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we introduce a general diploid population model with self-fertilization and possible overlapping generations and study the genealogy of a sample of n genes as the population size
n
reproduction number
we introduce a general diploid population model with self-fertilization and possible overlapping generations and study the genealogy of a sample of n genes as the population size n tends to infinity
artificial intelligence systems based on large language models
llms
models llms
artificial intelligence systems based on large language models llms can now generate coherent text music and images yet they operate without a persistent state each inference reconstructs context from scratch
we also review and discuss different methods for m_
bullet
black hole mass
we also review and discuss different methods for m_ bullet inference in tdes and find that approaches based on physical models of the early-time uv optical emission are not able to recover at a statistically significant level black hole-host galaxy scalings
our aim is to investigate different types of inner speech and push decoding performance by
collecting
large language models
our aim is to investigate different types of inner speech and push decoding performance by collecting a high number of trials and sessions from a few participants
limiting spectral distribution of high-dimensional
multivariate
spectral density matrices
limiting spectral distribution of high-dimensional multivariate kendall- τ
we quantify the dependence of magnetic fields on star formation
activity
star clusters
we quantify the dependence of magnetic fields on star formation activity including both regular and starburst galaxies
inspired by this principle we introduce a hierarchical multimodal
recurrent
recurrent neural networks
inspired by this principle we introduce a hierarchical multimodal recurrent neural network grounded in predictive processing under the free-energy principle capable of directly integrating over 30 000-dimensional visuo-proprioceptive inputs without dimensionality reduction
large language models llms and multi-agent systems mas
offer
language models
large language models llms and multi-agent systems mas offer opportunities to augment dispatchers
our work demonstrates that inputdsa is a robust and efficient method for comparing intrinsic dynamics and the effect of external input on
dynamical
dynamical systems
our work demonstrates that inputdsa is a robust and efficient method for comparing intrinsic dynamics and the effect of external input on dynamical systems
using the next generation matrix method we derive an analytical expression for the basic
reproduction
basic reproduction
using the next generation matrix method we derive an analytical expression for the basic reproduction number mathcal r_0
viral population dynamics at the cellular level considering the
replication
viral replication
viral population dynamics at the cellular level considering the replication cycle
the derived bases are fitted onto subsequences that are extracted with a sliding window in order to quantify how long
patterns
temporal patterns
the derived bases are fitted onto subsequences that are extracted with a sliding window in order to quantify how long patterns are dominant in the set of subsequences
we establish the convergence of the sequence generated by tkma to a fixed
point
linear convergence
we establish the convergence of the sequence generated by tkma to a fixed point of t
our results show substantial accuracy gains with fine-tuned models
compared
open-source models
our results show substantial accuracy gains with fine-tuned models compared to vanilla baselines
model-free robust beamforming in satellite downlink using
reinforcement
deep reinforcement learning
model-free robust beamforming in satellite downlink using reinforcement learning
when your ai agent succumbs to peer-pressure studying opinion-change
dynamics
opinion dynamics
when your ai agent succumbs to peer-pressure studying opinion-change dynamics of llms
to make inverse optimal issf controllers robust to gain variation we propose a
gain
optimal control
to make inverse optimal issf controllers robust to gain variation we propose a gain margin improvement approach at the expense of an increased control effort
we show that this not only dramatically improves the visual quality of the generated
images
image generation
we show that this not only dramatically improves the visual quality of the generated images but it also significantly speeds up the training
efficiency without cognitive change evidence from human
interaction
ai agents
efficiency without cognitive change evidence from human interaction with narrow ai systems
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
language models
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
in order to facilitate the future-proof application-dependent comparison of
mitigation
mitigation methods
in order to facilitate the future-proof application-dependent comparison of mitigation methods we develop a set of quantitative metrics that account for continual improvements in logical gate quality
we consider two waveguides one dedicated to transmission and one to reception and both of them are connected to a
base
base station
we consider two waveguides one dedicated to transmission and one to reception and both of them are connected to a base station bs
to improve the efficiency of clinical screening and enable the early detection of dr a variety of automated dr diagnosis systems have been recently established based on convolutional neural
network
deep network
to improve the efficiency of clinical screening and enable the early detection of dr a variety of automated dr diagnosis systems have been recently established based on convolutional neural network cnn or vision transformer vit
unveiling intrinsic text bias in multimodal large
language
large language models llms
unveiling intrinsic text bias in multimodal large language models through attention key-space analysis
these findings suggest that resting-state eeg
connectivity
functional connectivity
these findings suggest that resting-state eeg connectivity patterns can index stable cognitive traits such as creativity
we employ the plane-wave expansion method to compute distributions of
emission
spontaneous emission
we employ the plane-wave expansion method to compute distributions of emission rates that are relevant to many optical experiments where quantum emitters are distributed within a crystal
existing eeg foundation models struggle to generalize across these variations often restricting
pretraining
electroencephalography eeg
existing eeg foundation models struggle to generalize across these variations often restricting pretraining to a single setup resulting in suboptimal performance in particular under linear probing
in this work we propose a strategy that exploits the principles of non-hermitian physics--specifically the concept of exceptional points eps --to transcend these limitations and pave the way for the next
generation
nonlinear optical
in this work we propose a strategy that exploits the principles of non-hermitian physics--specifically the concept of exceptional points eps --to transcend these limitations and pave the way for the next generation of versatile high-performance photonic devices
in paper ii we will present the formalism for populating the galaxy cgm structures with multiphase photoionized and collisionally
ionized
ionized gas
in paper ii we will present the formalism for populating the galaxy cgm structures with multiphase photoionized and collisionally ionized gas and for generating absorption profiles for ions of interest
beyond mmlu acer enhances performance on knowledge-intensive
benchmarks
reasoning capabilities
beyond mmlu acer enhances performance on knowledge-intensive benchmarks like arc and gpqa by over 2 absolute points while maintaining stable performance on general reasoning tasks
these findings highlight the potential of shared control strategies to balance
stability
predictive control
these findings highlight the potential of shared control strategies to balance stability efficiency and driver acceptance
we extend an experimentally-feasible object detection and range finding
quantum
quantum technologies
we extend an experimentally-feasible object detection and range finding quantum illumination-based protocol to include spoofing resilience
non-uniformity would have significant implications for exoplanet occurrence
rate
stellar population
non-uniformity would have significant implications for exoplanet occurrence rate calculations so future work should explore the longevity of these biases driven by the star-cloud connection
tree ensembles have demonstrated state-of-the-art
predictive
predictive performance
tree ensembles have demonstrated state-of-the-art predictive performance across a wide range of problems involving tabular data
this problem is inherently non-convex due to the strong coupling among its decision parameters making it challenging to solve using traditional
optimization
optimization problem
this problem is inherently non-convex due to the strong coupling among its decision parameters making it challenging to solve using traditional optimization methods
lagmemo language 3d gaussian splatting memory for multi-modal open-vocabulary
multi-goal
multi-modal open-vocabulary
lagmemo language 3d gaussian splatting memory for multi-modal open-vocabulary multi-goal visual navigation
we study the statistical properties of nonparametric distance-based isotropic local polynomial regression estimators of the boundary average treatment effect curve a key causal functional parameter capturing heterogeneous treatment effects in
boundary
effect boundaries
we study the statistical properties of nonparametric distance-based isotropic local polynomial regression estimators of the boundary average treatment effect curve a key causal functional parameter capturing heterogeneous treatment effects in boundary discontinuity designs
the sga method outperforms the gradient method by including
second-order
first-order methods
the sga method outperforms the gradient method by including second-order mixed derivatives computed at each iterate which requires considerably larger computational effort
we suggest that their low black hole masses are unlikely to be due to their small angles of inclination to the
line
black hole mass
we suggest that their low black hole masses are unlikely to be due to their small angles of inclination to the line of sight
we statistically validate links in each cohort
comorbidity
comorbidity networks
we statistically validate links in each cohort comorbidity network and furthermore partition the networks into communities of diseases
we also establish that under a suitable sample-based detectability condition known as sample-based incremental input output-to-state stability i-ioss the proposed
sample-based
state estimation
we also establish that under a suitable sample-based detectability condition known as sample-based incremental input output-to-state stability i-ioss the proposed sample-based mhe achieves robust global exponential stability rges
in this work we focus on applying these methods to fundamental problems in image processing and classical optics such as wave-front propagation and optical image formation by using directly or indirectly parallels with
quantum
quantum technologies
in this work we focus on applying these methods to fundamental problems in image processing and classical optics such as wave-front propagation and optical image formation by using directly or indirectly parallels with quantum mechanics and computation
in robotics likelihood-free inference lfi can provide the domain distribution that adapts a learnt
agent
policy learning
in robotics likelihood-free inference lfi can provide the domain distribution that adapts a learnt agent in a parametric set of deployment conditions
in this work we propose a general hierarchical motion modeling method that learns structured interpretable
motion
point tracking
in this work we propose a general hierarchical motion modeling method that learns structured interpretable motion relationships directly from data
the result is a suite of tuning-free sampling algorithms including
tuning-free
debiased machine learning
the result is a suite of tuning-free sampling algorithms including tuning-free variants of the unadjusted langevin algorithm ula stochastic gradient langevin dynamics sgld mean-field langevin dynamics mfld stein variational gradient descent svgd and variational gradient descent vgd
finally we outline several potential applications of nonlinear
sis
nonlinear sis
finally we outline several potential applications of nonlinear sis in wireless communication scenarios
we propose a weaker operational form of aoe that still leads to inequalities that quantum
mechanics
quantum mechanics
we propose a weaker operational form of aoe that still leads to inequalities that quantum mechanics violates
a graph neural network-based model is designed to generate projections tailored to each qp instance enabling us to produce high-quality
solutions
neural network
a graph neural network-based model is designed to generate projections tailored to each qp instance enabling us to produce high-quality solutions even for previously unseen problems
these include the feeling of spatial extendedness temporal flow of objects
binding
receptive fields
these include the feeling of spatial extendedness temporal flow of objects binding general concepts with particular configurations of features and of qualia such as colors and sounds
to this end inas qds are integrated in a hybrid photon-phonon patterned microcavity where the density of optical
states
quantum emitters
to this end inas qds are integrated in a hybrid photon-phonon patterned microcavity where the density of optical states is tailored by the lateral confinement of photons in um-sized traps defined lithographically in the microcavity spacer
as ai capabilities improve and ai is used to tackle more challenging
tasks
ai agents
as ai capabilities improve and ai is used to tackle more challenging tasks verifying quality and safety becomes increasingly challenging
the online method adapts to distribution shifts including human behavior evolving through interaction with ai a phenomenon we call human to
ai
distribution shift
the online method adapts to distribution shifts including human behavior evolving through interaction with ai a phenomenon we call human to ai adaptation
our results reinforce that quantum illumination is advantageous for
spoofing
spoofing resilience
our results reinforce that quantum illumination is advantageous for spoofing resilience compared to a classical illumination-based protocol
a tidal disruption event tde occurs when a star passes within the tidal radius of a
supermassive
black hole mass
a tidal disruption event tde occurs when a star passes within the tidal radius of a supermassive black hole smbh
our findings reveal that despite being designed to enhance efficiency cognitive strategies can reduce the abundance of the
species
ecological interactions
our findings reveal that despite being designed to enhance efficiency cognitive strategies can reduce the abundance of the species due to the constraints of cyclic dominance
to address this challenge this paper presents a forecast-integrated optimal
power
optimal power flow
to address this challenge this paper presents a forecast-integrated optimal power flow opf -based adaptive control framework
experiments show improved success rate and sample efficiency over single-algorithm baselines dqn or td3 alone and rule-based planners with better generalization to unseen
obstacle
obstacle avoidance
experiments show improved success rate and sample efficiency over single-algorithm baselines dqn or td3 alone and rule-based planners with better generalization to unseen obstacle configurations and reduced abrupt control changes
our results establish spatially engineered atomic
ensembles
single photons
our results establish spatially engineered atomic ensembles as a pathway to selective photon transfer between modes and precise control of many-body complexity
to address these issues this study introduces the
human-ai
ai literacy
to address these issues this study introduces the human-ai re synergy model hare-sm a conceptual framework that integrates ai-driven analysis with human oversight to improve requirements elicitation analysis and validation
this energy is believed to impact the star formation
activity
massive stars
this energy is believed to impact the star formation activity and contribute to the quenching of galaxies
moreover adapting existing code review approaches to target security
issues
code review
moreover adapting existing code review approaches to target security issues faces substantial challenges including data scarcity and inadequate evaluation metrics
inference-cost-aware dynamic tree construction for efficient inference in large
language
language models
inference-cost-aware dynamic tree construction for efficient inference in large language models
to address these limitations we propose a symbolic
regression
machine learning
to address these limitations we propose a symbolic regression sr -based ml framework
these results suggest lossy compression supports
mnemonic
working memory
these results suggest lossy compression supports mnemonic discrimination by discarding redundant and overlapping information
in these models the retrieval of a particular memory can be hampered by overlaps between the network state and other memories termed spurious overlaps since these overlaps collectively introduce noise in the
retrieval
working memory
in these models the retrieval of a particular memory can be hampered by overlaps between the network state and other memories termed spurious overlaps since these overlaps collectively introduce noise in the retrieval process
using network gradients it is possible to identify regions where the
network
convolutional neural
using network gradients it is possible to identify regions where the network pays attention during image recognition tasks
machine translation mt is widely employed to address resource scarcity in low-resource
languages
multilingual data
machine translation mt is widely employed to address resource scarcity in low-resource languages by generating synthetic data from high-resource counterparts
spade sparsity adaptive depth estimator for zero-shot real-time monocular depth
estimation
depth estimation
spade sparsity adaptive depth estimator for zero-shot real-time monocular depth estimation in underwater environments
we present the first such improvement a k -mismatch index with o n log k-1 n space and the same
query
query complexity
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 find that most of the stars in our bulges are formed in-situ but 33 of our
bulges
massive stars
we find that most of the stars in our bulges are formed in-situ but 33 of our bulges show a non-negligible contribution of stellar accretion from satellites which could add to about 35 of the population
this paper considers the textit zarankiewicz problem in
graphs
regular graphs
this paper considers the textit zarankiewicz problem in graphs with low-dimensional geometric representation i
the global system is described as the collection of landscapes of coexisting and interacting
collective
dynamical systems
the global system is described as the collection of landscapes of coexisting and interacting collective states each characterized both by continuous activity frequency and discrete class variables
large language models llms are increasingly shaping creative work and
problem-solving
large language
large language models llms are increasingly shaping creative work and problem-solving however prior research suggests that they may diminish unassisted creativity
this motivates the need for multimodal evaluation frameworks that extend beyond text-based
metrics
evaluation metrics
this motivates the need for multimodal evaluation frameworks that extend beyond text-based metrics to enable a more holistic assessment of slt outputs
with the help of low-loss distribution capability of optical fibers the system can be scaled into a distributed non-contact multiple vital signs monitoring
platform
optical communication
with the help of low-loss distribution capability of optical fibers the system can be scaled into a distributed non-contact multiple vital signs monitoring platform highlighting its potential for clinical and healthcare applications
here we implement an efficient quantum simulation
algorithm
quantum error correction
here we implement an efficient quantum simulation algorithm on quantinuum s system model h2 trapped-ion quantum computer for the time dynamics of a 56-qubit system that is too complex for exact classical simulation
these findings highlight the fine-grained discrepancies among
vlms
vision-language models vlms
these findings highlight the fine-grained discrepancies among vlms in chart understanding tasks and point to specific skills that need to be strengthened in current models
training a nn as a surrogate optimization solver amounts to estimating a global solution function that maps varying
problem
gradient descent
training a nn as a surrogate optimization solver amounts to estimating a global solution function that maps varying problem input parameters to the corresponding optimal solutions
star quasiconvexity an unified approach for linear
convergence
zeroth-order methods
star quasiconvexity an unified approach for linear convergence of first-order methods beyond convexity
violations of conditional exchangeability substantially limit the validity of ite estimates from
causal
causal effect
violations of conditional exchangeability substantially limit the validity of ite estimates from causal ml models in routinely collected observational data
we demonstrate that the treatment effects of interest can be consistently
estimated
treatment effect
we demonstrate that the treatment effects of interest can be consistently estimated using ordinary least squares with an appropriately specified working model and transformed regressors
large language models llms and multi-agent systems mas
offer
large language
large language models llms and multi-agent systems mas offer opportunities to augment dispatchers
large language models llms show strong potential to support creative
tasks
models llms
large language models llms show strong potential to support creative tasks but the role of the interface design is poorly understood
for thin films of pt our calculations show that the relative contributions of the orbital edelstein and surface pockels effects are comparable and that they have different effects on kerr rotation of s and
p
optical properties
for thin films of pt our calculations show that the relative contributions of the orbital edelstein and surface pockels effects are comparable and that they have different effects on kerr rotation of s and p polarized light theta_k s and theta_k p
we propose a data-driven framework for efficiently solving quadratic
programming
quadratic programming
we propose a data-driven framework for efficiently solving quadratic programming qp problems by reducing the number of variables in high-dimensional qps using instance-specific projection
land use change perceptions included deforestation coastal degradation
habitat
climate change
land use change perceptions included deforestation coastal degradation habitat protection renewable energy facilities wetlands and others
here we propose a machine learning ml approach for image repair and
super-resolution
image reconstruction
here we propose a machine learning ml approach for image repair and super-resolution to alleviate both challenges
interstellar comet 3i atlas evidence for galactic
cosmic
galactic nuclei
interstellar comet 3i atlas evidence for galactic cosmic ray processing
these robots exhibit intelligent behavior that emerges from their structural dynamics and the
physical
robotic systems
these robots exhibit intelligent behavior that emerges from their structural dynamics and the physical interaction between their components and with the environment
as ai capabilities improve and ai is used to tackle more challenging tasks verifying quality and
safety
artificial intelligence
as ai capabilities improve and ai is used to tackle more challenging tasks verifying quality and safety becomes increasingly challenging
egoemotion egocentric vision and physiological
signals
physiological signals
egoemotion egocentric vision and physiological signals for emotion and personality recognition in real-world tasks
reinforcement learning from human feedback rlhf has emerged as a key technique for post-training large
language
large language
reinforcement learning from human feedback rlhf has emerged as a key technique for post-training large language models
our results constitute a substantial development on existing corrections to mean-field theory for infectious individuals in
sis
viral replication
our results constitute a substantial development on existing corrections to mean-field theory for infectious individuals in sis processes and provide an in-depth characterization of how structural randomness in networks affects the dynamical trajectories of infectious diseases on networks
identifying and addressing security issues during the early phase of the development lifecycle is critical for mitigating the long-term negative impacts on
software
software engineering
identifying and addressing security issues during the early phase of the development lifecycle is critical for mitigating the long-term negative impacts on software systems
furthermore localized bessel-type modes at aa stacking regions can be excited nonlocally across the moire superlattice enabling
vortex
vortex phase
furthermore localized bessel-type modes at aa stacking regions can be excited nonlocally across the moire superlattice enabling vortex array generation
our results include that finding a solution is fixed-parameter tractable with respect to the vertex cover number of the food web assuming the
phylogenetic
phylogenetic tree
our results include that finding a solution is fixed-parameter tractable with respect to the vertex cover number of the food web assuming the phylogenetic tree is a star
the architecture incorporates a brain-region to image-feature cross-attention mechanism enabling nonlinear mappings between high-dimensional deep network features and semantic
patterns
brain-computer interface
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
to improve the quality of long-term light
curves
light curves
to improve the quality of long-term light curves for agns we bin and stack multi-epoch images balancing the image depths and temporal resolution
we hypothesize this gap stems from missing core gui knowledge which existing training schemes such as supervised fine tuning and
reinforcement
reinforcement learning
we hypothesize this gap stems from missing core gui knowledge which existing training schemes such as supervised fine tuning and reinforcement learning alone cannot fully address
validated in high-fidelity simulation with realistic quadrotor dynamics the resulting policies significantly outperform both a standard
reinforcement
control strategy
validated in high-fidelity simulation with realistic quadrotor dynamics the resulting policies significantly outperform both a standard reinforcement learning baseline and a state-of-the-art game-theoretic planner