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the modelling is based on a susceptible-infected-recovered sir - model and on a susceptible-exposed-infected-recovered seir - model through a kernel that dampens the activity based on the recent history of
infectious
infectious individuals
the modelling is based on a susceptible-infected-recovered sir - model and on a susceptible-exposed-infected-recovered seir - model through a kernel that dampens the activity based on the recent history of infectious individuals
a refiner agent synthesizes the search history which effectively compresses the researcher s perceived trajectory thereby reducing exploration cost and increasing the overall
reward
reward density
a refiner agent synthesizes the search history which effectively compresses the researcher s perceived trajectory thereby reducing exploration cost and increasing the overall reward density
this paper proposes a new method for estimating conditional
average
average treatment
this paper proposes a new method for estimating conditional average treatment effects cate in randomized experiments
while modern large language models llms are increasingly used to model neural responses to
language
encoding models
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
we introduce multicolleagues a multi-agent conversational system that shows how ai
agents
ai assistance
we introduce multicolleagues a multi-agent conversational system that shows how ai agents can act as colleagues by conversing with each other sharing new ideas and actively involving users in collaborative ideation
human feedback is critical for aligning ai
systems
ai agents
human feedback is critical for aligning ai systems to human values
we argue that such abstraction leads to oversimplification of
reasoning
natural language
we argue that such abstraction leads to oversimplification of reasoning methodologies from nlp ml and results in a distortion of llms empirically studied capabilities and un known limitations
two widely studied approaches are directive feedback which gives explicit explanations and reduces cognitive load to speed up learning and metacognitive
feedback
directive metacognitive
two widely studied approaches are directive feedback which gives explicit explanations and reduces cognitive load to speed up learning and metacognitive feedback which prompts learners to reflect track their progress and develop self-regulated learning srl skills
however the potential perceptual demands of viewing
virtual
physical virtual
however the potential perceptual demands of viewing virtual annotations while navigating a physical environment could impact user efficacy and safety and the implications of these demands are not well understood
a mathematical theory for understanding when
abstract
abstract representations
a mathematical theory for understanding when abstract representations emerge in neural networks
specifically our algorithm runs in k p-1 cdot mathrm
polylog
mathrm polylog
specifically our algorithm runs in k p-1 cdot mathrm polylog n bits of communication which is optimal up to polylogarithmic factors
such organisations are abundant in nature as sharing information is a key benefit of many biological
collective
collective systems
such organisations are abundant in nature as sharing information is a key benefit of many biological collective systems and have been influential in the design of many artificial collectives such as swarm robotics
nearest neighbor matching is equivalent to least squares
density
density-ratio estimation
nearest neighbor matching is equivalent to least squares density ratio estimation and riesz regression
to address the practical demands of multi-modal open-vocabulary goal queries and
multi-goal
multi-modal open-vocabulary
to address the practical demands of multi-modal open-vocabulary goal queries and multi-goal visual navigation we propose lagmemo a navigation system that leverages a language 3d gaussian splatting memory
can language models boost the power of randomized
experiments
randomized experiments
can language models boost the power of randomized experiments without statistical bias
2 of disagreement trials reporting less certainty and more
normative
normative reasoning
2 of disagreement trials reporting less certainty and more normative conformity
evaluations across digital and physical benchmarks show that a single blm _1 instance outperforms four model
families
vla models
evaluations across digital and physical benchmarks show that a single blm _1 instance outperforms four model families -- mllms ellms vlas and gmlms -- achieving sim
we develop a structural framework for modeling and
inferring
panel data
we develop a structural framework for modeling and inferring unobserved heterogeneity in dynamic panel-data models
the role of viral dynamics and infectivity in models of oncolytic virotherapy for
tumours
viral replication
the role of viral dynamics and infectivity in models of oncolytic virotherapy for tumours with different motility
the theoretical analysis presented in this work focuses on novel
convergence
convergence rate
the theoretical analysis presented in this work focuses on novel convergence estimates for the sga and lrsga methods including parameter bounds
language models can be used to provide interactive personalized student
feedback
human feedback
language models can be used to provide interactive personalized student feedback in educational settings
this development culminated with a new class of measures that are both time scale independent and
time
temporal resolution
this development culminated with a new class of measures that are both time scale independent and time resolved
unlike previous studies that treat either disturbance rejection or partial sensing this work combines the command filter
disturbance
bounded disturbances
unlike previous studies that treat either disturbance rejection or partial sensing this work combines the command filter disturbance observer and hgo to address both challenges simultaneously while avoiding the complexity growth typical of backstepping designs
debiased machine learning typically requires estimation of the
riesz
debiased machine
debiased machine learning typically requires estimation of the riesz representer and the regression function
these theoretical guarantees close an important gap in the literature providing
rigorous
theoretical guarantees
these theoretical guarantees close an important gap in the literature providing rigorous foundations for resampling-based confidence intervals and hypothesis tests
dyck edit distance measures how far a parenthesis string is from a well-parenthesized expression while tree edit distance quantifies the minimum number of node insertions deletions and substitutions required to transform one rooted ordered labeled
tree
tree edit
dyck edit distance measures how far a parenthesis string is from a well-parenthesized expression while tree edit distance quantifies the minimum number of node insertions deletions and substitutions required to transform one rooted ordered labeled tree into another
the intercalation of guest species into the gap of van
der
der waals
the intercalation of guest species into the gap of van der waals materials often leads to the emergence of intriguing phenomena such as superconductivity
we demonstrate the value of this approach by applying it to a set of experiments and find that our method would have reduced the variance of the treatment effect estimate by 10 -50 compared to simple randomization in our
empirical
empirical application
we demonstrate the value of this approach by applying it to a set of experiments and find that our method would have reduced the variance of the treatment effect estimate by 10 -50 compared to simple randomization in our empirical applications
our main results are bivariate central limit
theorems
central limit theorem
our main results are bivariate central limit theorems for a class of method-of-moments estimators under increasing-domain and fixed-domain asymptotics
spa provides highly accurate approximations to probability
densities
density estimation
spa provides highly accurate approximations to probability densities and distribution functions via the moment generating function
this enables early detection of operational context changes that impact
image
image classification
this enables early detection of operational context changes that impact image classification performance in the field
we present combined jwst nirspec and miri mrs integral field spectroscopy data of the nuclear and circumnuclear regions of the highly dust obscured seyfert 2 galaxy ngc 7582 which is part of the
sample
galactic nuclei
we present combined jwst nirspec and miri mrs integral field spectroscopy data of the nuclear and circumnuclear regions of the highly dust obscured seyfert 2 galaxy ngc 7582 which is part of the sample of agn in the galaxy activity torus and outflow survey gatos
we apply our method to variational quantum
algorithm
quantum computing
we apply our method to variational quantum algorithm vqa ansatz design for molecular ground state estimation max-cut and image classification key challenges in near-term quantum computing
in this study we used a data-driven network approach to examine whether resting-state eeg
connectivity
human cognition
in this study we used a data-driven network approach to examine whether resting-state eeg connectivity patterns differentiate individuals according to their creative abilities
we prove that combining diffusion models with an annealed variant of langevin
dynamics
diffusion models
we prove that combining diffusion models with an annealed variant of langevin dynamics achieves conditional sampling in polynomial time using merely an l 4 bound on the score error
we propose a new collision avoidance strategy that takes both energy use and travel
time
dynamic obstacles
we propose a new collision avoidance strategy that takes both energy use and travel time into account
to solve this high-dimensional non-convex problem under uncertain channels we develop a deep
reinforcement
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
the observation process is driven by k independent
brownian
brownian motion
the observation process is driven by k independent brownian motions while the hidden state takes n 1 values with k n
the loss function of lp-quantile regression circumvents the non-differentiability of the absolute loss function and the difficulty of the squares loss function requiring the finiteness of error s variance and thus promises excellent properties of
lp-quantile
quantile regression
the loss function of lp-quantile regression circumvents the non-differentiability of the absolute loss function and the difficulty of the squares loss function requiring the finiteness of error s variance and thus promises excellent properties of lp-quantile regression
the empirical performance of the proposed methods is demonstrated through
numerical
computationally efficient
the empirical performance of the proposed methods is demonstrated through numerical experiments
the resulting problem is modeled as a markov decision process
mdp
machine learning
the resulting problem is modeled as a markov decision process mdp and solved via the deep reinforcement learning drl method
non-asymptotic error bounds are established for the resulting estimators under heavy-tailed regime and the minimax optimal convergence
rate
convergence rate
non-asymptotic error bounds are established for the resulting estimators under heavy-tailed regime and the minimax optimal convergence rate is derived
in contrast to the standard situation involving no s-o
coupling
s-o coupling
in contrast to the standard situation involving no s-o coupling the system exhibits long-ranged casimir forces both in two and three dimensions d 2 and d 3
an extension of that algorithm along with
numerical
computationally efficient
an extension of that algorithm along with numerical benchmarks on various non-gaussian and high-dimensional examples are provided to demonstrate its effectiveness and practical potential
we also provide new data-driven control design methods in terms of linear matrix inequalities that complement the
conditions
predictive control
we also provide new data-driven control design methods in terms of linear matrix inequalities that complement the conditions for informativity
second we demonstrate that group size affects the
dynamics
group size
second we demonstrate that group size affects the dynamics in a non-linear way revealing model-dependent dynamical regimes
these findings suggest that geometry and tuning encode brain-region- or model-family-specific
signatures
encoding models
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
brain decoding is a key neuroscience field that reconstructs the visual stimuli from brain activity with
fmri
brain decoding
brain decoding is a key neuroscience field that reconstructs the visual stimuli from brain activity with fmri which helps illuminate how the brain represents the world
simultaneously strongly aligning with human
visual
convolutional neural
simultaneously strongly aligning with human visual attention
galaxy mergers trigger starburst activity and
galactic
quiescent galaxies
galaxy mergers trigger starburst activity and galactic outflows that enrich the circumgalactic medium profoundly impacting galaxy evolution
the confinement potential is not assumed a priori but emerges as a radial effective potential analogous to a
quantum
quantum dot
the confinement potential is not assumed a priori but emerges as a radial effective potential analogous to a quantum dot geometrically induced by the torsion of the material
we present glyph-sr a vision-language-guided
diffusion
language models
we present glyph-sr a vision-language-guided diffusion framework that aims to achieve both objectives jointly
to close this gap we introduce nano-scale
vision-language
vision-language models vlms
to close this gap we introduce nano-scale vision-language action nanovla a family of lightweight vla architectures that achieve high performance with minimal resources
monte carlo simulations demonstrate that our approach exhibits excellent finite sample performance for both oos prediction and
policy
policy evaluation
monte carlo simulations demonstrate that our approach exhibits excellent finite sample performance for both oos prediction and policy evaluation
the fast for the curious how to accelerate
fault-tolerant
quantum batteries
the fast for the curious how to accelerate fault-tolerant quantum applications
using multi-component light profile fitting we model the radial
brightness
surface brightness
using multi-component light profile fitting we model the radial brightness distributions of a subset 20 of galaxies with an inner spheroidal sersic component and an underlying exponential disk
we design a complex per-subcarrier distortion vector that increases sidelobes of the mismatched ambiguity function maf relative to its mainlobe using two objectives the sidelobe-to-peak level
ratio
signal-to-noise ratio
we design a complex per-subcarrier distortion vector that increases sidelobes of the mismatched ambiguity function maf relative to its mainlobe using two objectives the sidelobe-to-peak level ratio and the integrated sidelobe level
our findings reinforce the need for designing llm-based tools that more clearly communicate their
programming
large language models llms
our findings reinforce the need for designing llm-based tools that more clearly communicate their programming capabilities to users
we evaluate three convolutional neural network architectures of varying depth and complexity to assess their effectiveness for
periocular
convolutional neural
we evaluate three convolutional neural network architectures of varying depth and complexity to assess their effectiveness for periocular recognition
we evaluate our tokenizer using a gnn pcn and a transformer on gesture
recognition
action recognition
we evaluate our tokenizer using a gnn pcn and a transformer on gesture recognition and object detection
the goal of policy learning is to train a
policy
reinforcement learning
the goal of policy learning is to train a policy function that recommends a treatment given covariates to maximize population welfare
a convexity-dependent two-phase training algorithm for
deep
gradient descent
a convexity-dependent two-phase training algorithm for deep neural networks
our findings suggest that selecting the right
support
support vector machines
our findings suggest that selecting the right support vectors may matter more than their precise weighting
we present a human-llm collaborative framework to infer thinking
traces
thinking traces
we present a human-llm collaborative framework to infer thinking traces from label-only annotations
modeling the agent as a generic neural dynamical system coupled to such streams we show that accurate world-tracking imposes i emph structural constraints -- equivariance of the agent s constitutive equations and readouts -- and ii emph
dynamical
dynamical systems
modeling the agent as a generic neural dynamical system coupled to such streams we show that accurate world-tracking imposes i emph structural constraints -- equivariance of the agent s constitutive equations and readouts -- and ii emph dynamical constraints under static inputs symmetry induces conserved quantities noe...
this approach demonstrates that robust self-testing and publicly verifiable quantum randomness can be achieved with minimal optical
complexity
open quantum
this approach demonstrates that robust self-testing and publicly verifiable quantum randomness can be achieved with minimal optical complexity without jeopardizing security
in this work we investigate the bi-isotropic effects in the formation and tunability of hybrid surface
polaritons
phonon polaritons
in this work we investigate the bi-isotropic effects in the formation and tunability of hybrid surface polaritons in bilayer configurations
simulation results show that the proposed approach outperforms classical signal processing filtering and
deep
deep learning
simulation results show that the proposed approach outperforms classical signal processing filtering and deep learning benchmarks
filling the gap atom probe tomography of porous structures enabled by
site
electron microscopy
filling the gap atom probe tomography of porous structures enabled by site specific semglu curing
extensive experiments have shown that our approach exceeds these baselines in conditional structured
layout
layout generation
extensive experiments have shown that our approach exceeds these baselines in conditional structured layout generation
furthermore we incorporate focaler-siou to strengthen the model s bounding box matching capability and increase its sensitivity to small-object features thereby further enhancing
detection
object detection
furthermore we incorporate focaler-siou to strengthen the model s bounding box matching capability and increase its sensitivity to small-object features thereby further enhancing detection accuracy and robustness
importantly suppression is transient - the two rival images compete for dominance with stochastic switches between mutually exclusive
percepts
visual rivalry
importantly suppression is transient - the two rival images compete for dominance with stochastic switches between mutually exclusive percepts occurring every few seconds with law-like regularity
incorporating causal knowledge and mechanisms is essential for refining
causal
interventional constraints
incorporating causal knowledge and mechanisms is essential for refining causal models and improving downstream tasks such as designing new treatments
the integration of distributed energy resources ders into wholesale electricity markets as mandated by ferc order 2222 imposes new
challenges
power systems
the integration of distributed energy resources ders into wholesale electricity markets as mandated by ferc order 2222 imposes new challenges on system operations
we show that even absent nefarious behavior conventional confidence
intervals
confidence intervals
we show that even absent nefarious behavior conventional confidence intervals and point estimators are invalid due to the fact that non-preregistered estimates are only reported in a subset of potential data realizations
this framework establishes a quantitative link between turbulence-induced phase distortions and
quantum
optical communication
this framework establishes a quantitative link between turbulence-induced phase distortions and quantum statistical behavior of reconstructed optical fields
in this setting our second algorithm a 2 -off-c 2 pl actively selects samples that target the least-informative
dimensions
preference optimization
in this setting our second algorithm a 2 -off-c 2 pl actively selects samples that target the least-informative dimensions of the test user s preference
this situation can be further exacerbated by multiple anomalous response variables whose
errors
anomaly detection
this situation can be further exacerbated by multiple anomalous response variables whose errors propagate due to correlations between outputs
a common method for reading out the state of a
spin
spin 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
these annotations draw inspiration from cognitive science research on how humans identify and resolve anomalies providing a comprehensive framework for evaluating
vision-language
language models
these annotations draw inspiration from cognitive science research on how humans identify and resolve anomalies providing a comprehensive framework for evaluating vision-language models vlms in detecting and understanding anomalies
stochastic optimization in semi-discrete optimal
transport
optimal transport
stochastic optimization in semi-discrete optimal transport convergence analysis and minimax rate
we find that most of the stars in our bulges are formed in-situ but 33 of our
bulges
stellar population
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
accretion rates of stellar-mass compact objects
embedded
stellar mass function
accretion rates of stellar-mass compact objects embedded in agn discs
however when a food web representing predator-prey relationships is given finding a set of species that optimizes
phylogenetic
phylogenetic tree
however when a food web representing predator-prey relationships is given finding a set of species that optimizes phylogenetic diversity subject to the condition that each saved species should be able to find food among the preserved species is np-hard spillner et al
we find that for different types of network architectures and for both visual or neuronal stimuli these cross-supervising networks learn semantic representations that are easily decodable and that decoding accuracy is comparable to supervised networks -- both at the level of
single
neural codes
we find that for different types of network architectures and for both visual or neuronal stimuli these cross-supervising networks learn semantic representations that are easily decodable and that decoding accuracy is comparable to supervised networks -- both at the level of single networks and the ensemble
stopping rules for monte carlo methods of
martingale
stopping rules
stopping rules for monte carlo methods of martingale difference type
our framework offers a new lens for analyzing how both
biological
continual learning
our framework offers a new lens for analyzing how both biological and artificial neural systems learn complex features while maintaining robust information-rich representations of the world
our cross-system comparison also suggests actionable ways that
cooperation
collective systems
our cross-system comparison also suggests actionable ways that cooperation can be improved in large-scale common pool resources problems like climate change
then we incorporate two control actions namely vector
control
control strategies
then we incorporate two control actions namely vector control and incentives to adopt protection measures
while there are a plethora of works showing the effectiveness of llms in generating step-by-step solutions through chain-of-thought cot reasoning on reasoning benchmarks little is understood about whether the generated cot is helpful for end-users in improving their ability to comprehend mathematical
reasoning
thinking traces
while there are a plethora of works showing the effectiveness of llms in generating step-by-step solutions through chain-of-thought cot reasoning on reasoning benchmarks little is understood about whether the generated cot is helpful for end-users in improving their ability to comprehend mathematical reasoning problems...
scmd a kernel-based distance for structural
causal
causal effects
scmd a kernel-based distance for structural causal models to quantify transferability across environments
we resolve this question by presenting the first bidirectional reduction showing that suffix array queries are up to an additive o log log n term in query time equivalent to prefix-select
queries
compressed indexing
we resolve this question by presenting the first bidirectional reduction showing that suffix array queries are up to an additive o log log n term in query time equivalent to prefix-select queries in all parameters
the results demonstrate the model s capability to generate scenarios for the validation of intelligent driving functions involving multi-agent interactions as well as to augment
data
learning agents
the results demonstrate the model s capability to generate scenarios for the validation of intelligent driving functions involving multi-agent interactions as well as to augment data for their development and iterative improvement
to address these issues we propose in-context steered
policy
policy learning
to address these issues we propose in-context steered policy optimization icpo a unified framework that leverages the inherent in-context learning capability of lrms to provide expert guidance using existing datasets
learning to plan schedule with reinforcement-learned bimanual
robot
learning agents
learning to plan schedule with reinforcement-learned bimanual robot skills
robotic systems navigating in real-world settings
require
autonomous driving
robotic systems navigating in real-world settings require a semantic understanding of their environment to properly determine safe actions
in-situ infrared spectroscopy revealed non-monotonic behavior an initial increase in aliphatic ch bonds was observed followed by a decrease at higher
hydrogen
molecular dynamics
in-situ infrared spectroscopy revealed non-monotonic behavior an initial increase in aliphatic ch bonds was observed followed by a decrease at higher hydrogen fluences
such a computation is enabled by contextual influences through which a neuron s response to visual inputs is influenced by
contextual
surrogate brain
such a computation is enabled by contextual influences through which a neuron s response to visual inputs is influenced by contextual inputs outside its classical receptive fields
to further enhance performance an adaptive active ris configuration strategy is employed which refines the
beam
beamforming design
to further enhance performance an adaptive active ris configuration strategy is employed which refines the beam direction based on an initial user location estimate
ai-powered approaches specifically large language models llms natural
language
language models
ai-powered approaches specifically large language models llms natural language processing nlp and generative ai offer transformative solutions and reduce inefficiencies