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existing approaches often employ an external optimization loop such as an evolutionary
algorithm
diffusion models
existing approaches often employ an external optimization loop such as an evolutionary algorithm to the diffusion model
to address this issue this paper investigates blockage-resilient near-field beam training based on self-accelerating airy
beam
airy beam
to address this issue this paper investigates blockage-resilient near-field beam training based on self-accelerating airy beam which can propagate along a curved trajectory to circumvent obstacles
it provides smoother rewards based on the similarity between the model s actions and expert
actions
reward density
it provides smoother rewards based on the similarity between the model s actions and expert actions extracted from the sft dataset in a step-wise manner
to tackle this challenge we propose braincognizer a novel brain decoding model inspired by human visual cognition which explores multi-level semantics and
correlations
fmri data
to tackle this challenge we propose braincognizer a novel brain decoding model inspired by human visual cognition which explores multi-level semantics and correlations without fine-tuning of generative models
emotion-coherent reasoning for multimodal
llms
llm reasoning
emotion-coherent reasoning for multimodal llms via emotional rationale verifier
in this paper we systematically evaluate llms reasoning
capabilities
large language models llms
in this paper we systematically evaluate llms reasoning capabilities in the normative domain from both logical and modal perspectives
decentralized bilevel optimization has garnered
significant
bilevel optimization
decentralized bilevel optimization has garnered significant attention due to its critical role in solving large-scale machine learning problems
8 probability that arbitrarily advanced technology could induce vacuum decay if our
vacuum
vacuum decay
8 probability that arbitrarily advanced technology could induce vacuum decay if our vacuum is metastable
the factorization converts the nested bayesian dependency into a chain structure enabling efficient parallel computation at the lower level an invariant extended kalman filter with state augmentation estimates trajectories while a derivative
filter
state estimation
the factorization converts the nested bayesian dependency into a chain structure enabling efficient parallel computation at the lower level an invariant extended kalman filter with state augmentation estimates trajectories while a derivative filter computes analytical gradients in parallel for upper-level gradient upda...
through increased levels of perceived agency and immersive environments my work aims to merge the human elements of live theater with the dynamic potential of
virtual
physical virtual
through increased levels of perceived agency and immersive environments my work aims to merge the human elements of live theater with the dynamic potential of virtual entities and ai agents
this paper deals with the optimal synthesis of aperture fields for radiating near-field
communications
wireless communication
this paper deals with the optimal synthesis of aperture fields for radiating near-field communications in obstructed environments
while there are many available methods to assess machine learning calibration and recalibrate faulty predictions less effort has been spent on developing approaches that continually monitor predictive models for potential loss of
calibration
machine learning
while there are many available methods to assess machine learning calibration and recalibrate faulty predictions less effort has been spent on developing approaches that continually monitor predictive models for potential loss of calibration as time passes
stellar-mass compact objects cos embedded in active
galactic
active galactic
stellar-mass compact objects cos embedded in active galactic nucleus agn discs are commonly assumed to accrete via bondi or bondi-hoyle-lyttleton bhl prescriptions neglecting gas angular momentum
in the present study we introduce and characterize
network
quantum channels
in the present study we introduce and characterize network nonlocality breaking channels
we investigate the connection between accretion signatures and host galaxy properties in the context of how
active
massive galaxies
we investigate the connection between accretion signatures and host galaxy properties in the context of how active dwarf galaxies are identified
considering uncorrelated classically correlated and entangled initial
states
quantum correlations
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
we provide a view of climate networks through a triad of frameworks and associated paradigms a networks of data where both geographical
nodes
correlation network
we provide a view of climate networks through a triad of frameworks and associated paradigms a networks of data where both geographical nodes and their links arcs are determined according to some metrics and or statistical criteria b climate data over networks where the structure of the network for both vertices and ed...
based on this framework we formulate a max-min fairness optimization problem that jointly optimizes power
allocation
resource allocation
based on this framework we formulate a max-min fairness optimization problem that jointly optimizes power allocation message splitting and time slot scheduling to maximize the minimum achievable rate across uds
different from existing motion representations we aim to estimate an hr
motion
point tracking
different from existing motion representations we aim to estimate an hr motion trajectory with high-quality from a single motion-blurred image
our largest instance involves all-to-all connectivity with 2000 two-qubit gates which h2 can produce the target
peaked
peaked circuits
our largest instance involves all-to-all connectivity with 2000 two-qubit gates which h2 can produce the target peaked bitstring directly in under 2 hours
modeling and scheduling of fusion patterns in
autonomous
multi-drone racing
modeling and scheduling of fusion patterns in autonomous driving systems extended version
the observed behavior has origins in the electromagnetic
interference
optical interference
the observed behavior has origins in the electromagnetic interference effects taking place between the two surfaces of the thin crystals
we find that dwarf agn selected by infrared colors are the most distinct
population
star clusters
we find that dwarf agn selected by infrared colors are the most distinct population with the highest star formation rates and lowest stellar masses
our analysis reveals a rich phase diagram with four distinct phases -- paramagnetic baxter langle sigma rangle and antiferromagnetic -- and diverse types of
phase
phase transition
our analysis reveals a rich phase diagram with four distinct phases -- paramagnetic baxter langle sigma rangle and antiferromagnetic -- and diverse types of phase transitions
moreover we show how these two phases can be identified experimentally using inelastic
electron
electron microscopy
moreover we show how these two phases can be identified experimentally using inelastic electron tunneling spectroscopy iets
the diminishing of star formation is accompanied by size differentiating as
quiescent
bulge stars
the diminishing of star formation is accompanied by size differentiating as quiescent galaxies are more compact than star-forming galaxies at fixed stellar mass
a unified theory for causal inference direct debiased
machine
debiased machine learning
a unified theory for causal inference direct debiased machine learning via bregman-riesz regression
a classification model is said to be well
calibrated
predictive performance
a classification model is said to be well calibrated if its predicted probabilities correspond with the rates events actually occur
the system features a counter-propagating beam at the same wavelength as the
quantum
photonic circuits
the system features a counter-propagating beam at the same wavelength as the quantum state which simultaneously actively stabilizes the cavity and after transmission acts as the local oscillator for homodyne detection
in this paper we propose catch a plug-and-play framework for
cross-domain
models vlms
in this paper we propose catch a plug-and-play framework for cross-domain adaptation that improves the generalization of vqa models while requiring minimal changes to their core architecture
this enables mammals to maintain or even accelerate the time to initiate the
adaptive
adaptive immune
this enables mammals to maintain or even accelerate the time to initiate the adaptive immune response as body size increases
ai agents perform near the floor on rli with the highest-performing
agent
language agents
ai agents perform near the floor on rli with the highest-performing agent achieving an automation rate of 2
generative artificial intelligence genai can aid
humans
artificial intelligence
generative artificial intelligence genai can aid humans in a wide range of tasks but its effectiveness critically depends on users being able to evaluate the accuracy of genai outputs and their own expertise
this is a concise pedagogical introduction to the dynamic field of
open
quantum walk
this is a concise pedagogical introduction to the dynamic field of open quantum systems governed by markovian master equations
this note introduces a unified theory for causal inference that integrates
riesz
riesz regression
this note introduces a unified theory for causal inference that integrates riesz regression covariate balancing density-ratio estimation dre targeted maximum likelihood estimation tmle and the matching estimator in average treatment effect ate estimation
the proposed optimization problem is a highly coupled
non-convex
optimization problem
the proposed optimization problem is a highly coupled non-convex mixed-integer problem
we present the first such improvement a k -mismatch index with o n log k-1 n
space
compressed indexing
we present the first such improvement a k -mismatch index with o n log k-1 n space and the same query time as k -errata trees
we study a coordinated multi-point comp transmission where two base stations bss each supported by a pinching antenna system pass are deployed to jointly serve
communication
wireless communication
we study a coordinated multi-point comp transmission where two base stations bss each supported by a pinching antenna system pass are deployed to jointly serve communication users under spatial division multiple access sdma technology
our results indicate that although llms generally adhere to valid reasoning patterns they exhibit notable inconsistencies in specific types of normative
reasoning
reasoning capabilities
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
a unified theory for causal inference direct debiased machine learning via
bregman-riesz
riesz regression
a unified theory for causal inference direct debiased machine learning via bregman-riesz regression
thus the formulation and algorithms provide a multi-layer approach that spans target
tracking
multi-object tracking
thus the formulation and algorithms provide a multi-layer approach that spans target tracking bayesian signal processing and the gtnn for group intent inference
this leads to an imbalanced optimization that drives the model to prioritize simple reasoning skills while hindering its ability to tackle more complex
reasoning
reasoning curriculum
this leads to an imbalanced optimization that drives the model to prioritize simple reasoning skills while hindering its ability to tackle more complex reasoning tasks
for sources with more than 50 counts we obtain their x-ray
spectral
spectral energy distribution
for sources with more than 50 counts we obtain their x-ray spectral properties
we introduce human ai collaborative uncertainty
quantification
uncertainty quantification
we introduce human ai collaborative uncertainty quantification a framework that formalizes how an ai model can refine a human expert s proposed prediction set with two goals avoiding counterfactual harm ensuring the ai does not degrade correct human judgments and complementarity enabling recovery of correct outcomes th...
in the current study using the human connectome
project
functional connectivity
in the current study using the human connectome project hcp dataset we examined the relationship between brain activation and working memory performance in two conditions i
we explain the emergence of higher-order dtc
phases
-dtc phases
we explain the emergence of higher-order dtc phases through classical phase portraits of the system connected with spin coherent states scss
explicitly our model highlights an efficiency threshold for unconditional advantage of single photons over laser along with insights on the interplay between
single-photon
single photons
explicitly our model highlights an efficiency threshold for unconditional advantage of single photons over laser along with insights on the interplay between single-photon purity and collected brightness in the performances of bb84
such misconceptions may lead to over-reliance unproductive practices or insufficient
quality
llm responses
such misconceptions may lead to over-reliance unproductive practices or insufficient quality control in llm-assisted programming
by contrast m 3 and m 4 display explosive percolation that still corresponds to a continuous
phase
phase transition
by contrast m 3 and m 4 display explosive percolation that still corresponds to a continuous phase transition with m 4 producing a significantly sharper and clearer order-disorder transition
these results suggest that llms can serve as
practical
models llms
these results suggest that llms can serve as practical proxies for otherwise unrevealed human thinking traces enabling label-only corpora to be extended into thinking-trace-augmented resources that enhance the reliability of llm raters
our results underline the potential of generative
models
quantum channels
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
we demonstrate this digital approach over a
free-space
optical communication
we demonstrate this digital approach over a free-space optical delay line of 1 m and over an 40 km fiber link
third we propose a practical method for enhancing local calibration in neural networks which enforces alignment between predicted probabilities and
local
local calibration
third we propose a practical method for enhancing local calibration in neural networks which enforces alignment between predicted probabilities and local estimates of class frequencies using the jensen-shannon distance
a single graphical user interface gui controls all the rovers providing a simple overview of the
robotic
robotic systems
a single graphical user interface gui controls all the rovers providing a simple overview of the robotic mission
in the limited cases where ground truth is available through exact classical simulation we find that it agrees with the results we obtain from the
quantum
quantum technologies
in the limited cases where ground truth is available through exact classical simulation we find that it agrees with the results we obtain from the quantum device
trajectory planning for mobile robots in cluttered
environments
mobile robots
trajectory planning for mobile robots in cluttered environments remains a major challenge due to narrow passages where conventional methods often fail or generate suboptimal paths
the considered system consists of a multi-antenna access point ap
multiple
wireless networks
the considered system consists of a multi-antenna access point ap multiple heterogeneous user devices uds and an deployed irs to enhance both uplink and downlink transmission
generative artificial intelligence genai can aid
humans
ai systems
generative artificial intelligence genai can aid humans in a wide range of tasks but its effectiveness critically depends on users being able to evaluate the accuracy of genai outputs and their own expertise
in this paper we give a streaming algorithm that achieves a 1 varepsilon -approximation using frac rn varepsilon 2 log 2 n log r cdot text poly log log m bits of space matching the sample complexity of the best known offline algorithm up to text poly log log
m
online algorithm
in this paper we give a streaming algorithm that achieves a 1 varepsilon -approximation using frac rn varepsilon 2 log 2 n log r cdot text poly log log m bits of space matching the sample complexity of the best known offline algorithm up to text poly log log m factors
link-kg llm-driven coreference-resolved knowledge
graphs
coreference resolution
link-kg llm-driven coreference-resolved knowledge graphs for human smuggling networks
in such cases we observe that the performance of gc deteriorates significantly while existing robust
graph
graph neural
in such cases we observe that the performance of gc deteriorates significantly while existing robust graph learning technologies offer only limited effectiveness
such distributions of emission rates enable us to
compute
spontaneous emission
such distributions of emission rates enable us to compute and directly interpret the time-resolved decay as observed in experiments
to evaluate the system-level performance of nonlinear sis we present a case study where sis structures are optimized to minimize the symbol error rate ser in an mimo system with
sis
nonlinear sis
to evaluate the system-level performance of nonlinear sis we present a case study where sis structures are optimized to minimize the symbol error rate ser in an mimo system with sis deployed at both the transmitter and receiver sides using only statistical channel information
we suggest that interactions between parallel subnetworks in the brain may underlie such learning we present a model of representation learning by ensembles of neural networks where each network learns to encode stimuli into an
abstract
neural representations
we suggest that interactions between parallel subnetworks in the brain may underlie such learning we present a model of representation learning by ensembles of neural networks where each network learns to encode stimuli into an abstract representation space by cross-supervising interactions with other networks for inpu...
currently machine learning methods are developing rapidly in
polymer
machine learning
currently machine learning methods are developing rapidly in polymer material research although they have significantly accelerated material prediction and design their complexity has also caused difficulties in understanding and application for researchers in traditional fields
these solutions are then employed to evaluate the optical absorption coefficients and
refractive
refractive index
these solutions are then employed to evaluate the optical absorption coefficients and refractive index changes including both linear and third-order nonlinear contributions
together these results provide an explanation for the widely observed
abstract
neural networks
together these results provide an explanation for the widely observed abstract representations in both the brain and artificial neural networks as well as a mathematically tractable toolkit for understanding the emergence of different kinds of representations in task-optimized feature-learning network models
we develop a direct debiased machine learning framework comprising neyman targeted estimation and generalized
riesz
debiased machine learning
we develop a direct debiased machine learning framework comprising neyman targeted estimation and generalized riesz regression
we make use of this algorithmic framework to analyse memory effects in disease
evolution
evolutionary dynamics
we make use of this algorithmic framework to analyse memory effects in disease evolution in a population
quantization is the key method for reducing inference latency power and memory footprint of
generative
generative models
quantization is the key method for reducing inference latency power and memory footprint of generative ai models
through the judge s eyes inferred thinking
traces
retrospective confidence
through the judge s eyes inferred thinking traces improve reliability of llm raters
sequential activation of place-tuned neurons in an animal during navigation is typically interpreted as reflecting the
sequence
recurrent neural
sequential activation of place-tuned neurons in an animal during navigation is typically interpreted as reflecting the sequence of input from adjacent positions along the trajectory
currently hierarchically deep neural networks dnns have played a significant role as tools for mining the core features of
complex
neural networks
currently hierarchically deep neural networks dnns have played a significant role as tools for mining the core features of complex data
the system parses user intent into structured goals monitors execution via automated verification and exposes inter-agent dependencies through an
interactive
human-ai interaction
the system parses user intent into structured goals monitors execution via automated verification and exposes inter-agent dependencies through an interactive planning panel
order-unity star formation efficiencies sfe in early galaxies may explain the overabundance of bright galaxies observed by
jwst
massive galaxies
order-unity star formation efficiencies sfe in early galaxies may explain the overabundance of bright galaxies observed by jwst at high redshift
the prior network generates coarse localization maps that delineate the approximate roi serving as spatial guidance for the dual
encoder
deep network
the prior network generates coarse localization maps that delineate the approximate roi serving as spatial guidance for the dual encoder network
this study considers the estimation of the
average
average treatment
this study considers the estimation of the average treatment effect ate
the considered system consists of a multi-antenna access point ap multiple heterogeneous user devices uds and an deployed irs to enhance both
uplink
uplink communication
the considered system consists of a multi-antenna access point ap multiple heterogeneous user devices uds and an deployed irs to enhance both uplink and downlink transmission
while pruning and low-rank approximation have each
demonstrated
models llms
while pruning and low-rank approximation have each demonstrated promising performance individually their synergy for llms remains underexplored
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
fmri data
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
our work identifies how enhanced coulomb interactions in two dimensions can stabilize this phase at significantly higher temperatures proposing promising material candidates for observing these
collective
phase transitions
our work identifies how enhanced coulomb interactions in two dimensions can stabilize this phase at significantly higher temperatures proposing promising material candidates for observing these collective states
we formulate the safety filter design as a convex linear matrix inequality lmi optimization problem that simultaneously computes a robust controlled invariant rci
ellipsoidal
safety filter
we formulate the safety filter design as a convex linear matrix inequality lmi optimization problem that simultaneously computes a robust controlled invariant rci ellipsoidal set and its associated state-feedback control law
yet when these vlms are adapted to the action modality it remains unclear to what extent their original
vl
vla models
yet when these vlms are adapted to the action modality it remains unclear to what extent their original vl representations and knowledge are preserved
a gaussian decomposition of the molecular gas data reveals complex
oh
molecular gas
a gaussian decomposition of the molecular gas data reveals complex oh emission and absorption across our targets
human feedback is critical for aligning ai
systems
generative ai
human feedback is critical for aligning ai systems to human values
evolutionary systems must learn to generalize often extrapolating from a limited set of selective conditions to anticipate future
environmental
evolutionary game
evolutionary systems must learn to generalize often extrapolating from a limited set of selective conditions to anticipate future environmental changes
this way prior domain knowledge can be incorporated into the learning process and the learned driving
behaviour
prior knowledge
this way prior domain knowledge can be incorporated into the learning process and the learned driving behaviour can be constrained more easily
efficient spectral efficiency maximization design for irs-aided
mimo
spectral efficiency
efficient spectral efficiency maximization design for irs-aided mimo systems
we highlight weaknesses of the current ai and especially of its llm-based core show that root causes of
llms
models llms
we highlight weaknesses of the current ai and especially of its llm-based core show that root causes of llms weaknesses are unfixable by the current technologies and propose directions in the constructivist paradigm for the changes in education that ensure long-term advantages of humans over ai tools
we provide the first multi-wavelength polarisation decomposed characterisation of the principal modes of a
photonic
photonic crystal
we provide the first multi-wavelength polarisation decomposed characterisation of the principal modes of a photonic lantern
we develop a direct debiased machine learning framework comprising neyman targeted
estimation
machine learning
we develop a direct debiased machine learning framework comprising neyman targeted estimation and generalized riesz regression
our study addresses the challenges inherent in calculating mixed fermion-boson systems and explores the potential of quantum
computing
quantum algorithm
our study addresses the challenges inherent in calculating mixed fermion-boson systems and explores the potential of quantum computing to advance their analysis
yet the question of its point convergence had
remained
linear convergence
yet the question of its point convergence had remained open
in each round a design agent proposes targeted morphological modifications and a control
agent
learning agents
in each round a design agent proposes targeted morphological modifications and a control agent devises a reward function tailored to exploit the new design
we revisit a recent algorithm in the literature and show that it does not have a competitive
ratio
competitive ratio
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
we identify potential trade-offs between collective
size
group size
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
robust graph condensation via classification
complexity
graph neural
robust graph condensation via classification complexity mitigation
debiased machine learning typically requires estimation of the
riesz
riesz representer
debiased machine learning typically requires estimation of the riesz representer and the regression function
we show that for every learning algorithm there exists an auxiliary
algorithm
machine learning
we show that for every learning algorithm there exists an auxiliary algorithm that does not memorize and which yields comparable generalization error for any data distribution
simulation studies further demonstrate the accuracy and practical value of the
proposed
simulation studies
simulation studies further demonstrate the accuracy and practical value of the proposed approach