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quantum versus classical descriptions of spontaneous
emission
quantum emitters
quantum versus classical descriptions of spontaneous emission in nanophotonic cavities
performance varies significantly across games suggesting sensitivity to
interaction
task performance
performance varies significantly across games suggesting sensitivity to interaction complexity
as a further consequence we obtain a emph deterministic and emph combinatorial two-party
communication
network coding
as a further consequence we obtain a emph deterministic and emph combinatorial two-party communication protocol for computing a minimum s -- t cut using widetilde o n 11 7 bits of communication
this paper presents findings from a test-track study with 40 participants in a real-world rural
automated
automated driving
this paper presents findings from a test-track study with 40 participants in a real-world rural automated driving scenario
in linear time-invariant systems the sensitivity
function
data-driven stabilization
in linear time-invariant systems the sensitivity function to disturbances is designed under a sensitivity tradeoff known as the waterbed effect
nearest neighbor matching as least squares density ratio
estimation
density-ratio estimation
nearest neighbor matching as least squares density ratio estimation and riesz regression
these methods are generally classified into two categories numerical optimization-based methods emd vmd and spectral decomposition
methods
signal processing
these methods are generally classified into two categories numerical optimization-based methods emd vmd and spectral decomposition methods ssa that consider the physical meaning of signals
we find a total of 20 mechanisms of third-order nonlinear
transport
transport properties
we find a total of 20 mechanisms of third-order nonlinear transport by developing a comprehensive theory that treats the geometric effects and disorder scattering on an equal footing
we developed a dataset with actual warehouse operations and
demonstrated
real-world applications
we developed a dataset with actual warehouse operations and demonstrated the method s effectiveness for real-world applications
here we implement an efficient quantum simulation
algorithm
quantum batteries
here we implement an efficient quantum simulation algorithm on quantinuum s system model h2 trapped-ion quantum computer for the time dynamics of a 56-qubit system that is too complex for exact classical simulation
we show that the new formulation produces the same linear
programming
quadratic programming
we show that the new formulation produces the same linear programming bound as the tightest existing formulations for the studied problem which use 4-index variables outperforming existing supermodular formulations adapted to the considered problem
our key findings are that the amount of resources in the environment can lead to specific subgroup sizes being optimal for the group as a whole when individuals are homogeneous in their
information
collective systems
our key findings are that the amount of resources in the environment can lead to specific subgroup sizes being optimal for the group as a whole when individuals are homogeneous in their information gathering abilities
we construct reference frames using three different sets of external sources 1 stars with gaia dr3 data 2 stationary background galaxies and 3 a
combination
reference frame
we construct reference frames using three different sets of external sources 1 stars with gaia dr3 data 2 stationary background galaxies and 3 a combination of the two
recently reward models have been specifically designed to perform post-hoc selection of generated
images
image generation
recently reward models have been specifically designed to perform post-hoc selection of generated images and align them to a reward typically user preference
stochastic optimization in semi-discrete optimal
transport
optimal transport
stochastic optimization in semi-discrete optimal transport convergence analysis and minimax rate
life-cycle modeling and the walking behavior of the pedestrian-group as an emergent agent with empirical
data
human mobility
life-cycle modeling and the walking behavior of the pedestrian-group as an emergent agent with empirical data on the cohesion of the group formation
we use the line ratios of the ci fine-structure
lines
emission line
we use the line ratios of the ci fine-structure lines to constrain the density of the cold gas yielding n_ rm h sim 10 3 mathrm cm -3
debiased machine learning typically requires estimation of the
riesz
debiased machine learning
debiased machine learning typically requires estimation of the riesz representer and the regression function
we evaluate 15 foundation models across 79 problems spanning eight academic
domains
smaller models
we evaluate 15 foundation models across 79 problems spanning eight academic domains physics mathematics chemistry economics biology statistics calculus and optimization through three experimental phases 1 baseline establishment six models mixtral-8x7b phi-3 llama 3
theoretically we show that the generative models within the bo
process
gaussian process
theoretically we show that the generative models within the bo process approximately follow a sequence of distributions which asymptotically concentrate at the global optima under certain conditions
this is formulated as a bilevel optimization problem the inner optimization solves the qp under a given projection using a qp solver while the outer
optimization
optimization problem
this is formulated as a bilevel optimization problem the inner optimization solves the qp under a given projection using a qp solver while the outer optimization updates the model parameters
estimating brain activity with high spatial and temporal
resolution
temporal resolution
estimating brain activity with high spatial and temporal resolution using a naturalistic meg-fmri encoding model
twin-field quantum key distribution protocols security and
open
quantum error correction
twin-field quantum key distribution protocols security and open problems
while traditional approaches rely on data to reveal causal links a recently developed method assimilative
causal
causal inference
while traditional approaches rely on data to reveal causal links a recently developed method assimilative causal inference aci integrates observations with dynamical models
we establish fast o 1 n -type regret guarantees when propensities are known and extend these
guarantees
policy optimization
we establish fast o 1 n -type regret guarantees when propensities are known and extend these guarantees to the unknown-propensity case via a doubly robust dr objective
the former deterministically realizes nonlocal operations but
demands
network nonlocality
the former deterministically realizes nonlocal operations but demands extensive entanglement resources whereas the latter requires no entanglement yet suffers from exponential sampling overhead
reconstructing images seen by people from their
fmri
human brain
reconstructing images seen by people from their fmri brain recordings provides a non-invasive window into the human brain
multi-objective model predictive control mompc for fixed point
stabilization
predictive control
multi-objective model predictive control mompc for fixed point stabilization requires an automated a priori decision-making mechanism to translate a high-level preference into a single solution to be implemented
our findings connect entanglement dissipation-enhanced
scaling
entanglement entropy
our findings connect entanglement dissipation-enhanced scaling laws and superabsorption outlining a pathway towards scalable quantum batteries offering practical quantum advantage
leveraging the linearity of hilbert spaces gce also supports simple yet effective control algorithms for synthesizing
optimal
predictive control
leveraging the linearity of hilbert spaces gce also supports simple yet effective control algorithms for synthesizing optimal sequences
through horizontal comparison with three baselines the optimal
uav
optimal uav
through horizontal comparison with three baselines the optimal uav location scheme obtained by the proposed method can reach an improvement of up to 7
sensitive cells may acquire resistance through
mutation
adaptive immune
sensitive cells may acquire resistance through mutation which is coupled to a change in cellular fitness
in contrast both problems admit algorithms against oblivious adversaries that achieve operatorname
polylog
poly log
in contrast both problems admit algorithms against oblivious adversaries that achieve operatorname polylog n amortized update time behnezhad derakhshan hajiaghayi stein sudan focs 19
randomization of weighted networks has traditionally been done via the
weighted
network structures
randomization of weighted networks has traditionally been done via the weighted configuration model wcm a simple extension of the configuration model where weights are interpreted as bundles of edges
as a byproduct we obtain a complete characterization of the internal and bibo stability of linear
control
linear control
as a byproduct we obtain a complete characterization of the internal and bibo stability of linear control systems
we investigate the impact of synthetic data generation and lightweight fine-tuning techniques on the ability of large
language
large language
we investigate the impact of synthetic data generation and lightweight fine-tuning techniques on the ability of large language models llms to recognize fallacious arguments using the missci dataset and framework
in many practical settings however user interactions are limited or costly making offline
preference
preference learning
in many practical settings however user interactions are limited or costly making offline preference learning necessary
the advent of large language models llms has revolutionized
natural
natural language processing
the advent of large language models llms has revolutionized natural language processing yet their application in high-stakes specialized domains like religious question answering is hindered by challenges like hallucination and unfaithfulness to authoritative sources
compact accretion disks in the aftermath of
tidal
tidal disruption
compact accretion disks in the aftermath of tidal disruption events parameter inference from joint x-ray spectra and uv optical photometry fitting
it remains open whether these algorithms can provide tight provable
guarantees
online algorithm
it remains open whether these algorithms can provide tight provable guarantees in i o-costs on trees
by experimental results pvmark efficiently enables public verifiability on the state-of-the-art llm watermarking schemes yet without compromising the
watermarking
watermarking schemes
by experimental results pvmark efficiently enables public verifiability on the state-of-the-art llm watermarking schemes yet without compromising the watermarking performance promising to be deployed in practice
more recently a new class of prefix queries was introduced together with reductions that among others transform a simple tradeoff for prefix-select
queries
query time
more recently a new class of prefix queries was introduced together with reductions that among others transform a simple tradeoff for prefix-select queries into a suffix array tradeoff matching state-of-the-art space and query-time bounds while achieving sublinear construction time
in this work we extend our previous work on the extrapolation of single-time observables to demonstrate an efficient scheme for computing two-time impurity correlation functions by combining the non-markovian quantum mpemba effect nmqmpe with a dynamical map-based framework for
open
quantum walk
in this work we extend our previous work on the extrapolation of single-time observables to demonstrate an efficient scheme for computing two-time impurity correlation functions by combining the non-markovian quantum mpemba effect nmqmpe with a dynamical map-based framework for open quantum systems
the first part based on system decomposition constructs the object such as a stability function or condition for
stability
data-driven stabilization
the first part based on system decomposition constructs the object such as a stability function or condition for stability analysis as a scalable aggregation of multiple nns
we derive h beta -based single-epoch virial black-hole
masses
black hole mass
we derive h beta -based single-epoch virial black-hole masses median value rm 4
the numerical methods to do this efficiently depend on the properties of the
loss
gradient descent
the numerical methods to do this efficiently depend on the properties of the loss function
our approach directly models the conditional probability of a fund s portfolio weights given stock characteristics historical returns previous weights and a latent variable representing the
fund
risk prediction
our approach directly models the conditional probability of a fund s portfolio weights given stock characteristics historical returns previous weights and a latent variable representing the fund s strategy
applying consensus clustering with a hierarchical density-based
clustering
star clusters
applying consensus clustering with a hierarchical density-based clustering algorithm we demonstrate that the detected spatial dependencies are stable against perturbations by measurement uncertainties
in more complex scenarios we show that thinking of
selection
evolutionary game
in more complex scenarios we show that thinking of selection over discrete generations has significant advantages
quantum imaging with entangled photon pairs promises performance beyond classical limits yet phase-matching
nonlinear
quantum correlations
quantum imaging with entangled photon pairs promises performance beyond classical limits yet phase-matching nonlinear crystal properties and pump size jointly constrain its ultimate spatial resolution
reinforcement learning with verifiable rewards rlvr is a promising approach for enhancing agentic
deep
reinforcement learning rl
reinforcement learning with verifiable rewards rlvr is a promising approach for enhancing agentic deep search
applications span environmental economics
banking
european banking
applications span environmental economics banking and healthcare policy
we then apply it to a higher-order spin-glass hamiltonian with 156
qubits
quantum advantage
we then apply it to a higher-order spin-glass hamiltonian with 156 qubits executed on ibm quantum processors
we provide extensive simulation experiments
demonstrating
randomized experiments
we provide extensive simulation experiments demonstrating the validity of our sensitivity approach and diagnostics and apply our approach to two empirical applications
our main findings from simple human memory paradigms also generalize to a
sequence
recurrent neural networks
our main findings from simple human memory paradigms also generalize to a sequence completion task which more closely resembles the next-token prediction process in llm pre-training
we formulate the model as a system of ordinary differential equations and leverage monotone
systems
evolutionary dynamics
we formulate the model as a system of ordinary differential equations and leverage monotone systems theory to rigorously characterize the epidemic dynamics
it is shown however that under control gain variation the
safe
data-driven stabilization
it is shown however that under control gain variation the safe set of these controllers is locally asymptotically stable which implies that their safety is sensitive to large but bounded disturbances
our work not only clarifies the intimate connection between magnetism and cef in rare-earth compounds but more importantly it reveals a physical pathway to effectively tune
magnetic
magnetic anisotropy
our work not only clarifies the intimate connection between magnetism and cef in rare-earth compounds but more importantly it reveals a physical pathway to effectively tune magnetic anisotropy via anisotropic lattice distortion induced by chemical pressure
the main results are the existence and uniqueness of a regular mild solution to the
hjb
hjb equation
the main results are the existence and uniqueness of a regular mild solution to the hjb equation a verification theorem and the synthesis of optimal feedback controls
shock-driven heating in the circumnuclear star-forming regions of ngc 7582 insights from
jwst
quiescent galaxies
shock-driven heating in the circumnuclear star-forming regions of ngc 7582 insights from jwst nirspec and miri mrs spectroscopy
in quantum networks after passing through noisy channels or information processing residual
states
multipartite entanglement
in quantum networks after passing through noisy channels or information processing residual states may lack sufficient entanglement for further tasks yet they may retain hidden quantum resources that can be recycled
posterior sampling by combining diffusion models with
annealed
diffusion models
posterior sampling by combining diffusion models with annealed langevin dynamics
neyman targeted estimation includes riesz
representer
riesz regression
neyman targeted estimation includes riesz representer estimation and we measure discrepancies using the bregman divergence
reinforcement learning for pollution detection in a randomized sparse and nonstationary environment with an
autonomous
reinforcement learning
reinforcement learning for pollution detection in a randomized sparse and nonstationary environment with an autonomous underwater vehicle
from linear to nonlinear provable weak-to-strong generalization through
feature
deep learning
from linear to nonlinear provable weak-to-strong generalization through feature learning
we study the complementarity of different cnns for
periocular
deep network
we study the complementarity of different cnns for periocular verification at different distances on the ubipr database
human-in-the-loop online rejection sampling for
robotic
robotic systems
human-in-the-loop online rejection sampling for robotic manipulation
large language models llms have demonstrated exceptional
capabilities
large language models llms
large language models llms have demonstrated exceptional capabilities across multiple domains by leveraging massive pre-training and curated fine-tuning data
in this paper we systematically evaluate llms
reasoning
reasoning capabilities
in this paper we systematically evaluate llms reasoning capabilities in the normative domain from both logical and modal perspectives
this leads to an imbalanced optimization that drives the model to prioritize simple reasoning
skills
reasoning tasks
this leads to an imbalanced optimization that drives the model to prioritize simple reasoning skills while hindering its ability to tackle more complex reasoning tasks
to illustrate this we construct a class of structured datasets where incremental
adam
deep learning
to illustrate this we construct a class of structured datasets where incremental adam provably converges to the ell_2 -max-margin classifier in contrast to the ell_ infty -max-margin bias of full-batch adam
we consider two types of loss function the empirical l_1 distance between the intensity
functions
loss function
we consider two types of loss function the empirical l_1 distance between the intensity functions of the process and the l_1 norm on the parameters background rates and interaction functions
towards reliable sea ice drift estimation in the arctic
deep
sea ice
towards reliable sea ice drift estimation in the arctic deep learning optical flow on radarsat-2
we derive the dual concave formulation with explicit gradient hessian and sensitivity expressions and provide two provably convergent solvers a damped dual newton method with global convergence and local
quadratic
quadratic programming
we derive the dual concave formulation with explicit gradient hessian and sensitivity expressions and provide two provably convergent solvers a damped dual newton method with global convergence and local quadratic rate and a kl-projection scheme based on iterative proportional fitting and bregman-dykstra projections
the paradigm consists of two parts the neural stability
descriptor
deep neural
the paradigm consists of two parts the neural stability descriptor and the sample-augmented iterative training scheme
this improves upon known results in the literature that state that
convergence
convergence rate
this improves upon known results in the literature that state that convergence holds for a sufficiently small discount factor by establishing an explicit bound
our initial approach uses solve_bvp to approximate optimal
control
predictive control
our initial approach uses solve_bvp to approximate optimal control trajectories
we show that in many settings and in some sense generically
distribution
distribution shift
we show that in many settings and in some sense generically distribution shift can be beneficial and test performance can improve due to mismatched training proportions even if the components are unrelated and with no transfer between components
our results establish the learning algorithm as a primary determinant of emergent computation revealing how reward-based
optimization
artificial intelligence
our results establish the learning algorithm as a primary determinant of emergent computation revealing how reward-based optimization autonomously discovers sophisticated dynamical mechanisms that are less accessible to direct gradient-based optimization
our results imply an n omega o 1 -time strongly polynomial time algorithms for computing a maximum
bipartite
bipartite graphs
our results imply an n omega o 1 -time strongly polynomial time algorithms for computing a maximum bipartite b -matching where omega is the matrix multiplication constant
our design demonstrates complementary channel-specific
coupling
photonic devices
our design demonstrates complementary channel-specific coupling regimes and enables wavelength-dependent lorentzian-to-fano lineshaping
building upon previous works where a field approach to activity--connectivity dynamics formation of collective states and effective fields of collective states were successively introduced the present paper synthesizes and extends these results toward a general description of multiple hierarchical
collective
collective states
building upon previous works where a field approach to activity--connectivity dynamics formation of collective states and effective fields of collective states were successively introduced the present paper synthesizes and extends these results toward a general description of multiple hierarchical collective structures
the proposed framework uses a simple and effective rejection sampling method to reconstruct these
traces
thinking traces
the proposed framework uses a simple and effective rejection sampling method to reconstruct these traces at scale
in this paper we design a meta-algorithm that allows us to take any robust algorithm for exact
submodular
monotone submodular
in this paper we design a meta-algorithm that allows us to take any robust algorithm for exact submodular maximization as a black box and transform it into an algorithm for the noisy setting while retaining the approximation guarantee
our experiments agree excellently with 2d band
structure
nonlinear optical
our experiments agree excellently with 2d band structure calculations and with 2d finite-difference time-domain simulations confirming that our experimental methods truly pertain to nanophotonics in 2d
photonic band-gap crystals radically modulate the rldos thereby controlling
spontaneous
photonic circuits
photonic band-gap crystals radically modulate the rldos thereby controlling spontaneous emission
in this latter case the best known algorithm due to azar and jacob-fanani has competitiveness
mathcal
online algorithm
in this latter case the best known algorithm due to azar and jacob-fanani has competitiveness mathcal o m 0
here we present an information-theoretic framework to identify the most important correlations which provide the most
accurate
neural networks
here we present an information-theoretic framework to identify the most important correlations which provide the most accurate predictions of neural states
we demonstrate that the treatment effects of interest can be consistently
estimated
average treatment effect
we demonstrate that the treatment effects of interest can be consistently estimated using ordinary least squares with an appropriately specified working model and transformed regressors
we adapt a pre-trained vision-language-action vla model open-vla for dexterous human-robot collaboration with minimal
language
vision-language models vlms
we adapt a pre-trained vision-language-action vla model open-vla for dexterous human-robot collaboration with minimal language prompting
in challenging tasks requiring active information seeking only meta-rl with predictive modules successfully learns optimal representations and
policies
continual learning
in challenging tasks requiring active information seeking only meta-rl with predictive modules successfully learns optimal representations and policies whereas conventional meta-rl struggles with inadequate representation learning
the results indicate the potential of deep neural network based observer to enable personalized and adaptive
autonomous
autonomous driving
the results indicate the potential of deep neural network based observer to enable personalized and adaptive autonomous vehicle control
urban science has largely relied on universal models rendering the heterogeneous and locally specific nature of
cities
urban systems
urban science has largely relied on universal models rendering the heterogeneous and locally specific nature of cities effectively invisible
however in biological brains structural plasticity - where new
connections
continual learning
however in biological brains structural plasticity - where new connections are created and others removed - is also vital not only for effective learning but also for recovery from damage and optimal resource usage
we study the telephone broadcasting problem in
sparse
regular graphs
we study the telephone broadcasting problem in sparse graphs
while resting-state fmri studies have revealed large-scale network correlates of creative potential electroencephalography
eeg
human brain
while resting-state fmri studies have revealed large-scale network correlates of creative potential electroencephalography eeg offers a temporally precise and scalable approach to capture the fast oscillatory dynamics that underlie spontaneous neural organization
orbit offers a standardized evaluation framework of public datasets with reproducible splits and transparent settings for its
public
data curation
orbit offers a standardized evaluation framework of public datasets with reproducible splits and transparent settings for its public leaderboard
nearest neighbor matching as least squares density ratio estimation and
riesz
riesz regression
nearest neighbor matching as least squares density ratio estimation and riesz regression
moreover we quantitatively interpret our components from a neuroscience perspective and analyze the associations between different visual
patterns
fmri data
moreover we quantitatively interpret our components from a neuroscience perspective and analyze the associations between different visual patterns and brain functions
this approach demonstrates that robust self-testing and publicly verifiable quantum randomness can be achieved with minimal optical
complexity
quantum key distribution
this approach demonstrates that robust self-testing and publicly verifiable quantum randomness can be achieved with minimal optical complexity without jeopardizing security