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experimental results show that our method reconstructs the intrinsic
motion
optical flow
experimental results show that our method reconstructs the intrinsic motion hierarchy in 1d and 2d cases and produces more realistic and interpretable deformations compared to the baseline on dynamic 3d gaussian splatting scenes
imbalanced data where the positive samples represent only a small proportion compared to the negative samples makes it challenging for classification problems to balance the false positive and false
negative
imbalanced data
imbalanced data where the positive samples represent only a small proportion compared to the negative samples makes it challenging for classification problems to balance the false positive and false negative rates
here we present an information-theoretic framework to identify the most important
correlations
fmri data
here we present an information-theoretic framework to identify the most important correlations which provide the most accurate predictions of neural states
cole gottlieb and lewenstein stoc 2004 proposed k -errata
trees
-errata trees
cole gottlieb and lewenstein stoc 2004 proposed k -errata trees a family of text indexes supporting approximate pattern matching queries of several types
by uniting researchers policymakers industry practitioners and community advocates we aim to identify shared challenges in online
safety
online safety
by uniting researchers policymakers industry practitioners and community advocates we aim to identify shared challenges in online safety research highlight gaps in current knowledge and establish common research priorities
we introduce debate2create d2c a framework in which large
language
large language
we introduce debate2create d2c a framework in which large language model llm agents engage in a structured dialectical debate to jointly optimize a robot s design and its reward function
we study the convergence of off-policy td 0 with linear function approximation when used to approximate the expected discounted
reward
reinforcement learning
we study the convergence of off-policy td 0 with linear function approximation when used to approximate the expected discounted reward in a markov chain
this result unifies prior approaches and shows that essentially all efficient suffix
array
suffix array
this result unifies prior approaches and shows that essentially all efficient suffix array representations can be expressed via prefix-select structures
distributed quantum computing dqc provides a promising route toward scalable quantum
computation
quantum computing
distributed quantum computing dqc provides a promising route toward scalable quantum computation where entanglement-assisted locc and circuit knitting represent two complementary approaches
hybrid dqn-td3 reinforcement learning for autonomous navigation in
dynamic
reinforcement learning
hybrid dqn-td3 reinforcement learning for autonomous navigation in dynamic environments
this paper develops a risk-adjusted alternative to standard optimal policy learning opl for observational data by importing roy s 1952 safety-first principle into the treatment
assignment
treatment assignment
this paper develops a risk-adjusted alternative to standard optimal policy learning opl for observational data by importing roy s 1952 safety-first principle into the treatment assignment problem
to address these challenges we propose a spatial prior-guided cross dual encoder network spg-cdenet a novel two-stage segmentation paradigm designed to improve multi-organ
segmentation
multi-organ segmentation
to address these challenges we propose a spatial prior-guided cross dual encoder network spg-cdenet a novel two-stage segmentation paradigm designed to improve multi-organ segmentation accuracy
the picard-lagrange framework for higher-order langevin
monte
monte carlo
the picard-lagrange framework for higher-order langevin monte carlo
through sed fitting we determine the luminosities of these
quasars
quiescent galaxies
through sed fitting we determine the luminosities of these quasars and find that their dust torus sizes follow the established r_ dust -l_ agn relation reported in previous studies
statistical analysis of these silver datasets shows that six discourse relations namely cause purpose contrast
cause
pragmatic theories
statistical analysis of these silver datasets shows that six discourse relations namely cause purpose contrast cause belief concession and condition play a crucial role in persuasive texts especially in the use of loaded language exaggeration minimisation repetition and to cast doubt
tailoring reproducing kernels for optimal
control
control systems
tailoring reproducing kernels for optimal control via policy iteration
we provide a detailed structural description along with an abstract model of this
data
data structure
we provide a detailed structural description along with an abstract model of this data structure
these associations are recorded in combinatorial neural codes motivating the following mathematical question which neural
codes
neural codes
these associations are recorded in combinatorial neural codes motivating the following mathematical question which neural codes are generated by a collection of convex open sets in euclidean space
allowing the order of quantum operations to exist in superposition is known to
open
quantum channels
allowing the order of quantum operations to exist in superposition is known to open new routes for thermodynamic tasks
extensions to longitudinal data dynamic treatment regimes and multiplicative
instrumental
instrumental variable
extensions to longitudinal data dynamic treatment regimes and multiplicative instrumental variables are further developed
this work lays out the architectural blueprint for extending hpcqc integration to support pulse-level quantum
operations
quantum computing
this work lays out the architectural blueprint for extending hpcqc integration to support pulse-level quantum operations without disrupting state-of-the-art classical workflows
this study presents a promising step toward a more flexible and integrated real-time traffic incident
detection
incident detection
this study presents a promising step toward a more flexible and integrated real-time traffic incident detection system with significant implications for the operational efficiency and responsiveness of modern transportation management
while autonomous racing performance in time-trial scenarios has seen significant progress and development
autonomous
autonomous driving
while autonomous racing performance in time-trial scenarios has seen significant progress and development autonomous wheel-to-wheel racing and overtaking are still severely limited
such simulations -- for which classical methods are often inaccurate -- are critical to advancing our knowledge and understanding of
quantum
quantum technologies
such simulations -- for which classical methods are often inaccurate -- are critical to advancing our knowledge and understanding of quantum chemistry and materials underpinning a wide range of fields from biochemistry to clean-energy technologies and chemical synthesis
2023 shows that nn matching is an instance of density-ratio
estimation
density-ratio estimation
2023 shows that nn matching is an instance of density-ratio estimation with their new density-ratio estimator
to enhance the interaction between the global and
local
cross dual encoder network
to enhance the interaction between the global and local encoders a symmetric cross-attention module is proposed across all layers of the encoders to fuse and refine features
the prompts are collaboratively optimized via traditional
federated
prompt tuning
the prompts are collaboratively optimized via traditional federated averaging technique on the same
we characterize the properties of the categorical
fisher
fisher information
we characterize the properties of the categorical fisher information showing that its eigenvectors give the most discriminant directions at each point of the projection space
this work advances this vision by identifying the fundamental principles of human ai
collaboration
human-ai interaction
this work advances this vision by identifying the fundamental principles of human ai collaboration within uncertainty quantification a key component of reliable decision making
the focus is on the mathematical description of these interactions and their role in deriving differential
systems
transient dynamics
the focus is on the mathematical description of these interactions and their role in deriving differential systems that describe the aforementioned dynamics
we adapt the cross-validated log likelihood cvll function to simultaneously select the order of the prewhitening
vector
vector autoregression
we adapt the cross-validated log likelihood cvll function to simultaneously select the order of the prewhitening vector autoregression var and the bandwidth
a central transmitter equipped with a spontaneous parametric down-conversion spdc source emits time-energy entangled photon pairs directing user photons toward spatially defined grids based on estimated user positions while retaining reference
photons
optical communication
a central transmitter equipped with a spontaneous parametric down-conversion spdc source emits time-energy entangled photon pairs directing user photons toward spatially defined grids based on estimated user positions while retaining reference photons for timestamping
from an ecological perspective the sign of the propagation speed determines the long-term outcome of competition between two species and thus plays a central role in predicting the success or failure of invasion of an alien
species
ecological interactions
from an ecological perspective the sign of the propagation speed determines the long-term outcome of competition between two species and thus plays a central role in predicting the success or failure of invasion of an alien species into habitats occupied by a native species
to promote climate adaptation and mitigation it is crucial to understand stakeholder perspectives and knowledge gaps on land use and
climate
environmental change
to promote climate adaptation and mitigation it is crucial to understand stakeholder perspectives and knowledge gaps on land use and climate changes
we consider populations evolving according to natural selection mutation and recombination and assume that the genomes of all or a representative
selection
evolutionary game
we consider populations evolving according to natural selection mutation and recombination and assume that the genomes of all or a representative selection of individuals are known
overall we suggest that hvc ac-iii is entering the
galactic
active galactic
overall we suggest that hvc ac-iii is entering the galactic wim layer and being sculpted by ram pressure into a droplet-like morphology providing a valuable case for studying the structure formation turbulence origin and dynamic evolution of hvcs as well as the physical properties of the ambient medium
we develop a novel bicriteria approximation algorithm and show a significant
reduction
-approximation algorithm
we develop a novel bicriteria approximation algorithm and show a significant reduction of medical deserts across states in the u
finally we identify three distinct causal archetypes tightly coupled pattern-heterogeneous and workday-attenuated which map pathways from
causal
comorbidity networks
finally we identify three distinct causal archetypes tightly coupled pattern-heterogeneous and workday-attenuated which map pathways from causal diagnosis to intervention
these subclumps exhibit parabolic morphologies consistent with ram-pressure-confined droplets with their heads tending to point toward the
galactic
star formation
these subclumps exhibit parabolic morphologies consistent with ram-pressure-confined droplets with their heads tending to point toward the galactic plane
a multi-agent large language model framework to automatically assess performance of a clinical ai
triage
triage tool
a multi-agent large language model framework to automatically assess performance of a clinical ai triage tool
using first-principles calculations we show that the resulting magneto-electric
coupling
magnetic anisotropy
using first-principles calculations we show that the resulting magneto-electric coupling alpha zz can be as large as 0
in this work we attempt to add robustness into existing region proposal network-deep convolutional neural
network
deep network
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
designing efficient quantum circuits is a central bottleneck to exploring the potential of quantum
computing
fault-tolerant quantum
designing efficient quantum circuits is a central bottleneck to exploring the potential of quantum computing particularly for noisy intermediate-scale quantum nisq devices where circuit efficiency and resilience to errors are paramount
comparison with exact quantum-mechanical results in one- and two-dimensional
models
quantum mechanics
comparison with exact quantum-mechanical results in one- and two-dimensional models demonstrates that it has a reasonably high accuracy similar to that reported for instanton theory in the symmetric case
human activity recognition har via wi-fi channel state information csi presents a privacy-preserving contactless
sensing
activity recognition
human activity recognition har via wi-fi channel state information csi presents a privacy-preserving contactless sensing approach suitable for smart homes healthcare monitoring and mobile iot systems
through state machine simulation we show that meta-rl with predictive modules consistently generates more interpretable representations that better approximate bayes-optimal belief states compared to conventional
meta-rl
reinforcement learning
through state machine simulation we show that meta-rl with predictive modules consistently generates more interpretable representations that better approximate bayes-optimal belief states compared to conventional meta-rl across a wide variety of tasks even when both achieve optimal policies
transmission neural networks transnns proposed by gao and caines 2022 serve as both virus spread models over networks and
neural
neural network
transmission neural networks transnns proposed by gao and caines 2022 serve as both virus spread models over networks and neural network models with tuneable activation functions
by developing generator-to-load carbon emission distribution factors through data-driven technique the analytical formulas for both average and marginal
carbon
carbon emissions
by developing generator-to-load carbon emission distribution factors through data-driven technique the analytical formulas for both average and marginal carbon emissions can be derived and integrated seamlessly into dc opf models as linear constraints
key topological metrics including scale-free exponents estrada heterogeneity and assortativity reveal that both the speed and variability of
learning
scale-free networks
key topological metrics including scale-free exponents estrada heterogeneity and assortativity reveal that both the speed and variability of learning critically shape system rationality and network architecture
jogs joint optimization of pose estimation and 3d
gaussian
pose estimation
jogs joint optimization of pose estimation and 3d gaussian splatting
we evaluate its reasoning behavior across 12 dimensions including spatial geometric physical temporal and embodied
logic
reasoning tasks
we evaluate its reasoning behavior across 12 dimensions including spatial geometric physical temporal and embodied logic systematically characterizing both its strengths and failure modes
although mean-field approximations are commonly invoked to simplify
disease
disease transmission
although mean-field approximations are commonly invoked to simplify disease forecasting on networks they fail to account for these correlations by assuming that infectious individuals are well-mixed within a population leading to inaccurate predictions of infection numbers over time
to this end we introduce a new dataset covering a wide range of formal patterns of
reasoning
reasoning capabilities
to this end we introduce a new dataset covering a wide range of formal patterns of reasoning in both normative and epistemic domains while also incorporating non-formal cognitive factors that influence human reasoning
twin-field quantum key distribution protocols security and
open
quantum correlations
twin-field quantum key distribution protocols security and open problems
the authors developed a new forward kinematics-based
calibration
calibration plate
the authors developed a new forward kinematics-based calibration method using denavit-hartenberg convention and used the stewart platform tiger 66
together our results show that sparse input synergizes with sequence-generating dynamics providing both a mechanistic account of place cell sequences in the mammalian hippocampus and a simple inductive bias for reinforcement learning based on sparse egocentric inputs in
navigation
recurrent neural
together our results show that sparse input synergizes with sequence-generating dynamics providing both a mechanistic account of place cell sequences in the mammalian hippocampus and a simple inductive bias for reinforcement learning based on sparse egocentric inputs in navigation tasks
josephson parametric devices are widely used in superconducting quantum
computing
super-heisenberg scaling
josephson parametric devices are widely used in superconducting quantum computing research but suffer from an inherent gain-bandwidth trade-off
to address this issue we propose a learning-based
csi
channel estimation
to address this issue we propose a learning-based csi prediction framework that leverages temporal correlations in wireless channels to forecast future signal to interference plus noise ratio sinr values
this entanglement biases conventional brain
encoding
neural representations
this entanglement biases conventional brain encoding analyses toward linguistically shallow features e
44 and an additional 47 galaxies belonging to a
diffuse
milky way
44 and an additional 47 galaxies belonging to a diffuse foreground structure at z 3
among tuning mechanisms all-optical coherent control stands out because it requires no material or geometric change enabling ultrafast low-energy interference-based modulation of
amplitude
coherent control
among tuning mechanisms all-optical coherent control stands out because it requires no material or geometric change enabling ultrafast low-energy interference-based modulation of amplitude phase and polarization in ultrathin devices
to date such directional control has been achieved using
components
coherent control
to date such directional control has been achieved using components whose overall footprint is larger than the emission wavelength and often rely on lossy plasmonic components
engineering atom-photon hybridization with density-modulated atomic
ensembles
single photons
engineering atom-photon hybridization with density-modulated atomic ensembles in coupled cavities
overall our model provides a framework for investigating the importance of cognitive and
social
spatial structure
overall our model provides a framework for investigating the importance of cognitive and social structures in determining human-environment dynamics
to address these issues this study introduces the
human-ai
ai assistance
to address these issues this study introduces the human-ai re synergy model hare-sm a conceptual framework that integrates ai-driven analysis with human oversight to improve requirements elicitation analysis and validation
laplace noise user interference while retaining competitive
accuracy
high accuracy
laplace noise user interference while retaining competitive accuracy under matched conditions
designing efficient quantum circuits is a central bottleneck to exploring the potential of quantum
computing
quantum advantage
designing efficient quantum circuits is a central bottleneck to exploring the potential of quantum computing particularly for noisy intermediate-scale quantum nisq devices where circuit efficiency and resilience to errors are paramount
riesz regression covariate balancing dre and the matching estimator are methods for estimating the
balancing
covariate balancing
riesz regression covariate balancing dre and the matching estimator are methods for estimating the balancing weights where riesz regression is essentially equivalent to dre in the ate context the matching estimator is a special case of dre and dre is in a dual relationship with covariate balancing
through simulations we demonstrate and evaluate the efficacy of the proposed method in identifying important
covariates
covariate balancing
through simulations we demonstrate and evaluate the efficacy of the proposed method in identifying important covariates in the presence of non-gaussian model errors
this paper presents a human-automation shared lane-changing control framework that preserves driver authority while allowing automated assistance to achieve stable maneuvers in the presence of
driver
collision avoidance
this paper presents a human-automation shared lane-changing control framework that preserves driver authority while allowing automated assistance to achieve stable maneuvers in the presence of driver s behavioral uncertainty
this work presents a theoretical and numerical investigation of the symplectic gradient adjustment sga method and of a low-rank sga lrsga method for efficiently
solving
augmented lagrangian
this work presents a theoretical and numerical investigation of the symplectic gradient adjustment sga method and of a low-rank sga lrsga method for efficiently solving two-objective optimization problems in the framework of nash games
this research provides an indication of potential advances in the exploration of all four fundamental aspects of tool usage enabling robots to master the intricate art of
tool
tool usage
this research provides an indication of potential advances in the exploration of all four fundamental aspects of tool usage enabling robots to master the intricate art of tool manipulation across diverse tasks
the control design is integrated into the model via an optimal
control
optimal control
the control design is integrated into the model via an optimal control framework solved numerically using the forward backward sweep method enabling the exploration of intervention strategies on epidemic dynamics including infection prevalence hospitalization burden and the effective reproduction number
these findings provide both mechanistic insights into neural computation and actionable principles for designing adaptive
ai
artificial intelligence
these findings provide both mechanistic insights into neural computation and actionable principles for designing adaptive ai systems
to project the future of common biodiversity in these scenarios we translate these pressure changes into expected variations of abundances for all common bird and
butterfly
butterfly species
to project the future of common biodiversity in these scenarios we translate these pressure changes into expected variations of abundances for all common bird and butterfly species as well as for the multi-species indicators used to monitor common biodiversity status in europe
using the next generation matrix method we derive an analytical expression for the basic
reproduction
reproduction number
using the next generation matrix method we derive an analytical expression for the basic reproduction number mathcal r_0
spatial-kinematic absorption models of the
circumgalactic
circumgalactic medium
spatial-kinematic absorption models of the circumgalactic medium
we propose reasoning curriculum a simple two-stage curriculum that first elicits
reasoning
mathematical reasoning
we propose reasoning curriculum a simple two-stage curriculum that first elicits reasoning skills in pretraining-aligned domains such as math then adapts and refines these skills across other domains via joint rl
this variation is reflected as a bimodal split in the estimated route parameters indicating that for certain attributes commuters fall into two distinct
groups
human mobility
this variation is reflected as a bimodal split in the estimated route parameters indicating that for certain attributes commuters fall into two distinct groups with contrasting preference signs
extensive experiments across five additional benchmarks math
reasoning
reasoning capabilities
extensive experiments across five additional benchmarks math reasoning code generation and question answering and various sota foundation models validate the broad applicability and robustness of our approach
furthermore using a synthetic preference dataset that enables controlled manipulation of values we find that different preference optimization algorithms lead to different value alignment outcomes even when
preference
preference optimization
furthermore using a synthetic preference dataset that enables controlled manipulation of values we find that different preference optimization algorithms lead to different value alignment outcomes even when preference data is held constant
3 we show this dependency is almost optimal to get a
poly
time complexity
3 we show this dependency is almost optimal to get a poly n log lambda -time complexity
in third-order nonlinear transport a voltage can be measured in response to the cube of a driving current as a result of the quantum
geometric
third-order nonlinear transport
in third-order nonlinear transport a voltage can be measured in response to the cube of a driving current as a result of the quantum geometric effects which has attracted tremendous attention
additionally the refined annotation guidelines increase agreement among different
llm
large language models llms
additionally the refined annotation guidelines increase agreement among different llm models
adaptive surrogate gradients for sequential reinforcement
learning
imitation learning
adaptive surrogate gradients for sequential reinforcement learning in spiking neural networks
the dominant paradigm in machine learning is to assess model performance based on average loss across all samples in some
test
machine learning
the dominant paradigm in machine learning is to assess model performance based on average loss across all samples in some test set
i argue that the classical lot hypothesis fodor 1975 is ruled out the version of lot that is best supported by empirical evidence is the nonclassical lot thesis that some neural representations mirror some of the structure of natural
language
large language models
i argue that the classical lot hypothesis fodor 1975 is ruled out the version of lot that is best supported by empirical evidence is the nonclassical lot thesis that some neural representations mirror some of the structure of natural language and represent in a language-like way yet they encode information nondigitally and are processed by ordinary nondigital and hence nonclassical neural computations that rely not only on syntactic structure but many other features
results demonstrate that the gains in precision and
efficiency
efficiency bound
results demonstrate that the gains in precision and efficiency can be substantial
this essay provides a critical overview of the
mathematical
statistical physics
this essay provides a critical overview of the mathematical kinetic theory of active particles which is used to model and study collective systems consisting of interacting living entities such as those involved in behavior and evolution
a novel two-stage optimization approach mitigates numerical issues
arising
optimization problem
a novel two-stage optimization approach mitigates numerical issues arising from the structure of the resulting optimal control problem
additionally the dynamics features the periodic transfer of the spin to the maximally stretched state starting from a
superposition
quantum mechanics
additionally the dynamics features the periodic transfer of the spin to the maximally stretched state starting from a superposition state
the growing integration of artificial intelligence ai into human cognition raises a fundamental question does ai merely
improve
ai systems
the growing integration of artificial intelligence ai into human cognition raises a fundamental question does ai merely improve efficiency or does it alter how we think
the simulations include detailed modeling of star formation
chemical
stellar mass
the simulations include detailed modeling of star formation chemical enrichment and supernova feedback using the textsc celib and textsc grackle libraries achieving baryonic resolutions of sim2 times10 3 m_ odot
we propose digitized counterdiabatic quantum sampling dcqs a hybrid quantum-classical
algorithm
quantum algorithm
we propose digitized counterdiabatic quantum sampling dcqs a hybrid quantum-classical algorithm for efficient sampling from energy-based models such as low-temperature boltzmann distributions
however as typical to any quantum resource network
nonlocality
quantum coherence
however as typical to any quantum resource network nonlocality is also vulnerable to environmental noise which sometimes prove to be detrimental
to fill this gap via an iterative and deductive process we develop the interaction-augmented instruction iai model a compact entity-relation graph formalizing how the combination of interactions and
text
generative ai
to fill this gap via an iterative and deductive process we develop the interaction-augmented instruction iai model a compact entity-relation graph formalizing how the combination of interactions and text prompts enhances human-generative ai communication
this paper introduces the trust-based optimization tbo a novel extension of the island model in evolutionary
computation
genetic algorithm
this paper introduces the trust-based optimization tbo a novel extension of the island model in evolutionary computation that replaces conventional periodic migrations with a flexible agent-driven interaction mechanism based on trust or reputation
this paper introduces a stochastic optimal control framework to address these issues
simultaneously
inverse optimal
this paper introduces a stochastic optimal control framework to address these issues simultaneously without excessively conservative approximations
we release baseline experiments and preprocessing
tools
extensive experiments
we release baseline experiments and preprocessing tools to facilitate adoption
as a result srl enables small models to learn
challenging
reinforcement learning rl
as a result srl enables small models to learn challenging problems previously unlearnable by sft or rlvr