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perseus with its known formation history through
interstellar
interstellar medium
perseus with its known formation history through interstellar structure e
we observe that it is attributed to the secret key mostly used in the watermark detection -- it cannot be public or the adversary may launch removal attacks provided the key nor can it be private or the
watermarking
watermarking schemes
we observe that it is attributed to the secret key mostly used in the watermark detection -- it cannot be public or the adversary may launch removal attacks provided the key nor can it be private or the watermarking detection is opaque to the public
the debiased plug-in j -test delivers satisfactory
finite-sample
randomized experiments
the debiased plug-in j -test delivers satisfactory finite-sample inference and slim scales smoothly to n 10 6
more importantly the thinking structure in this protocol can be further optimized through
reinforcement
reinforcement learning rl
more importantly the thinking structure in this protocol can be further optimized through reinforcement learning
using these benchmarks researchers have found that
llms
proxy llms
using these benchmarks researchers have found that llms sometimes fail to keep secrets private when responding to complex tasks e
in this paper we study the truncated random
return
truncated random
in this paper we study the truncated random return in the distributional lqr
contrary to expectations our investigations reveal that the alignment between textual and image modalities in existing
diffusion
diffusion models
contrary to expectations our investigations reveal that the alignment between textual and image modalities in existing diffusion models is inadequate
this paper studies an online cost optimization problem for
distributed
online algorithm
this paper studies an online cost optimization problem for distributed storage and access
this study experimentally tested whether short-term exposure to narrow ai tools enhances core
cognitive
ai literacy
this study experimentally tested whether short-term exposure to narrow ai tools enhances core cognitive abilities or simply optimizes task performance
using a brain encoder as a digital twin offers a powerful data-driven framework for generating and testing hypotheses about visual selectivity in the human brain - hypotheses that can guide future
fmri
brain decoding
using a brain encoder as a digital twin offers a powerful data-driven framework for generating and testing hypotheses about visual selectivity in the human brain - hypotheses that can guide future fmri experiments
while this perspective has had enormous impact in imitation learning for robotics and understanding dynamic choices in economics practical
learning
policy learning
while this perspective has had enormous impact in imitation learning for robotics and understanding dynamic choices in economics practical learning algorithms often involve delicate inner-loop optimization repeated dynamic programming or adversarial training all of which complicate the use of modern highly expressive function approximators like neural nets and boosting
88 on a held-out test set and when applied to 146 who-classified uncertain variants identified 41 candidates with convergent emergence across multiple
lineages
phylogenetic diversity
88 on a held-out test set and when applied to 146 who-classified uncertain variants identified 41 candidates with convergent emergence across multiple lineages consistent with adaptive evolution
building ai literacy at home how families navigate children s self-directed
learning
ai use
building ai literacy at home how families navigate children s self-directed learning with ai
we outline a multi-phase research methodology focused on preparing re datasets fine-tuning ai models and designing collaborative
human-ai
ai assistance
we outline a multi-phase research methodology focused on preparing re datasets fine-tuning ai models and designing collaborative human-ai workflows
by further exploiting the underlying graph structure we show that gsi with musielak regularization gsi-m reduces to a simple emph univariate
optimization
gradient descent
by further exploiting the underlying graph structure we show that gsi with musielak regularization gsi-m reduces to a simple emph univariate optimization problem achieving remarkably computational efficiency
existing methods either overlook coreference
resolution
coreference resolution
existing methods either overlook coreference resolution or fail to scale beyond short text spans leading to fragmented graphs and inconsistent entity linking
this allows us to build a fully-correlated approximate
posterior
approximate posterior
this allows us to build a fully-correlated approximate posterior reflecting the overparametrization that tunes easy-to-interpret hyperparameters
colony-level heterogeneity urges a fundamental question how is it possible that one colony as a
collective
collective systems
colony-level heterogeneity urges a fundamental question how is it possible that one colony as a collective unit behaves differently from another one
however the most representative skeleton sequences may not necessarily be the most informative for the
action
action recognition
however the most representative skeleton sequences may not necessarily be the most informative for the action recognizer as the model may have already acquired similar knowledge from previously seen skeleton samples
in addition in the critical weak competition case b a 1 c d 0 we rigorously prove for the first time the existence of front-pulse
traveling
traveling waves
in addition in the critical weak competition case b a 1 c d 0 we rigorously prove for the first time the existence of front-pulse traveling waves
a mathematical theory for understanding when
abstract
deep learning
a mathematical theory for understanding when abstract representations emerge in neural networks
the simulated galaxies reproduce the observed stellar-to-halo mass mass--metallicity and size--mass relations yielding stellar
masses
quiescent galaxies
the simulated galaxies reproduce the observed stellar-to-halo mass mass--metallicity and size--mass relations yielding stellar masses of 10 6-10 8 m_ odot and metallicities consistent with those of local group dwarf galaxies
unlike the curie-weiss type divergence typically observed in diluted magnetic systems our findings reveal a distinct enhancement of magnetization per er 3 ion under high
magnetic
magnetic anisotropy
unlike the curie-weiss type divergence typically observed in diluted magnetic systems our findings reveal a distinct enhancement of magnetization per er 3 ion under high magnetic fields suggesting an unconventional mechanism
here we harness the emission of a quantum
dot
quantum dot
here we harness the emission of a quantum dot embedded in a micropillar and explore a hybrid approach where the information is encoded on a mixture of single photons and laser pulses
we propose viz-coast a method of leveraging the common-sense spatial reasoning of
large
multi-goal visual
we propose viz-coast a method of leveraging the common-sense spatial reasoning of large pretrained vision-language models to identify issues with downward refinement a priori bypassing the need to fix these failures during planning
inspired by this principle we introduce a hierarchical multimodal
recurrent
artificial neural
inspired by this principle we introduce a hierarchical multimodal recurrent neural network grounded in predictive processing under the free-energy principle capable of directly integrating over 30 000-dimensional visuo-proprioceptive inputs without dimensionality reduction
self-improvement has emerged as a mainstream paradigm for advancing the reasoning capabilities of large
vision-language
vision-language models
self-improvement has emerged as a mainstream paradigm for advancing the reasoning capabilities of large vision-language models lvlms where models explore and learn from successful trajectories iteratively
results show that lower and more homogeneously distributed learning rates promote scale-free networks while higher or more heterogeneously distributed learning
rates
learning rates
results show that lower and more homogeneously distributed learning rates promote scale-free networks while higher or more heterogeneously distributed learning rates lead to the emergence of core-periphery topologies
2024 develops riesz regression for automatic debiased
machine
debiased machine learning
2024 develops riesz regression for automatic debiased machine learning which directly estimates the riesz representer or equivalently the bias-correction term by minimizing the mean squared error
plugging regression functions estimated by machine learning methods into the identifying equations can yield poor performance because of
first-stage
regression function
plugging regression functions estimated by machine learning methods into the identifying equations can yield poor performance because of first-stage bias
these findings highlight risks of treating
llms
llm agents
these findings highlight risks of treating llms as neutral decision aids and underline the need to elicit ai judgments prior to exposing them to human opinions
by integrating spatial factors and network representation the framework provides a versatile decision-support tool for bss planning in complex
urban
urban systems
by integrating spatial factors and network representation the framework provides a versatile decision-support tool for bss planning in complex urban environments
to fill this gap via an iterative and deductive process we develop the interaction-augmented instruction iai model a compact entity-relation graph formalizing how the combination of
interactions
human-ai interaction
to fill this gap via an iterative and deductive process we develop the interaction-augmented instruction iai model a compact entity-relation graph formalizing how the combination of interactions and text prompts enhances human-generative ai communication
data selection is a critical aspect of reinforcement learning with verifiable rewards rlvr for enhancing the reasoning capabilities of large
language
language models
data selection is a critical aspect of reinforcement learning with verifiable rewards rlvr for enhancing the reasoning capabilities of large language models llms
stellar-mass compact objects cos embedded in active
galactic
galactic disk
stellar-mass compact objects cos embedded in active galactic nucleus agn discs are commonly assumed to accrete via bondi or bondi-hoyle-lyttleton bhl prescriptions neglecting gas angular momentum
here we exploit the spatial structure of atomic
ensembles
single photons
here we exploit the spatial structure of atomic ensembles to control the coupling between modes of distinct cavities thereby reshaping the resulting atom-photon spectra
my research in building a well-designed ar stage features avatars and interactive
elements
mobile ar
my research in building a well-designed ar stage features avatars and interactive elements that allow users to engage with stories at their own pace granting them full agency over their experience
together these results provide a principled account of memory as an active constructive and
resource-bounded
memory demand
together these results provide a principled account of memory as an active constructive and resource-bounded process
bridging a key theoretical challenge in diffusion-based generative modeling our results extend convergence theory to more realistic data
distributions
langevin dynamics
bridging a key theoretical challenge in diffusion-based generative modeling our results extend convergence theory to more realistic data distributions and practical ode solvers
here we implement an efficient quantum simulation algorithm on quantinuum s system model h2 trapped-ion
quantum
classical simulation
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
by making predictability an explicit metric for crafting the data diet poyo-ssl turns heterogeneity from a liability into an asset providing a robust
biologically
neural representations
by making predictability an explicit metric for crafting the data diet poyo-ssl turns heterogeneity from a liability into an asset providing a robust biologically grounded recipe for scalable neural decoding and a path toward foundation models of neural dynamics
the civic tool was developed in partnership with municipal authorities law enforcement ngos and social services and reflects their
institutional
civic tool
the civic tool was developed in partnership with municipal authorities law enforcement ngos and social services and reflects their institutional priorities while centering community knowledge
here we harness the emission of a quantum dot embedded in a micropillar and explore a
hybrid
quantum key distribution
here we harness the emission of a quantum dot embedded in a micropillar and explore a hybrid approach where the information is encoded on a mixture of single photons and laser pulses
we present a suite of 100 cosmologically motivated controlled n-body simulations designed to advance the understanding of the role of purely gravitational dynamics in the early formation of
low-mass
dark matter
we present a suite of 100 cosmologically motivated controlled n-body simulations designed to advance the understanding of the role of purely gravitational dynamics in the early formation of low-mass galaxy groups 1-5 x 10 13 m_sun
while the sample complexity of agnostic learning is well understood its time
complexity
time complexity
while the sample complexity of agnostic learning is well understood its time complexity has received much less attention
the fourth example has two binary and absorbing
treatments
treatment effect
the fourth example has two binary and absorbing treatments where the second treatment always happens after the first
we also explore applications of coherence to
graph
regular graphs
we also explore applications of coherence to graph problems
we formulate hierarchy inference as a differentiable graph learning problem where vertices represent elemental motions and directed edges capture learned parent-child dependencies through
graph
graph neural
we formulate hierarchy inference as a differentiable graph learning problem where vertices represent elemental motions and directed edges capture learned parent-child dependencies through graph neural networks
despite the widespread adoption of parallel join-based
trees
spanning trees
despite the widespread adoption of parallel join-based trees a major drawback of previous work on such data structures is the inefficiency of their input output i o access patterns
the structure of relation decoding linear operators in large
language
large language
the structure of relation decoding linear operators in large language models
data-enabled predictive control and guidance for autonomous
underwater
underwater vehicles
data-enabled predictive control and guidance for autonomous underwater vehicles
accurate macroeconomic forecasting has become harder amid geopolitical disruptions
policy
monetary policy
accurate macroeconomic forecasting has become harder amid geopolitical disruptions policy reversals and volatile financial markets
2 textbf failure-guided hints injecting corrective guidance into
stalled
success rate
2 textbf failure-guided hints injecting corrective guidance into stalled trajectories to increase the probability of successful outcomes
the discovery of the proof was heavily assisted by chatgpt a proprietary large
language
large language
the discovery of the proof was heavily assisted by chatgpt a proprietary large language model and we describe the process through which its assistance was
for fair and rigorous evaluation we curate goat-core a high-quality core split distilled from goat-bench tailored to multi-modal open-vocabulary multi-goal
visual
multi-goal visual
for fair and rigorous evaluation we curate goat-core a high-quality core split distilled from goat-bench tailored to multi-modal open-vocabulary multi-goal visual navigation
we present a suite of 100 cosmologically motivated controlled n-body simulations designed to advance the understanding of the
role
quiescent galaxies
we present a suite of 100 cosmologically motivated controlled n-body simulations designed to advance the understanding of the role of purely gravitational dynamics in the early formation of low-mass galaxy groups 1-5 x 10 13 m_sun
we suggest demographic synchrony provides a general mechanism for understanding why human populations remain vulnerable across all scales scale still stabilizes synchronous populations via density increases but synchrony ensures that stability grows only slowly with size leaving large
populations
population genetics
we suggest demographic synchrony provides a general mechanism for understanding why human populations remain vulnerable across all scales scale still stabilizes synchronous populations via density increases but synchrony ensures that stability grows only slowly with size leaving large populations more volatile and more vulnerable than classical demographic theory predicts
without imposing any a priori structures on the spatial linkages between
variables
causal inference
without imposing any a priori structures on the spatial linkages between variables we let the data speak for themselves
these eeg data adaptively manipulates the audiovisual parameters in real-time generating a distinct
experience
user experience
these eeg data adaptively manipulates the audiovisual parameters in real-time generating a distinct experience for each user
as an alternative to established hole-based photonic crystal cavities we introduce corrugated triangular dinosaur
photonic
photonic crystal
as an alternative to established hole-based photonic crystal cavities we introduce corrugated triangular dinosaur photonic crystal cavities and develop a tapered quasi loss-free cavity-waveguide interface to adiabatically interconvert bloch and waveguide modes
in this work we explore how human-machine
teaming
human-machine teaming
in this work we explore how human-machine teaming can support this process by accelerating iterations while preserving human judgment
this study introduces a fully-supervised three-stage framework the staged bayesian domain-adversarial neural network staged b-dann that combines parameter transfer and shared
latent
generative models
this study introduces a fully-supervised three-stage framework the staged bayesian domain-adversarial neural network staged b-dann that combines parameter transfer and shared latent space adaptation
leveraging this equivalence we propose a novel regularization method for
policy
preference learning
leveraging this equivalence we propose a novel regularization method for policy learning
the stochastic evolutionary game dynamics in the regime of replication-selection reveals the emergence of spiteful behaviour as a dominant behaviour via a first order phase transition -- a discontinuous jump in the frequency of spiteful
individuals
evolutionary game
the stochastic evolutionary game dynamics in the regime of replication-selection reveals the emergence of spiteful behaviour as a dominant behaviour via a first order phase transition -- a discontinuous jump in the frequency of spiteful individuals at a threshold value of prejudicity
the graph constructed from the power system model requires only knowledge of the dependencies between state-to-state and output-to-state variables within a
state-space
state estimation
the graph constructed from the power system model requires only knowledge of the dependencies between state-to-state and output-to-state variables within a state-space framework
programming assistants powered by large language
models
language models
programming assistants powered by large language models llms have become widely available with conversational assistants like chatgpt proving particularly accessible to less experienced programmers
here we employ a two-color pump-probe system to reveal the anisotropic electron-phonon coupling epc and coherent
phonon
phonon polaritons
here we employ a two-color pump-probe system to reveal the anisotropic electron-phonon coupling epc and coherent phonon dynamics in bulk tairte4
the limiting distributions derived here are obtained under stationarity and nonstationarity assumptions and analytically tractable expressed as finite sums of weighted independent chi 2
random
central limit theorem
the limiting distributions derived here are obtained under stationarity and nonstationarity assumptions and analytically tractable expressed as finite sums of weighted independent chi 2 random variables
for this reason a lrsga method is proposed where the approximation to
second-order
first-order methods
for this reason a lrsga method is proposed where the approximation to second-order mixed derivatives are obtained by rank-one updates
our initial approach uses solve_bvp to approximate optimal
control
dynamic programming
our initial approach uses solve_bvp to approximate optimal control trajectories
this work presents a theoretical and numerical investigation of the symplectic gradient adjustment sga method and of a low-rank
sga
bilevel optimization
this work presents a theoretical and numerical investigation of the symplectic gradient adjustment sga method and of a low-rank sga lrsga method for efficiently solving two-objective optimization problems in the framework of nash games
a linear convergence rate is also obtained under the
strong
linear convergence
a linear convergence rate is also obtained under the strong monotonicity assumption
characterizing cities through urban structure form and function the framework uncovers bidirectional causal patterns between
urban
urban systems
characterizing cities through urban structure form and function the framework uncovers bidirectional causal patterns between urban systems and traffic dynamics across 30 cities on six continents
it is well known that the combination of off-policy learning and
function
reinforcement learning
it is well known that the combination of off-policy learning and function approximation can lead to divergence of the algorithm
carbon-aware optimal power flow with data-driven
carbon
optimal power flow
carbon-aware optimal power flow with data-driven carbon emission tracing
latent class logit kernel framework for surrogate
safety
crash risk
latent class logit kernel framework for surrogate safety identifying behavioural thresholds through conflict indicator profiles
with stesso it has been experimentally proven that n 1 -bit toffoli
gates
-bit toffoli
with stesso it has been experimentally proven that n 1 -bit toffoli gates always have lower quantum costs than using conventional composition methods
amo-bench large language models still struggle in
high
language models
amo-bench large language models still struggle in high school math competitions
by assuming efficient suppression of inter-sub-band interference between radar and communication sub-bands
uplink
uplink communication
by assuming efficient suppression of inter-sub-band interference between radar and communication sub-bands uplink communication and radar signals can be efficiently processed without mutual interference
this network is proposed to leverage the information dependencies in
supply
supply chain
this network is proposed to leverage the information dependencies in supply chain to derive invisible states of samples in the database
finally we investigate how multi-slice electron ptychography could provide even further insight on nanotube defect structures thanks to its close to 3d imaging capabilities at
atomic
atomic force microscopy
finally we investigate how multi-slice electron ptychography could provide even further insight on nanotube defect structures thanks to its close to 3d imaging capabilities at atomic resolution
llms as in-context meta-learners for model and
hyperparameter
models llms
llms as in-context meta-learners for model and hyperparameter selection
adaptive channel estimation and quantized feedback for ris assisted optical
wireless
channel state information
adaptive channel estimation and quantized feedback for ris assisted optical wireless communication systems
leveraging this equivalence we propose a novel regularization method for
policy
reinforcement learning
leveraging this equivalence we propose a novel regularization method for policy learning
the impact of navigation aids on search performance and object recall in wide-area
augmented
augmented reality
the impact of navigation aids on search performance and object recall in wide-area augmented reality
the combination of efficient tunneling leakage management by composite pt cap ptox
pt
ptox pt
the combination of efficient tunneling leakage management by composite pt cap ptox pt 1
existing methods often simplify these complexities limiting their
real-world
existing methods
existing methods often simplify these complexities limiting their real-world performance
in first phase of experiments turbulence impacted vortex beam shaping technique has been introduced for a propagating
beam
beam shaping
in first phase of experiments turbulence impacted vortex beam shaping technique has been introduced for a propagating beam which is effected by kolmogorov type lab based turbulence simulator using pseudo random phase plate prpp
this approach restores high-sensitivity operation in the maximum-pulse-efficiency configuration without detailed beam characterisation providing a practical route towards next-generation time-resolved
atom
atom interferometry
this approach restores high-sensitivity operation in the maximum-pulse-efficiency configuration without detailed beam characterisation providing a practical route towards next-generation time-resolved atom interferometers operating at the 10 -5 rad noise level
while encompassing several update rules from the literature this framework allows for priority on
updates
data-driven stabilization
while encompassing several update rules from the literature this framework allows for priority on updates of particular blocks and correlations in the block selection between iterations which is not permitted under the classical convergent stochastic framework
quantum imaging with entangled photon pairs promises
performance
quantum channels
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
using information geometry to characterize higher-order
interactions
brain-computer interface
using information geometry to characterize higher-order interactions in eeg
here we develop a tripartite entanglement distillation scheme using an eight-photon quantum platform
demonstrating
single photons
here we develop a tripartite entanglement distillation scheme using an eight-photon quantum platform demonstrating entanglement superactivation phenomena which are unique to multipartite systems
our findings reveal that despite being designed to enhance efficiency cognitive
strategies
control strategies
our findings reveal that despite being designed to enhance efficiency cognitive strategies can reduce the abundance of the species due to the constraints of cyclic dominance
in visuomotor policy learning diffusion-based imitation
learning
learning agents
in visuomotor policy learning diffusion-based imitation learning has become widely adopted for its ability to capture diverse behaviors
in this study we first prove that the density-ratio estimation method proposed in
lin
density-ratio estimation
in this study we first prove that the density-ratio estimation method proposed in lin et al
the results show that individual robot movements can be validated with over 80 accuracy under baseline conditions using four different classifiers support vector machine svm deep neural network dnn recurrent neural network rnn and convolutional
neural
mobile robots
the results show that individual robot movements can be validated with over 80 accuracy under baseline conditions using four different classifiers support vector machine svm deep neural network dnn recurrent neural network rnn and convolutional neural network cnn
we aim to assess what types of sfhs are consistent with the observed present-day star formation rates text sfr _0 and time-averaged star formation rates langle text
sfr
star formation rates
we aim to assess what types of sfhs are consistent with the observed present-day star formation rates text sfr _0 and time-averaged star formation rates langle text sfr rangle of galaxies in the local volume without assuming any fixed functional form
existing benchmarks have widely leveraged high school math competitions for evaluating mathematical
reasoning
reasoning capabilities
existing benchmarks have widely leveraged high school math competitions for evaluating mathematical reasoning capabilities of large language models llms
scribe combines domain-specific tools with a self-reflective
inference
reasoning curriculum
scribe combines domain-specific tools with a self-reflective inference pipeline that supports iterative reasoning tool use and error recovery