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understanding how individual learning behavior and structural
dynamics
complex networks
understanding how individual learning behavior and structural dynamics interact is essential to modeling emergent phenomena in socioeconomic networks
the recent advancement of multimodal large
language
language models
the recent advancement of multimodal large language models mllms is transforming human-computer interaction hci from surface-level exchanges into more nuanced and emotionally intelligent communication
to compensate for a quasiperiodic disturbance a quasiperiodic disturbance
observer
disturbance observer
to compensate for a quasiperiodic disturbance a quasiperiodic disturbance observer using time delays was proposed
in this work we propose get-use a two-step procedure that learns to perform real-robot generalized tool usage by
learning
learning agents
in this work we propose get-use a two-step procedure that learns to perform real-robot generalized tool usage by learning first to extend the robot s embodiment in simulation and then transferring the learned strategies to real-robot visuomotor policies
we investigate a process of growth of a signed network that strictly adheres to heider
structural
network structures
we investigate a process of growth of a signed network that strictly adheres to heider structural balance rules resulting in two opposing growing factions
for the monotone patterns pi 1 2 3 and 3 2 1 we present a nonadaptive tester with
polylogarithmic
mathrm polylog
for the monotone patterns pi 1 2 3 and 3 2 1 we present a nonadaptive tester with polylogarithmic query complexity giving an exponential separation between monotone and nonmonotone patterns unlike the one-dimensional case
we also show that there are differences in the blueshift of the civ lambda 1549 aa line and the equivalent width of the heii lambda 1640 aa line in radio loud and radio quiet
quasars
black hole
we also show that there are differences in the blueshift of the civ lambda 1549 aa line and the equivalent width of the heii lambda 1640 aa line in radio loud and radio quiet quasars that persist even after accounting for differences in the mass and accretion rate of the central black hole
we hope the geometric view of parametric memory encourages revisiting the default intuitions that guide researchers in areas like
knowledge
reasoning capabilities
we hope the geometric view of parametric memory encourages revisiting the default intuitions that guide researchers in areas like knowledge acquisition capacity discovery and unlearning
experiments on both synthetic and real-world datasets validate our theoretical insights and show that the proposed method effectively improves fairness while preserving
predictive
predictive performance
experiments on both synthetic and real-world datasets validate our theoretical insights and show that the proposed method effectively improves fairness while preserving predictive performance
based on this discussion and drawing on jane jacobs notion of eyes on the
street
everyday publics
based on this discussion and drawing on jane jacobs notion of eyes on the street we put forward the relational notion of reciprocity deficits between ai infrastructures and everyday publics in the street
quantum random number generators provide true physical randomness based on quantum processes essential for cryptographic and
scientific
quantum batteries
quantum random number generators provide true physical randomness based on quantum processes essential for cryptographic and scientific applications
overall our study links the structure of stimuli task and learning rule to representational drift and could pave the way for using
drift
working memory
overall our study links the structure of stimuli task and learning rule to representational drift and could pave the way for using drift as a signal for uncovering underlying computation in the brain
for the first time we effectively evaluate
diagrams
evaluation metrics
for the first time we effectively evaluate diagrams produced by state-of-the-art llms on recent research literature quantitatively demonstrating the validity of our metrics
the results demonstrate that the agent s training against opponents enables deliberate overtaking behaviours with an
overtaking
collision avoidance
the results demonstrate that the agent s training against opponents enables deliberate overtaking behaviours with an overtaking rate of 87 compared 56 for an agent trained just to race
this paper introduces a new structural design method to effectively decompose n 1 -bit toffoli
gates
-bit toffoli
this paper introduces a new structural design method to effectively decompose n 1 -bit toffoli gates by utilizing configurable ancilla qubits which we named the support qubits
our analysis disentangles the effects of post-training algorithms and datasets measuring both the magnitude and time of
value
training data
our analysis disentangles the effects of post-training algorithms and datasets measuring both the magnitude and time of value drifts during training
the poisson generalized linear model glm is a foundational tool for analyzing neural
spike
spike train
the poisson generalized linear model glm is a foundational tool for analyzing neural spike train data
these innovations mitigate model bias and restore approximate exchangeability enabling
uncertainty
uncertainty quantification
these innovations mitigate model bias and restore approximate exchangeability enabling uncertainty quantification even under policy shifts
as a first foray into quantum computational chemistry of actinides this paper compares the method of quantum computed moments qcm as a noisy intermediate-scale quantum algorithm with a single-ancilla version of quantum phase
estimation
quantum dot
as a first foray into quantum computational chemistry of actinides this paper compares the method of quantum computed moments qcm as a noisy intermediate-scale quantum algorithm with a single-ancilla version of quantum phase estimation qpe a quantum algorithm expected to run on fault-tolerant quantum computers
observational studies developing causal machine learning ml models for the prediction of individualized treatment effects ites seldom conduct empirical evaluations to assess the conditional
exchangeability
causal effect
observational studies developing causal machine learning ml models for the prediction of individualized treatment effects ites seldom conduct empirical evaluations to assess the conditional exchangeability assumption
in this work we systematically assess the performance of pdfxm relative to monochromatic dfxm when using a compound
refractive
refractive index
in this work we systematically assess the performance of pdfxm relative to monochromatic dfxm when using a compound refractive lens crl as the objective
twin-field quantum key distribution protocols security and
open
fault-tolerant quantum
twin-field quantum key distribution protocols security and open problems
to determine the half-light radii we derived surface
brightness
surface brightness
to determine the half-light radii we derived surface brightness and number density profiles for each system and fitted them with exponential plummer and s ersic models
while prior work often imposes object-centric
attention
working memory
while prior work often imposes object-centric attention e
remarkably however we show that if the system is stabilizable then using this as prior knowledge leads to necessary and sufficient
conditions
linear control
remarkably however we show that if the system is stabilizable then using this as prior knowledge leads to necessary and sufficient conditions that are weaker than those for data-driven stabilization without prior knowledge
we propose a new collision avoidance strategy that takes both
energy
motion planning
we propose a new collision avoidance strategy that takes both energy use and travel time into account
however since the synthetic data depends on the observed data and fails to replicate the original data distribution accurately prediction accuracy is reduced when the
synthetic
synthetic data
however since the synthetic data depends on the observed data and fails to replicate the original data distribution accurately prediction accuracy is reduced when the synthetic data is naively treated as the true data
in these systems the magnetic activity is not uniformly distributed along the edges but localized on specific
magnetic
magnetic properties
in these systems the magnetic activity is not uniformly distributed along the edges but localized on specific magnetic islands around molybdenum edge atoms
yet the relationship between parametric-oscillator implementations and traditional oscillator-based
ising
parametric oscillator
yet the relationship between parametric-oscillator implementations and traditional oscillator-based ising machines remains underexplored
to address these limitations we propose a symbolic
regression
support vector machines
to address these limitations we propose a symbolic regression sr -based ml framework
this design incorporates a temporal attention in vision encoder enabling the model to better capture the progression of actions and the relationships between frames before passing
visual
vision transformers
this design incorporates a temporal attention in vision encoder enabling the model to better capture the progression of actions and the relationships between frames before passing visual tokens to the llm
we find that the gas content and the dynamical coldness of the disk jointly regulate
spiral
quiescent galaxies
we find that the gas content and the dynamical coldness of the disk jointly regulate spiral growth galaxies with higher gas fractions and colder disks develop more prominent spirals
while modern large language models llms are increasingly used to model neural responses to
language
inner speech
while modern large language models llms are increasingly used to model neural responses to language their internal representations are highly entangled mixing information about lexicon syntax meaning and reasoning
these results establish arp as a robust and scalable approach for coherent control in inas qd ensembles with potential applications for ultrafast and broadband optical
communication
optical communication
these results establish arp as a robust and scalable approach for coherent control in inas qd ensembles with potential applications for ultrafast and broadband optical communication in the thz spectral region
our study addresses the challenges inherent in calculating mixed fermion-boson systems and explores the potential of quantum
computing
quantum networks
our study addresses the challenges inherent in calculating mixed fermion-boson systems and explores the potential of quantum computing to advance their analysis
we also find an apparent dependence on both
quiescent
galaxy cgm
we also find an apparent dependence on both quiescent galaxy mass and environment with 75 of the most massive log m_ mathrm m_ odot geq10
our work extends the study of predator-prey
models
quantum advantage
our work extends the study of predator-prey models to the quantum realm and advances quantum simulation stratagies that leverage engineered many-body nonequilibrium effects
in this paper we formalize this challenge as the textbf reward density optimization problem which aims to improve the
reward
reward density
in this paper we formalize this challenge as the textbf reward density optimization problem which aims to improve the reward obtained per unit of exploration cost
we learn visual features by captioning images with an image-conditioned masked diffusion
language
vision-language models
we learn visual features by captioning images with an image-conditioned masked diffusion language model a formulation we call masked diffusion captioning mdc
our key finding is that network connectivity declined by 45 from 2018 to 2023 implying a 26 reduction in the
contagion
network fragility
our key finding is that network connectivity declined by 45 from 2018 to 2023 implying a 26 reduction in the contagion decay parameter
artificial intelligence systems based on large
language
ai agents
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
such approaches are limited by large upfront
costs
computational cost
such approaches are limited by large upfront costs an inability to immediately handle new benchmarks cold-start and the fragile assumption that future models will share the failure patterns of their predecessors
the idea is to combine the statistical principle of rejection sampling with the geometric
principle
density estimation
the idea is to combine the statistical principle of rejection sampling with the geometric principle of volume comparison
optimal random access and conditional lower bounds for 2d
compressed
compressed indexing
optimal random access and conditional lower bounds for 2d compressed strings
to address these challenges we present expertflow a runtime system for moe
inference
moe inference
to address these challenges we present expertflow a runtime system for moe inference that combines adaptive expert prefetching and cache-aware routing
empathic prompting non-verbal context integration for multimodal
llm
llm reasoning
empathic prompting non-verbal context integration for multimodal llm conversations
behavioral cloning is a simple yet effective technique for
learning
reinforcement learning
behavioral cloning is a simple yet effective technique for learning sequential decision-making from demonstrations
quantum enhanced dark-matter search with entangled fock
states
quantum computing
quantum enhanced dark-matter search with entangled fock states in high-quality cavities
furthermore we extend our scheme to generate a three-photon state capable of extracting an einstein-podolsky-rosen pair from two initial
states
entanglement entropy
furthermore we extend our scheme to generate a three-photon state capable of extracting an einstein-podolsky-rosen pair from two initial states lacking this capability revealing a previously unobserved entanglement superactivation phenomenon
in this work we take a step toward building a truly accurate
world
world models
in this work we take a step toward building a truly accurate world model by addressing a fundamental yet open problem constructing a model that can fully clone and overfit to a deterministic 3d world
inspired by societally relevant applications in networked infrastructure systems these problems consist of simultaneously finding an unreweighted sparsified graph and
nodal
optimal power flow
inspired by societally relevant applications in networked infrastructure systems these problems consist of simultaneously finding an unreweighted sparsified graph and nodal potentials that satisfy fixed demands where the objective is to minimize some congestion criterion e
generating pivot gray codes for spanning trees of complete
graphs
spanning trees
generating pivot gray codes for spanning trees of complete graphs in constant amortized time
on the data-driven level we propose an orthogonal residual
learning
machine learning
on the data-driven level we propose an orthogonal residual learning scheme
these accretion-based m_ bullet values which in this sample span nearly three orders of magnitude are consistent with
galactic
dwarf galaxies
these accretion-based m_ bullet values which in this sample span nearly three orders of magnitude are consistent with galactic scaling relations but are significantly more precise 68 credible interval pm 0
we then propose novel estimators that are asymptotically
efficient
ate estimation
we then propose novel estimators that are asymptotically efficient achieving this theoretical bound
self-improvement has emerged as a mainstream paradigm for advancing the reasoning
capabilities
reasoning tasks
self-improvement has emerged as a mainstream paradigm for advancing the reasoning capabilities of large vision-language models lvlms where models explore and learn from successful trajectories iteratively
we then show that electron-phonon interactions critically modify optical spectra and exciton
lifetimes
phonon polaritons
we then show that electron-phonon interactions critically modify optical spectra and exciton lifetimes at finite temperatures
experimental results indicate improved performance compared to baseline methods
particularly
extensive experiments
experimental results indicate improved performance compared to baseline methods particularly for large environments
researchers have investigated various methods to extract
motion
optical flow
researchers have investigated various methods to extract motion information from a single blurred image including blur kernels and optical flow
incorporating these two modules enhances intra-region
semantic
brain decoding
incorporating these two modules enhances intra-region semantic consistency and maintains inter-region contextual associations thereby facilitating fine-grained brain decoding
0 percentage point on average compared to parametric exponential
decay
spatial decay
0 percentage point on average compared to parametric exponential decay assumptions with largest improvements at policy-relevant distances 2
the mean viral count is expressed via probability density functions representing the completion time for each step in the
replication
viral replication
the mean viral count is expressed via probability density functions representing the completion time for each step in the replication process
first-order methods based on the pdhg algorithm have recently emerged as a viable option for efficiently solving large-scale linear
programming
efficiently solving
first-order methods based on the pdhg algorithm have recently emerged as a viable option for efficiently solving large-scale linear programming problems
building upon over a hundred references we explore the essential aspects of these methods such as core assumptions numerical algorithms convergence properties and practical trade-offs to guide further developments particularly at the intersection of sequential
stopping
stopping rules
building upon over a hundred references we explore the essential aspects of these methods such as core assumptions numerical algorithms convergence properties and practical trade-offs to guide further developments particularly at the intersection of sequential stopping rules and related areas of research
the relativistic spin-momentum locking for mnte is
composed
spin-momentum locking
the relativistic spin-momentum locking for mnte is composed of dxz- dyz- and s-wave
8 ev and broadening of the pe lines for all atoms upon polymerization indicating a major redistribution of valence electron
density
electronic structure
8 ev and broadening of the pe lines for all atoms upon polymerization indicating a major redistribution of valence electron density between the monomer fragments
the increasing integration of renewable energy sources and distributed energy resources der into modern
power
power systems
the increasing integration of renewable energy sources and distributed energy resources der into modern power systems introduces significant uncertainty posing challenges for maintaining grid flexibility and reliability
in this work we develop a kuramoto-style canonical phase description of parametric oscillator ising machines by starting from the stuart-landau oscillator model -- the canonical normal form near a hopf bifurcation and a natural reduced description for many parametric oscillator implementations such as the degenerate optical parametric
oscillator
parametric oscillator
in this work we develop a kuramoto-style canonical phase description of parametric oscillator ising machines by starting from the stuart-landau oscillator model -- the canonical normal form near a hopf bifurcation and a natural reduced description for many parametric oscillator implementations such as the degenerate optical parametric oscillator dopo among others
swiech stochastic optimal control in infinite dimensions dynamic programming and
hjb
linear control
swiech stochastic optimal control in infinite dimensions dynamic programming and hjb equations springer 2017
experimental results reveal that while advanced models achieve moderate accuracy in
persona
persona simulation
experimental results reveal that while advanced models achieve moderate accuracy in persona simulation they still fall short of capabilities such as syntactic style and memory recall
we study a weighting estimator that sets weights by a minimax procedure solving a convex optimization
problem
maximum likelihood
we study a weighting estimator that sets weights by a minimax procedure solving a convex optimization problem that trades off worst-case conditional bias against variance
the theoretical results found in the evolutionarily
equilibrium
evolutionary dynamics
the theoretical results found in the evolutionarily equilibrium in the mathematical model are in line with the empirical results observed in oncology namely the coexistence of both primary and metastatic tumors and the conditions that favor a metastatic process
there has been a surge of recent interest in automatically
learning
policy evaluation
there has been a surge of recent interest in automatically learning policies to target treatment decisions based on rich individual covariates
we examine how context can help with the drc task with our experiments showing that
context
context engineering
we examine how context can help with the drc task with our experiments showing that context as defined by discourse structure is generally helpful
while viewing an ever-changing fractal-inspired artwork in an
immersive
virtual reality
while viewing an ever-changing fractal-inspired artwork in an immersive environment the user s eeg stream is analyzed and mapped into vr
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 anisotropy
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
a critical visual computation is to construct global scene properties from activities of early visual cortical neurons which have small
receptive
higher-order visual
a critical visual computation is to construct global scene properties from activities of early visual cortical neurons which have small receptive fields
experiments across multiple model families and sizes show that angular
steering
angular steering
experiments across multiple model families and sizes show that angular steering achieves robust behavioral control while maintaining general language modeling performance underscoring its flexibility generalization and robustness compared to prior approaches
analyses were put on the bulge stars including retrograde
stars
bulge stars
analyses were put on the bulge stars including retrograde stars their elemental abundances and the - plane to investigate potential accreted components
crash data objectively characterize road safety but are rare and often unsuitable for proactive
safety
crash risk
crash data objectively characterize road safety but are rare and often unsuitable for proactive safety management
in comparison to conventional measurement methods like laser tracker or motion capture systems the
calibration
calibration plate
in comparison to conventional measurement methods like laser tracker or motion capture systems the calibration plate provides a more mechanically robust and cheaper alternative which is furthermore easier to transport due to its small size
to overcome this limitation we propose to generalize sobolev ipm through the lens of emph orlicz geometric structure which employs convex functions to capture nuanced geometric relationships building upon recent advances in optimal transport theory -- particularly orlicz-wasserstein ow and generalized
sobolev
sobolev ipm
to overcome this limitation we propose to generalize sobolev ipm through the lens of emph orlicz geometric structure which employs convex functions to capture nuanced geometric relationships building upon recent advances in optimal transport theory -- particularly orlicz-wasserstein ow and generalized sobolev transport -- that have proven instrumental in advancing machine learning methodologies
notably we further complement the global analysis with local
convergence
local convergence
notably we further complement the global analysis with local convergence guarantees by demonstrating that the rescaled iterates exhibit asymptotic normality with a limiting covariance matrix resembling the minimax optimal covariance achieved by derivative-based methods albeit larger due to the absence of derivative information
physics-informed neural networks pinns are
neural
neural networks
physics-informed neural networks pinns are neural networks that embed the laws of dynamical systems modeled by differential equations into their loss function as constraints
we demonstrate that for spiking neural networks constrained to only four unique synaptic weight values m 4 our
spikefit
spike train
we demonstrate that for spiking neural networks constrained to only four unique synaptic weight values m 4 our spikefit method not only outperforms state-of-the-art snns compression methods and conventional baselines combining extreme quantization schemes and clustering algorithms but also meets a wider range of neuromorphic hardware requirements and provides the lowest energy use in experiments
ssf adapts the concept of retransmission from
communication
communication systems
ssf adapts the concept of retransmission from communication to sensing
the model which is built extending the classical susceptible-infected-susceptible model accounts for two populations -- humans and vectors -- and for cross-contagion between the two species whereby humans become infected upon interaction with carrier vectors and vectors become carriers after interaction with
infected
infectious individuals
the model which is built extending the classical susceptible-infected-susceptible model accounts for two populations -- humans and vectors -- and for cross-contagion between the two species whereby humans become infected upon interaction with carrier vectors and vectors become carriers after interaction with infected humans
efficient collision-avoidance constraints for ellipsoidal
obstacles
collision avoidance
efficient collision-avoidance constraints for ellipsoidal obstacles in optimal control application to path-following mpc and uavs
this paper proposes a noncoherent low probability of detection lpd
communication
communication systems
this paper proposes a noncoherent low probability of detection lpd communication system based on direct sequence spread spectrum dsss and grassmannian signaling
large language models llms face significant
inference
large language models llms
large language models llms face significant inference latency challenges stemming from their autoregressive design and large size
the state of brain emulation report 2025 provides a comprehensive reassessment of the field s progress since sandberg and bostrom s 2008 whole brain
emulation
brain regions
the state of brain emulation report 2025 provides a comprehensive reassessment of the field s progress since sandberg and bostrom s 2008 whole brain emulation roadmap
our findings point toward future control applications and open new avenues for probing the
intrinsic
functional connectivity
our findings point toward future control applications and open new avenues for probing the intrinsic temporal structure of neural activity
across synthetic and real-world benchmarks we show that
llms
large language models llms
across synthetic and real-world benchmarks we show that llms can exploit dataset metadata to recommend competitive models and hyperparameters without search and that improvements from meta-informed prompting demonstrate their capacity for in-context meta-learning
by first probing an lm to identify feature-specific layers our method iteratively regresses out lower-level representations to produce four nearly orthogonal embeddings for lexicon syntax meaning and
critically
abstract representations
by first probing an lm to identify feature-specific layers our method iteratively regresses out lower-level representations to produce four nearly orthogonal embeddings for lexicon syntax meaning and critically reasoning
the advent of artificial intelligence ai -native wireless communication is fundamentally reshaping the design paradigm of next-generation nextg systems where
intelligent
reconfigurable intelligent surface
the advent of artificial intelligence ai -native wireless communication is fundamentally reshaping the design paradigm of next-generation nextg systems where intelligent air interfaces are expected to operate adaptively and efficiently in highly dynamic environments
we present an adaptive and parallel implementation of the basin hopping bh algorithm for the global optimization of
atomic
molecular dynamics
we present an adaptive and parallel implementation of the basin hopping bh algorithm for the global optimization of atomic clusters interacting via the lennard-jones lj potential
finally we present a proof-of-principle simulation on the ibm quantum experience platform realizing a quantum switch of two measurement
channels
quantum key distribution
finally we present a proof-of-principle simulation on the ibm quantum experience platform realizing a quantum switch of two measurement channels with tunable strengths and experimentally confirming the predicted efficiency enhancement enabled by correlation-assisted superposed causal order
for a wide range of envisioned integrated
sensing
integrated sensing
for a wide range of envisioned integrated sensing and communication isac use cases it is necessary to incorporate tracking techniques into cellular communication systems
the optimization campaign successfully identified a
control
optimal control
the optimization campaign successfully identified a control strategy that yields a drag reduction of approximately 10
time-optimal model predictive control for
linear
predictive control
time-optimal model predictive control for linear systems with multiplicative uncertainties