prompt
stringlengths
41
511
target
stringlengths
1
25
keyword
stringclasses
697 values
full_sentence
stringlengths
48
1.25k
second motivated by the consistency between multi-head self-attention in
transformers
vision transformers
second motivated by the consistency between multi-head self-attention in transformers and semantic level affinity we propose structure-affinity transformation to transform semantic features with class-specific affinity and combine it with a transformer decoder for structure-aware reasoning
the bound electron-hole pairs known as excitons govern the optical
properties
quantum materials
the bound electron-hole pairs known as excitons govern the optical properties of insulating solids
empirical evaluations conducted on road networks and synthetic graphs under both dynamic and stationary prize distributions show that 1 the state-aliasing induced by or-conditioning enables
learning
reinforcement learning
empirical evaluations conducted on road networks and synthetic graphs under both dynamic and stationary prize distributions show that 1 the state-aliasing induced by or-conditioning enables learning policies that scale more efficiently to large team sizes than those trained with the global index and 2 policies trained with forl generalize better to imbalanced prize distributions than those with other multi-agent training methods
we opt to model human driver decisions as a
markov
markov decision
we opt to model human driver decisions as a markov decision process and propose a method for handling collision avoidance between non-convex vehicle shapes by imposing a positive distance constraint between compact sets
we design a practical reward shaping scheme direction distance obstacle
avoidance
obstacle avoidance
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
graph-theoretical mapping of resting-state
eeg
cognitive science
graph-theoretical mapping of resting-state eeg reveals neural signatures of creativity
however as typical to any quantum resource
network
quantum channels
however as typical to any quantum resource network nonlocality is also vulnerable to environmental noise which sometimes prove to be detrimental
trishul technique for reconstructing magnetic
interstellar
galactic disk
trishul technique for reconstructing magnetic interstellar structure using starlight polarization
however as typical to any quantum resource
network
quantum advantage
however as typical to any quantum resource network nonlocality is also vulnerable to environmental noise which sometimes prove to be detrimental
the orbital angular momentum oam of light is a versatile
degree
light-matter interactions
the orbital angular momentum oam of light is a versatile degree of freedom with transformative impact across optical communication imaging and micromanipulation
modern vision-language models vlms excel at many multimodal tasks yet their grasp of
temporal
temporal understanding
modern vision-language models vlms excel at many multimodal tasks yet their grasp of temporal information in video remains weak and crucially under-evaluated
however the performance of all previous digital quantum simulations has been matched by classical methods and it has thus far remained unclear whether near-term intermediate-scale quantum hardware could offer any computational
advantage
quantum walk
however the performance of all previous digital quantum simulations has been matched by classical methods and it has thus far remained unclear whether near-term intermediate-scale quantum hardware could offer any computational advantage in this area
we therefore ask whether improving representation learning alone can substantially improve
world-model
representation learning
we therefore ask whether improving representation learning alone can substantially improve world-model performance
we provide an inferential framework to assess variable importance for heterogeneous
treatment
average treatment effect
we provide an inferential framework to assess variable importance for heterogeneous treatment effects
revisiting generative infrared and visible image
fusion
vision transformers
revisiting generative infrared and visible image fusion based on human cognitive laws
understanding the dynamics of the spread of diseases within populations is critical for effective public
health
public health
understanding the dynamics of the spread of diseases within populations is critical for effective public health interventions
the framework provides practical tools for detecting when localized treatments become systemic and identifying critical thresholds for
policy
policy evaluation
the framework provides practical tools for detecting when localized treatments become systemic and identifying critical thresholds for policy intervention
central to this advancement is a new class of quasi-bound state in the continuum bessel-type
modes
waveguide modes
central to this advancement is a new class of quasi-bound state in the continuum bessel-type modes emerging from moire-induced interlayer coupling which generate vortex beams with tailored spiral phase distributions
they are low-mass median log m _ star m _
odot
stellar mass
they are low-mass median log m _ star m _ odot 8
the nonlinear disturbance observer attenuates constant and nonlinear
disturbances
external disturbances
the nonlinear disturbance observer attenuates constant and nonlinear disturbances as well as band-limited white noise
the fixed-domain result follows immediately from the increasing-domain result from the self-similarity of gaussian
random
random field
the fixed-domain result follows immediately from the increasing-domain result from the self-similarity of gaussian random fields with power-law generalized covariances cite istaslang1997 coeurjolly2001 zhustein2002
in the scope of this work we assume a narrowband and static scenario aiming to focus on the
beamforming
beamforming design
in the scope of this work we assume a narrowband and static scenario aiming to focus on the beamforming and power allocation strategies
the growing success of vision-language-action vla models stems from the promise that pretrained vision-language models vlms can endow agents with transferable world knowledge and vision-language vl grounding laying a foundation for action
models
vision-language models vlms
the growing success of vision-language-action vla models stems from the promise that pretrained vision-language models vlms can endow agents with transferable world knowledge and vision-language vl grounding laying a foundation for action models with broader generalization
group based reinforcement learning rl has shown impressive results on complex
reasoning
reinforcement learning rl
group based reinforcement learning rl has shown impressive results on complex reasoning and mathematical tasks
get-use learning generalized tool usage for bimanual mobile
manipulation
tool usage
get-use learning generalized tool usage for bimanual mobile manipulation via simulated embodiment extensions
it jointly incorporates emph explicit evidence from direct evaluations of selected tasks and emph implicit
evidence
extensive experiments
it jointly incorporates emph explicit evidence from direct evaluations of selected tasks and emph implicit evidence inferred from these evaluations for unselected tasks with thompson sampling ensuring a principled balance between exploration and exploitation
we systematically explore techniques ranging from logic-based frameworks to computational legal reasoning approaches emphasizing their capability to
ensure
control law
we systematically explore techniques ranging from logic-based frameworks to computational legal reasoning approaches emphasizing their capability to ensure regulatory compliance and interpretability in dynamic and uncertain driving environments
we present a rigorous mathematical analysis for the
performance
randomized algorithm
we present a rigorous mathematical analysis for the performance of our algorithms
in this work we study how different cosmic-ray
transport
circumgalactic medium
in this work we study how different cosmic-ray transport mechanisms impact the gamma -ray luminosity of a turbulent multiphase medium formed from an initially diffuse medium
an understanding of the assembly history of the complex star cluster omega centauri has long been sought after with many studies separating the
stars
star clusters
an understanding of the assembly history of the complex star cluster omega centauri has long been sought after with many studies separating the stars on the color-magnitude diagram into multiple groupings across small magnitude ranges
we illustrate the framework in an empirical application estimating the
causal
causal inference
we illustrate the framework in an empirical application estimating the causal effect of private health insurance on health outcomes
this work advances the reliability of emcd as a quantitative tool for magnetic characterization at the nanoscale with unknown
magnetic
magnetic properties
this work advances the reliability of emcd as a quantitative tool for magnetic characterization at the nanoscale with unknown magnetic structures
our work provides a scalable method to overcome a measurement bottleneck in
cognitive
cognitive neuroscience
our work provides a scalable method to overcome a measurement bottleneck in cognitive science and demonstrates that foundation models can learn a representational geometry that is functionally relevant for modeling key aspects of human cognition such as categorization
our work lays the foundation for this new direction by establishing upper and
lower
lower bound
our work lays the foundation for this new direction by establishing upper and lower bounds on space complexity of key variants of the problem
the online method adapts to distribution shifts including human behavior evolving through
interaction
ai systems
the online method adapts to distribution shifts including human behavior evolving through interaction with ai a phenomenon we call human to ai adaptation
this framework offers a practical analytical tool for
traffic
traffic dynamics
this framework offers a practical analytical tool for traffic engineers and planners to design adaptive signal control and pedestrian safety interventions before crashes occur
we design polynomial-time approximations to the optimum
online
online algorithm
we design polynomial-time approximations to the optimum online algorithm achieving guarantees of 7 8 for vertex-weighted graphs and 2 sqrt 2 -2 approx 0
efficient collision-avoidance constraints for ellipsoidal obstacles in optimal
control
optimal control
efficient collision-avoidance constraints for ellipsoidal obstacles in optimal control application to path-following mpc and uavs
classical metrics such as the colless and sackin indices quantify
tree
phylogenetic tree
classical metrics such as the colless and sackin indices quantify tree imbalance and have been extensively used to characterize phylogenies
with this approach we determine and study the hidden spin textures of the upper valence bands of the ptte2 monolayer together with the spatial behavior of the probability densities and spin polarization densities of the corresponding maximally segregated
spin
hidden spin
with this approach we determine and study the hidden spin textures of the upper valence bands of the ptte2 monolayer together with the spatial behavior of the probability densities and spin polarization densities of the corresponding maximally segregated spin states
prevailing methods often rely on the one-lora-per-effect paradigm which is resource-intensive and fundamentally incapable of generalizing to unseen
effects
extensive experiments
prevailing methods often rely on the one-lora-per-effect paradigm which is resource-intensive and fundamentally incapable of generalizing to unseen effects thus limiting scalability and creation
brain decoding is a key neuroscience field that reconstructs the visual stimuli from brain activity with
fmri
receptive fields
brain decoding is a key neuroscience field that reconstructs the visual stimuli from brain activity with fmri which helps illuminate how the brain represents the world
the generic population concept - agent-based model henceforth short gepoc abm is one of the models within gepoc a
generic
large population
the generic population concept - agent-based model henceforth short gepoc abm is one of the models within gepoc a generic concept to model a country s population and its dynamics using causal modelling approaches
numerical studies based on stochastic gradient
descent
gradient descent
numerical studies based on stochastic gradient descent provide empirical backing for our theoretical findings
our analysis precisely characterizes conditions under which disparities persist or diminish with a particular focus on the
role
resource allocation
our analysis precisely characterizes conditions under which disparities persist or diminish with a particular focus on the role of the scarcity of available spots in the program and its effectiveness
the present article avoids these tools and is entirely confined to the
domain
discrete distributions
the present article avoids these tools and is entirely confined to the domain of distribution functions
galaxy mergers trigger starburst activity and
galactic
dwarf galaxies
galaxy mergers trigger starburst activity and galactic outflows that enrich the circumgalactic medium profoundly impacting galaxy evolution
accordingly redundancy reduction has been suggested as a prominent design principle of neural
encoding
encoding models
accordingly redundancy reduction has been suggested as a prominent design principle of neural encoding but its mechanistic biological implementation is unclear
this system includes an additional key quadratic term--a distinction from classical optimal stopping where stopping
conditions
linear control
this system includes an additional key quadratic term--a distinction from classical optimal stopping where stopping conditions depend only on comparing the value function to the instantaneous reward
fermionic dynamics on a trapped-ion quantum computer beyond exact
classical
quantum technologies
fermionic dynamics on a trapped-ion quantum computer beyond exact classical simulation
this energy is believed to impact the star formation activity and contribute to the
quenching
active galactic
this energy is believed to impact the star formation activity and contribute to the quenching of galaxies
first it supports evaluation using a quantum simulator environment beyond conventional python execution allowing feedback of domain-specific metrics such as circuit depth execution time and
error
quantum error correction
first it supports evaluation using a quantum simulator environment beyond conventional python execution allowing feedback of domain-specific metrics such as circuit depth execution time and error classification which can be used to guide better generation
low probability of detection communication using
noncoherent
communication systems
low probability of detection communication using noncoherent grassmannian signaling
by fitting up to three three epochs per source with a fully
relativistic
black hole mass
by fitting up to three three epochs per source with a fully relativistic disk model we show that many system properties can be reliably recovered including importantly the black hole mass m_ bullet
large language models llms have advanced code generation at the function level yet their ability to
produce
models llms
large language models llms have advanced code generation at the function level yet their ability to produce correct class-level implementations in authentic software projects remains poorly understood
in this paper we propose the first high-resolution hr
motion
point tracking
in this paper we propose the first high-resolution hr motion trajectory estimation framework using diffusion models motdiff
taking a solution-centric approach we introduce the concept of provably small set of solutions p called a it portfolio such that for every objective function h cdot in the given class mathbf c there exists some solution in
p
approximation guarantee
taking a solution-centric approach we introduce the concept of provably small set of solutions p called a it portfolio such that for every objective function h cdot in the given class mathbf c there exists some solution in p which is an alpha -approximation for h cdot
the network coding problem asks whether data throughput in a network can be increased using
coding
network coding
the network coding problem asks whether data throughput in a network can be increased using coding compared to treating bits as commodities in a flow
our method outperforms existing intervention techniques on steering and hallucination mitigation benchmarks for
vlms
models vlms
our method outperforms existing intervention techniques on steering and hallucination mitigation benchmarks for vlms and proposes a robust solution for multimodal model control through activation engineering
integrated sensing and communication isac enables simultaneous localization
environment
communication isac
integrated sensing and communication isac enables simultaneous localization environment perception and data exchange for connected autonomous vehicles
moreover since the size of mathcal s grows exponentially with l and t we propose heuristics -- including conditional and deep-learning based approaches -- that exploit these
structural
deep neural
moreover since the size of mathcal s grows exponentially with l and t we propose heuristics -- including conditional and deep-learning based approaches -- that exploit these structural insights while maintaining low computational complexity
this framework offers a scalable and interpretable approach for context-aware
reasoning
spatial reasoning
this framework offers a scalable and interpretable approach for context-aware reasoning advancing zero-shot generalization in dynamic real-world settings
we apply our method to variational quantum
algorithm
quantum walk
we apply our method to variational quantum algorithm vqa ansatz design for molecular ground state estimation max-cut and image classification key challenges in near-term quantum computing
we present a quantum-enhanced protocol for detecting wave-like dark matter using an array of
n
quantum computing
we present a quantum-enhanced protocol for detecting wave-like dark matter using an array of n entangled superconducting cavities initialized in an m -photon fock state
in robotics likelihood-free inference lfi can provide the domain distribution that adapts a learnt
agent
learning agents
in robotics likelihood-free inference lfi can provide the domain distribution that adapts a learnt agent in a parametric set of deployment conditions
each state in the controller and observer is replaced with its
estimate
state estimation
each state in the controller and observer is replaced with its estimate from the hgo
the considered system consists of a multi-antenna access point ap multiple
heterogeneous
wireless systems
the considered system consists of a multi-antenna access point ap multiple heterogeneous user devices uds and an deployed irs to enhance both uplink and downlink transmission
specifically we design a central agent that dynamically optimizes
context
context length
specifically we design a central agent that dynamically optimizes context length via temporal gradient analysis enhancing exploration to facilitate convergence to global optima in marl
debiased machine learning typically requires estimation of the
riesz
debiased machine
debiased machine learning typically requires estimation of the riesz representer and the regression function
we explain these capacities via a computational
cognitive
working memory
we explain these capacities via a computational cognitive model that we call the intuitive gamer
the higher stress in films grown on copper substrate holder can likely be associated with enhanced ion bombardment due to the
higher
film thickness
the higher stress in films grown on copper substrate holder can likely be associated with enhanced ion bombardment due to the higher electrical conductivity of copper
inspired by predictive coding in neuroscience--which suggests that the brain predicts sensory inputs as a neural implementation of bayesian inference--and by auxiliary predictive objectives in deep rl we investigate whether integrating self-supervised
predictive
neural representations
inspired by predictive coding in neuroscience--which suggests that the brain predicts sensory inputs as a neural implementation of bayesian inference--and by auxiliary predictive objectives in deep rl we investigate whether integrating self-supervised predictive coding modules into meta-rl can facilitate learning of bayes-optimal representations
in thisproject we have used machine learning techniques like logistic regression random forest and support
vector
support vector machines
in thisproject we have used machine learning techniques like logistic regression random forest and support vector machines to analyze the health claims data and identify demographic and medical factors that play a crucial role in predicting all-cause readmissions
to address this issue we propose a learning-based
csi
channel state information csi
to address this issue we propose a learning-based csi prediction framework that leverages temporal correlations in wireless channels to forecast future signal to interference plus noise ratio sinr values
we open-source our complete data generation pipeline and training code providing a reproducible
foundation
synthetic data
we open-source our complete data generation pipeline and training code providing a reproducible foundation for future research
specifically we first develop a new method called composite
lp-quantile
quantile regression
specifically we first develop a new method called composite lp-quantile regression clpqr
the walk operates on the space of spanning
trees
spanning trees
the walk operates on the space of spanning trees with marked edges allowing for calculable transition probabilities for use in the metropolis-hastings algorithm
pinching antenna technology was introduced as a
promising
pinching antenna
pinching antenna technology was introduced as a promising solution to overcome the large-scale fading that has been shown to be an impediment in multiple-input multiple-output mimo systems
empirical results across several domains show that our algorithms substantially reduce training costs without sacrificing prediction
accuracy
predictive performance
empirical results across several domains show that our algorithms substantially reduce training costs without sacrificing prediction accuracy demonstrating the practical value of our budget-aware deferral algorithms
we introduce textsc flowq-net flow-based quantum design network a generative framework for automated quantum circuit synthesis based on
generative
generative models
we introduce textsc flowq-net flow-based quantum design network a generative framework for automated quantum circuit synthesis based on generative flow networks gflownets
to this end we introduce a new dataset covering a wide range of formal patterns of
reasoning
mathematical reasoning
to this end we introduce a new dataset covering a wide range of formal patterns of reasoning in both normative and epistemic domains while also incorporating non-formal cognitive factors that influence human reasoning
by linking behavioral attributes with environmental context mobilitygen reproduces key patterns such as scaling laws for location visits activity time allocation and the coupled evolution of
travel
travel information
by linking behavioral attributes with environmental context mobilitygen reproduces key patterns such as scaling laws for location visits activity time allocation and the coupled evolution of travel mode and destination choices
cluster 0 associated with lower creativity scores exhibited stronger overall
connectivity
functional connectivity
cluster 0 associated with lower creativity scores exhibited stronger overall connectivity strength reduced modularity and higher local clustering
we design a deterministic algorithm that given n points in a emph typical constant degree regular graph queries o n distances to output a constant factor approximation to the average distance among those
points
approximation factor
we design a deterministic algorithm that given n points in a emph typical constant degree regular graph queries o n distances to output a constant factor approximation to the average distance among those points thus answering a question posed in cite mn14
during inference slideagent selectively activates specialized
agents
language agents
during inference slideagent selectively activates specialized agents for multi-level reasoning and integrates their outputs into coherent context-aware answers
current paradigms either myopically optimize single-turn attributes or rely on brittle high-cost
user
persona simulation
current paradigms either myopically optimize single-turn attributes or rely on brittle high-cost user simulators creating a persistent reality gap
across three real-world tasks and two embodiments hi-ors fine-tunes a pi-base policy to master contact-rich
manipulation
multi-robot collaboration
across three real-world tasks and two embodiments hi-ors fine-tunes a pi-base policy to master contact-rich manipulation in just 1
this body of theory can accommodate a range of social dilemmas or games as well as real-world complexities such as
spatial
evolutionary game
this body of theory can accommodate a range of social dilemmas or games as well as real-world complexities such as spatial structure or behaviors conditioned on reputations
here we introduce a methodology to probe the internal geometry of
vision-language
large language models
here we introduce a methodology to probe the internal geometry of vision-language models vlms by having them generate pairwise similarity judgments for a complex set of natural objects
to complement this we derive a tight upper bound of 2n k-1 for chordal bigraphs and 54n k-1 for grid intersection graphs gig a prominent graph class residing in four ferrers dimensions and capturing planar bipartite graphs as well as bipartite intersection
graphs
regular graphs
to complement this we derive a tight upper bound of 2n k-1 for chordal bigraphs and 54n k-1 for grid intersection graphs gig a prominent graph class residing in four ferrers dimensions and capturing planar bipartite graphs as well as bipartite intersection graphs of rectangles
we investigate the chemical content of 22 well-studied massive protostars from the sofia massive soma star formation survey aiming to identify correlations between
chemical
stellar mass function
we investigate the chemical content of 22 well-studied massive protostars from the sofia massive soma star formation survey aiming to identify correlations between chemical and physical parameters
we propose the first known computationally tractable algorithm for computing the solution to the graves-lai
optimization
bilevel optimization
we propose the first known computationally tractable algorithm for computing the solution to the graves-lai optimization problem which in turn enables the implementation of asymptotically optimal algorithms for this bandit problem
this work enables us to model the robot s collapse behavior in any open
environment
mobile robots
this work enables us to model the robot s collapse behavior in any open environment and understand the parameters it needs to succeed in 3d navigation tasks
in this work we study how different cosmic-ray
transport
interstellar medium
in this work we study how different cosmic-ray transport mechanisms impact the gamma -ray luminosity of a turbulent multiphase medium formed from an initially diffuse medium
this work advances this vision by identifying the fundamental principles of human ai
collaboration
ai assistance
this work advances this vision by identifying the fundamental principles of human ai collaboration within uncertainty quantification a key component of reliable decision making
here we describe mechanisms of internal and external embodiment in humans and relate these to
current
abstract representations
here we describe mechanisms of internal and external embodiment in humans and relate these to current advances in mllms in early stages of aligning to human representations
its interactive interface provides a transparent workflow where users can trace validate and refine the
agent
ai agents
its interactive interface provides a transparent workflow where users can trace validate and refine the agent s reasoning supporting both adaptability and trustworthiness
in this paper we study linear control systems with positive
bounded
linear control
in this paper we study linear control systems with positive bounded orbits
enhancing ecg classification robustness with lightweight unsupervised
anomaly
anomaly detection
enhancing ecg classification robustness with lightweight unsupervised anomaly detection filters
we benchmark against a random sampling approach and we find that our optimization-based approach always finds larger
load
optimal power flow
we benchmark against a random sampling approach and we find that our optimization-based approach always finds larger load shedding