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i develop a nonparametric framework for identifying
spatial
dynamic spatial
i develop a nonparametric framework for identifying spatial boundaries of treatment effects without imposing parametric functional form restrictions
while deep optics with elements like does can encode depth they introduce trade-offs in fabrication complexity and
chromatic
optical properties
while deep optics with elements like does can encode depth they introduce trade-offs in fabrication complexity and chromatic aberrations compromising simplicity
as an alternative to established hole-based photonic crystal cavities we introduce corrugated triangular dinosaur
photonic
integrated photonics
as an alternative to established hole-based photonic crystal cavities we introduce corrugated triangular dinosaur photonic crystal cavities and develop a tapered quasi loss-free cavity-waveguide interface to adiabatically interconvert bloch and waveguide modes
we describe how we adapt the applied soft actor-critic
learning
deep reinforcement learning
we describe how we adapt the applied soft actor-critic learning algorithm to the problem of downlink satellite beamforming and show numerically that the resulting precoding algorithm adjusts to all investigated scenarios
a new neural network paradigm for scalable and generalizable
stability
neural network
a new neural network paradigm for scalable and generalizable stability analysis of power systems
exploring liveability profiles and active
travel
travel information
exploring liveability profiles and active travel patterns in london
however current watermarking solutions hardly resolve the trust issue the non-public watermark
detection
watermarking schemes
however current watermarking solutions hardly resolve the trust issue the non-public watermark detection cannot prove itself faithfully conducting the detection
we find that despite surfacing errors different
language
large language
we find that despite surfacing errors different language models learn interchangeable representations of numbers that are systematic highly accurate and universal across their hidden states and the types of input contexts
difference-in-differences analysis reveals this structural change was driven by regulatory-induced deleveraging of systemically important banks which experienced
differential
european banking
difference-in-differences analysis reveals this structural change was driven by regulatory-induced deleveraging of systemically important banks which experienced differential asset reductions of 17 relative to smaller institutions
specifically we employ a set of variational autoencoder architectures to embed item
values
generative models
specifically we employ a set of variational autoencoder architectures to embed item values into a shared latent space for each time point
we find that this compressibility is strikingly consistent across different individuals and
cognitive
human cognition
we find that this compressibility is strikingly consistent across different individuals and cognitive tasks and that counterintuitively the most important correlations are not necessarily the strongest
specifically it optimizes not only for the estimated objective value but also for the chances that the policy will be statistically significantly better than the observational
policy
policy evaluation
specifically it optimizes not only for the estimated objective value but also for the chances that the policy will be statistically significantly better than the observational policy used to collect data
deviations from the classical description due to the quantum nature of the atomic spins as well as
quantum
quantum technologies
deviations from the classical description due to the quantum nature of the atomic spins as well as quantum fluctuations are usually treated as negligible if long-range order is preserved and are rarely quantified for actual materials
we analyse 19 spiral disks from the cosmological rtnscrimhd azahar suite deriving line-of-sight integrated maps to measure median magnetic-field strength b specific energies thermal turbulent magnetic and cosmic-ray and star formation rate sfr star formation surface density sigma_ mathrm sfr and specific
sfr
star formation rates
we analyse 19 spiral disks from the cosmological rtnscrimhd azahar suite deriving line-of-sight integrated maps to measure median magnetic-field strength b specific energies thermal turbulent magnetic and cosmic-ray and star formation rate sfr star formation surface density sigma_ mathrm sfr and specific sfr ssfr
to evaluate the system-level performance of nonlinear sis we present a case study where sis structures are optimized to minimize the symbol error rate ser in an mimo system with
sis
communication systems
to evaluate the system-level performance of nonlinear sis we present a case study where sis structures are optimized to minimize the symbol error rate ser in an mimo system with sis deployed at both the transmitter and receiver sides using only statistical channel information
our solutions assume a constant star formation efficiency a constant mass-loading factor and that the yields are linearly dependent on the interstellar medium
abundance
massive stars
our solutions assume a constant star formation efficiency a constant mass-loading factor and that the yields are linearly dependent on the interstellar medium abundance with the option of a saturation of the yields at high metallicity
intercalation with various metal species preserves the
phonon
phonon polaritons
intercalation with various metal species preserves the phonon polariton lifetimes while modulating the dielectric permittivity in agreement with the density functional theory and analytical calculations
our results herald the use of quantum computing for simulating strongly
correlated
strongly correlated
our results herald the use of quantum computing for simulating strongly correlated electronic systems beyond the capacity of classical computing
ai agents perform near the floor on rli with the highest-performing
agent
fm agent
ai agents perform near the floor on rli with the highest-performing agent achieving an automation rate of 2
both approximation ratios match known upper bounds on the integrality gap of the natural fractional relaxation
improving
approximation ratio
both approximation ratios match known upper bounds on the integrality gap of the natural fractional relaxation improving upon the best-known approximation of 0
beam shaping techniques for pulsed laser ablation in
liquids
ablation liquids
beam shaping techniques for pulsed laser ablation in liquids unlocking tunable control of nanoparticle synthesis in liquids
witnessing genuine multipartite entanglement in phase space with controlled
gaussian
genuine multipartite
witnessing genuine multipartite entanglement in phase space with controlled gaussian unitaries
second we theoretically analyze the pitfalls of existing evaluation metrics when applied to multiclass
local
local calibration
second we theoretically analyze the pitfalls of existing evaluation metrics when applied to multiclass local calibration
this is important as reliable navigation around other vehicles is vital for safe autonomous
wheel-to-wheel
wheel-to-wheel racing
this is important as reliable navigation around other vehicles is vital for safe autonomous wheel-to-wheel racing
importantly we introduce antagonism effect to describe the intensities with which the two camps incite opposition and exert voting pressure in the run-up to the
election
swing voters
importantly we introduce antagonism effect to describe the intensities with which the two camps incite opposition and exert voting pressure in the run-up to the election typically via us-versus-them framing
although mean-field approximations are commonly invoked to simplify disease forecasting on networks they fail to account for these correlations by assuming that infectious individuals are well-mixed within a population leading to inaccurate predictions of
infection
infectious individuals
although mean-field approximations are commonly invoked to simplify disease forecasting on networks they fail to account for these correlations by assuming that infectious individuals are well-mixed within a population leading to inaccurate predictions of infection numbers over time
plugging regression functions estimated by
machine
machine learning
plugging regression functions estimated by machine learning methods into the identifying equations can yield poor performance because of first-stage bias
numerical studies based on stochastic gradient
descent
gradient descent
numerical studies based on stochastic gradient descent provide empirical backing for our theoretical findings
x-ray and variability selected agn have higher average star formation
rates
star formation rates
x-ray and variability selected agn have higher average star formation rates than those selected with optical narrow line spectroscopic diagrams
our main technical building block is a dynamic balanced binary search tree which we call the
compressed
compressed indexing
our main technical building block is a dynamic balanced binary search tree which we call the compressed tabulation-weighted treap that itself achieves a surprising time space tradeoff
we find that dwarf agn selected by infrared colors are the most distinct
population
dwarf galaxies
we find that dwarf agn selected by infrared colors are the most distinct population with the highest star formation rates and lowest stellar masses
during exploration lagmemo constructs a unified 3d
language
large language
during exploration lagmemo constructs a unified 3d language memory
our main technical contribution is a new reduction that converts locally-decodable codes in the low-error regime into multicast
coding
network coding
our main technical contribution is a new reduction that converts locally-decodable codes in the low-error regime into multicast coding instances
we demonstrate the capabilities of this laser-arpes system by investigating several prototypical materials showcasing its potential for elucidating complex phenomena in
quantum
electronic structure
we demonstrate the capabilities of this laser-arpes system by investigating several prototypical materials showcasing its potential for elucidating complex phenomena in quantum materials
we develop a unified model that relates these factors to the transverse correlations observed in both near and far-field planes treating both
degenerate
-dtc phases
we develop a unified model that relates these factors to the transverse correlations observed in both near and far-field planes treating both degenerate and non-degenerate type-i spdc processes equally
focusing on simpler statistical methods we examine the design-based properties of regression-based methods for
estimating
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
we introduce a corrected ola distance index over
ola
ola vectors
we introduce a corrected ola distance index over ola vectors of k trees which is easily computable in linear time
the intrinsic coupling among electrical conductivity sigma seebeck coefficient s and lattice thermal
conductivity
heat conduction
the intrinsic coupling among electrical conductivity sigma seebeck coefficient s and lattice thermal conductivity kappa_ mathrm l imposes a fundamental limit on the dimensionless figure of merit zt in thermoelectric te materials
combining spectra from the 17th data release of the sloan digital sky survey sdss with radio fluxes from the 2nd data release of the low frequency array lofar two-meter sky survey lotss we statistically characterise a radio loud and radio quiet population using a two-component gaussian mixture model and perform
population
stellar population
combining spectra from the 17th data release of the sloan digital sky survey sdss with radio fluxes from the 2nd data release of the low frequency array lofar two-meter sky survey lotss we statistically characterise a radio loud and radio quiet population using a two-component gaussian mixture model and perform population matching in black hole mass and eddington fraction
centi-combs low-noise sub-ghz repetition-rate soliton frequency
combs
frequency combs
centi-combs low-noise sub-ghz repetition-rate soliton frequency combs from crystalline resonators
however most convergence guarantees rely on restrictive regularity
assumptions
theoretical guarantees
however most convergence guarantees rely on restrictive regularity assumptions on the target distribution -- such as strong log-concavity or bounded support
reinforcement finetuning rft is a key technique for aligning large language models llms with
human
reinforcement learning
reinforcement finetuning rft is a key technique for aligning large language models llms with human preferences and enhancing reasoning yet its effectiveness is highly sensitive to which tasks are explored during training
we present results from what we believe to be the first structured survey of physics experts n 20 regarding both the theoretical possibility of
vacuum
vacuum decay
we present results from what we believe to be the first structured survey of physics experts n 20 regarding both the theoretical possibility of vacuum decay and its potential technological inducibility
we find that sufficiently high levels of noise can reduce the success of
collective
collective action
we find that sufficiently high levels of noise can reduce the success of collective action
in this paper we systematically evaluate llms reasoning capabilities in the
normative
llm inference
in this paper we systematically evaluate llms reasoning capabilities in the normative domain from both logical and modal perspectives
we then separately solve a dirichlet problem for laplace s equation to synthesize a safe textit guidance field that encodes variable levels of caution around
obstacles
obstacle avoidance
we then separately solve a dirichlet problem for laplace s equation to synthesize a safe textit guidance field that encodes variable levels of caution around obstacles -- by enforcing a tunable flux boundary condition
as such the development of mathematical frameworks that account for spatially correlated infections is of great interest as they offer a compromise between accurate
disease
disease transmission
as such the development of mathematical frameworks that account for spatially correlated infections is of great interest as they offer a compromise between accurate disease forecasting and analytic tractability
to address these issues we propose in-context steered
policy
federated 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
we investigate the performance of this approach against naive baselines and assess its
robustness
llm inference
we investigate the performance of this approach against naive baselines and assess its robustness through case studies on both human and llm-judges biases
the operator associated with the underlying partial differential equations pdes is then approximated by a simple multi-layer perceptron mlp which takes as input a
latent
gradient flow
the operator associated with the underlying partial differential equations pdes is then approximated by a simple multi-layer perceptron mlp which takes as input a latent code along with spatiotemporal coordinates to produce the solution in the physical space
joint inference for the regression discontinuity effect and its
external
external validity
joint inference for the regression discontinuity effect and its external validity
model-free robust beamforming in satellite downlink using
reinforcement
reinforcement learning
model-free robust beamforming in satellite downlink using reinforcement learning
the magnetic field lines could be dragged along the
filament
magnetic field
the magnetic field lines could be dragged along the filament as a result of the gas motion induced by the gravitational potential of the filament
we study the problem of adding native pulse-level control to heterogeneous high performance computing-quantum
computing
quantum algorithm
we study the problem of adding native pulse-level control to heterogeneous high performance computing-quantum computing hpcqc software stacks using the munich quantum software stack mqss as a case study
reinforcement learning rl offers a data-driven approach to derive control
policies
deep reinforcement
reinforcement learning rl offers a data-driven approach to derive control policies for such challenges
overall pls-sem provides competitive accuracy with interpretable levers for design and evaluation in
mobile
mobile ar
overall pls-sem provides competitive accuracy with interpretable levers for design and evaluation in mobile ar
in this letter we propose a parametric channel
estimation
channel state information
in this letter we propose a parametric channel estimation method tailored for active riss
in this regime langevin dynamics yields posterior samples when the exact scores of p x are available but it is brittle to score--estimation
error
langevin dynamics
in this regime langevin dynamics yields posterior samples when the exact scores of p x are available but it is brittle to score--estimation error requiring an mgf bound sub-exponential error
here we probe at the single-atom level ultracold
atomic
atom interferometry
here we probe at the single-atom level ultracold atomic fermi gases made of two interacting spin states obtaining direct access to their counting statistics in situ
a three-dimensional reconstruction of the
interstellar
star-forming region
a three-dimensional reconstruction of the interstellar magnetic field toward a star-forming region
the localization of magnetic moments is robust even in non-equilateral nanoflake geometries highlighting their intrinsic stability regardless of the
high
magnetic anisotropy
the localization of magnetic moments is robust even in non-equilateral nanoflake geometries highlighting their intrinsic stability regardless of the high symmetry of the hosting structure
we extend a formal framework that previously derived
time
phylogenetic tree
we extend a formal framework that previously derived time from the multifractal structure of biological lineages hudnall d souza 2025
extending the 3dsi model to heterogeneous networks with a community
structure
mobility networks
extending the 3dsi model to heterogeneous networks with a community structure allows us to devise new analytical formulas for e
in this work we aim to explain this conflict by exploring how
language
large language models llms
in this work we aim to explain this conflict by exploring how language models manipulate numbers and quantify the lower bounds of accuracy of these mechanisms
this paper proposes a coherence-aware communication-efficient framework for joint channel training and model updating in practical
wireless
wireless systems
this paper proposes a coherence-aware communication-efficient framework for joint channel training and model updating in practical wireless fl systems operating under heterogeneous fading dynamics
this involves jointly optimizing active beamforming power allocation receiving filters and ma position configurations which is a highly
non-convex
optimization problem
this involves jointly optimizing active beamforming power allocation receiving filters and ma position configurations which is a highly non-convex problem
these individuals contribute to the spread of the
epidemic
disease transmission
these individuals contribute to the spread of the epidemic and pose a significant challenge to public health policies
occupations generated by four large language
models
large language
occupations generated by four large language models with different ai safety commitments and countries of origin u
we study the online multi-class selection problem with group fairness
guarantees
online algorithm
we study the online multi-class selection problem with group fairness guarantees where limited resources must be allocated to sequentially arriving agents
this algorithm is deterministic and does not need to know the
metric
online algorithm
this algorithm is deterministic and does not need to know the metric space or m in advance
in the limited cases where ground truth is available through exact classical
simulation
classical simulation
in the limited cases where ground truth is available through exact classical simulation we find that it agrees with the results we obtain from the quantum device
shock-driven heating in the circumnuclear star-forming regions of ngc 7582 insights from jwst
nirspec
galactic disk
shock-driven heating in the circumnuclear star-forming regions of ngc 7582 insights from jwst nirspec and miri mrs spectroscopy
we apply inputdsa on recurrent neural networks
rnns
recurrent neural
we apply inputdsa on recurrent neural networks rnns trained with deep reinforcement learning identifying that high-performing networks are dynamically similar to one another while low-performing networks are more diverse
this paper considers the problem of regression over distributions which is becoming increasingly important in
machine
support vector machines
this paper considers the problem of regression over distributions which is becoming increasingly important in machine learning
the quality and completeness of light curves have a direct impact on variability studies particularly for faint
agns
light curves
the quality and completeness of light curves have a direct impact on variability studies particularly for faint agns and high-redshift agns
we learn visual features by captioning images with an image-conditioned masked diffusion
language
vision-language models vlms
we learn visual features by captioning images with an image-conditioned masked diffusion language model a formulation we call masked diffusion captioning mdc
sample-efficient and scalable exploration in
continuous-time
reinforcement learning
sample-efficient and scalable exploration in continuous-time rl
notably under a similar performance guarantee as in our tree
embedding
-errata trees
notably under a similar performance guarantee as in our tree embedding algorithms i
however the convergence behavior of the relative-type inexact variant remains
insufficiently
local convergence
however the convergence behavior of the relative-type inexact variant remains insufficiently understood
predictive coding enhances meta-rl to achieve
interpretable
predictive processing
predictive coding enhances meta-rl to achieve interpretable bayes-optimal belief representation under partial observability
we release gaperon a fully open suite of french-english-coding language models designed to advance
transparency
open-source models
we release gaperon a fully open suite of french-english-coding language models designed to advance transparency and reproducibility in large-scale model training
yet an important question still remains are video models ready to serve as zero-shot reasoners in challenging visual
reasoning
vision-language models
yet an important question still remains are video models ready to serve as zero-shot reasoners in challenging visual reasoning scenarios
to address these limitations we propose adaptive vmc with large language model llm - and lyapunov-based
reinforcement
reinforcement learning
to address these limitations we propose adaptive vmc with large language model llm - and lyapunov-based reinforcement learning rl which preserves the physical interpretability of vmc while supporting stability-guaranteed online adaptation
imports of green intermediates can lower costs while preserving jobs and production whereas
broad
green finance
imports of green intermediates can lower costs while preserving jobs and production whereas broad subsidies are economically unsustainable
this allows us to create universal probes for each
llm
models llms
this allows us to create universal probes for each llm and to trace information -- including the causes of output errors -- to specific layers
our findings introduce a new paradigm in integrated
photonics
photonic devices
our findings introduce a new paradigm in integrated photonics paving the way for ultracompact modulators and highly tunable on-chip communication systems with reduced power consumption
2 we prove conditional lower bounds for pattern matching over 2d-grammar
compressed
compressed indexing
2 we prove conditional lower bounds for pattern matching over 2d-grammar compressed strings
heterogeneous graph neural networks hgnns have emerged as a promising paradigm for
anomaly
graph neural
heterogeneous graph neural networks hgnns have emerged as a promising paradigm for anomaly detection by incorporating type-aware transformations and relation-sensitive aggregation enabling more expressive modeling of complex cyber data
previous work showed that the average entanglement growth after a quantum quench can be explained in terms of pairs of
entangled
multipartite entanglement
previous work showed that the average entanglement growth after a quantum quench can be explained in terms of pairs of entangled quasiparticles performing random walks leading to diffusive entanglement spreading
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
quantum algorithm
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
darts a drone-based ai-powered real-time traffic incident
detection
incident detection
darts a drone-based ai-powered real-time traffic incident detection system
spatial structure can play an important role in the evolution of cooperative behavior and the achievement of
collective
collective systems
spatial structure can play an important role in the evolution of cooperative behavior and the achievement of collective success of a population
self-localization based on a camera often uses a convolutional neural
network
object detection
self-localization based on a camera often uses a convolutional neural network cnn that can extract local features that are calculated by nearby pixels
momentum-resolved reflectivity of a 2d photonic
crystal
photonic crystal
momentum-resolved reflectivity of a 2d photonic crystal in the near-infrared
a minimal quantitative model of perceptual
suppression
visual stimuli
a minimal quantitative model of perceptual suppression and breakthrough in visual rivalry
despite significant advances in multimodal large language models mllms understanding complex
temporal
temporal understanding
despite significant advances in multimodal large language models mllms understanding complex temporal dynamics in videos remains a major challenge
arp 220 w reveals a b-field parallel to the red- and blueshifted outflows in both the dust and emission
line
emission line
arp 220 w reveals a b-field parallel to the red- and blueshifted outflows in both the dust and emission line polarization maps
our study contributes to the scheduling and combinatorial optimization literature with new heuristics discovered by leveraging the power of large
language
large language
our study contributes to the scheduling and combinatorial optimization literature with new heuristics discovered by leveraging the power of large language models llms
distributional evaluation of generative models via relative
density
density-ratio estimation
distributional evaluation of generative models via relative density ratio
reinforcement learning rl is widely used to produce robust robotic manipulation policies but fine-tuning vision-language-action vla
models
vision-language-action vla
reinforcement learning rl is widely used to produce robust robotic manipulation policies but fine-tuning vision-language-action vla models with rl can be unstable due to inaccurate value estimates and sparse supervision at intermediate steps