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in this paper we unify the perspectives of stochastic processes and
reinforcement
reinforcement learning
in this paper we unify the perspectives of stochastic processes and reinforcement learning through action-driven processes and illustrate their application to spiking neural networks
quantifying the uncertainty in the output of a neural network is essential for deployment in scientific or engineering applications where
decisions
uncertainty quantification
quantifying the uncertainty in the output of a neural network is essential for deployment in scientific or engineering applications where decisions must be made under limited or noisy data
furthermore using a synthetic preference dataset that enables controlled manipulation of values we find that different preference optimization algorithms lead to different value alignment outcomes even when
preference
preference data
furthermore using a synthetic preference dataset that enables controlled manipulation of values we find that different preference optimization algorithms lead to different value alignment outcomes even when preference data is held constant
these results have implications for amplified oversight -- the challenge of combining humans and ai to supervise ai
systems
trustworthy ai
these results have implications for amplified oversight -- the challenge of combining humans and ai to supervise ai systems even as they surpass human expert performance
extensions to longitudinal data dynamic treatment regimes and multiplicative
instrumental
average treatment effect
extensions to longitudinal data dynamic treatment regimes and multiplicative instrumental variables are further developed
while the parallel trends assumption is counterfactual and cannot be tested directly researchers often examine pre-treatment periods to check whether the time trends are
parallel
parallel trends
while the parallel trends assumption is counterfactual and cannot be tested directly researchers often examine pre-treatment periods to check whether the time trends are parallel before treatment is administered
for this problem we develop a direct debiased machine learning framework with an
end-to-end
debiased machine
for this problem we develop a direct debiased machine learning framework with an end-to-end algorithm
there are k sources broadcasting each to a subset of nodes in a
graph
bipartite graphs
there are k sources broadcasting each to a subset of nodes in a graph of size n
cooperation is fundamental to the functioning of biological and
social
social interactions
cooperation is fundamental to the functioning of biological and social systems in both human and animal populations with the structure of interactions playing a crucial role
in this paper we show new strongly polynomial work-depth tradeoffs for computing single-source shortest paths
sssp
strongly polynomial
in this paper we show new strongly polynomial work-depth tradeoffs for computing single-source shortest paths sssp in non-negatively weighted directed graphs in parallel
energy approach from varepsilon -graph to continuum
diffusion
langevin dynamics
energy approach from varepsilon -graph to continuum diffusion model with connectivity functional
evolutionary systems must learn to generalize often extrapolating from a limited set of selective conditions to anticipate future
environmental
evolutionary dynamics
evolutionary systems must learn to generalize often extrapolating from a limited set of selective conditions to anticipate future environmental changes
during training text tokens in each image-caption pair are masked at a randomly chosen ratio and a decoder conditioned on
visual
vision-language models
during training text tokens in each image-caption pair are masked at a randomly chosen ratio and a decoder conditioned on visual features is trained to reconstruct the original text
qualitative quantitative and subjective evaluations collectively show that our model substantially surpasses previous ones in both
estimation
pose estimation
qualitative quantitative and subjective evaluations collectively show that our model substantially surpasses previous ones in both estimation accuracy and speed for sketch-to-pose tasks
we present a novel technique to decompose line-of-sight los stellar polarization as a
function
dwarf galaxies
we present a novel technique to decompose line-of-sight los stellar polarization as a function of distance aimed at reconstructing three dimensional 3d plane-of-sky pos magnetic structures in the interstellar medium ism
the geometry of dialogue graphing language models to reveal
synergistic
language models
the geometry of dialogue graphing language models to reveal synergistic teams for multi-agent collaboration
this article studies people interactions with
groups
collective action
this article studies people interactions with groups and the emergence of group proxemics
in this work we present a clear and robust morphological analysis of a sample of 190 galaxies at z 6 demonstrating that distinct
bulge
bulge stars
in this work we present a clear and robust morphological analysis of a sample of 190 galaxies at z 6 demonstrating that distinct bulge and disk components were already beginning to emerge during this early epoch
direct bias-correction term estimation for
propensity
bias-correction term
direct bias-correction term estimation for propensity scores and average treatment effect estimation
anomaly detection in time-series data is a critical challenge with significant
implications
anomaly detection
anomaly detection in time-series data is a critical challenge with significant implications for network security
we reveal that in environments with higher external density enhanced mass inflow from the envelope leads to bondi-like accretion as the
protostellar
interstellar medium
we reveal that in environments with higher external density enhanced mass inflow from the envelope leads to bondi-like accretion as the protostellar mass increases
deep neural networks dnns have been used to model complex optimization problems in many applications yet have difficulty guaranteeing solution optimality and feasibility despite
training
neural network
deep neural networks dnns have been used to model complex optimization problems in many applications yet have difficulty guaranteeing solution optimality and feasibility despite training on large datasets
the local feature extraction capability of cnn and the global
feature
feature extraction
the local feature extraction capability of cnn and the global feature capturing ability of vit
harnessing topological effects offers a promising route to protect quantum states of light from imperfections potentially enabling more robust platforms for
quantum
quantum dot
harnessing topological effects offers a promising route to protect quantum states of light from imperfections potentially enabling more robust platforms for quantum information processing
unfortunately classical high-resolution techniques require multi-element arrays and
extensive
real-world datasets
unfortunately classical high-resolution techniques require multi-element arrays and extensive snapshot collection while generic machine learning ml approaches often yield black-box models that lack physical interpretability
this effect exhibits a lower junction capacitance as compared to its injection counterparts leading to a higher electro-optic bandwidth although the effective
refractive
refractive index
this effect exhibits a lower junction capacitance as compared to its injection counterparts leading to a higher electro-optic bandwidth although the effective refractive index change is low
controllers designed using an accurate model is
robust
predictive control
controllers designed using an accurate model is robust against disturbance and small mismatch between the physical setup and the mathematical model derived from first principles while a poor model results in a controller that performs well in simulation but fails in physical experiments
experimental results indicate improved performance compared to
baseline
extensive experiments
experimental results indicate improved performance compared to baseline methods particularly for large environments
self-localization on a 3d map by fusing global and local features from a
monocular
autonomous driving
self-localization on a 3d map by fusing global and local features from a monocular camera
our results establish spatially engineered atomic ensembles as a pathway to selective photon transfer between
modes
photonic crystal
our results establish spatially engineered atomic ensembles as a pathway to selective photon transfer between modes and precise control of many-body complexity
yet the question of its point convergence had
remained
point convergence
yet the question of its point convergence had remained open
the appearance of a virtual avatar significantly influences its perceived appropriateness and the user s
experience
physical virtual
the appearance of a virtual avatar significantly influences its perceived appropriateness and the user s experience particularly in healthcare applications
identification in these models is based on the conditional parallel trends assumption in the absence of treatment the average outcome of the treated and untreated group are assumed to evolve in
parallel
parallel trends
identification in these models is based on the conditional parallel trends assumption in the absence of treatment the average outcome of the treated and untreated group are assumed to evolve in parallel over time conditional on pre-treatment covariates
twin-field quantum key distribution protocols security and
open
quantum channels
twin-field quantum key distribution protocols security and open problems
in a recently developed visual rivalry paradigm - tracking continuous flash suppression tcfs - it was shown that the transition between awareness and suppression is hysteretic with a higher contrast threshold required for a stimulus to breakthrough
suppression
visual stimuli
in a recently developed visual rivalry paradigm - tracking continuous flash suppression tcfs - it was shown that the transition between awareness and suppression is hysteretic with a higher contrast threshold required for a stimulus to breakthrough suppression into awareness than to be suppressed from awareness
comparison with exact quantum-mechanical results in one- and two-dimensional
models
quantum networks
comparison with exact quantum-mechanical results in one- and two-dimensional models demonstrates that it has a reasonably high accuracy similar to that reported for instanton theory in the symmetric case
we derive the physical properties by conducting
spectral
spectral energy
we derive the physical properties by conducting spectral energy distribution sed fitting revealing that our target is a young age sim2 myr starburst galaxy with intense radiation field
we focus on multi-agent misalignment building on recent evidence that interacting llms playing a simple coordination game can generate
collective
emergent behaviors
we focus on multi-agent misalignment building on recent evidence that interacting llms playing a simple coordination game can generate collective biases absent in individual models
we demonstrate this method in simulation and discuss how textit a priori understandings of obstacle risk can be directly incorporated into the
safety
safety filter
we demonstrate this method in simulation and discuss how textit a priori understandings of obstacle risk can be directly incorporated into the safety filter to generate safe behaviors that are risk-aware
furthermore ablation studies further validate the effectiveness of the proposed modules in improving
segmentation
multi-organ segmentation
furthermore ablation studies further validate the effectiveness of the proposed modules in improving segmentation accuracy
then it generates detailed parts each within its own
fixed
layout generation
then it generates detailed parts each within its own fixed full-resolution voxel grid
we explore whether a calibrated predictor of object
recall
object recall
we explore whether a calibrated predictor of object recall can help shield against such cognitive attacks
we construct a non-parametric framework by
generating
galaxy cgm
we construct a non-parametric framework by generating large ensembles of randomized sfhs for each galaxy in the sample
images are high-dimensional and lossy or entangled latents make dynamics
learning
representation learning
images are high-dimensional and lossy or entangled latents make dynamics learning unnecessarily hard
a two-step approach originally introduced for network reconstruction in which one first randomizes the structure then the weights with a suitable distribution restores scale invariance and allows us to conduct unbiased assessments of
significance
correlation network
a two-step approach originally introduced for network reconstruction in which one first randomizes the structure then the weights with a suitable distribution restores scale invariance and allows us to conduct unbiased assessments of significance on weighted networks
these results demonstrate that flexible data-driven
spatial
dynamic spatial
these results demonstrate that flexible data-driven spatial methods substantially outperform restrictive parametric assumptions in environmental policy applications
weighted food webs make computing phylogenetic
diversity
phylogenetic tree
weighted food webs make computing phylogenetic diversity so much harder
however these methods assume that only one object is provided and that it is possible with the correct grasp to perform the task they are not capable of identifying grasping and using the best object for a
task
obstacle avoidance
however these methods assume that only one object is provided and that it is possible with the correct grasp to perform the task they are not capable of identifying grasping and using the best object for a task when many are available especially when the optimal tool is absent
the difference-in-differences did research design is a key identification strategy which allows researchers to estimate causal effects under the
parallel
parallel trends
the difference-in-differences did research design is a key identification strategy which allows researchers to estimate causal effects under the parallel trends assumption
our results suggest that mossnet is a compelling new direction for
efficient
models llms
our results suggest that mossnet is a compelling new direction for efficient high-performing recurrent llm architectures
kernel logistic regression klr is a powerful
classification
image classification
kernel logistic regression klr is a powerful classification method widely applied across diverse domains
our approach jointly addresses seven astrophysical domains - including
dark
dark matter
our approach jointly addresses seven astrophysical domains - including dark matter gas neutral hydrogen stellar mass temperature and magnetic field strength - while introducing physics-aware evaluation metrics that quantify structural realism beyond standard computer vision measures
reconstructing images seen by people from their
fmri
visual stimuli
reconstructing images seen by people from their fmri brain recordings provides a non-invasive window into the human brain
our witnesses can detect paradigmatic gme states like the dicke and multipartite n00n states which include the w
states
multipartite entanglement
our witnesses can detect paradigmatic gme states like the dicke and multipartite n00n states which include the w states as a special case and ghz-type entangled cat states
the learned representations can be effectively transferred to downstream robotic
manipulation
imitation learning
the learned representations can be effectively transferred to downstream robotic manipulation tasks via action value map prediction
test-time alignment of large language models llms attracts attention because fine-tuning
llms
test-time alignment
test-time alignment of large language models llms attracts attention because fine-tuning llms requires high computational costs
we believe that this work outlines a significant route for next-generation programmable photonics delivering subwavelength confinement energy-efficient operation and high-dimensional optical reconfigurability within an
integrated
photonic devices
we believe that this work outlines a significant route for next-generation programmable photonics delivering subwavelength confinement energy-efficient operation and high-dimensional optical reconfigurability within an integrated scalable and manufacturable platform
a maaf represents an optimal breakdown of k trees into reticulation-free subtrees with the roots of these subtrees representing
reticulation
reticulation events
a maaf represents an optimal breakdown of k trees into reticulation-free subtrees with the roots of these subtrees representing reticulation events
numerous mitigation methods exist for quantum noise suppression making it challenging to identify the optimum approach for a specific application especially as ongoing advances in hardware tuning and error correction are expected to reduce logical
error
quantum error correction
numerous mitigation methods exist for quantum noise suppression making it challenging to identify the optimum approach for a specific application especially as ongoing advances in hardware tuning and error correction are expected to reduce logical error rates
this allows us to infer a dense turn-by-turn
reward
human feedback
this allows us to infer a dense turn-by-turn reward signal grounded in the expert s revealed strategy decomposing the intractable long-horizon problem into a series of supervised learning tasks and training a policy to output a structured texttt action state_assessment tuple governing both textbf what to ask and crucia...
these systems aim to emulate the human brain s ability to flexibly learn reason remember perceive and
act
predictive processing
these systems aim to emulate the human brain s ability to flexibly learn reason remember perceive and act in real-world settings with minimal supervision
as ai writing support becomes ubiquitous how disclosing its
use
ai literacy
as ai writing support becomes ubiquitous how disclosing its use affects reader perception remains a critical underexplored question
recently deep multi-agent reinforcement learning marl has demonstrated promising performance for solving
challenging
continual learning
recently deep multi-agent reinforcement learning marl has demonstrated promising performance for solving challenging tasks such as long-term dependencies and non-markovian environments
in this paper we propose the first high-resolution hr motion trajectory estimation framework using
diffusion
diffusion models
in this paper we propose the first high-resolution hr motion trajectory estimation framework using diffusion models motdiff
a freeable matrix characterization of bipartite graphs of ferrers
dimension
ferrers dimension
a freeable matrix characterization of bipartite graphs of ferrers dimension three
newton s method may exhibit slower convergence than vanilla
gradient
gradient descent
newton s method may exhibit slower convergence than vanilla gradient descent in its initial phase on strongly convex problems
we show here that in models equipped with a learning rule inferred from neurobiological data spurious overlaps collectively reduce the mean synaptic inputs to
neurons
brain activity
we show here that in models equipped with a learning rule inferred from neurobiological data spurious overlaps collectively reduce the mean synaptic inputs to neurons and that this mean reduction causes in turn an increase in storage capacity through a sparsening of network activity
statistical physics of deep learning optimal learning of a
multi-layer
deep learning
statistical physics of deep learning optimal learning of a multi-layer perceptron near interpolation
all you need for object detection from pixels points and prompts to next-gen fusion and multimodal llms vlms in
autonomous
autonomous driving
all you need for object detection from pixels points and prompts to next-gen fusion and multimodal llms vlms in autonomous vehicles
articulated 3d objects are central to many
applications
pose estimation
articulated 3d objects are central to many applications in robotics ar vr and animation
experiments on synthetic and real-world datasets demonstrate that scmd effectively captures both structural and distributional differences between scms providing a practical tool to assess
causal
causal inference
experiments on synthetic and real-world datasets demonstrate that scmd effectively captures both structural and distributional differences between scms providing a practical tool to assess causal transferability and generalization difficulty
here we demonstrate that reinforcement learning rl and supervised learning sl drive
recurrent
recurrent neural networks
here we demonstrate that reinforcement learning rl and supervised learning sl drive recurrent neural networks rnns toward fundamentally different computational solutions when trained on identical decision-making tasks
optimal bidding and coordinated dispatch of hybrid
energy
optimal power flow
optimal bidding and coordinated dispatch of hybrid energy systems in regulation markets
we also provide a proof-of-concept case study of further adapting dms for better
wireless
wireless communication
we also provide a proof-of-concept case study of further adapting dms for better wireless receiver performance
in computer vision this long-standing challenge remains limited to industrial defects or unrealistic synthetically generated
anomalies
anomaly detection
in computer vision this long-standing challenge remains limited to industrial defects or unrealistic synthetically generated anomalies failing to capture the richness and unpredictability of real-world anomalies
finally simulation results confirm the efficacy of the proposed method in managing trajectory tracking and
cable
cable length
finally simulation results confirm the efficacy of the proposed method in managing trajectory tracking and cable length adjustments effectively
large language models llms have seen rapid adoption for
tasks
large language
large language models llms have seen rapid adoption for tasks such as drafting emails summarizing meetings and answering health questions
data-driven projection generation for efficiently solving heterogeneous quadratic
programming
quadratic programming
data-driven projection generation for efficiently solving heterogeneous quadratic programming problems
self-evidencing through hierarchical gradient decomposition a dissipative system that maintains non-equilibrium steady-state by minimizing variational
free
dynamical systems
self-evidencing through hierarchical gradient decomposition a dissipative system that maintains non-equilibrium steady-state by minimizing variational free energy
to bridge this gap we develop a unifying framework based on heat
kernels
heat kernels
to bridge this gap we develop a unifying framework based on heat kernels which we derive in a systematic way and express as simple closed-form expressions
specifically by formulating channel estimation as a generative ai problem generative ai methods such as diffusion
models
channel estimation
specifically by formulating channel estimation as a generative ai problem generative ai methods such as diffusion models dms can efficiently deal with rough initial estimations and have great potential to cooperate with traditional signal processing methods
theoretical calculations revealed that epitaxial strain effectively modulates the strength and energy positions of vhs of specific ru orbitals driving correlated phase transitions in the
electronic
electronic structure
theoretical calculations revealed that epitaxial strain effectively modulates the strength and energy positions of vhs of specific ru orbitals driving correlated phase transitions in the electronic and magnetic ground states
we present a suite of 100 cosmologically motivated controlled n-body simulations designed to advance the understanding of the
role
active galactic
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
this work establishes a clean dichotomy the optimal time complexity to support central string
queries
query time
this work establishes a clean dichotomy the optimal time complexity to support central string queries in compressed space is either theta log n log log n or theta log log n
experiments on systems ranging from 57 to 793 buses demonstrate scalability speed and reliability bridging the gap between ml acceleration and safe real-time deployment of ac-opf solutions - and paving the way toward
data-driven
data-driven stabilization
experiments on systems ranging from 57 to 793 buses demonstrate scalability speed and reliability bridging the gap between ml acceleration and safe real-time deployment of ac-opf solutions - and paving the way toward data-driven optimal control
selecting a small set of species that maximizes phylogenetic diversity for a given
phylogenetic
phylogenetic tree
selecting a small set of species that maximizes phylogenetic diversity for a given phylogenetic tree is an essential task in preservation planning where limited resources naturally prevent saving all species
this study establishes strain engineering as a promising platform for tuning vhss and resultant itinerant ferromagnetism of low-dimensional correlated
quantum
quantum technologies
this study establishes strain engineering as a promising platform for tuning vhss and resultant itinerant ferromagnetism of low-dimensional correlated quantum systems
we provide strong evidence that the 2-cycle for the ricker
growth
growth rates
we provide strong evidence that the 2-cycle for the ricker growth function can be rigorously proven using a similar approach
in this survey we provide a comprehensive review of multimodal spatial reasoning tasks with large models categorizing recent progress in multimodal large
language
language models
in this survey we provide a comprehensive review of multimodal spatial reasoning tasks with large models categorizing recent progress in multimodal large language models mllms and introducing open benchmarks for evaluation
understanding how distributed brain regions coordinate to produce behavior requires models that are both
predictive
predictive processing
understanding how distributed brain regions coordinate to produce behavior requires models that are both predictive and interpretable
the gamma -ray luminosity of a plasma depends on where cosmic rays are if they are in denser gas they produce more
gamma
gamma -ray
the gamma -ray luminosity of a plasma depends on where cosmic rays are if they are in denser gas they produce more gamma -rays
most prior work focuses on minimizing its time
complexity
query complexity
most prior work focuses on minimizing its time complexity i
by deriving the local oscillator directly from the spopo cavity the setup establishes an intrinsically excellent spatial mode overlap and high
interference
optical interference
by deriving the local oscillator directly from the spopo cavity the setup establishes an intrinsically excellent spatial mode overlap and high interference visibility forming a distinctive self-referenced architecture
we characterize their minimax optimal decision rule via a duality argument and show that surprisingly trusting the predictions and acting accordingly is recovered in this
minimax
minimax optimal
we characterize their minimax optimal decision rule via a duality argument and show that surprisingly trusting the predictions and acting accordingly is recovered in this minimax sense by emph decision calibration and any strictly stronger notion of calibration a substantially weaker and more tractable condition than f...
human evaluation confirms 100 correctness of the
synthesized
synthetic data
human evaluation confirms 100 correctness of the synthesized data
the system features a counter-propagating beam at the same wavelength as the quantum state which simultaneously actively stabilizes the cavity and after transmission acts as the
local
quantum dot
the system features a counter-propagating beam at the same wavelength as the quantum state which simultaneously actively stabilizes the cavity and after transmission acts as the local oscillator for homodyne detection
simulating the dynamics of quantum impurity models remains a fundamental challenge due to the complex
memory
memory effects
simulating the dynamics of quantum impurity models remains a fundamental challenge due to the complex memory effects that arise from system-environment interactions
to bridge this gap we systematically evaluated eight probabilistic deep learning models including two foundation models that have demonstrated strong performance on general
forecasting
deep learning
to bridge this gap we systematically evaluated eight probabilistic deep learning models including two foundation models that have demonstrated strong performance on general forecasting benchmarks
for example comparing two neural systems can shed light on the nature of emergent
computations
artificial neural
for example comparing two neural systems can shed light on the nature of emergent computations in the brain and deep neural networks
xray reflectivity measurements shows the progressive formation of an additional layer in between the 3
nm
film thickness
xray reflectivity measurements shows the progressive formation of an additional layer in between the 3 nm thick si capping layer and the ndco compound film