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the advent of mass measurements of radial velocities of stars has recently led to a number of interesting results obtained from the analysis of spatial velocities of
stars
stellar mass function
the advent of mass measurements of radial velocities of stars has recently led to a number of interesting results obtained from the analysis of spatial velocities of stars and open star clusters
however existing models primarily focus on general-purpose code
review
code review
however existing models primarily focus on general-purpose code review their effectiveness in identifying and addressing security-related issues remains underexplored
understanding how creativity is represented in the brain s intrinsic
functional
fmri data
understanding how creativity is represented in the brain s intrinsic functional architecture remains a central challenge in cognitive neuroscience
importantly although our approach relies on regression our design-based framework allows for
misspecification
regression function
importantly although our approach relies on regression our design-based framework allows for misspecification of the regression model
next we consider the case of multiple sensors each with its own
wireless
wireless networks
next we consider the case of multiple sensors each with its own wireless transmitter queue and show that our analysis extends to the case of multiple homogeneous sensors
we relate the maximum likelihood estimation problems of these distributions to norm minimization over a group and build a correspondence between stability of data with respect to the group action and the properties of the
likelihood
maximum likelihood
we relate the maximum likelihood estimation problems of these distributions to norm minimization over a group and build a correspondence between stability of data with respect to the group action and the properties of the likelihood function
several researchers introduced different methods to decompose n 1 -bit toffoli gates in a quantum circuit into a set of standard 3-bit toffoli gates or a set of elementary quantum
gates
-bit toffoli
several researchers introduced different methods to decompose n 1 -bit toffoli gates in a quantum circuit into a set of standard 3-bit toffoli gates or a set of elementary quantum gates such as single-qubit and two-qubit gates
leveraging the linearity of hilbert spaces gce also supports simple yet effective control algorithms for synthesizing
optimal
inverse optimal
leveraging the linearity of hilbert spaces gce also supports simple yet effective control algorithms for synthesizing optimal sequences
cycle-factors subject to parity constraints arise naturally in the study of structural
graph
bipartite graphs
cycle-factors subject to parity constraints arise naturally in the study of structural graph theory and algorithmic complexity
building on the eth matrix ansatz and the structure of the out-of-time-order correlator otoc we show that the chaos bound directly constrains the
error
quantum algorithm
building on the eth matrix ansatz and the structure of the out-of-time-order correlator otoc we show that the chaos bound directly constrains the error of an approximate quantum error-correcting code
on hardness and approximation of broadcasting in
sparse
regular graphs
on hardness and approximation of broadcasting in sparse graphs
the value of the basic reproduction number r_0 depends on the attractiveness of traps adjusted relative to infected individuals the dependence on the relative
attractiveness
basic reproduction
the value of the basic reproduction number r_0 depends on the attractiveness of traps adjusted relative to infected individuals the dependence on the relative attractiveness of susceptibles is non-monotone suggesting that there exists an optimal mosquito preference that maximizes disease transmission
mapping habitat connectivity takes geographic analyses a step further evaluating the potential roles of locations in biological invasions pandemics or
species
ecological interactions
mapping habitat connectivity takes geographic analyses a step further evaluating the potential roles of locations in biological invasions pandemics or species conservation
these results highlight the significant room for improving the mathematical
reasoning
llm agents
these results highlight the significant room for improving the mathematical reasoning in current llms
together with other strategies such as immunotherapies nanoparticles and adjunct therapies the use of
viral
viral replication
together with other strategies such as immunotherapies nanoparticles and adjunct therapies the use of viral vectors in clinical trials and in the clinics has been and is still widely studied and pursued
this work propose a new network formation model the vari-linear network which includes two core mechanisms exponential probabilistic growth and
vari-linear
correlation network
this work propose a new network formation model the vari-linear network which includes two core mechanisms exponential probabilistic growth and vari-linear preferential attachment
it introduces two key capabilities automated feedback generation using a fine-tuned large
language
large language models llms
it introduces two key capabilities automated feedback generation using a fine-tuned large language model and visualization of student code submissions to uncover learning patterns
crucially we uncover an emergent capability for instruction-based decoding control the model learns to interpret
natural
natural language
crucially we uncover an emergent capability for instruction-based decoding control the model learns to interpret natural language commands e
pass-enhanced mec joint optimization of task offloading and uplink
pass
beamforming design
pass-enhanced mec joint optimization of task offloading and uplink pass beamforming
these results deepen the understanding of adaptive decision-making in spatial
ecology
ecological interactions
these results deepen the understanding of adaptive decision-making in spatial ecology linking cognitive complexity to ecosystem resilience and extinction risk
to streamline the generation of review comments various automated code review approaches have been proposed where llm-based methods have significantly advanced the capabilities of automated
review
review comments
to streamline the generation of review comments various automated code review approaches have been proposed where llm-based methods have significantly advanced the capabilities of automated review generation
trishul technique for reconstructing magnetic
interstellar
star formation
trishul technique for reconstructing magnetic interstellar structure using starlight polarization
however if the spin chirality were staggered with the opposite signs in the adjacent co layers the net
ahe
magnetic anisotropy
however if the spin chirality were staggered with the opposite signs in the adjacent co layers the net ahe would disappear yielding instead the topological magneto-electric effect
out of the many deep reinforcement learning approaches for autonomous driving only few make use of the
options
reinforcement learning
out of the many deep reinforcement learning approaches for autonomous driving only few make use of the options or skills framework
we introduce textsc flowq-net flow-based quantum
design
quantum algorithm
we introduce textsc flowq-net flow-based quantum design network a generative framework for automated quantum circuit synthesis based on generative flow networks gflownets
although we use nbfesb as an example the method we employ is material agnostic and can be broadly applied efficiently for electronic and thermoelectric materials in general with more than 10x reduction in computational cost compared to fully
ab
ab initio calculations
although we use nbfesb as an example the method we employ is material agnostic and can be broadly applied efficiently for electronic and thermoelectric materials in general with more than 10x reduction in computational cost compared to fully ab initio methods while retaining ab-initio accuracy
riesz regression covariate balancing dre and the matching estimator are methods for estimating the
balancing
riesz regression
riesz regression covariate balancing dre and the matching estimator are methods for estimating the balancing weights where riesz regression is essentially equivalent to dre in the ate context the matching estimator is a special case of dre and dre is in a dual relationship with covariate balancing
integrated sensing and communication isac is a key enabler in 6g networks where
sensing
integrated sensing
integrated sensing and communication isac is a key enabler in 6g networks where sensing and communication capabilities are designed to complement and enhance each other
we note that all the main predictions on galaxy quenched fractions and smbh growth histories and scaling relations are degenerate with those
expected
galactic disk
we note that all the main predictions on galaxy quenched fractions and smbh growth histories and scaling relations are degenerate with those expected in a halo quenching model
waveguide-coupled photonic crystal cavities with a triangular cross section fabricated by angled etching are suitable to interface embedded color centers with flying
photonic
photonic devices
waveguide-coupled photonic crystal cavities with a triangular cross section fabricated by angled etching are suitable to interface embedded color centers with flying photonic qubits in quantum information applications
sequential decision making under uncertainty is central to many
process
decision process
sequential decision making under uncertainty is central to many process systems engineering pse challenges where traditional methods often face limitations related to controlling and optimizing complex and stochastic systems
the system is implemented in ros gazebo turtlebot3 and evaluated with pathbench metrics including success rate
collision
obstacle avoidance
the system is implemented in ros gazebo turtlebot3 and evaluated with pathbench metrics including success rate collision rate path efficiency and re-planning efficiency in dynamic and partially observable environments
in this work we propose a framework for modeling diverse persona-based
preferences
preference data
in this work we propose a framework for modeling diverse persona-based preferences by learning to aggregate outputs from multiple rubric-conditioned judges
specifically braincognizer introduces two modules the cognitive integration module which incorporates prior human knowledge to extract hierarchical region semantics and the
cognitive
cognitive science
specifically braincognizer introduces two modules the cognitive integration module which incorporates prior human knowledge to extract hierarchical region semantics and the cognitive correlation module which captures contextual semantic relationships across regions
here we present the latest generation of lsm-ms2 a large-scale
deep
deep learning
here we present the latest generation of lsm-ms2 a large-scale deep learning foundation model trained on millions of spectra to learn a semantic chemical space
multimodal large language models mllms exhibit a pronounced preference for textual inputs when processing vision-language
data
large language models llms
multimodal large language models mllms exhibit a pronounced preference for textual inputs when processing vision-language data limiting their ability to reason effectively from visual evidence
in this study we employ recent nonlinear dynamical system techniques to uncover the core dynamics of several
rnns
recurrent neural networks
in this study we employ recent nonlinear dynamical system techniques to uncover the core dynamics of several rnns used in contemporary neuroscience
the algorithm first computes a fractional solution using a resource reservation approach -- referred to as the set-aside mechanism -- to enforce
fairness
integer programs
the algorithm first computes a fractional solution using a resource reservation approach -- referred to as the set-aside mechanism -- to enforce fairness across classes
the potential applications of our sensing protocols range from measuring
single-photon
single photons
the potential applications of our sensing protocols range from measuring single-photon scattering to searches for dark matter
the appearance of a virtual avatar significantly influences its perceived appropriateness and the user s
experience
virtual reality
the appearance of a virtual avatar significantly influences its perceived appropriateness and the user s experience particularly in healthcare applications
a critical visual computation is to construct global scene properties from activities of early visual cortical
neurons
brain decoding
a critical visual computation is to construct global scene properties from activities of early visual cortical neurons which have small receptive fields
geohabnet an r package for mapping habitat
connectivity
ecological communities
geohabnet an r package for mapping habitat connectivity for biosecurity and conservation
we propose tokenization of events and present a tokenizer
spiking
spiking patches
we propose tokenization of events and present a tokenizer spiking patches specifically designed for event cameras
in this paper we prove that a graph is of ferrer dimension three equivalent to the intersection bigraph of orthants and points in mathbb r 3 if and only if it admits a biadjacency matrix representation that does not contain gamma begin bmatrix 1 1 0 1 0 1 end bmatrix and
delta
regular graphs
in this paper we prove that a graph is of ferrer dimension three equivalent to the intersection bigraph of orthants and points in mathbb r 3 if and only if it admits a biadjacency matrix representation that does not contain gamma begin bmatrix 1 1 0 1 0 1 end bmatrix and delta begin bmatrix 1 0 1 1 0 1 end bmatrix wher...
we find a total of 20 mechanisms of third-order
nonlinear
third-order nonlinear transport
we find a total of 20 mechanisms of third-order nonlinear transport by developing a comprehensive theory that treats the geometric effects and disorder scattering on an equal footing
we determine the asymptotic type distribution observed in a single host in the limit of
large
large population
we determine the asymptotic type distribution observed in a single host in the limit of large host and virus populations under asymptotic rate assumptions by tracing back the ancestry of the sample
here we introduce the biased-independence q -voter model a generalization of the q -voter model with independence one of the most popular agent-based models of
opinion
opinion dynamics
here we introduce the biased-independence q -voter model a generalization of the q -voter model with independence one of the most popular agent-based models of opinion dynamics
in addition to the vertical excitation the effect of structural relaxation is also estimated using analytical
atomic
molecular dynamics
in addition to the vertical excitation the effect of structural relaxation is also estimated using analytical atomic forces
building upon multidimensional opinion model our results highlight the possibility of manipulating swing
voters
swing voters
building upon multidimensional opinion model our results highlight the possibility of manipulating swing voters and shaping electoral outcomes through antagonistic strategies of political parties
tidal disruption events with sph-exa resolving the
return
tidal field
tidal disruption events with sph-exa resolving the return of the stream
moreover we observe a positive correlation between forecasting and
classification
support vector machines
moreover we observe a positive correlation between forecasting and classification performance
we derived the formalism for arbitrary observed orientation of the galaxy cgm model and quasar line of
sight
galaxy cgm
we derived the formalism for arbitrary observed orientation of the galaxy cgm model and quasar line of sight positioning
our result generalizes prior almost-optimal parallel 1
epsilon
approximation guarantee
our result generalizes prior almost-optimal parallel 1 epsilon -approximation algorithms for these special cases including shortest paths rozhen haeupler marinsson grunau zuzic stoc 23 and max flow with only edge capacities
extensive numerical experiments demonstrate that adasdbo delivers competitive performance compared to existing decentralized
bilevel
bilevel optimization
extensive numerical experiments demonstrate that adasdbo delivers competitive performance compared to existing decentralized bilevel optimization methods while exhibiting remarkable robustness across diverse stepsize configurations
furthermore we extend our scheme to generate a three-photon state capable of extracting an einstein-podolsky-rosen pair from two initial
states
multipartite entanglement
furthermore we extend our scheme to generate a three-photon state capable of extracting an einstein-podolsky-rosen pair from two initial states lacking this capability revealing a previously unobserved entanglement superactivation phenomenon
monte carlo studies support our theory demonstrating reduced bias and improved
coverage
monte carlo
monte carlo studies support our theory demonstrating reduced bias and improved coverage relative to existing procedures using bart
simulating and experimenting with social media mobilization using
llm
llm agents
simulating and experimenting with social media mobilization using llm agents
there are two main problems in the task of predicting the dynamic evolution of complex networks on the one hand existing methods usually use simple graphs to describe the relationships in
complex
complex systems
there are two main problems in the task of predicting the dynamic evolution of complex networks on the one hand existing methods usually use simple graphs to describe the relationships in complex networks however this approach can only capture pairwise relationships while there may be rich non-pairwise structured relat...
while large language models llms have demonstrated remarkable performance across various reasoning tasks their ability to handle
normative
reasoning capabilities
while large language models llms have demonstrated remarkable performance across various reasoning tasks their ability to handle normative reasoning remains underexplored
quadratic truncated random return in distributional
lqr
truncated random return
quadratic truncated random return in distributional lqr positive definiteness density and log-concavity
data-driven stabilization using prior knowledge on
stabilizability
linear control
data-driven stabilization using prior knowledge on stabilizability and controllability
we demonstrate a fully integrable and reconfigurable platform for controlling
quantum
integrated photonics
we demonstrate a fully integrable and reconfigurable platform for controlling quantum emission by harnessing chiral bound states in the continuum bics as a higher-order non-hermitian singularity
however current research often adopts techno-centric approaches focusing primarily on technical attributes such as reliability robustness and fairness while overlooking the sociotechnical dimensions critical to understanding ai
trustworthiness
artificial intelligence
however current research often adopts techno-centric approaches focusing primarily on technical attributes such as reliability robustness and fairness while overlooking the sociotechnical dimensions critical to understanding ai trustworthiness in real-world contexts
recent advances in data collection and technology enable a deeper understanding of complex
urban
urban systems
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
this work lays a foundation for future quantum
computing
quantum networks
this work lays a foundation for future quantum computing investigations of more complex and physically rich fermion-boson quantum field theories in higher dimensions
we find that simply imposing the m_ rm bh
-
quiescent galaxies
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
a unified theory for causal inference direct
debiased
machine learning
a unified theory for causal inference direct debiased machine learning via bregman-riesz regression
using crystal structure prediction and first-principles calculations we mapped the
phase
phase transition
using crystal structure prediction and first-principles calculations we mapped the phase diagram of binary he-rg systems up to 1 tpa
stellar-mass compact objects cos embedded in active
galactic
dwarf galaxies
stellar-mass compact objects cos embedded in active galactic nucleus agn discs are commonly assumed to accrete via bondi or bondi-hoyle-lyttleton bhl prescriptions neglecting gas angular momentum
for small-scale open-source models reinforcement
learning
reinforcement learning
for small-scale open-source models reinforcement learning with verifiable rewards rlvr fails when correct solutions are rarely sampled even after many attempts while supervised fine-tuning sft tends to overfit long demonstrations through rigid token-by-token imitation
this entanglement biases conventional brain
encoding
brain decoding
this entanglement biases conventional brain encoding analyses toward linguistically shallow features e
quantitative evaluation with metrics such as the clip maximum mean discrepancy cmmd score and structural similarity demonstrates that our models are able to effectively restore images and offer a two- to fourfold reduction in image acquisition time by accurately reconstructing
images
image reconstruction
quantitative evaluation with metrics such as the clip maximum mean discrepancy cmmd score and structural similarity demonstrates that our models are able to effectively restore images and offer a two- to fourfold reduction in image acquisition time by accurately reconstructing images from sparsely sampled data
however achieving coherent control across such broad
spectral
coherent control
however achieving coherent control across such broad spectral ranges remains challenging due to detuning and spatial field inhomogeneities which reduce pe efficiency
spikefit towards optimal deployment of spiking networks on
neuromorphic
spike train
spikefit towards optimal deployment of spiking networks on neuromorphic hardware
effect of intratumor heterogeneity in managing the go-or-grow dichotomy of cancer cells a
game
game theory
effect of intratumor heterogeneity in managing the go-or-grow dichotomy of cancer cells a game theory modeling to understand metastasis
in addition we obtain several further structural results about global and local minimizers of
tensor
tensor product
in addition we obtain several further structural results about global and local minimizers of tensor product energies
in this work we study the limits of compressed
data
compressed indexing
in this work we study the limits of compressed data structures i
for calibration guarantees that fall short of decision
calibration
predictive performance
for calibration guarantees that fall short of decision calibration the minimax optimal decision rule is still efficiently computable and we provide an empirical evaluation of a natural one that applies to any regression model solved to optimize squared error
with the rapid development of large language models llms various
llm-based
llm inference
with the rapid development of large language models llms various llm-based works have been widely applied in educational fields
efficiency without cognitive change evidence from human
interaction
ai assistance
efficiency without cognitive change evidence from human interaction with narrow ai systems
hybrid deep learning approaches for classifying
autism
deep learning
hybrid deep learning approaches for classifying autism from brain mri
when compared to the latter in a centralized dse setting our method reduced computation
time
computationally efficient
when compared to the latter in a centralized dse setting our method reduced computation time by 1440x
existing benchmarks assess only end-to-end build
test
evaluation metrics
existing benchmarks assess only end-to-end build test success obscuring where and why agents succeed or fail
within this system framework we formulate an optimization problem for the purpose of maximizing the minimum rate of users for each cell via designing the transmit beamforming of the trtc subject to the
power
power allocation
within this system framework we formulate an optimization problem for the purpose of maximizing the minimum rate of users for each cell via designing the transmit beamforming of the trtc subject to the power constraints of each trtc unit
this paper investigates the construction of channel knowledge map ckm from sparse
channel
channel estimation
this paper investigates the construction of channel knowledge map ckm from sparse channel measurements
we study the propagation speed of bistable traveling
waves
traveling waves
we study the propagation speed of bistable traveling waves in the classical two-component diffusive lotka-volterra system under strong competition
the depth of the observational data used here enables us to
construct
host galaxy
the depth of the observational data used here enables us to construct complete unbiased samples of galaxies down to mstar 10 7 msun and out to z 0
self-supervised learning ssl holds a great deal of promise for applications in neuroscience due to the lack of
large-scale
cognitive neuroscience
self-supervised learning ssl holds a great deal of promise for applications in neuroscience due to the lack of large-scale consistently labeled neural datasets
in the single-user scenario it is proved that the
optimal
beamforming design
in the single-user scenario it is proved that the optimal pinching antenna pa positions are independent of the transmit beamforming
moderating role of presence in eeg responses to visuo-haptic prediction error in
virtual
physical virtual
moderating role of presence in eeg responses to visuo-haptic prediction error in virtual reality
we argue that such abstraction leads to oversimplification of reasoning methodologies from nlp ml and results in a distortion of
llms
large language models llms
we argue that such abstraction leads to oversimplification of reasoning methodologies from nlp ml and results in a distortion of llms empirically studied capabilities and un known limitations
the ppo agent uses an actor-critic neural
network
deep reinforcement
the ppo agent uses an actor-critic neural network trained from trajectories generated by the python simulator with configurable mobility e
we find that the gas in the core is blueshifted by v_z sim-200 km s -1 relative to the brightest
cluster
star clusters
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
in these models the retrieval of a particular memory can be hampered by overlaps between the network state and other memories termed spurious overlaps since these overlaps collectively introduce
noise
continual learning
in these models the retrieval of a particular memory can be hampered by overlaps between the network state and other memories termed spurious overlaps since these overlaps collectively introduce noise in the retrieval process
identifying geometric third-order nonlinear
transport
nonlinear transport
identifying geometric third-order nonlinear transport in disordered materials
we find that simply imposing the m_ rm bh
-
massive galaxies
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
we aim to assess what types of sfhs are consistent with the observed present-day star formation rates text sfr _0 and time-averaged star formation
rates
stellar mass
we aim to assess what types of sfhs are consistent with the observed present-day star formation rates text sfr _0 and time-averaged star formation rates langle text sfr rangle of galaxies in the local volume without assuming any fixed functional form
reciprocity deficits observing ai in the street with
everyday
trustworthy ai
reciprocity deficits observing ai in the street with everyday publics
we outline a multi-phase research methodology focused on preparing re datasets fine-tuning ai models and designing collaborative
human-ai
ai systems
we outline a multi-phase research methodology focused on preparing re datasets fine-tuning ai models and designing collaborative human-ai workflows
leveraging semiparametric theory we derive efficient influence functions and construct consistent asymptotically normal estimators via debiased
machine
debiased machine learning
leveraging semiparametric theory we derive efficient influence functions and construct consistent asymptotically normal estimators via debiased machine learning