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all d otimes d dimensional entangled states are useful for the antidiscrimination of quantum measurements when
d
entanglement entropy
all d otimes d dimensional entangled states are useful for the antidiscrimination of quantum measurements when d is even
we hope the geometric view of parametric memory encourages revisiting the default intuitions that guide researchers in areas
like
thinking traces
we hope the geometric view of parametric memory encourages revisiting the default intuitions that guide researchers in areas like knowledge acquisition capacity discovery and unlearning
these findings suggest that geometry and tuning encode brain-region- or model-family-specific signatures while linearly decodable information tends to be more globally shared across
regions
human brain
these findings suggest that geometry and tuning encode brain-region- or model-family-specific signatures while linearly decodable information tends to be more globally shared across regions or models
third we develop a unified efficient algorithm for fitting high-dimensional lp-quantile
regression
linear regression
third we develop a unified efficient algorithm for fitting high-dimensional lp-quantile regression by combining the cyclic coordinate descent and an augmented proximal gradient algorithm
in all three scenarios we observe an inverse correlation between
x-ray
gamma -ray
in all three scenarios we observe an inverse correlation between x-ray and optical emissions
finally we applied the new solver to a cosmological zoom-in
simulation
host galaxy
finally we applied the new solver to a cosmological zoom-in simulation of a galaxy cluster demonstrating its capability to model anisotropic transport and plasma microphysics in realistic large-scale environments
existing array-agnostic approaches typically rely on either raw microphone
signals
beamforming design
existing array-agnostic approaches typically rely on either raw microphone signals or beamformer outputs but both have drawbacks under changing geometries
our findings not only provide a comprehensive understanding on the role of various weight constraints but also practical guidance for empirical researchers how to choose relevant
constraints
weight constraints
our findings not only provide a comprehensive understanding on the role of various weight constraints but also practical guidance for empirical researchers how to choose relevant constraints based on prior information and targets
large language models llms are widely used in generative applications such as chatting
code
open-source models
large language models llms are widely used in generative applications such as chatting code generation and reasoning
we surveyed 415 software practitioners to capture their perceptions of productivity changes associated with ai-assisted development using the space framework - satisfaction and well-being performance activity communication and
collaboration
ai literacy
we surveyed 415 software practitioners to capture their perceptions of productivity changes associated with ai-assisted development using the space framework - satisfaction and well-being performance activity communication and collaboration and efficiency and flow
the result is a suite of tuning-free sampling algorithms including tuning-free variants of the unadjusted langevin algorithm ula stochastic gradient langevin dynamics sgld mean-field langevin dynamics mfld stein variational gradient descent svgd and variational
gradient
gradient descent
the result is a suite of tuning-free sampling algorithms including tuning-free variants of the unadjusted langevin algorithm ula stochastic gradient langevin dynamics sgld mean-field langevin dynamics mfld stein variational gradient descent svgd and variational gradient descent vgd
this challenge also underscores the importance of
open
quantum batteries
this challenge also underscores the importance of open reproducible experimentation and interdisciplinary collaboration highlighting how shared benchmarks can accelerate progress in quantum-enhanced learning
in this paper we formalize this challenge as the textbf reward density optimization problem which aims to improve the
reward
reinforcement learning
in this paper we formalize this challenge as the textbf reward density optimization problem which aims to improve the reward obtained per unit of exploration cost
head data but struggles with more complex
ones
synthetic data
head data but struggles with more complex ones i
these datasets capture valuable insights but their scale and structure present a unique challenge for large language models llms which otherwise excel at few-shot
reasoning
reasoning curriculum
these datasets capture valuable insights but their scale and structure present a unique challenge for large language models llms which otherwise excel at few-shot reasoning over open-ended text
this framework provides a practical and scalable strategy to accelerate basin hopping searches directly extendable to
ab
ab initio calculations
this framework provides a practical and scalable strategy to accelerate basin hopping searches directly extendable to ab initio calculations such as density functional theory dft on high-performance computing architectures
this paper develops a sensitivity analysis framework that transfers the
average
treatment effect boundaries
this paper develops a sensitivity analysis framework that transfers the average total treatment effect atte from source data with a fully observed network to target data whose network is completely unknown
it is shown however that under control gain variation the safe set of these
controllers
control strategy
it is shown however that under control gain variation the safe set of these controllers is locally asymptotically stable which implies that their safety is sensitive to large but bounded disturbances
we provide rigorous analyses of its non-asymptotic
convergence
convergence rate
we provide rigorous analyses of its non-asymptotic convergence rates showing an improvement over prior double-loop algorithms -- form o epsilon -3 log epsilon -1 to o epsilon -3
our general interaction framework which reduces to several previously studied
models
quantum error correction
our general interaction framework which reduces to several previously studied models as special cases provides a versatile platform for engineering quantum correlations with applications in quantum simulation state preparation and sensing protocols
phonon polaritons and epsilon near zero modes in
sapphire
phonon polaritons
phonon polaritons and epsilon near zero modes in sapphire nanostructures
experiments across multiple model families and sizes show that angular
steering
angular steering
experiments across multiple model families and sizes show that angular steering achieves robust behavioral control while maintaining general language modeling performance underscoring its flexibility generalization and robustness compared to prior approaches
a sliding-window filter for online continuous-time continuum robot
state
state estimation
a sliding-window filter for online continuous-time continuum robot state estimation
end-to-end autonomous driving maps raw sensor inputs directly into ego-vehicle trajectories to avoid cascading errors from
perception
autonomous driving
end-to-end autonomous driving maps raw sensor inputs directly into ego-vehicle trajectories to avoid cascading errors from perception modules and to leverage rich semantic cues
artificial intelligence systems based on large language
models
generative ai
artificial intelligence systems based on large language models llms can now generate coherent text music and images yet they operate without a persistent state each inference reconstructs context from scratch
refractive index-correlated pseudocoloring for adaptive
color
refractive index
refractive index-correlated pseudocoloring for adaptive color fusion in holotomographic cytology
large language models llms have demonstrated exceptional capabilities across multiple
domains
models llms
large language models llms have demonstrated exceptional capabilities across multiple domains by leveraging massive pre-training and curated fine-tuning data
the proposed tracker is designed to operate flexibly for both pre-recorded videos and camera live streams where future
frames
event cameras
the proposed tracker is designed to operate flexibly for both pre-recorded videos and camera live streams where future frames are unavailable
we show that a simple and efficient algorithm -- based on singular value decomposition and standard perturbation mechanisms -- returns a private rank- r approximation whose error depends only on the emph rank- r coherence of u_1 ldots u_r and the spectral gap sigma_r - sigma_
r
-approximation algorithm
we show that a simple and efficient algorithm -- based on singular value decomposition and standard perturbation mechanisms -- returns a private rank- r approximation whose error depends only on the emph rank- r coherence of u_1 ldots u_r and the spectral gap sigma_r - sigma_ r 1
dynamic dyck and tree edit distance decompositions and reductions to string
edit
edit distance
dynamic dyck and tree edit distance decompositions and reductions to string edit distance
causal inference in high-dimensional generalized linear
models
causal inference
causal inference in high-dimensional generalized linear models with binary outcomes
our method is designed for versatility allowing integration with any state-of-the-art deep
reinforcement
reinforcement learning
our method is designed for versatility allowing integration with any state-of-the-art deep reinforcement learning drl algorithms within its self-play framework
however identifying super-eddington accreting
quasars
black hole
however identifying super-eddington accreting quasars observationally is challenging due to uncertain black-hole mass estimates and other complications
5 percent post-shock amplifying treatment effects through supply
chain
supply chain
5 percent post-shock amplifying treatment effects through supply chain spillovers in a manner analogous to financial contagion documented in our recent study
while large language models llms have demonstrated remarkable performance across various reasoning tasks their ability to handle
normative
normative reasoning
while large language models llms have demonstrated remarkable performance across various reasoning tasks their ability to handle normative reasoning remains underexplored
here we show that spin-1 nanographenes can also be used to explore the topological phase transition between the haldane phase and a dimerized
phase
molecular dynamics
here we show that spin-1 nanographenes can also be used to explore the topological phase transition between the haldane phase and a dimerized phase predicted for spin-1 chains with bond-alternation
in total we evaluate 234 model configurations derived from 16 base classifiers across more than 1980 time series and we propose the first extensive experimental evaluation of time series classification as model selection for
anomaly
anomaly detection
in total we evaluate 234 model configurations derived from 16 base classifiers across more than 1980 time series and we propose the first extensive experimental evaluation of time series classification as model selection for anomaly detection
the proposed approach provides formal guarantees on safety and timing and is computationally lightweight model-free and robust to
unknown
disturbance observer
the proposed approach provides formal guarantees on safety and timing and is computationally lightweight model-free and robust to unknown disturbances
88 on a held-out test set and when applied to 146 who-classified uncertain variants identified 41 candidates with convergent emergence across multiple
lineages
phylogenetic tree
88 on a held-out test set and when applied to 146 who-classified uncertain variants identified 41 candidates with convergent emergence across multiple lineages consistent with adaptive evolution
the resulting synthetic corpus is used for continual pretraining with an interleaved
curriculum
reasoning curriculum
the resulting synthetic corpus is used for continual pretraining with an interleaved curriculum schedule aligning learning across both content and cognitive dimensions
our framework provides a tractable model for exploring autonomy and
evolution
evolutionary game
our framework provides a tractable model for exploring autonomy and evolution in alife
posterior sampling by combining diffusion models with annealed
langevin
langevin dynamics
posterior sampling by combining diffusion models with annealed langevin dynamics
these spectral singularities can give rise to counterintuitive mode switching where a
mode
waveguide modes
these spectral singularities can give rise to counterintuitive mode switching where a mode with a lower quality factor and initially smaller increase of modal gain reaches the lasing threshold ahead of a more favorable competitor
yet when these vlms are adapted to the action modality it remains unclear to what extent their original vl
representations
vision-language-action vla
yet when these vlms are adapted to the action modality it remains unclear to what extent their original vl representations and knowledge are preserved
we show that our estimation strategy fully exploits the multidimensional
assignment
treatment assignment
we show that our estimation strategy fully exploits the multidimensional assignment rule and reveals heterogeneous effects along the treatment boundaries
for these environments to be considered for long term adoption and use they must support multiple-input-multiple- mimo technology rapidly fluctuating channel conditions in these environments place a heavy burden on traditional time-frequency
csi
channel state information csi
for these environments to be considered for long term adoption and use they must support multiple-input-multiple- mimo technology rapidly fluctuating channel conditions in these environments place a heavy burden on traditional time-frequency csi feedback schemes required for mimo precoding
second users recalled fewer physical objects than
virtual
virtual reality
second users recalled fewer physical objects than virtual objects in the environment suggesting reduced awareness of the physical environment
we release code pretrained weights and tutorials to support standardized
eeg
electroencephalography eeg
we release code pretrained weights and tutorials to support standardized eeg research and accelerate progress in clinical neuroscience
accurate world models are essential for enabling
agents
real-world scenarios
accurate world models are essential for enabling agents to think plan and reason effectively in complex dynamic settings
5 cdot mathrm polylog frac w delta time algorithm for solving the generalized maximum flow and generalized minimum cost flow problems in this setting where delta is the target accuracy and w is the maximum of all costs capacities and loss
factors
mathrm polylog
5 cdot mathrm polylog frac w delta time algorithm for solving the generalized maximum flow and generalized minimum cost flow problems in this setting where delta is the target accuracy and w is the maximum of all costs capacities and loss factors and their inverses
galaxy mergers trigger starburst activity and
galactic
galactic disk
galaxy mergers trigger starburst activity and galactic outflows that enrich the circumgalactic medium profoundly impacting galaxy evolution
galaxy mergers trigger starburst activity and
galactic
star clusters
galaxy mergers trigger starburst activity and galactic outflows that enrich the circumgalactic medium profoundly impacting galaxy evolution
our study addresses the challenges inherent in calculating mixed fermion-boson systems and explores the potential of quantum
computing
quantum mechanics
our study addresses the challenges inherent in calculating mixed fermion-boson systems and explores the potential of quantum computing to advance their analysis
electrical detection methods for solid-state spins are attractive for quantum technologies being readily chip-scalable and not subject to the
small
quantum batteries
electrical detection methods for solid-state spins are attractive for quantum technologies being readily chip-scalable and not subject to the small photon budgets of single emitters
through extensive experimentation with state-of-the-art classical
simulation
classical simulation
through extensive experimentation with state-of-the-art classical simulation strategies we identify a clear gap between classical and quantum runtimes
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
learning agents
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 ...
however recent investigations suggest that incorporating novel
higher-dimensional
optical interference
however recent investigations suggest that incorporating novel higher-dimensional linear-optical splitters in interferometer design can lead to several improvements
this study examines the feasibility and engagement of senior fit a standalone mobile fitness app designed for
older
older adults
this study examines the feasibility and engagement of senior fit a standalone mobile fitness app designed for older adults
to standardize this study we curate the evaluation data into mme-cof a compact
benchmark
reasoning curriculum
to standardize this study we curate the evaluation data into mme-cof a compact benchmark that enables in-depth and thorough assessment of chain-of-frame cof reasoning
9999 against a proxy measure of the transportation service level that is the
increase
route choice
9999 against a proxy measure of the transportation service level that is the increase in the maximum shortest path
these findings highlight the fine-grained discrepancies among
vlms
models vlms
these findings highlight the fine-grained discrepancies among vlms in chart understanding tasks and point to specific skills that need to be strengthened in current models
in this regard it is an opportune time to reevaluate the past classifications of different surface
brightness
surface brightness
in this regard it is an opportune time to reevaluate the past classifications of different surface brightness types
on multiple robustness of proximal dynamic
treatment
dynamic treatment
on multiple robustness of proximal dynamic treatment regimes
this makes this approach limited to ordered
discrete
categorical outcomes
this makes this approach limited to ordered discrete outcomes
in this work we extend our previous work on the extrapolation of single-time observables to demonstrate an efficient scheme for
computing
quantum networks
in this work we extend our previous work on the extrapolation of single-time observables to demonstrate an efficient scheme for computing two-time impurity correlation functions by combining the non-markovian quantum mpemba effect nmqmpe with a dynamical map-based framework for open quantum systems
estimating optimal dynamic treatment regimes via
sequential
treatment regimes
estimating optimal dynamic treatment regimes via sequential randomized trials might face costly and ethical hurdles often necessitating the use of historical observational data
we propose that atomic hydrogen facilitates the formation of radical sites which promotes covalent bond
formation
atomic force
we propose that atomic hydrogen facilitates the formation of radical sites which promotes covalent bond formation between adjacent particles or molecular units creating a more interconnected and rigid network with smaller interlayer distance
the numerical methods to do this efficiently
depend
numerical experiments
the numerical methods to do this efficiently depend on the properties of the loss function
recent causality-based methods address this challenge by learning invariant
causal
causal inference
recent causality-based methods address this challenge by learning invariant causal relationships in the underlying data-generating process
we evaluate infoflow on multiple agentic search benchmarks where it significantly outperforms strong baselines enabling lightweight
llms
models llms
we evaluate infoflow on multiple agentic search benchmarks where it significantly outperforms strong baselines enabling lightweight llms to achieve performance comparable to advanced proprietary llms
importantly i its width scales as the input dimension making it more prone to
feature
representation learning
importantly i its width scales as the input dimension making it more prone to feature learning than ultra wide networks and more expressive than narrow ones or with fixed embedding layers and ii we focus on the challenging interpolation regime where the number of trainable parameters and data are comparable which force...
we find that for different types of network architectures and for both visual or
neuronal
artificial neural
we find that for different types of network architectures and for both visual or neuronal stimuli these cross-supervising networks learn semantic representations that are easily decodable and that decoding accuracy is comparable to supervised networks -- both at the level of single networks and the ensemble
moreover it gives access at the same time to the spatial distribution within the monolayer of the individual spin-segregated states responsible for the hidden
spin
hidden spin
moreover it gives access at the same time to the spatial distribution within the monolayer of the individual spin-segregated states responsible for the hidden spin textures not provided by other techniques
to fill this gap we revisit encoder-decoder llm redllm enhancing it with
recent
large language models llms
to fill this gap we revisit encoder-decoder llm redllm enhancing it with recent recipes from decoder-only llm decllm
moreover all the detected communities for all the
cohorts
comorbidity networks
moreover all the detected communities for all the cohorts can be organized into a hierarchical tree
in this paper we estimate host galaxy properties from
spectral
spectral energy
in this paper we estimate host galaxy properties from spectral energy distribution models
we demonstrate that nonlinear quantum scrambling facilitates the achievement of super-heisenberg
scaling
super-heisenberg scaling
we demonstrate that nonlinear quantum scrambling facilitates the achievement of super-heisenberg scaling t - beta when the generator of the parameter is time-independent
the basic reproduction number mathcal r_0 calculated from models that assume homogeneous mixing or single-layer contact structures have limited
applicability
basic reproduction
the basic reproduction number mathcal r_0 calculated from models that assume homogeneous mixing or single-layer contact structures have limited applicability to complex social systems
prediction of a topological phase transition in
exchange
phase transition
prediction of a topological phase transition in exchange alternating spin-1 nanographene chains
finally simulation results demonstrate that the airy beam effectively mitigates blockage effects and the proposed scheme achieves comparable performance to exhaustive
beam
beamforming design
finally simulation results demonstrate that the airy beam effectively mitigates blockage effects and the proposed scheme achieves comparable performance to exhaustive beam sweeping while significantly reducing training overhead
instead of volatility declining as the square root of population
size
population size
instead of volatility declining as the square root of population size it falls much more slowly
in pulse broadening and compression schemes mi is a parasitic effect that induces significant shot-to-shot fluctuations of the peak power of compressed
pulses
pulsed laser
in pulse broadening and compression schemes mi is a parasitic effect that induces significant shot-to-shot fluctuations of the peak power of compressed pulses and increases rapidly over a narrow range of input pulse energies
we present reve representation for eeg with versatile embeddings a pretrained model explicitly designed to generalize across diverse
eeg
electroencephalography eeg
we present reve representation for eeg with versatile embeddings a pretrained model explicitly designed to generalize across diverse eeg signals
unlike conventional bias compensation methods that treat the bias as an augmented state within a single filter the proposed dual-filter structure decouples residual
bias
residual bias
unlike conventional bias compensation methods that treat the bias as an augmented state within a single filter the proposed dual-filter structure decouples residual bias estimation from electrochemical state estimation
motion imitation is a promising approach for humanoid
locomotion
imitation learning
motion imitation is a promising approach for humanoid locomotion enabling agents to acquire humanlike behaviors
moreover we quantitatively interpret our components from a neuroscience
perspective
higher-order visual
moreover we quantitatively interpret our components from a neuroscience perspective and analyze the associations between different visual patterns and brain functions
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
star clusters
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
atomic force microscopy showed a significant increase in the young
s
atomic force microscopy
atomic force microscopy showed a significant increase in the young s modulus of the film for both sample types after hydrogenation
all you need for object detection from pixels points and prompts to next-gen fusion and multimodal llms
vlms
vision-language models vlms
all you need for object detection from pixels points and prompts to next-gen fusion and multimodal llms vlms in autonomous vehicles
overall we suggest that hvc ac-iii is entering the
galactic
massive galaxies
overall we suggest that hvc ac-iii is entering the galactic wim layer and being sculpted by ram pressure into a droplet-like morphology providing a valuable case for studying the structure formation turbulence origin and dynamic evolution of hvcs as well as the physical properties of the ambient medium
reconfigurable intelligent surface ris technology has emerged as a key enabler for future
wireless
wireless systems
reconfigurable intelligent surface ris technology has emerged as a key enabler for future wireless communications
the tri-infrastructure methodology and 79-problem benchmark enable longitudinal tracking of reasoning
capabilities
reasoning capabilities
the tri-infrastructure methodology and 79-problem benchmark enable longitudinal tracking of reasoning capabilities as foundation models evolve
the street has emerged as a primary site where everyday publics are confronted with ai as an infrastructural phenomenon as
machine
machine learning
the street has emerged as a primary site where everyday publics are confronted with ai as an infrastructural phenomenon as machine learning-based systems are now commonly deployed in this setting in the form of automated cars facial recognition smart billboards and the like
the ability of llm agents to plan and invoke tools exposes them to new safety risks making a comprehensive
red-teaming
llm agents
the ability of llm agents to plan and invoke tools exposes them to new safety risks making a comprehensive red-teaming system crucial for discovering vulnerabilities and ensuring their safe deployment
we investigate whether large language models
llms
language models
we investigate whether large language models llms can act as in-context meta-learners for this task
stesso a reconfigurable decomposition of n -bit toffoli
gates
toffoli gates
stesso a reconfigurable decomposition of n -bit toffoli gates using symmetrical logical structures and adjustable support qubits
interpreting visual observations and natural
language
natural language
interpreting visual observations and natural language instructions for complex task execution remains a key challenge in robotics and ai
these methods operate on pareto fronts and
leverage
bilevel optimization
these methods operate on pareto fronts and leverage the individual minima im which are characteristic pareto-optimal points
unravelling the mechanisms of manipulating
numbers
large language models llms
unravelling the mechanisms of manipulating numbers in language models
hierarchical structures of motion exist across research fields including
computer
computer vision
hierarchical structures of motion exist across research fields including computer vision graphics and robotics where complex dynamics typically arise from coordinated interactions among simpler motion components