eamag/beyond_benchmarks · Datasets at Hugging Face

as photonic systems progress toward enhanced miniaturization dynamic reconfigurability and improved energy efficiency a central challenge endures the accurate and independent control of optical losses and resonant properties on

scalable

integrated photonics

as photonic systems progress toward enhanced miniaturization dynamic reconfigurability and improved energy efficiency a central challenge endures the accurate and independent control of optical losses and resonant properties on scalable cmos-compatible platforms

we find the most significant determinant of efficient

mitigation

mitigation methods

we find the most significant determinant of efficient mitigation is accurate and precise characterisation

using fourier spectroscopy we carefully select wave vectors in the 2d plane of periodicity of the

photonic

waveguide modes

using fourier spectroscopy we carefully select wave vectors in the 2d plane of periodicity of the photonic crystal

in the strong bi-isotropic coupling regime the

surface

phonon polaritons

in the strong bi-isotropic coupling regime the surface dirac plasmon--phonon--magnon polariton dppmp dispersion undergoes a pronounced redshift accompanied by suppression of the characteristic anticrossing between the dirac plasmon and the phonon

here we introduce a topological framework that defines and detects localities in human

mobility

scale-free networks

here we introduce a topological framework that defines and detects localities in human mobility networks

quantum computation can be formulated through various models each highlighting distinct structural and resource-theoretic aspects of quantum

computational

quantum dot

quantum computation can be formulated through various models each highlighting distinct structural and resource-theoretic aspects of quantum computational power

by tailoring the reproducing kernel and rkhs to the dynamics of the nonlinear optimal

control

predictive control

by tailoring the reproducing kernel and rkhs to the dynamics of the nonlinear optimal control problem we leverage recent advancements in characterizing error bounds from statistical and machine learning theory

we propose a mathematically principled pde

gradient

gradient flow

we propose a mathematically principled pde gradient flow framework for distributionally robust optimization dro

overall we conclude that despite ongoing research efforts and scaling model capabilities frontier

llms

models llms

overall we conclude that despite ongoing research efforts and scaling model capabilities frontier llms remain vulnerable to very simple prompt injections in realistic scenarios

we propose a joint beamforming strategy for designing

transmit

beamforming design

we propose a joint beamforming strategy for designing transmit and receive beamformer vectors at the bs

we argue that statistical physics provides a suitable and necessary framework for analyzing the unfolding of these complex

dynamics

statistical physics

we argue that statistical physics provides a suitable and necessary framework for analyzing the unfolding of these complex dynamics on socio-technological systems

we compare these models in terms of theoretical properties optimization strategies and empirical performance and discuss

applications

machine learning

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

while demonstrated in orchard monitoring the approach can be applied to other outdoor domains

requiring

point tracking

while demonstrated in orchard monitoring the approach can be applied to other outdoor domains requiring robust multimodal perception

nevertheless when networks exhibit a high spectral gap the usual global

centrality

scale-free networks

nevertheless when networks exhibit a high spectral gap the usual global centrality measures typically do not add significant information with respect to the degree i

deep networks have shown remarkable performance across a wide range of tasks yet getting a

global

neural network

deep networks have shown remarkable performance across a wide range of tasks yet getting a global concept-level understanding of how they function remains a key challenge

modeling predator-prey dynamics with stochastic

differential

stochastic differential

modeling predator-prey dynamics with stochastic differential equations patterns of collective hunting and nonlinear predation effects

compared to systems with fixed-length cables introducing the variable-length

cable

cable length

compared to systems with fixed-length cables introducing the variable-length cable adds a new degree of freedom

our experiments reveal that low wer does not necessarily guarantee high key-information accuracy exposing a gap between traditional

metrics

evaluation metrics

our experiments reveal that low wer does not necessarily guarantee high key-information accuracy exposing a gap between traditional metrics and practical intelligibility

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 regions

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

we prove convergence guarantees for both standard and preconditioned vpal under mild assumptions and show that variable projection leads to sharper

convergence

convergence guarantees

we prove convergence guarantees for both standard and preconditioned vpal under mild assumptions and show that variable projection leads to sharper convergence and higher solution quality

recently researchers have been cautioned against using preliminary tests which aim to detect violations of

parallel

treatment effect boundaries

recently researchers have been cautioned against using preliminary tests which aim to detect violations of parallel trends in the pre-treatment period

our results highlight the need for simpler models that align with the available data and propose a distribution-based approach to better capture ecosystem

diversity

phylogenetic diversity

our results highlight the need for simpler models that align with the available data and propose a distribution-based approach to better capture ecosystem diversity stability and competition

simulations and validation on real-world data where ground truth is available

demonstrate

real datasets

simulations and validation on real-world data where ground truth is available demonstrate the advantages of our approach over existing methods

wigner negativity and genuine multipartite entanglement gme are key nonclassical resources that enable

computational

quantum networks

wigner negativity and genuine multipartite entanglement gme are key nonclassical resources that enable computational advantages and broader quantum-information tasks

our method supports sublinear query time batch

queries

query time

our method supports sublinear query time batch queries and extends to the more general turnstile model

these results demonstrate efficiency gains without cognitive change suggesting that current narrow ai

systems

ai systems

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

while this approach has advantages such as independence from appearance the existing methods may break down under

real-world

existing methods

while this approach has advantages such as independence from appearance the existing methods may break down under real-world conditions

acknowledging recent breakthroughs in the context of

deep

deep network

acknowledging recent breakthroughs in the context of deep networks several architectural options have been deployed to analyze supply chain datasets

among these methods deep neural networks have been widely adopted due to their

performance

deep learning

among these methods deep neural networks have been widely adopted due to their performance and accessibility but they require large high-quality datasets

we bring down the quantum computation runtime from 22 years to just 1 day

achieving

quantum error correction

we bring down the quantum computation runtime from 22 years to just 1 day achieving a significant 7

in particular our analysis shows that applying one round of luby s algorithm on the line graph of a delta -regular

graph

regular graphs

in particular our analysis shows that applying one round of luby s algorithm on the line graph of a delta -regular graph results in an almost delta 2 -regular graph

hybrid consistency policy decoupling multi-modal diversity and real-time efficiency in

robotic

multi-robot collaboration

hybrid consistency policy decoupling multi-modal diversity and real-time efficiency in robotic manipulation

the competition format makes it possible to evaluate

overtaking

collision avoidance

the competition format makes it possible to evaluate overtaking and wheel-to-wheel racing algorithms against the state-of-the-art

to solve it we reformulate semi-supervised 3d

action

action recognition

to solve it we reformulate semi-supervised 3d action recognition via active learning from a novel perspective by casting it as a markov decision process mdp

a prevailing theory in cognitive neuroscience proposes that the human brain operates through hierarchical predictive

processing

predictive processing

a prevailing theory in cognitive neuroscience proposes that the human brain operates through hierarchical predictive processing which underlies flexible cognition and behavior by integrating multimodal sensory signals

we present single-zone analytic solutions for the

chemical

stellar mass function

we present single-zone analytic solutions for the chemical evolution of galaxies when the stellar yields are metallicity-dependent

additionally for each generation sample we retrieve a semantically related understanding example to form a retrieved pair linking different but

additionally for each generation sample we retrieve a semantically related understanding example to form a retrieved pair linking different but related data points

this study demonstrates that providing language models with pragmatic theories as prompts is an effective in-context

learning

natural language

this study demonstrates that providing language models with pragmatic theories as prompts is an effective in-context learning approach for tasks to understand implied meanings

1 via low-rank adaptation lora to align neural signals with

visual

vision-language models

1 via low-rank adaptation lora to align neural signals with visual semantics while a controlnet branch conditions generation on saliency maps for spatial control

taken together our study revealed a novel difference of

functional

cognitive science

taken together our study revealed a novel difference of functional property in response to task performance in the sensorimotor areas versus the association areas

contrastive language-image pre-training clip delivers strong cross modal generalization by aligning images and texts in a shared

embedding

representation learning

contrastive language-image pre-training clip delivers strong cross modal generalization by aligning images and texts in a shared embedding space yet it persistently fails at compositional reasoning over objects attributes and relations often behaving like a bag-of-words matcher

we find that the gas in the core is blueshifted by v_z sim-200 km s -1 relative to the brightest cluster galaxy while the low-entropy gas inside the cold front is redshifted by v_z sim 200

km

dense gas

we find that the gas in the core is blueshifted by v_z sim-200 km s -1 relative to the brightest cluster galaxy while the low-entropy gas inside the cold front is redshifted by v_z sim 200 km s -1

our aim is to investigate different types of inner

speech

inner speech

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

we further formalize the generalization problem in meta-reinforcement learning and

establish

minimax optimal

we further formalize the generalization problem in meta-reinforcement learning and establish corresponding generalization bounds

oriented towards reliable estimation of traffic states we propose an index feasible domain size as the uncertainty measurement and transform the optimal uav deployment problem into minimizing the observation uncertainty of network-wide

traffic

reliable estimation

oriented towards reliable estimation of traffic states we propose an index feasible domain size as the uncertainty measurement and transform the optimal uav deployment problem into minimizing the observation uncertainty of network-wide traffic states

the cost function of the mhe optimization

problem

optimization problem

the cost function of the mhe optimization problem is suitably designed to accommodate these irregular output sequences

the emergence of large vision-language models vlms such as gpt and claude enables zero-shot semantic

reasoning

vision-language-action vla

the emergence of large vision-language models vlms such as gpt and claude enables zero-shot semantic reasoning from visual and textual inputs

we evaluate its reasoning behavior across 12 dimensions including spatial geometric physical temporal and embodied

logic

reasoning capabilities

we evaluate its reasoning behavior across 12 dimensions including spatial geometric physical temporal and embodied logic systematically characterizing both its strengths and failure modes

we further find that first-ranked group members and the igl follow distinct growth histories with the igl assembled from a more numerous and systematically lower-mass

population

stellar population

we further find that first-ranked group members and the igl follow distinct growth histories with the igl assembled from a more numerous and systematically lower-mass population than the central object

focusing on simpler statistical methods we examine the design-based properties of regression-based methods for estimating

treatment

average treatment effect

focusing on simpler statistical methods we examine the design-based properties of regression-based methods for estimating treatment effects in time-series experiments

pvmark hinges upon the proof of correct execution of watermark

detection

watermarking schemes

pvmark hinges upon the proof of correct execution of watermark detection on which a set of zkp constraints are built including mapping random number generation comparison and summation

a recent result by anand saranurak and wang soda 2025 also matched this upper

bound

upper bound

a recent result by anand saranurak and wang soda 2025 also matched this upper bound via a deterministic algorithm based on blocking flows

we also provide a novel static and dynamic decomposition achieving an o k log n -approximation when the tree

edit

edit distance

we also provide a novel static and dynamic decomposition achieving an o k log n -approximation when the tree edit distance is at most k

understanding how creativity is represented in the brain s intrinsic functional architecture remains a central challenge in

cognitive

human brain

understanding how creativity is represented in the brain s intrinsic functional architecture remains a central challenge in cognitive neuroscience

molecule and text representation learning has gained increasing interest due to its potential for enhancing the understanding of

chemical

representation learning

molecule and text representation learning has gained increasing interest due to its potential for enhancing the understanding of chemical information

this work theoretically investigates possibilities of using the stimulated raman adiabatic passage stirap and its variants to control a

coherent

coherent control

this work theoretically investigates possibilities of using the stimulated raman adiabatic passage stirap and its variants to control a coherent superposition of quantum states

we compared them against four classical statistical models and two baseline

methods

brain activity

we compared them against four classical statistical models and two baseline methods on spontaneous neural activity recorded from mouse cortex via widefield imaging

augmented reality is projected to be a primary mode of information consumption on the go seamlessly integrating

virtual

virtual reality

augmented reality is projected to be a primary mode of information consumption on the go seamlessly integrating virtual content into the physical world

under this setting we propose a derivative-free stochastic sequential quadratic

programming

quadratic programming

under this setting we propose a derivative-free stochastic sequential quadratic programming df-ssqp method

understanding and modeling human mobility is central to challenges in transport planning sustainable

urban

mobility networks

understanding and modeling human mobility is central to challenges in transport planning sustainable urban design and public health

our recursive algorithm generates each spanning tree in constant

amortized

polynomial time

our recursive algorithm generates each spanning tree in constant amortized time using o n 2 space

moderating role of presence in eeg responses to visuo-haptic prediction error in

virtual

virtual reality

moderating role of presence in eeg responses to visuo-haptic prediction error in virtual reality

we provide faster strongly polynomial time algorithms solving maximum

flow

maximum flow

we provide faster strongly polynomial time algorithms solving maximum flow in structured n -node m -arc networks

skeb provides a foundation for assessing unlearning completeness

robustness

evaluation metrics

skeb provides a foundation for assessing unlearning completeness robustness and overall behavior in llms

finally our model yields a series of novel behavioural predictions the first of which - distributions of dominance and

suppression

human cognition

finally our model yields a series of novel behavioural predictions the first of which - distributions of dominance and suppression durations during tcfs should be approximately equal - we empirically validate in human psychophysical data

this work advances this vision by identifying the fundamental principles of human ai

collaboration

human-machine teaming

this work advances this vision by identifying the fundamental principles of human ai collaboration within uncertainty quantification a key component of reliable decision making

statistical analysis of these silver datasets shows that six discourse relations namely cause purpose contrast cause belief concession and condition play a crucial role in persuasive texts

especially

natural language processing

statistical analysis of these silver datasets shows that six discourse relations namely cause purpose contrast cause belief concession and condition play a crucial role in persuasive texts especially in the use of loaded language exaggeration minimisation repetition and to cast doubt

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 population

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

when such estimates are insufficient to extrapolate effects for broader policy questions such as external validity and general-equilibrium ge effects researchers combine trials with external evidence from reduced-form or

structural

causal inference

when such estimates are insufficient to extrapolate effects for broader policy questions such as external validity and general-equilibrium ge effects researchers combine trials with external evidence from reduced-form or structural observational estimates or prior experiments

however existing approaches to multiclass calibration lack a notion of distance among inputs which makes them vulnerable to proximity bias

predictions

local calibration

however existing approaches to multiclass calibration lack a notion of distance among inputs which makes them vulnerable to proximity bias predictions in sparse regions of the feature space are systematically miscalibrated

through iterative decomposition of the problem into tractable subgoals selection of appropriate analytical

methods

existing methods

through iterative decomposition of the problem into tractable subgoals selection of appropriate analytical methods and validation of intermediate results we reveal how human intuition and machine computation can complement one another

we propose geometrically-regularized world models grwm which enforces that consecutive points along a natural sensory trajectory remain close in latent

representation

world models

we propose geometrically-regularized world models grwm which enforces that consecutive points along a natural sensory trajectory remain close in latent representation space

the main difficulties to treat such problems are the lack of smoothing properties of the linear part of the

hjb

hjb equation

the main difficulties to treat such problems are the lack of smoothing properties of the linear part of the hjb equation the presence of unbounded control operators the presence of state-dependent costs

we perform empirical evaluations on synthetic datasets and

real-world

synthetic data

we perform empirical evaluations on synthetic datasets and real-world examples to characterize and compare mka to its contemporaries

in addition we also contribute to existing reflected sdes based constrained

generative

generative models

in addition we also contribute to existing reflected sdes based constrained generative models where the stochastic dynamics is restricted through an abstract local time term

inference on welfare and value functionals under

optimal

treatment assignment

inference on welfare and value functionals under optimal treatment assignment