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our method is designed for versatility allowing integration with any state-of-the-art deep
reinforcement
deep reinforcement learning
our method is designed for versatility allowing integration with any state-of-the-art deep reinforcement learning drl algorithms within its self-play framework
we demonstrate the capabilities of this laser-arpes system by investigating several prototypical materials showcasing its potential for elucidating complex phenomena in
quantum
quantum materials
we demonstrate the capabilities of this laser-arpes system by investigating several prototypical materials showcasing its potential for elucidating complex phenomena in quantum materials
to address these limitations we propose catg a novel planning framework that leverages constrained
flow
flow matching
to address these limitations we propose catg a novel planning framework that leverages constrained flow matching
their predictions align with observed frequencies is a minimal and fundamental requirement for
classifiers
image classification
their predictions align with observed frequencies is a minimal and fundamental requirement for classifiers to be viewed as trustworthy
models must appropriately capture both aleatoric and epistemic uncertainty to effectively combine fine-grained local insights with broader
ecological
ecological interactions
models must appropriately capture both aleatoric and epistemic uncertainty to effectively combine fine-grained local insights with broader ecological patterns
data selection is a critical aspect of reinforcement learning with verifiable rewards rlvr for enhancing the
reasoning
policy learning
data selection is a critical aspect of reinforcement learning with verifiable rewards rlvr for enhancing the reasoning capabilities of large language models llms
our method represents observed motions using graph-based hierarchies explicitly decomposing global absolute motions into parent-inherited patterns and local
motion
point tracking
our method represents observed motions using graph-based hierarchies explicitly decomposing global absolute motions into parent-inherited patterns and local motion residuals
results show that introducing distractors in the virtual environment significantly raises users
translation
translation gain
results show that introducing distractors in the virtual environment significantly raises users translation gain thresholds
sequences of logits reveal the low rank structure of
language
large language
sequences of logits reveal the low rank structure of language models
formation control simplifies minimizing multi-robot cost functions by encoding a cost function as a shape the
robots
multi-robot collaboration
formation control simplifies minimizing multi-robot cost functions by encoding a cost function as a shape the robots maintain
spin-dependent anisotropic electron-phonon
coupling
magnetic anisotropy
spin-dependent anisotropic electron-phonon coupling in ktao _3
we propose a constrained generative model using kinetic underdamped
langevin
langevin dynamics
we propose a constrained generative model using kinetic underdamped langevin dynamics with specular reflection of velocity on the boundary defining constraints
process-level trajectory evaluation for environment configuration in
software
software engineering
process-level trajectory evaluation for environment configuration in software engineering agents
we demonstrate that a nonlinear sis can improve communication reliability compared to a linear sis by forming complex signal patterns across the sis surface which provide higher diversity against
noise
signal-to-noise ratio
we demonstrate that a nonlinear sis can improve communication reliability compared to a linear sis by forming complex signal patterns across the sis surface which provide higher diversity against noise disturbances while still allowing the receiver to discern these patterns
existing benchmarks have widely leveraged high school math competitions for evaluating mathematical reasoning
capabilities
reasoning tasks
existing benchmarks have widely leveraged high school math competitions for evaluating mathematical reasoning capabilities of large language models llms
in this work we attempt to add robustness into existing region proposal network-deep convolutional neural network rpn-dcnn object detection networks through two distinct scene-based information
fusion
image fusion
in this work we attempt to add robustness into existing region proposal network-deep convolutional neural network rpn-dcnn object detection networks through two distinct scene-based information fusion techniques
finally the trained csi model is combined with an
openmax
information csi
finally the trained csi model is combined with an openmax function to estimate the likelihood of unknown ones
we present results from what we believe to be the first structured survey of physics experts n 20 regarding both the theoretical possibility of
vacuum
vacuum metastable
we present results from what we believe to be the first structured survey of physics experts n 20 regarding both the theoretical possibility of vacuum decay and its potential technological inducibility
urban form and function shape mobility more profoundly than structure even though structure often exhibits higher correlations as observed in cities such as singapore new delhi
london
human mobility
urban form and function shape mobility more profoundly than structure even though structure often exhibits higher correlations as observed in cities such as singapore new delhi london chicago and moscow
cosmological hydrodynamical simulations are essential tools for studying the formation and evolution of galaxies and their central
supermassive
black hole mass
cosmological hydrodynamical simulations are essential tools for studying the formation and evolution of galaxies and their central supermassive black holes
in the limited cases where ground truth is available through exact classical
simulation
quantum advantage
in the limited cases where ground truth is available through exact classical simulation we find that it agrees with the results we obtain from the quantum device
we focus on an important safety problem that is already challenging for
humans
ai agents
we focus on an important safety problem that is already challenging for humans fact-verification of ai outputs
a scenario-based approach for stochastic economic model
predictive
predictive control
a scenario-based approach for stochastic economic model predictive control with an expected shortfall constraint
this energy is believed to impact the star formation
activity
star-forming region
this energy is believed to impact the star formation activity and contribute to the quenching of galaxies
the prohibitive cost of evaluating large language models
llms
large language
the prohibitive cost of evaluating large language models llms on comprehensive benchmarks necessitates the creation of small yet representative data subsets i
our analysis loosely favours local starburst
activity
massive stars
our analysis loosely favours local starburst activity as the driver of the shocks and circumnuclear gas dynamics in ngc 7582 though the possibility of an agn jet contribution cannot be excluded
accurate world models are essential for enabling agents to think plan and reason effectively in complex
dynamic
world models
accurate world models are essential for enabling agents to think plan and reason effectively in complex dynamic settings
102 250501 2009 proved that all entangled
states
quantum coherence
102 250501 2009 proved that all entangled states are useful for discrimination of quantum channels
we introduce the textbf boundless large model blm _1 a multimodal spatial foundation model that preserves instruction following and
reasoning
vision-language models
we introduce the textbf boundless large model blm _1 a multimodal spatial foundation model that preserves instruction following and reasoning incorporates embodied knowledge and supports robust cross-embodiment control
we provide a theoretical analysis of the generalization ability of solving qps with
projection
quadratic programming
we provide a theoretical analysis of the generalization ability of solving qps with projection matrices generated by neural networks
meta-heuristic algorithms are widely used to tackle complex optimization problems including
nonlinear
genetic algorithm
meta-heuristic algorithms are widely used to tackle complex optimization problems including nonlinear multimodal and high-dimensional tasks
distributionally robust dynamic structural estimation serial dependence and
sensitivity
nonparametric identification
distributionally robust dynamic structural estimation serial dependence and sensitivity analysis
user misconceptions of llm-based conversational
programming
llm responses
user misconceptions of llm-based conversational programming assistants
unlike capability benchmarks that assess task
performance
task performance
unlike capability benchmarks that assess task performance the nct examines whether an llm remains the same interlocutor across time and interaction gaps
vfxmaster unlocking dynamic visual effect
generation
image generation
vfxmaster unlocking dynamic visual effect generation via in-context learning
we develop the associated theory we establish identifiability of the loadings and noise variance and show that-unlike in matrix ppca-the maximum
likelihood
maximum likelihood
we develop the associated theory we establish identifiability of the loadings and noise variance and show that-unlike in matrix ppca-the maximum likelihood estimator mle exists even from a single tensor sample
a critical visual computation is to construct global scene properties from activities of early visual cortical
neurons
brain regions
a critical visual computation is to construct global scene properties from activities of early visual cortical neurons which have small receptive fields
notably our algorithm can be viewed as an optimized diffusion
process
diffusion models
notably our algorithm can be viewed as an optimized diffusion process and can be integrated into existing methods to further improve their performance
we develop a stochastic variational expectation-maximization
algorithm
minimax optimal
we develop a stochastic variational expectation-maximization algorithm to jointly optimize the neural and probabilistic components
we release code pretrained weights and tutorials to support standardized
eeg
cognitive neuroscience
we release code pretrained weights and tutorials to support standardized eeg research and accelerate progress in clinical neuroscience
while many flavors of the top exist for planning in multi-robot systems they assume that all the robots cooperate toward a single objective thus they do not extend to settings where the
robots
optimal control
while many flavors of the top exist for planning in multi-robot systems they assume that all the robots cooperate toward a single objective thus they do not extend to settings where the robots compete in reward-scarce environments
by fully accounting for all radiative transfer coefficients in the prt
calculations
radiative transfer
by fully accounting for all radiative transfer coefficients in the prt calculations the uniform background light becomes more depolarized near the cluster center with the effect growing more pronounced as background intensity decreases
while it is well-known that a network coding advantage exists in directed graphs the situation in undirected graphs is much less understood -- in particular despite significant effort it is not even known whether network
coding
network coding
while it is well-known that a network coding advantage exists in directed graphs the situation in undirected graphs is much less understood -- in particular despite significant effort it is not even known whether network coding is helpful at all for unicast sessions
for these environments to be considered for long term adoption and use they must support multiple-input-multiple- mimo technology rapidly fluctuating
channel
channel state information
for these environments to be considered for long term adoption and use they must support multiple-input-multiple- mimo technology rapidly fluctuating channel conditions in these environments place a heavy burden on traditional time-frequency csi feedback schemes required for mimo precoding
using score matching within a corresponding
diffusion
diffusion models
using score matching within a corresponding diffusion model we obtain an estimator of the bayesian posterior mathbb p x mid y in this setup
we test the assumption that galaxies permanently quench when their central smbhs approach the limit imposed by the observed m_ rm bh
-
massive galaxies
we test the assumption that galaxies permanently quench when their central smbhs approach the limit imposed by the observed m_ rm bh - sigma_ star relation as a proxy of smbh disruptive feedback
conceptually the swes bridge real-space semiclassical intuition with many-body solid-state
optics
optical properties
conceptually the swes bridge real-space semiclassical intuition with many-body solid-state optics offering a numerically robust and gauge-clean alternative to reciprocal-space approaches for nonlinear optical response and attosecond spectroscopy in solids
revisiting generative infrared and visible image
fusion
multimodal reasoning
revisiting generative infrared and visible image fusion based on human cognitive laws
how external medium outside prestellar cores affects
protostellar
interstellar medium
how external medium outside prestellar cores affects protostellar growth variations in accretion rate and evolution of disks and outflows
while this approach has advantages such as independence from appearance the
existing
real-world applications
while this approach has advantages such as independence from appearance the existing methods may break down under real-world conditions
dynamic context-aware scene reasoning using
vision-language
vision-language models vlms
dynamic context-aware scene reasoning using vision-language alignment in zero-shot real-world scenarios
scalable solid state single-photon sources spss with triggered single-photon emission rates exceeding a few ghz would aid in the wide technological adoption of photonic
quantum
quantum technologies
scalable solid state single-photon sources spss with triggered single-photon emission rates exceeding a few ghz would aid in the wide technological adoption of photonic quantum technologies
generative fusion methods reconstruct fused
images
image reconstruction
generative fusion methods reconstruct fused images by learning from data distributions but their generative capabilities remain limited
in contrast to lstm agents hidden sequence units
develop
recurrent neural
in contrast to lstm agents hidden sequence units develop localized place fields distance-dependent spatial kernels and task-dependent remapping while inputs orthogonalize and spatial information increases across layers
control-var can estimate average treatment effects on the treated for dummy policies or average
causal
causal inference
control-var can estimate average treatment effects on the treated for dummy policies or average causal responses over time for continuous policies
dynamic spatial treatment effect boundaries a continuous functional framework from
navier-stokes
effect boundaries
dynamic spatial treatment effect boundaries a continuous functional framework from navier-stokes equations
we predict that polarization can be reduced by decreasing the role of
prejudice
opinion formation
we predict that polarization can be reduced by decreasing the role of prejudice or increasing the willingness to consider opposing opinions
evaluations across state-of-the-art llms and agent frameworks show that while
agents
llm agents
evaluations across state-of-the-art llms and agent frameworks show that while agents can localize errors they struggle to translate feedback into effective corrections limiting end-to-end performance
in-situ infrared spectroscopy revealed non-monotonic behavior an
initial
photoemission spectroscopy
in-situ infrared spectroscopy revealed non-monotonic behavior an initial increase in aliphatic ch bonds was observed followed by a decrease at higher hydrogen fluences
classical demographic theory predicts that
volatility
large population
classical demographic theory predicts that volatility in growth should decline rapidly with size due to the averaging effects of the law of large numbers
while photonic lanterns efficiently and uniquely map a set of input modes to single-mode outputs or vice versa the optical mode transfer matrix of any particular fabricated device cannot be constrained at the
design
photonic devices
while photonic lanterns efficiently and uniquely map a set of input modes to single-mode outputs or vice versa the optical mode transfer matrix of any particular fabricated device cannot be constrained at the design stage due to manufacturing imperfections
besides producing simple algorithms simulating random bits through random arrivals enhances our understanding of the comparative strength of
randomized
randomized algorithm
besides producing simple algorithms simulating random bits through random arrivals enhances our understanding of the comparative strength of randomized online algorithms with adversarial input sequence and deterministic algorithms in the rom
to address this we propose the jacobian-based nonlinearity evaluation jne an interpretability metric for nonlinear neural
encoding
encoding models
to address this we propose the jacobian-based nonlinearity evaluation jne an interpretability metric for nonlinear neural encoding models
we partially answer this by showing that for every d otimes d
entangled
entanglement entropy
we partially answer this by showing that for every d otimes d entangled state with even d there exist three projective measurements which are antidiscriminable but not discriminable with that input state but those three measurements are not antidiscriminable with the product probe
road traffic accidents remain a major public
health
public health
road traffic accidents remain a major public health challenge worldwide with urbanisation and population density identified as key factors influencing risk
these results highlight the significant room for improving the mathematical reasoning in
current
llm post-training
these results highlight the significant room for improving the mathematical reasoning in current llms
understanding the dynamics of the spread of
diseases
disease transmission
understanding the dynamics of the spread of diseases within populations is critical for effective public health interventions
spectral and energy efficiency tradeoff for
pinching-antenna
pinching antenna
spectral and energy efficiency tradeoff for pinching-antenna systems
however existing algorithms such as reversible recombination revrecom and metropolized forest recombination mfr are constrained to sampling from distributions related to
spanning
spanning trees
however existing algorithms such as reversible recombination revrecom and metropolized forest recombination mfr are constrained to sampling from distributions related to spanning trees
we compare these models in terms of theoretical properties optimization strategies and empirical
performance
support vector machines
we compare these models in terms of theoretical properties optimization strategies and empirical performance and discuss applications in fields such as computer vision natural language processing and bioinformatics
early-assembling high-concentration halos form
stars
galactic nuclei
early-assembling high-concentration halos form stars efficiently and become gas-poor by z 0 while late-assembling low-concentration halos remain gas-rich due to delayed star formation and rejuvenated gas accretion
quantifying transient dynamics in heterogeneous networks under
various
transient dynamics
quantifying transient dynamics in heterogeneous networks under various inputs
in this study we investigate the effectiveness of advanced feature engineering and hybrid model architectures for anomaly detection in a multivariate industrial time
series
time series classification
in this study we investigate the effectiveness of advanced feature engineering and hybrid model architectures for anomaly detection in a multivariate industrial time series focusing on a steam turbine system
our study uses machine-learning-driven molecular-dynamics
simulations
molecular dynamics
our study uses machine-learning-driven molecular-dynamics simulations which describe the temperature evolution of pair distribution functions in close agreement with experiment
text prompt is the most common way for human-generative ai
genai
ai literacy
text prompt is the most common way for human-generative ai genai communication
in this article we review the computational-driven discoveries and the recent developments in the field from various essential aspects including the theoretical computational and specifically artificial intelligence ai machine
learning
machine learning ml
in this article we review the computational-driven discoveries and the recent developments in the field from various essential aspects including the theoretical computational and specifically artificial intelligence ai machine learning ml based approaches emerging within the paradigm of materials informatics
the model naturally accepts interleaved vision-language inputs and generates interleaved
vision-language
language models
the model naturally accepts interleaved vision-language inputs and generates interleaved vision-language outputs
we introduce a novel framework that decodes spatial information from
grid
grid cell
we introduce a novel framework that decodes spatial information from grid cell activity using topology
convergence results and convergence rate estimates in these nonlinear
settings
convergence rate
convergence results and convergence rate estimates in these nonlinear settings are already well established
particularly we design an additional control input using
reinforcement
control strategy
particularly we design an additional control input using reinforcement learning rl to be applied to the vehicle powertrain along with the input commanded by the battery
focusing on a reduced and hardware-feasible version of the mnist digit classification task or near-term photonic processors it offers a concrete framework to evaluate how
photonic
single photons
focusing on a reduced and hardware-feasible version of the mnist digit classification task or near-term photonic processors it offers a concrete framework to evaluate how photonic quantum circuits learn and generalize from limited data
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
to address this gap we propose supervised
reinforcement
reinforcement learning
to address this gap we propose supervised reinforcement learning srl a framework that reformulates problem solving as generating a sequence of logical actions
learning generalizable robotic manipulation
policies
reinforcement learning
learning generalizable robotic manipulation policies remains a key challenge due to the scarcity of diverse real-world training data
the european space agency esa and the european space resources innovation centre esric created the space resources challenge to invite researchers and companies to propose innovative solutions for
multi-robot
multi-robot collaboration
the european space agency esa and the european space resources innovation centre esric created the space resources challenge to invite researchers and companies to propose innovative solutions for multi-robot systems mrs space prospection
this extreme sub-thermal photoemission signal is consistent with direct emission of electrons photoexcited from the fe dopant states into the low effective mass and positive electron affinity primary conduction band and it is superimposed on a stronger signal with a larger mte associated with an optical phonon-mediated momentum resonant franck-condon fc emission process from a thermally populated and negative
electron
photoemission spectroscopy
this extreme sub-thermal photoemission signal is consistent with direct emission of electrons photoexcited from the fe dopant states into the low effective mass and positive electron affinity primary conduction band and it is superimposed on a stronger signal with a larger mte associated with an optical phonon-mediated momentum resonant franck-condon fc emission process from a thermally populated and negative electron affinity upper conduction band
tracing the evolution of brightest galaxies and
diffuse
host galaxy
tracing the evolution of brightest galaxies and diffuse light in galaxy groups
we also provide a novel static and dynamic decomposition achieving an o k log n -approximation when the
tree
tree edit
we also provide a novel static and dynamic decomposition achieving an o k log n -approximation when the tree edit distance is at most k
our method outperforms existing intervention techniques on steering and hallucination mitigation benchmarks for
vlms
vision-language models vlms
our method outperforms existing intervention techniques on steering and hallucination mitigation benchmarks for vlms and proposes a robust solution for multimodal model control through activation engineering
a conceptually simple and appealing idea is to directly replace the forward dynamic model in lam with a powerful
world
world models
a conceptually simple and appealing idea is to directly replace the forward dynamic model in lam with a powerful world model and training them jointly but it is non-trivial and prone to representational collapse
in a tidal disruption event tde a star is disrupted by the
tidal
tidal field
in a tidal disruption event tde a star is disrupted by the tidal field of a massive black hole creating a debris stream that returns to the black hole forms an accretion flow and powers a luminous flare
here we present a refractive index-correlated pseudocoloring ricp framework that integrates quantitative
refractive
nonlinear optical
here we present a refractive index-correlated pseudocoloring ricp framework that integrates quantitative refractive index ri maps obtained by holotomography ht with color bf images to enhance diagnostic interpretability
lastly we apply inputdsa to neural data recorded from rats performing a
cognitive
brain activity
lastly we apply inputdsa to neural data recorded from rats performing a cognitive task demonstrating that it identifies a transition from input-driven evidence accumulation to intrinsically-driven decision-making
evaluated on benchmark datasets our method achieves a high
time-series
time series classification
evaluated on benchmark datasets our method achieves a high time-series aware f1 score taf1 of 89
this task can be viewed as a language generation
task
language agents
this task can be viewed as a language generation task that bridges natural language human knowledge and programming logic
i show that the interpretation of pvars depends on the distribution of the causing variable and can range from average treatment effects to
average
average treatment effect
i show that the interpretation of pvars depends on the distribution of the causing variable and can range from average treatment effects to average causal responses to a combination of the two
we also discuss the symmetry rules governing the shape in the brillouin zone of the hidden
spin
magnetic anisotropy
we also discuss the symmetry rules governing the shape in the brillouin zone of the hidden spin texture which can be straightforwardly predicted within the present framework
in addition we also contribute to existing reflected sdes based constrained generative models where the stochastic
dynamics
langevin dynamics
in addition we also contribute to existing reflected sdes based constrained generative models where the stochastic dynamics is restricted through an abstract local time term
in this article we provide a self-contained introduction to the limit theory of dense multiplex networks analogous to the theory of graphons limit
theory
limit theory
in this article we provide a self-contained introduction to the limit theory of dense multiplex networks analogous to the theory of graphons limit theory of dense graphs
in this letter we propose using the mean information gain mig as a metric to quantify
emergence
emergent behaviors
in this letter we propose using the mean information gain mig as a metric to quantify emergence in agent-based models