorescent fields, and electron and nuclear motion are treated
on equal footing and exactly. The model accounts for several optical response
temporal scenarios: a Mollow spectrum hindered by electron correlations, a
competition of harmonic generation and molecular dissociation, a dependence of
fluorescence on photon pumping rate and dissipation. It is thus a general and
flexible template for insight into experiments where quantum photon
confinement, leakage, nuclear motion and electronic correlations are at
interplay.
| arxiv topic:quant-ph cond-mat.mes-hall physics.atom-ph physics.optics |
arxiv_dataset-179402302.09066 | Towards optimal and robust $f_{\rm NL}$ constraints with multi-tracer
analyses
astro-ph.CO
We discuss the potential of the multi-tracer technique to improve
observational constraints of the local primordial non-Gaussianity (PNG)
parameter $f_{\rm NL}$ from the galaxy power spectrum. For two galaxy samples
$A$ and $B$, the constraining power is $\propto |b_1^B b_\phi^A -
b_1^Ab_\phi^B|$, where $b_1$ and $b_\phi$ are the linear and PNG galaxy bias
parameters. We show this allows for significantly improved constraints compared
to the traditional expectation $\propto |b_1^A - b_1^B|$ based on naive
universality-like relations where $b_\phi \propto b_1$. Using IllustrisTNG
galaxy simulation data, we find that different equal galaxy number splits of
the full sample lead to different $|b_1^B b_\phi^A - b_1^Ab_\phi^B|$, and thus
have different constraining power. Of all of the strategies explored, splitting
by $g-r$ color is the most promising, more than doubling the significance of
detecting $f_{\rm NL}b_\phi \neq 0$. Importantly, since these are constraints
on $f_{\rm NL}b_\phi$ and not $f_{\rm NL}$, they do not require priors on the
$b_\phi(b_1)$ relation. For direct constraints on $f_{\rm NL}$, we show that
multi-tracer constraints can be significantly more robust than single-tracer to
$b_\phi$ misspecifications and uncertainties; this relaxes the precision and
accuracy requirements for $b_\phi$ priors. Our results present new
opportunities to improve our chances to detect and robustly constrain $f_{\rm
NL}$, and strongly motivate galaxy formation simulation campaigns to calibrate
the $b_\phi(b_1)$ relation.
| arxiv topic:astro-ph.CO |
arxiv_dataset-179412302.09166 | Machine Learning for Cutting Planes in Integer Programming: A Survey
math.OC cs.AI cs.LG
We survey recent work on machine learning (ML) techniques for selecting
cutting planes (or cuts) in mixed-integer linear programming (MILP). Despite
the availability of various classes of cuts, the task of choosing a set of cuts
to add to the linear programming (LP) relaxation at a given node of the
branch-and-bound (B&B) tree has defied both formal and heuristic solutions to
date. ML offers a promising approach for improving the cut selection process by
using data to identify promising cuts that accelerate the solution of MILP
instances. This paper presents an overview of the topic, highlighting recent
advances in the literature, common approaches to data collection, evaluation,
and ML model architectures. We analyze the empirical results in the literature
in an attempt to quantify the progress that has been made and conclude by
suggesting avenues for future research.
| arxiv topic:math.OC cs.AI cs.LG |
arxiv_dataset-179422302.09266 | Ionized carbon as a tracer of the assembly of interstellar clouds
astro-ph.GA
Molecular hydrogen clouds are a key component of the interstellar medium
because they are the birthplaces for stars. They are embedded in atomic gas
that pervades the interstellar space. However, the details of how molecular
clouds assemble from and interact with the atomic gas are still largely
unknown. As a result of new observations of the 158~$\mu$m line of ionized
carbon CII in the Cygnus region within the FEEDBACK program on SOFIA
(Stratospheric Observatory for Infrared Astronomy), we present compelling
evidence that CII unveils dynamic interactions between cloud ensembles. This
process is neither a head-on collision of fully molecular clouds nor a gentle
merging ofonly atomic clouds. Moreover, we demonstrate that the dense molecular
clouds associated with the DR21 and W75N star-forming regions and a cloud at
higher velocity are embedded in atomic gas and all components interact over a
large range of velocities (20 km/s). The atomic gas has a density of 100
cm$^{-3}$ and a temperature of 100 K. We conclude that the CII 158 $\mu$m line
is an excellent tracer to witness the processes involved in cloud interactions
and anticipate further detections of this phenomenon in other regions
| arxiv topic:astro-ph.GA |
arxiv_dataset-179432302.09366 | Gyro-Groups, Gyro-splittings and Co-homology
math.GR
In this paper, we study gyro-groups associated to groups, group extensions
admitting gyro-sections, and corresponding co-homologies. We also describe the
obstructions in terms of co-homomology. The notion of gyro-Schur Multiplier and
that of gyro-Milnor $K_{2}$ group are introduced.
| arxiv topic:math.GR |
arxiv_dataset-179442302.09466 | RePrompt: Automatic Prompt Editing to Refine AI-Generative Art Towards
Precise Expressions
cs.HC cs.AI
Generative AI models have shown impressive ability to produce images with
text prompts, which could benefit creativity in visual art creation and
self-expression. However, it is unclear how precisely the generated images
express contexts and emotions from the input texts. We explored the emotional
expressiveness of AI-generated images and developed RePrompt, an automatic
method to refine text prompts toward precise expression of the generated
images. Inspired by crowdsourced editing strategies, we curated intuitive text
features, such as the number and concreteness of nouns, and trained a proxy
model to analyze the feature effects on the AI-generated image. With model
explanations of the proxy model, we curated a rubric to adjust text prompts to
optimize image generation for precise emotion expression. We conducted
simulation and user studies, which showed that RePrompt significantly improves
the emotional expressiveness of AI-generated images, especially for negative
emotions.
| arxiv topic:cs.HC cs.AI |
arxiv_dataset-179452302.09566 | Optimization Methods in Deep Learning: A Comprehensive Overview
cs.LG math.OC
In recent years, deep learning has achieved remarkable success in various
fields such as image recognition, natural language processing, and speech
recognition. The effectiveness of deep learning largely depends on the
optimization methods used to train deep neural networks. In this paper, we
provide an overview of first-order optimization methods such as Stochastic
Gradient Descent, Adagrad, Adadelta, and RMSprop, as well as recent
momentum-based and adaptive gradient methods such as Nesterov accelerated
gradient, Adam, Nadam, AdaMax, and AMSGrad. We also discuss the challenges
associated with optimization in deep learning and explore techniques for
addressing these challenges, including weight initialization, batch
normalization, and layer normalization. Finally, we provide recommendations for
selecting optimization methods for different deep learning tasks and datasets.
This paper serves as a comprehensive guide to optimization methods in deep
learning and can be used as a reference for researchers and practitioners in
the field.
| arxiv topic:cs.LG math.OC |
arxiv_dataset-179462302.09666 | Synchronizing Many Filesystems in Near Linear Time
cs.IT math.IT
Finding a provably correct subquadratic synchronization algorithm for many
filesystem replicas is one of the main theoretical problems in Operational
Transformation (OT) and Conflict-free Replicated Data Types (CRDT) frameworks.
Based on the Algebraic Theory of Filesystems, which incorporates
non-commutative filesystem commands natively, we developed and built a
proof-of-concept implementation of an algorithm suite which synchronizes an
arbitrary number of replicas. The result is provably correct, and the
synchronized system is created in linear space and time after an initial
sorting phase. It works by identifying conflicting command pairs and requesting
one of the commands to be removed. The method can be guided to reach any of the
theoretically possible synchronized states. The algorithm also allows
asynchronous usage. After the client sends a synchronization request, the local
replica remains available for further modifications. When the synchronization
instructions arrive, they can be merged with the changes made since the
synchronization request. The suite also works on filesystems with directed
acyclic graph-based path structure in place of the traditional tree-like
arrangement. Consequently, our algorithms apply to filesystems with hard or
soft links as long as the links create no loops.
| arxiv topic:cs.IT math.IT |
arxiv_dataset-179472302.09766 | A One-Sample Decentralized Proximal Algorithm for Non-Convex Stochastic
Composite Optimization
math.OC cs.DC cs.LG stat.ML
We focus on decentralized stochastic non-convex optimization, where $n$
agents work together to optimize a composite objective function which is a sum
of a smooth term and a non-smooth convex term. To solve this problem, we
propose two single-time scale algorithms: Prox-DASA and Prox-DASA-GT. These
algorithms can find $\epsilon$-stationary points in
$\mathcal{O}(n^{-1}\epsilon^{-2})$ iterations using constant batch sizes (i.e.,
$\mathcal{O}(1)$). Unlike prior work, our algorithms achieve comparable
complexity without requiring large batch sizes, more complex per-iteration
operations (such as double loops), or stronger assumptions. Our theoretical
findings are supported by extensive numerical experiments, which demonstrate
the superiority of our algorithms over previous approaches. Our code is
available at https://github.com/xuxingc/ProxDASA.
| arxiv topic:math.OC cs.DC cs.LG stat.ML |
arxiv_dataset-179482302.09866 | Hydrodynamic limit of the Schelling model with spontaneous Glauber and
Kawasaki dynamics
math.PR
In the present article we consider the Schelling model, an agent-based model
describing a segregation dynamics when we have a cohabitation of two social
groups. As for several social models, the behaviour of the Schelling model was
analyzed along several directions, notably by exploiting theoretical physics
tools and computer simulations. This approach led to conjecture a phase diagram
in which either different social groups were segregated in two large clusters
or they were mixed. In this article, we describe and analyze a perturbation of
the Schelling model as a particle systems model by adding a Glauber and
Kawasaki dynamics to the original Schelling dynamics. As far as the authors
know, this is the first rigorous mathematical analysis of the perturbed
Schelling model. We prove the existence of an hydrodynamic limit described by a
reaction-diffusion equation with a discontinuous non-linear reaction term. The
existence and uniqueness of the solution is non trivial and the analysis of the
limit PDE is interesting in its own. Based on our results, we conjecture, as in
other variations of this model, the existence of a phase diagram in which we
have a mixed, a segregated and a metastable segregation phase. We also describe
how this phase transition can be viewed as a transition between a relevant and
irrelevant disorder regime in the model.
| arxiv topic:math.PR |
arxiv_dataset-179492302.09966 | Density functional description of long-range electron Coulomb
interactions in bulk SnS
cond-mat.mtrl-sci physics.chem-ph
A high-throughput benchmarking technique for testing the performance of
different exchange-correlation functionals and pseudopotentials is proposed and
applied to bulk SnS. It is shown that, contrary to the popular view that the
local density approximation can best describe layered materials, a semilocal
pseudopotential with a functional having a gradient dependence better described
lattice vectors and `tetragonicity' of the lattice. We classify the
pseudopotentials based on this value and show that the participation ratio of
maximally localized Wannier functions follows the theory which states that more
distorted structures have higher anti-bonding hybridization as stabilizing
factor. In order to classify pseudopotentials, the local and nonlocal potential
contributions to the dynamical Born effective charges are taken for each
pseudopotential. Finally, a strategy is proposed for learning
exchange-correlation functionals based on the distinction between short and
long range parts of the Kohn-Sham potential.
| arxiv topic:cond-mat.mtrl-sci physics.chem-ph |
arxiv_dataset-179502302.10066 | Sharp analysis of EM for learning mixtures of pairwise differences
math.ST cs.LG stat.ML stat.TH
We consider a symmetric mixture of linear regressions with random samples
from the pairwise comparison design, which can be seen as a noisy version of a
type of Euclidean distance geometry problem. We analyze the
expectation-maximization (EM) algorithm locally around the ground truth and
establish that the sequence converges linearly, providing an $\ell_\infty$-norm
guarantee on the estimation error of the iterates. Furthermore, we show that
the limit of the EM sequence achieves the sharp rate of estimation in the
$\ell_2$-norm, matching the information-theoretically optimal constant. We also
argue through simulation that convergence from a random initialization is much
more delicate in this setting, and does not appear to occur in general. Our
results show that the EM algorithm can exhibit several unique behaviors when
the covariate distribution is suitably structured.
| arxiv topic:math.ST cs.LG stat.ML stat.TH |
arxiv_dataset-179512302.10166 | Learning Deep Semantics for Test Completion
cs.SE cs.CL cs.LG
Writing tests is a time-consuming yet essential task during software
development. We propose to leverage recent advances in deep learning for text
and code generation to assist developers in writing tests. We formalize the
novel task of test completion to automatically complete the next statement in a
test method based on the context of prior statements and the code under test.
We develop TeCo -- a deep learning model using code semantics for test
completion. The key insight underlying TeCo is that predicting the next
statement in a test method requires reasoning about code execution, which is
hard to do with only syntax-level data that existing code completion models
use. TeCo extracts and uses six kinds of code semantics data, including the
execution result of prior statements and the execution context of the test
method. To provide a testbed for this new task, as well as to evaluate TeCo, we
collect a corpus of 130,934 test methods from 1,270 open-source Java projects.
Our results show that TeCo achieves an exact-match accuracy of 18, which is 29%
higher than the best baseline using syntax-level data only. When measuring
functional correctness of generated next statement, TeCo can generate runnable
code in 29% of the cases compared to 18% obtained by the best baseline.
Moreover, TeCo is significantly better than prior work on test oracle
generation.
| arxiv topic:cs.SE cs.CL cs.LG |
arxiv_dataset-179522302.10266 | Kernel function impact on convolutional neural networks
cs.CV
This paper investigates the usage of kernel functions at the different layers
in a convolutional neural network. We carry out extensive studies of their
impact on convolutional, pooling and fully-connected layers. We notice that the
linear kernel may not be sufficiently effective to fit the input data
distributions, whereas high order kernels prone to over-fitting. This leads to
conclude that a trade-off between complexity and performance should be reached.
We show how one can effectively leverage kernel functions, by introducing a
more distortion aware pooling layers which reduces over-fitting while keeping
track of the majority of the information fed into subsequent layers. We further
propose Kernelized Dense Layers (KDL), which replace fully-connected layers,
and capture higher order feature interactions. The experiments on conventional
classification datasets i.e. MNIST, FASHION-MNIST and CIFAR-10, show that the
proposed techniques improve the performance of the network compared to
classical convolution, pooling and fully connected layers. Moreover,
experiments on fine-grained classification i.e. facial expression databases,
namely RAF-DB, FER2013 and ExpW demonstrate that the discriminative power of
the network is boosted, since the proposed techniques improve the awareness to
slight visual details and allows the network reaching state-of-the-art results.
| arxiv topic:cs.CV |
arxiv_dataset-179532302.10366 | Programmable System Call Security with eBPF
cs.OS cs.CR
System call filtering is a widely used security mechanism for protecting a
shared OS kernel against untrusted user applications. However, existing system
call filtering techniques either are too expensive due to the context switch
overhead imposed by userspace agents, or lack sufficient programmability to
express advanced policies. Seccomp, Linux's system call filtering module, is
widely used by modern container technologies, mobile apps, and system
management services. Despite the adoption of the classic BPF language (cBPF),
security policies in Seccomp are mostly limited to static allow lists,
primarily because cBPF does not support stateful policies. Consequently, many
essential security features cannot be expressed precisely and/or require kernel
modifications.
In this paper, we present a programmable system call filtering mechanism,
which enables more advanced security policies to be expressed by leveraging the
extended BPF language (eBPF). More specifically, we create a new Seccomp eBPF
program type, exposing, modifying or creating new eBPF helper functions to
safely manage filter state, access kernel and user state, and utilize
synchronization primitives. Importantly, our system integrates with existing
kernel privilege and capability mechanisms, enabling unprivileged users to
install advanced filters safely. Our evaluation shows that our eBPF-based
filtering can enhance existing policies (e.g., reducing the attack surface of
early execution phase by up to 55.4% for temporal specialization), mitigate
real-world vulnerabilities, and accelerate filters.
| arxiv topic:cs.OS cs.CR |
arxiv_dataset-179542302.10466 | Multiple stellar populations at less evolved stages-III: a possible
helium spread in NGC 2210
astro-ph.SR astro-ph.GA
Helium variations are common features of globular clusters (GCs) with
multiple stellar populations. All the formation scenarios predict that
secondary population stars are enhanced in helium but the exact helium content
depends on the polluters. Therefore, searching for helium variations in a star
cluster is a straightforward method to understand if it hosts multiple
populations or not, and constrain the formation scenario. Although this topic
has been well explored for Galactic GCs, GCs beyond the Milky Way are
challenging to study because of their large distances. This work studies the
helium distribution of GK-type main sequence dwarfs in an old ($\sim$12.5 Gyr)
GC in the Large Magellanic Cloud, NGC 2210, using the deep photometry observed
by the {\sl Hubble Space Telescope}. We compare the observed morphology of the
MS with that of synthetic populations with different helium distributions. We
confirm that NGC 2210 dwarfs have a helium spread, with an internal dispersion
of $\delta{Y}\sim$0.06--0.07. The fraction of helium enriched stars depends on
the $\delta{Y}$ distribution. A continuous $\delta{Y}$ distribution would
indicate that more than half of MS stars are helium enriched ($\sim$55\%). If
the $\delta{Y}$ distribution is discrete (bimodal), a fraction of $\sim$30\%
enriched stars is able to explain the observed morphology of the MS. We also
find that the He-enriched population stars are more centrally concentrated than
He-normal stars.
| arxiv topic:astro-ph.SR astro-ph.GA |
arxiv_dataset-179552302.10566 | The commissioning phase
astro-ph.IM
In May 1997 a consistent part of the services and structures committed to the
industry had already been released to the commissioning group. The telescope
itself was, with the exception of the Nasmyth derotators, motors and all the
optics groups, basically ready in its mechanical parts to accept the
integration of all services and control equipment. Also the verification of the
cabling (interlocks, data-nets, power and controls) already mounted was started
in the same period. Starting from June 1998 (telescope first-light date) the
telescope went gradually in use, several nights per week, in order to test and
tune the tracking and pointing system, the optics and the first derotator
system (Nasmyth A station). At the end of the commissioning period and with the
first scientific instruments mounted (April 1999) also the first routinely
observations started. In this moment the telescope is doing astronomy 80% of
time and the complete first-light instrumentation is mounted.
| arxiv topic:astro-ph.IM |
arxiv_dataset-179562302.10666 | Supervisory Control of Modular Discrete-Event Systems under Partial
Observation: Normality
cs.FL
Complex systems are often composed of many small communicating components
called modules. We investigate the synthesis of supervisory controllers for
modular systems under partial observation that, as the closed-loop system,
realize the supremal normal sublanguage of the specification. We call such
controllers maximally permissive normal supervisors. The challenge in modular
systems is to find conditions under which the global nonblocking and maximally
permissive normal supervisor can be achieved locally as the parallel
composition of local normal supervisors. We show that a structural concept of
hierarchical supervisory control called modified observation consistency (MOC)
is such a condition. However, the algorithmic verification of MOC is an open
problem, and therefore it is necessary to find easily-verifiable conditions
that ensure MOC. We show that the condition that all shared events are
observable is such a condition. Considering specifications, we examine both
local specifications, where each module has its own specification, and global
specifications. We combine our results for normality with the existing results
for controllability to locally synthesize the nonblocking and maximally
permissive controllable and normal supervisor. Finally, we illustrate the
results on an industrial case study of the patient table of an MRI scanner.
| arxiv topic:cs.FL |
arxiv_dataset-179572302.10766 | Bridging the Transparency Gap: What Can Explainable AI Learn From the AI
Act?
cs.AI cs.CY
The European Union has proposed the Artificial Intelligence Act which
introduces detailed requirements of transparency for AI systems. Many of these
requirements can be addressed by the field of explainable AI (XAI), however,
there is a fundamental difference between XAI and the Act regarding what
transparency is. The Act views transparency as a means that supports wider
values, such as accountability, human rights, and sustainable innovation. In
contrast, XAI views transparency narrowly as an end in itself, focusing on
explaining complex algorithmic properties without considering the
socio-technical context. We call this difference the ``transparency gap''.
Failing to address the transparency gap, XAI risks leaving a range of
transparency issues unaddressed. To begin to bridge this gap, we overview and
clarify the terminology of how XAI and European regulation -- the Act and the
related General Data Protection Regulation (GDPR) -- view basic definitions of
transparency. By comparing the disparate views of XAI and regulation, we arrive
at four axes where practical work could bridge the transparency gap: defining
the scope of transparency, clarifying the legal status of XAI, addressing
issues with conformity assessment, and building explainability for datasets.
| arxiv topic:cs.AI cs.CY |
arxiv_dataset-179582302.10866 | Hyena Hierarchy: Towards Larger Convolutional Language Models
cs.LG cs.CL
Recent advances in deep learning have relied heavily on the use of large
Transformers due to their ability to learn at scale. However, the core building
block of Transformers, the attention operator, exhibits quadratic cost in
sequence length, limiting the amount of context accessible. Existing
subquadratic methods based on low-rank and sparse approximations need to be
combined with dense attention layers to match Transformers, indicating a gap in
capability. In this work, we propose Hyena, a subquadratic drop-in replacement
for attention constructed by interleaving implicitly parametrized long
convolutions and data-controlled gating. In recall and reasoning tasks on
sequences of thousands to hundreds of thousands of tokens, Hyena improves
accuracy by more than 50 points over operators relying on state-spaces and
other implicit and explicit methods, matching attention-based models. We set a
new state-of-the-art for dense-attention-free architectures on language
modeling in standard datasets (WikiText103 and The Pile), reaching Transformer
quality with a 20% reduction in training compute required at sequence length
2K. Hyena operators are twice as fast as highly optimized attention at sequence
length 8K, and 100x faster at sequence length 64K.
| arxiv topic:cs.LG cs.CL |
arxiv_dataset-179592302.10966 | Dirac bracket and time dependent constraints
gr-qc math-ph math.MP quant-ph
We provide a compact derivation of the Dirac bracket and of the equations of
motion for second class constrained systems when the constraints are time
dependent. The examples of Parameterized Mechanics and of General Relativity
after gauge fixing are given, and the need for the use of time dependent gauge
fixing conditions in these examples is illustrated geometrically.
| arxiv topic:gr-qc math-ph math.MP quant-ph |
arxiv_dataset-179602302.11066 | Reinforcement Learning for Block Decomposition of CAD Models
cs.LG cs.AI
We present a novel AI-assisted method for decomposing (segmenting) planar CAD
(computer-aided design) models into well shaped rectangular blocks as a
proof-of-principle of a general decomposition method applicable to complex 2D
and 3D CAD models. The decomposed blocks are required for generating good
quality meshes (tilings of quadrilaterals or hexahedra) suitable for numerical
simulations of physical systems governed by conservation laws. The problem of
hexahedral mesh generation of general CAD models has vexed researchers for over
3 decades and analysts often spend more than 50% of the design-analysis cycle
time decomposing complex models into simpler parts meshable by existing
techniques. Our method uses reinforcement learning to train an agent to perform
a series of optimal cuts on the CAD model that result in a good quality block
decomposition. We show that the agent quickly learns an effective strategy for
picking the location and direction of the cuts and maximizing its rewards as
opposed to making random cuts. This paper is the first successful demonstration
of an agent autonomously learning how to perform this block decomposition task
effectively thereby holding the promise of a viable method to automate this
challenging process.
| arxiv topic:cs.LG cs.AI |
arxiv_dataset-179612302.11166 | Characterizing the Conditional Galaxy Property Distribution using
Gaussian Mixture Models
astro-ph.GA astro-ph.CO
Line-intensity mapping (LIM) is a promising technique to constrain the global
distribution of galaxy properties. To combine LIM experiments probing different
tracers with traditional galaxy surveys and fully exploit the scientific
potential of these observations, it is necessary to have a physically motivated
modeling framework. As part of developing such a framework, in this work we
introduce and model the conditional galaxy property distribution (CGPD), i.e.
the distribution of galaxy properties conditioned on the host halo mass and
redshift. We consider five galaxy properties, including the galaxy stellar
mass, molecular gas mass, galaxy radius, gas phase metallicity and star
formation rate (SFR), which are important for predicting the emission lines of
interest. The CGPD represents the full distribution of galaxies in the five
dimensional property space; many important galaxy distribution functions and
scaling relations, such as the stellar mass function and SFR main sequence, can
be derived from integrating and projecting it. We utilize two different kinds
of cosmological galaxy simulations, a semi-analytic model and the IllustrisTNG
hydrodynamic simulation, to characterize the CGPD and explore how well it can
be represented using a Gaussian mixture model (GMM). We find that with just a
few ($\sim 3$) Gaussian components, a GMM can describe the CGPD of the
simulated galaxies to high accuracy for both simulations. The CGPD can be
mapped to LIM or other observables by constructing the appropriate relationship
between galaxy properties and the relevant observable tracers.
| arxiv topic:astro-ph.GA astro-ph.CO |
arxiv_dataset-179622302.11266 | One-Shot Labeling for Automatic Relevance Estimation
cs.IR
Dealing with unjudged documents ("holes") in relevance assessments is a
perennial problem when evaluating search systems with offline experiments.
Holes can reduce the apparent effectiveness of retrieval systems during
evaluation and introduce biases in models trained with incomplete data. In this
work, we explore whether large language models can help us fill such holes to
improve offline evaluations. We examine an extreme, albeit common, evaluation
setting wherein only a single known relevant document per query is available
for evaluation. We then explore various approaches for predicting the relevance
of unjudged documents with respect to a query and the known relevant document,
including nearest neighbor, supervised, and prompting techniques. We find that
although the predictions of these One-Shot Labelers (1SL) frequently disagree
with human assessments, the labels they produce yield a far more reliable
ranking of systems than the single labels do alone. Specifically, the strongest
approaches can consistently reach system ranking correlations of over 0.86 with
the full rankings over a variety of measures. Meanwhile, the approach
substantially increases the reliability of t-tests due to filling holes in
relevance assessments, giving researchers more confidence in results they find
to be significant. Alongside this work, we release an easy-to-use software
package to enable the use of 1SL for evaluation of other ad-hoc collections or
systems.
| arxiv topic:cs.IR |
arxiv_dataset-179632302.11366 | Non-Adiabatic Approximations in Time-Dependent Density Functional
Theory: Progress and Prospects
physics.chem-ph cond-mat.mtrl-sci
Time-dependent density functional theory continues to draw a large number of
users in a wide range of fields exploring myriad applications involving
electronic spectra and dynamics. Although in principle exact, the predictivity
of the calculations is limited by the available approximations for the
exchange-correlation functional. In particular, it is known that the exact
exchange-correlation functional has memory-dependence, but in practise
adiabatic approximations are used which ignore this. Here we review the
development of non-adiabatic functional approximations, their impact on
calculations, and challenges in developing practical and accurate
memory-dependent functionals for general purposes.
| arxiv topic:physics.chem-ph cond-mat.mtrl-sci |
arxiv_dataset-179642302.11466 | Advancements in Federated Learning: Models, Methods, and Privacy
cs.AI cs.CL
Federated learning (FL) is a promising technique for addressing the rising
privacy and security issues. Its main ingredient is to cooperatively learn the
model among the distributed clients without uploading any sensitive data. In
this paper, we conducted a thorough review of the related works, following the
development context and deeply mining the key technologies behind FL from both
theoretical and practical perspectives. Specifically, we first classify the
existing works in FL architecture based on the network topology of FL systems
with detailed analysis and summarization. Next, we abstract the current
application problems, summarize the general techniques and frame the
application problems into the general paradigm of FL base models. Moreover, we
provide our proposed solutions for model training via FL. We have summarized
and analyzed the existing FedOpt algorithms, and deeply revealed the
algorithmic development principles of many first-order algorithms in depth,
proposing a more generalized algorithm design framework. Based on these
frameworks, we have instantiated FedOpt algorithms. As privacy and security is
the fundamental requirement in FL, we provide the existing attack scenarios and
the defense methods. To the best of our knowledge, we are among the first tier
to review the theoretical methodology and propose our strategies since there
are very few works surveying the theoretical approaches. Our survey targets
motivating the development of high-performance, privacy-preserving, and secure
methods to integrate FL into real-world applications.
| arxiv topic:cs.AI cs.CL |
arxiv_dataset-179652302.11566 | Vid2Avatar: 3D Avatar Reconstruction from Videos in the Wild via
Self-supervised Scene Decomposition
cs.CV
We present Vid2Avatar, a method to learn human avatars from monocular
in-the-wild videos. Reconstructing humans that move naturally from monocular
in-the-wild videos is difficult. Solving it requires accurately separating
humans from arbitrary backgrounds. Moreover, it requires reconstructing
detailed 3D surface from short video sequences, making it even more
challenging. Despite these challenges, our method does not require any
groundtruth supervision or priors extracted from large datasets of clothed
human scans, nor do we rely on any external segmentation modules. Instead, it
solves the tasks of scene decomposition and surface reconstruction directly in
3D by modeling both the human and the background in the scene jointly,
parameterized via two separate neural fields. Specifically, we define a
temporally consistent human representation in canonical space and formulate a
global optimization over the background model, the canonical human shape and
texture, and per-frame human pose parameters. A coarse-to-fine sampling
strategy for volume rendering and novel objectives are introduced for a clean
separation of dynamic human and static background, yielding detailed and robust
3D human geometry reconstructions. We evaluate our methods on publicly
available datasets and show improvements over prior art.
| arxiv topic:cs.CV |
arxiv_dataset-179662302.11666 | Oscillation probabilities for a PT-symmetric non-Hermitian two-state
system
quant-ph hep-ph hep-th
There is growing interest in viable quantum theories with PT-symmetric
non-Hermitian Hamiltonians, but a formulation of transition matrix elements
consistent with positivity and perturbative unitarity has so far proved
elusive. This Letter provides such a formulation, which relies crucially on the
ability to span the state space in such a way that the interaction and energy
eigenstates are orthonormal with respect to the same positive-definite inner
product. We mention possible applications to the oscillations of mesons and
neutrinos.
| arxiv topic:quant-ph hep-ph hep-th |
arxiv_dataset-179672302.11766 | MUTANT: A Multi-sentential Code-mixed Hinglish Dataset
cs.CL cs.LG
The multi-sentential long sequence textual data unfolds several interesting
research directions pertaining to natural language processing and generation.
Though we observe several high-quality long-sequence datasets for English and
other monolingual languages, there is no significant effort in building such
resources for code-mixed languages such as Hinglish (code-mixing of
Hindi-English). In this paper, we propose a novel task of identifying
multi-sentential code-mixed text (MCT) from multilingual articles. As a use
case, we leverage multilingual articles from two different data sources and
build a first-of-its-kind multi-sentential code-mixed Hinglish dataset i.e.,
MUTANT. We propose a token-level language-aware pipeline and extend the
existing metrics measuring the degree of code-mixing to a multi-sentential
framework and automatically identify MCT in the multilingual articles. The
MUTANT dataset comprises 67k articles with 85k identified Hinglish MCTs. To
facilitate future research, we make the publicly available.
| arxiv topic:cs.CL cs.LG |
arxiv_dataset-179682302.11866 | DCNetBench: Scaleable Data Center Network Benchmarking
cs.NI
Data center networking is the central infrastructure of the modern
information society. However, benchmarking them is very challenging as the
real-world network traffic is difficult to model, and Internet service giants
treat the network traffic as confidential. Several industries have published a
few publicly available network traces. However, these traces are collected from
specific data center environments, e.g., applications, network topology,
protocols, and hardware devices, and thus cannot be scaled to different users,
underlying technologies, and varying benchmarking requirements.
This article argues we should scale different data center applications and
environments in designing, implementing, and evaluating data center networking
benchmarking. We build DCNetBench, the first application-driven data center
network benchmarking that can scale to different users, underlying
technologies, and varying benchmarking requirements. The methodology is as
follows. We built an emulated system that can simulate networking with
different configurations. Then we run applications on the emulated systems to
capture the realistic network traffic patterns; we analyze and classify these
patterns to model and replay those traces. Finally, we provide an automatic
benchmarking framework to support this pipeline. The evaluations on DCNetBench
show its scaleability, effectiveness, and diversity for data center network
benchmarking.
| arxiv topic:cs.NI |
arxiv_dataset-179692302.11966 | Machine Learning for QoS Prediction in Vehicular Communication:
Challenges and Solution Approaches
cs.NI cs.LG
As cellular networks evolve towards the 6th generation, machine learning is
seen as a key enabling technology to improve the capabilities of the network.
Machine learning provides a methodology for predictive systems, which can make
networks become proactive. This proactive behavior of the network can be
leveraged to sustain, for example, a specific quality of service requirement.
With predictive quality of service, a wide variety of new use cases, both
safety- and entertainment-related, are emerging, especially in the automotive
sector. Therefore, in this work, we consider maximum throughput prediction
enhancing, for example, streaming or high-definition mapping applications. We
discuss the entire machine learning workflow highlighting less regarded aspects
such as the detailed sampling procedures, the in-depth analysis of the dataset
characteristics, the effects of splits in the provided results, and the data
availability. Reliable machine learning models need to face a lot of challenges
during their lifecycle. We highlight how confidence can be built on machine
learning technologies by better understanding the underlying characteristics of
the collected data. We discuss feature engineering and the effects of different
splits for the training processes, showcasing that random splits might
overestimate performance by more than twofold. Moreover, we investigate diverse
sets of input features, where network information proved to be most effective,
cutting the error by half. Part of our contribution is the validation of
multiple machine learning models within diverse scenarios. We also use
explainable AI to show that machine learning can learn underlying principles of
wireless networks without being explicitly programmed. Our data is collected
from a deployed network that was under full control of the measurement team and
covered different vehicular scenarios and radio environments.
| arxiv topic:cs.NI cs.LG |
arxiv_dataset-179702302.12066 | Teaching CLIP to Count to Ten
cs.CV
Large vision-language models (VLMs), such as CLIP, learn rich joint
image-text representations, facilitating advances in numerous downstream tasks,
including zero-shot classification and text-to-image generation. Nevertheless,
existing VLMs exhibit a prominent well-documented limitation - they fail to
encapsulate compositional concepts such as counting. We introduce a simple yet
effective method to improve the quantitative understanding of VLMs, while
maintaining their overall performance on common benchmarks. Specifically, we
propose a new counting-contrastive loss used to finetune a pre-trained VLM in
tandem with its original objective. Our counting loss is deployed over
automatically-created counterfactual examples, each consisting of an image and
a caption containing an incorrect object count. For example, an image depicting
three dogs is paired with the caption "Six dogs playing in the yard". Our loss
encourages discrimination between the correct caption and its counterfactual
variant which serves as a hard negative example. To the best of our knowledge,
this work is the first to extend CLIP's capabilities to object counting.
Furthermore, we introduce "CountBench" - a new image-text counting benchmark
for evaluating a model's understanding of object counting. We demonstrate a
significant improvement over state-of-the-art baseline models on this task.
Finally, we leverage our count-aware CLIP model for image retrieval and
text-conditioned image generation, demonstrating that our model can produce
specific counts of objects more reliably than existing ones.
| arxiv topic:cs.CV |
arxiv_dataset-179712302.12166 | Analysis of nonlinear poroviscoelastic flows with discontinuous
porosities
math.AP
Existence and uniqueness of solutions is shown for a class of viscoelastic
flows in porous media with particular attention to problems with nonsmooth
porosities. The considered models are formulated in terms of the time-dependent
nonlinear interaction between porosity and effective pressure, which in certain
cases leads to porosity waves. In particular, conditions for well-posedness in
the presence of initial porosities with jump discontinuities are identified.
| arxiv topic:math.AP |
arxiv_dataset-179722302.12266 | SHAPER: Can You Hear the Shape of a Jet?
hep-ph cs.NA hep-ex math.NA
The identification of interesting substructures within jets is an important
tool for searching for new physics and probing the Standard Model at colliders.
Many of these substructure tools have previously been shown to take the form of
optimal transport problems, in particular the Energy Mover's Distance (EMD). In
this work, we show that the EMD is in fact the natural structure for comparing
collider events, which accounts for its recent success in understanding event
and jet substructure. We then present a Shape Hunting Algorithm using
Parameterized Energy Reconstruction (SHAPER), which is a general framework for
defining and computing shape-based observables. SHAPER generalizes N-jettiness
from point clusters to any extended, parametrizable shape. This is accomplished
by efficiently minimizing the EMD between events and parameterized manifolds of
energy flows representing idealized shapes, implemented using the
dual-potential Sinkhorn approximation of the Wasserstein metric. We show how
the geometric language of observables as manifolds can be used to define novel
observables with built-in infrared-and-collinear safety. We demonstrate the
efficacy of the SHAPER framework by performing empirical jet substructure
studies using several examples of new shape-based observables.
| arxiv topic:hep-ph cs.NA hep-ex math.NA |
arxiv_dataset-179732302.12366 | Less is More: Data Pruning for Faster Adversarial Training
cs.LG cs.CV
Deep neural networks (DNNs) are sensitive to adversarial examples, resulting
in fragile and unreliable performance in the real world. Although adversarial
training (AT) is currently one of the most effective methodologies to robustify
DNNs, it is computationally very expensive (e.g., 5-10X costlier than standard
training). To address this challenge, existing approaches focus on single-step
AT, referred to as Fast AT, reducing the overhead of adversarial example
generation. Unfortunately, these approaches are known to fail against stronger
adversaries. To make AT computationally efficient without compromising
robustness, this paper takes a different view of the efficient AT problem.
Specifically, we propose to minimize redundancies at the data level by
leveraging data pruning. Extensive experiments demonstrate that the data
pruning based AT can achieve similar or superior robust (and clean) accuracy as
its unpruned counterparts while being significantly faster. For instance,
proposed strategies accelerate CIFAR-10 training up to 3.44X and CIFAR-100
training to 2.02X. Additionally, the data pruning methods can readily be
reconciled with existing adversarial acceleration tricks to obtain the striking
speed-ups of 5.66X and 5.12X on CIFAR-10, 3.67X and 3.07X on CIFAR-100 with
TRADES and MART, respectively.
| arxiv topic:cs.LG cs.CV |
arxiv_dataset-179742302.12466 | Theory of Quantum Circuits with Abelian Symmetries
quant-ph cond-mat.str-el hep-th math-ph math.MP
Quantum circuits with gates (local unitaries) respecting a global symmetry
have broad applications in quantum information science and related fields, such
as condensed matter theory and quantum thermodynamics. However, despite their
widespread use, fundamental properties of such circuits are not
well-understood. Recently, it was found that generic unitaries respecting a
global symmetry cannot be realized, even approximately, using gates that
respect the same symmetry. This observation raises important open questions:
What unitary transformations can be realized with k-local gates that respect a
global symmetry? In other words, in the presence of a global symmetry, how does
the locality of interactions constrain the possible time evolution of a
composite system? In this work, we address these questions for the case of
Abelian (commutative) symmetries and develop constructive methods for
synthesizing circuits with such symmetries. Remarkably, as a corollary, we find
that, while the locality of interactions still imposes additional constraints
on realizable unitaries, certain restrictions observed in the case of
non-Abelian symmetries do not apply to circuits with Abelian symmetries. For
instance, in circuits with a general non-Abelian symmetry such as SU($d$), the
unitary realized in a subspace with one irreducible representation (charge) of
the symmetry dictates the realized unitaries in multiple other sectors with
inequivalent representations of the symmetry. Furthermore, in certain sectors,
rather than all unitaries respecting the symmetry, the realizable unitaries are
the symplectic or orthogonal subgroups of this group. We prove that none of
these restrictions appears in the case of Abelian symmetries. This result
suggests that global non-Abelian symmetries may affect the thermalization of
quantum systems in ways not possible under Abelian symmetries.
| arxiv topic:quant-ph cond-mat.str-el hep-th math-ph math.MP |
arxiv_dataset-179752302.12566 | Observing M Dwarfs UV and optical flares from a CubeSat and their
implications for exoplanets habitability
astro-ph.IM astro-ph.EP astro-ph.SR
M dwarfs show the highest rocky planet occurrence among all spectral types,
in some instances within the Habitable Zone. Because some of them are very
active stars, they are often subject to frequent and powerful flaring, which
can be a double-edged sword in regard of exoplanet habitability. On one hand,
the increased flux during flare events can trigger the chemical reactions that
are necessary to build the basis of prebiotic chemistry. On the other hand,
sufficiently strong flares may erode exoplanets' atmospheres and reduce their
UV protection. Recent observations of flares have shown that the flaring flux
can be x100 times stronger in UV than in the optical. UV is also preferable to
constrain more accurately both the prebiotic abiogenesis and the atmospheric
erosion. For these reasons, we are developing a CubeSat payload concept to
complement current flare surveys operating in the optical. This CubeSat will
observe a high number of flaring M dwarfs, following an all-sky scanning law
coverage, both in the UV and the optical to better understand the different
effective temperatures as wavelengths and flaring status go. This will
complement the bright optical flares data acquired from the current
ground-based, high-cadence, wide FoV surveys. Another scientific planned goal
is to conduct few-minute after-the-flare follow-up optical ground-based
time-resolved spectroscopy, that will be triggered by the detection of UV
flares in space on board of the proposed CubeSat. Finally, the study of M
dwarfs stellar activity in the UV band will provide useful data for larger
forthcoming missions that will survey exoplanets, such as PLATO, ARIEL, HabEx
and LUVOIR.
| arxiv topic:astro-ph.IM astro-ph.EP astro-ph.SR |
arxiv_dataset-179762302.12666 | Modelling Temporal Document Sequences for Clinical ICD Coding
cs.LG cs.AI cs.CL
Past studies on the ICD coding problem focus on predicting clinical codes
primarily based on the discharge summary. This covers only a small fraction of
the notes generated during each hospital stay and leaves potential for
improving performance by analysing all the available clinical notes. We propose
a hierarchical transformer architecture that uses text across the entire
sequence of clinical notes in each hospital stay for ICD coding, and
incorporates embeddings for text metadata such as their position, time, and
type of note. While using all clinical notes increases the quantity of data
substantially, superconvergence can be used to reduce training costs. We
evaluate the model on the MIMIC-III dataset. Our model exceeds the prior
state-of-the-art when using only discharge summaries as input, and achieves
further performance improvements when all clinical notes are used as input.
| arxiv topic:cs.LG cs.AI cs.CL |
arxiv_dataset-179772302.12766 | Language-Driven Representation Learning for Robotics
cs.RO cs.AI cs.CL cs.CV cs.LG
Recent work in visual representation learning for robotics demonstrates the
viability of learning from large video datasets of humans performing everyday
tasks. Leveraging methods such as masked autoencoding and contrastive learning,
these representations exhibit strong transfer to policy learning for visuomotor
control. But, robot learning encompasses a diverse set of problems beyond
control including grasp affordance prediction, language-conditioned imitation
learning, and intent scoring for human-robot collaboration, amongst others.
First, we demonstrate that existing representations yield inconsistent results
across these tasks: masked autoencoding approaches pick up on low-level spatial
features at the cost of high-level semantics, while contrastive learning
approaches capture the opposite. We then introduce Voltron, a framework for
language-driven representation learning from human videos and associated
captions. Voltron trades off language-conditioned visual reconstruction to
learn low-level visual patterns, and visually-grounded language generation to
encode high-level semantics. We also construct a new evaluation suite spanning
five distinct robot learning problems $\unicode{x2013}$ a unified platform for
holistically evaluating visual representations for robotics. Through
comprehensive, controlled experiments across all five problems, we find that
Voltron's language-driven representations outperform the prior
state-of-the-art, especially on targeted problems requiring higher-level
features.
| arxiv topic:cs.RO cs.AI cs.CL cs.CV cs.LG |
arxiv_dataset-179782302.12866 | Permutation tests for assessing potential non-linear associations
between treatment use and multivariate clinical outcomes
stat.ME stat.AP
In many psychometric applications, the relationship between the mean of an
outcome and a quantitative covariate is too complex to be described by simple
parametric functions; instead, flexible nonlinear relationships can be
incorporated using penalized splines. Penalized splines can be conveniently
represented as a linear mixed effects model (LMM), where the coefficients of
the spline basis functions are random effects. The LMM representation of
penalized splines makes the extension to multivariate outcomes relatively
straightforward. In the LMM, no effect of the quantitative covariate on the
outcome corresponds to the null hypothesis that a fixed effect and a variance
component are both zero. Under the null, the usual asymptotic chi-square
distribution of the likelihood ratio test for the variance component does not
hold. Therefore, we propose three permutation tests for the likelihood ratio
test statistic: one based on permuting the quantitative covariate, the other
two based on permuting residuals. We compare via simulation the Type I error
rate and power of the three permutation tests obtained from joint models for
multiple outcomes, as well as a commonly used parametric test. The tests are
illustrated using data from a stimulant use disorder psychosocial clinical
trial.
| arxiv topic:stat.ME stat.AP |
arxiv_dataset-179792302.12966 | SUPS: A Simulated Underground Parking Scenario Dataset for Autonomous
Driving
cs.CV cs.RO
Automatic underground parking has attracted considerable attention as the
scope of autonomous driving expands. The auto-vehicle is supposed to obtain the
environmental information, track its location, and build a reliable map of the
scenario. Mainstream solutions consist of well-trained neural networks and
simultaneous localization and mapping (SLAM) methods, which need numerous
carefully labeled images and multiple sensor estimations. However, there is a
lack of underground parking scenario datasets with multiple sensors and
well-labeled images that support both SLAM tasks and perception tasks, such as
semantic segmentation and parking slot detection. In this paper, we present
SUPS, a simulated dataset for underground automatic parking, which supports
multiple tasks with multiple sensors and multiple semantic labels aligned with
successive images according to timestamps. We intend to cover the defect of
existing datasets with the variability of environments and the diversity and
accessibility of sensors in the virtual scene. Specifically, the dataset
records frames from four surrounding fisheye cameras, two forward pinhole
cameras, a depth camera, and data from LiDAR, inertial measurement unit (IMU),
GNSS. Pixel-level semantic labels are provided for objects, especially ground
signs such as arrows, parking lines, lanes, and speed bumps. Perception, 3D
reconstruction, depth estimation, and SLAM, and other relative tasks are
supported by our dataset. We also evaluate the state-of-the-art SLAM algorithms
and perception models on our dataset. Finally, we open source our virtual 3D
scene built based on Unity Engine and release our dataset at
https://github.com/jarvishou829/SUPS.
| arxiv topic:cs.CV cs.RO |
arxiv_dataset-179802302.13066 | Estimating Fiscal Multipliers by Combining Statistical Identification
with Potentially Endogenous Proxies
econ.EM
Different proxy variables used in fiscal policy SVARs lead to contradicting
conclusions regarding the size of fiscal multipliers. We show that the
conflicting results are due to violations of the exogeneity assumptions, i.e.
the commonly used proxies are endogenously related to the structural shocks. We
propose a novel approach to include proxy variables into a Bayesian
non-Gaussian SVAR, tailored to accommodate for potentially endogenous proxy
variables. Using our model, we show that increasing government spending is a
more effective tool to stimulate the economy than reducing taxes.
| arxiv topic:econ.EM |
arxiv_dataset-179812302.13166 | Stellar Dynamical Modeling -- Counting Conserved Quantities
astro-ph.GA
Knowing the conserved quantities that a galaxy's stellar orbits conform to is
important in helping us understand the stellar distribution and structures
within the galaxy. Isolating integrals of motion and resonances are
particularly important, non-isolating integrals less so. We compare the
behavior and results of two methods for counting the number of conserved
quantities, one based on the correlation integral approach and the other a more
recent method using machine learning. Both methods use stellar orbit
trajectories in phase space as their only input, and we create such
trajectories from theoretical spherical, axisymmetric and triaxial model
galaxies. The orbits have known isolating integrals and resonances. We find
that neither method is fully effective in recovering the numbers of these
quantities, nor in determining the number of non-isolating integrals. From a
computer performance perspective, we find the correlation integral approach to
be the faster. Determining the algebraic formulae of (multiple) conserved
quantities from the trajectories has not been possible due to the lack of an
appropriate symbolic regression capability. Notwithstanding the shortcomings we
have noted, it may be that the methods are usable as part of a trajectory
analysis tool kit.
| arxiv topic:astro-ph.GA |
arxiv_dataset-179822302.13266 | Profinite non-rigidity of arithmetic groups
math.GR
We show that for a typical high rank arithmetic lattice $\Gamma$, there exist
finite index subgroups $\Gamma_{1}$ and $\Gamma_{2}$ such that $\Gamma_{1}
\not\simeq \Gamma_{2}$ while $\widehat{\Gamma_{1}} \simeq
\widehat{\Gamma_{2}}$. But there are exceptions to that rule.
| arxiv topic:math.GR |
arxiv_dataset-179832302.13366 | On the existence of solutions of the Dirichlet problem for $p$-Laplacian
on Riemannian manifolds
math.AP
We obtain a criterion for the existence of solutions of the problem $$
\Delta_p u = 0
\quad
\mbox{in } M \setminus \partial M,
\quad
\left.
u
\right|_{
\partial M
}
=
h, $$ with the bounded Dirichlet integral, where $M$ is an oriented complete
Riemannian manifold with boundary and $h \in W_{p, loc}^1 (M)$, $p > 1$.
| arxiv topic:math.AP |
arxiv_dataset-179842302.13466 | Higher-group structure in lattice Abelian gauge theory under
instanton-sum modification
hep-th hep-lat
We consider the $U(1)$ gauge theory on a four-dimensional torus, where the
instanton number is restricted to an integral multiple of $p$. This theory
possesses the nontrivial higher-group structure, which can be regarded as a
generalization of the Green--Schwarz mechanism, between $\mathbb{Z}_q$ $1$-form
and $\mathbb{Z}_{pq}$ $3$-form symmetries. Here, $\mathbb{Z}_q$ is a subgroup
of the center of~$U(1)$. Following the recent study of the lattice construction
of the $U(1)/\mathbb{Z}_q$ principal bundle, we examine how such a structure is
realized on the basis of lattice regularization.
| arxiv topic:hep-th hep-lat |
arxiv_dataset-179852302.13566 | Enzyme kinetics simulation at the scale of individual particles
q-bio.QM cond-mat.stat-mech
Enzyme-catalysed reactions involve two distinct timescales. There is a short
timescale on which enzymes bind to substrate molecules to produce bound
complexes, and a comparatively long timescale on which the complex is
transformed into a product. The rate at which the substrate is converted into
product is characteristically non-linear and is traditionally derived by
applying singular perturbation theory to the system's governing equations.
Central to this analysis is the assumption that complex formation is
effectively instantaneous on the timescale over which significant substrate
degradation occurs. This prevents accurate modelling of enzyme kinetics by many
particle-based simulations of reaction-diffusion systems as they rely on
proximity-based reaction conditions that do not correctly model the fast
reactions associated with the complex on the long timescale. In this paper we
derive a new proximity-based reaction condition that correctly incorporates the
reactions that occur on the short timescale for a specific enzymatic system. We
present proof of concept particle-based simulations and demonstrate that
non-linear reaction rates typical of enzyme kinetics can be reproduced without
needing to explicitly simulate reactions on the short timescale.
| arxiv topic:q-bio.QM cond-mat.stat-mech |
arxiv_dataset-179862302.13666 | Correlated Noise and Critical Dimensions
cond-mat.stat-mech cond-mat.dis-nn
In equilibrium, the Mermin-Wagner theorem prohibits the continuous symmetry
breaking for all dimensions $d\leq 2$. In this work, we discuss that this
limitation can be circumvented in non-equilibrium systems driven by the
spatio-temporally long-range anticorrelated noise. We first compute the lower
and upper critical dimensions of the $O(n)$ model driven by the
spatio-temporally correlated noise by means of the dimensional analysis. Next,
we consider the spherical model, which corresponds to the large $n$ limit of
the $O(n)$ model and allows us to compute the critical dimensions and critical
exponents, analytically. Both results suggest that the critical dimensions
increase when the noise is positively correlated in space and time, and
decrease when anticorrelated. We also report that the spherical model with the
correlated noise shows the hyperuniformity and giant number fluctuation even
well above the critical point.
| arxiv topic:cond-mat.stat-mech cond-mat.dis-nn |
arxiv_dataset-179872302.13766 | Learning to Super-Resolve Blurry Images with Events
cs.CV
Super-Resolution from a single motion Blurred image (SRB) is a severely
ill-posed problem due to the joint degradation of motion blurs and low spatial
resolution. In this paper, we employ events to alleviate the burden of SRB and
propose an Event-enhanced SRB (E-SRB) algorithm, which can generate a sequence
of sharp and clear images with High Resolution (HR) from a single blurry image
with Low Resolution (LR). To achieve this end, we formulate an event-enhanced
degeneration model to consider the low spatial resolution, motion blurs, and
event noises simultaneously. We then build an event-enhanced Sparse Learning
Network (eSL-Net++) upon a dual sparse learning scheme where both events and
intensity frames are modeled with sparse representations. Furthermore, we
propose an event shuffle-and-merge scheme to extend the single-frame SRB to the
sequence-frame SRB without any additional training process. Experimental
results on synthetic and real-world datasets show that the proposed eSL-Net++
outperforms state-of-the-art methods by a large margin. Datasets, codes, and
more results are available at https://github.com/ShinyWang33/eSL-Net-Plusplus.
| arxiv topic:cs.CV |
arxiv_dataset-179882302.13866 | Topological or not? A unified pattern description in the one-dimensional
anisotropic quantum XY model with a transverse field
cond-mat.stat-mech
The nature of phase transitions involving the questions why and how phase
transitions take place has not been sufficiently touched in the literature. In
contrast, the current attention to certain extent still focus on the
description of critical phenomena and the classification of the associated
phase transition along with the Ginzburg-Landau-Wilson paradigm, where the key
issue is to identify phenomenologically order parameters and related
symmetries. This brings the question to topological phase transitions (TPTs),
where no obvious order parameter and the broken symmetry are identified. Here
we present a unified pattern description of the second-order quantum phase
transition (QPT) and TPT, both involved in the one-dimensional anisotropic
quantum XY model in a transverse field, which contains the transverse Ising
model (TIM) as a limit case. Away from the TIM, the XY model enters the
ferromagnetic phase (marked by a second-order QPT or a direct TPT) as
increasing ferromagentic exchange coupling, a series of TPTs occur, which are
absent in the TIM. The TPTs behave like the first-order QPTs. In the isotropic
and large exchange coupling cases, the ground state of the XY model is
dominated by two topologically different vortices along positive and negative
direction of the transverse field. We confirm the above conclusion by analyzing
the energy contributions of the patterns to the ground state and calculating
the ground state pattern occupations of the XY model. The results have been
obtained in a unified and self-evident way and answer the questions why and how
the QPT and TPTs take place in the XY model.
| arxiv topic:cond-mat.stat-mech |
arxiv_dataset-179892302.13966 | Hydrodynamic instabilities in a 2-D sheet of microswimmers embedded in a
3-D fluid
cond-mat.soft physics.flu-dyn
A collection of microswimmers immersed in an incompressible fluid is
characterised by strong interactions due to the long-range nature of the
hydrodynamic fields generated by individual organisms. As a result, suspensions
of rear-actuated `pusher' swimmers such as bacteria exhibit a collective motion
state often referred to as `bacterial turbulence', characterised by large-scale
chaotic flows. The onset of collective motion in pusher suspensions is
classically understood within the framework of mean-field kinetic theories for
dipolar swimmers. In bulk 2-D and 3-D, the theory predicts that the instability
leading to bacterial turbulence is due to mutual swimmer reorientation and sets
in at the largest length scale available to the suspension. Here, we construct
a similar kinetic theory for the case of a dipolar microswimmer suspension
restricted to a two-dimensional plane embedded in a three-dimensional
incompressible fluid. This setting qualitatively mimics the effect of swimming
close to a two-dimensional interface. We show that the in-plane flow fields are
effectively compressible in spite of the incompressibility of the 3-D bulk
fluid, and that microswimmers on average act as sources (pushers) or sinks
(pullers). We analyse stability of the homogeneous and isotropic state, and
find two types of instability that are qualitatively different from the bulk,
three-dimensional case: First, we show that the analogue of the orientational
pusher instability leading to bacterial turbulence in bulk systems instead
occurs at the smallest length-scale available to the system. Second, an
instability associated with density variations arises in puller suspensions as
a generic consequence of the effective in-plane compressibility. We conclude
that confinement can have a crucial role in determining the collective
behaviour of microswimmer suspensions.
| arxiv topic:cond-mat.soft physics.flu-dyn |
arxiv_dataset-179902302.14066 | Query-optimal estimation of unitary channels in diamond distance
quant-ph cs.DS
We consider process tomography for unitary quantum channels. Given access to
an unknown unitary channel acting on a $\textsf{d}$-dimensional qudit, we aim
to output a classical description of a unitary that is $\varepsilon$-close to
the unknown unitary in diamond norm. We design an algorithm achieving error
$\varepsilon$ using $O(\textsf{d}^2/\varepsilon)$ applications of the unknown
channel and only one qudit. This improves over prior results, which use
$O(\textsf{d}^3/\varepsilon^2)$ [via standard process tomography] or
$O(\textsf{d}^{2.5}/\varepsilon)$ [Yang, Renner, and Chiribella, PRL 2020]
applications. To show this result, we introduce a simple technique to
"bootstrap" an algorithm that can produce constant-error estimates to one that
can produce $\varepsilon$-error estimates with the Heisenberg scaling. Finally,
we prove a complementary lower bound showing that estimation requires
$\Omega(\textsf{d}^2/\varepsilon)$ applications, even with access to the
inverse or controlled versions of the unknown unitary. This shows that our
algorithm has both optimal query complexity and optimal space complexity.
| arxiv topic:quant-ph cs.DS |
arxiv_dataset-179912302.14166 | GLOW: Global Layout Aware Attacks on Object Detection
cs.CV
Adversarial attacks aim to perturb images such that a predictor outputs
incorrect results. Due to the limited research in structured attacks, imposing
consistency checks on natural multi-object scenes is a promising yet practical
defense against conventional adversarial attacks. More desired attacks, to this
end, should be able to fool defenses with such consistency checks. Therefore,
we present the first approach GLOW that copes with various attack requests by
generating global layout-aware adversarial attacks, in which both categorical
and geometric layout constraints are explicitly established. Specifically, we
focus on object detection task and given a victim image, GLOW first localizes
victim objects according to target labels. And then it generates multiple
attack plans, together with their context-consistency scores. Our proposed
GLOW, on the one hand, is capable of handling various types of requests,
including single or multiple victim objects, with or without specified victim
objects. On the other hand, it produces a consistency score for each attack
plan, reflecting the overall contextual consistency that both semantic category
and global scene layout are considered. In experiment, we design multiple types
of attack requests and validate our ideas on MS COCO and Pascal. Extensive
experimental results demonstrate that we can achieve about 30$\%$ average
relative improvement compared to state-of-the-art methods in conventional
single object attack request; Moreover, our method outperforms SOTAs
significantly on more generic attack requests by about 20$\%$ in average;
Finally, our method produces superior performance under challenging zero-query
black-box setting, or 20$\%$ better than SOTAs. Our code, model and attack
requests would be made available.
| arxiv topic:cs.CV |
arxiv_dataset-179922302.14266 | Precise measurements of $D$ meson lifetimes
hep-ex
We report the result of $D^0$ and $D^+$ lifetime measurement using $D^0\to
K^-\pi^+$ and $D^+\to K^-\pi^+\pi^+$ decays reconstructed using $72~{\rm
fb^{-1}}$ of data collected by the Belle II experiment at SuperKEKB
asymmetric-energy $e^{+}e^{-}$ collider. The results,
$\tau(D^0)=410.5\pm1.1({\rm stat})\pm0.8({\rm syst})~{\rm fs}$ and
$\tau(D^+)=1030.4\pm4.7({\rm stat})\pm 3.1({\rm syst})~{\rm fs}$, are the most
precise to date and are consistent with previous measurements.
| arxiv topic:hep-ex |
arxiv_dataset-179932302.14366 | $\mathbb{Z}_2$ Non-Hermitian skin effect in equilibrium heavy-fermions
cond-mat.str-el
We demonstrate that a correlated equilibrium $f$-electron system with
time-reversal symmetry can exhibit a $\mathbb{Z}_2$ non-Hermitian skin effect
of quasi-particles. In particular, we analyze a two-dimensional periodic
Anderson model with spin-orbit coupling by combining the dynamical mean-field
theory (DMFT) and the numerical renormalization group. We prove the existence
of the $\mathbb{Z}_2$ skin effect by explicitly calculating the topological
invariant and show that spin-orbit interaction is essential to this effect. Our
DMFT analysis demonstrates that the $\mathbb{Z}_2$ skin effect of
quasi-particles is reflected on the pseudo-spectrum. Furthermore, we analyze
temperature effects on this skin effect using the generalized Brillouin zone
technique, which clarifies that the skin modes are strongly localized above the
Kondo temperature.
| arxiv topic:cond-mat.str-el |
arxiv_dataset-179942302.14466 | Approximation properties of Fell bundles over inverse semigroups and
non-Hausdorff groupoids
math.OA
In this paper we study the nuclearity and weak containment property of
reduced cross-sectional C*-algebras of Fell bundles over inverse semigroups. In
order to develop the theory, we first prove an analogue of Fell's absorption
trick in the context of Fell bundles over inverse semigroups. In parallel, the
approximation property of Exel can be reformulated in this context, and Fell's
absorption trick can be used to prove that the approximation property, as
defined here, implies that the full and reduced cross-sectional C*-algebras are
isomorphic via the left regular representation, i.e., the Fell bundle has the
weak containment property.
We then use this machinery to prove that a Fell bundle with the approximation
property and nuclear unit fiber has a nuclear cross-sectional \cstar{}algebra.
This result gives nuclearity of a large class of C*-algebras, as, remarkably,
all the machinery in this paper works for \'{e}tale non-Hausdorff groupoids
just as well.
| arxiv topic:math.OA |
arxiv_dataset-179952302.14566 | Continuous interaction with a smart speaker via low-dimensional
embeddings of dynamic hand pose
cs.HC
This paper presents a new continuous interaction strategy with visual
feedback of hand pose and mid-air gesture recognition and control for a smart
music speaker, which utilizes only 2 video frames to recognize gestures.
Frame-based hand pose features from MediaPipe Hands, containing 21 landmarks,
are embedded into a 2 dimensional pose space by an autoencoder. The
corresponding space for interaction with the music content is created by
embedding high-dimensional music track profiles to a compatible two-dimensional
embedding. A PointNet-based model is then applied to classify gestures which
are used to control the device interaction or explore music spaces. By jointly
optimising the autoencoder with the classifier, we manage to learn a more
useful embedding space for discriminating gestures. We demonstrate the
functionality of the system with experienced users selecting different musical
moods by varying their hand pose.
| arxiv topic:cs.HC |
arxiv_dataset-179962302.14666 | Entanglement and Expansion
hep-th gr-qc
We study the entanglement entropy resulting from tracing out local degrees of
freedom of a quantum scalar field in an expanding universe. It is known that
when field modes become superhorizon during inflation they evolve to
increasingly squeezed states. We argue that this causes the entanglement
entropy to grow continuously as successive modes cross the horizon. The
resulting entropy is proportional to the total duration of inflation. It is
preserved during a subsequent radiation or matter dominated era, and thus it
may be relevant for today's universe. We demonstrate explicitly these features
in a toy model of a scalar field in 1+1 dimensions.
| arxiv topic:hep-th gr-qc |
arxiv_dataset-179972302.14766 | Combining randomized and non-randomized data to predict heterogeneous
effects of competing treatments
stat.ME
Some patients benefit from a treatment while others may do so less or do not
benefit at all. We have previously developed a two-stage network
meta-regression prediction model that synthesized randomized trials and
evaluates how treatment effects vary across patient characteristics. In this
article, we extended this model to combine different sources of types in
different formats: aggregate data (AD) and individual participant data (IPD)
from randomized and non-randomized evidence. In the first stage, a prognostic
model is developed to predict the baseline risk of the outcome using a large
cohort study. In the second stage, we recalibrated this prognostic model to
improve our predictions for patients enrolled in randomized trials. In the
third stage, we used the baseline risk as effect modifier in a network
meta-regression model combining AD, IPD RCTs to estimate heterogeneous
treatment effects. We illustrated the approach in the re-analysis of a network
of studies comparing three drugs for relapsing-remitting multiple sclerosis.
Several patient characteristics influence the baseline risk of relapse, which
in turn modifies the effect of the drugs. The proposed model makes personalized
predictions for health outcomes under several treatment options and encompasses
all relevant randomized and non-randomized evidence.
| arxiv topic:stat.ME |
arxiv_dataset-179982303.00003 | Is a spectrograph of hidden variables possible?
quant-ph
A new definition of "Realism" is proposed: it is that a gedanken
"spectrograph" of hidden variables behaves as an actual (say, wavelength)
spectrograph. The question is: does this definition allow, by itself, the
derivation of Bell's inequalities? If it were, then such a spectrograph would
be impossible, for Bell's inequalities are observed to be violated. In this
short paper it is reported that, on the contrary, such spectrograph is
compatible with the violation of Bell's inequalities. This result puts some new
light on the controversy about the hypotheses necessary to derive Bell's
inequalities. In particular, "Spectrograph's Realism", and "Locality", are
proven to be different, and both necessary, hypotheses to derive Bell's
inequalities.
| arxiv topic:quant-ph |
arxiv_dataset-179992303.00103 | Flat Bands and High Chern Numbers in Twisted Multilayer Graphene
math-ph cond-mat.mtrl-sci cond-mat.str-el math.MP math.SP quant-ph
Motivated by recent Physical Review Letters of Wang-Liu and
Ledwith-Vishwanath-Khalaf, we study Tarnopolsky-Kruchkov-Vishwanath chiral
model of two sheets of $n$-layer Bernal stacked graphene twisted by a small
angle using the framework developed by Becker-Embree-Wittsten-Zworski. We show
that magic angles of this model are exactly the same as magic angles of chiral
twisted bilayer graphene with multiplicity. For small inter-layer tunneling
potentials, we compute the band separation at Dirac points as we turning on the
tunneling parameter. Flat band eigenfunctions are also constructed using a new
theta function argument and this yields a complex line bundle with the Chern
number $-n$.
| arxiv topic:math-ph cond-mat.mtrl-sci cond-mat.str-el math.MP math.SP quant-ph |
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