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incorporating social awareness into control of unknown multi-agent
systems
multi-robot collaboration
incorporating social awareness into control of unknown multi-agent systems a real-time spatiotemporal tubes approach
despite many years of research the quest to identify neural correlates of perceptual
consciousness
surrogate brain
despite many years of research the quest to identify neural correlates of perceptual consciousness ncc remains unresolved
we introduce ztrs zero-imitation end-to-end autonomous
driving
autonomous driving
we introduce ztrs zero-imitation end-to-end autonomous driving with trajectory scoring a framework that combines the strengths of both worlds sensor inputs without losing information and rl training for robust planning
together our findings reveal how information retrieval
demands
working memory
together our findings reveal how information retrieval demands model architecture and structural attention dynamics during model training can jointly produce positional bias observed in llms
we advance mechanistic interpretability in medical vision by applying medical
sparse
sparse autoencoders
we advance mechanistic interpretability in medical vision by applying medical sparse autoencoders medsaes to the latent space of medclip a vision-language model trained on chest radiographs and reports
pose refinement is guided by a probabilistic lidar-based depth consistency term back-propagated through the
camera
depth estimation
pose refinement is guided by a probabilistic lidar-based depth consistency term back-propagated through the camera projection to tighten geometry-appearance coupling
we propose that this behavior is not simply a flaw indicative of information loss but an adaptation to different information retrieval demands during pre-training some tasks require uniform recall across the entire input a long-term memory
demand
continual learning
we propose that this behavior is not simply a flaw indicative of information loss but an adaptation to different information retrieval demands during pre-training some tasks require uniform recall across the entire input a long-term memory demand while others prioritize the most recent information a short-term memory demand
we suggest that the robustness of connectivity is enhanced by constructing long
loops
network structures
we suggest that the robustness of connectivity is enhanced by constructing long loops of o log n
constructing a quantum algorithm for this problem with a query complexity improving the upper bound o
n
query complexity
constructing a quantum algorithm for this problem with a query complexity improving the upper bound o n 7 4 is an open problem
we utilise a deep learning model fine-tuned on galaxy zoo volunteer classifications to identify strongly and weakly
barred
host galaxy
we utilise a deep learning model fine-tuned on galaxy zoo volunteer classifications to identify strongly and weakly barred and unbarred disc galaxies in hyper suprime-cam subaru strategic program i -band images
we apply this theory to identify the geometric mechanisms of
third-order
third-order nonlinear
we apply this theory to identify the geometric mechanisms of third-order nonlinear transport in materials both with and without time-reversal symmetry such as 2d materials topological materials and altermagnets
in this scenario a large language model llm based
conversational
large language
in this scenario a large language model llm based conversational agent ca was designed to check in with drivers and re-engage them with their surroundings
experiments demonstrate that policies trained with fieldgen achieve higher success rates and improved stability compared to teleoperation-based baselines while significantly
reducing
human-machine teaming
experiments demonstrate that policies trained with fieldgen achieve higher success rates and improved stability compared to teleoperation-based baselines while significantly reducing human effort in long-term real-world data collection
in contrast to conventional hypothesis-driven biophysical models the ai-based
surrogate
surrogate brain
in contrast to conventional hypothesis-driven biophysical models the ai-based surrogate brain encompasses a broad spectrum of data-driven approaches to solve the inverse problem with the primary objective of accurately predicting future whole-brain dynamics with historical data
then a cable length generator has been developed that achieves online optimization of the cable length while satisfying state constraints thus balancing the multirotor s motion and
cable
cable length
then a cable length generator has been developed that achieves online optimization of the cable length while satisfying state constraints thus balancing the multirotor s motion and cable length changes without the need for manual trajectory planning
while ai-assisted participants completed several
tasks
human-ai interaction
while ai-assisted participants completed several tasks faster and more accurately no significant pre-post differences emerged in standardized measures of problem solving or verbal comprehension
to formalize this we extend the concept of data informativity by requiring the existence of a controller that stabilizes all systems consistent with the data and the
prior
predictive control
to formalize this we extend the concept of data informativity by requiring the existence of a controller that stabilizes all systems consistent with the data and the prior knowledge
a closed-form approximation-free control law is derived to ensure that each agent remains within its evolving stt thereby avoiding
dynamic
predictive control
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
through expert-centered case studies and quantitative evaluation we show that sia effectively discovers diverse and meaningful insights from social media while supporting human-agent
collaboration
ai agents
through expert-centered case studies and quantitative evaluation we show that sia effectively discovers diverse and meaningful insights from social media while supporting human-agent collaboration in complex analytical tasks
additionally we propose adaptive angular steering a selective variant that rotates only activations aligned with the
target
angular steering
additionally we propose adaptive angular steering a selective variant that rotates only activations aligned with the target feature further enhancing stability and coherence
i develop a comprehensive theoretical framework for dynamic
spatial
dynamic spatial
i develop a comprehensive theoretical framework for dynamic spatial treatment effect boundaries using continuous functional definitions grounded in navier-stokes partial differential equations
the simulator enables codesign of source filtering and feedforward settings for specific quantum memories and integrates as a building block for end to end quantum
network
quantum key distribution
the simulator enables codesign of source filtering and feedforward settings for specific quantum memories and integrates as a building block for end to end quantum network studies
bike-sharing systems bss are key components of
urban
urban systems
bike-sharing systems bss are key components of urban mobility promoting active travel and complementing public transport
while a multi-agent approach based on large language models llms represents a promising strategy to surpass the
capabilities
language agents
while a multi-agent approach based on large language models llms represents a promising strategy to surpass the capabilities of single models its success is critically dependent on synergistic team composition
the ac optimal power flow ac-opf problem is central to
power
inverse optimal issf
the ac optimal power flow ac-opf problem is central to power system operation but challenging to solve efficiently due to its nonconvex and nonlinear nature
such integration is particularly beneficial for
visual
visual navigation
such integration is particularly beneficial for visual tasks like simultaneous localization and mapping slam where cnn representations enriched with spatially attentive object locations can enhance performance
this capability is particularly relevant for active photonic circuits that generate
quantum
optical communication
this capability is particularly relevant for active photonic circuits that generate quantum light directly on-chip
comparison with exact quantum-mechanical results in one- and two-dimensional
models
quantum advantage
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
it emphasizes traceability interpretability and data-driven decision making offering a unified human-understandable framework for machine learning and achieves at or near state-of-the-art performance across most common
machine
debiased machine learning
it emphasizes traceability interpretability and data-driven decision making offering a unified human-understandable framework for machine learning and achieves at or near state-of-the-art performance across most common machine learning tasks
in this paper we study the truncated random
return
random return
in this paper we study the truncated random return in the distributional lqr
bots a unified framework for bayesian online task selection in llm
reinforcement
reinforcement learning rl
bots a unified framework for bayesian online task selection in llm reinforcement finetuning
our method is interpretable and can easily be adapted to other
datasets
machine learning
our method is interpretable and can easily be adapted to other datasets offering many future directions for research and practical applications
however conventional moe inference approaches which select active experts independently at each layer often introduce
considerable
moe inference
however conventional moe inference approaches which select active experts independently at each layer often introduce considerable latency because of frequent parameter transfers between host and gpu memory
formation control simplifies minimizing multi-robot cost functions by encoding a
cost
cost function
formation control simplifies minimizing multi-robot cost functions by encoding a cost function as a shape the robots maintain
integrated sensing and communication isac is a key enabler in 6g networks where
sensing
communication isac
integrated sensing and communication isac is a key enabler in 6g networks where sensing and communication capabilities are designed to complement and enhance each other
census demographic distributions authentic twitter network topology and heterogeneous large
language
large language models llms
census demographic distributions authentic twitter network topology and heterogeneous large language model llm agents to examine the effect of mobilization messages on voter turnout
grid cells in the medial entorhinal cortex provide a periodic spatial
code
neural codes
grid cells in the medial entorhinal cortex provide a periodic spatial code that are organized near a toroidal manifold independent of the spatial environment
the stochastic evolutionary game dynamics in the regime of replication-selection reveals the
emergence
emergent behaviors
the stochastic evolutionary game dynamics in the regime of replication-selection reveals the emergence of spiteful behaviour as a dominant behaviour via a first order phase transition -- a discontinuous jump in the frequency of spiteful individuals at a threshold value of prejudicity
bulge stars were divided into sub-samples with line-of-sight velocity dispersion analyzed and the
peaks
bulge stars
bulge stars were divided into sub-samples with line-of-sight velocity dispersion analyzed and the peaks of mdf were detected by both gaussian mixture models gmm and texttt scipy
next the max-min objective function can be approximated using a differentiable
function
optimization problem
next the max-min objective function can be approximated using a differentiable function derived from smooth approximation theory
numerical results reveal that the proposed approach significantly improves estimation accuracy compared with conventional parameterized dms and other baseline methods particularly in cases with limited
pilot
pilot overhead
numerical results reveal that the proposed approach significantly improves estimation accuracy compared with conventional parameterized dms and other baseline methods particularly in cases with limited pilot overhead
reinforcement learning from human feedback rlhf has emerged as a key technique for post-training large
language
reward models
reinforcement learning from human feedback rlhf has emerged as a key technique for post-training large language models
integrated sensing and communication isac enables simultaneous localization environment perception and data exchange for connected
autonomous
integrated sensing
integrated sensing and communication isac enables simultaneous localization environment perception and data exchange for connected autonomous vehicles
the proposed szbvarx offers g7 policymakers a transparent well calibrated tool for modern
macroeconomic
monetary policy
the proposed szbvarx offers g7 policymakers a transparent well calibrated tool for modern macroeconomic forecasting under pervasive uncertainty
we resolve this question by proving a sharp
lower
lower bound
we resolve this question by proving a sharp lower bound of n log varepsilon -1 n log e - o n bits for varepsilon o 1 regardless of operation time
artificial intelligence systems based on large
language
ai literacy
artificial intelligence systems based on large language models llms can now generate coherent text music and images yet they operate without a persistent state each inference reconstructs context from scratch
notably we show monotonicity properties of the frontier building on which we transform the bi-objective
problem
inverse optimal
notably we show monotonicity properties of the frontier building on which we transform the bi-objective problem into several single-objective problems
we propose to quantify how long temporal patterns appear without relying on their repetition or singularity enabling to extract such temporal
patterns
temporal patterns
we propose to quantify how long temporal patterns appear without relying on their repetition or singularity enabling to extract such temporal patterns from a small dataset
2024 develops riesz regression for automatic
debiased
debiased machine learning
2024 develops riesz regression for automatic debiased machine learning which directly estimates the riesz representer or equivalently the bias-correction term by minimizing the mean squared error
stochastic agent-based simulations confirm these results demonstrating a direct correspondence between tau and the average number of secondary infections in the early
epidemic
infectious individuals
stochastic agent-based simulations confirm these results demonstrating a direct correspondence between tau and the average number of secondary infections in the early epidemic phase in line with the interpretation of mathcal r_0
these findings suggest that reinforcement
learning
reinforcement learning rl
these findings suggest that reinforcement learning approaches can be effectively adapted for use in random nonstationary and reward-sparse environments
sequences of logits reveal the low rank structure of
language
language models
sequences of logits reveal the low rank structure of language models
this study introduces a two-dimensional opinion model that classifies
voters
opinion dynamics
this study introduces a two-dimensional opinion model that classifies voters into five groups enabling precise characterization of the swing group s interactive behaviors
it is observed that for thinner as-deposited chromium
films
film thickness
it is observed that for thinner as-deposited chromium films the stress showed a pronounced irreversible increase when measured immediately after deposition and after several days of aging
we first formalize weaker conditions for identification which
motivates
covariate balancing
we first formalize weaker conditions for identification which motivates estimators that can efficiently control for many covariates
the tn network comprises three ground station nodes each of which are equipped to support direct-detection bidirectional optical
communication
optical communication
the tn network comprises three ground station nodes each of which are equipped to support direct-detection bidirectional optical communication with leo spacecraft
in this paper we study the multi-source multicast network
coding
network coding
in this paper we study the multi-source multicast network coding problem in undirected graphs
in this work we extend our previous work on the extrapolation of single-time observables to demonstrate an efficient scheme for
computing
quantum computing
in this work we extend our previous work on the extrapolation of single-time observables to demonstrate an efficient scheme for computing two-time impurity correlation functions by combining the non-markovian quantum mpemba effect nmqmpe with a dynamical map-based framework for open quantum systems
we present brain-it a brain-inspired approach that addresses this challenge through a brain interaction transformer bit allowing effective
interactions
brain decoding
we present brain-it a brain-inspired approach that addresses this challenge through a brain interaction transformer bit allowing effective interactions between clusters of functionally-similar brain-voxels
in addition to detailing the system we also share the empirical mode transfer matrices enabling future work in astrophotonic
design
integrated photonics
in addition to detailing the system we also share the empirical mode transfer matrices enabling future work in astrophotonic design computational imaging device fabrication feedback loops and beam shaping
progressive prompt engineering experiments demonstrate that analytical query handling without explicit guidance remains unreliable despite models general
reasoning
reasoning capabilities
progressive prompt engineering experiments demonstrate that analytical query handling without explicit guidance remains unreliable despite models general reasoning capabilities
however global prompt tuning struggles to generalize across heterogeneous clients while personalized
tuning
prompt tuning
however global prompt tuning struggles to generalize across heterogeneous clients while personalized tuning overfits to local data and lacks generalization
models must appropriately capture both aleatoric and epistemic uncertainty to effectively combine fine-grained local insights with broader
ecological
ecological communities
models must appropriately capture both aleatoric and epistemic uncertainty to effectively combine fine-grained local insights with broader ecological patterns
by synthesizing the current state of rl algorithm development and implications for pse this work identifies successes challenges trends and outlines
avenues
computationally efficient
by synthesizing the current state of rl algorithm development and implications for pse this work identifies successes challenges trends and outlines avenues for future research at the interface of these fields
the method is particularly effective when treatment effects are small relative to the variability of
outcomes
average treatment effect
the method is particularly effective when treatment effects are small relative to the variability of outcomes which is often the case in empirical applications
lagmemo language 3d gaussian splatting memory for multi-modal open-vocabulary multi-goal
visual
multi-goal visual
lagmemo language 3d gaussian splatting memory for multi-modal open-vocabulary multi-goal visual navigation
evaluated on benchmark datasets our method achieves a high
time-series
time series
evaluated on benchmark datasets our method achieves a high time-series aware f1 score taf1 of 89
several future directions are outlined for further developing the
multiplex
multiplex networks
several future directions are outlined for further developing the multiplex limit theory
at the neural level sensorimotor disruptions elicited robust event-related potential effects at fcz and pz accompanied by
increases
sensorimotor disruptions
at the neural level sensorimotor disruptions elicited robust event-related potential effects at fcz and pz accompanied by increases in frontal midline theta and posterior alpha suppression
in addition to the vertical excitation the effect of structural relaxation is also estimated using analytical
atomic
atomic force microscopy
in addition to the vertical excitation the effect of structural relaxation is also estimated using analytical atomic forces
these findings highlight challenges in achieving logical consistency in llms normative
reasoning
reasoning curriculum
these findings highlight challenges in achieving logical consistency in llms normative reasoning and provide insights for enhancing their reliability
however the effectiveness of this process in atomically thin flakes and the extent of the
magnetic
magnetic properties
however the effectiveness of this process in atomically thin flakes and the extent of the magnetic tunability remain unclear
we further characterize the minimax high-probability
risk
minimax risk
we further characterize the minimax high-probability risk for any estimator and demonstrate that it can be attained through a simple smoothing strategy
by manipulating the spectral content of both the target and distractor we found that the upper limit was reduced if and only if the
distractor
visual stimuli
by manipulating the spectral content of both the target and distractor we found that the upper limit was reduced if and only if the distractor spectrally overlaps with the target in the frequency range relevant for front back discrimination energetic masking thus explains the upper limit reduction by the distractor
stacked intelligent surfaces sis are a promising technology for next-generation
wireless
wireless systems
stacked intelligent surfaces sis are a promising technology for next-generation wireless systems offering an opportunity to enhance communication performance with low power consumption
this formulation progressively reduces projection errors particularly in challenging scenarios with large viewpoint variations and sparse feature
distributions
image reconstruction
this formulation progressively reduces projection errors particularly in challenging scenarios with large viewpoint variations and sparse feature distributions where traditional methods struggle
however no analytic expression exists for the viral population at the cellular level when the completion time for each process constituting
viral
viral replication
however no analytic expression exists for the viral population at the cellular level when the completion time for each process constituting viral replication is a random variable
perception learning a formal separation of sensory representation learning from
decision
policy learning
perception learning a formal separation of sensory representation learning from decision learning
this method has profound implications for the design of quantum
networks
multipartite entanglement
this method has profound implications for the design of quantum networks where preservation and purification of entanglement with minimal resource overhead is critical
vision-language-action vla models have significantly advanced robotic manipulation by integrating vision-language
models
vision-language models vlms
vision-language-action vla models have significantly advanced robotic manipulation by integrating vision-language models vlms and action decoders into a unified architecture
we explain how apparent heterogeneity in mr estimates between
populations
population genetics
we explain how apparent heterogeneity in mr estimates between populations can arise from differences in genetic variant frequencies and correlation patterns as well as from differences in the distribution of phenotypic variables complicating the detection of true differences in the causal pathway
this survey bridges that gap by delivering a forward-looking analysis of object
detection
object detection
this survey bridges that gap by delivering a forward-looking analysis of object detection in avs emphasizing emerging paradigms such as vision-language models vlms large language models llms and generative ai rather than re-examining outdated techniques
existing automated approaches though increasingly leveraging large
language
large language models llms
existing automated approaches though increasingly leveraging large language models llms remain largely confined to structured tabular data and cannot adequately address the heterogeneity of social media analysis
in many problems involving causal effects or structural models the parameters of interest
depend
regression function
in many problems involving causal effects or structural models the parameters of interest depend on regression functions
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 learning
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
we develop a direct debiased machine learning framework comprising neyman targeted estimation and generalized
riesz
debiased machine
we develop a direct debiased machine learning framework comprising neyman targeted estimation and generalized riesz regression
how muon s spectral design benefits generalization a study on
imbalanced
imbalanced data
how muon s spectral design benefits generalization a study on imbalanced data
we construct reference frames using three different sets of external sources 1 stars with
gaia
milky way
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
the extent to which different neural or artificial neural networks models rely on equivalent representations to support similar tasks remains a central
question
abstract representations
the extent to which different neural or artificial neural networks models rely on equivalent representations to support similar tasks remains a central question in neuroscience and machine learning
our results demonstrate that ssf significantly improves
sensing
integrated sensing
our results demonstrate that ssf significantly improves sensing quality while preserving communication efficiency
specifically we examine how fabrication-induced inhomogeneities in qubit resonant frequencies and coupling strengths affect quantum state propagation and the fidelity of
fundamental
quantum mechanics
specifically we examine how fabrication-induced inhomogeneities in qubit resonant frequencies and coupling strengths affect quantum state propagation and the fidelity of fundamental quantum operations
instead of extracting fingerprints directly from raw csi measurements csi2q first transforms frequency-domain
csi
information csi
instead of extracting fingerprints directly from raw csi measurements csi2q first transforms frequency-domain csi measurements into time-domain signals that share the same feature space with iq samples
we observe that the ability to recall an object physical or virtual that was encountered in a mobile ar experience depends on many possible impact factors and attributes with some objects being readily recalled while others are not and some people recalling
objects
object recall
we observe that the ability to recall an object physical or virtual that was encountered in a mobile ar experience depends on many possible impact factors and attributes with some objects being readily recalled while others are not and some people recalling objects overall much better or worse than others
by combining polarization diversity and angular-spectrum
modulation
nonlinear optical
by combining polarization diversity and angular-spectrum modulation idtt achieves speckle-free and vibration-robust mapping of biaxial birefringence with submicron resolution
swiech stochastic optimal control in infinite dimensions
dynamic
predictive control
swiech stochastic optimal control in infinite dimensions dynamic programming and hjb equations springer 2017
rather than experiencing a blend of both percepts often only one
eye
visual rivalry
rather than experiencing a blend of both percepts often only one eye s image is experienced whilst the other is suppressed from awareness
we find an almost universal magnetic-field-sfr scaling with
slope
magnetic field
we find an almost universal magnetic-field-sfr scaling with slope alpha approx 0
nearest neighbor matching as least squares
density
density-ratio estimation
nearest neighbor matching as least squares density ratio estimation and riesz regression
we conclude that ly alpha radiation pressure severely limits a possible extremely efficient feedback-free phase of star formation in
dense
star-forming region
we conclude that ly alpha radiation pressure severely limits a possible extremely efficient feedback-free phase of star formation in dense metal-poor clouds
these results highlight a fundamental gap in current
multimodal
vision-language models
these results highlight a fundamental gap in current multimodal systems while they capture rich visual-semantic correlations they lack the inductive biases required for temporal continuity and causal understanding