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the model jointly predicts semantic segmentation and depth estimation giving richer spatial features for
planning
autonomous driving
the model jointly predicts semantic segmentation and depth estimation giving richer spatial features for planning and control
this is an exponential improvement over previous results and only a polylogarithmic factor away from the
lower
lower bound
this is an exponential improvement over previous results and only a polylogarithmic factor away from the lower bound
the conclusion reflects on the consequences of this form of social invisibility of ai for situated engagement with ai by everyday publics in the street and for public
trust
trustworthy ai
the conclusion reflects on the consequences of this form of social invisibility of ai for situated engagement with ai by everyday publics in the street and for public trust in urban governance
yet an important question still remains are video models ready to serve as
zero-shot
vision-language models
yet an important question still remains are video models ready to serve as zero-shot reasoners in challenging visual reasoning scenarios
we present results from crocodile-dwarf a suite of cosmological zoom-in hydrodynamic simulations of isolated field dwarf galaxies with halo
masses
stellar mass
we present results from crocodile-dwarf a suite of cosmological zoom-in hydrodynamic simulations of isolated field dwarf galaxies with halo masses of sim10 10 m_ odot at z 0 performed with the textsc gadget4-osaka code
interestingly for the trapped system a cat-like superposition state corresponds to maximum entanglement
entropy
entanglement entropy
interestingly for the trapped system a cat-like superposition state corresponds to maximum entanglement entropy below the transition highlighting the relevance of the present model for studying the effect of decoherence on intra-particle entanglement in the context of quantum information processing
the architecture incorporates a brain-region to image-feature cross-attention mechanism enabling nonlinear mappings between high-dimensional deep network features and semantic
patterns
fmri data
the architecture incorporates a brain-region to image-feature cross-attention mechanism enabling nonlinear mappings between high-dimensional deep network features and semantic patterns encoded in the brain activity
we conducted manual expert evaluation of observed
groups
collective action
we conducted manual expert evaluation of observed groups produced new manual annotations for relevant events pertaining to group behavior and extracted metrics relevant group formation
we present a unified field-theoretic framework for the dynamics of activity and
connectivity
brain activity
we present a unified field-theoretic framework for the dynamics of activity and connectivity in interacting neuronal systems
more importantly the thinking structure in this protocol can be further optimized through
reinforcement
reinforcement learning
more importantly the thinking structure in this protocol can be further optimized through reinforcement learning
adapting pre-trained video generation models into controllable world models via latent actions is a promising step towards creating generalist
world
world models
adapting pre-trained video generation models into controllable world models via latent actions is a promising step towards creating generalist world models
the quantum walk technique is a general framework for constructing quantum algorithms by transforming a classical random walk search into a quantum search and has been successfully
applied
quantum coherence
the quantum walk technique is a general framework for constructing quantum algorithms by transforming a classical random walk search into a quantum search and has been successfully applied to constructing an algorithm with a tight query complexity for another problem
we provide a theoretical analysis of the generalization ability of solving qps with projection matrices generated by
neural
reinforcement learning
we provide a theoretical analysis of the generalization ability of solving qps with projection matrices generated by neural networks
our findings connect entanglement dissipation-enhanced
scaling
quantum walk
our findings connect entanglement dissipation-enhanced scaling laws and superabsorption outlining a pathway towards scalable quantum batteries offering practical quantum advantage
these results confirm the effectiveness efficiency and robustness of our approach for low-resource domain
adaptation
llm post-training
these results confirm the effectiveness efficiency and robustness of our approach for low-resource domain adaptation of llms
this manuscript revisits the essence of generative image fusion under the inspiration of human cognitive laws and proposes a novel infrared and visible image
fusion
multimodal reasoning
this manuscript revisits the essence of generative image fusion under the inspiration of human cognitive laws and proposes a novel infrared and visible image fusion method termed hclfuse
this makes this approach limited to ordered
discrete
potential outcomes
this makes this approach limited to ordered discrete outcomes
we prove certain monotonicity properties of the
optimal
optimal control
we prove certain monotonicity properties of the optimal policy in the state space mathcal s and identify classes of unreachable states
nonparametric estimation of homogenized invariant measures from
multiscale
density estimation
nonparametric estimation of homogenized invariant measures from multiscale data via hermite expansion
interest in the random order model rom leads us to initiate a study of utilizing random-order arrivals to extract
random
randomized algorithm
interest in the random order model rom leads us to initiate a study of utilizing random-order arrivals to extract random bits with the goal of de-randomizing algorithms
in this paper we present the first systematic survey of data-efficient llm
post-training
llm raters
in this paper we present the first systematic survey of data-efficient llm post-training from a data-centric perspective
leakage is especially harmful as it corrupts all subsequent syndrome
measurements
qubit readout
leakage is especially harmful as it corrupts all subsequent syndrome measurements and can spread to neighboring qubits
we apply our method to variational quantum
algorithm
quantum error correction
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
compressed indexing is a powerful technique that enables efficient querying over data stored in
compressed
query complexity
compressed indexing is a powerful technique that enables efficient querying over data stored in compressed form significantly reducing memory usage and often accelerating computation
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
moreover we quantitatively interpret our components from a neuroscience perspective and analyze the associations between different visual
patterns
cognitive science
moreover we quantitatively interpret our components from a neuroscience perspective and analyze the associations between different visual patterns and brain functions
this paper presents a comprehensive cross-platform evaluation of
reasoning
reasoning capabilities
this paper presents a comprehensive cross-platform evaluation of reasoning capabilities in contemporary foundation models establishing an infrastructure-agnostic benchmark across three computational paradigms hpc supercomputing marenostrum 5 cloud platforms nebius ai studio and university clusters a node with eight h20...
these results demonstrate a direct link between lattice distortions and coulomb interactions in transition-metal dichalcogenides providing a microscopic mechanism for light-induced control of correlated phases in two-dimensional
quantum
quantum technologies
these results demonstrate a direct link between lattice distortions and coulomb interactions in transition-metal dichalcogenides providing a microscopic mechanism for light-induced control of correlated phases in two-dimensional quantum materials
in this work we propose a new inferential framework for
autocorrelation
time-series experiments
in this work we propose a new inferential framework for autocorrelation in time series data under frequent mean shifts
furthermore we also characterize quantum channels according to their ability in preserving quantum
resources
quantum advantage
furthermore we also characterize quantum channels according to their ability in preserving quantum resources i
direct feedback alignment dfa enables local parallelizable updates with lower memory requirements but is limited by unstructured feedback and poor scalability in deeper architectures specially
convolutional
deep network
direct feedback alignment dfa enables local parallelizable updates with lower memory requirements but is limited by unstructured feedback and poor scalability in deeper architectures specially convolutional neural networks
graph-theoretical mapping of resting-state
eeg
electroencephalography eeg
graph-theoretical mapping of resting-state eeg reveals neural signatures of creativity
point convergence analysis of the accelerated
gradient
accelerated gradient
point convergence analysis of the accelerated gradient method for multiobjective optimization continuous and discrete
experiments on synthetic and real-world datasets demonstrate that scmd effectively captures both structural and distributional differences between scms providing a practical tool to assess
causal
causal effects
experiments on synthetic and real-world datasets demonstrate that scmd effectively captures both structural and distributional differences between scms providing a practical tool to assess causal transferability and generalization difficulty
in this paper we give sufficient conditions under which linear abstract
control
linear control
in this paper we give sufficient conditions under which linear abstract control systems for which the semigroup is analytic are stabilizable with a bounded feedback
causal machine learning has emerged as a powerful tool for flexibly estimating
causal
causal inference
causal machine learning has emerged as a powerful tool for flexibly estimating causal effects from observational data in both industry and academia
the quantum walk technique is a general framework for constructing quantum algorithms by transforming a classical random walk search into a quantum search and has been successfully applied to constructing an
algorithm
open quantum
the quantum walk technique is a general framework for constructing quantum algorithms by transforming a classical random walk search into a quantum search and has been successfully applied to constructing an algorithm with a tight query complexity for another problem
reinforcement learning rl is widely used to produce robust robotic manipulation
policies
reinforcement learning
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
when inputs are held constant learning strength declines linearly as the delay between input and
error
continual learning
when inputs are held constant learning strength declines linearly as the delay between input and error approaches the stimulus duration explaining observed robustness and failure across network depths
this paper provides a nonparametric framework for causal inference with
categorical
categorical outcomes
this paper provides a nonparametric framework for causal inference with categorical outcomes under binary treatment and binary instrument settings
our simulations with fast transport mechanisms either diffusion or streaming are degenerate they each produce a lower
gamma
gamma -ray
our simulations with fast transport mechanisms either diffusion or streaming are degenerate they each produce a lower gamma -ray luminosity than slow transport simulations by two orders of magnitude
this approach demonstrates that robust self-testing and publicly verifiable quantum randomness can be achieved with minimal optical
complexity
fault-tolerant quantum
this approach demonstrates that robust self-testing and publicly verifiable quantum randomness can be achieved with minimal optical complexity without jeopardizing security
learning to defer uncertain predictions to costly experts offers a
powerful
machine learning
learning to defer uncertain predictions to costly experts offers a powerful strategy for improving the accuracy and efficiency of machine learning systems
we propose a novel framework grounded in the hypothesis that
loss
gradient descent
we propose a novel framework grounded in the hypothesis that loss functions in real-world tasks swap from initial non-convexity to convexity towards the optimum
however the neural mechanisms facilitating the
continual
recurrent neural
however the neural mechanisms facilitating the continual learning and flexible re- composition of skills remain elusive
our results provide robust evidence that massive
etgs
dark matter
our results provide robust evidence that massive etgs have undergone significant kinematic evolution losing angular momentum as they evolve towards the present day consistent with theoretical models where processes such as dry mergers play a crucial role in shaping the dynamical state of galaxies
a dynamic reasoning module refines predictions by combining global
scene
spatial reasoning
a dynamic reasoning module refines predictions by combining global scene cues and object-level interactions guided by linguistic priors
trainium an ai accelerator recently developed by amazon web services aws provides an attractive option for llm training and
inference
llm inference
trainium an ai accelerator recently developed by amazon web services aws provides an attractive option for llm training and inference through its heterogeneous architecture
here we extend evolutionary game theory to account for random changes in the
social
game theory
here we extend evolutionary game theory to account for random changes in the social environment so that mutual cooperation may bring different rewards today than it brings tomorrow for example
for typical cortical stimuli tens of milliseconds this places the functional plasticity window in the few-second range a testable prediction that identifies seconds-scale eligibility traces as necessary for error-driven learning in
biological
artificial neural
for typical cortical stimuli tens of milliseconds this places the functional plasticity window in the few-second range a testable prediction that identifies seconds-scale eligibility traces as necessary for error-driven learning in biological circuits
we present a lateral ventricular brain-computer interface lv-bci that deploys an expandable flexible
electrode
electroencephalography eeg
we present a lateral ventricular brain-computer interface lv-bci that deploys an expandable flexible electrode into the lateral ventricle through a minimally invasive external ventricular drainage pathway
these artificial time variations enable opportunistic user
scheduling
resource allocation
these artificial time variations enable opportunistic user scheduling and exploitation of multiuser diversity under slow channel dynamics
this work expands the operational range of kerr soliton microcombs from the terahertz to the sub-gigahertz domain opening new frontiers for
frequency
frequency combs
this work expands the operational range of kerr soliton microcombs from the terahertz to the sub-gigahertz domain opening new frontiers for frequency comb technologies
user perceptions of privacy and helpfulness in
llm
llm reasoning
user perceptions of privacy and helpfulness in llm responses to privacy-sensitive scenarios
in this work we demonstrate an on-demand high fidelity 99 re-initialization of a quantum dot qubit out of a latched
readout
qubit readout
in this work we demonstrate an on-demand high fidelity 99 re-initialization of a quantum dot qubit out of a latched readout state
a two-step approach originally introduced for network reconstruction in which one first randomizes the structure then the weights with a suitable distribution restores scale invariance and allows us to conduct unbiased assessments of significance on
weighted
scale-free networks
a two-step approach originally introduced for network reconstruction in which one first randomizes the structure then the weights with a suitable distribution restores scale invariance and allows us to conduct unbiased assessments of significance on weighted networks
in this work we address this main shortcoming by introducing a
local
local calibration
in this work we address this main shortcoming by introducing a local perspective on multiclass calibration
building on this algorithm we next give a one-sided adaptive algorithm for this problem that does not need to be given the value of n and with high probability makes
tilde
query complexity
building on this algorithm we next give a one-sided adaptive algorithm for this problem that does not need to be given the value of n and with high probability makes tilde o log n epsilon samples and queries
our results indicate that although llms generally adhere to valid reasoning patterns they exhibit notable inconsistencies in specific types of
normative
normative reasoning
our results indicate that although llms generally adhere to valid reasoning patterns they exhibit notable inconsistencies in specific types of normative reasoning and display cognitive biases similar to those observed in psychological studies of human reasoning
the ewm approach is analogous to a classification problem where one first builds an estimator of the population welfare which is a functional of policy functions and then trains a
policy
policy learning
the ewm approach is analogous to a classification problem where one first builds an estimator of the population welfare which is a functional of policy functions and then trains a policy by maximizing the estimated welfare
learning complex network dynamics is fundamental to understanding modelling and controlling real-world
complex
complex systems
learning complex network dynamics is fundamental to understanding modelling and controlling real-world complex systems
all such methods require multiple applications of the operator
defining
augmented lagrangian
all such methods require multiple applications of the operator defining the forward problem and of its adjoint
here we demonstrate that annealed population
heterogeneity
population genetics
here we demonstrate that annealed population heterogeneity wherein distinct individuals in the population experience different instances of a complex environment over time can act as a form of implicit regularization and facilitate evolutionary generalization
in this two-paper series we present a straightforward mathematical model for synthesizing quasar absorption line profiles from sight lines through idealized spatial-kinematic models of the
circumgalactic
circumgalactic medium
in this two-paper series we present a straightforward mathematical model for synthesizing quasar absorption line profiles from sight lines through idealized spatial-kinematic models of the circumgalactic medium cgm and their host galaxies
we further prove that no deterministic online algorithm can achieve a competitive
ratio
competitive ratio
we further prove that no deterministic online algorithm can achieve a competitive ratio bounded by 2 for the general cost optimization problem
many believe that intracortical axons conduct signals too slowly to bring the contextual information from
receptive
neural representations
many believe that intracortical axons conduct signals too slowly to bring the contextual information from receptive fields of other neurons
performance characteristics of the eight open-source models and consensus were
compared
open-source models
performance characteristics of the eight open-source models and consensus were compared to gpt-4o
the latter is decomposed into individual and coupling costs with the distinctive feature that the
coupling
linear control
the latter is decomposed into individual and coupling costs with the distinctive feature that the coupling term is a pairwise interaction function between the controls
here we develop a mathematical model which uses an optimisation framework to determine the higher-order spatial structure of a
collective
collective systems
here we develop a mathematical model which uses an optimisation framework to determine the higher-order spatial structure of a collective that optimises group-level knowledge transfer
for typical cortical stimuli tens of milliseconds this places the functional plasticity window in the few-second range a testable prediction that identifies seconds-scale eligibility traces as necessary for error-driven learning in
biological
brain decoding
for typical cortical stimuli tens of milliseconds this places the functional plasticity window in the few-second range a testable prediction that identifies seconds-scale eligibility traces as necessary for error-driven learning in biological circuits
meanwhile the resource allocation problem is solved using a successive convex
approximation
optimization problem
meanwhile the resource allocation problem is solved using a successive convex approximation sca -based algorithm
we hope this work provides a practical and economically viable blueprint for transforming passive llms into
proactive
llm agents
we hope this work provides a practical and economically viable blueprint for transforming passive llms into proactive goal-oriented llm applications
we hope the geometric view of parametric memory encourages revisiting the default intuitions that guide researchers in areas like
knowledge
reasoning capabilities
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
the tri-infrastructure methodology and 79-problem benchmark enable longitudinal tracking of reasoning
capabilities
reasoning tasks
the tri-infrastructure methodology and 79-problem benchmark enable longitudinal tracking of reasoning capabilities as foundation models evolve
we use the database constructed in wasleske baldassare 2024 which contains dwarf galaxies that were
selected
stellar population
we use the database constructed in wasleske baldassare 2024 which contains dwarf galaxies that were selected as active galaxies by optical spectroscopy infrared colors x-ray brightness and photometric variability
a neural network framework for discovering closed-form solutions to
quadratic
quadratic programming
a neural network framework for discovering closed-form solutions to quadratic programs with linear constraints
stakeholders generally perceive climate change impacts on ecosystem services as negative with natural
habitat
climate change
stakeholders generally perceive climate change impacts on ecosystem services as negative with natural habitat destruction and biodiversity loss identified as top issues
for this problem we develop a direct debiased
machine
machine learning
for this problem we develop a direct debiased machine learning framework with an end-to-end algorithm
yet it remains unclear which correlations -- and how many -- are needed to predict
large-scale
fmri data
yet it remains unclear which correlations -- and how many -- are needed to predict large-scale neural activity
we use the database constructed in wasleske baldassare 2024 which contains dwarf galaxies that were selected as active galaxies by optical
spectroscopy
host galaxy
we use the database constructed in wasleske baldassare 2024 which contains dwarf galaxies that were selected as active galaxies by optical spectroscopy infrared colors x-ray brightness and photometric variability
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
when both the input and output bitstrings of a peaked circuit are unknown determining whether the circuit is
peaked
peaked circuits
when both the input and output bitstrings of a peaked circuit are unknown determining whether the circuit is peaked constitutes a qcma-complete problem meaning the problem remains hard even for a quantum polynomial-time machine under commonly accepted complexity assumptions
starting with the dynamical system representing
collective
collective states
starting with the dynamical system representing collective states in terms of connections activity levels and internal frequencies we analyze its stability emphasizing the possibility of transitions between configurations
our results indicate that although llms generally adhere to valid reasoning patterns they exhibit notable inconsistencies in specific types of normative
reasoning
mathematical reasoning
our results indicate that although llms generally adhere to valid reasoning patterns they exhibit notable inconsistencies in specific types of normative reasoning and display cognitive biases similar to those observed in psychological studies of human reasoning
recent alignment techniques such as reinforcement learning from human feedback have been widely adopted to align large
language
language models
recent alignment techniques such as reinforcement learning from human feedback have been widely adopted to align large language models with human preferences by learning and leveraging reward models
our solution jointly optimizes the transmission
power
transmit power
our solution jointly optimizes the transmission power and the neural network split point
the predominant climatic characteristic is temperature and the predominant
land
land use
the predominant climatic characteristic is temperature and the predominant land use characteristic is deforestation
unifying regression-based and design-based
causal
causal effects
unifying regression-based and design-based causal inference in time-series experiments
infoflow reinforcing search agent via reward
density
reinforcement learning
infoflow reinforcing search agent via reward density optimization
evolutionary systems must learn to generalize often extrapolating from a limited set of selective conditions to anticipate future
environmental
climate change
evolutionary systems must learn to generalize often extrapolating from a limited set of selective conditions to anticipate future environmental changes
a bandwise analysis shows that the hybrid approach leverages beamformer directivity at high frequencies and microphone cues at low
frequencies
beamforming design
a bandwise analysis shows that the hybrid approach leverages beamformer directivity at high frequencies and microphone cues at low frequencies outperforming either method alone across all bands
this study demonstrates that providing language models with pragmatic theories as prompts is an effective in-context
learning
language models
this study demonstrates that providing language models with pragmatic theories as prompts is an effective in-context learning approach for tasks to understand implied meanings
these results and the simplicity of the hcf-based system pave the way to a high-performance and scalable solution for ultra-stable
laser
pulsed laser ablation liquids
these results and the simplicity of the hcf-based system pave the way to a high-performance and scalable solution for ultra-stable laser sources
perception learning a formal separation of sensory
representation
representation learning
perception learning a formal separation of sensory representation learning from decision learning
simulation studies indicate that the proposed method performs favorably
compared
simulation studies
simulation studies indicate that the proposed method performs favorably compared to existing approaches
education unexpectedly has now become burdened by an especially crucial role of charting long-range strategies for discovering viable human skills that would guarantee their place in the world of the ubiquitous
use
trustworthy ai
education unexpectedly has now become burdened by an especially crucial role of charting long-range strategies for discovering viable human skills that would guarantee their place in the world of the ubiquitous use of ai in the intellectual sphere
scout a lightweight framework for scenario
coverage
scenario coverage
scout a lightweight framework for scenario coverage assessment in autonomous driving
molecular dynamics md simulations were performed to investigate the influence of mechanical strain on irradiation-induced defect and dislocation evolution in nickel single crystals subjected to cumulative overlapping 5 kev collision
cascades
molecular dynamics
molecular dynamics md simulations were performed to investigate the influence of mechanical strain on irradiation-induced defect and dislocation evolution in nickel single crystals subjected to cumulative overlapping 5 kev collision cascades at 300 k
moreover we derive an explicit expression for the
value
value function
moreover we derive an explicit expression for the value function by solving a system of differential equations
instrumental variable methods are fundamental to
causal
instrumental variable
instrumental variable methods are fundamental to causal inference when treatment assignment is confounded by unobserved variables