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the third example has a discrete multivalued treatment that can increase or
decrease
treatment assignment
the third example has a discrete multivalued treatment that can increase or decrease multiple times over time
orchvis advances human-centered design for multi-agent
systems
human-ai interaction
orchvis advances human-centered design for multi-agent systems by combining transparent visualization with adaptive autonomy
we demonstrate that the precession of the disk and wind drive time-dependent obscuration and reprocessing of
x-ray
gamma -ray
we demonstrate that the precession of the disk and wind drive time-dependent obscuration and reprocessing of x-ray radiation
deep neural networks dnns have been used to model complex optimization problems in many applications yet have difficulty guaranteeing solution optimality and feasibility despite
training
deep learning
deep neural networks dnns have been used to model complex optimization problems in many applications yet have difficulty guaranteeing solution optimality and feasibility despite training on large datasets
interaction-augmented instruction modeling the synergy of prompts and
interactions
human-ai interaction
interaction-augmented instruction modeling the synergy of prompts and interactions in human-genai collaboration
model and hyperparameter selection are critical but challenging in machine learning typically
requiring
variable selection
model and hyperparameter selection are critical but challenging in machine learning typically requiring expert intuition or expensive automated search
advancing interdisciplinary approaches to online
safety
online safety
advancing interdisciplinary approaches to online safety research
even when these global minima are aligned to the hidden configuration there can be exponentially many higher
energy
global minima
even when these global minima are aligned to the hidden configuration there can be exponentially many higher energy local minima that are all unaligned with the hidden solution
while successful in language and vision their adoption in
eeg
electroencephalography eeg
while successful in language and vision their adoption in eeg has lagged due to the heterogeneity of public datasets which are collected under varying protocols devices and electrode configurations
we then apply it to a higher-order spin-glass hamiltonian with 156
qubits
quantum computing
we then apply it to a higher-order spin-glass hamiltonian with 156 qubits executed on ibm quantum processors
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
surrogate brain
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
we study the impact of static disorder on a globally-controlled superconducting quantum
computing
super-heisenberg scaling
we study the impact of static disorder on a globally-controlled superconducting quantum computing architecture based on a quasi-two-dimensional ladder geometry r
stage 1 performs a brief cold start and then math-only
rl
reinforcement learning rl
stage 1 performs a brief cold start and then math-only rl with verifiable rewards to develop reasoning skills
large language models llms such as chatgpt are
increasingly
language models
large language models llms such as chatgpt are increasingly integrated into high-stakes decision-making yet little is known about their susceptibility to social influence
these subclumps exhibit parabolic morphologies consistent with ram-pressure-confined droplets with their heads tending to point toward the
galactic
quiescent galaxies
these subclumps exhibit parabolic morphologies consistent with ram-pressure-confined droplets with their heads tending to point toward the galactic plane
contribution of task-irrelevant stimuli to
drift
receptive fields
contribution of task-irrelevant stimuli to drift of neural representations
kinematic elemental and structural dependences on
metallicity
bulge stars
kinematic elemental and structural dependences on metallicity in the galactic bulge
we investigate potential heating mechanisms including direct agn photoionisation uv fluorescent excitation from young star
clusters
star formation rates
we investigate potential heating mechanisms including direct agn photoionisation uv fluorescent excitation from young star clusters and shock excitation
reinforcement learning rl is widely used to produce robust robotic manipulation policies but fine-tuning vision-language-action vla
models
vision-language models vlms
reinforcement learning rl is widely used to produce robust robotic manipulation policies but fine-tuning vision-language-action vla models with rl can be unstable due to inaccurate value estimates and sparse supervision at intermediate steps
the results illustrate how safety-first opl provides an implementable interpretable tool for risk-sensitive
policy
policy evaluation
the results illustrate how safety-first opl provides an implementable interpretable tool for risk-sensitive policy design quantifying the efficiency-insurance trade-off that policymakers face when outcomes are volatile
in this work we present a fully unsupervised machine learning ml workflow that detects and classifies these defects directly from
molecular
molecular dynamics
in this work we present a fully unsupervised machine learning ml workflow that detects and classifies these defects directly from molecular dynamics data
there are two main problems in the task of predicting the dynamic evolution of complex networks on the one hand existing methods usually use simple graphs to describe the relationships in complex networks however this approach can only capture pairwise relationships while there may be rich non-pairwise structured
relationships
correlation network
there are two main problems in the task of predicting the dynamic evolution of complex networks on the one hand existing methods usually use simple graphs to describe the relationships in complex networks however this approach can only capture pairwise relationships while there may be rich non-pairwise structured relationships in the network
ct reconstruction provides radiologists with
images
image reconstruction
ct reconstruction provides radiologists with images for diagnosis and treatment yet current deep learning methods are typically limited to specific anatomies and datasets hindering generalization ability to unseen anatomies and lesions
although most related studies have focused on prediction methods research on the predictability of complex
systems
complex systems
although most related studies have focused on prediction methods research on the predictability of complex systems has received increasing attention across disciplines--aiming to provide theories and tools to address a key question what are the limits of prediction accuracy
score-based generative models based on stochastic differential equations sdes achieve impressive performance in sampling from unknown
distributions
generative models
score-based generative models based on stochastic differential equations sdes achieve impressive performance in sampling from unknown distributions but often fail to satisfy underlying constraints
in this study we investigate how the ai mathematician aim system can operate as a research
partner
mathematical reasoning
in this study we investigate how the ai mathematician aim system can operate as a research partner rather than a mere problem solver
this real-time control strategy is then benchmarked against an offline
optimal
control strategy
this real-time control strategy is then benchmarked against an offline optimal dispatch to evaluate flexibility performance
this work tackles the problem of identifying asymptomatic individuals considering a classic si susceptible-infected network epidemic model where a fraction of the infected nodes are not observed as
infected
viral replication
this work tackles the problem of identifying asymptomatic individuals considering a classic si susceptible-infected network epidemic model where a fraction of the infected nodes are not observed as infected i
we develop a stochastic variational expectation-maximization
algorithm
debiased machine learning
we develop a stochastic variational expectation-maximization algorithm to jointly optimize the neural and probabilistic components
on the limitation of evaluating machine unlearning using only a single
training
machine learning
on the limitation of evaluating machine unlearning using only a single training seed
in the cold dark environments of pre-stellar cores where the temperatures are below 10 k ne can condense onto the surface of
interstellar
star formation
in the cold dark environments of pre-stellar cores where the temperatures are below 10 k ne can condense onto the surface of interstellar grains
dual-channel technology diffusion spatial decay and network contagion in supply
chain
supply chain
dual-channel technology diffusion spatial decay and network contagion in supply chain networks
furthermore for specific pairs of models and riesz representer estimation methods we can automatically obtain the covariate balancing property without explicitly solving the covariate
balancing
covariate balancing
furthermore for specific pairs of models and riesz representer estimation methods we can automatically obtain the covariate balancing property without explicitly solving the covariate balancing objective
0 and sample-efficient reinforcement learning on
continuous
reinforcement learning
0 and sample-efficient reinforcement learning on continuous control tasks without replay buffers
quantum computing could greatly aid in this understanding as it can potentially provide exponential speedups over classical approaches thereby
offering
quantum batteries
quantum computing could greatly aid in this understanding as it can potentially provide exponential speedups over classical approaches thereby offering insights into the complex electronic structure of actinide compounds
the dataset allows for a clearer analysis of road conditions by compiling essential
data
autonomous driving
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
we further establish unconditional lower bounds demonstrating that the time and query complexities of our algorithms are optimal up to mathrm polylog n
factors
lower bound
we further establish unconditional lower bounds demonstrating that the time and query complexities of our algorithms are optimal up to mathrm polylog n factors hidden within the tilde o cdot notation below
in this paper we study the problem of continuous-time
reinforcement
optimal control
in this paper we study the problem of continuous-time reinforcement learning where the unknown system dynamics are represented using nonlinear ordinary differential equations odes
a problem of achieving minimum time consensus for a
set
optimal control
a problem of achieving minimum time consensus for a set of n second-order lti system agents with bounded inputs and fuel constraints is considered
on the randomized locality of matching problems in
regular
bipartite graphs
on the randomized locality of matching problems in regular graphs
by dynamically adjusting the coding sequence the metasurface could enable multi-mode orbital angular momentum oam beam generation dynamic
beam
beam pattern
by dynamically adjusting the coding sequence the metasurface could enable multi-mode orbital angular momentum oam beam generation dynamic beam scanning and precise direction finding
nanovla routing decoupled vision-language understanding for nano-sized generalist
robotic
vision-language models vlms
nanovla routing decoupled vision-language understanding for nano-sized generalist robotic policies
test-time alignment of large language models llms attracts attention because fine-tuning
llms
large language
test-time alignment of large language models llms attracts attention because fine-tuning llms requires high computational costs
through numerical simulations validated with real population and temperature data it is possible to understand the
disease
disease transmission
through numerical simulations validated with real population and temperature data it is possible to understand the disease dynamics under many different scenarios and make future projections offering insights for potential effective control strategies as well as addressing the timing for these strategies to be adopted
while large language models llms have demonstrated remarkable performance across various reasoning tasks their ability to handle
normative
llm inference
while large language models llms have demonstrated remarkable performance across various reasoning tasks their ability to handle normative reasoning remains underexplored
while large language models llms have demonstrated remarkable performance across various reasoning tasks their ability to handle
normative
reasoning tasks
while large language models llms have demonstrated remarkable performance across various reasoning tasks their ability to handle normative reasoning remains underexplored
certification and classification of linear quantum
error
quantum error correction
certification and classification of linear quantum error mitigation methods
simultaneously strongly aligning with human
visual
computer vision
simultaneously strongly aligning with human visual attention
such simulations -- for which classical methods are often inaccurate -- are critical to advancing our knowledge and understanding of
quantum
classical simulation
such simulations -- for which classical methods are often inaccurate -- are critical to advancing our knowledge and understanding of quantum chemistry and materials underpinning a wide range of fields from biochemistry to clean-energy technologies and chemical synthesis
bridging the gap between empirical welfare maximization and conditional average treatment effect estimation in
policy
policy learning
bridging the gap between empirical welfare maximization and conditional average treatment effect estimation in policy learning
in this work we aim to explain this conflict by exploring how
language
large language
in this work we aim to explain this conflict by exploring how language models manipulate numbers and quantify the lower bounds of accuracy of these mechanisms
vision-language models vlms such as clip which are pre-trained on large image-text pairs offer a promising solution by enhancing robustness and data efficiency in
medical
sparse autoencoders
vision-language models vlms such as clip which are pre-trained on large image-text pairs offer a promising solution by enhancing robustness and data efficiency in medical imaging tasks
extensive experiments demonstrate the effectiveness of our model in panoramic visual perception and graphics-ready 3d scene
generation
image 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
in network-based sis models of infectious disease transmission
infection
viral replication
in network-based sis models of infectious disease transmission infection can only occur between directly connected individuals
we give the first super-constant bound to this problem demonstrating an example with a coding advantage of
omega
upper bound
we give the first super-constant bound to this problem demonstrating an example with a coding advantage of omega log k
to address these challenges we argue that post-hoc attribution can be reframed as a reasoning problem where
answers
question answering
to address these challenges we argue that post-hoc attribution can be reframed as a reasoning problem where answers are decomposed into constituent units each tied to specific context
a unified theory for causal inference direct debiased
machine
machine learning
a unified theory for causal inference direct debiased machine learning via bregman-riesz regression
however in recent years with the rise of generative ai especially large
language
large language models llms
however in recent years with the rise of generative ai especially large language models llm and particularly with the widespread popularity of the chatgpt model that concern became practical
in this paper we explore the stellar mass profiles of a sample of disk galaxies with similar stellar
masses
bulge stars
in this paper we explore the stellar mass profiles of a sample of disk galaxies with similar stellar masses sim 10 10 m _ odot using iac stripe82 legacy project data
here we introduce a topological framework that defines and detects localities in human
mobility
human mobility
here we introduce a topological framework that defines and detects localities in human mobility networks
in numerous experiments the results reveal the importance of the proposed method in hierarchical networks in the visual tasks and also validate the hypothesis that the
hierarchical
higher-order visual
in numerous experiments the results reveal the importance of the proposed method in hierarchical networks in the visual tasks and also validate the hypothesis that the hierarchical information content in brain regions of the visual system can be quantified by decoding outcomes to reflect an information hierarchy
for a graph g the parameter treedepth measures the minimum depth among all forests f called elimination
forests
tree edit distance
for a graph g the parameter treedepth measures the minimum depth among all forests f called elimination forests such that g is a subgraph of the ancestor-descendant closure of f
previous work established one-way reductions showing how suffix array queries can be answered using for example rank
queries
compressed indexing
previous work established one-way reductions showing how suffix array queries can be answered using for example rank queries on the burrows-wheeler transform
this study proposes to quantify the structural modifications implied by the disruption of single elements in a transportation network through the
gromov-wasserstein
gromov-wasserstein distance
this study proposes to quantify the structural modifications implied by the disruption of single elements in a transportation network through the gromov-wasserstein distance
nearest neighbor matching as least squares
density
density estimation
nearest neighbor matching as least squares density ratio estimation and riesz regression
to effectively learn from these enriched alignments molbridge employs substructure-aware contrastive learning coupled with a self-refinement mechanism that filters out noisy
alignment
test-time alignment
to effectively learn from these enriched alignments molbridge employs substructure-aware contrastive learning coupled with a self-refinement mechanism that filters out noisy alignment signals
researchers often use specifications that correctly estimate the
average
treatment effect boundaries
researchers often use specifications that correctly estimate the average treatment effect under the assumption of constant effects
our empirical analysis shows that contractionary monetary policy shocks have significant negative effects on the
macroeconomic
monetary policy
our empirical analysis shows that contractionary monetary policy shocks have significant negative effects on the macroeconomic stars highlighting the nonzero long-run effects of transitory monetary policy shocks
this framework significantly broadens the set of tools available for analyzing selection in categorical and other discrete
outcomes
potential outcomes
this framework significantly broadens the set of tools available for analyzing selection in categorical and other discrete outcomes offering substantial relevance for empirical work across economics health sciences and social sciences
to this end we introduce a novel metric for comparing both intrinsic
recurrent
recurrent neural
to this end we introduce a novel metric for comparing both intrinsic recurrent and input-driven dynamics called inputdsa idsa
artificial intelligence ai systems increasingly match or surpass human experts in
biomedical
physiological signals
artificial intelligence ai systems increasingly match or surpass human experts in biomedical signal interpretation
we describe this simulation method and compare it with
alternative
classical simulation
we describe this simulation method and compare it with alternative approaches
our findings introduce a new paradigm in integrated
photonics
photonic circuits
our findings introduce a new paradigm in integrated photonics paving the way for ultracompact modulators and highly tunable on-chip communication systems with reduced power consumption
beyond lamno _ 3 our work opens an avenue for studying a wider range of
correlated
electronic structure
beyond lamno _ 3 our work opens an avenue for studying a wider range of correlated materials
in this work we propose a strategy that exploits the principles of non-hermitian physics--specifically the concept of exceptional
points
exceptional points
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
in particular we show that narrower degree distributions contain longer shortest
loops
scale-free networks
in particular we show that narrower degree distributions contain longer shortest loops as a universal property in a wide class of random networks
to generate physically and semantically plausible supervision signals we introduce a spatial prior labeling method that guides a
vision-language
vision-language-action vla
to generate physically and semantically plausible supervision signals we introduce a spatial prior labeling method that guides a vision-language model to produce reasonable manipulation orders for distillation
reasoning about reasoning towards informed and reflective use of
llm
llm reasoning
reasoning about reasoning towards informed and reflective use of llm reasoning in hci
twinkle twinkle little star roman sees where you are predicting exoplanet transit
yields
interstellar medium
twinkle twinkle little star roman sees where you are predicting exoplanet transit yields in the rosette nebula with the nancy grace roman space telescope
the external medium also influences the evolution of circumstellar disks and protostellar outflows with the high-density external medium disks grow rapidly but their mass becomes smaller relative to the
protostellar
interstellar medium
the external medium also influences the evolution of circumstellar disks and protostellar outflows with the high-density external medium disks grow rapidly but their mass becomes smaller relative to the protostellar mass and the outflow is sustained over a long duration
the conditions for the clt exclude certain canonical examples such as the empirical sub-gaussian norm of normally distributed
random
central limit theorem
the conditions for the clt exclude certain canonical examples such as the empirical sub-gaussian norm of normally distributed random variables
it further decomposes the evaluation of llm performance into six fundamental
capabilities
llm post-training
it further decomposes the evaluation of llm performance into six fundamental capabilities including opinion consistency memory recall logical reasoning lexical fidelity persona tone and syntactic style
additionally we connect for the first time the chromosome diagram to the two-stream age-metallicity relation allowing us to link the p1 and p2
stars
star clusters
additionally we connect for the first time the chromosome diagram to the two-stream age-metallicity relation allowing us to link the p1 and p2 stars to the distinct star formation tracks proposed to be in-situ and ex-situ contributions to the cluster s assembly
the feynman path integral formalism has inspired the development of memory-efficient and parallelizable classical
algorithms
quantum walk
the feynman path integral formalism has inspired the development of memory-efficient and parallelizable classical algorithms for simulating quantum computers
the probability of vacuum metastability and artificial
vacuum
vacuum decay
the probability of vacuum metastability and artificial vacuum decay expert survey results
we conclude that this framework can be used in the future to design compare and benchmark
obstacle
dynamic obstacles
we conclude that this framework can be used in the future to design compare and benchmark obstacle avoidance methods
the problem of optimizing the computing resources for distributed machine learning
ml
machine learning
the problem of optimizing the computing resources for distributed machine learning ml and optimization is considered in this paper
an analytical model supports our experimental observations by linking this robustness to the
band-structure
coupling regimes
an analytical model supports our experimental observations by linking this robustness to the band-structure properties of the interacting modes
we finally provide numerical evidence for our
theoretical
theoretical guarantees
we finally provide numerical evidence for our theoretical results
we further discover that issameobject is encoded in a low-dimensional subspace on top of
object
receptive fields
we further discover that issameobject is encoded in a low-dimensional subspace on top of object features and that this signal actively guides attention
the goal of policy learning is to train a
policy
policy learning
the goal of policy learning is to train a policy function that recommends a treatment given covariates to maximize population welfare
we adapt the degree-based mean-field dbmf sir model for single-layered complex networks to the
multiplex
multiplex networks
we adapt the degree-based mean-field dbmf sir model for single-layered complex networks to the multiplex setting where each layer has its own degree distribution and infection rate
communication impact is quantied by a capacity-motivated lower bound obtained from the linear minimum mean-squared error error covariance with a mismatched
channel
wireless communication
communication impact is quantied by a capacity-motivated lower bound obtained from the linear minimum mean-squared error error covariance with a mismatched channel estimate
tuning magnetic anisotropy through chemical doping is a powerful strategy for designing functional materials with enhanced
magnetic
magnetic properties
tuning magnetic anisotropy through chemical doping is a powerful strategy for designing functional materials with enhanced magnetic properties
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 circuits
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
we introduce a framework that captures regimes of containment mitigation and
failure
control strategies
we introduce a framework that captures regimes of containment mitigation and failure to control
reinforcement finetuning rft is a key technique for aligning large
language
language models
reinforcement finetuning rft is a key technique for aligning large language models llms with human preferences and enhancing reasoning yet its effectiveness is highly sensitive to which tasks are explored during training
reward models rms play a critical role in aligning large language models llms with
human
reward density
reward models rms play a critical role in aligning large language models llms with human preferences
experimental results showed that compared to the state-of-the-art method sota the accuracy improvement rate in a cg dataset with dynamic
obstacles
obstacle avoidance
experimental results showed that compared to the state-of-the-art method sota the accuracy improvement rate in a cg dataset with dynamic obstacles is 1
2024 develops riesz regression for automatic debiased machine learning which directly estimates the riesz representer or equivalently the
bias-correction
bias-correction term
2024 develops riesz regression for automatic debiased machine learning which directly estimates the riesz representer or equivalently the bias-correction term by minimizing the mean squared error