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the waterbed effect on quasiperiodic disturbance
observer
bounded disturbances
the waterbed effect on quasiperiodic disturbance observer avoidance of sensitivity tradeoff with time delays
inference latency stands as a critical bottleneck in the large-scale deployment of large
language
large language models llms
inference latency stands as a critical bottleneck in the large-scale deployment of large language models llms
across 125 participants we found that interactive interfaces
significantly
user experience
across 125 participants we found that interactive interfaces significantly improved performance
to this end we compare different representation extraction strategies and introduce two model-agnostic
embedding
representation learning
to this end we compare different representation extraction strategies and introduce two model-agnostic embedding augmentations
to solve this high-dimensional non-convex problem under uncertain channels we develop a deep
reinforcement
deep reinforcement
to solve this high-dimensional non-convex problem under uncertain channels we develop a deep reinforcement learning solution framework based on the proximal policy optimization ppo algorithm that integrates distribution-aware action modeling and a multi-branch actor network
for dyck edit distance our reduction incurs only polylogarithmic overheads in approximation and
update
tree edit distance
for dyck edit distance our reduction incurs only polylogarithmic overheads in approximation and update time yielding an n o 1 -approximation with n o 1 updates
through a comprehensive evaluation across various
configurations
multilingual data
through a comprehensive evaluation across various configurations we demonstrate that the system effectively performs diacritization without relying on complex explicit linguistic analysis
our findings reveal that while current video models demonstrate promising reasoning patterns on short-horizon spatial coherence fine-grained grounding and locally consistent dynamics they remain limited in long-horizon causal
reasoning
reasoning capabilities
our findings reveal that while current video models demonstrate promising reasoning patterns on short-horizon spatial coherence fine-grained grounding and locally consistent dynamics they remain limited in long-horizon causal reasoning strict geometric constraints and abstract logic
fractalbrain presents an experience combining a surreal virtual
reality
virtual reality
fractalbrain presents an experience combining a surreal virtual reality vr program with an electroencephalogram eeg interface
formulas for calculating the mean square errors and the spectral characteristics of the optimal linear estimate of the functional are derived in the case where the spectral
density
density estimation
formulas for calculating the mean square errors and the spectral characteristics of the optimal linear estimate of the functional are derived in the case where the spectral density matrices are exactly known
the ability to accurately interpret implied meanings plays a crucial
role
natural language
the ability to accurately interpret implied meanings plays a crucial role in human communication and language use and language models are also expected to possess this capability
we investigate the gain margin of a general nonlinear system under an inverse optimal input-to-state safe issf controller of the form u u0 x u x u0 where u0 is the nominal control and u is the inverse
optimal
optimal control
we investigate the gain margin of a general nonlinear system under an inverse optimal input-to-state safe issf controller of the form u u0 x u x u0 where u0 is the nominal control and u is the inverse optimal safety filter that minimally modifies the nominal controller s unsafe actions over the infinite horizon
revisiting generative infrared and visible image
fusion
image fusion
revisiting generative infrared and visible image fusion based on human cognitive laws
under this assumption we establish identification and derive a uniform
confidence
confidence intervals
under this assumption we establish identification and derive a uniform confidence band for the extrapolated treatment effects
it also introduces a behavior-normalized modified
crash
crash risk
it also introduces a behavior-normalized modified crash risk mrc formula to account for pedestrians habitual risk-taking behavior
the emergence of hidden spin polarization in centrosymmetric nonmagnetic crystals due to local
symmetry
spin-momentum locking
the emergence of hidden spin polarization in centrosymmetric nonmagnetic crystals due to local symmetry breaking has created new opportunities for potential spintronic applications and for enhancing our understanding of mechanisms to electrically manipulate spin-related phenomena
the composite loss integrates data fidelity dynamical residuals and barrier-based physical constraints
derived
loss functions
the composite loss integrates data fidelity dynamical residuals and barrier-based physical constraints derived from kramers escape theory
contribution of task-irrelevant stimuli to
drift
recurrent neural
contribution of task-irrelevant stimuli to drift of neural representations
identifying and addressing security issues during the
early
security issues
identifying and addressing security issues during the early phase of the development lifecycle is critical for mitigating the long-term negative impacts on software systems
our work establishes a new framework for mobility simulation paving the way for fine-grained data-driven studies of
human
mobility networks
our work establishes a new framework for mobility simulation paving the way for fine-grained data-driven studies of human behavior and its societal implications
reconstructing evolutionary histories and estimating the rate of evolution from molecular sequence data is of central importance in
evolutionary
phylogenetic diversity
reconstructing evolutionary histories and estimating the rate of evolution from molecular sequence data is of central importance in evolutionary biology and infectious disease research
our general interaction framework which reduces to several previously studied
models
quantum walk
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
inspired by the structure and function of biological cognition this paper introduces the concept of neurocognitive-inspired intelligence nii a hybrid approach that combines neuroscience
cognitive
surrogate brain
inspired by the structure and function of biological cognition this paper introduces the concept of neurocognitive-inspired intelligence nii a hybrid approach that combines neuroscience cognitive science computer vision and ai to develop more general adaptive and robust intelligent systems capable of rapid learning learning from less data and leveraging prior experience
in this paper we introduce a new sampling algorithm built on a general k th-order
langevin
langevin dynamics
in this paper we introduce a new sampling algorithm built on a general k th-order langevin dynamics extending beyond second- and third-order methods
orchvis advances human-centered design for multi-agent
systems
ai agents
orchvis advances human-centered design for multi-agent systems by combining transparent visualization with adaptive autonomy
hence we divide the main problem into two sub-problems the antenna activation sub-problem and the
power
transmit power
hence we divide the main problem into two sub-problems the antenna activation sub-problem and the power allocation sub-problem
public discourse emerges from the interplay between individuals willingness to voice their
opinions
opinion dynamics
public discourse emerges from the interplay between individuals willingness to voice their opinions and the structural features of the social networks in which they are embedded
5ev a transition from a long to a short transport regime is forced by an absorption depth reduction to below 100nm and both mte signals exhibit spectral trends consistent with phonon-mediated fc emission if the polaron formation self-energy is included in the initial photoexcited
electron
photoemission spectroscopy
5ev a transition from a long to a short transport regime is forced by an absorption depth reduction to below 100nm and both mte signals exhibit spectral trends consistent with phonon-mediated fc emission if the polaron formation self-energy is included in the initial photoexcited electron thermalization
these individuals contribute to the spread of the epidemic and pose a significant challenge to public
health
public health
these individuals contribute to the spread of the epidemic and pose a significant challenge to public health policies
this paper studies nonparametric local over- identification in the sense of chen and santos 2018 and the associated semiparametric efficiency in modern
causal
causal effects
this paper studies nonparametric local over- identification in the sense of chen and santos 2018 and the associated semiparametric efficiency in modern causal frameworks
eventually we obtain a polynomial-time reduction of asymmetric a priori tsp to a problem of finding a path in an acyclic digraph
minimizing
polynomial time
eventually we obtain a polynomial-time reduction of asymmetric a priori tsp to a problem of finding a path in an acyclic digraph minimizing a particular objective function for which we give an o log n -approximation algorithm in quasi-polynomial time
these results highlight the substantial room for improvement and underscore the
challenges
models llms
these results highlight the substantial room for improvement and underscore the challenges of applying llms in education
recent advances in representation learning reveal that widely used objectives such as
contrastive
representation learning
recent advances in representation learning reveal that widely used objectives such as contrastive and non-contrastive implicitly perform spectral decomposition of a contextual kernel induced by the relationship between inputs and their contexts
the competition format makes it possible to evaluate overtaking and wheel-to-wheel
racing
wheel-to-wheel racing
the competition format makes it possible to evaluate overtaking and wheel-to-wheel racing algorithms against the state-of-the-art
results from perturbation theory along with lyapunov stability and eigen-spectrum analysis are used to prove the
convergence
convergence rate
results from perturbation theory along with lyapunov stability and eigen-spectrum analysis are used to prove the convergence towards the optimal case
in the present study we introduce and characterize network
nonlocality
network nonlocality
in the present study we introduce and characterize network nonlocality breaking channels
we note that all the main predictions on galaxy quenched fractions and smbh growth histories and scaling relations are degenerate with those expected in a halo
quenching
galaxy cgm
we note that all the main predictions on galaxy quenched fractions and smbh growth histories and scaling relations are degenerate with those expected in a halo quenching model
transfer learning which leverages relevant data-abundant source domains to assist learning in data-scarce target
domains
continual learning
transfer learning which leverages relevant data-abundant source domains to assist learning in data-scarce target domains has shown efficacy
an analogous relaxation in the lower energy singlet state using
spin
spin-momentum locking
an analogous relaxation in the lower energy singlet state using spin purified atomic forces is estimated to be 0
the external validity of regression discontinuity
rd
external validity
the external validity of regression discontinuity rd designs is essential for informing policy and remains an active research area in econometrics and statistics
joint uplink and downlink resource allocation and antenna activation for
pinching
pinching antenna
joint uplink and downlink resource allocation and antenna activation for pinching antenna systems
we study a weighting estimator that sets weights by a minimax procedure solving a convex
optimization
policy optimization
we study a weighting estimator that sets weights by a minimax procedure solving a convex optimization problem that trades off worst-case conditional bias against variance
classical metrics such as the colless and sackin indices quantify
tree
phylogenetic diversity
classical metrics such as the colless and sackin indices quantify tree imbalance and have been extensively used to characterize phylogenies
in this work we study data-driven stabilization of linear time-invariant systems using
prior
data-driven stabilization
in this work we study data-driven stabilization of linear time-invariant systems using prior knowledge of system-theoretic properties specifically stabilizability and controllability
despite orders-of-magnitude differences in size and metabolic rate we show that the time to initiate
adaptive
adaptive immune
despite orders-of-magnitude differences in size and metabolic rate we show that the time to initiate adaptive immunity is remarkably consistent across species
here we show that the magnetic properties of mnps3 can be tailored through the intercalation of
different
magnetic properties
here we show that the magnetic properties of mnps3 can be tailored through the intercalation of different guest molecules
we investigate how peer pressure influences the opinions of large language model llm agents across a spectrum of cognitive commitments by embedding them in social networks where they update
opinions
opinion dynamics
we investigate how peer pressure influences the opinions of large language model llm agents across a spectrum of cognitive commitments by embedding them in social networks where they update opinions based on peer perspectives
here we present resmatching a novel csr method that uses guided conditional
flow
flow matching
here we present resmatching a novel csr method that uses guided conditional flow matching to learn such improved data-priors
to address this tension a coach-like llm environment was developed that embodies divergent and convergent
thinking
llm responses
to address this tension a coach-like llm environment was developed that embodies divergent and convergent thinking personas as two complementary processes
in this work we propose a general hierarchical motion modeling method that learns structured interpretable
motion
action recognition
in this work we propose a general hierarchical motion modeling method that learns structured interpretable motion relationships directly from data
structured tree representations derived from raw database tables guide llm-based
task
reasoning tasks
structured tree representations derived from raw database tables guide llm-based task generation followed by interaction-oriented filtering and expert validation
finally we present a proof-of-principle simulation on the ibm quantum experience platform realizing a quantum switch of two measurement
channels
quantum networks
finally we present a proof-of-principle simulation on the ibm quantum experience platform realizing a quantum switch of two measurement channels with tunable strengths and experimentally confirming the predicted efficiency enhancement enabled by correlation-assisted superposed causal order
self-supervised learning ssl holds a great deal of promise for applications in neuroscience due to the lack of
large-scale
fmri data
self-supervised learning ssl holds a great deal of promise for applications in neuroscience due to the lack of large-scale consistently labeled neural datasets
through extensive experimentation with state-of-the-art classical
simulation
numerical simulations
through extensive experimentation with state-of-the-art classical simulation strategies we identify a clear gap between classical and quantum runtimes
gravothermal collapse of self-interacting dark-matter
halos
dark matter
gravothermal collapse of self-interacting dark-matter halos with anisotropic velocity distributions
inspired by the success of direct preference optimization dpo we show that one can train a
generative
generative models
inspired by the success of direct preference optimization dpo we show that one can train a generative model with noisy simple utility values directly computed from observations to then form proposal distributions whose densities are proportional to the expected utility i
while recent breakthroughs in computer vision cv and artificial intelligence ai have driven remarkable progress the field still faces a critical challenge as knowledge remains fragmented across multimodal perception contextual
reasoning
vision-language models
while recent breakthroughs in computer vision cv and artificial intelligence ai have driven remarkable progress the field still faces a critical challenge as knowledge remains fragmented across multimodal perception contextual reasoning and cooperative intelligence
we use land-use and land-use-intensity change scenarios produced previously using the ipbes nature futures framework to support the achievement of conservation objectives as well as
climate
land use
we use land-use and land-use-intensity change scenarios produced previously using the ipbes nature futures framework to support the achievement of conservation objectives as well as climate change scenarios in order to project the future of biodiversity pressures in europe up to 2050
for the practical computation we develop a gradient-descent
algorithm
gradient descent
for the practical computation we develop a gradient-descent algorithm based on artificial iterates that employs an exact computation for the arising conditional expectations thereby eliminating costly monte carlo sampling
the proposed approach provides formal guarantees on safety and timing and is computationally lightweight model-free and
robust
predictive control
the proposed approach provides formal guarantees on safety and timing and is computationally lightweight model-free and robust to unknown disturbances
we present one algorithm under each methodology the first operates prior to prediction selecting a custom object
network
deep network
we present one algorithm under each methodology the first operates prior to prediction selecting a custom object network to use based on the identified background scene and the second operates after detection fusing scene knowledge into initial object scores output by the rpn
this work enables us to model the robot s collapse behavior in any open
environment
dynamic obstacles
this work enables us to model the robot s collapse behavior in any open environment and understand the parameters it needs to succeed in 3d navigation tasks
i show that under independence assumptions vars can identify average treatment effects
average
average treatment effect
i show that under independence assumptions vars can identify average treatment effects average causal responses or a mix of the two depending on the distribution of the policy
this study presents five effects that augment the appearance of a
physical
virtual reality
this study presents five effects that augment the appearance of a physical room to subtly encourage user motion
x-ray and variability selected agn have higher average star formation
rates
star clusters
x-ray and variability selected agn have higher average star formation rates than those selected with optical narrow line spectroscopic diagrams
here we demonstrate that quantum and classical descriptions generally yield different results for the spontaneous
emission
spontaneous emission
here we demonstrate that quantum and classical descriptions generally yield different results for the spontaneous emission in nanophotonic cavities
unmanned aerial vehicles uavs with their airborne full-sample continuous trajectory observation bring new opportunities for
traffic
traffic states
unmanned aerial vehicles uavs with their airborne full-sample continuous trajectory observation bring new opportunities for traffic state estimation
in this environment second-order minimizing methods such as the conjugate
gradient
accelerated gradient
in this environment second-order minimizing methods such as the conjugate gradient cg give a guaranteed superlinear convergence
driven by sub-100 fs pulses with approximately 200 pj
pulse
pulsed laser
driven by sub-100 fs pulses with approximately 200 pj pulse energy the generated mid-infrared light covers wavelengths from 3200 to 4800 nm
federated learning fl has emerged as a key paradigm for collaborative model training across multiple
clients
federated learning
federated learning fl has emerged as a key paradigm for collaborative model training across multiple clients without sharing raw data enabling privacy-preserving applications in areas such as radiology and pathology
simulated galaxies with higher halo masses higher median cgm gas density and higher star formation
rates
star formation rates
simulated galaxies with higher halo masses higher median cgm gas density and higher star formation rates produce brighter and more widespread o vi emission in their cgm
the model achieved a classification accuracy exceeding previous cnn and lstm-based
approaches
deep learning
the model achieved a classification accuracy exceeding previous cnn and lstm-based approaches and was benchmarked against a temporal convolutional network tcn baseline
many existing genuine multipartite entanglement gme witnesses for continuous-variable cv quantum systems typically rely on quadrature measurements which is challenging to implement in platforms where the cv degrees of freedom can be indirectly accessed only through
qubit
qubit readout
many existing genuine multipartite entanglement gme witnesses for continuous-variable cv quantum systems typically rely on quadrature measurements which is challenging to implement in platforms where the cv degrees of freedom can be indirectly accessed only through qubit readouts
discovering causal relationships using proxy variables under
unmeasured
causal inference
discovering causal relationships using proxy variables under unmeasured confounding
we also show that our novel cutting-plane
algorithm
efficiently solving
we also show that our novel cutting-plane algorithm enables the solution of very large mga problems with integer investment decisions
there are two major approaches in policy learning the empirical
welfare
policy evaluation
there are two major approaches in policy learning the empirical welfare maximization ewm approach and the plug-in approach
understanding and modeling human mobility is central to challenges in transport planning sustainable
urban
travel information
understanding and modeling human mobility is central to challenges in transport planning sustainable urban design and public health
we then evaluate the impact of heuristic support adaptation on parameter inference and policy
learning
optimal control
we then evaluate the impact of heuristic support adaptation on parameter inference and policy learning for a dynamic deformable linear object dlo manipulation task
we develop a stochastic variational expectation-maximization
algorithm
gradient descent
we develop a stochastic variational expectation-maximization algorithm to jointly optimize the neural and probabilistic components
we compare these models in terms of theoretical properties
optimization
machine learning
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
26 10 1 m _ sun yr -1 kpc -2 our results indicate a rapid building of inner stellar mass and
bulge
stellar mass function
26 10 1 m _ sun yr -1 kpc -2 our results indicate a rapid building of inner stellar mass and bulge assembly within these young systems
in addition such data could also be used to improve the training of both pathologists and ai
systems
ai assistance
in addition such data could also be used to improve the training of both pathologists and ai systems that might support human experts
interestingly we reveal that the beam pattern not only captures the receiver s location information but also implicitly encodes the spatial relationship between the receiver and obstacle which facilitates identifying the optimal airy
beam
near-field beam
interestingly we reveal that the beam pattern not only captures the receiver s location information but also implicitly encodes the spatial relationship between the receiver and obstacle which facilitates identifying the optimal airy beam configuration
the phylogenetic diversity pd of a set of species a is the total weight of edges that are on any path between the root of the
phylogenetic
phylogenetic diversity
the phylogenetic diversity pd of a set of species a is the total weight of edges that are on any path between the root of the phylogenetic tree and a species in a
for maximal matching our techniques further allow us to establish a strong separation between the node-averaged complexity and worst-case complexity of maximal matching in regular
graphs
regular graphs
for maximal matching our techniques further allow us to establish a strong separation between the node-averaged complexity and worst-case complexity of maximal matching in regular graphs by showing that the former is only o 1
this research proposes the use of two separate
distributions
income distribution
this research proposes the use of two separate distributions to more accurately represent the overall data rather than relying on a single distribution
manipulation in cluttered environments is challenging due to spatial dependencies among objects where an improper
manipulation
manipulation ordering
manipulation in cluttered environments is challenging due to spatial dependencies among objects where an improper manipulation order can cause collisions or blocked access
in this paper we estimate host galaxy properties from
spectral
spectral energy distribution
in this paper we estimate host galaxy properties from spectral energy distribution models
in the cold dark environments of pre-stellar cores where the temperatures are below 10 k ne can condense onto the surface of
interstellar
galactic nuclei
in the cold dark environments of pre-stellar cores where the temperatures are below 10 k ne can condense onto the surface of interstellar grains
5 kv while the field-plate pt 011 b eta -ga2o3 sbds achieved an increased
breakdown
breakdown voltage
5 kv while the field-plate pt 011 b eta -ga2o3 sbds achieved an increased breakdown voltage of 2
controllers designed using an accurate model is
robust
control strategy
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
rubin observatory around spiral galaxy m61 ngc 4303 in virgo first
look
milky way
rubin observatory around spiral galaxy m61 ngc 4303 in virgo first look imaging
this paper introduces cypress crop yield prediction via regression on prithvi s encoder for satellite sensing a
deep
deep learning
this paper introduces cypress crop yield prediction via regression on prithvi s encoder for satellite sensing a deep learning model designed for high-resolution intra-field canola yield prediction
modern vision-language models vlms excel at many multimodal tasks yet their grasp of temporal information in video
remains
language agents
modern vision-language models vlms excel at many multimodal tasks yet their grasp of temporal information in video remains weak and crucially under-evaluated
we train neural networks to compress perceptual and semantic factors of stimuli measuring lossiness using the
mathematical
recurrent neural networks
we train neural networks to compress perceptual and semantic factors of stimuli measuring lossiness using the mathematical framework underlying compression
our solutions assume a constant star formation efficiency a constant mass-loading factor and that the yields are linearly dependent on the
interstellar
star-forming region
our solutions assume a constant star formation efficiency a constant mass-loading factor and that the yields are linearly dependent on the interstellar medium abundance with the option of a saturation of the yields at high metallicity
based on this insight we formulate the blockage-resilient beam training task as a multitask learning problem and propose a lightweight attention-based multi-parameter beam training network ampbt-net to jointly predict the angle distance and curvature parameters of the optimal airy beam based on the
beam
airy beam
based on this insight we formulate the blockage-resilient beam training task as a multitask learning problem and propose a lightweight attention-based multi-parameter beam training network ampbt-net to jointly predict the angle distance and curvature parameters of the optimal airy beam based on the beam pattern
dynamic context-aware scene reasoning using vision-language alignment in
zero-shot
spatial reasoning
dynamic context-aware scene reasoning using vision-language alignment in zero-shot real-world scenarios
we provide a comparison of qudit decompositions for several types of trapped
ions
trapped ions
we provide a comparison of qudit decompositions for several types of trapped ions specifically 171 text yb 137 text ba 40 text ca 86 text rb with different selection rules and also decomposition for superconducting qudits
we model dust emission using the themis dust model with the soc radiative
transfer
radiative transfer
we model dust emission using the themis dust model with the soc radiative transfer code