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our result nearly matches the o log 2 n approximation guarantee of the
quasi-polynomial-time
polynomial time
our result nearly matches the o log 2 n approximation guarantee of the quasi-polynomial-time algorithm by li xu and zhang icalp 2025
calibration has emerged as a foundational goal in trustworthy machine learning in part because of its strong
decision
debiased machine learning
calibration has emerged as a foundational goal in trustworthy machine learning in part because of its strong decision theoretic semantics
coupled opinion-environmental dynamics in
polarized
opinion formation
coupled opinion-environmental dynamics in polarized and prejudiced populations
on a real robot with 22 degrees of freedom dofs get-use outperforms state-of-the-art methods by 30-60 success rates across three vision-based bimanual
mobile
mobile robots
on a real robot with 22 degrees of freedom dofs get-use outperforms state-of-the-art methods by 30-60 success rates across three vision-based bimanual mobile manipulation tool-usage tasks
in this work we propose a strategy that exploits the principles of non-hermitian physics--specifically the concept of exceptional points eps --to transcend these limitations and pave the way for the next generation of versatile high-performance
photonic
integrated photonics
in this work we propose a strategy that exploits the principles of non-hermitian physics--specifically the concept of exceptional points eps --to transcend these limitations and pave the way for the next generation of versatile high-performance photonic devices
star incorporates a streamlined gated recurrent unit gru -based
recurrent
deep learning
star incorporates a streamlined gated recurrent unit gru -based recurrent neural network reducing model parameters by 33 compared to conventional lstm models while maintaining effective temporal modeling capability
leveraging this equivalence we propose a novel regularization method for
policy
policy optimization
leveraging this equivalence we propose a novel regularization method for policy learning
using network gradients it is possible to identify regions where the
network
object detection
using network gradients it is possible to identify regions where the network pays attention during image recognition tasks
we investigate the connection between accretion signatures and host galaxy properties in the context of how
active
active galactic
we investigate the connection between accretion signatures and host galaxy properties in the context of how active dwarf galaxies are identified
metacognition and confidence dynamics in advice taking from
generative
generative ai
metacognition and confidence dynamics in advice taking from generative ai
we further prove that no deterministic online algorithm can achieve a
competitive
online algorithm
we further prove that no deterministic online algorithm can achieve a competitive ratio bounded by 2 for the general cost optimization problem
linear-quadratic zero-sum stochastic differential
game
quadratic programming
linear-quadratic zero-sum stochastic differential game with partial observation
these findings suggest that resting-state eeg
connectivity
brain regions
these findings suggest that resting-state eeg connectivity patterns can index stable cognitive traits such as creativity
results demonstrate that icpo consistently enhances reinforcement learning performance and training stability on mathematical
reasoning
reasoning capabilities
results demonstrate that icpo consistently enhances reinforcement learning performance and training stability on mathematical reasoning benchmarks revealing a scalable and effective rlvr paradigm for lrms
in the current study using the human connectome project hcp dataset we examined the
relationship
task performance
in the current study using the human connectome project hcp dataset we examined the relationship between brain activation and working memory performance in two conditions i
randomization of weighted networks has traditionally been done via the
weighted
mobility networks
randomization of weighted networks has traditionally been done via the weighted configuration model wcm a simple extension of the configuration model where weights are interpreted as bundles of edges
large language models llms and multi-agent systems mas
offer
large language models llms
large language models llms and multi-agent systems mas offer opportunities to augment dispatchers
estimation of causal dose-response functions under
data
data fusion
estimation of causal dose-response functions under data fusion
we release amo-bench to facilitate further research into advancing the
reasoning
reasoning capabilities
we release amo-bench to facilitate further research into advancing the reasoning abilities of language models
magnetic field dragging in the filamentary
high-mass
massive stars
magnetic field dragging in the filamentary high-mass star-forming region g35
notably this algorithm gives an o n 1 2 -approximation for the densest connected k -subgraph problem
improving
-approximation algorithm
notably this algorithm gives an o n 1 2 -approximation for the densest connected k -subgraph problem improving upon the previous best-known approximation ratio of o n 2 3
here we present an analytically-tractable model of visual rivalry that quantitatively explains the hysteretic transition between periods of awareness and
suppression
human cognition
here we present an analytically-tractable model of visual rivalry that quantitatively explains the hysteretic transition between periods of awareness and suppression in tcfs
moreover spikefit introduces a new hardware-friendly fisher
spike
spike train
moreover spikefit introduces a new hardware-friendly fisher spike contribution fsc pruning method showing the state-of-the-art performance
human driving behavior is modeled as a markov jump
process
markov decision
human driving behavior is modeled as a markov jump process with transitions driven by task difficulty providing a tractable representation of stochastic state switching
our framework unifies riesz regression for automatic
debiased
debiased machine learning
our framework unifies riesz regression for automatic debiased machine learning covariate balancing targeted maximum likelihood estimation tmle and density-ratio estimation
the advancements in automatic modulation classification amc have propelled the development of signal sensing and identification technologies in non-cooperative communication scenarios but also enable eavesdroppers to effectively intercept user signals in
wireless
wireless communication
the advancements in automatic modulation classification amc have propelled the development of signal sensing and identification technologies in non-cooperative communication scenarios but also enable eavesdroppers to effectively intercept user signals in wireless communication environments
as a result the gromov-wasserstein distance can be used to rank edges
depending
gromov-wasserstein distance
as a result the gromov-wasserstein distance can be used to rank edges depending on their criticality with respect to their individual impact on the overall infrastructure and level allowing for prioritizing maintenance emergency planning and enhancing the resilience of the urban transport network
overall they are not yet reliable as standalone zero-shot reasoners but exhibit encouraging signs as complementary visual engines alongside dedicated
reasoning
spatial reasoning
overall they are not yet reliable as standalone zero-shot reasoners but exhibit encouraging signs as complementary visual engines alongside dedicated reasoning models
in this work we introduce a general form of a two-parameter family of
local
quantum dot
in this work we introduce a general form of a two-parameter family of local interactions between quantum walkers conditioned on the internal state of their coins
the complex kinematic relations among the six actuator paths connecting the fixed base to the moving platform further compound the difficulty in establishing a straightforward and efficient
calibration
calibration plate
the complex kinematic relations among the six actuator paths connecting the fixed base to the moving platform further compound the difficulty in establishing a straightforward and efficient calibration method
in the past the voter model has been explicitly used to model the impact of propaganda on a dynamic interconnected population and certain factors have been identified that influence the behavior of
voters
opinion dynamics
in the past the voter model has been explicitly used to model the impact of propaganda on a dynamic interconnected population and certain factors have been identified that influence the behavior of voters when under outside influence
the current best quantum algorithm has the query complexity o n 7 4 which is an improvement over the trivial bound o
n
query complexity
the current best quantum algorithm has the query complexity o n 7 4 which is an improvement over the trivial bound o n 2
soliton dynamics in coupled kerr microcavities is an important aspect of
frequency
frequency combs
soliton dynamics in coupled kerr microcavities is an important aspect of frequency comb technologies with applications in optical communication and precision metrology
although per-iteration cost can exceed that of classical multilevel schemes the method is efficient and consistently outperforms newton s method
gradient
gradient descent
although per-iteration cost can exceed that of classical multilevel schemes the method is efficient and consistently outperforms newton s method gradient descent and the multilevel newton method indicating that second-order methods can outperform first-order methods even when newton s method is initially slow
current progress in electro-optical modulation within silicon
integrated
photonic circuits
current progress in electro-optical modulation within silicon integrated photonics driven by the unique capabilities of advanced functional materials has led to significant improvements in device performance
decentralized bilevel optimization has garnered significant attention due to its critical role in solving large-scale machine
learning
bilevel optimization
decentralized bilevel optimization has garnered significant attention due to its critical role in solving large-scale machine learning problems
accurate world models are essential for enabling agents to think plan and reason effectively in complex
dynamic
spatial reasoning
accurate world models are essential for enabling agents to think plan and reason effectively in complex dynamic settings
under this hypothesis pattern separation is facilitated when more information from similar stimuli can be
discarded
mutual information
under this hypothesis pattern separation is facilitated when more information from similar stimuli can be discarded rather than preserved
in this paper we explore the challenges and possibilities of
everyday
everyday publics
in this paper we explore the challenges and possibilities of everyday public engagement with ai in the situated environment of city streets under these paradoxical conditions
the system supports various sensor models as evidenced by evaluations
conducted
sensor data
the system supports various sensor models as evidenced by evaluations conducted with velodyne hdl-64e ouster os2 128 aeva aeries ii and livox avia sensors
to further balance performance and automated effort we then develop a minimal intervention
controller
disturbance observer
to further balance performance and automated effort we then develop a minimal intervention controller mic that retains acceptable stability while limiting automation
our study addresses the challenges inherent in calculating mixed fermion-boson systems and explores the potential of quantum
computing
multipartite entanglement
our study addresses the challenges inherent in calculating mixed fermion-boson systems and explores the potential of quantum computing to advance their analysis
we conduct a comprehensive comparison between redllm pretrained with prefix language
modeling
large language
we conduct a comprehensive comparison between redllm pretrained with prefix language modeling lm and decllm pretrained with causal lm at different model scales ranging from sim 150m to sim 8b
spiking neural networks snns represent a promising algorithmic approach for these systems yet their application to complex control tasks faces two critical challenges 1 the non-differentiable nature of spiking neurons necessitates surrogate gradients with unclear optimization properties and 2 the stateful dynamics of s...
learning
reinforcement learning
spiking neural networks snns represent a promising algorithmic approach for these systems yet their application to complex control tasks faces two critical challenges 1 the non-differentiable nature of spiking neurons necessitates surrogate gradients with unclear optimization properties and 2 the stateful dynamics of s...
while a multi-agent approach based on large language models llms represents a promising strategy to surpass the
capabilities
llm 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
for ate estimation we estimate the propensity score through direct
bias-correction
ate estimation
for ate estimation we estimate the propensity score through direct bias-correction term estimation
we investigate the impact of synthetic data generation and lightweight fine-tuning techniques on the ability of large
language
language models
we investigate the impact of synthetic data generation and lightweight fine-tuning techniques on the ability of large language models llms to recognize fallacious arguments using the missci dataset and framework
these results highlight the significant room for improving the
mathematical
reasoning curriculum
these results highlight the significant room for improving the mathematical reasoning in current llms
we also demonstrate this effect through experiments on challenging
optimization
optimization problem
we also demonstrate this effect through experiments on challenging optimization problems involving large batches in high dimensions
this study demonstrates that providing language models with
pragmatic
pragmatic theories
this study demonstrates that providing language models with pragmatic theories as prompts is an effective in-context learning approach for tasks to understand implied meanings
here we go beyond this result and show that mai techniques can significantly enhance the detection capability of
witnesses
multipartite entanglement
here we go beyond this result and show that mai techniques can significantly enhance the detection capability of witnesses for quantum correlations
besides ac conductivity electric modulus and dielectric properties have been investigated to illustrate the microscopic
conduction
heat conduction
besides ac conductivity electric modulus and dielectric properties have been investigated to illustrate the microscopic conduction mechanism
our study establishes a framework for integrating ecological interactions into population genetics models and helps illuminate how ecology can affect
evolutionary
phylogenetic diversity
our study establishes a framework for integrating ecological interactions into population genetics models and helps illuminate how ecology can affect evolutionary outcomes
2 we design an algorithm that gathers all of them in poly n log
lambda
polynomial time
2 we design an algorithm that gathers all of them in poly n log lambda time where n resp
however most methods often overlook the dynamic generation process of neural data such as
hierarchical
higher-order visual
however most methods often overlook the dynamic generation process of neural data such as hierarchical brain s visual data within the brain s structure
under the potential outcomes framework recent research has studied time-series
experiments
time-series experiments
under the potential outcomes framework recent research has studied time-series experiments from the design-based perspective relying solely on the randomness in the design to drive the statistical inference
we introduce an approach to study the low-dimensional structure of
language
large language
we introduce an approach to study the low-dimensional structure of language models at a model-agnostic level as sequential probabilistic models
a robust and streamlined method is presented for efficiently extracting spectral diffusion from two-dimensional coherent
spectra
spectral range
a robust and streamlined method is presented for efficiently extracting spectral diffusion from two-dimensional coherent spectra by employing the projection-slice theorem
in computer vision this long-standing challenge remains limited to
industrial
computer vision
in computer vision this long-standing challenge remains limited to industrial defects or unrealistic synthetically generated anomalies failing to capture the richness and unpredictability of real-world anomalies
the growing prevalence of negative experiences in online spaces
demands
user experience
the growing prevalence of negative experiences in online spaces demands urgent attention from the human-computer interaction hci community
extensions to longitudinal data dynamic treatment regimes and multiplicative instrumental
variables
causal effects
extensions to longitudinal data dynamic treatment regimes and multiplicative instrumental variables are further developed
inference latency stands as a critical bottleneck in the large-scale deployment of large
language
large language
inference latency stands as a critical bottleneck in the large-scale deployment of large language models llms
starting with an impractical scheme based on the standard martingale central
limit
central limit
starting with an impractical scheme based on the standard martingale central limit theorem we progressively address its limitations from implementation perspectives in the non-asymptotic regime
for typical cortical stimuli tens of milliseconds this places the
functional
fmri data
for typical cortical stimuli tens of milliseconds this places the functional plasticity window in the few-second range a testable prediction that identifies seconds-scale eligibility traces as necessary for error-driven learning in biological circuits
however the potential perceptual demands of viewing virtual annotations while navigating a physical environment could impact user efficacy and safety and the implications of these
demands
user experience
however the potential perceptual demands of viewing virtual annotations while navigating a physical environment could impact user efficacy and safety and the implications of these demands are not well understood
we develop an efficient algorithm to solve this bilevel
optimization
gradient descent
we develop an efficient algorithm to solve this bilevel optimization problem which computes parameter gradients without backpropagating through the solver
dyck edit distance measures how far a parenthesis string is from a well-parenthesized expression while tree edit distance quantifies the minimum number of node insertions deletions and substitutions required to transform one rooted ordered labeled
tree
tree edit distance
dyck edit distance measures how far a parenthesis string is from a well-parenthesized expression while tree edit distance quantifies the minimum number of node insertions deletions and substitutions required to transform one rooted ordered labeled tree into another
wimhf a method to explain feedback data using
sparse
sparse autoencoders
wimhf a method to explain feedback data using sparse autoencoders
our recursive algorithm generates each spanning
tree
tree embedding
our recursive algorithm generates each spanning tree in constant amortized time using o n 2 space
accurate and timely travel information is an asset for enhancing passenger
travel
travel information
accurate and timely travel information is an asset for enhancing passenger travel experience during normal traffic and for mitigating the discomforts during disruptions
the proposed approach yields a numerical method that provably executes in linear
time
computationally efficient
the proposed approach yields a numerical method that provably executes in linear time with respect to the number of nodes and edges in a graph
when treatment effects are heterogeneous however such specifications generally fail to recover this
average
average treatment effect
when treatment effects are heterogeneous however such specifications generally fail to recover this average effect
most classical visual navigation methods are restricted to single-goal single-modality and closed set
goal
multi-goal visual
most classical visual navigation methods are restricted to single-goal single-modality and closed set goal settings
immersive applications call for synthesizing spatiotemporal 4d content from casual
videos
video generation
immersive applications call for synthesizing spatiotemporal 4d content from casual videos without costly 3d supervision
anomaly detection in time-series data is a critical challenge with significant
implications
time series
anomaly detection in time-series data is a critical challenge with significant implications for network security
our results lay a fundamental understanding of how pre-trained llms manipulate numbers and outline the potential of more accurate probing techniques in addressed refinements of
llms
models llms
our results lay a fundamental understanding of how pre-trained llms manipulate numbers and outline the potential of more accurate probing techniques in addressed refinements of llms architectures
our extensive evaluation demonstrates that our approach can localize within 200m more than 68 of queries of a dataset covering a
large
large language
our extensive evaluation demonstrates that our approach can localize within 200m more than 68 of queries of a dataset covering a large part of europe
this observation reduces irl to two off-the-shelf supervised
learning
machine learning
this observation reduces irl to two off-the-shelf supervised learning problems probabilistic classification to estimate the behavior policy and iterative regression to solve the fixed point
to address these problems we develop a general nonparametric approach that accommodates both discrete and continuous settings for testing
causal
causal effects
to address these problems we develop a general nonparametric approach that accommodates both discrete and continuous settings for testing causal hypothesis under unmeasured confounders
designing efficient quantum circuits is a central bottleneck to exploring the potential of quantum
computing
quantum networks
designing efficient quantum circuits is a central bottleneck to exploring the potential of quantum computing particularly for noisy intermediate-scale quantum nisq devices where circuit efficiency and resilience to errors are paramount
then we incorporate two control actions namely vector
control
optimal control
then we incorporate two control actions namely vector control and incentives to adopt protection measures
to account for uncertainty the underlying optimal power
flow
power flow
to account for uncertainty the underlying optimal power flow opf routines have to be modified
this study develops a real-time framework for estimating
pedestrian
traffic dynamics
this study develops a real-time framework for estimating pedestrian crash risk at signalized intersections under heterogeneous non-lane-based traffic
5 v reduction in operating voltage at a current level of 400 a cm 2 and a
decrease
breakdown voltage
5 v reduction in operating voltage at a current level of 400 a cm 2 and a decrease in differential on-resistance from 6
pragmatic theories enhance understanding of
implied
pragmatic theories
pragmatic theories enhance understanding of implied meanings in llms
by separately selecting actions for longitudinal and lateral control the introduced policies over combined and hybrid options obtain the same expressiveness and flexibility that human drivers have while being easier to interpret than classical
policies
control strategy
by separately selecting actions for longitudinal and lateral control the introduced policies over combined and hybrid options obtain the same expressiveness and flexibility that human drivers have while being easier to interpret than classical policies over continuous actions
among these methods deep neural networks have been widely adopted due to their performance and
accessibility
neural network
among these methods deep neural networks have been widely adopted due to their performance and accessibility but they require large high-quality datasets
we present advancements in decoding visual stimuli using
linear
encoding models
we present advancements in decoding visual stimuli using linear models at the individual subject level
wilson-cowan and amari-type models capture nonlinear neural population dynamics providing a fundamental framework for modeling how
sensory
receptive fields
wilson-cowan and amari-type models capture nonlinear neural population dynamics providing a fundamental framework for modeling how sensory and other exogenous inputs shape activity in neural tissue
viewing llms as implicit repositories of human knowledge we propose evontree a novel framework that leverages a small set of high-quality ontology rules to systematically extract validate and enhance domain knowledge within
llms
large language models llms
viewing llms as implicit repositories of human knowledge we propose evontree a novel framework that leverages a small set of high-quality ontology rules to systematically extract validate and enhance domain knowledge within llms without requiring extensive external datasets
to resolve the dilemma we propose pvmark a plugin based on zero-knowledge proof zkp enabling the watermark
detection
watermark detection
to resolve the dilemma we propose pvmark a plugin based on zero-knowledge proof zkp enabling the watermark detection process to be publicly verifiable by third parties without disclosing any secret key
as an application we determine optimal solutions of both the primal and the dual problem using this duality in the case of quantum bits and distinguished cost operators with certain restrictions on the
states
quantum advantage
as an application we determine optimal solutions of both the primal and the dual problem using this duality in the case of quantum bits and distinguished cost operators with certain restrictions on the states involved
stochastic state estimation methods for continuum robots
crs
state estimation
stochastic state estimation methods for continuum robots crs often struggle to balance accuracy and computational efficiency
in this paper we systematically evaluate llms reasoning capabilities in the
normative
normative reasoning
in this paper we systematically evaluate llms reasoning capabilities in the normative domain from both logical and modal perspectives
all d otimes d dimensional entangled states are useful for the antidiscrimination of quantum measurements when
d
multipartite entanglement
all d otimes d dimensional entangled states are useful for the antidiscrimination of quantum measurements when d is even
we robustify our test both against this term and finite sample bias and illustrate its excellent
performance
test statistics
we robustify our test both against this term and finite sample bias and illustrate its excellent performance and practical relevance in a monte carlo study and a real data empirical example
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
learning agents
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
the continuous functional perspective unifies
spatial
spatial decay
the continuous functional perspective unifies spatial econometrics with mathematical physics providing theoretically grounded methods for boundary detection exposure quantification and policy evaluation across environmental economics banking and healthcare applications
using network gradients it is possible to identify regions where the
network
neural network
using network gradients it is possible to identify regions where the network pays attention during image recognition tasks
robotic assistant completing collaborative
tasks
multi-robot collaboration
robotic assistant completing collaborative tasks with dexterous vision-language-action models