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the general data protection regulation gdpr are cross-referential and normative while runtime contexts are expressed in
unstructured
natural language
the general data protection regulation gdpr are cross-referential and normative while runtime contexts are expressed in unstructured natural language
effective hamiltonians are powerful tools for understanding the emergent
phenomena
quantum materials
effective hamiltonians are powerful tools for understanding the emergent phenomena in condensed matter systems
among the tested magnitude gaps the difference between the first- and fourth-ranked galaxies delta m4-1 proves a more robust indicator of dynamical age for
low-mass
dark matter
among the tested magnitude gaps the difference between the first- and fourth-ranked galaxies delta m4-1 proves a more robust indicator of dynamical age for low-mass groups than the conventional delta m2-1 gap
among these methods deep neural networks have been widely adopted due to their
performance
real-world datasets
among these methods deep neural networks have been widely adopted due to their performance and accessibility but they require large high-quality datasets
evolution du principe d exclusion compétitive le rôle
des
weak competition
evolution du principe d exclusion compétitive le rôle des mathématiques
for fluorescence-based readout we selectively couple one of the t
center
spin readout
for fluorescence-based readout we selectively couple one of the t center spin-conserving transitions to a single-mode photonic cavity exploiting the enhancement of the fluorescence emission and cyclicity
2023 offers a nonparametric identification using a
local
nonparametric identification
2023 offers a nonparametric identification using a local gaussian representation lgr that holds for any bivariate joint distributions
given r as an input we present a polynomial-time o n r-1 2 -approximation algorithm for this
maximization
-approximation algorithm
given r as an input we present a polynomial-time o n r-1 2 -approximation algorithm for this maximization problem which does not require prior knowledge of the specific decomposition
at the same time large language models llms are
increasingly
large language models llms
at the same time large language models llms are increasingly used in health coaching yet cgm is rarely incorporated as a first-class signal
using network gradients it is possible to identify regions where the network pays attention during image
recognition
action recognition
using network gradients it is possible to identify regions where the network pays attention during image recognition tasks
the learned representations successfully capture known investment styles such as growth and
value
reward density
the learned representations successfully capture known investment styles such as growth and value while also revealing implicit manager objectives
randomizing the structure of a network is a classic procedure used to estimate the statistical significance of properties of the network such as transitivity
centrality
scale-free networks
randomizing the structure of a network is a classic procedure used to estimate the statistical significance of properties of the network such as transitivity centrality and community structure
two models are considered a fully connected
deep
deep learning
two models are considered a fully connected deep neural network dnn and an long short-term memory lstm -based network
our analysis loosely favours local starburst
activity
star formation
our analysis loosely favours local starburst activity as the driver of the shocks and circumnuclear gas dynamics in ngc 7582 though the possibility of an agn jet contribution cannot be excluded
adam is the de facto optimizer in deep learning yet its
theoretical
deep learning
adam is the de facto optimizer in deep learning yet its theoretical understanding remains limited
optimal transmit field distribution for partially obstructed continuous radiating surfaces in near-field
communication
wireless communication
optimal transmit field distribution for partially obstructed continuous radiating surfaces in near-field communication systems
we therefore ask whether improving representation
learning
representation learning
we therefore ask whether improving representation learning alone can substantially improve world-model performance
our results lay a fundamental understanding of how pre-trained llms manipulate numbers and outline the
potential
llm post-training
our results lay a fundamental understanding of how pre-trained llms manipulate numbers and outline the potential of more accurate probing techniques in addressed refinements of llms architectures
coherent all-optical tuning of large-area
phase-gradient
refractive index
coherent all-optical tuning of large-area phase-gradient metasurface
furthermore we show that concept activations produce
spatial
multimodal reasoning
furthermore we show that concept activations produce spatial attributions that align with semantically meaningful image regions
existing approaches from vision-language-action vla
models
learning agents
existing approaches from vision-language-action vla models to hierarchical frameworks fall short due to their reliance on limited or dividual-agent memory
in the rapidly evolving research on artificial intelligence
ai
artificial intelligence
in the rapidly evolving research on artificial intelligence ai the demand for fast computationally efficient and scalable solutions has increased in recent years
optimal unmanned aerial vehicle deployment for macro-micro
traffic
optimal uav
optimal unmanned aerial vehicle deployment for macro-micro traffic monitoring fused with connected vehicles
here we compare three different ar navigation aids on-screen compass on-screen radar and in-world vertical arrows in a wide-area outdoor user study n 24 where participants search for hidden virtual target items amongst
physical
augmented reality
here we compare three different ar navigation aids on-screen compass on-screen radar and in-world vertical arrows in a wide-area outdoor user study n 24 where participants search for hidden virtual target items amongst physical and virtual objects
large language models llms excel at general tasks but underperform in specialized
domains
large language models llms
large language models llms excel at general tasks but underperform in specialized domains like economics and psychology which require deep principled understanding
while modern large language models llms are increasingly used to model neural responses to
language
large language
while modern large language models llms are increasingly used to model neural responses to language their internal representations are highly entangled mixing information about lexicon syntax meaning and reasoning
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
galactic nuclei
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
formation control simplifies minimizing multi-robot cost functions by encoding a
cost
optimal control
formation control simplifies minimizing multi-robot cost functions by encoding a cost function as a shape the robots maintain
securereviewer enhancing large language models for secure code
review
code review
securereviewer enhancing large language models for secure code review through secure-aware fine-tuning
nearest neighbor matching is equivalent to least squares density ratio
estimation
density-ratio estimation
nearest neighbor matching is equivalent to least squares density ratio estimation and riesz regression
comparison with exact quantum-mechanical results in one- and two-dimensional models demonstrates that it has a reasonably high accuracy similar to that reported for
instanton
instanton theory
comparison with exact quantum-mechanical results in one- and two-dimensional models demonstrates that it has a reasonably high accuracy similar to that reported for instanton theory in the symmetric case
using transient encounter rates to quantify
spatial
spatial structure
using transient encounter rates to quantify spatial patterns of home-range organization
building ai literacy at home how families navigate children s self-directed
learning
ai literacy
building ai literacy at home how families navigate children s self-directed learning with ai
scale invariance and statistical significance in
complex
network structures
scale invariance and statistical significance in complex weighted networks
a common method for reading out the state of a spin qubit is by latching one logical
qubit
qubit readout
a common method for reading out the state of a spin qubit is by latching one logical qubit state either 1 rangle or 0 rangle onto a different metastable charge state
the performance of artificial intelligence ai
systems
artificial intelligence
the performance of artificial intelligence ai systems fundamentally depends on high-quality training data
the proposed approach integrates pre-trained vision transformers and large language models to align visual semantics with natural
language
multimodal reasoning
the proposed approach integrates pre-trained vision transformers and large language models to align visual semantics with natural language descriptions enhancing contextual comprehension
we also show that in mixed graphs even deciding the existence of any
cycle
bipartite graphs
we also show that in mixed graphs even deciding the existence of any cycle factor is np-complete
low-rank adaptation lora has become a popular technique for parameter-efficient fine-tuning of large
language
large language models llms
low-rank adaptation lora has become a popular technique for parameter-efficient fine-tuning of large language models llms
leveraging semiparametric theory we derive efficient influence functions and construct consistent asymptotically normal estimators via
debiased
debiased machine learning
leveraging semiparametric theory we derive efficient influence functions and construct consistent asymptotically normal estimators via debiased machine learning
however problems in rendering can create sensorimotor
disruptions
sensorimotor disruptions
however problems in rendering can create sensorimotor disruptions that undermine presence and task performance
randomizing the structure of a network is a classic procedure used to estimate the statistical significance of properties of the network such as transitivity
centrality
network structures
randomizing the structure of a network is a classic procedure used to estimate the statistical significance of properties of the network such as transitivity centrality and community structure
our work significantly extends the existing results on the
convergence
convergence rate
our work significantly extends the existing results on the convergence of gd and sgd by guaranteeing that they apply to practical neural network settings and has the potential to unlock further exploration of learning dynamics
a single-loop first-order algorithm for linearly constrained
bilevel
bilevel optimization
a single-loop first-order algorithm for linearly constrained bilevel optimization
in the non-adaptive case our lower bounds essentially match the
complexity
time complexity
in the non-adaptive case our lower bounds essentially match the complexity of the algorithm that we provide
trishul technique for reconstructing magnetic
interstellar
interstellar medium
trishul technique for reconstructing magnetic interstellar structure using starlight polarization
this encoder applies posterior probability modeling and information decomposition to extract accurate and concise low-level modal
information
dual encoder
this encoder applies posterior probability modeling and information decomposition to extract accurate and concise low-level modal information thereby supporting the generation of high-fidelity structural details
while large language models llms offer opportunities in document understanding
current
large language models llms
while large language models llms offer opportunities in document understanding current systems struggle with complex multi-page visual documents particularly in fine-grained reasoning over elements and pages
however this work argues that the reasoning behavior of
llms
llm responses
however this work argues that the reasoning behavior of llms in hci is often decontextualized from the underlying mechanics and subjective decisions that condition the emergence and human interpretation of this behavior
this algorithm leverages ell_ infty lewis
weight
-time algorithm
this algorithm leverages ell_ infty lewis weight overestimates and achieves this iteration complexity via a simple lightweight irls approach inspired by the work of
we present a quantum-enhanced protocol for detecting wave-like dark matter using an array of
n
open quantum
we present a quantum-enhanced protocol for detecting wave-like dark matter using an array of n entangled superconducting cavities initialized in an m -photon fock state
this assumption generalizes existing assumptions of differential basis support used for identification of the
causal
causal inference
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
large language models llms excel at general
tasks
models llms
large language models llms excel at general tasks but underperform in specialized domains like economics and psychology which require deep principled understanding
integrating legal and logical specifications in perception prediction and
planning
collision avoidance
integrating legal and logical specifications in perception prediction and planning for automated driving a survey of methods
extracting pulsive temporal patterns from a small dataset without their repetition or singularity
shows
temporal patterns
extracting pulsive temporal patterns from a small dataset without their repetition or singularity shows significant importance in manufacturing applications but does not sufficiently attract scientific attention
over iterations this imbalance becomes increasingly pronounced--a dynamic we term the matthew
effect
superior performance
over iterations this imbalance becomes increasingly pronounced--a dynamic we term the matthew effect --which ultimately hinders further model improvement and leads to performance bottlenecks
the analytical calculation of suitable precodings for perfect
channel
channel estimation
the analytical calculation of suitable precodings for perfect channel information is well studied however their performance can quickly deteriorate when faced with e
to each gonosomic algebra an evolution operator noted w is associated that gives the state of the
offspring
population genetics
to each gonosomic algebra an evolution operator noted w is associated that gives the state of the offspring population at the birth stage
in this paper we present an inverse-free pure quantum state estimation protocol that achieves
heisenberg
super-heisenberg scaling
in this paper we present an inverse-free pure quantum state estimation protocol that achieves heisenberg scaling
several future directions are outlined for further developing the multiplex
limit
limit theory
several future directions are outlined for further developing the multiplex limit theory
these findings confirm that entity aware retrieval improves both efficiency and accuracy in clinical
natural
natural language processing
these findings confirm that entity aware retrieval improves both efficiency and accuracy in clinical natural language processing
we used these disentangled embeddings to model intracranial ecog brain
recordings
electroencephalography eeg
we used these disentangled embeddings to model intracranial ecog brain recordings from neurosurgical patients listening to natural speech
shortcuts spurious rules that perform well during training but fail to generalize present a major challenge to the reliability of
deep
gradient descent
shortcuts spurious rules that perform well during training but fail to generalize present a major challenge to the reliability of deep networks geirhos et al
using the specific stellar angular momentum proxy lambda_r we quantify the balance between ordered
rotation
angular momentum
using the specific stellar angular momentum proxy lambda_r we quantify the balance between ordered rotation and random motions
results show that the curvature-aware calibration maintains consistent force accuracy across all surfaces while flat-surface
calibration
calibration plate
results show that the curvature-aware calibration maintains consistent force accuracy across all surfaces while flat-surface calibration underestimates force as curvature increases
next we introduce a structured categorization of av datasets that moves beyond simple collections positioning ego-vehicle infrastructure-based and cooperative
datasets
dataset comprising
next we introduce a structured categorization of av datasets that moves beyond simple collections positioning ego-vehicle infrastructure-based and cooperative datasets e
enhancing the reachability of variational quantum
algorithms
quantum mechanics
enhancing the reachability of variational quantum algorithms via input-state design
by providing a mathematically rigorous framework for controlling amari-type neural fields this work advances our understanding of
nonlinear
neural codes
by providing a mathematically rigorous framework for controlling amari-type neural fields this work advances our understanding of nonlinear neural population control with potential applications in computational neuroscience psychophysics and neurostimulation
interstellar comet 3i atlas evidence for galactic
cosmic
massive galaxies
interstellar comet 3i atlas evidence for galactic cosmic ray processing
we identify potential trade-offs between collective size and coordination noise for example a
collective
collective action
we identify potential trade-offs between collective size and coordination noise for example a collective that is twice as big but with four times more noise experiencing worse outcomes than the smaller more coordinated one
supervised reinforcement learning from expert
trajectories
reinforcement learning
supervised reinforcement learning from expert trajectories to step-wise reasoning
the introduction of integrated sensing and communications
isac
integrated sensing
the introduction of integrated sensing and communications isac in cellular systems is not expected to result in a shift away from the popular choice of cost- and energy-efficient analog or hybrid beamforming structures
second we study the canonical form of such optimizers which is spectral
gradient
gradient descent
second we study the canonical form of such optimizers which is spectral gradient descent specgd -- each update step is uv t where u sigma v t is the truncated svd of the gradient
a unified theory for causal inference direct
debiased
debiased machine learning
a unified theory for causal inference direct debiased machine learning via bregman-riesz regression
amo-bench large language models still struggle in
high
large language
amo-bench large language models still struggle in high school math competitions
however existing models often struggle to capture subtle differences between molecules and their descriptions as they lack the ability to
learn
language models
however existing models often struggle to capture subtle differences between molecules and their descriptions as they lack the ability to learn fine-grained alignments between molecular substructures and chemical phrases
by presenting efficient numerical samplers which converge with optimal rate in terms of discretizations step we provide a comprehensive comparison of models based on confined specularly reflected kinetic
langevin
langevin dynamics
by presenting efficient numerical samplers which converge with optimal rate in terms of discretizations step we provide a comprehensive comparison of models based on confined specularly reflected kinetic langevin diffusion with models based on reflected diffusion with local time
ai predictive systems are increasingly embedded in
decision
predictive performance
ai predictive systems are increasingly embedded in decision making pipelines shaping high stakes choices once made solely by humans
the new level of understanding of rnns obtained from ssm reduction enables the interpretation of mathematically well-defined and robust structures in
neuronal
artificial neural
the new level of understanding of rnns obtained from ssm reduction enables the interpretation of mathematically well-defined and robust structures in neuronal dynamics leading to novel predictions about the neural computations underlying behavior
finally we validate these theoretical guarantees on several real-world datasets
demonstrating
real datasets
finally we validate these theoretical guarantees on several real-world datasets demonstrating improved personalization of human preferences
beyond regularization we employ model inversion to synthesize data from the
trained
continual learning
beyond regularization we employ model inversion to synthesize data from the trained model enabling replay without storing samples
it is equipped with a commercial-grade adaptive
optics
nonlinear optical
it is equipped with a commercial-grade adaptive optics system for efficient single-mode fibre coupling
to avoid repeated differentiation of states and virtual controls a first-order command filter is introduced and a nonlinear disturbance observer is added to provide
disturbance
external disturbances
to avoid repeated differentiation of states and virtual controls a first-order command filter is introduced and a nonlinear disturbance observer is added to provide disturbance estimates
in this work we utilize proximal causal inference framework for learning optimal dynamic
treatment
causal inference
in this work we utilize proximal causal inference framework for learning optimal dynamic treatment regimes when the unconfoundedness assumption fails
bell inequality violation is the phenomenon where multiple non-communicating parties can exhibit correlations using quantum
resources
entanglement entropy
bell inequality violation is the phenomenon where multiple non-communicating parties can exhibit correlations using quantum resources that are impossible if they can only use classical resources
we present fm agent a novel and general-purpose
multi-agent
llm agents
we present fm agent a novel and general-purpose multi-agent framework that leverages a synergistic combination of llm-based reasoning and large-scale evolutionary search to address complex real-world challenges
instrumental variable methods are fundamental to
causal
causal inference
instrumental variable methods are fundamental to causal inference when treatment assignment is confounded by unobserved variables
when the decomposition v_i f_i _ i 1 m is known an additional
connectivity
submodular maximization
when the decomposition v_i f_i _ i 1 m is known an additional connectivity requirement is introduced to the problem
we address this problem by proposing a grammar of temporal graphics and an associated software implementation ggtime that encodes temporal semantics into a declarative grammar for visualizing
temporal
temporal semantics
we address this problem by proposing a grammar of temporal graphics and an associated software implementation ggtime that encodes temporal semantics into a declarative grammar for visualizing temporal data
we investigate the properties of gas giving rise to
o
vi emission
we investigate the properties of gas giving rise to o vi emission from the cgm that upcoming missions such as the aspera smallsat will be able to map in local galaxies
we show that assignment rules with more than one variable allow the estimation of a more comprehensive set of treatment effects relaxing in a research-driven style the
local
treatment effect boundaries
we show that assignment rules with more than one variable allow the estimation of a more comprehensive set of treatment effects relaxing in a research-driven style the local and sometimes limiting nature of univariate rd designs
stabilizability with bounded feedback for analytic linear
control
linear control
stabilizability with bounded feedback for analytic linear control systems
our results underline the potential of generative
models
quantum walk
our results underline the potential of generative models as a general-purpose methodology for automated quantum circuit design offering a promising path towards more efficient quantum algorithms and accelerating scientific discovery in the quantum domain
the continuous functional perspective unifies
spatial
dynamic spatial
the continuous functional perspective unifies spatial econometrics with mathematical physics providing theoretically grounded methods for boundary detection exposure quantification and policy evaluation across environmental economics banking and healthcare applications
causal machine learning has emerged as a powerful tool for flexibly estimating
causal
causal effects
causal machine learning has emerged as a powerful tool for flexibly estimating causal effects from observational data in both industry and academia
fitting a suite of paris-durham shock models to the rotational h _2 lines as well as rovibrational 1-0 s 1 1-0 s 2 and 2-1 s 1 h _2 emission
lines
emission line
fitting a suite of paris-durham shock models to the rotational h _2 lines as well as rovibrational 1-0 s 1 1-0 s 2 and 2-1 s 1 h _2 emission lines we find that a slow v_s sim 10 km s c-type shock is likely responsible for the elevated temperatures
these quantum-inspired methods are expected to yield faster algorithms with applications ranging from astronomy and earth observation to microscopy and
classical
quantum emitters
these quantum-inspired methods are expected to yield faster algorithms with applications ranging from astronomy and earth observation to microscopy and classical imaging more broadly
continual learning cl aims to incrementally train a model on a sequence of tasks while retaining
performance
reinforcement learning
continual learning cl aims to incrementally train a model on a sequence of tasks while retaining performance on prior ones
in the proposed system the target illumination signal is transmitted on the downlink radar sub-band whereas uplink users transmit on the
uplink
uplink communication
in the proposed system the target illumination signal is transmitted on the downlink radar sub-band whereas uplink users transmit on the uplink communication sub-band
our experiments show that the best forecasting models achieve
classification
time series classification
our experiments show that the best forecasting models achieve classification accuracy that matches or even surpasses that of state-of-the-art models pre-trained specifically for classification