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this assumption generalizes existing assumptions of differential basis support used for identification of the
causal
causal effects
this assumption generalizes existing assumptions of differential basis support used for identification of the causal effect under spatial confounding and does not require prior knowledge of which basis functions satisfy this support condition
our results demonstrate that model selection methods outperform every single
anomaly
anomaly detection
our results demonstrate that model selection methods outperform every single anomaly detection method while being in the same order of magnitude regarding execution time
in this paper we address the bias introduced by
synthetic
synthetic data
in this paper we address the bias introduced by synthetic data and provide consistent estimators for this bias by borrowing information from the majority group
harnessing topological effects offers a promising route to protect quantum states of light from imperfections potentially enabling more robust platforms for
quantum
quantum emitters
harnessing topological effects offers a promising route to protect quantum states of light from imperfections potentially enabling more robust platforms for quantum information processing
we show the existence of pseudo-optimal text encoders that achieve perfect modal-invariant alignment yet are
provably
sparse autoencoders
we show the existence of pseudo-optimal text encoders that achieve perfect modal-invariant alignment yet are provably insensitive to swap replace and add operations over atomic concepts thereby failing to distinguish correct captions from hard negatives despite optimizing the same training objective as true-optimal enc...
in this study we first prove that the density-ratio estimation method
proposed
density estimation
in this study we first prove that the density-ratio estimation method proposed in lin et al
variational quantum algorithms vqas face an inherent trade-off between expressivity and trainability deeper circuits can represent richer
states
quantum channels
variational quantum algorithms vqas face an inherent trade-off between expressivity and trainability deeper circuits can represent richer states but suffer from noise accumulation and barren plateaus while shallow circuits remain trainable and implementable but lack expressive power
considering uncorrelated classically correlated and entangled initial
states
multipartite entanglement
considering uncorrelated classically correlated and entangled initial states we show that entanglement enables the superposed causal order to generate coherence in the working medium thereby enhancing work extraction and efficiency beyond the separable and uncorrelated cases
5 dex at all significantly above the values observed in milky way
stars
galactic nuclei
5 dex at all significantly above the values observed in milky way stars where remains close to solar reflecting comparable production of r-process and alpha -capture elements
finally we prove that the proximal point algorithm converges linearly to the unique solution when applied to
strongly
strongly convex
finally we prove that the proximal point algorithm converges linearly to the unique solution when applied to strongly star quasiconvex functions defined over closed star-shaped sets which are not necessarily convex
102 250501 2009 proved that all entangled
states
entanglement entropy
102 250501 2009 proved that all entangled states are useful for discrimination of quantum channels
graph-theoretical mapping of resting-state
eeg
brain regions
graph-theoretical mapping of resting-state eeg reveals neural signatures of creativity
this work advances this vision by identifying the fundamental principles of human ai collaboration within uncertainty quantification a key component of reliable
decision
artificial intelligence
this work advances this vision by identifying the fundamental principles of human ai collaboration within uncertainty quantification a key component of reliable decision making
to solve this high-dimensional non-convex problem under uncertain channels we develop a deep
reinforcement
deep reinforcement learning
to solve this high-dimensional non-convex problem under uncertain channels we develop a deep reinforcement learning solution framework based on the proximal policy optimization ppo algorithm that integrates distribution-aware action modeling and a multi-branch actor network
in this two-paper series we present a straightforward mathematical model for synthesizing quasar
absorption
absorption line
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
these inferred thinking traces are applied to two complementary tasks 1 fine-tuning open llm
raters
thinking traces
these inferred thinking traces are applied to two complementary tasks 1 fine-tuning open llm raters and 2 synthesizing clearer annotation guidelines for proprietary llm raters
while the parallel trends assumption is counterfactual and cannot be tested directly researchers often examine pre-treatment periods to check whether the time trends are
parallel
treatment effect
while the parallel trends assumption is counterfactual and cannot be tested directly researchers often examine pre-treatment periods to check whether the time trends are parallel before treatment is administered
additionally we explore the use of pretrained
foundation
foundation models
additionally we explore the use of pretrained foundation models specifically ct-fm and radimagenet to extract image features which are then used with traditional classifiers
first-order methods based on the pdhg algorithm have recently emerged as a viable option for efficiently solving large-scale linear
programming
quadratic programming
first-order methods based on the pdhg algorithm have recently emerged as a viable option for efficiently solving large-scale linear programming problems
rather than discrete treatment parameters the framework characterizes treatment intensity as continuous functions tau mathbf x t over space-time enabling rigorous analysis of boundary evolution
spatial
spatial treatment
rather than discrete treatment parameters the framework characterizes treatment intensity as continuous functions tau mathbf x t over space-time enabling rigorous analysis of boundary evolution spatial gradients and cumulative exposure
we establish the theoretical properties of shide including pointwise consistency bias-variance decomposition and asymptotic mise showing that shide attains the classical n -4 5 convergence
rate
convergence rate
we establish the theoretical properties of shide including pointwise consistency bias-variance decomposition and asymptotic mise showing that shide attains the classical n -4 5 convergence rate while mitigating boundary bias
this represents a paradigm shift long-residence interstellar objects primarily reveal gcr-processed material rather than pristine
material
dark matter
this represents a paradigm shift long-residence interstellar objects primarily reveal gcr-processed material rather than pristine material representative of their primordial formation environments
crucially this assumption has been used to prove that gd avoids saddle points and maxima and to establish the existence of a computable quantity that determines the
convergence
convergence guarantees
crucially this assumption has been used to prove that gd avoids saddle points and maxima and to establish the existence of a computable quantity that determines the convergence to global minima both for gd and stochastic gd
rewards where the expected reward function is
multimodal
reward models
rewards where the expected reward function is multimodal with at most m modes
finally simulation results demonstrate that the proposed scma-empowered ura scheme enjoys higher maximum throughput compared to the conventional orthogonal multiple
access
uplink communication
finally simulation results demonstrate that the proposed scma-empowered ura scheme enjoys higher maximum throughput compared to the conventional orthogonal multiple access oma based ura scheme
in particular the gradient descent gd optimization
algorithm
gradient descent
in particular the gradient descent gd optimization algorithm has been extensively studied in recent years
response length discrimination sycophancy and
conceptual
theoretical findings
response length discrimination sycophancy and conceptual bias which is a problem that has received increasing attention
we revisit a recent algorithm in the literature and show that it does not have a
competitive
online algorithm
we revisit a recent algorithm in the literature and show that it does not have a competitive ratio of 2 as claimed by constructing a counterexample
our system achieves accuracy increases of at least 16 on the synthetic csi dataset 20 on the in-lab csi dataset and 17 on the in-the-wild
csi
information csi
our system achieves accuracy increases of at least 16 on the synthetic csi dataset 20 on the in-lab csi dataset and 17 on the in-the-wild csi dataset
to explain this surprising redundancy we develop a cross-evaluation protocol in which we apply each linear
decoder
dual encoder
to explain this surprising redundancy we develop a cross-evaluation protocol in which we apply each linear decoder operator to the subjects of every other relation
we compare these models in terms of theoretical properties optimization strategies and empirical performance and discuss
applications
deep learning
we compare these models in terms of theoretical properties optimization strategies and empirical performance and discuss applications in fields such as computer vision natural language processing and bioinformatics
we propose the first known computationally tractable algorithm for computing the solution to the graves-lai
optimization
minimax optimal
we propose the first known computationally tractable algorithm for computing the solution to the graves-lai optimization problem which in turn enables the implementation of asymptotically optimal algorithms for this bandit problem
furthermore the moving mass dynamics are expressed as an extension to the manoeuvring model for
underwater
underwater vehicles
furthermore the moving mass dynamics are expressed as an extension to the manoeuvring model for underwater vehicles originally introduced by fossen 1991
surprisingly the pa agrees well with monte carlo simulations on some empirical networks even small ones highlighting its potential as a computationally efficient bridge between individual decision-making and
collective
collective action
surprisingly the pa agrees well with monte carlo simulations on some empirical networks even small ones highlighting its potential as a computationally efficient bridge between individual decision-making and collective actions
5 kv while the field-plate pt 011 b eta -ga2o3 sbds achieved an increased
breakdown
breakdown voltage
5 kv while the field-plate pt 011 b eta -ga2o3 sbds achieved an increased breakdown voltage of 2
our results strongly suggest the role of predictive learning as a guiding
principle
predictive processing
our results strongly suggest the role of predictive learning as a guiding principle for effective representation learning in agents navigating partial observability
in this work we investigate how and at which stage
value
training data
in this work we investigate how and at which stage value alignment arises during the course of a model s post-training
we present brain-it a brain-inspired approach that addresses this challenge through a brain interaction transformer bit allowing effective
interactions
brain-computer interface
we present brain-it a brain-inspired approach that addresses this challenge through a brain interaction transformer bit allowing effective interactions between clusters of functionally-similar brain-voxels
such models rely on key assumptions about the underlying process in order to enable faithful learning of governing
dynamics
world models
such models rely on key assumptions about the underlying process in order to enable faithful learning of governing dynamics that mimic the actual system behavior
for approximate matching we develop randomized algorithms to show that 1
epsilon
approximation guarantee
for approximate matching we develop randomized algorithms to show that 1 epsilon -approximate matching in regular graphs is truly local i
these nns remain fixed across varying power system structures and parameters and are repeatedly shared within each system instance defined by these variations thereby enabling the generalization of the neural stability descriptor across a class of
power
power systems
these nns remain fixed across varying power system structures and parameters and are repeatedly shared within each system instance defined by these variations thereby enabling the generalization of the neural stability descriptor across a class of power systems
we thus confirm that adaptive higher-order methods achieve superlinear convergence for certain degenerate problems as long as p is large enough and provide sharp bounds on the order of
convergence
first-order methods
we thus confirm that adaptive higher-order methods achieve superlinear convergence for certain degenerate problems as long as p is large enough and provide sharp bounds on the order of convergence one can expect in the limit
our results demonstrate that a modified monte carlo-based approach significantly outperforms traditional q-learning and two exhaustive search patterns illustrating its potential in adapting
rl
reinforcement learning
our results demonstrate that a modified monte carlo-based approach significantly outperforms traditional q-learning and two exhaustive search patterns illustrating its potential in adapting rl to complex environments
there has been a surge of recent interest in automatically
learning
policy learning
there has been a surge of recent interest in automatically learning policies to target treatment decisions based on rich individual covariates
quantum gated recurrent gan with gaussian uncertainty for network
anomaly
anomaly detection
quantum gated recurrent gan with gaussian uncertainty for network anomaly detection
we propose a novel embodied agent framework for robots which comprises a human-robot voice interaction module a
vision-language
vision-language models
we propose a novel embodied agent framework for robots which comprises a human-robot voice interaction module a vision-language agent module and an action execution module
these limitations are particularly apparent in real-life
driving
motion planning
these limitations are particularly apparent in real-life driving scenarios where state-of-the-art algorithms struggle to safely or reliably complete overtaking manoeuvres
that can handle only 10 6-7 photons s whereas sources may easily produce 10 10-13
photons
quantum emitters
that can handle only 10 6-7 photons s whereas sources may easily produce 10 10-13 photons s or more if properly designed
we robustify our test both against this term and finite sample
bias
randomized experiments
we robustify our test both against this term and finite sample bias and illustrate its excellent performance and practical relevance in a monte carlo study and a real data empirical example
we present a systematic analysis of estimation errors for a
class
density estimation
we present a systematic analysis of estimation errors for a class of optimal transport based algorithms for filtering and data assimilation
pulse-level quantum programs can be fully described with only three low-level abstractions ports input output
channels
quantum channels
pulse-level quantum programs can be fully described with only three low-level abstractions ports input output channels frames reference signals and waveforms pulse envelopes
an n 1 -bit toffoli gate is mainly utilized to construct other quantum gates and operators such as fredkin
gates
-bit toffoli gates
an n 1 -bit toffoli gate is mainly utilized to construct other quantum gates and operators such as fredkin gates arithmetical adders and logical comparators where n geq 2
to address this we leverage a suite of representational similarity metrics-each capturing a distinct facet of representational correspondence such as geometry unit-level tuning or linear decodability-and assess brain region or model separability using
multiple
brain regions
to address this we leverage a suite of representational similarity metrics-each capturing a distinct facet of representational correspondence such as geometry unit-level tuning or linear decodability-and assess brain region or model separability using multiple complementary measures
our work provides a scalable method to overcome a measurement bottleneck in cognitive science and demonstrates that
foundation
foundation models
our work provides a scalable method to overcome a measurement bottleneck in cognitive science and demonstrates that foundation models can learn a representational geometry that is functionally relevant for modeling key aspects of human cognition such as categorization
object-context shortcuts remain a persistent challenge in
vision-language
vision-language models
object-context shortcuts remain a persistent challenge in vision-language models undermining zero-shot reliability when test-time scenes differ from familiar training co-occurrences
learning-based blockage-resilient beam training in
near-field
near-field beam
learning-based blockage-resilient beam training in near-field terahertz communications
we test our method in a grid with two interconnecting points and analyze the properties of the resulting high-dimensional for from a
power
power systems
we test our method in a grid with two interconnecting points and analyze the properties of the resulting high-dimensional for from a power systems perspective
the spatial distribution of optical and far-infrared fir emission
lines
emission line
the spatial distribution of optical and far-infrared fir emission lines differs in morphology likely resulting from different critical densities and inhomogeneous density distributions within the galaxy
recent large language model llm research has undergone an architectural shift from encoder-decoder
modeling
large language
recent large language model llm research has undergone an architectural shift from encoder-decoder modeling to nowadays the dominant decoder-only modeling
point convergence of nesterov s accelerated
gradient
superlinear convergence
point convergence of nesterov s accelerated gradient method an ai-assisted proof
in this paper we propose a strong baseline basicavsr for avsr by integrating four key components 1 adaptive multi-scale frequency priors generated from image laplacian pyramids 2 a flow-guided propagation unit to aggregate spatiotemporal information from adjacent frames 3 a second-order motion compensation unit for mor...
unit
image fusion
in this paper we propose a strong baseline basicavsr for avsr by integrating four key components 1 adaptive multi-scale frequency priors generated from image laplacian pyramids 2 a flow-guided propagation unit to aggregate spatiotemporal information from adjacent frames 3 a second-order motion compensation unit for mor...
evontree ontology rule-guided self-evolution of large
language
language models
evontree ontology rule-guided self-evolution of large language models
to realize this vision we introduce asynchronous thinking asyncthink as a new paradigm of reasoning with large language models which organizes the internal
thinking
reasoning capabilities
to realize this vision we introduce asynchronous thinking asyncthink as a new paradigm of reasoning with large language models which organizes the internal thinking process into concurrently executable structures
we introduce debate2create d2c a framework in which large
language
vision-language models vlms
we introduce debate2create d2c a framework in which large language model llm agents engage in a structured dialectical debate to jointly optimize a robot s design and its reward function
transient thermoreflectance measurements show that thermal conductivity thc of the
aln
aln buffer
transient thermoreflectance measurements show that thermal conductivity thc of the aln buffer increases with the thickness reaching 188 w m
a three-dimensional reconstruction of the
interstellar
interstellar medium
a three-dimensional reconstruction of the interstellar magnetic field toward a star-forming region
our extensive evaluation demonstrates that our approach can localize within 200m more than 68 of queries of a
dataset
real-world datasets
our extensive evaluation demonstrates that our approach can localize within 200m more than 68 of queries of a dataset covering a large part of europe
estimating causal effects of spatially structured exposures is complicated by unmeasured spatial confounders which undermine identifiability in spatial linear regression models unless
structural
causal inference
estimating causal effects of spatially structured exposures is complicated by unmeasured spatial confounders which undermine identifiability in spatial linear regression models unless structural assumptions are imposed
determining the precise geographic location of an
image
local calibration
determining the precise geographic location of an image at a global scale remains an unsolved challenge
ai agents perform near the floor on rli with the highest-performing
agent
llm agents
ai agents perform near the floor on rli with the highest-performing agent achieving an automation rate of 2
this study experimentally tested whether short-term exposure to narrow ai tools enhances core
cognitive
ai agents
this study experimentally tested whether short-term exposure to narrow ai tools enhances core cognitive abilities or simply optimizes task performance
this study bridges the gap between the two approaches by showing that both are based on essentially the same
optimization
learning algorithm
this study bridges the gap between the two approaches by showing that both are based on essentially the same optimization problem
contrary to the static case where the analogy between
phylogenetic
phylogenetic diversity
contrary to the static case where the analogy between phylogenetic trees and the tree that grows in soil is drawn our framework shows that the living tree of life is analogous to a cantor dust where each branch is a distinct fractal curve
these results obtained with a hybrid density
functional
density functional
these results obtained with a hybrid density functional improve on previously published results using local and semi-local functionals which are known to underestimate the band gap
our results provide 2 mathcal o k n mathcal o 1 time and n
mathcal
polynomial time
our results provide 2 mathcal o k n mathcal o 1 time and n mathcal o 1 space algorithms for problems for which the existence of such algorithms was previously unknown
the second-order stark shift exceeds 10 ghz which is of the same order of magnitude as the 15 ghz inhomogeneous distribution of siv - observed in emitters embedded in optical nanostructures such as
photonic
photonic crystal
the second-order stark shift exceeds 10 ghz which is of the same order of magnitude as the 15 ghz inhomogeneous distribution of siv - observed in emitters embedded in optical nanostructures such as photonic crystal nanocavities
to address these shortcomings we propose scout scenario coverage oversight and understanding tool a lightweight surrogate model designed to predict scenario coverage labels directly from an
agent
learning agents
to address these shortcomings we propose scout scenario coverage oversight and understanding tool a lightweight surrogate model designed to predict scenario coverage labels directly from an agent s latent sensor representations
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
nevertheless limitations remain in fully reproducing the rise and decline of the bright quasar population over cosmic time and in matching the black hole
masses
black hole mass
nevertheless limitations remain in fully reproducing the rise and decline of the bright quasar population over cosmic time and in matching the black hole masses inferred from quasar spectra
these insights are then consolidated into a set of
future
findings highlight
these insights are then consolidated into a set of future research directions
contextual inference facilitates the creation learning and reuse of low-rank rnn components as new tasks are introduced sequentially enabling
continual
recurrent neural
contextual inference facilitates the creation learning and reuse of low-rank rnn components as new tasks are introduced sequentially enabling continual learning without catastrophic forgetting
evaluation with a human-aligned gpt-judge and a user study with 108 students shows that 8b-scribe models achieve comparable or
superior
evaluation metrics
evaluation with a human-aligned gpt-judge and a user study with 108 students shows that 8b-scribe models achieve comparable or superior quality to much larger models in key dimensions such as relevance and actionability while being perceived on par with gpt-4o and llama-3
visual prompt tuning vpt of pre-trained vision
transformers
vision transformers
visual prompt tuning vpt of pre-trained vision transformers vits has proven highly effective as a parameter-efficient fine-tuning technique for adapting large models to downstream tasks with limited data
from zonal to nodal capacity expansion planning
spatial
expansion planning
from zonal to nodal capacity expansion planning spatial aggregation impacts on a realistic test-case
the received pilot signals are preprocessed and passed through a cnn-based feature extractor followed by a gpt-2 model that captures temporal dependencies across multiple frames and directly predicts the near-field
beam
near-field beam
the received pilot signals are preprocessed and passed through a cnn-based feature extractor followed by a gpt-2 model that captures temporal dependencies across multiple frames and directly predicts the near-field beam index in an end-to-end manner
we also prove general lower bounds for k 3 every nonadaptive tester requires omega n queries and every adaptive tester requires omega sqrt n queries yielding the first super-logarithmic
lower
lower bound
we also prove general lower bounds for k 3 every nonadaptive tester requires omega n queries and every adaptive tester requires omega sqrt n queries yielding the first super-logarithmic lower bounds for pi -freeness
we empirically demonstrate that these human mobility network localities are rigorous geometric entities that map directly to geographic localities revealing that human
mobility
scale-free networks
we empirically demonstrate that these human mobility network localities are rigorous geometric entities that map directly to geographic localities revealing that human mobility networks lie on manifolds of dimension 5
uplink scma-empowered uncoordinated random
access
uplink communication
uplink scma-empowered uncoordinated random access for future mmtc
however most neural datasets contain heterogeneous populations that mix stable predictable cells with highly stochastic stimulus-contingent ones which has made it hard to identify consistent activity
patterns
brain activity
however most neural datasets contain heterogeneous populations that mix stable predictable cells with highly stochastic stimulus-contingent ones which has made it hard to identify consistent activity patterns during ssl
aisp applies the gaussian perturbation into pre-logits which are outputs of the penultimate layer so as to maximize expected
rewards
reward density
aisp applies the gaussian perturbation into pre-logits which are outputs of the penultimate layer so as to maximize expected rewards with respect to the mean of the perturbation
2024 develops riesz regression for automatic
debiased
debiased machine learning
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
spg-cdenet spatial prior-guided cross dual
encoder
cross dual encoder network
spg-cdenet spatial prior-guided cross dual encoder network for multi-organ segmentation
overcoming this challenge advances key applications such as creating reliable reward models for
reinforcement
reinforcement learning
overcoming this challenge advances key applications such as creating reliable reward models for reinforcement learning from human feedback rlhf and building effective routing systems that select the best-suited model for a given user query
empirical results across several domains show that our
algorithms
computational cost
empirical results across several domains show that our algorithms substantially reduce training costs without sacrificing prediction accuracy demonstrating the practical value of our budget-aware deferral algorithms
here we investigate the connection between
ola
ola vectors
here we investigate the connection between ola vectors and the maximum acyclic agreement forest maaf problem
when multiple distinct prngs are presented together during training the model can jointly learn them identifying structures from
different
generative models
when multiple distinct prngs are presented together during training the model can jointly learn them identifying structures from different permutations
our results suggest a geometric foundation for randomness and open the door to an
equivalence
theoretical guarantees
our results suggest a geometric foundation for randomness and open the door to an equivalence principle for information
benchmarking quantum key distribution by mixing single
photons
single photons
benchmarking quantum key distribution by mixing single photons and laser light
sensitivity analysis for treatment effects in
difference-in-differences
treatment effect
sensitivity analysis for treatment effects in difference-in-differences models using riesz representation
this fully demonstrates the advantages of this
generative
image generation
this fully demonstrates the advantages of this generative image fusion method drawing inspiration from human cognition in enhancing structural consistency and detail quality