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using the derived mathematical tools we assess the impact of these two control actions and determine the optimal
control
control strategies
using the derived mathematical tools we assess the impact of these two control actions and determine the optimal control policy
additionally path estimates identify lighting augmentation density ar registration stability cognitive load and
ar
mobile ar
additionally path estimates identify lighting augmentation density ar registration stability cognitive load and ar familiarity as primary drivers
in this paper we propose csi2q a novel csi fingerprinting system that
achieves
information csi
in this paper we propose csi2q a novel csi fingerprinting system that achieves comparable performance to iq-based approaches
however causal inference from observational data relies on untestable
assumptions
causal effects
however causal inference from observational data relies on untestable assumptions about the data-generating process such as the absence of unobserved confounders
during inference slideagent selectively activates
specialized
reasoning curriculum
during inference slideagent selectively activates specialized agents for multi-level reasoning and integrates their outputs into coherent context-aware answers
yet it remains unclear which correlations -- and how many -- are needed to
predict
brain activity
yet it remains unclear which correlations -- and how many -- are needed to predict large-scale neural activity
in decode we grow galaxies with their sfr linked to halo accretion rate distributions via
abundance
star clusters
in decode we grow galaxies with their sfr linked to halo accretion rate distributions via abundance matching
a unified theory for causal inference direct
debiased
debiased machine learning
a unified theory for causal inference direct debiased machine learning via bregman-riesz regression
to compensate for a quasiperiodic disturbance a quasiperiodic
disturbance
bounded disturbances
to compensate for a quasiperiodic disturbance a quasiperiodic disturbance observer using time delays was proposed
in terms of graph size we obtain a lower bound of 2 tilde
omega
omega log
in terms of graph size we obtain a lower bound of 2 tilde omega sqrt log log n
for typical cortical stimuli tens of milliseconds this places the
functional
cognitive science
for typical cortical stimuli tens of milliseconds this places the functional plasticity window in the few-second range a testable prediction that identifies seconds-scale eligibility traces as necessary for error-driven learning in biological circuits
we measure abundance ratios in 209 giant stars that are confirmed members of the smc providing the first extensive dataset of eu abundances in this galaxy across its full
metallicity
star formation rates
we measure abundance ratios in 209 giant stars that are confirmed members of the smc providing the first extensive dataset of eu abundances in this galaxy across its full metallicity range spanning more than 1
we first study how the boundary of the phase
transition
phase transition
we first study how the boundary of the phase transition is modified by non-linear exchange known to be present in spin-1 nanographenes using density matrix renormalization group dmrg
the results show that the vari-linear network model maintains high fitting accuracy across
multiple
correlation network
the results show that the vari-linear network model maintains high fitting accuracy across multiple real-world networks of varying types and scales
in such cases to ensure the human maintains an accurate understanding of
critical
artificial intelligence
in such cases to ensure the human maintains an accurate understanding of critical task elements an assistive agent must not only identify the highest priority information but also estimate how and when this information can be communicated most effectively given that human attention represents a zero-sum cognitive resou...
inputdsa demixing then comparing recurrent and externally
driven
temporal resolution
inputdsa demixing then comparing recurrent and externally driven dynamics
these results highlight a promising new role for
llms
large language models llms
these results highlight a promising new role for llms as lightweight general-purpose assistants for model selection and hyperparameter optimization
we also prove for the first time the non-singularity of the gradient descent gd map on the loss landscape of realistic
neural
deep learning
we also prove for the first time the non-singularity of the gradient descent gd map on the loss landscape of realistic neural network architectures with fully connected convolutional or softmax attention layers and piecewise analytic activations which includes sigmoid relu leaky relu etc
to account for uncertainty the underlying optimal
power
optimal power
to account for uncertainty the underlying optimal power flow opf routines have to be modified
this improves both upon a folklore solution that attains the optimal
query
query time
this improves both upon a folklore solution that attains the optimal query complexity but requires omega n time and upon the only previously known sublinear-time property tester by chan golan kociumaka kopelowitz and porat with time complexity tilde o n sqrt k
we begin by isolating a clean and analyzable instance of transformer
reasoning
reasoning capabilities
we begin by isolating a clean and analyzable instance of transformer reasoning that is incompatible with memory as strictly a storage of the local co-occurrences specified during training
our findings reinforce the need for designing llm-based tools that more
clearly
proxy llms
our findings reinforce the need for designing llm-based tools that more clearly communicate their programming capabilities to users
to address this gap we propose supervised
reinforcement
reinforcement learning rl
to address this gap we propose supervised reinforcement learning srl a framework that reformulates problem solving as generating a sequence of logical actions
we further investigate the transition of the
ground
quantum dot
we further investigate the transition of the ground state in the presence of harmonic confinement which effectively models a quantum dot-like nanostructure under the influence of the environment
porous microstructures while central to many functional materials remain difficult to characterize quantitatively by
atom
atomic force microscopy
porous microstructures while central to many functional materials remain difficult to characterize quantitatively by atom probe tomography apt
to avoid repeated differentiation of states and virtual controls a first-order command filter is introduced and a nonlinear disturbance
observer
disturbance observer
to avoid repeated differentiation of states and virtual controls a first-order command filter is introduced and a nonlinear disturbance observer is added to provide disturbance estimates
robotic systems navigating in real-world settings require a semantic understanding of their
environment
robotic systems
robotic systems navigating in real-world settings require a semantic understanding of their environment to properly determine safe actions
this drive towards greater fidelity however conflicts with a simultaneous push towards greater model representation of inherent complexity in
decision
decision process
this drive towards greater fidelity however conflicts with a simultaneous push towards greater model representation of inherent complexity in decision making including methods like modelling to generate alternatives mga
we consider a class of algorithms for this problem which is provably minimax
optimal
optimal transport
we consider a class of algorithms for this problem which is provably minimax optimal up to a constant factor
structlayoutformer conditional structured
layout
layout generation
structlayoutformer conditional structured layout generation via structure serialization and disentanglement
these results suggest that llms can serve as practical proxies for otherwise unrevealed human thinking
traces
thinking traces
these results suggest that llms can serve as practical proxies for otherwise unrevealed human thinking traces enabling label-only corpora to be extended into thinking-trace-augmented resources that enhance the reliability of llm raters
for the latter we discover a nonstandard rate of n 1 4 log n -3 8 with a heavy-tailed stable
limit
asymptotic normality
for the latter we discover a nonstandard rate of n 1 4 log n -3 8 with a heavy-tailed stable limit distribution
stellar-mass compact objects cos embedded in
active
star clusters
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
certification and classification of linear quantum
error
quantum advantage
certification and classification of linear quantum error mitigation methods
on the other hand reinforcement learning rl has recently shown potential in scaling up with simulations but is
typically
reinforcement learning
on the other hand reinforcement learning rl has recently shown potential in scaling up with simulations but is typically confined to low-dimensional symbolic inputs e
in this paper we propose a deep reinforcement
learning
reinforcement learning
in this paper we propose a deep reinforcement learning drl -based approach for dynamic beamforming and power allocation in isac systems
this ability is widely thought to be supported by expanding the dimensionality of
relevant
visual stimuli
this ability is widely thought to be supported by expanding the dimensionality of relevant neural codes such that neural representations for similar stimuli are maximally distinct or separated
existing benchmarks for speech understanding largely rely on multiple-choice question
answering
question answering
existing benchmarks for speech understanding largely rely on multiple-choice question answering mcqa formats which are prone to failure and therefore unreliable in capturing the nuanced ways paralinguistic features influence model behaviour
neural networks for ac optimal power flow
improving
power flow
neural networks for ac optimal power flow improving worst-case guarantees during training
in the limited cases where ground truth is available through exact classical
simulation
quantum key distribution
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
in many problems involving causal effects or structural models the parameters of interest depend on
regression
regression function
in many problems involving causal effects or structural models the parameters of interest depend on regression functions
our analysis loosely favours local starburst
activity
galactic nuclei
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
existing stationarity tests either fail to account for long-memory processes or exhibit poor empirical size particularly near the boundary between
stationarity
time-series experiments
existing stationarity tests either fail to account for long-memory processes or exhibit poor empirical size particularly near the boundary between stationarity and nonstationarity
instrumental variable methods are fundamental to
causal
causal inference
instrumental variable methods are fundamental to causal inference when treatment assignment is confounded by unobserved variables
lane changes are common yet challenging driving maneuvers that require continuous decision-making and
dynamic
collision avoidance
lane changes are common yet challenging driving maneuvers that require continuous decision-making and dynamic interaction with surrounding vehicles
stabilizability with bounded feedback for analytic linear
control
optimal control
stabilizability with bounded feedback for analytic linear control systems
applied to both synthetic and real spike-time data from rodent hippocampus our methods demonstrate superior accuracy and scalability compared to traditional binned glms enabling functional
connectivity
functional connectivity
applied to both synthetic and real spike-time data from rodent hippocampus our methods demonstrate superior accuracy and scalability compared to traditional binned glms enabling functional connectivity inference in large-scale neural recordings that are temporally precise on the order of synaptic dynamical timescales a...
using analytical and numerical methods we characterized the conditions under which polarization consensus opinion changes and cyclic dynamics emerge depending on the costs of mitigation environmental damage and the factors influencing
opinion
opinion formation
using analytical and numerical methods we characterized the conditions under which polarization consensus opinion changes and cyclic dynamics emerge depending on the costs of mitigation environmental damage and the factors influencing opinion formation
in this paper we study the truncated random
return
truncated random return
in this paper we study the truncated random return in the distributional lqr
we further use the tools we develop to explore a wealth of
related
solution discovery
we further use the tools we develop to explore a wealth of related tasks
we propose geometrically-regularized world models grwm which enforces that consecutive points along a natural sensory trajectory remain close in latent
representation
world models
we propose geometrically-regularized world models grwm which enforces that consecutive points along a natural sensory trajectory remain close in latent representation space
including our target star-forming galaxies at z 6 detected by alma are generally very
young
stellar population
including our target star-forming galaxies at z 6 detected by alma are generally very young but more massive and brighter in uv than galaxies identified by only jwst
such an approach does not require full access to the original dataset or the model addressing the challenges of
existing
existing methods
such an approach does not require full access to the original dataset or the model addressing the challenges of existing methods
cluster 1 characterized by consistently higher performance across multiple creativity variables icaa dat mapt and sr showed broad alpha-band hypoconnectivity relatively preserved left frontal
connectivity
brain regions
cluster 1 characterized by consistently higher performance across multiple creativity variables icaa dat mapt and sr showed broad alpha-band hypoconnectivity relatively preserved left frontal connectivity and greater network modularity
bell inequality violation is the phenomenon where multiple non-communicating parties can exhibit
correlations
quantum mechanics
bell inequality violation is the phenomenon where multiple non-communicating parties can exhibit correlations using quantum resources that are impossible if they can only use classical resources
instrumental variable methods are fundamental to causal inference when treatment
assignment
treatment assignment
instrumental variable methods are fundamental to causal inference when treatment assignment is confounded by unobserved variables
this learned geometric knowledge can then be distilled to perform generalized
tool
tool usage
this learned geometric knowledge can then be distilled to perform generalized tool usage tasks by selecting and using the best available real-world object as tool
this combined approach of pmma encapsulation and xef2-assisted fib patterning offers a robust cost-effective and scalable single-step fabrication route for integrating 2d tmdcs into high-performance
photonic
photonic devices
this combined approach of pmma encapsulation and xef2-assisted fib patterning offers a robust cost-effective and scalable single-step fabrication route for integrating 2d tmdcs into high-performance photonic devices thereby maintaining their intrinsic optical functionality essential for advancing quantum technologies a...
in this work we investigate within density functional theory the properties of the hidden
spin
hidden spin texture
in this work we investigate within density functional theory the properties of the hidden spin texture and spin-layer segregation in a prototype centrosymmetric dichalcogenide-monolayer material using an electric-field-based method
this work represents the theoretical foundations of this cooperative
manipulation
multi-robot collaboration
this work represents the theoretical foundations of this cooperative manipulation control framework and thus the experiments are presented in an abstract way while giving pointers towards potential future applications
we demonstrate the capabilities of this laser-arpes system by investigating several prototypical materials showcasing its potential for elucidating complex phenomena in
quantum
quantum technologies
we demonstrate the capabilities of this laser-arpes system by investigating several prototypical materials showcasing its potential for elucidating complex phenomena in quantum materials
consequently each iteration has a computational cost that is proportional to the number of spatial degrees of freedom producing a scalable method that preserves the established strong
convergence
convergence rate
consequently each iteration has a computational cost that is proportional to the number of spatial degrees of freedom producing a scalable method that preserves the established strong convergence rates
we further develop an optional second-order refinement and inference procedures based on random scaling and plug-in methods including plug-in
debiased
debiased machine learning
we further develop an optional second-order refinement and inference procedures based on random scaling and plug-in methods including plug-in debiased plug-in and online versions of the sargan--hansen j -test tailored to stochastic learning
1 developed in their lab for experimenting with the photogrammetry-based
calibration
calibration plate
1 developed in their lab for experimenting with the photogrammetry-based calibration strategies described in this paper
we study a coordinated multi-point comp transmission where two base stations bss each supported by a pinching antenna system pass are deployed to jointly serve
communication
uplink communication
we study a coordinated multi-point comp transmission where two base stations bss each supported by a pinching antenna system pass are deployed to jointly serve communication users under spatial division multiple access sdma technology
the cross dual encoder network comprises four essential components a global encoder a local
encoder
cross dual encoder network
the cross dual encoder network comprises four essential components a global encoder a local encoder a symmetric cross-attention module and a flow-based decoder
our general interaction framework which reduces to several previously studied
models
quantum mechanics
our general interaction framework which reduces to several previously studied models as special cases provides a versatile platform for engineering quantum correlations with applications in quantum simulation state preparation and sensing protocols
while this approach has advantages such as independence from appearance the
existing
real-world scenarios
while this approach has advantages such as independence from appearance the existing methods may break down under real-world conditions
vision-language models vlms such as clip which are pre-trained on
large
language models
vision-language models vlms such as clip which are pre-trained on large image-text pairs offer a promising solution by enhancing robustness and data efficiency in medical imaging tasks
neurodob a deep neural observer-based controller for vehicle
lateral
disturbance observer
neurodob a deep neural observer-based controller for vehicle lateral dynamics
one of the most radical shifts is the rejection of the sandwich model of
cognition
cognitive science
one of the most radical shifts is the rejection of the sandwich model of cognition which holds that mental processes are located be- tween action and perception
this research presents a novel racing and
overtaking
wheel-to-wheel racing
this research presents a novel racing and overtaking agent capable of learning to reliably navigate a track and overtake opponents in both simulation and reality
the performance of large language models llms often degrades when crucial information is in the middle of a long
context
large language models
the performance of large language models llms often degrades when crucial information is in the middle of a long context a lost-in-the-middle phenomenon that mirrors the primacy and recency effects in human memory
in this paper we prove that the iterates of the
accelerated
accelerated gradient
in this paper we prove that the iterates of the accelerated nesterov s algorithm in the critical regime do converge in the weak topology to a global minimizer of an l -smooth function in a real hilbert space hence answering positively a conjecture posed by h
to reduce such bias debiased machine learning employs
neyman
debiased machine learning
to reduce such bias debiased machine learning employs neyman orthogonal estimating equations
machine learning models including support vector machines svm naive bayes long short-term memory lstm networks and
bidirectional
machine learning
machine learning models including support vector machines svm naive bayes long short-term memory lstm networks and bidirectional encoder representations from transformers bert were employed to classify sentiments and predict trends
existing reinforcement learning from verifiable rewards rlvr methods such as group relative
policy
reinforcement learning
existing reinforcement learning from verifiable rewards rlvr methods such as group relative policy optimization grpo have achieved remarkable progress in improving the reasoning capabilities of large reasoning models lrms
by making predictability an explicit metric for crafting the data diet poyo-ssl turns heterogeneity from a liability into an asset providing a
robust
recurrent neural
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
in this study we investigate the effectiveness of advanced feature engineering and hybrid model architectures for
anomaly
anomaly detection
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
we introduce multicolleagues a multi-agent conversational system that shows how ai
agents
ai literacy
we introduce multicolleagues a multi-agent conversational system that shows how ai agents can act as colleagues by conversing with each other sharing new ideas and actively involving users in collaborative ideation
recently machine learning ml models mostly based on the application of large
language
large language
recently machine learning ml models mostly based on the application of large language models to sequence information have been developed to predict antibody properties
our framework clarifies the conditions under which benchmark scores can support diverse scientific claims bringing
predictive
debiased machine learning
our framework clarifies the conditions under which benchmark scores can support diverse scientific claims bringing predictive benchmarking into perspective as an epistemological practice and a key site of conceptual and theoretical reasoning in machine learning
this paper presents a carbon-aware optimal
power
optimal power flow
this paper presents a carbon-aware optimal power flow opf framework that incorporates data-driven carbon tracing enabling rapid estimation of nodal carbon emissions from electric loads
we formulate the problem as a markov decision process and analyze the structure of the optimal
policy
deep reinforcement learning
we formulate the problem as a markov decision process and analyze the structure of the optimal policy pi star for l 3 extending insights to arbitrary l
user misconceptions of llm-based conversational
programming
llm reasoning
user misconceptions of llm-based conversational programming assistants
lagged macroeconomic indicators should provide naturally valid but
frequently
monetary policy
lagged macroeconomic indicators should provide naturally valid but frequently weak instruments
don t blind your vla aligning visual representations for
ood
multi-goal visual
don t blind your vla aligning visual representations for ood generalization
the tri-infrastructure methodology and 79-problem benchmark enable longitudinal tracking of reasoning capabilities as
foundation
foundation models
the tri-infrastructure methodology and 79-problem benchmark enable longitudinal tracking of reasoning capabilities as foundation models evolve
we describe how weak phase modulations applied to classical
coherent
optical interference
we describe how weak phase modulations applied to classical coherent light in specially modified linear interferometers can be used to perform primitive computational tasks
overall our results highlight that the implicit
bias
debiased machine learning
overall our results highlight that the implicit bias of adam crucially depends on both the batching scheme and the dataset while signum remains invariant
nearest neighbor matching is equivalent to least squares density ratio estimation and
riesz
riesz regression
nearest neighbor matching is equivalent to least squares density ratio estimation and riesz regression
we study policy learning with abstention where a
policy
policy optimization
we study policy learning with abstention where a policy may defer to a safe default or an expert
results reveal that gromov-wasserstein is largely uncorrelated with
edge
gromov-wasserstein distance
results reveal that gromov-wasserstein is largely uncorrelated with edge betweenness rho 0
self-improvement has emerged as a mainstream paradigm for advancing the
reasoning
reasoning capabilities
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
experimental verification is scarce however especially in the telecom range because real
photonic
integrated photonics
experimental verification is scarce however especially in the telecom range because real photonic crystals and experimental methods inherently cannot be homogeneous in the third dimension
in this study we summarize prevalent weight
constraints
weight constraints
in this study we summarize prevalent weight constraints used in the literature and theoretically and numerically compare how they influence the properties of the combined forecast
in this work we propose a strategy that exploits the principles of non-hermitian physics--specifically the concept of exceptional points eps --to transcend these limitations and pave the way for the next generation of versatile high-performance
photonic
quantum emitters
in this work we propose a strategy that exploits the principles of non-hermitian physics--specifically the concept of exceptional points eps --to transcend these limitations and pave the way for the next generation of versatile high-performance photonic devices
this study proves that nearest neighbor nn matching can be interpreted as an instance of riesz regression for automatic debiased
machine
debiased machine learning
this study proves that nearest neighbor nn matching can be interpreted as an instance of riesz regression for automatic debiased machine learning
these estimators minimize a kernel-weighted convex-in-parameter function over observation pairs that are similar in terms of certain covariates where the similarity is governed by a localization
bandwidth
nonparametric identification
these estimators minimize a kernel-weighted convex-in-parameter function over observation pairs that are similar in terms of certain covariates where the similarity is governed by a localization bandwidth parameter
finally we validate our theoretical results through simulations on synthetic and
real-world
real-world scenarios
finally we validate our theoretical results through simulations on synthetic and real-world datasets