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we show that if the system is controllable then incorporating this as
prior
prior knowledge
we show that if the system is controllable then incorporating this as prior knowledge does not relax the conditions required for data-driven stabilization
furthermore we demonstrate the ability of the model to generalize across
diverse
world models
furthermore we demonstrate the ability of the model to generalize across diverse geographical contexts by utilizing datasets from two distinct regions abu dhabi and san francisco
the quantum walk technique is a general framework for constructing quantum algorithms by transforming a classical random walk search into a quantum search and has been successfully applied to constructing an
algorithm
quantum error correction
the quantum walk technique is a general framework for constructing quantum algorithms by transforming a classical random walk search into a quantum search and has been successfully applied to constructing an algorithm with a tight query complexity for another problem
it has been proven that super-heisenberg scaling can be achieved when the hamiltonian of the system involves
many-body
quantum mechanics
it has been proven that super-heisenberg scaling can be achieved when the hamiltonian of the system involves many-body interactions or the time-dependent terms
the usefulness of the method is demonstrated through an
empirical
empirical application
the usefulness of the method is demonstrated through an empirical application highlighting its complementarity to existing approaches
slot attention explicitly to probe these benefits it remains unclear whether this
ability
visual stimuli
slot attention explicitly to probe these benefits it remains unclear whether this ability naturally emerges in pre-trained vision transformers vits
we present an extremely simple polynomial-space exponential-time 1- varepsilon -approximation algorithm for max-k-sat that is slightly faster than the previous known
polynomial-space
polynomial time
we present an extremely simple polynomial-space exponential-time 1- varepsilon -approximation algorithm for max-k-sat that is slightly faster than the previous known polynomial-space 1- varepsilon -approximation algorithms by hirsch discrete applied mathematics 2003 and escoffier paschos and tourniaire theoretical comp...
the faster the rate of change of the environment the larger the effect of the
mutations
environmental change
the faster the rate of change of the environment the larger the effect of the mutations that are utilised
these challenges reveal key limitations of standard numerical techniques in multi-agent
control
optimal control
these challenges reveal key limitations of standard numerical techniques in multi-agent control and underscore the need for more robust nonlinear strategies for coordinating interacting agents
our general interaction framework which reduces to several previously studied models as special cases provides a versatile platform for engineering quantum
correlations
entanglement entropy
our general interaction framework which reduces to several previously studied models as special cases provides a versatile platform for engineering quantum correlations with applications in quantum simulation state preparation and sensing protocols
we study the convergence of off-policy td 0 with linear function approximation when used to approximate the expected discounted
reward
reward models
we study the convergence of off-policy td 0 with linear function approximation when used to approximate the expected discounted reward in a markov chain
we derive closed-form solutions for optimal encoding and decoding coefficients via lagrangian duality and convex
optimization
inverse optimal issf
we derive closed-form solutions for optimal encoding and decoding coefficients via lagrangian duality and convex optimization and propose data allocation strategies that reduce both redundancy and computation load
we define a new measure called the synchronization bottleneck of a graph which we denote by xi this new network property provides a quantification of the limiting bottleneck of the flow between any subset of
nodes
complex networks
we define a new measure called the synchronization bottleneck of a graph which we denote by xi this new network property provides a quantification of the limiting bottleneck of the flow between any subset of nodes regardless of its order and the rest of the networked system
whereas existing work typically imposes strong conditions to restore just-identification before deriving the efficiency bound we relax such assumptions and characterize the general efficiency
bound
ate estimation
whereas existing work typically imposes strong conditions to restore just-identification before deriving the efficiency bound we relax such assumptions and characterize the general efficiency bound along with efficient estimators in the overidentified models ii and iii
in this paper we formalize this challenge as the textbf reward density
optimization
preference optimization
in this paper we formalize this challenge as the textbf reward density optimization problem which aims to improve the reward obtained per unit of exploration cost
neyman targeted estimation also yields tmle as a special case for
regression
regression function
neyman targeted estimation also yields tmle as a special case for regression function estimation
we aim at exploring the existence of a stellar mass-metallicity relation of bulges mz r and analyze the possible imprint of characteristics features by accretion and migration of
stars
stellar mass function
we aim at exploring the existence of a stellar mass-metallicity relation of bulges mz r and analyze the possible imprint of characteristics features by accretion and migration of stars which could store information on their assembly histories
causal inference in high-dimensional generalized linear
models
causal effects
causal inference in high-dimensional generalized linear models with binary outcomes
thus tokenization constitutes a novel direction in event-based vision and marks a step towards methods that preserve the properties of
event
event cameras
thus tokenization constitutes a novel direction in event-based vision and marks a step towards methods that preserve the properties of event cameras
the emergence of hidden spin polarization in centrosymmetric nonmagnetic crystals due to local symmetry breaking has created new opportunities for potential spintronic applications and for enhancing our understanding of
mechanisms
hidden spin
the emergence of hidden spin polarization in centrosymmetric nonmagnetic crystals due to local symmetry breaking has created new opportunities for potential spintronic applications and for enhancing our understanding of mechanisms to electrically manipulate spin-related phenomena
propagation speed of traveling waves for diffusive
lotka-volterra
traveling waves
propagation speed of traveling waves for diffusive lotka-volterra system with strong competition
these findings challenge the assumption that task-specific pre-training is necessary and suggest that learning to forecast may provide a powerful route toward constructing general-purpose time
series
time series classification
these findings challenge the assumption that task-specific pre-training is necessary and suggest that learning to forecast may provide a powerful route toward constructing general-purpose time series foundation models
the model also naturally generates smbh accretion
rates
accretion rate
the model also naturally generates smbh accretion rates peaking within 1 gyr of their host sfhs
experimental results on several benchmarks as well as real-world deployments demonstrate that nanovla achieves up to 52x faster inference on edge devices compared to previous state-of-the-art
vla
vla models
experimental results on several benchmarks as well as real-world deployments demonstrate that nanovla achieves up to 52x faster inference on edge devices compared to previous state-of-the-art vla models with 98 less parameters while maintaining or surpassing their task accuracy and generalization
fixed and increasing domain asymptotics for the roughness and scale of isotropic gaussian
random
random field
fixed and increasing domain asymptotics for the roughness and scale of isotropic gaussian random fields
to mitigate the prohibitive overhead associated with full channel state information at the transmitter csit we propose a partial-csit-based
beamforming
beamforming design
to mitigate the prohibitive overhead associated with full channel state information at the transmitter csit we propose a partial-csit-based beamforming scheme that leverages randomized steering vectors and limited user-side feedback based on signal quality measurements
causal inference identifies cause-and-effect
relationships
causal effects
causal inference identifies cause-and-effect relationships between variables
our results show that approximate bayesian inference applied to deep neural networks is far from a lost cause when constructing
inference
generative models
our results show that approximate bayesian inference applied to deep neural networks is far from a lost cause when constructing inference mechanisms that reflect the geometry of reparametrizations
gui knowledge bench revealing the knowledge gap behind vlm failures in
gui
gui knowledge
gui knowledge bench revealing the knowledge gap behind vlm failures in gui tasks
5 nm anode contact to take advantage of the enhanced reverse blocking capabilities enabled by ptox while allowing low turn-on voltage by the interfacing thin
pt
ptox pt
5 nm anode contact to take advantage of the enhanced reverse blocking capabilities enabled by ptox while allowing low turn-on voltage by the interfacing thin pt layer
the recent advancement of multimodal large
language
large language models llms
the recent advancement of multimodal large language models mllms is transforming human-computer interaction hci from surface-level exchanges into more nuanced and emotionally intelligent communication
benchmarks on molecular systems show excellent agreement with exact diagonalization and demonstrate access to dynamical timescales beyond the reach of purely classical methods highlighting its suitability for near-term and early fault-tolerant
quantum
quantum technologies
benchmarks on molecular systems show excellent agreement with exact diagonalization and demonstrate access to dynamical timescales beyond the reach of purely classical methods highlighting its suitability for near-term and early fault-tolerant quantum hardware
quantization is the key method for reducing inference latency power and memory footprint of
generative
sparse autoencoders
quantization is the key method for reducing inference latency power and memory footprint of generative ai models
the external medium also influences the evolution of circumstellar disks and protostellar outflows with the high-density
external
dense gas
the external medium also influences the evolution of circumstellar disks and protostellar outflows with the high-density external medium disks grow rapidly but their mass becomes smaller relative to the protostellar mass and the outflow is sustained over a long duration
5 times smaller than star-forming populations at
similar
black hole mass
5 times smaller than star-forming populations at similar mass and the typical black hole mass of lrds is elevated by 1
we demonstrate the value of this approach by applying it to a set of
experiments
randomized experiments
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
we develop an efficient algorithm to solve this bilevel
optimization
optimization problem
we develop an efficient algorithm to solve this bilevel optimization problem which computes parameter gradients without backpropagating through the solver
in our model individuals of one species possess cognitive abilities to perceive environmental cues and assess the local density of the
species
ecological communities
in our model individuals of one species possess cognitive abilities to perceive environmental cues and assess the local density of the species they dominate in the spatial competition for natural resources
the prediction of delivery status of dataco
supply
supply chain
the prediction of delivery status of dataco supply chain is done for risk administration
let x_i d_i y_i _ i 1 n be the observations where x_i in mathbb r p denotes p -dimensional covariates d_i in 0 1 denotes a binary treatment
assignment
treatment assignment
let x_i d_i y_i _ i 1 n be the observations where x_i in mathbb r p denotes p -dimensional covariates d_i in 0 1 denotes a binary treatment assignment indicator and y_i in mathbb r is an outcome
we present the first known pivot gray code for spanning trees of complete graphs listing all
spanning
spanning trees
we present the first known pivot gray code for spanning trees of complete graphs listing all spanning trees such that consecutive trees differ by pivoting a single edge around a vertex
59 which is exponentially worse than the best known
lower
lower bound
59 which is exponentially worse than the best known lower bound of omega log m log log m by ashlagi et al
though convenient it is challenging to convey
fine-grained
natural language
though convenient it is challenging to convey fine-grained and referential intent
self-improvement has emerged as a mainstream paradigm for advancing the reasoning capabilities of
large
large language models llms
self-improvement has emerged as a mainstream paradigm for advancing the reasoning capabilities of large vision-language models lvlms where models explore and learn from successful trajectories iteratively
an understanding of the assembly history of the complex star cluster omega centauri has long been sought after with many studies separating the
stars
star-forming region
an understanding of the assembly history of the complex star cluster omega centauri has long been sought after with many studies separating the stars on the color-magnitude diagram into multiple groupings across small magnitude ranges
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
kagome metals are an intriguing class of quantum materials as the presence of both flat bands and dirac points provides access to functional
properties
electronic structure
kagome metals are an intriguing class of quantum materials as the presence of both flat bands and dirac points provides access to functional properties present in strongly correlated and topological materials
simulation results demonstrate that the proposed aircnn
architectures
neural network
simulation results demonstrate that the proposed aircnn architectures can achieve satisfactory classification performance
most existing methods rely on the generalization ability of pre-trained
vision-language
vision-language models
most existing methods rely on the generalization ability of pre-trained vision-language models vlms to recognize potentially anomalous regions through feature similarity between text descriptions and images
cross-sectional fib-tem revealed thin tribofilms 12-17 nm in
thickness
film thickness
cross-sectional fib-tem revealed thin tribofilms 12-17 nm in thickness on si- and co-doped surfaces
bidirectional yet asymmetric causality between urban systems and
traffic
traffic dynamics
bidirectional yet asymmetric causality between urban systems and traffic dynamics in 30 cities worldwide
the resulting control law enables the quadrotor to follow its path despite internal and external disturbances with each subsystem allowed its own
disturbance
bounded disturbances
the resulting control law enables the quadrotor to follow its path despite internal and external disturbances with each subsystem allowed its own disturbance type for realism
to address this we introduce the multi-organ medical image
reconstruction
image reconstruction
to address this we introduce the multi-organ medical image reconstruction more dataset comprising ct scans across 9 diverse anatomies with 15 lesion types
however the performance of all previous digital quantum simulations has been matched by classical methods and it has thus far remained unclear whether near-term intermediate-scale quantum hardware could offer any computational
advantage
quantum key distribution
however the performance of all previous digital quantum simulations has been matched by classical methods and it has thus far remained unclear whether near-term intermediate-scale quantum hardware could offer any computational advantage in this area
colloidal transition metal dichalcogenides nanostructures are experimentally accessible and chemically
tunable
colloidal synthesis
colloidal transition metal dichalcogenides nanostructures are experimentally accessible and chemically tunable platforms for spintronics deserving dedicated research to assess their potential
in the cold dark environments of pre-stellar
cores
star formation rates
in the cold dark environments of pre-stellar cores where the temperatures are below 10 k ne can condense onto the surface of interstellar grains
offline clustering of preference learning with
active-data
preference learning
offline clustering of preference learning with active-data augmentation
we find that simply imposing the m_ rm bh
-
dark matter
we find that simply imposing the m_ rm bh - sigma_ star condition is sufficient to generate a fraction of quenched galaxies consistent with current data including the newest ones from euclid
as a consequence of the high variability of load demand and renewable generation long-term and high-resolution inputs are required for power system expansion
planning
expansion planning
as a consequence of the high variability of load demand and renewable generation long-term and high-resolution inputs are required for power system expansion planning making the problem intractable in real-world applications
to establish a solid foundation for formal
reasoning
mathematical reasoning
to establish a solid foundation for formal reasoning in physics we also introduce physlib a community-driven repository containing fundamental unit systems and theorems essential for formal physics reasoning
error bounds and optimal schedules for masked
diffusions
diffusion models
error bounds and optimal schedules for masked diffusions with factorized approximations
while generative models especially large language models
llms
language models
while generative models especially large language models llms are ubiquitous in today s world principled mechanisms to assess their in correctness are limited
moire-enabled optical vortex with tunable
topological
vortex phase
moire-enabled optical vortex with tunable topological charge in twisted bilayer photonic crystals
out of the many deep reinforcement learning approaches for
autonomous
deep reinforcement learning
out of the many deep reinforcement learning approaches for autonomous driving only few make use of the options or skills framework
using the proposed model we derive an analytical formula for viral populations at the cellular level based on viewing
viral
infectious individuals
using the proposed model we derive an analytical formula for viral populations at the cellular level based on viewing viral replication as a birth-death process
however their success is tied to one core capability reliable object
detection
object detection
however their success is tied to one core capability reliable object detection in complex and multimodal environments
media signals or mobilizing rhetoric can trigger coherence across the population resulting in large-scale synchronized
collective
collective action
media signals or mobilizing rhetoric can trigger coherence across the population resulting in large-scale synchronized collective actions such as protests or ideological shifts
we investigate the fundamental problem of leveraging offline data to accelerate online reinforcement
learning
policy learning
we investigate the fundamental problem of leveraging offline data to accelerate online reinforcement learning - a direction with strong potential but limited theoretical grounding
as a result srl enables small models to learn challenging problems previously unlearnable by
sft
open-source models
as a result srl enables small models to learn challenging problems previously unlearnable by sft or rlvr
practically this model motivates a transparent control layer with predictable incentives where the agent learns to defer when risky and act when safe while its pretrained
policy
policy learning
practically this model motivates a transparent control layer with predictable incentives where the agent learns to defer when risky and act when safe while its pretrained policy and the environment s reward structure remain untouched
understanding how this quantity can be manipulated and transformed efficiently is crucial for advancing quantum
energy
quantum batteries
understanding how this quantity can be manipulated and transformed efficiently is crucial for advancing quantum energy management technologies
we provide asymptotic bounds on its overestimation and underestimation
probabilities
theoretical guarantees
we provide asymptotic bounds on its overestimation and underestimation probabilities and demonstrate first-order b-robustness of the criteria
this observation indicates that ne and co create a mixture in which ne can diffuse and stabilize at the surface which isolates co
molecules
molecular gas
this observation indicates that ne and co create a mixture in which ne can diffuse and stabilize at the surface which isolates co molecules from the accreting h atoms
we then employ methods from large deviation theory on discrete-time markov processes to study
stochastic
stochastic differential
we then employ methods from large deviation theory on discrete-time markov processes to study stochastic evolutionary dynamics
local overidentification and efficiency gains in modern
causal
causal inference
local overidentification and efficiency gains in modern causal inference and data combination
these structural changes are mirrored by an
electronic
electronic structure
these structural changes are mirrored by an electronic rearrangement at the interface quantified by a charge-accumulation descriptor that strongly correlates with adhesion
we suggest that their low black hole masses are unlikely to be due to their small angles of inclination to the
line
black hole
we suggest that their low black hole masses are unlikely to be due to their small angles of inclination to the line of sight
where galaxies go to die the environments of massive
quiescent
host galaxy
where galaxies go to die the environments of massive quiescent galaxies at 3 z 5
to address these challenges a theoretically grounded and computationally efficient proximal linearized
algorithm
gradient descent
to address these challenges a theoretically grounded and computationally efficient proximal linearized algorithm is developed
this paper introduces a morphology-aware reinforcement
learning
learning agents
this paper introduces a morphology-aware reinforcement learning framework that integrates a graph neural network gnn into the soft actor-critic sac algorithm
compared to baseline dense models our sparse
classifiers
deep learning
compared to baseline dense models our sparse classifiers reduce training time by up to 10x while the deep r rewiring enables them to perform as well as the original models
here we demonstrate that such an optical component can be designed using fano
interference
optical interference
here we demonstrate that such an optical component can be designed using fano interference and stark effect in a nonlinear nano-plasmonic system
while graph-based observability analysis methods exist for power system static-state estimation the approach presented here is the first for dynamic-state
estimation
state estimation
while graph-based observability analysis methods exist for power system static-state estimation the approach presented here is the first for dynamic-state estimation dse
our first result is a one-sided non-adaptive algorithm for this problem that makes tilde o log n epsilon samples and
queries
query complexity
our first result is a one-sided non-adaptive algorithm for this problem that makes tilde o log n epsilon samples and queries where n f -1 1 is the number of satisfying assignments of the function that is being tested and the value of n is given as an input parameter to the algorithm
in this paper a novel uncoordinated random access ura protocol is presented to address the pressing demand for massive connectivity with low access latency in future massive machine type
communication
uplink communication
in this paper a novel uncoordinated random access ura protocol is presented to address the pressing demand for massive connectivity with low access latency in future massive machine type communication mmtc scenarios
variational quantum algorithms vqas face an inherent trade-off between expressivity and trainability deeper circuits can represent richer states but suffer from noise accumulation and barren plateaus while shallow
circuits
fault-tolerant quantum
variational quantum algorithms vqas face an inherent trade-off between expressivity and trainability deeper circuits can represent richer states but suffer from noise accumulation and barren plateaus while shallow circuits remain trainable and implementable but lack expressive power
this approach is particularly relevant for predicting the emergence of paradoxical neural representations such as discordant visual illusions that occur in response to overt
sensory
artificial neural
this approach is particularly relevant for predicting the emergence of paradoxical neural representations such as discordant visual illusions that occur in response to overt sensory stimuli
human feedback can alter language models in unpredictable and undesirable ways as practitioners lack a clear
understanding
language agents
human feedback can alter language models in unpredictable and undesirable ways as practitioners lack a clear understanding of what feedback data encodes
this work provides a scalable framework for task-specific
pre-training
predictive processing
this work provides a scalable framework for task-specific pre-training and highlights its benefit in generalizable affective computing
furthermore for specific pairs of models and
riesz
riesz regression
furthermore for specific pairs of models and riesz representer estimation methods we can automatically obtain the covariate balancing property without explicitly solving the covariate balancing objective
based on these results we derive nn matching from
riesz
riesz regression
based on these results we derive nn matching from riesz regression
extensive experiments on both synthetic and real-world
datasets
real-world datasets
extensive experiments on both synthetic and real-world datasets demonstrate the superior performance of our algorithms particularly in complex environments
this method results in efficient calculations of individual
signal
signal processing
this method results in efficient calculations of individual signal and distortion components
we show that this version is np-hard even when both structures the food web and the
phylogenetic
phylogenetic tree
we show that this version is np-hard even when both structures the food web and the phylogenetic tree are stars
here we show that lyman- alpha ly alpha radiation pressure limits the gas mass converted into
stars
massive galaxies
here we show that lyman- alpha ly alpha radiation pressure limits the gas mass converted into stars particularly in primordial environments
more broadly we argue that considering scale and incorporating human-natural system feedbacks are not just interesting special cases within non-cooperative
game
game theory
more broadly we argue that considering scale and incorporating human-natural system feedbacks are not just interesting special cases within non-cooperative game theory but rather should be the starting point for the study of altruism and human cooperation
moreover we show that a poly-logarithmic approximation ratio and hence an approximation
ratio
approximation factor
moreover we show that a poly-logarithmic approximation ratio and hence an approximation ratio below the adaptivity gap can be achieved by a randomized algorithm with quasi-polynomial running time
simulation results demonstrate that the proposed approach significantly outperforms some baseline approaches and other state-of-the-art deep
reinforcement
deep reinforcement learning
simulation results demonstrate that the proposed approach significantly outperforms some baseline approaches and other state-of-the-art deep reinforcement learning algorithms
specifically we propose a thinking protocol where an organizer dynamically assigns sub-queries to workers merges intermediate
knowledge
reasoning capabilities
specifically we propose a thinking protocol where an organizer dynamically assigns sub-queries to workers merges intermediate knowledge and produces coherent solutions
conditions under which gonosomic algebras are not gonosomal and several algebraic constructions of gonosomic
algebras
gonosomic algebras
conditions under which gonosomic algebras are not gonosomal and several algebraic constructions of gonosomic algebras are given