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dinosaur photonic crystal cavity interfaces for color center
coupling
photonic devices
dinosaur photonic crystal cavity interfaces for color center coupling to triangular nanostructures
lastly we apply inputdsa to neural data recorded from rats performing a
cognitive
cognitive neuroscience
lastly we apply inputdsa to neural data recorded from rats performing a cognitive task demonstrating that it identifies a transition from input-driven evidence accumulation to intrinsically-driven decision-making
we propose pep-fedpt prompt estimation from prototypes for federated prompt tuning a unified framework designed to achieve both generalization and personalization in federated
prompt
prompt tuning
we propose pep-fedpt prompt estimation from prototypes for federated prompt tuning a unified framework designed to achieve both generalization and personalization in federated prompt tuning of vits
we propose a debiased machine learning estimator that is based on nuisance
functions
debiased machine learning
we propose a debiased machine learning estimator that is based on nuisance functions restricted to a partially linear form
our objective is to find a velocity field generated by hamiltonian
flows
gradient flow
our objective is to find a velocity field generated by hamiltonian flows that minimizes the kinetic energy while ensuring that the initial scalar distribution reaches a prescribed degree of mixedness by a finite time
by integrating density functional theory calculations with the stochastic self-consistent harmonic approximation assisted by on the fly machine learned force field we obtain
accurate
density functional
by integrating density functional theory calculations with the stochastic self-consistent harmonic approximation assisted by on the fly machine learned force field we obtain accurate structural information and dynamical properties under varying strain conditions while incorporating higher-order anharmonic effects
posterior sampling by combining diffusion models with annealed
langevin
langevin dynamics
posterior sampling by combining diffusion models with annealed langevin dynamics
this work advances both the theoretical understanding of surrogate gradient
learning
imitation learning
this work advances both the theoretical understanding of surrogate gradient learning in snns and practical training methodologies for neuromorphic controllers demonstrated in real-world robotic systems
conventional autograders while efficient act as black-box systems that simply return pass fail results offering little insight into student
thinking
reasoning curriculum
conventional autograders while efficient act as black-box systems that simply return pass fail results offering little insight into student thinking or learning needs
furthermore we incorporate focaler-siou to strengthen the model s bounding box matching capability and increase its sensitivity to small-object features thereby further enhancing
detection
computer vision
furthermore we incorporate focaler-siou to strengthen the model s bounding box matching capability and increase its sensitivity to small-object features thereby further enhancing detection accuracy and robustness
dinosaur photonic crystal cavity interfaces for color
center
photonic crystal
dinosaur photonic crystal cavity interfaces for color center coupling to triangular nanostructures
building on recent progress in large-scale multi-animal models we introduce neuropaint a masked autoencoding approach for inferring the dynamics of
unrecorded
brain regions
building on recent progress in large-scale multi-animal models we introduce neuropaint a masked autoencoding approach for inferring the dynamics of unrecorded brain areas
the system integrates industrial cameras to monitor equipment operation alignment and hot
bar
achieves state-of-the-art
the system integrates industrial cameras to monitor equipment operation alignment and hot bar motion in real time along the process line
irradiation-induced defects that distort the lattice generate elastic
strain
strain engineering
irradiation-induced defects that distort the lattice generate elastic strain so we use excess potential energy as a measure of defect content
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
curvature-aware calibration of tactile sensors for accurate
force
tactile sensors
curvature-aware calibration of tactile sensors for accurate force estimation on non-planar surfaces
these relationships generalize fisher s fundamental theorem of natural
selection
population genetics
these relationships generalize fisher s fundamental theorem of natural selection and also make clear some of its limitation
normative reasoning is a type of reasoning that involves
normative
reasoning curriculum
normative reasoning is a type of reasoning that involves normative or deontic modality such as obligation and permission
we use this life cycle to project future concentration in
large
large cities
we use this life cycle to project future concentration in large cities
our results show that atlas performs strongly in logical reasoning tasks like sudoku completing puzzles significantly faster than human baselines but struggles substantially in real-time games requiring precise timing and motor control often failing to
progress
reasoning capabilities
our results show that atlas performs strongly in logical reasoning tasks like sudoku completing puzzles significantly faster than human baselines but struggles substantially in real-time games requiring precise timing and motor control often failing to progress beyond initial obstacles
inference on local variable importance measures for heterogeneous
treatment
average treatment
inference on local variable importance measures for heterogeneous treatment effects
corvs person identification via video trajectory-sensor
correspondence
point tracking
corvs person identification via video trajectory-sensor correspondence in a real-world warehouse
stellar-mass compact objects cos embedded in active
galactic
massive galaxies
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
unlike previous studies that treat either disturbance rejection or partial sensing this work combines the command filter disturbance
observer
disturbance observer
unlike previous studies that treat either disturbance rejection or partial sensing this work combines the command filter disturbance observer and hgo to address both challenges simultaneously while avoiding the complexity growth typical of backstepping designs
unlike iterative approximation using dynamic programming in the drl a closed-form expression for the random return can be exactly characterized in the distributional lqr which is defined over infinitely many
random
truncated random return
unlike iterative approximation using dynamic programming in the drl a closed-form expression for the random return can be exactly characterized in the distributional lqr which is defined over infinitely many random variables
these findings suggest that combining deep learning and traditional machine learning methods could enhance the reliability of
mri-based
deep learning
these findings suggest that combining deep learning and traditional machine learning methods could enhance the reliability of mri-based research on asd
however it remains unclear which search direction to construct a
gradient
accelerated gradient
however it remains unclear which search direction to construct a gradient estimator is more appropriate and how to set the algorithmic parameters
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
large language models llms
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
the findings challenge conventional scaling assumptions establish training data quality as more critical than model
size
smaller models
the findings challenge conventional scaling assumptions establish training data quality as more critical than model size and provide actionable guidelines for model selection across educational production and research contexts
large language models llms have increasingly been applied to automatic programming
code
open-source models
large language models llms have increasingly been applied to automatic programming code generation
dynamic dyck and tree edit distance decompositions and reductions to string
edit
tree edit distance
dynamic dyck and tree edit distance decompositions and reductions to string edit distance
our analysis extends the understanding of z_ abs approx z_ em
absorption
absorption line
our analysis extends the understanding of z_ abs approx z_ em absorption line systems and provides valuable constraints on the interplay between dust metals and neutral gas in the ism of early galaxies
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
photonic crystal
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 yields toolpref-pairwise-30k a diverse balanced and challenging dataset of critique tasks that
supports
human feedback
this yields toolpref-pairwise-30k a diverse balanced and challenging dataset of critique tasks that supports reinforcement learning with verifiable feedback
various plots underline our results and illustrate the capabilities of our
functions
regression function
various plots underline our results and illustrate the capabilities of our functions with regard to estimation
our findings demonstrate that acoustic signals can support real-time low-cost passive verification in sensitive
robotic
robotic systems
our findings demonstrate that acoustic signals can support real-time low-cost passive verification in sensitive robotic environments without requiring hardware modifications
large language models llms are increasingly used for long-document question
answering
question answering
large language models llms are increasingly used for long-document question answering where reliable attribution to sources is critical for trust
our results show that atlas performs strongly in
logical
reasoning curriculum
our results show that atlas performs strongly in logical reasoning tasks like sudoku completing puzzles significantly faster than human baselines but struggles substantially in real-time games requiring precise timing and motor control often failing to progress beyond initial obstacles
these s cells are modulated by a third inhibitory subtype vip
neurons
brain regions
these s cells are modulated by a third inhibitory subtype vip neurons that receive inputs from other cortical areas
finally it summarizes the current technical challenges in research such as data quality and model generalization ability and looks forward to future development trends including multi-scale modeling physics-informed machine
learning
machine learning ml
finally it summarizes the current technical challenges in research such as data quality and model generalization ability and looks forward to future development trends including multi-scale modeling physics-informed machine learning standardized data sharing and interpretable machine learning
through these principles our method achieves image reconstructions from fmri that faithfully reconstruct the seen
images
higher-order visual
through these principles our method achieves image reconstructions from fmri that faithfully reconstruct the seen images and surpass current sota approaches both visually and by standard objective metrics
it reflects spatio-temporal variability and generates diverse plausible and novel mobility
patterns
emergent behaviors
it reflects spatio-temporal variability and generates diverse plausible and novel mobility patterns consistent with the built environment
this is formulated as a bilevel optimization problem the inner optimization solves the qp under a given projection using a qp solver while the outer
optimization
bilevel optimization
this is formulated as a bilevel optimization problem the inner optimization solves the qp under a given projection using a qp solver while the outer optimization updates the model parameters
in a tidal disruption event tde a star is disrupted by the
tidal
black hole
in a tidal disruption event tde a star is disrupted by the tidal field of a massive black hole creating a debris stream that returns to the black hole forms an accretion flow and powers a luminous flare
for ate estimation we estimate the propensity
score
propensity score
for ate estimation we estimate the propensity score through direct bias-correction term estimation
neon is an inhibitor of co hydrogenation in pre-stellar
core
star formation
neon is an inhibitor of co hydrogenation in pre-stellar core conditions
the prior network generates coarse localization maps that delineate the approximate roi serving as spatial guidance for the dual
encoder
dual encoder
the prior network generates coarse localization maps that delineate the approximate roi serving as spatial guidance for the dual encoder network
we investigate the spontaneous emission of light in three-dimensional 3d photonic
crystals
spontaneous emission
we investigate the spontaneous emission of light in three-dimensional 3d photonic crystals through theoretical calculations and simulations
this real-time control strategy is then benchmarked against an offline
optimal
optimal control
this real-time control strategy is then benchmarked against an offline optimal dispatch to evaluate flexibility performance
while successful in language and vision their adoption in
eeg
brain activity
while successful in language and vision their adoption in eeg has lagged due to the heterogeneity of public datasets which are collected under varying protocols devices and electrode configurations
to this end we generalize a technique of linhares and swamy used to obtain a low-cost chain-constrained spanning
tree
spanning trees
to this end we generalize a technique of linhares and swamy used to obtain a low-cost chain-constrained spanning tree from an algorithm without cost guarantees
the impact of different multilingual data mixtures in
pretraining
language agents
the impact of different multilingual data mixtures in pretraining large language models llms has been a topic of ongoing debate often raising concerns about potential trade-offs between language coverage and model performance i
however this trajectory-to-trajectory formulation often entangles camera
motion
motion trajectory
however this trajectory-to-trajectory formulation often entangles camera motion with scene dynamics and complicates both modeling and inference
carbonaceous nano-grains are a significant component of
interstellar
interstellar medium
carbonaceous nano-grains are a significant component of interstellar dust and dominate the mid-infrared emission of photodissociation regions pdrs
fully programmable plasmonic pt-symmetric dimer with epsilon near zero and phase-change materials for
integrated
photonic devices
fully programmable plasmonic pt-symmetric dimer with epsilon near zero and phase-change materials for integrated photonics
adaptive context length optimization with low-frequency truncation for multi-agent
reinforcement
reinforcement learning
adaptive context length optimization with low-frequency truncation for multi-agent reinforcement learning
biological and artificial learners are inherently exposed to a stream of data and experience throughout their lifetimes and must constantly adapt to learn from or selectively
ignore
continual learning
biological and artificial learners are inherently exposed to a stream of data and experience throughout their lifetimes and must constantly adapt to learn from or selectively ignore the ongoing input
these results illustrate our iai model s descriptive discriminative and generative power for shaping future
genai
ai systems
these results illustrate our iai model s descriptive discriminative and generative power for shaping future genai systems
large language models llms excel at general tasks but underperform in specialized
domains
language models
large language models llms excel at general tasks but underperform in specialized domains like economics and psychology which require deep principled understanding
given past observations and actions it predicts the future of both the
embodied
temporal understanding
given past observations and actions it predicts the future of both the embodied agent and its environment
this process is indeed analogous to a variant of the mini-batching strategy employed in
stochastic
stochastic differential
this process is indeed analogous to a variant of the mini-batching strategy employed in stochastic gradient descent where an effective multiplicative noise produces an inductive bias by triggering noise-induced transitions
evaluation using mean absolute error mae root
mean
high accuracy
evaluation using mean absolute error mae root mean squared error rmse and r squared r2 shows high accuracy
in such cases to ensure the human maintains an accurate understanding of critical
task
task performance
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...
debiased machine learning typically requires estimation of the
riesz
debiased machine
debiased machine learning typically requires estimation of the riesz representer and the regression function
to address these limitations we propose securereviewer a new approach designed for enhancing llms ability to identify and resolve security-related
issues
security issues
to address these limitations we propose securereviewer a new approach designed for enhancing llms ability to identify and resolve security-related issues during code review
ai-powered approaches specifically large language models llms natural
language
large language models llms
ai-powered approaches specifically large language models llms natural language processing nlp and generative ai offer transformative solutions and reduce inefficiencies
our steering module gains model control through dimension-wise activation modulation and adaptive
steering
angular steering
our steering module gains model control through dimension-wise activation modulation and adaptive steering across layers without requiring pre-extracted static vectors or manual tuning of intervention points
we construct reference frames using three different sets of
external
interstellar medium
we construct reference frames using three different sets of external sources 1 stars with gaia dr3 data 2 stationary background galaxies and 3 a combination of the two
an empirical application to a two-way error components model shows that the proposed
test
test statistics
an empirical application to a two-way error components model shows that the proposed test can provide more informative inference than the conventional t -test
cooperation is fundamental to the functioning of biological and social
systems
collective systems
cooperation is fundamental to the functioning of biological and social systems in both human and animal populations with the structure of interactions playing a crucial role
notably the insertion of four alkylammonium ions introduces different populations of mn2 vacancies leading to a transition from the pristine antiferromagnetic state to more complex magnetic textures including a ferrimagnetic state displaying a
magnetic
magnetic properties
notably the insertion of four alkylammonium ions introduces different populations of mn2 vacancies leading to a transition from the pristine antiferromagnetic state to more complex magnetic textures including a ferrimagnetic state displaying a magnetic saturation of 1 ub atom
the theory of training deep networks has become a central question of modern machine
learning
deep learning
the theory of training deep networks has become a central question of modern machine learning and has inspired many practical advancements
accelerating real-world overtaking in f1tenth
racing
wheel-to-wheel racing
accelerating real-world overtaking in f1tenth racing employing reinforcement learning methods
our model produces imbalanced trees with power-law exponents matching empirical and numerical observations revealing the mathematical basis of observed scaling laws and offering new tools to interpret tree imbalance in
evolutionary
evolutionary dynamics
our model produces imbalanced trees with power-law exponents matching empirical and numerical observations revealing the mathematical basis of observed scaling laws and offering new tools to interpret tree imbalance in evolutionary contexts
self-localization on a 3d map by using an inexpensive monocular camera is required to realize
autonomous
autonomous driving
self-localization on a 3d map by using an inexpensive monocular camera is required to realize autonomous driving
this paper develops a unified theoretical framework for detecting and estimating boundaries in treatment effects across both
spatial
treatment effect
this paper develops a unified theoretical framework for detecting and estimating boundaries in treatment effects across both spatial and temporal dimensions
here we show that the magnetic properties of mnps3 can be tailored through the
intercalation
magnetic anisotropy
here we show that the magnetic properties of mnps3 can be tailored through the intercalation of different guest molecules
to address this we propose a one problem multiple solutions 1pns training paradigm that exposes the model to a variety of
valid
question answering
to address this we propose a one problem multiple solutions 1pns training paradigm that exposes the model to a variety of valid reasoning trajectories and thus increases inference diversity
these results highlight the significant room for improving the
mathematical
reasoning capabilities
these results highlight the significant room for improving the mathematical reasoning in current llms
our results underline the potential of generative
models
quantum computing
our results underline the potential of generative models as a general-purpose methodology for automated quantum circuit design offering a promising path towards more efficient quantum algorithms and accelerating scientific discovery in the quantum domain
debiased machine learning typically requires estimation of the
riesz
riesz regression
debiased machine learning typically requires estimation of the riesz representer and the regression function
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
these results highlight the potential of drl for enabling real-time high-performance
isac
communication isac
these results highlight the potential of drl for enabling real-time high-performance isac in dynamic scenarios
within this context ai-based channel characterization and
estimation
channel estimation
within this context ai-based channel characterization and estimation become the focus since these methods have not been solved by traditional methods very well and have become the bottleneck of transceiver efficiency in large-scale orthogonal frequency division multiplexing ofdm systems
point convergence analysis of the accelerated
gradient
objective function
point convergence analysis of the accelerated gradient method for multiobjective optimization continuous and discrete
our results indicate that although llms generally adhere to valid reasoning patterns they exhibit notable inconsistencies in specific types of
normative
reasoning capabilities
our results indicate that although llms generally adhere to valid reasoning patterns they exhibit notable inconsistencies in specific types of normative reasoning and display cognitive biases similar to those observed in psychological studies of human reasoning
however existing vision-language models vlms still lack accurate perception of details and struggle to extract
fine-grained
vision-language models vlms
however existing vision-language models vlms still lack accurate perception of details and struggle to extract fine-grained structures from charts
the quantum walk technique is a general framework for constructing quantum algorithms by transforming a classical random walk search into a quantum search and has been successfully applied to constructing an
algorithm
quantum batteries
the quantum walk technique is a general framework for constructing quantum algorithms by transforming a classical random walk search into a quantum search and has been successfully applied to constructing an algorithm with a tight query complexity for another problem
we propose a new approach that reformulates the maximum likelihood estimation problem as an
optimization
policy optimization
we propose a new approach that reformulates the maximum likelihood estimation problem as an optimization problem with equilibrium constraints where both the structural parameters and the value functions are treated as decision variables
building on this synthetic dataset we introduce an end-to-end data-driven framework for estimating human
poses
pose estimation
building on this synthetic dataset we introduce an end-to-end data-driven framework for estimating human poses and shapes from diverse sketch styles
we close by distinguishing these qualitative changes from density-dependent
phase
phase transition
we close by distinguishing these qualitative changes from density-dependent phase transitions and by discussing how our approach could generalize to broader classes of collective behaviors
extensive experiments validate the state-of-the-art performance of the
proposed
real-world applications
extensive experiments validate the state-of-the-art performance of the proposed framework
this study demonstrates that providing language models with pragmatic theories as prompts is an effective in-context
learning
reasoning tasks
this study demonstrates that providing language models with pragmatic theories as prompts is an effective in-context learning approach for tasks to understand implied meanings
in ate estimation the bias-correction term h_0 x_i d_i frac 1 e_0 x_i - frac 1 1 - e_0 x_i plays an important role where e_0 x_i is the
propensity
propensity score
in ate estimation the bias-correction term h_0 x_i d_i frac 1 e_0 x_i - frac 1 1 - e_0 x_i plays an important role where e_0 x_i is the propensity score the probability of being assigned treatment 1
however significant limitations remain mllms generalize poorly across digital-physical spaces and embodiments vision-language-action models vlas produce low-level actions yet lack robust high-level embodied reasoning and most embodied large
language
vision-language models vlms
however significant limitations remain mllms generalize poorly across digital-physical spaces and embodiments vision-language-action models vlas produce low-level actions yet lack robust high-level embodied reasoning and most embodied large language models ellms are constrained to digital-space with poor generalization...
experimental results complemented with theoretical predictions show that in the low absorption and thin sample limits the signal reproduce the vert chi 3 vert
spectral
spectral range
experimental results complemented with theoretical predictions show that in the low absorption and thin sample limits the signal reproduce the vert chi 3 vert spectral profile
this setting presents two central challenges 1 identifying similarity across users to effectively aggregate data especially under scenarios where offline data is imbalanced across dimensions and 2 handling the imbalanced offline data where some
preference
preference optimization
this setting presents two central challenges 1 identifying similarity across users to effectively aggregate data especially under scenarios where offline data is imbalanced across dimensions and 2 handling the imbalanced offline data where some preference dimensions are underrepresented
yet robust decisions under uncertainty still rely on capabilities that
current
trustworthy ai
yet robust decisions under uncertainty still rely on capabilities that current ai lacks domain knowledge not captured by data long horizon context and reasoning grounded in the physical world
the model is based on mechanisms of fast and flat depth-limited goal-directed probabilistic simulation--analogous to those used in monte carlo tree-search models of expert game-play but scaled down to use very few stochastic samples simple goal heuristics for evaluating
actions
artificial intelligence
the model is based on mechanisms of fast and flat depth-limited goal-directed probabilistic simulation--analogous to those used in monte carlo tree-search models of expert game-play but scaled down to use very few stochastic samples simple goal heuristics for evaluating actions and no deep search
for approximate matching we develop randomized algorithms to show that 1 epsilon -approximate matching in regular
graphs
-approximation algorithm
for approximate matching we develop randomized algorithms to show that 1 epsilon -approximate matching in regular graphs is truly local i