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despite the notable advancements and versatility of multi-modal
diffusion
diffusion models
despite the notable advancements and versatility of multi-modal diffusion models such as text-to-image models their susceptibility to adversarial inputs remains underexplored
to evaluate llms ability to identify and redact such private information prior work developed
benchmarks
llm reasoning
to evaluate llms ability to identify and redact such private information prior work developed benchmarks e
here we exploit the spatial structure of atomic ensembles to
control
atom interferometry
here we exploit the spatial structure of atomic ensembles to control the coupling between modes of distinct cavities thereby reshaping the resulting atom-photon spectra
the increasing frequency and severity of high impact and low probability events such as hurricanes and windstorms pose significant challenges to the resilience of electrical
power
power systems
the increasing frequency and severity of high impact and low probability events such as hurricanes and windstorms pose significant challenges to the resilience of electrical power distribution systems particularly in regions of new england where there is a significant amount of overhead infrastructure in areas where vegetation is predominant
ai mathematician as a partner in advancing
mathematical
artificial intelligence
ai mathematician as a partner in advancing mathematical discovery -- a case study in homogenization theory
our work incorporates state-of-the-art innovations in all key aspects of the fault-tolerant quantum
computing
quantum computing
our work incorporates state-of-the-art innovations in all key aspects of the fault-tolerant quantum computing ftqc stack to show how quantum computers could realistically and practically tackle co _2 utilization for green energy production
although recent work has developed robust did estimators for complex settings like staggered treatment adoption these methods typically assume complete data and fail to address the critical challenge of
outcomes
average treatment
although recent work has developed robust did estimators for complex settings like staggered treatment adoption these methods typically assume complete data and fail to address the critical challenge of outcomes that are missing at random mar -- a common problem that invalidates standard estimators
by treating them as instances of the same phenomenon we show that
critical
critical numbers
by treating them as instances of the same phenomenon we show that critical numbers across physical scales and scientific domains commonly arise from competing feedbacks that scale differently with number
automating the co-design of a robot s morphology and
control
mobile robots
automating the co-design of a robot s morphology and control is a long-standing challenge due to the vast design space and the tight coupling between body and behavior
in the case of purely real kerr coefficients a split-step fourier method is
appropriate
kerr rotation
in the case of purely real kerr coefficients a split-step fourier method is appropriate for numerical simulations
this paper proposes a coherence-aware communication-efficient framework for joint channel training and model updating in practical
wireless
wireless networks
this paper proposes a coherence-aware communication-efficient framework for joint channel training and model updating in practical wireless fl systems operating under heterogeneous fading dynamics
this article presents a comparison between two data-driven state
estimation
state estimation
this article presents a comparison between two data-driven state estimation methods based on the unscented kalman filter ukf fusing pressure demand and flow data for head and flow estimation
beyond summarising comparisons we analyse reported
performance
predictive performance
beyond summarising comparisons we analyse reported performance metrics using mixed-effects modelling to investigate potential characteristics that can explain and quantify observed differences including application area study year sample size number of predictors and neural network complexity
this superior performance not only sets a new benchmark for device responsivity and compactness but also opens promising avenues for future research including the incorporation of gain media for loss compensation at eps and the exploration of alternative tunable materials for next-generation ultracompact
photonic
integrated photonics
this superior performance not only sets a new benchmark for device responsivity and compactness but also opens promising avenues for future research including the incorporation of gain media for loss compensation at eps and the exploration of alternative tunable materials for next-generation ultracompact photonic devices
however traditional one-dimensional opinion models -- assuming support for one party equals opposition to another -- fail to capture the nuanced
dynamics
opinion dynamics
however traditional one-dimensional opinion models -- assuming support for one party equals opposition to another -- fail to capture the nuanced dynamics of swing voters including neutrals left leaners and right leaners who are critical for the final election outcomes
the former deterministically realizes nonlocal operations but demands extensive entanglement
resources
multipartite entanglement
the former deterministically realizes nonlocal operations but demands extensive entanglement resources whereas the latter requires no entanglement yet suffers from exponential sampling overhead
with enough data optimal performance is attained through model s specialisation towards the target but it can be hard to reach for
training
machine learning
with enough data optimal performance is attained through model s specialisation towards the target but it can be hard to reach for training algorithms which get attracted by sub-optimal solutions predicted by the theory
in this study we incorporate a homogeneous environment into the
evolutionary
evolutionary game
in this study we incorporate a homogeneous environment into the evolutionary dynamics of a three-state system comprising cooperators defectors and empty nodes
experimental results show that our method reconstructs the intrinsic motion hierarchy in 1d and 2d cases and produces more realistic and interpretable deformations compared to the baseline on dynamic 3d gaussian
splatting
gaussian splatting
experimental results show that our method reconstructs the intrinsic motion hierarchy in 1d and 2d cases and produces more realistic and interpretable deformations compared to the baseline on dynamic 3d gaussian splatting scenes
in this paper we propose a new test-time alignment method called adaptive importance sampling on pre-logits aisp on the basis of the sampling-based model predictive control with the stochastic
control
test-time alignment
in this paper we propose a new test-time alignment method called adaptive importance sampling on pre-logits aisp on the basis of the sampling-based model predictive control with the stochastic control input
identifying asymptomatic individuals is critical for measuring and controlling an epidemic but periodic and widespread testing of healthy
individuals
disease transmission
identifying asymptomatic individuals is critical for measuring and controlling an epidemic but periodic and widespread testing of healthy individuals is often too costly
in this work we propose get-use a two-step procedure that learns to perform real-robot generalized tool usage by
learning
imitation learning
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
in this paper we introduce a novel concept in
causal
interventional constraints
in this paper we introduce a novel concept in causal discovery termed interventional constraints which differs fundamentally from interventional data
however the spatial scales over which the initial conditions can exert a significant
influence
scaling relations
however the spatial scales over which the initial conditions can exert a significant influence are not well constrained
on three real-world road networks with 17 to 177 heterogeneous
intersections
heterogeneous intersections
on three real-world road networks with 17 to 177 heterogeneous intersections extensive experiments show that reg-tsc reduces travel time by 42
while current models show promise it remains an open question whether this
alignment
encoding models
while current models show promise it remains an open question whether this alignment is superficial or reflects a deeper correspondence in the underlying dimensions of representation
they also allow fine-grained personalization on the community alignment dataset we learn annotator-specific weights over subjective features that improve
preference
preference data
they also allow fine-grained personalization on the community alignment dataset we learn annotator-specific weights over subjective features that improve preference prediction
to answer this at inference time we estimate object and background expectations within clip s representation space and synthesize counterfactual embeddings by recombining
object
vision-language models
to answer this at inference time we estimate object and background expectations within clip s representation space and synthesize counterfactual embeddings by recombining object features with diverse alternative contexts sampled from external datasets batch neighbors or text-derived descriptions
a prevailing theory in cognitive neuroscience proposes that the human brain operates through hierarchical predictive processing which underlies flexible
cognition
brain-computer interface
a prevailing theory in cognitive neuroscience proposes that the human brain operates through hierarchical predictive processing which underlies flexible cognition and behavior by integrating multimodal sensory signals
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 advantage
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
our findings suggest the smc has a higher production of eu with respect to the alpha -elements than the milky
way
active galactic
our findings suggest the smc has a higher production of eu with respect to the alpha -elements than the milky way but in line with what observed in other dwarf systems within the local group
to address this we introduce robart a posterior
bias-correction
bias-correction term
to address this we introduce robart a posterior bias-correction that robustifies bart for valid inference on the mean response
we reveal that under fef populations are less
cooperative
collective systems
we reveal that under fef populations are less cooperative and network heterogeneity can provide an advantage only if targeting specific network properties clarifying seemingly contradictory experimental results and evolutionary predictions
this paper explores a multi-cell multiple-input single-output miso
downlink
uplink communication
this paper explores a multi-cell multiple-input single-output miso downlink communication system enabled by a unique transmissive reconfigurable intelligent surface ris transceiver trtc configuration
this paper develops and empirically implements a continuous functional framework for analyzing systemic risk in financial networks building on the dynamic
spatial
dynamic spatial
this paper develops and empirically implements a continuous functional framework for analyzing systemic risk in financial networks building on the dynamic spatial treatment effect methodology established in our previous studies
to enhance the interaction between the global and
local
encoder network
to enhance the interaction between the global and local encoders a symmetric cross-attention module is proposed across all layers of the encoders to fuse and refine features
we apply our framework to the simplex method an algorithm which is beloved for its excellent performance in
practice
online algorithm
we apply our framework to the simplex method an algorithm which is beloved for its excellent performance in practice and notorious for its high running time under worst-case analysis
this is a concise pedagogical introduction to the dynamic field of
open
quantum mechanics
this is a concise pedagogical introduction to the dynamic field of open quantum systems governed by markovian master equations
we propose viz-coast a method of leveraging the common-sense spatial reasoning of large pretrained
vision-language
vision-language-action vla
we propose viz-coast a method of leveraging the common-sense spatial reasoning of large pretrained vision-language models to identify issues with downward refinement a priori bypassing the need to fix these failures during planning
understanding how learning algorithms shape the computational strategies that
emerge
neural networks
understanding how learning algorithms shape the computational strategies that emerge in neural networks remains a fundamental challenge in machine intelligence
we adopt inverse probability weighting ipw for identification however ipw-transformed outcomes are known to be noisy even when true
propensity
propensity score
we adopt inverse probability weighting ipw for identification however ipw-transformed outcomes are known to be noisy even when true propensity scores are used
this study not only enriches the theoretical framework of evolutionary
game
game theory
this study not only enriches the theoretical framework of evolutionary game theory but also provides a foundation for the management of ecological systems and the design of cooperative mechanisms in society
using a dataset of only 36 pristine experimental images of si 001 h we demonstrate that a physics-informed synthetic data generation pipeline can be used to train several state-of-the-art flow-matching and
diffusion
flow matching
using a dataset of only 36 pristine experimental images of si 001 h we demonstrate that a physics-informed synthetic data generation pipeline can be used to train several state-of-the-art flow-matching and diffusion models
for this problem we develop a direct debiased
machine
debiased machine learning
for this problem we develop a direct debiased machine learning framework with an end-to-end algorithm
under mild assumptions on the noise we derive sharp
non-asymptotic
asymptotic normality
under mild assumptions on the noise we derive sharp non-asymptotic perturbation bounds that reveal how the error scales with the eigengap spectral decay and noise alignment with low-curvature directions of a
the dataset allows for a clearer analysis of road conditions by compiling essential
data
sensor data
the dataset allows for a clearer analysis of road conditions by compiling essential data including vehicle speed acceleration rotation rates and magnetic field intensity along with the visual and spatial context provided by gis weather and video data
these features allow the design of flat band materials with ultra large
electronic
electronic structure
these features allow the design of flat band materials with ultra large electronic gaps in low-dimensional systems making these materials promising for devices operation at higher voltages and temperatures than conventional semiconductor materials
physicalism posits consciousness is a physical process that can be modeled computationally
natural
cognitive science
physicalism posits consciousness is a physical process that can be modeled computationally natural dualism rejects this hypothesis
combobench can llms manipulate physical devices to play virtual
reality
virtual reality
combobench can llms manipulate physical devices to play virtual reality games
self-improvement has emerged as a mainstream paradigm for advancing the reasoning
capabilities
large language models llms
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
this research provides an indication of potential advances in the exploration of all four fundamental aspects of tool usage enabling
robots
robotic systems
this research provides an indication of potential advances in the exploration of all four fundamental aspects of tool usage enabling robots to master the intricate art of tool manipulation across diverse tasks
for this recurrence time as well as for measures of clonal diversity and the size of the largest resistant clone at recurrence we derive corresponding law of large
number
reproduction number
for this recurrence time as well as for measures of clonal diversity and the size of the largest resistant clone at recurrence we derive corresponding law of large number limits
moreover we quantitatively interpret our components from a neuroscience perspective and analyze the associations between different visual
patterns
brain-computer interface
moreover we quantitatively interpret our components from a neuroscience perspective and analyze the associations between different visual patterns and brain functions
neutral atom quantum computers hold promise for scaling up to hundreds of thousands of qubits but their progress is constrained by slow
qubit
qubit readout
neutral atom quantum computers hold promise for scaling up to hundreds of thousands of qubits but their progress is constrained by slow qubit readout
will this trend towards greater concentration in large
cities
urban systems
will this trend towards greater concentration in large cities continue or level off
to mitigate the prohibitive overhead associated with full
channel
channel state information
to mitigate the prohibitive overhead associated with full channel state information at the transmitter csit we propose a partial-csit-based beamforming scheme that leverages randomized steering vectors and limited user-side feedback based on signal quality measurements
for statistical models on circles we investigate performance of estimators defined as the projections of the
empirical
density estimation
for statistical models on circles we investigate performance of estimators defined as the projections of the empirical distribution with respect to the wasserstein distance
we find that urban growth follows a consistent
life
large cities
we find that urban growth follows a consistent life cycle
existing benchmarks have widely leveraged high school math competitions for evaluating mathematical
reasoning
reasoning curriculum
existing benchmarks have widely leveraged high school math competitions for evaluating mathematical reasoning capabilities of large language models llms
in this paper we present a novel method that dynamically adjusts
translation
translation gain
in this paper we present a novel method that dynamically adjusts translation gain by leveraging visual distractors
our results demonstrate that large electrical stark shifts can overcome the inhomogeneous distribution of transition frequencies representing a significant step toward scalable siv - -based quantum technologies such as
quantum
quantum technologies
our results demonstrate that large electrical stark shifts can overcome the inhomogeneous distribution of transition frequencies representing a significant step toward scalable siv - -based quantum technologies such as quantum repeaters
by having two parties transmit phase encoded weak coherent pulses wcp to an untrusted central node the tf-qkd exploits single-photon interference to achieve secret
key
quantum key distribution
by having two parties transmit phase encoded weak coherent pulses wcp to an untrusted central node the tf-qkd exploits single-photon interference to achieve secret key rates scaling as square-root of channel length enabling quantum-secured communication over unprecedented distances
we make use of this algorithmic framework to analyse memory effects in disease
evolution
population genetics
we make use of this algorithmic framework to analyse memory effects in disease evolution in a population
mapping habitat connectivity takes geographic analyses a step further evaluating the potential roles of locations in biological invasions pandemics or
species
ecological communities
mapping habitat connectivity takes geographic analyses a step further evaluating the potential roles of locations in biological invasions pandemics or species conservation
each file represents a unique subject emotion and the ecg
signals
physiological signals
each file represents a unique subject emotion and the ecg signals recorded at 1000 hz were segmented into 10-second epochs to reflect real-world usage
to address the non-convex and temporally coupled optimization problem we propose a mixture-of-experts-augmented soft actor-critic moe-sac algorithm that employs a sparse top-k gated mixture-of-shallow-experts architecture to represent multimodal
policy
deep reinforcement
to address the non-convex and temporally coupled optimization problem we propose a mixture-of-experts-augmented soft actor-critic moe-sac algorithm that employs a sparse top-k gated mixture-of-shallow-experts architecture to represent multimodal policy distributions arising from the conflicting optimization objectives
our core innovations include 1 vision-language decoupling that moves conventional early vision and
language
vision-language models
our core innovations include 1 vision-language decoupling that moves conventional early vision and language inputs fusion in vlm to late stage achieving better performance while enabling caching and reduce inference overhead and latency 2 long-short action chunking to ensure smooth coherent multi-step planning without sacrificing real-time responsiveness 3 dynamic routing that adaptively assigns lightweight or heavy backbones based on task complexity further optimizing inference efficiency
our main theoretical results show that in the limit of high dimension d this
posterior
approximate posterior
our main theoretical results show that in the limit of high dimension d this posterior mathbb p x mid y is concentrated near the desired metric projection pi_ mathscr m y
deep reinforcement learning approach to qosaware load balancing in 5g cellular networks under user
mobility
deep reinforcement learning
deep reinforcement learning approach to qosaware load balancing in 5g cellular networks under user mobility and observation uncertainty
thus unified models that operate seamlessly across
digital
vision-language models vlms
thus unified models that operate seamlessly across digital and physical spaces while generalizing across embodiments and tasks remain absent
we compare models that use only macro-level incidence models that add
mobility
mobility networks
we compare models that use only macro-level incidence models that add mobility network features and their interactions with macro incidence and autoregressive ar models that include town-level recent cases
inspired by this principle we introduce a hierarchical multimodal recurrent neural network grounded in
predictive
predictive processing
inspired by this principle we introduce a hierarchical multimodal recurrent neural network grounded in predictive processing under the free-energy principle capable of directly integrating over 30 000-dimensional visuo-proprioceptive inputs without dimensionality reduction
these results demonstrate the advantages of incorporating structural priors into reinforcement
learning
learning agents
these results demonstrate the advantages of incorporating structural priors into reinforcement learning for tensegrity robot control
a high-resource language that serves as a catalyst for
multilingual
multilingual data
a high-resource language that serves as a catalyst for multilingual generalization yields benefits across language families and contrary to expectations selecting a pivot language from within a specific family does not consistently improve performance for languages within that family
while a large number of studies have been devoted to developing and comparing various weight choice criteria the role of weight constraints on the properties of combination forecasts is relatively less understood and the use of various
constraints
weight constraints
while a large number of studies have been devoted to developing and comparing various weight choice criteria the role of weight constraints on the properties of combination forecasts is relatively less understood and the use of various constraints in practice is also rather arbitrary
a-tpt angular diversity calibration properties for test-time prompt tuning of
vision-language
language models
a-tpt angular diversity calibration properties for test-time prompt tuning of vision-language models
mightee-hi the hi mass-stellar mass relation of massive galaxies and the hi mass
function
host galaxy
mightee-hi the hi mass-stellar mass relation of massive galaxies and the hi mass function at 0
for a long-range energy planning problem it was
able
expansion planning
for a long-range energy planning problem it was able to produce optimal and feasible solutions for millions of input parameters within seconds
learning to defer uncertain predictions to costly experts offers a powerful strategy for improving the
accuracy
debiased machine learning
learning to defer uncertain predictions to costly experts offers a powerful strategy for improving the accuracy and efficiency of machine learning systems
understanding how urban systems and traffic dynamics co-evolve is crucial for advancing
sustainable
urban systems
understanding how urban systems and traffic dynamics co-evolve is crucial for advancing sustainable and resilient cities
the land use-climate change-biodiversity nexus in european
islands
land use
the land use-climate change-biodiversity nexus in european islands stakeholders
we address this gap with a token-aware causal representation
learning
reinforcement learning rl
we address this gap with a token-aware causal representation learning crl framework grounded in a sequential language-token scm
although it is often regarded as a recent innovation of the
agent
llm agents
although it is often regarded as a recent innovation of the agent era we argue that related practices can be traced back more than twenty years
among possible strategies to meet this challenge is exploiting the twist degree of freedom in layered structures which enables both emerging moire physics and unprecedented reconfigurability of
photonic
photonic crystal
among possible strategies to meet this challenge is exploiting the twist degree of freedom in layered structures which enables both emerging moire physics and unprecedented reconfigurability of photonic and electronic properties
leveraging predictive channel models and predictive uav trajectories we formulate a bi-objective pareto
optimization
multi-drone racing
leveraging predictive channel models and predictive uav trajectories we formulate a bi-objective pareto optimization problem over a long-term planning horizon to jointly optimize the sampling timing for aerial traffic and the power and spectrum allocation for fair coexistence
task and motion planning tamp integrates high-level task
planning
motion planning
task and motion planning tamp integrates high-level task planning with low-level motion feasibility but existing methods are costly in long-horizon problems due to excessive motion sampling
furthermore we construct a large-scale synthetic panorama
dataset
real-world datasets
furthermore we construct a large-scale synthetic panorama dataset containing high-quality multimodal panoramas from diverse indoor and outdoor scenes
unfortunately in too many cases today s ai is not
accountable
ai systems
unfortunately in too many cases today s ai is not accountable -- we cannot question it enter into a discussion with it let alone sanction it
using fourier spectroscopy we carefully select wave vectors in the 2d plane of periodicity of the
photonic
photonic crystal
using fourier spectroscopy we carefully select wave vectors in the 2d plane of periodicity of the photonic crystal
i propose compositional did codid which identifies counterfactual totals and shares under a parallel growths assumption absent treatment each category
s
treatment assignment
i propose compositional did codid which identifies counterfactual totals and shares under a parallel growths assumption absent treatment each category s size grows or shrinks at the same proportional rate in treated and control groups
the joint transmit and pinching beamforming
design
beamforming design
the joint transmit and pinching beamforming design for spectral efficiency se and energy efficiency ee tradeoff in pinching-antenna systems pass is proposed
extensive experiments demonstrate the effectiveness of our model in panoramic visual perception and graphics-ready 3d scene
generation
video generation
extensive experiments demonstrate the effectiveness of our model in panoramic visual perception and graphics-ready 3d scene generation opening new possibilities for immersive and physically realistic virtual world generation
strongly polynomial parallel work-depth tradeoffs for
directed
strongly polynomial
strongly polynomial parallel work-depth tradeoffs for directed sssp
we quantify the gas flows from a scale of up to several parsecs down to the
sub-parsec
dense gas
we quantify the gas flows from a scale of up to several parsecs down to the sub-parsec scale along filamentary structures in the three high-mass star-forming regions g75
our result nearly matches the o log 2 n approximation
guarantee
-approximation algorithm
our result nearly matches the o log 2 n approximation guarantee of the quasi-polynomial-time algorithm by li xu and zhang icalp 2025
generative artificial intelligence genai can aid
humans
human-ai interaction
generative artificial intelligence genai can aid humans in a wide range of tasks but its effectiveness critically depends on users being able to evaluate the accuracy of genai outputs and their own expertise
along these lines we contend that co-design is not only a method for empirical optimization under constraints but also an enabler of ei offering a scalable and robust alternative to classical
control
optimal control
along these lines we contend that co-design is not only a method for empirical optimization under constraints but also an enabler of ei offering a scalable and robust alternative to classical control for robotics at the mm-to-cm-scale
3 we show this dependency is almost optimal to get a
poly
poly log
3 we show this dependency is almost optimal to get a poly n log lambda -time complexity
derivative-free sequential quadratic programming for equality-constrained
stochastic
dynamic programming
derivative-free sequential quadratic programming for equality-constrained stochastic optimization
our findings reveal that while current video models demonstrate promising reasoning patterns on short-horizon spatial coherence fine-grained grounding and locally consistent dynamics they remain limited in long-horizon causal
reasoning
multimodal reasoning
our findings reveal that while current video models demonstrate promising reasoning patterns on short-horizon spatial coherence fine-grained grounding and locally consistent dynamics they remain limited in long-horizon causal reasoning strict geometric constraints and abstract logic