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text network and behavioral data intermediate outputs mined analytical results and visualization artifacts -- through coordinated
agent
ai agents
text network and behavioral data intermediate outputs mined analytical results and visualization artifacts -- through coordinated agent flows
to overcome the challenge this work suggests leveraging a multi-frequency
neural
deep learning
to overcome the challenge this work suggests leveraging a multi-frequency neural network named mfnn embedding prior physical knowledge into the network
segmentation over complexity evaluating ensemble and hybrid approaches for
anomaly
anomaly detection
segmentation over complexity evaluating ensemble and hybrid approaches for anomaly detection in industrial time series
during inference slideagent selectively activates
specialized
question answering
during inference slideagent selectively activates specialized agents for multi-level reasoning and integrates their outputs into coherent context-aware answers
more broadly they point to an intrinsic neural signature of adaptive brain function marked by efficient yet
flexible
cognitive science
more broadly they point to an intrinsic neural signature of adaptive brain function marked by efficient yet flexible network organization that may support creative and adaptive cognition
our work significantly extends the existing results on the convergence of gd and sgd by guaranteeing that they apply to practical neural network settings and has the potential to unlock further exploration of
learning
deep learning
our work significantly extends the existing results on the convergence of gd and sgd by guaranteeing that they apply to practical neural network settings and has the potential to unlock further exploration of learning dynamics
to this end we introduce a principled two-stage framework the first stage uses offline data to derive upper and lower bounds on value functions while the second incorporates these learned
bounds
lower bounds
to this end we introduce a principled two-stage framework the first stage uses offline data to derive upper and lower bounds on value functions while the second incorporates these learned bounds into online algorithms
this work sharpens existing guarantees and advances the
theoretical
density-ratio estimation
this work sharpens existing guarantees and advances the theoretical understanding of divergence-based estimation
simulating finite temperature phase transitions from
first-principles
phase transition
simulating finite temperature phase transitions from first-principles is computationally challenging
the results highlight the potential of modeling in joint latent
representations
representation learning
the results highlight the potential of modeling in joint latent representations for addressing small data challenges
this optical design naturally encodes depth into its depth-varying point spread functions psfs without requiring
complex
nonlinear optical
this optical design naturally encodes depth into its depth-varying point spread functions psfs without requiring complex diffractive or freeform elements
in contrast both problems admit algorithms against oblivious adversaries that achieve operatorname
polylog
mathrm polylog
in contrast both problems admit algorithms against oblivious adversaries that achieve operatorname polylog n amortized update time behnezhad derakhshan hajiaghayi stein sudan focs 19
magnetic phase transitions between ordered phases are often understood on the basis of semi-classical
spin
hidden spin texture
magnetic phase transitions between ordered phases are often understood on the basis of semi-classical spin models
in this work we present a fully unsupervised machine
learning
machine learning
in this work we present a fully unsupervised machine learning ml workflow that detects and classifies these defects directly from molecular dynamics data
the architecture incorporates a brain-region to image-feature cross-attention mechanism enabling nonlinear mappings between high-dimensional deep network features and semantic
patterns
brain activity
the architecture incorporates a brain-region to image-feature cross-attention mechanism enabling nonlinear mappings between high-dimensional deep network features and semantic patterns encoded in the brain activity
the search space of gate sequences grows combinatorially and
handcrafted
-bit toffoli gates
the search space of gate sequences grows combinatorially and handcrafted templates often waste scarce qubit and depth budgets
republican evolve in parallel and counterfactual distributions follow
parallel
parallel trends
republican evolve in parallel and counterfactual distributions follow parallel trajectories in the probability simplex
a major goal of computational astrophysics is to simulate the milky way galaxy with sufficient
resolution
host galaxy
a major goal of computational astrophysics is to simulate the milky way galaxy with sufficient resolution down to individual stars
when minimizing a variety of cost functions simultaneously using a formation planner with adaptive weights can reduce the
cost
optimal control
when minimizing a variety of cost functions simultaneously using a formation planner with adaptive weights can reduce the cost by 20-40
it also exhibits generalizable world-modeling abilities enabling spatiotemporally consistent
world
world models
it also exhibits generalizable world-modeling abilities enabling spatiotemporally consistent world exploration and open-world embodied manipulation across diverse scenarios and tasks
unlike existing gaussian process-based approaches our method constructs an approximate posterior distribution using samples drawn from a
gaussian
gaussian process
unlike existing gaussian process-based approaches our method constructs an approximate posterior distribution using samples drawn from a gaussian process model fitted to the observed data which does not require any structural assumption about the underlying pde
the approach leverages adversarial networks to model the complex dynamics of
tool
tool usage
the approach leverages adversarial networks to model the complex dynamics of tool and object manipulation as well as the aim of the manipulation task
we first demonstrate how increasing photo-excited carrier
density
photoemission spectroscopy
we first demonstrate how increasing photo-excited carrier density leads to a redshift-blueshift crossover of excitons
the cost function was designed to achieve a net energy saving by simultaneously minimizing aerodynamic drag and penalizing the actuation s
energy
energy consumption
the cost function was designed to achieve a net energy saving by simultaneously minimizing aerodynamic drag and penalizing the actuation s energy consumption
through numerical simulations and theoretical analysis we discuss the conditions under which variation in how individuals experience
environmental
ecological interactions
through numerical simulations and theoretical analysis we discuss the conditions under which variation in how individuals experience environmental selection can naturally promote evolutionary strategies that generalize across environments and anticipate novel challenges
we develop a structural framework for modeling and inferring
unobserved
causal effects
we develop a structural framework for modeling and inferring unobserved heterogeneity in dynamic panel-data models
in this work we propose mossnet a novel mixture-of-state-space-experts architecture that emulates a linear multi-head
attention
vision-language models
in this work we propose mossnet a novel mixture-of-state-space-experts architecture that emulates a linear multi-head attention mha
extensive experiments on real-world datasets
demonstrate
real-world datasets
extensive experiments on real-world datasets demonstrate that our methods outperform baseline approaches
5-4b models across visual reasoning tasks demonstrate that our methods consistently improve visual
reasoning
multimodal reasoning
5-4b models across visual reasoning tasks demonstrate that our methods consistently improve visual reasoning capabilities outperforming vanilla self-improvement by 3
high-speed generation processing and detection of polarization bell-states is therefore critical for
quantum
quantum emitters
high-speed generation processing and detection of polarization bell-states is therefore critical for quantum technology
simulation of the time-dynamics of fermionic many-body systems has long been predicted to be one of the key applications of
quantum
classical simulation
simulation of the time-dynamics of fermionic many-body systems has long been predicted to be one of the key applications of quantum computers
taken together our analysis clarifies the trade-off between
action
vision-language-action vla
taken together our analysis clarifies the trade-off between action fine-tuning and the degradation of vl representations and highlights practical approaches to recover inherited vl capabilities
efficiency without cognitive change evidence from human
interaction
ai use
efficiency without cognitive change evidence from human interaction with narrow ai systems
to address this issue we propose a learning-based
csi
channel state information
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
in this study we introduce a physics-informed operator learning approach by extending the resolution
independent
policy learning
in this study we introduce a physics-informed operator learning approach by extending the resolution independent neural operator rino framework to a fully data-free setup addressing both challenges simultaneously
the results demonstrate consistent enhancements over traditional optimization techniques and competitive accuracy relative to current
deep
deep learning
the results demonstrate consistent enhancements over traditional optimization techniques and competitive accuracy relative to current deep learning models
many post-hoc concept-based approaches have been introduced to
understand
existing methods
many post-hoc concept-based approaches have been introduced to understand their workings yet they are not always faithful to the model
as an application we improve on the best-known characterization of the metric entropy of the sobolev ellipsoid and extend pinsker s sobolev theorem in two ways i to any bounded open domain in arbitrary finite dimension and ii by providing the second-order term in the asymptotic expansion of the
minimax
minimax risk
as an application we improve on the best-known characterization of the metric entropy of the sobolev ellipsoid and extend pinsker s sobolev theorem in two ways i to any bounded open domain in arbitrary finite dimension and ii by providing the second-order term in the asymptotic expansion of the minimax risk
biological learning unfolds continuously in time yet most algorithmic models rely on discrete updates and separate
inference
continual learning
biological learning unfolds continuously in time yet most algorithmic models rely on discrete updates and separate inference and learning phases
keywords small language models factual grounding directed
reasoning
ai systems
keywords small language models factual grounding directed reasoning fine-tuning model alignment cost-efficient ai
we propose a taxonomy of data-efficient llm post-training methods covering data
selection
data curation
we propose a taxonomy of data-efficient llm post-training methods covering data selection data quality enhancement synthetic data generation data distillation and compression and self-evolving data ecosystems
an audiovisual experiment with 14 participants was conducted using 15
virtual
physical virtual
an audiovisual experiment with 14 participants was conducted using 15 virtual reality scenarios featuring a passing car
this work represents the theoretical foundations of this cooperative
manipulation
robotic systems
this work represents the theoretical foundations of this cooperative manipulation control framework and thus the experiments are presented in an abstract way while giving pointers towards potential future applications
therefore there may be a non-linear relationship between task performance and brain
activation
task performance
therefore there may be a non-linear relationship between task performance and brain activation if a full range of task performance is considered
despite its original definition within the economic field the
genepy
correlation network
despite its original definition within the economic field the genepy can be easily applied and interpreted on a wide range of networks characterized by high spectral gap including monopartite and bipartite networks systems
the first design yields a locally just-identified statistical model implying that all regular asymptotically linear estimators of the treatment effect share the same asymptotic variance equal to the trivial semiparametric
efficiency
efficiency bound
the first design yields a locally just-identified statistical model implying that all regular asymptotically linear estimators of the treatment effect share the same asymptotic variance equal to the trivial semiparametric efficiency bound
01 pc for musca we will resolve magnetic field structures at scales necessary for understanding cloud fragmentation and star formation efficiency and the
roles
magnetic field
01 pc for musca we will resolve magnetic field structures at scales necessary for understanding cloud fragmentation and star formation efficiency and the roles that magnetic fields play in these processes
despite significant advancements in recent decades autonomous vehicles avs continue to face challenges in navigating certain traffic
scenarios
collision avoidance
despite significant advancements in recent decades autonomous vehicles avs continue to face challenges in navigating certain traffic scenarios where human drivers excel
this overview of integrated information theory iit emphasizes iit s
consciousness-first
mutual information
this overview of integrated information theory iit emphasizes iit s consciousness-first approach to what exists
the current advancements in beam shaping techniques their impact on the nanoparticle characteristics and their broader implications for scaling pulsed laser
ablation
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
this study examines llms ability to infer user intent embedded in context-driven
prompts
context engineering
this study examines llms ability to infer user intent embedded in context-driven prompts and whether understanding implicature improves response generation
based on these results we derive nn matching from
riesz
riesz regression
based on these results we derive nn matching from riesz regression
however their success is tied to one core capability reliable
object
computer vision
however their success is tied to one core capability reliable object detection in complex and multimodal environments
the sga method outperforms the gradient method by including
second-order
zeroth-order methods
the sga method outperforms the gradient method by including second-order mixed derivatives computed at each iterate which requires considerably larger computational effort
our system achieves accuracy increases of at least 16 on the synthetic csi dataset 20 on the in-lab csi dataset and 17 on the in-the-wild
csi
csi dataset
our system achieves accuracy increases of at least 16 on the synthetic csi dataset 20 on the in-lab csi dataset and 17 on the in-the-wild csi dataset
a robust trajectory tracking controller based on feedback linearization augmented with an lqr scheme compensates for the
platform
trajectory tracking
a robust trajectory tracking controller based on feedback linearization augmented with an lqr scheme compensates for the platform s nonlinear dynamics to achieve precise motion control
purpose the purpose of this study was to determine if an ensemble of multiple llm
agents
llm agents
purpose the purpose of this study was to determine if an ensemble of multiple llm agents could be used collectively to provide a more reliable assessment of a pixel-based ai triage tool than a single llm
monte carlo simulations demonstrate that the nonparametric approach exhibits lower bias and correctly identifies the absence of
boundaries
effect boundaries
monte carlo simulations demonstrate that the nonparametric approach exhibits lower bias and correctly identifies the absence of boundaries when none exist unlike parametric methods that may impose spurious spatial patterns
the growing integration of artificial intelligence ai into human cognition raises a fundamental question does ai merely
improve
ai assistance
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
we propose several setups for hierarchical control with options and derive practical algorithms following state-of-the-art
reinforcement
reinforcement learning
we propose several setups for hierarchical control with options and derive practical algorithms following state-of-the-art reinforcement learning techniques
by leveraging multiple reconfigurable intelligent
surfaces
reconfigurable intelligent surface
by leveraging multiple reconfigurable intelligent surfaces riss and transceiver designs we engineer the ambient wireless propagation environment to emulate the operations of a cnn layer
moreover the lack of interpretability in modal information selection further affects the reliability and consistency of
fusion
image fusion
moreover the lack of interpretability in modal information selection further affects the reliability and consistency of fusion results in complex scenarios
in this study we propose estimating h_0 or equivalently the
propensity
propensity score
in this study we propose estimating h_0 or equivalently the propensity score e_0 by directly minimizing the prediction error of h_0
we study the problem of estimating the average treatment effect ate under sequentially adaptive
treatment
average treatment
we study the problem of estimating the average treatment effect ate under sequentially adaptive treatment assignment mechanisms
our key insight is to reframe the offline policy
learning
reinforcement learning
our key insight is to reframe the offline policy learning problem by leveraging the textbf observed future of each expert trajectory
here we demonstrate that annealed population heterogeneity wherein distinct individuals in the population experience different instances of a complex environment over time can act as a form of implicit regularization and facilitate
evolutionary
evolutionary game
here we demonstrate that annealed population heterogeneity wherein distinct individuals in the population experience different instances of a complex environment over time can act as a form of implicit regularization and facilitate evolutionary generalization
from the perspective of dynamical systems theory visual rivalry
offers
human cognition
from the perspective of dynamical systems theory visual rivalry offers an experimentally tractable window into the dynamical mechanisms governing perceptual awareness
these results indicate that the privacy and helpfulness of
llm
llm responses
these results indicate that the privacy and helpfulness of llm responses are often specific to individuals and proxy llms are poor estimates of how real users would perceive these responses in privacy-sensitive scenarios
using this framework we conduct a comprehensive evaluation of nine representative lvg models finding that while current methods perform well on basic visual and temporal aspects they struggle with inter-event consistency fine-grained
alignment
world models
using this framework we conduct a comprehensive evaluation of nine representative lvg models finding that while current methods perform well on basic visual and temporal aspects they struggle with inter-event consistency fine-grained alignment and high-level thematic adherence etc
this framework enables the direct inclusion of complex physical and dynamic constraints such as nonlinear friction and
collision
collision avoidance
this framework enables the direct inclusion of complex physical and dynamic constraints such as nonlinear friction and collision avoidance for both payload and rope
we propose reasoning curriculum a simple two-stage
curriculum
reasoning curriculum
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
here we present a systematic analysis of four
quasars
dwarf galaxies
here we present a systematic analysis of four quasars initially selected by their ks-band variability amplitudes in the vista variables in the v i a l actea survey vvv vvvx
instead of sharing a global low-resolution space each part in our method - even small ones - is
generated
video generation
instead of sharing a global low-resolution space each part in our method - even small ones - is generated at full resolution enabling the synthesis of intricate details
stellar abundances of elements with production channels that are metallicity-dependent most notably aluminium have provided an empirical route for separating different
galactic
active galactic
stellar abundances of elements with production channels that are metallicity-dependent most notably aluminium have provided an empirical route for separating different galactic components
long-horizon contact-rich bimanual manipulation presents a significant challenge requiring complex
coordination
manipulation ordering
long-horizon contact-rich bimanual manipulation presents a significant challenge requiring complex coordination involving a mixture of parallel execution and sequential collaboration between arms
certification and classification of linear quantum
error
quantum algorithm
certification and classification of linear quantum error mitigation methods
in this paper we propose a deep reinforcement
learning
deep reinforcement learning
in this paper we propose a deep reinforcement learning drl -based approach for dynamic beamforming and power allocation in isac systems
a unified theory for causal inference direct
debiased
riesz regression
a unified theory for causal inference direct debiased machine learning via bregman-riesz regression
frequency-dependent selection also gives rise to prolonged parent-mutant coexistence in
complex
population genetics
frequency-dependent selection also gives rise to prolonged parent-mutant coexistence in complex communities a phenomenon absent in classical population genetics
central to the approach is a computationally efficient and continuously differentiable condition for detecting
collisions
dynamic obstacles
central to the approach is a computationally efficient and continuously differentiable condition for detecting collisions with ellipsoidal obstacles
the findings highlight the need for ethical and
educational
ai literacy
the findings highlight the need for ethical and educational frameworks that promote critical and autonomous thinking in an increasingly ai-augmented cognitive ecology
in this paper we design a meta-algorithm that allows us to take any robust algorithm for exact
submodular
submodular maximization
in this paper we design a meta-algorithm that allows us to take any robust algorithm for exact submodular maximization as a black box and transform it into an algorithm for the noisy setting while retaining the approximation guarantee
in summary this work advances the modeling of epidemics to a more local scope offering a more expressive tool for epidemiological research and public
health
disease transmission
in summary this work advances the modeling of epidemics to a more local scope offering a more expressive tool for epidemiological research and public health planning
however the fundamental question of emph which problems with a
deterministic
randomized algorithm
however the fundamental question of emph which problems with a deterministic complexity of omega log n can be solved exponentially faster using randomization still remains wide open
the usefulness of the method is demonstrated through an empirical application highlighting its
complementarity
existing methods
the usefulness of the method is demonstrated through an empirical application highlighting its complementarity to existing approaches
the proposed carbon-aware opf model enables market operators to optimize
energy
carbon emissions
the proposed carbon-aware opf model enables market operators to optimize energy dispatch while reducing greenhouse gas emissions
slim stochastic learning and inference in
overidentified
ate estimation
slim stochastic learning and inference in overidentified models
given a complete point cloud of an articulated object we utilize a discrete haoi representation to model each
hand
vision-language-action vla
given a complete point cloud of an articulated object we utilize a discrete haoi representation to model each hand object interaction frame
our main findings from simple human memory paradigms also generalize to a sequence completion task which more closely resembles the next-token
prediction
predictive processing
our main findings from simple human memory paradigms also generalize to a sequence completion task which more closely resembles the next-token prediction process in llm pre-training
this approach demonstrates that robust self-testing and publicly verifiable quantum randomness can be achieved with minimal optical
complexity
quantum error correction
this approach demonstrates that robust self-testing and publicly verifiable quantum randomness can be achieved with minimal optical complexity without jeopardizing security
moreover we demonstrate that when the interspecific competition coefficients differ significantly the outcome of
competition
weak competition
moreover we demonstrate that when the interspecific competition coefficients differ significantly the outcome of competition cannot be reversed by adjusting diffusion or growth rates
bridging the gap between empirical welfare maximization and conditional average treatment effect estimation in
policy
policy evaluation
bridging the gap between empirical welfare maximization and conditional average treatment effect estimation in policy learning
we also incorporate an action projection module that explicitly enforces per-time-slot
power
power allocation
we also incorporate an action projection module that explicitly enforces per-time-slot power budget constraints and antenna position constraints
secondly our algorithm matches the trajectories and sensor measurements over time using the
predicted
predictive processing
secondly our algorithm matches the trajectories and sensor measurements over time using the predicted probabilities and reliabilities
in this model informational change over time is fitted to an advection-diffusion
equation
stochastic differential
in this model informational change over time is fitted to an advection-diffusion equation a normal distribution with a time component
understanding how these systems may be spatially distributed to optimise their collective potential is therefore of importance in both ecology and in collective
systems
evolutionary dynamics
understanding how these systems may be spatially distributed to optimise their collective potential is therefore of importance in both ecology and in collective systems design
however existing approaches to multiclass calibration lack a notion of
distance
local calibration
however existing approaches to multiclass calibration lack a notion of distance among inputs which makes them vulnerable to proximity bias predictions in sparse regions of the feature space are systematically miscalibrated
we also provide new data-driven control design methods in terms of
linear
predictive control
we also provide new data-driven control design methods in terms of linear matrix inequalities that complement the conditions for informativity
in defense of the pre-test valid inference when testing violations of
parallel
parallel trends
in defense of the pre-test valid inference when testing violations of parallel trends for difference-in-differences
moreover by simulation experiments on multiple products pricing and strategic classification applications we show practical performance of
zeroth-order
zeroth-order methods
moreover by simulation experiments on multiple products pricing and strategic classification applications we show practical performance of zeroth-order methods with various gradient estimators