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accordingly several prior studies identified people in videos by comparing their trajectories and wearable
sensor
activity recognition
accordingly several prior studies identified people in videos by comparing their trajectories and wearable sensor measurements
for typical cortical stimuli tens of milliseconds this places the
functional
cognitive neuroscience
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
adaptive trajectory refinement for optimization-based local
planning
motion planning
adaptive trajectory refinement for optimization-based local planning in narrow passages
using the proposed model we derive an analytical formula for viral populations at the cellular level based on viewing
viral
reproduction number
using the proposed model we derive an analytical formula for viral populations at the cellular level based on viewing viral replication as a birth-death process
our framework unifies riesz regression for automatic debiased
machine
machine learning
our framework unifies riesz regression for automatic debiased machine learning covariate balancing targeted maximum likelihood estimation tmle and density-ratio estimation
for typical cortical stimuli tens of milliseconds this places the
functional
functional connectivity
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
certification and classification of linear quantum
error
quantum networks
certification and classification of linear quantum error mitigation methods
this study provides a new perspective for understand
biological
moving mass
this study provides a new perspective for understand biological reaching movement and offers a potential platform for future hydrodynamic research
in this work we introduce cave the first benchmark of real-world
visual
anomaly detection
in this work we introduce cave the first benchmark of real-world visual anomalies
we show that if the system is controllable then incorporating this as prior knowledge does not relax the conditions required for
data-driven
optimal control
we show that if the system is controllable then incorporating this as prior knowledge does not relax the conditions required for data-driven stabilization
the theory conceptualizes social systems as ensembles of social atoms capable of absorbing and emitting quantized units of
social
complex systems
the theory conceptualizes social systems as ensembles of social atoms capable of absorbing and emitting quantized units of social energy
a view-conditional video inpainting model is trained to learn a robust geometry prior by denoising realistically synthesized warped
images
image fusion
a view-conditional video inpainting model is trained to learn a robust geometry prior by denoising realistically synthesized warped images and to inpaint occluded or missing regions across virtual viewpoints eliminating the need for explicit 3d annotations
symbiosis emergence and abandonment in nature a coordination
game
collective systems
symbiosis emergence and abandonment in nature a coordination game approach
our main contribution is a set of reductions and decompositions that transform dyck and
tree
tree edit distance
our main contribution is a set of reductions and decompositions that transform dyck and tree edit distance instances into efficiently maintainable string edit distance instances which can be approximated within a n o 1 factor in n o 1 update time
this work establishes a new design paradigm for high-efficiency te materials by exploiting substantial orbital overlap in structurally weakly bonded quasi-one-dimensional
systems
electronic structure
this work establishes a new design paradigm for high-efficiency te materials by exploiting substantial orbital overlap in structurally weakly bonded quasi-one-dimensional systems opening promising avenues for the discovery and engineering of next-generation high-performance te materials
results show consistent integration of non-verbal input into coherent
llm
llm responses
results show consistent integration of non-verbal input into coherent llm outputs with participants highlighting conversational fluidity
here we aim to characterize misconceptions that users of conversational llm-based assistants may have in
programming
llm reasoning
here we aim to characterize misconceptions that users of conversational llm-based assistants may have in programming contexts
the projected trends are improved while still declining for birds in particular farmland
species
butterfly species
the projected trends are improved while still declining for birds in particular farmland species under the scenarios that meet the conservation objectives with few effects on butterflies
these individuals contribute to the spread of the
epidemic
infectious individuals
these individuals contribute to the spread of the epidemic and pose a significant challenge to public health policies
most networks encountered in nature society and technology have
weighted
network structures
most networks encountered in nature society and technology have weighted edges representing the strength of the interaction association between their vertices
however it remains unclear which search direction to construct a
gradient
gradient descent
however it remains unclear which search direction to construct a gradient estimator is more appropriate and how to set the algorithmic parameters
this theory further promotes nonlinear transport as a probe of
geometric
nonlinear transport
this theory further promotes nonlinear transport as a probe of geometric effects and phase transitions in quantum materials
monte carlo simulations demonstrate that our approach exhibits excellent finite sample performance for both oos prediction and
policy
policy learning
monte carlo simulations demonstrate that our approach exhibits excellent finite sample performance for both oos prediction and policy evaluation
wimhf characterizes both 1 the preferences a dataset is capable of measuring and 2 the
preferences
preference data
wimhf characterizes both 1 the preferences a dataset is capable of measuring and 2 the preferences that the annotators actually express
speculative decoding sd accelerates autoregressive generation in serving
systems
speculative decoding
speculative decoding sd accelerates autoregressive generation in serving systems but its behavior under rl training remains largely unexplored
on hardness and approximation of broadcasting in
sparse
compressed indexing
on hardness and approximation of broadcasting in sparse graphs
to address these gaps we propose a hybrid visual
tracking
multi-object tracking
to address these gaps we propose a hybrid visual tracking framework that bridges advanced perception with real-time servo control
our augmented reality experience involves interacting with ever-shifting sound bubbles that the user engages with by stepping into color-coded bubbles within the assigned area using a standalone
ar
augmented reality
our augmented reality experience involves interacting with ever-shifting sound bubbles that the user engages with by stepping into color-coded bubbles within the assigned area using a standalone ar headset
during exploration lagmemo constructs a unified 3d
language
vision-language models
during exploration lagmemo constructs a unified 3d language memory
central to the approach is a computationally efficient and continuously differentiable condition for detecting
collisions
obstacle avoidance
central to the approach is a computationally efficient and continuously differentiable condition for detecting collisions with ellipsoidal obstacles
results show that lower and more homogeneously distributed learning rates promote
scale-free
scale-free networks
results show that lower and more homogeneously distributed learning rates promote scale-free networks while higher or more heterogeneously distributed learning rates lead to the emergence of core-periphery topologies
bridging a key theoretical challenge in diffusion-based generative modeling our results extend convergence
theory
diffusion models
bridging a key theoretical challenge in diffusion-based generative modeling our results extend convergence theory to more realistic data distributions and practical ode solvers
estimating the causal dose-response function is challenging particularly when data from a single source are insufficient to estimate responses precisely across all
exposure
causal inference
estimating the causal dose-response function is challenging particularly when data from a single source are insufficient to estimate responses precisely across all exposure levels
in ate estimation the balancing weights and the regression functions of the outcome play important roles where the
balancing
riesz regression
in ate estimation the balancing weights and the regression functions of the outcome play important roles where the balancing weights are referred to as the riesz representer bias-correction term and clever covariates depending on the context
we prove that combining diffusion models with an annealed variant of
langevin
langevin dynamics
we prove that combining diffusion models with an annealed variant of langevin dynamics achieves conditional sampling in polynomial time using merely an l 4 bound on the score error
the key task of machine learning is to minimize the
loss
loss function
the key task of machine learning is to minimize the loss function that measures the model fit to the training data
understanding the diversity of star formation histories sfhs of galaxies is
key
star formation rates
understanding the diversity of star formation histories sfhs of galaxies is key to reconstructing their evolutionary paths
building on this insight we propose attncache a framework that accelerates the
prefill
llm post-training
building on this insight we propose attncache a framework that accelerates the prefill stage of llm inference by retrieving and reusing similar attention maps
exploring emergent topological properties in socio-economic networks through
learning
scale-free networks
exploring emergent topological properties in socio-economic networks through learning heterogeneity
in our model individuals of one species possess cognitive abilities to perceive environmental cues and assess the local density of the
species
ecological interactions
in our model individuals of one species possess cognitive abilities to perceive environmental cues and assess the local density of the species they dominate in the spatial competition for natural resources
after a short period of evolution during which physical relevant information can be loaded a quantum weak measurement is
applied
quantum coherence
after a short period of evolution during which physical relevant information can be loaded a quantum weak measurement is applied to the internal state of the atoms
look at that distractor dynamic translation
gain
translation gain
look at that distractor dynamic translation gain under low perceptual load in virtual reality
a human-vector susceptible-infected-susceptible model for analyzing and controlling the
spread
infectious individuals
a human-vector susceptible-infected-susceptible model for analyzing and controlling the spread of vector-borne diseases
allowing the order of quantum operations to exist in superposition is known to
open
quantum mechanics
allowing the order of quantum operations to exist in superposition is known to open new routes for thermodynamic tasks
inference on local variable importance measures for heterogeneous
treatment
treatment regimes
inference on local variable importance measures for heterogeneous treatment effects
the structure of relation decoding linear operators in large
language
large language models llms
the structure of relation decoding linear operators in large language models
these measurements provided useful insights into how water and lubricants were retained and
distributed
measurements demonstrated
these measurements provided useful insights into how water and lubricants were retained and distributed under static conditions
despite decades of effort simulating individual
mobility
mobility networks
despite decades of effort simulating individual mobility remains challenging because of its complex context-dependent and exploratory nature
together these findings suggest that while navigational aids presented in
ar
augmented reality
together these findings suggest that while navigational aids presented in ar can enhance search task performance users may pay less attention to the physical environment which could have undesirable side-effects
the fourth example has two binary and absorbing
treatments
treatment effect boundaries
the fourth example has two binary and absorbing treatments where the second treatment always happens after the first
5 spectroscopically-confirmed quiescent galaxies in the uds and egs fields at 3 z 5 with nirspec prism spectroscopy from rubies and other public jwst
nirspec
dark matter
5 spectroscopically-confirmed quiescent galaxies in the uds and egs fields at 3 z 5 with nirspec prism spectroscopy from rubies and other public jwst nirspec programs
approximately optimal distributed controls for high-dimensional stochastic
systems
predictive control
approximately optimal distributed controls for high-dimensional stochastic systems with pairwise interaction through controls
specifically we focus on downlink dl bistatic sensing where the
user
wireless systems
specifically we focus on downlink dl bistatic sensing where the user equipment ue performs measurements from reflected sensing signals and provides feedback to the network nw
control-var can estimate average treatment effects on the treated for dummy policies or
average
average treatment effect
control-var can estimate average treatment effects on the treated for dummy policies or average causal responses over time for continuous policies
these findings suggest that resting-state eeg connectivity patterns can index stable
cognitive
cognitive science
these findings suggest that resting-state eeg connectivity patterns can index stable cognitive traits such as creativity
evontree ontology rule-guided self-evolution of large
language
large language models llms
evontree ontology rule-guided self-evolution of large language models
by using the meta-algorithm with the measured
continuous
approximation ratio
by using the meta-algorithm with the measured continuous greedy algorithm we obtain a 1-1 e -approximation resp
here we show that lyman- alpha ly alpha radiation pressure limits the gas mass converted into
stars
stellar mass
here we show that lyman- alpha ly alpha radiation pressure limits the gas mass converted into stars particularly in primordial environments
we review the limitations of current ai methods define core principles of neurocognitive-inspired
intelligence
artificial intelligence
we review the limitations of current ai methods define core principles of neurocognitive-inspired intelligence and propose a modular biologically inspired architecture that emphasizes integration embodiment and adaptability
we introduce two new city population datasets that use consistent
city
large cities
we introduce two new city population datasets that use consistent city definitions across countries and over time
the rate improves with the order k increases yielding
better
convergence rate
the rate improves with the order k increases yielding better rates than existing first to third-order approaches
leveraging this equivalence we propose a novel regularization method for
policy
policy evaluation
leveraging this equivalence we propose a novel regularization method for policy learning
we begin our journey by recalling the fundamentals of
probability
density estimation
we begin our journey by recalling the fundamentals of probability theory that underlie one of its most significant applications to real-world problems parametric estimation
we develop a direct debiased machine learning framework comprising neyman targeted estimation and generalized
riesz
riesz regression
we develop a direct debiased machine learning framework comprising neyman targeted estimation and generalized riesz regression
however they exhibit limited exploration due to reliance on on-policy rollouts where confined to the current
policy
policy learning
however they exhibit limited exploration due to reliance on on-policy rollouts where confined to the current policy s distribution resulting in narrow trajectory diversity
our results lay a fundamental understanding of how pre-trained llms manipulate numbers and outline the
potential
large language models llms
our results lay a fundamental understanding of how pre-trained llms manipulate numbers and outline the potential of more accurate probing techniques in addressed refinements of llms architectures
we empirically validate the performance of our online algorithm with
experiments
real-world datasets
we empirically validate the performance of our online algorithm with experiments on real datasets
robust non-negative proximal gradient algorithm for
inverse
gradient descent
robust non-negative proximal gradient algorithm for inverse problems
to address this we analyze one year of smartphone position
data
human mobility
to address this we analyze one year of smartphone position data from over one million users in the tokyo metropolitan area to extract high-resolution commuting trajectories
as a third alternative we propose artificial
intelligence
artificial intelligence
as a third alternative we propose artificial intelligence ai enabled representatives trained on individual shareholder preferences to act as proxies and vote on their behalf
mightee-hi the hi mass-stellar mass relation of massive galaxies and the
hi
hi mass
mightee-hi the hi mass-stellar mass relation of massive galaxies and the hi mass function at 0
reinforcement learning rl algorithms are designed to optimize problem-solving by
learning
reinforcement learning
reinforcement learning rl algorithms are designed to optimize problem-solving by learning actions that maximize rewards a task that becomes particularly challenging in random and nonstationary environments
modulating the free-electron wave function with light brings new opportunities to create attosecond electron pulse trains to probe the
quantum
photonic circuits
modulating the free-electron wave function with light brings new opportunities to create attosecond electron pulse trains to probe the quantum coherence of systems with significantly improved spatial resolution and to generate classical and non-classical states of light with wide tunability
the current advancements in beam shaping techniques their impact on the nanoparticle characteristics and their broader implications for scaling pulsed laser
ablation
pulsed laser ablation liquids
the current advancements in beam shaping techniques their impact on the nanoparticle characteristics and their broader implications for scaling pulsed laser ablation in liquids to meet industrial demands are highlighted offering a comprehensive perspective on the future of this dynamic field
in rigorous in-house evaluations our model was launched and achieved performance even
superior
evaluation metrics
in rigorous in-house evaluations our model was launched and achieved performance even superior to human experts proving our framework s ability to translate offline data into tangible real-world impact
random utility models rums are a classical framework for modeling user preferences and play a key role in reward modeling for reinforcement
learning
policy learning
random utility models rums are a classical framework for modeling user preferences and play a key role in reward modeling for reinforcement learning from human feedback rlhf
integrating legal and logical specifications in perception prediction and planning for automated
driving
autonomous driving
integrating legal and logical specifications in perception prediction and planning for automated driving a survey of methods
we present a dataset generated to investigate
urban
urban systems
we present a dataset generated to investigate urban heat and thermal perception across five neighborhoods in the barcelona metropolitan area
centered on proposing jne as a novel interpretability metric we validated its effectiveness through controlled simulation experiments on various activation functions and network architectures and further verified it on real fmri data demonstrating a hierarchical progression of
nonlinear
artificial neural
centered on proposing jne as a novel interpretability metric we validated its effectiveness through controlled simulation experiments on various activation functions and network architectures and further verified it on real fmri data demonstrating a hierarchical progression of nonlinear characteristics from primary to ...
we justify the feasibility of this constraint set and
leverage
bilevel optimization
we justify the feasibility of this constraint set and leverage benamou--brenier s results to establish the existence of a global optimal solution
model inversion with layer-specific modeling and alignment for data-free
continual
continual learning
model inversion with layer-specific modeling and alignment for data-free continual learning
an alternative hypothesis is that discrimination is supported by lossy
compression
visual stimuli
an alternative hypothesis is that discrimination is supported by lossy compression of visual inputs efficiently coding sensory information by discarding seemingly irrelevant details
in this paper we investigate the distinct observational signatures produced from such tde disks by performing radiative
transfer
radiative transfer
in this paper we investigate the distinct observational signatures produced from such tde disks by performing radiative transfer calculations upon previous super-eddington disk simulations
x-ray and variability selected agn have higher average star formation rates than those
selected
stellar population
x-ray and variability selected agn have higher average star formation rates than those selected with optical narrow line spectroscopic diagrams
01 pc and follow the gas inflows from the
interstellar
circumgalactic medium
01 pc and follow the gas inflows from the interstellar medium ism to the black hole bh allowing for the self-consistent emergence of circumnuclear discs cnds
we show that a classical learning-based method as a simple baseline in real-world affect prediction produces better estimates from
signals
physiological signals
we show that a classical learning-based method as a simple baseline in real-world affect prediction produces better estimates from signals captured on egocentric vision systems than processing physiological signals
in this two-paper series we present a straightforward mathematical model for synthesizing quasar absorption line profiles from sight lines through idealized spatial-kinematic models of the
circumgalactic
circumgalactic medium
in this two-paper series we present a straightforward mathematical model for synthesizing quasar absorption line profiles from sight lines through idealized spatial-kinematic models of the circumgalactic medium cgm and their host galaxies
our approach employs linear models to decode spiking activity into multiple latent visual spaces including clip and vdvae embeddings and reconstruct
images
higher-order visual
our approach employs linear models to decode spiking activity into multiple latent visual spaces including clip and vdvae embeddings and reconstruct images using state-of-the-art generative models
risks and opportunities in human-machine teaming in operationalizing machine learning
target
human-machine teaming
risks and opportunities in human-machine teaming in operationalizing machine learning target variables
pattern formation in agent-based and pde models for
evolutionary
game theory
pattern formation in agent-based and pde models for evolutionary games with payoff-driven motion
we present a suite of high-resolution simulations to study how different stellar feedback channels regulate the growth of central intermediate-mass black
holes
black hole mass
we present a suite of high-resolution simulations to study how different stellar feedback channels regulate the growth of central intermediate-mass black holes imbhs in dwarf galaxies hosting nuclear star clusters nscs
a closed-form approximation-free control law is derived to ensure that each
agent
control strategy
a closed-form approximation-free control law is derived to ensure that each agent remains within its evolving stt thereby avoiding dynamic obstacles while also preventing inter-agent collisions in a socially aware manner and reaching the target within a prescribed time
the theoretical findings are validated through comprehensive
simulation
simulation studies
the theoretical findings are validated through comprehensive simulation studies
our agent reliably solves a continuous maze without explicit geometric cues with performance depending on the length of the
recurrent
recurrent neural
our agent reliably solves a continuous maze without explicit geometric cues with performance depending on the length of the recurrent sequence
spiral structure diversity in milky way analogs from tng50 the
role
quiescent galaxies
spiral structure diversity in milky way analogs from tng50 the role of gas and disk dynamics
such an approach does not require full access to the
original
existing methods
such an approach does not require full access to the original dataset or the model addressing the challenges of existing methods
the simulated galaxies reproduce the observed stellar-to-halo mass mass--metallicity and size--mass relations yielding stellar
masses
star formation rates
the simulated galaxies reproduce the observed stellar-to-halo mass mass--metallicity and size--mass relations yielding stellar masses of 10 6-10 8 m_ odot and metallicities consistent with those of local group dwarf galaxies
this entanglement biases conventional brain
encoding
cognitive neuroscience
this entanglement biases conventional brain encoding analyses toward linguistically shallow features e
we consider the problem of estimating a causal
effect
causal effect
we consider the problem of estimating a causal effect in a multi-domain setting
the c-ddpm-assisted framework achieves significantly lower estimation errors in both angle and distance tracking demonstrating the potential of generative models for integrated
sensing
integrated sensing
the c-ddpm-assisted framework achieves significantly lower estimation errors in both angle and distance tracking demonstrating the potential of generative models for integrated sensing and communications