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6 rated at a difficulty level of 46 out of 50 and imposes stricter conditions than existing revolving-door or edge-exchange gray codes for
spanning
spanning trees
6 rated at a difficulty level of 46 out of 50 and imposes stricter conditions than existing revolving-door or edge-exchange gray codes for spanning trees of complete graphs
the bound electron-hole pairs known as excitons govern the optical
properties
electronic structure
the bound electron-hole pairs known as excitons govern the optical properties of insulating solids
ai-powered approaches specifically large language models llms natural language processing nlp and
generative
ai literacy
ai-powered approaches specifically large language models llms natural language processing nlp and generative ai offer transformative solutions and reduce inefficiencies
efficiency without cognitive change evidence from human
interaction
human-ai interaction
efficiency without cognitive change evidence from human interaction with narrow ai systems
we investigate the spontaneous emission of light in three-dimensional 3d
photonic
photonic crystal
we investigate the spontaneous emission of light in three-dimensional 3d photonic crystals through theoretical calculations and simulations
these results demonstrate efficiency gains without cognitive change suggesting that current narrow ai
systems
ai use
these results demonstrate efficiency gains without cognitive change suggesting that current narrow ai systems serve as cognitive scaffolds extending performance without transforming underlying mental capacities
in this study we first prove that the density-ratio
estimation
density-ratio estimation
in this study we first prove that the density-ratio estimation method proposed in lin et al
such approaches are limited by large upfront costs an inability to immediately handle new benchmarks cold-start and the fragile assumption that
future
existing approaches
such approaches are limited by large upfront costs an inability to immediately handle new benchmarks cold-start and the fragile assumption that future models will share the failure patterns of their predecessors
then simulations based on three real datasets are used to
demonstrate
real datasets
then simulations based on three real datasets are used to demonstrate the estimators properties
the results reveal systematic patterns navigation success depends predictably on platform capability and
scene
visual navigation
the results reveal systematic patterns navigation success depends predictably on platform capability and scene geometry and different algorithms exhibit distinct preferences and failure modes across the evaluated conditions
the geometry of dialogue graphing language models to reveal
synergistic
language models
the geometry of dialogue graphing language models to reveal synergistic teams for multi-agent collaboration
the control of localized magnetic domains at the
nanoscale
magnetic anisotropy
the control of localized magnetic domains at the nanoscale holds great promise for next-generation spintronic applications
our framework leverages a system of differential equations to simulate
disease
disease transmission
our framework leverages a system of differential equations to simulate disease transmission across a network of interconnected cities capturing more realistic patterns
based on the similarity transformations of these cooperative geometric primitives we derive an abstraction of complex
robotic
robotic manipulation
based on the similarity transformations of these cooperative geometric primitives we derive an abstraction of complex robotic systems that enables representing these systems in a way that directly corresponds to single-arm systems
these findings establish quadratic nonlinear waveguide arrays as a promising platform to explore the interplay of nonlinearity topology and disorder in quantum
photonic
integrated photonics
these findings establish quadratic nonlinear waveguide arrays as a promising platform to explore the interplay of nonlinearity topology and disorder in quantum photonic circuits
we highlight weaknesses of the current ai and especially of its llm-based core show that root causes of
llms
llm reasoning
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
a unified theory for causal inference direct
debiased
riesz regression
a unified theory for causal inference direct debiased machine learning via bregman-riesz regression
in task and motion planning high-level task
planning
motion planning
in task and motion planning high-level task planning is done over an abstraction of the world to enable efficient search in long-horizon robotics problems
we investigate the gain margin of a general nonlinear system under an inverse optimal input-to-state safe issf controller of the form u u0 x u x u0 where u0 is the nominal control and u is the
inverse
inverse optimal
we investigate the gain margin of a general nonlinear system under an inverse optimal input-to-state safe issf controller of the form u u0 x u x u0 where u0 is the nominal control and u is the inverse optimal safety filter that minimally modifies the nominal controller s unsafe actions over the infinite horizon
we then benchmark the performance of our methods against comparable
existing
existing methods
we then benchmark the performance of our methods against comparable existing approaches
with co-added images we construct light curves for 73
agns
light curves
with co-added images we construct light curves for 73 agns in the egs field
we also find that significant disparities can
arise
statistical physics
we also find that significant disparities can arise due to chance even from completely symmetric initial conditions especially when populations are small
to solve the formulated problem we proposed a two-timescale multi-agent
deep
reinforcement learning
to solve the formulated problem we proposed a two-timescale multi-agent deep deterministic policy gradient tts-maddpg algorithm based on the centralized training and distributed execution paradigm
an extension of that algorithm along with
numerical
numerical experiments
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
our approach is validated with experiments notably on real-world question-answering datasets using embeddings derived from state-of-the-art large
language
language models
our approach is validated with experiments notably on real-world question-answering datasets using embeddings derived from state-of-the-art large language models
however this trajectory-to-trajectory formulation often entangles camera
motion
point tracking
however this trajectory-to-trajectory formulation often entangles camera motion with scene dynamics and complicates both modeling and inference
in this survey we provide a comprehensive review of multimodal spatial
reasoning
spatial reasoning
in this survey we provide a comprehensive review of multimodal spatial reasoning tasks with large models categorizing recent progress in multimodal large language models mllms and introducing open benchmarks for evaluation
solving this problem to global optimality is challenging due to ac
power
optimal power
solving this problem to global optimality is challenging due to ac power flow and nn nonconvexities so our approach exploits a convex relaxation of the ac physics combined with a local nn search to find a guaranteed lower bound on worst--case load shedding
reality distortion room a study of user locomotion responses to spatial augmented
reality
virtual reality
reality distortion room a study of user locomotion responses to spatial augmented reality effects
the goal of policy learning is to train a
policy
policy learning
the goal of policy learning is to train a policy function that recommends a treatment given covariates to maximize population welfare
the presence of occlusions has provided substantial challenges to typically-powerful
object
vision transformers
the presence of occlusions has provided substantial challenges to typically-powerful object recognition algorithms
mean-shift theory and its applications in swarm robotics a new way to enhance the efficiency of
multi-robot
multi-robot collaboration
mean-shift theory and its applications in swarm robotics a new way to enhance the efficiency of multi-robot collaboration
enhancing the reachability of variational quantum
algorithms
quantum walk
enhancing the reachability of variational quantum algorithms via input-state design
multimodal large language models mllms have advanced vision-language reasoning and are increasingly deployed in
embodied
large language
multimodal large language models mllms have advanced vision-language reasoning and are increasingly deployed in embodied agents
look at that distractor dynamic translation gain under low perceptual load in
virtual
virtual reality
look at that distractor dynamic translation gain under low perceptual load in virtual reality
for maximal matching our techniques further allow us to establish a strong separation between the node-averaged complexity and worst-case
complexity
query complexity
for maximal matching our techniques further allow us to establish a strong separation between the node-averaged complexity and worst-case complexity of maximal matching in regular graphs by showing that the former is only o 1
posterior sampling by combining diffusion models with annealed
langevin
diffusion models
posterior sampling by combining diffusion models with annealed langevin dynamics
we show that 1 this isolated reasoning embedding
exhibits
abstract representations
we show that 1 this isolated reasoning embedding exhibits unique predictive power accounting for variance in neural activity not explained by other linguistic features and even extending to the recruitment of visual regions beyond classical language areas
this approach overcomes the ambiguity of qualitative inspection near regime boundaries particularly in large systems and provides a compact extensible framework for identifying and comparing
emergent
emergent behaviors
this approach overcomes the ambiguity of qualitative inspection near regime boundaries particularly in large systems and provides a compact extensible framework for identifying and comparing emergent behaviors in complex systems
here we propose and investigate two theoretical protocols for fast
single-shot
s-o coupling
here we propose and investigate two theoretical protocols for fast single-shot readout of cavity-coupled single t center electronic spins
furthermore we identify a quadratic dependence between the strain-induced resistance change and the threshold voltage confirming that piezoresistive modulation governs the strain-tunability of the
phase
phase transition
furthermore we identify a quadratic dependence between the strain-induced resistance change and the threshold voltage confirming that piezoresistive modulation governs the strain-tunability of the phase transition
the tree supports text polylog n -time operations and requires a static lookup table of size text poly n text polylog u -- but in exchange for these the
tree
tree edit distance
the tree supports text polylog n -time operations and requires a static lookup table of size text poly n text polylog u -- but in exchange for these the tree is able to achieve a remarkable space guarantee
controllers designed using an accurate model is
robust
optimal control
controllers designed using an accurate model is robust against disturbance and small mismatch between the physical setup and the mathematical model derived from first principles while a poor model results in a controller that performs well in simulation but fails in physical experiments
we investigate the fine-grained complexity of direct access to conjunctive
query
query time
we investigate the fine-grained complexity of direct access to conjunctive query cq answers according to their position ordered by the minimum or maximum value between attributes
distinguishing long-memory behaviour from
nonstationarity
time-series experiments
distinguishing long-memory behaviour from nonstationarity is challenging as both produce slowly decaying sample autocovariances
initial science highlights from the catalogue include the detection of 213 radio quiet wise
hii
hii regions
initial science highlights from the catalogue include the detection of 213 radio quiet wise hii region candidates previously undetected in radio continuum studies
we further discover that issameobject is encoded in a low-dimensional subspace on top of
object
visual stimuli
we further discover that issameobject is encoded in a low-dimensional subspace on top of object features and that this signal actively guides attention
the expansion of large language models is
increasingly
large language
the expansion of large language models is increasingly limited by the constrained memory capacity of modern gpus
reality distortion room a study of user locomotion responses to spatial augmented
reality
augmented reality
reality distortion room a study of user locomotion responses to spatial augmented reality effects
why do human populations remain vulnerable to
collapse
population size
why do human populations remain vulnerable to collapse even when they are large
however real-world matrices are often observed with
noise
spectral density matrices
however real-world matrices are often observed with noise arising from sampling sketching and quantization
we compare models that use only macro-level incidence models that add
mobility
human mobility
we compare models that use only macro-level incidence models that add mobility network features and their interactions with macro incidence and autoregressive ar models that include town-level recent cases
approximate quantum error correction eigenstate thermalization and the
chaos
quantum error correction
approximate quantum error correction eigenstate thermalization and the chaos bound
we further validate our theoretical findings with experiments on both synthetic and real datasets
demonstrating
real datasets
we further validate our theoretical findings with experiments on both synthetic and real datasets demonstrating that our method offers practical accuracy-privacy trade-offs
learning to plan schedule with reinforcement-learned bimanual
robot
reinforcement learning
learning to plan schedule with reinforcement-learned bimanual robot skills
args and their succinct representation tree
sequences
phylogenetic tree
args and their succinct representation tree sequences are increasingly central to modern population genetics methods yet building an intuition for args remains challenging
finally these step-wise rewards are used to calculate fork-relative advantages blended with trajectory-relative
advantages
llm post-training
finally these step-wise rewards are used to calculate fork-relative advantages blended with trajectory-relative advantages to train the llm for tool use
few-shot anomaly detection fsad methods identify
anomalous
anomaly detection
few-shot anomaly detection fsad methods identify anomalous regions with few known normal samples
more broadly they point to an intrinsic neural signature of adaptive brain function marked by efficient yet
flexible
brain-computer interface
more broadly they point to an intrinsic neural signature of adaptive brain function marked by efficient yet flexible network organization that may support creative and adaptive cognition
on these sub-finsler structures we study the normal
curves
normal curves
on these sub-finsler structures we study the normal curves in the sense of control theory
we measure abundance ratios in 209 giant stars that are confirmed members of the smc providing the first extensive dataset of eu abundances in this galaxy across its full
metallicity
stellar mass function
we measure abundance ratios in 209 giant stars that are confirmed members of the smc providing the first extensive dataset of eu abundances in this galaxy across its full metallicity range spanning more than 1
these findings highlight pulse optimization as a powerful strategy to enhance the resilience to disorder of solid-state globally-driven quantum
computing
quantum dot
these findings highlight pulse optimization as a powerful strategy to enhance the resilience to disorder of solid-state globally-driven quantum computing platforms
this work enables us to model the robot s collapse behavior in any open environment and understand the parameters it needs to succeed in 3d
navigation
obstacle avoidance
this work enables us to model the robot s collapse behavior in any open environment and understand the parameters it needs to succeed in 3d navigation tasks
these findings highlight challenges in achieving logical consistency in
llms
models llms
these findings highlight challenges in achieving logical consistency in llms normative reasoning and provide insights for enhancing their reliability
we study the problem of estimating the average treatment effect ate under sequentially adaptive
treatment
average treatment effect
we study the problem of estimating the average treatment effect ate under sequentially adaptive treatment assignment mechanisms
code review serves as an effective practice that enables developers to check their teammates
code
code review
code review serves as an effective practice that enables developers to check their teammates code before integration into the codebase
infoflow reinforcing search agent via reward
density
reward density
infoflow reinforcing search agent via reward density optimization
when the resulting posteriors are used as domain distributions for sim-based policy
learning
reinforcement learning
when the resulting posteriors are used as domain distributions for sim-based policy learning they lead to more robust object-centric agent performance
stacked intelligent surfaces sis are a promising technology for next-generation
wireless
wireless networks
stacked intelligent surfaces sis are a promising technology for next-generation wireless systems offering an opportunity to enhance communication performance with low power consumption
we then use this to create a physics informed complex exponential basis expansion model prediction framework that maximizes the usefulness of outdated
channel
channel state information
we then use this to create a physics informed complex exponential basis expansion model prediction framework that maximizes the usefulness of outdated channel state information csi in the presence of integer and fractional delay-doppler channels and facilitates high mobility mimo communication
researchers often use specifications that correctly estimate the
average
average treatment effect
researchers often use specifications that correctly estimate the average treatment effect under the assumption of constant effects
despite its longstanding presence and significant attention within the optimization community most works focusing on understanding its
convergence
convergence guarantees
despite its longstanding presence and significant attention within the optimization community most works focusing on understanding its convergence guarantees assume the strong l-lipschitz condition
our work incorporates state-of-the-art innovations in all key aspects of the fault-tolerant quantum
computing
fault-tolerant quantum
our work incorporates state-of-the-art innovations in all key aspects of the fault-tolerant quantum computing ftqc stack to show how quantum computers could realistically and practically tackle co _2 utilization for green energy production
we provide rigorous analyses of its non-asymptotic
convergence
convergence guarantees
we provide rigorous analyses of its non-asymptotic convergence rates showing an improvement over prior double-loop algorithms -- form o epsilon -3 log epsilon -1 to o epsilon -3
this approach unifies empirical likelihood bayesian empirical
likelihood
density estimation
this approach unifies empirical likelihood bayesian empirical likelihood and generalized method-of-moments estimation within a common predictive geometry
in particular we show the possibility of detecting einstein-podolsky-rosen steering and mode entanglement of non-gaussian
states
quantum correlations
in particular we show the possibility of detecting einstein-podolsky-rosen steering and mode entanglement of non-gaussian states from linear measurements only
while diffusion language models dlms enable fine-grained refinement their practical controllability
remains
diffusion models
while diffusion language models dlms enable fine-grained refinement their practical controllability remains fragile
recent advances in data collection and technology enable a deeper understanding of complex
urban
human mobility
recent advances in data collection and technology enable a deeper understanding of complex urban commuting yet few studies have rigorously analyzed the temporal stability and origin-destination od heterogeneity of route choice
these results confirm the effectiveness efficiency and
robustness
llm inference
these results confirm the effectiveness efficiency and robustness of our approach for low-resource domain adaptation of llms
we prove that combining diffusion models with an annealed variant of
langevin
langevin dynamics
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
while modern large language models llms are increasingly used to model neural responses to
language
neural representations
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
the functionality of this method was demonstrated in experiments that led to promising results correlated with one of a laser
tracker
calibration plate
the functionality of this method was demonstrated in experiments that led to promising results correlated with one of a laser tracker calibration
specifically this procedure s conservatism when analyzing nncs using transcendental activation functions and the restriction to feedforward nncs are addressed by a introducing novel semialgebraic activation functions that preserve key properties of common transcendental activations and b proving compatibility of
nncs
deep neural
specifically this procedure s conservatism when analyzing nncs using transcendental activation functions and the restriction to feedforward nncs are addressed by a introducing novel semialgebraic activation functions that preserve key properties of common transcendental activations and b proving compatibility of nncs f...
pvmark enabling public verifiability for llm
watermarking
watermark detection
pvmark enabling public verifiability for llm watermarking schemes
rlmeval provides a new challenging benchmark designed to guide and accelerate
progress
reinforcement learning rl
rlmeval provides a new challenging benchmark designed to guide and accelerate progress in automated reasoning for formal mathematics
whereas generic dnn cannot guarantee accuracy outside the training distribution the closed-form nn model produces exact solutions for every discovered critical region of the
solution
neural network
whereas generic dnn cannot guarantee accuracy outside the training distribution the closed-form nn model produces exact solutions for every discovered critical region of the solution function
this algorithm is deterministic and does not
need
randomized algorithm
this algorithm is deterministic and does not need to know the metric space or m in advance
we opt to model human driver decisions as a markov decision process and propose a method for handling collision avoidance between non-convex
vehicle
autonomous driving
we opt to model human driver decisions as a markov decision process and propose a method for handling collision avoidance between non-convex vehicle shapes by imposing a positive distance constraint between compact sets
in this paper we propose the first high-resolution hr
motion
motion trajectory
in this paper we propose the first high-resolution hr motion trajectory estimation framework using diffusion models motdiff
but despite their promising theoretical advantages contemporary mode sorters often feature large crosstalk high loss or sort
modes
waveguide modes
but despite their promising theoretical advantages contemporary mode sorters often feature large crosstalk high loss or sort modes that are poorly adapted to conventional imaging systems e
the model is trained on heterogeneous qps to minimize the
expected
reward models
the model is trained on heterogeneous qps to minimize the expected objective value evaluated on the projected solutions
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
dynamic obstacles
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
we believe that our findings serve a basis for future
urban
urban systems
we believe that our findings serve a basis for future urban route choice modeling by suggesting the importance of elabolating the model of transfer in railway
wifi channel state information csi -based human activity recognition har provides a privacy-preserving device-free
sensing
activity recognition
wifi channel state information csi -based human activity recognition har provides a privacy-preserving device-free sensing solution for smart environments
of particular interest are two-time correlation functions of an impurity which are central to the characterization of these many-body systems and are a cornerstone of the description of correlated materials in dynamical mean field
theory
quantum correlations
of particular interest are two-time correlation functions of an impurity which are central to the characterization of these many-body systems and are a cornerstone of the description of correlated materials in dynamical mean field theory dmft
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
in this aspect we formulate a sum rate optimization problem that jointly optimizes the antenna activation factor the bs transmit power and the ue s transmit power subject to
power
transmit power
in this aspect we formulate a sum rate optimization problem that jointly optimizes the antenna activation factor the bs transmit power and the ue s transmit power subject to power budget constraints for the bs and the ues as well as minimum rate requirements for the ues
many domains of human intellectual labour have to adapt to the new ai tools that give
humans
ai systems
many domains of human intellectual labour have to adapt to the new ai tools that give humans new functionality and opportunity but also question the viability and necessity of some human work that used to be considered intellectual yet has now become an easily automatable commodity
starting with the dynamical system representing
collective
dynamical systems
starting with the dynamical system representing collective states in terms of connections activity levels and internal frequencies we analyze its stability emphasizing the possibility of transitions between configurations
while a multi-agent approach based on large language models llms represents a promising strategy to surpass the
capabilities
large language
while a multi-agent approach based on large language models llms represents a promising strategy to surpass the capabilities of single models its success is critically dependent on synergistic team composition