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while resting-state fmri studies have revealed large-scale network correlates of creative potential electroencephalography
eeg
brain decoding
while resting-state fmri studies have revealed large-scale network correlates of creative potential electroencephalography eeg offers a temporally precise and scalable approach to capture the fast oscillatory dynamics that underlie spontaneous neural organization
our approach is the first data-driven approach that achieves conditional structured layout generation and produces realistic
layout
layout generation
our approach is the first data-driven approach that achieves conditional structured layout generation and produces realistic layout structures explicitly
learning to defer uncertain predictions to costly experts offers a powerful strategy for improving the accuracy and efficiency of
machine
machine learning
learning to defer uncertain predictions to costly experts offers a powerful strategy for improving the accuracy and efficiency of machine learning systems
here we combine information theory and synthetic forward modeling to estimate the
mutual
mutual information
here we combine information theory and synthetic forward modeling to estimate the mutual information between latent cortical sources and eeg recordings
to overcome these challenges we developed darts a drone-based ai-powered real-time traffic incident
detection
incident detection
to overcome these challenges we developed darts a drone-based ai-powered real-time traffic incident detection system
this hybrid approach enhances both sampling and entanglement
efficiency
quantum key distribution
this hybrid approach enhances both sampling and entanglement efficiency enabling more resource-practical implementations of distributed quantum computation
we prove kantorovich duality for a linearized version of a recently proposed non-quadratic quantum optimal transport problem where quantum
channels
quantum channels
we prove kantorovich duality for a linearized version of a recently proposed non-quadratic quantum optimal transport problem where quantum channels realize the transport
this paper presents a curriculum-based reinforcement
learning
optimal control
this paper presents a curriculum-based reinforcement learning framework for training precise and high-performance jumping policies for the robot olympus
land use change impacts are also negative but more complex with more
explanatory
land use
land use change impacts are also negative but more complex with more explanatory variables
if the researcher is willing to postulate a no residual autocorrelation assumption and some units can be thought of as controls pvar can identify
average
average treatment effect
if the researcher is willing to postulate a no residual autocorrelation assumption and some units can be thought of as controls pvar can identify average treatment effects on the treated
by comparing the evolution of a superposition-state to a superposition of individually-evolved basis states we test linearity and
observe
super-heisenberg scaling
by comparing the evolution of a superposition-state to a superposition of individually-evolved basis states we test linearity and observe clear violations which we quantify across the exceptional-point ep degeneracy of the non-hermitian hamiltonian
continual learning cl aims to incrementally train a model on a
sequence
continual learning
continual learning cl aims to incrementally train a model on a sequence of tasks while retaining performance on prior ones
the optimization problem aims to maximize the
communication
communication systems
the optimization problem aims to maximize the communication sum-rate which is critical for ensuring high-quality service to users while also maintaining accurate sensing performance for detection tasks and adhering to maximum power constraints for efficient resource usage
the methodology employed compares annotation time
coverage
findings highlight
the methodology employed compares annotation time coverage and diversity in three experimental settings manual automatic and semi-automatic annotation
our results present a compelling new case for the connection between the liquid-state properties of phase-change materials and their unique ability to combine high amorphous-phase stability with
ultrafast
phase transition
our results present a compelling new case for the connection between the liquid-state properties of phase-change materials and their unique ability to combine high amorphous-phase stability with ultrafast crystallization
specifically we first analyze the trajectory of the airy beam and the
beam
near-field beam
specifically we first analyze the trajectory of the airy beam and the beam pattern at the receiver using a discrete fourier transform dft codebook in the presence of obstacles
unlike autoregressive captioning the strength of the
visual
vision-language models
unlike autoregressive captioning the strength of the visual learning signal in mdc does not depend on each token s position in the sequence reducing the need for auxiliary objectives
this limits the ability of vision systems to capture key internal
drivers
automated driving
this limits the ability of vision systems to capture key internal drivers of behavior
dynamic spatial treatment effect boundaries a continuous
functional
spatial treatment
dynamic spatial treatment effect boundaries a continuous functional framework from navier-stokes equations
we introduce adaptive tuning-free step size schedules for gradient-based sampling algorithms obtained as time-discretizations of wasserstein
gradient
gradient flow
we introduce adaptive tuning-free step size schedules for gradient-based sampling algorithms obtained as time-discretizations of wasserstein gradient flows
this paper investigates large-population stochastic
control
predictive control
this paper investigates large-population stochastic control problems in which agents share their state information and cooperate to minimize a convex cost functional
extending the analysis to cavity magnomechanics we show that logarithmic plots can exhibit apparent polaromechanical normal
mode
waveguide modes
extending the analysis to cavity magnomechanics we show that logarithmic plots can exhibit apparent polaromechanical normal mode splitting whereas linear scale spectra display no true splitting
several models achieve sub kilometer accuracy epe 6 to 8 pixels 300 to 400 m a small error relative to the spatial scales of sea
ice
sea ice
several models achieve sub kilometer accuracy epe 6 to 8 pixels 300 to 400 m a small error relative to the spatial scales of sea ice motion and typical navigation requirements in the arctic
transition metals mn and co are chosen as substitutes for fe to reduce or increase the d-band electron count thereby moving the
fermi
fermi level
transition metals mn and co are chosen as substitutes for fe to reduce or increase the d-band electron count thereby moving the fermi level accordingly
we provide an inferential framework to assess variable importance for heterogeneous
treatment
causal inference
we provide an inferential framework to assess variable importance for heterogeneous treatment effects
recent efforts have introduced several combinatorial
kernels
heat kernels
recent efforts have introduced several combinatorial kernels but the relationships among them are not well understood
while we empirically observe that decomposing the original
flow
flow matching
while we empirically observe that decomposing the original flow enhances diversity in the target space generating semantically aligned outputs still requires consistent guidance toward the full target prompt
notably we show monotonicity properties of the frontier building on which we transform the bi-objective
problem
inverse optimal issf
notably we show monotonicity properties of the frontier building on which we transform the bi-objective problem into several single-objective problems
we present sia social insight agents an llm
agent
ai agents
we present sia social insight agents an llm agent system that links heterogeneous multi-modal data -- including raw inputs e
we introduce a class of generalized convex functions termed star quasiconvexity to ensure the linear convergence of
gradient
gradient descent
we introduce a class of generalized convex functions termed star quasiconvexity to ensure the linear convergence of gradient and proximal point methods
we initiate this study by focusing on collective
behaviors
social interactions
we initiate this study by focusing on collective behaviors that change abruptly at certain critical numbers of individuals
fully automated vehicles favs hold promise for
enhancing
automated driving
fully automated vehicles favs hold promise for enhancing the mobility of blind and low-vision blv individuals
i propose an alternative method defined as control-var which uses control variables to estimate
causal
causal inference
i propose an alternative method defined as control-var which uses control variables to estimate causal effects
in addition novel deep learning techniques are
currently
deep learning
in addition novel deep learning techniques are currently being investigated to further improve such optimization approaches
a-tpt angular diversity calibration properties for test-time prompt tuning of
vision-language
vision-language models
a-tpt angular diversity calibration properties for test-time prompt tuning of vision-language models
polynomial chaos expansions pce are widely used for
uncertainty
uncertainty quantification
polynomial chaos expansions pce are widely used for uncertainty quantification uq tasks particularly in the applied mathematics community
however the uniquely strict requirements for high-fidelity
qubit
quantum coherence
however the uniquely strict requirements for high-fidelity qubit transmission complicate the extent to which classical solutions may apply to future quantum networks particularly in terms of recognizing noise sources present in low-flux nonunitary channels
saddle point approximation and central limit
theorem
central limit
saddle point approximation and central limit theorem for densities in high dimensions
we design a practical reward shaping scheme direction distance
obstacle
dynamic obstacles
we design a practical reward shaping scheme direction distance obstacle avoidance action smoothness collision penalty time penalty and progress together with a lidar-based safety gate that prevents unsafe motions
these technologies offer personalized experiences and transform familiar
spaces
user experience
these technologies offer personalized experiences and transform familiar spaces by layering new narratives onto the real world
we validated the method on both continuous attractor network simulations and experimental recordings of grid cells demonstrating that local trajectories can be reliably reconstructed from a single
grid
grid cell
we validated the method on both continuous attractor network simulations and experimental recordings of grid cells demonstrating that local trajectories can be reliably reconstructed from a single grid cell module without external position information or training data
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
human-ai interaction
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
discussion about the replacement of intellectual human labour by thinking machines has been present in the public and expert discourse since the creation of
artificial
artificial intelligence
discussion about the replacement of intellectual human labour by thinking machines has been present in the public and expert discourse since the creation of artificial intelligence ai as an idea and terminology since the middle of the twentieth century
we formulate a network latency minimization problem to joint optimize
uplink
uplink communication
we formulate a network latency minimization problem to joint optimize uplink pass beamforming and task offloading
the design is subject to a transmit-power
budget
transmit power
the design is subject to a transmit-power budget and a proximity dissimilarity constraint around the communication-optimal pilot
different from existing motion representations we aim to estimate an hr
motion
motion trajectory
different from existing motion representations we aim to estimate an hr motion trajectory with high-quality from a single motion-blurred image
yet while most efforts have focused on document layout analysis dla its generative counterpart document
layout
layout generation
yet while most efforts have focused on document layout analysis dla its generative counterpart document layout generation remains underexplored
understanding how creativity is represented in the brain s intrinsic
functional
functional connectivity
understanding how creativity is represented in the brain s intrinsic functional architecture remains a central challenge in cognitive neuroscience
the appearance of a virtual avatar significantly influences its perceived appropriateness and the user s
experience
user experience
the appearance of a virtual avatar significantly influences its perceived appropriateness and the user s experience particularly in healthcare applications
second users recalled fewer physical objects than virtual
objects
object recall
second users recalled fewer physical objects than virtual objects in the environment suggesting reduced awareness of the physical environment
by incorporating a novel two-stage inference workflow the benchmark can further evaluate
vlms
vision-language models vlms
by incorporating a novel two-stage inference workflow the benchmark can further evaluate vlms capability to align and compare elements attributes across two charts
controlling specific behaviors in large language models while preserving their general
capabilities
language models
controlling specific behaviors in large language models while preserving their general capabilities is a central challenge for safe and reliable artificial intelligence deployment
specifically we establish a taylor expansion for the gradient of the biconjugation operator--that is the operator obtained by applying the fenchel transform twice--around a strictly
convex
strongly convex
specifically we establish a taylor expansion for the gradient of the biconjugation operator--that is the operator obtained by applying the fenchel transform twice--around a strictly convex function with assistance from gpt-5-pro openai s latest model
this paper analyses how these two model classes have performed on
real-world
real-world datasets
this paper analyses how these two model classes have performed on real-world tabular data
second we propose vimogen a flow-matching-based diffusion transformer that unifies
priors
optical flow
second we propose vimogen a flow-matching-based diffusion transformer that unifies priors from mocap data and vigen models through gated multimodal conditioning
an analytical end-to-end model is developed to characterize the system s electromechanical response noise behavior and information-theoretic performance including signal-to-noise
ratio
signal-to-noise ratio
an analytical end-to-end model is developed to characterize the system s electromechanical response noise behavior and information-theoretic performance including signal-to-noise ratio snr and channel capacity
the fact that the loss function can have and frequently has non-convex regions has led to a widespread commitment to
non-convex
gradient descent
the fact that the loss function can have and frequently has non-convex regions has led to a widespread commitment to non-convex methods such as adam
traditional graph_theoretic metrics such as betweenness and degree centrality offer insights into local network
structure
network structures
traditional graph_theoretic metrics such as betweenness and degree centrality offer insights into local network structure but often fail to capture global structural distortions resulting from link failures
our results demonstrate that models trained across animals with partial observations can successfully in-paint the dynamics of unrecorded
areas
brain regions
our results demonstrate that models trained across animals with partial observations can successfully in-paint the dynamics of unrecorded areas enabling multi-area analyses that transcend the limitations of any single experiment
large language models llms are increasingly
used
models llms
large language models llms are increasingly used as raters for evaluation tasks
accurate real-time wireless signal prediction is essential for
next-generation
wireless communication
accurate real-time wireless signal prediction is essential for next-generation networks
to tackle this challenge we propose braincognizer a novel brain decoding model inspired by human visual
cognition
brain-computer interface
to tackle this challenge we propose braincognizer a novel brain decoding model inspired by human visual cognition which explores multi-level semantics and correlations without fine-tuning of generative models
quantum error correction thermalization and quantum
chaos
quantum error correction
quantum error correction thermalization and quantum chaos are fundamental aspects of quantum many-body physics that have each developed largely independently despite their deep conceptual overlap
however the use of ai in re also brings challenges like algorithmic bias lack of explainability and
ethical
trustworthy ai
however the use of ai in re also brings challenges like algorithmic bias lack of explainability and ethical concerns related to automation
we establish the asymptotic properties of the test statistics which
hold
asymptotic normality
we establish the asymptotic properties of the test statistics which hold under both fixed- and high-dimensional regimes
in this work we study out-of-distribution
generalization
policy learning
in this work we study out-of-distribution generalization in meta-learning from an information-theoretic perspective
selecting a small set of species that maximizes phylogenetic diversity for a given
phylogenetic
phylogenetic diversity
selecting a small set of species that maximizes phylogenetic diversity for a given phylogenetic tree is an essential task in preservation planning where limited resources naturally prevent saving all species
dissecting the mass quenching in tng50 galaxy size determines the
quenching
galaxy cgm
dissecting the mass quenching in tng50 galaxy size determines the quenching mode
the virial mass of clumps increases exponentially with this normalized time revealing an
accelerating
dense gas
the virial mass of clumps increases exponentially with this normalized time revealing an accelerating buildup of star-forming gas within protoclusters
this represents a paradigm shift long-residence
interstellar
galactic nuclei
this represents a paradigm shift long-residence interstellar objects primarily reveal gcr-processed material rather than pristine material representative of their primordial formation environments
debiased machine learning typically requires estimation of the riesz representer and the
regression
machine learning
debiased machine learning typically requires estimation of the riesz representer and the regression function
motivated by practical applications we use this baseline to develop a new framework for fast
approximate
-approximation algorithm
motivated by practical applications we use this baseline to develop a new framework for fast approximate matrix multiplication amm via low-degree approximations of the cksu polynomials
slt thus provides a formal framework for understanding the amplification and coherence of social energy leading to abrupt phase-like transitions in
collective
emergent behaviors
slt thus provides a formal framework for understanding the amplification and coherence of social energy leading to abrupt phase-like transitions in collective behavior
understanding and modeling human mobility is central to challenges in transport planning sustainable
urban
human mobility
understanding and modeling human mobility is central to challenges in transport planning sustainable urban design and public health
as ai capabilities improve and ai is used to tackle more challenging
tasks
reasoning capabilities
as ai capabilities improve and ai is used to tackle more challenging tasks verifying quality and safety becomes increasingly challenging
the protocol utilizes a probabilistic approach to dynamically determine
channel
channel state
the protocol utilizes a probabilistic approach to dynamically determine channel polling listening intervals
the integration of non-linear link functions enhances model flexibility capturing the non-linear nature of
traffic
traffic dynamics
the integration of non-linear link functions enhances model flexibility capturing the non-linear nature of traffic extremes
the widespread adoption of generative ai genai has introduced new challenges in crowdsourced
data
generative ai
the widespread adoption of generative ai genai has introduced new challenges in crowdsourced data collection particularly in survey-based research
this paper introduces spikefit a novel training method for spiking neural networks snns that enables efficient inference on neuromorphic hardware considering all its stringent requirements the number of
neurons
neural codes
this paper introduces spikefit a novel training method for spiking neural networks snns that enables efficient inference on neuromorphic hardware considering all its stringent requirements the number of neurons and synapses that can fit on a single device and lower bit-width representations e
we quantify the dependence of magnetic fields on star formation
activity
star formation rates
we quantify the dependence of magnetic fields on star formation activity including both regular and starburst galaxies
furthermore the indirect optimization of a local region of attraction roa estimate using a restricted set of candidate lyapunov functions is greatly improved via c the introduction of a richer parameterization of candidate
lyapunov
predictive control
furthermore the indirect optimization of a local region of attraction roa estimate using a restricted set of candidate lyapunov functions is greatly improved via c the introduction of a richer parameterization of candidate lyapunov functions than previously reported and d the formulation of novel semidefinite programs sdps that directly optimize the resulting roa estimate
the fast for the curious how to accelerate
fault-tolerant
quantum coherence
the fast for the curious how to accelerate fault-tolerant quantum applications
the proposed mrc metric aligns crash risk estimates with real-world
pedestrian
crash risk
the proposed mrc metric aligns crash risk estimates with real-world pedestrian behavior in mixed-traffic environments
recent work shows that the problem remains np-hard even on restricted graph classes such as cactus
graphs
regular graphs
recent work shows that the problem remains np-hard even on restricted graph classes such as cactus graphs of pathwidth 2 aminian et al
in this paper we present an inverse-free pure quantum state
estimation
quantum dot
in this paper we present an inverse-free pure quantum state estimation protocol that achieves heisenberg scaling
fast high-fidelity baseband reset of a latched state for quantum
dot
quantum dot
fast high-fidelity baseband reset of a latched state for quantum dot qubit readout
to address these challenges we propose a unified framework that jointly optimizes 3d gaussian points and
camera
pose estimation
to address these challenges we propose a unified framework that jointly optimizes 3d gaussian points and camera poses without requiring pre-calibrated inputs
beyond identifying causal connections an equally important challenge is determining the associated causal influence range cir indicating when
causal
causal inference
beyond identifying causal connections an equally important challenge is determining the associated causal influence range cir indicating when causal influences emerged and for how long they persist
numerical experiments validate the efficiency and multiple robustness of our
proposed
computationally efficient
numerical experiments validate the efficiency and multiple robustness of our proposed methods
i develop a comprehensive theoretical framework for dynamic spatial treatment effect
boundaries
effect boundaries
i develop a comprehensive theoretical framework for dynamic spatial treatment effect boundaries using continuous functional definitions grounded in navier-stokes partial differential equations
these results highlight the substantial room for improvement and underscore the
challenges
llm inference
these results highlight the substantial room for improvement and underscore the challenges of applying llms in education
this leads to an imbalanced optimization that drives the model to prioritize simple reasoning
skills
reasoning curriculum
this leads to an imbalanced optimization that drives the model to prioritize simple reasoning skills while hindering its ability to tackle more complex reasoning tasks
we use this life cycle to project future concentration in
large
urban systems
we use this life cycle to project future concentration in large cities
we extend this theoretical foundation by proving the consistency of a truncated version of hohl and deriving explicit convergence rates when
hohl
continual learning
we extend this theoretical foundation by proving the consistency of a truncated version of hohl and deriving explicit convergence rates when hohl is used as a regularizer in fully supervised learning
the noise is sampled from a class of bounded polynomial kernel densities constructed through convolutions of uniform distributions with a natural bandwidth parameter defined by the
kernel
density estimation
the noise is sampled from a class of bounded polynomial kernel densities constructed through convolutions of uniform distributions with a natural bandwidth parameter defined by the kernel s support bound
the alignment trend becomes particularly pronounced in regions of high
tidal
tidal disruption
the alignment trend becomes particularly pronounced in regions of high tidal anisotropy and high overdensity
our results constitute a substantial development on existing corrections to mean-field theory for infectious individuals in sis processes and provide an in-depth characterization of how structural randomness in
networks
collective systems
our results constitute a substantial development on existing corrections to mean-field theory for infectious individuals in sis processes and provide an in-depth characterization of how structural randomness in networks affects the dynamical trajectories of infectious diseases on networks
boundary vector cells bvcs are a class of
neurons
surrogate brain
boundary vector cells bvcs are a class of neurons in the brains of vertebrates that encode environmental boundaries at specific distances and allocentric directions playing a central role in forming place fields in the hippocampus
furthermore we use our bounds to investigate the problem of data memorization raised in those works and which asserts that there are learning problem instances for which any learning algorithm that has good prediction there exist distributions under which the
algorithm
machine learning
furthermore we use our bounds to investigate the problem of data memorization raised in those works and which asserts that there are learning problem instances for which any learning algorithm that has good prediction there exist distributions under which the algorithm must memorize a big fraction of the training dataset
integrating legal and logical specifications in perception prediction and planning for automated
driving
autonomous driving
integrating legal and logical specifications in perception prediction and planning for automated driving a survey of methods