178 research outputs found
Sparse Bilinear Logistic Regression
In this paper, we introduce the concept of sparse bilinear logistic
regression for decision problems involving explanatory variables that are
two-dimensional matrices. Such problems are common in computer vision,
brain-computer interfaces, style/content factorization, and parallel factor
analysis. The underlying optimization problem is bi-convex; we study its
solution and develop an efficient algorithm based on block coordinate descent.
We provide a theoretical guarantee for global convergence and estimate the
asymptotical convergence rate using the Kurdyka-{\L}ojasiewicz inequality. A
range of experiments with simulated and real data demonstrate that sparse
bilinear logistic regression outperforms current techniques in several
important applications.Comment: 27 pages, 5 figure
Primordial Black Hole Formation in Starobinsky's Linear Potential Model
We study the power spectrum of the comoving curvature perturbation
in the model that glues two linear potentials of different slopes, originally
proposed by Starobinsky. We find that the enhanced power spectrum reaches its
maximum at the wavenumber which is times the junction scale. The peak is
times larger than the ultraviolet plateau. We also show that its
near-peak behavior can be well approximated by a constant-roll model, once we
define the effective ultra-slow-roll -folding number appropriately by
considering the contribution from non-single-clock phase only. Such an abrupt
transition to non-attractor phase can leave some interesting characteristic
features in the energy spectrum of the scalar-induced gravitational waves,
which are detectable in the space-borne interferometers if the primordial black
holes generated at such a high peak are all the dark matter.Comment: 45 pages, 8 figure
Video Compressive Sensing for Dynamic MRI
We present a video compressive sensing framework, termed kt-CSLDS, to
accelerate the image acquisition process of dynamic magnetic resonance imaging
(MRI). We are inspired by a state-of-the-art model for video compressive
sensing that utilizes a linear dynamical system (LDS) to model the motion
manifold. Given compressive measurements, the state sequence of an LDS can be
first estimated using system identification techniques. We then reconstruct the
observation matrix using a joint structured sparsity assumption. In particular,
we minimize an objective function with a mixture of wavelet sparsity and joint
sparsity within the observation matrix. We derive an efficient convex
optimization algorithm through alternating direction method of multipliers
(ADMM), and provide a theoretical guarantee for global convergence. We
demonstrate the performance of our approach for video compressive sensing, in
terms of reconstruction accuracy. We also investigate the impact of various
sampling strategies. We apply this framework to accelerate the acquisition
process of dynamic MRI and show it achieves the best reconstruction accuracy
with the least computational time compared with existing algorithms in the
literature.Comment: 30 pages, 9 figure
Rapidity scan approach for net-baryon cumulants with a statistical thermal model
Utilizing rapidity-dependent measurements to map the QCD phase diagram
provides a complementary approach to traditional beam-energy-dependent
measurements around midrapidity. The changing nature of thermodynamic
properties of QCD matter along the beam axis in heavy-ion collisions at low
collision energies both motivates and poses challenges for this method. In this
study, we derive the analytical cumulant-generating function for subsystems
within distinct rapidity windows, while accounting for global net-baryon charge
conservation of the full system. Rapidity-dependent net-baryon cumulants are
then calculated for a system exhibiting inhomogeneity along the beam axis, and
their sensitivity to finite acceptances through changing rapidity bin widths is
explored. We highlight the nontrivial behaviors exhibited by these cumulants,
underscoring their importance in establishing a noncritical baseline for
interpreting net-proton cumulants in the search for the QCD critical point.
Finally, we discuss the implications of the rapidity scan for mapping the QCD
phase diagram within the current context.Comment: 16 pages, 9 figures. Content, figures, and references are updated to
the publication versio
Optimal treatment allocation for efficient policy evaluation in sequential decision making
A/B testing is critical for modern technological companies to evaluate the effectiveness of newly developed products against standard baselines. This paper studies optimal designs that aim to maximize the amount of information obtained from online experiments to estimate treatment effects accurately. We propose three optimal allocation strategies in a dynamic setting where treatments are sequentially assigned over time. These strategies are designed to minimize the variance of the treatment effect estimator when data follow a non-Markov decision process or a (time-varying) Markov decision process. We further develop estimation procedures based on existing off-policy evaluation (OPE) methods and conduct extensive experiments in various environments to demonstrate the effectiveness of the proposed methodologies. In theory, we prove the optimality of the proposed treatment allocation design and establish upper bounds for the mean squared errors of the resulting treatment effect estimator
Integrating multi-type aberrations from DNA and RNA through dynamic mapping gene space for subtype-specific breast cancer driver discovery
Driver event discovery is a crucial demand for breast cancer diagnosis and
therapy. Especially, discovering subtype-specificity of drivers can prompt the
personalized biomarker discovery and precision treatment of cancer patients.
still, most of the existing computational driver discovery studies mainly
exploit the information from DNA aberrations and gene interactions. Notably,
cancer driver events would occur due to not only DNA aberrations but also RNA
alternations, but integrating multi-type aberrations from both DNA and RNA is
still a challenging task for breast cancer drivers. On the one hand, the data
formats of different aberration types also differ from each other, known as
data format incompatibility. One the other hand, different types of aberrations
demonstrate distinct patterns across samples, known as aberration type
heterogeneity. To promote the integrated analysis of subtype-specific breast
cancer drivers, we design a "splicing-and-fusing" framework to address the
issues of data format incompatibility and aberration type heterogeneity
respectively. To overcome the data format incompatibility, the "splicing-step"
employs a knowledge graph structure to connect multi-type aberrations from the
DNA and RNA data into a unified formation. To tackle the aberration type
heterogeneity, the "fusing-step" adopts a dynamic mapping gene space
integration approach to represent the multi-type information by vectorized
profiles. The experiments also demonstrate the advantages of our approach in
both the integration of multi-type aberrations from DNA and RNA and the
discovery of subtype-specific breast cancer drivers. In summary, our
"splicing-and-fusing" framework with knowledge graph connection and dynamic
mapping gene space fusion of multi-type aberrations data from DNA and RNA can
successfully discover potential breast cancer drivers with subtype-specificity
indication.Comment: 14 pages, 5 figures, 1 tabl
Generalist Vision Foundation Models for Medical Imaging: A Case Study of Segment Anything Model on Zero-Shot Medical Segmentation
In this paper, we examine the recent Segment Anything Model (SAM) on medical
images, and report both quantitative and qualitative zero-shot segmentation
results on nine medical image segmentation benchmarks, covering various imaging
modalities, such as optical coherence tomography (OCT), magnetic resonance
imaging (MRI), and computed tomography (CT), as well as different applications
including dermatology, ophthalmology, and radiology. Those benchmarks are
representative and commonly used in model development. Our experimental results
indicate that while SAM presents remarkable segmentation performance on images
from the general domain, its zero-shot segmentation ability remains restricted
for out-of-distribution images, e.g., medical images. In addition, SAM exhibits
inconsistent zero-shot segmentation performance across different unseen medical
domains. For certain structured targets, e.g., blood vessels, the zero-shot
segmentation of SAM completely failed. In contrast, a simple fine-tuning of it
with a small amount of data could lead to remarkable improvement of the
segmentation quality, showing the great potential and feasibility of using
fine-tuned SAM to achieve accurate medical image segmentation for a precision
diagnostics. Our study indicates the versatility of generalist vision
foundation models on medical imaging, and their great potential to achieve
desired performance through fine-turning and eventually address the challenges
associated with accessing large and diverse medical datasets in support of
clinical diagnostics.Comment: Published in Diagnostic
Dynamic characteristics and drivers of the regional household energy-carbon-water nexus in China
Being a node of the energy-water consumer and carbon dioxide (CO2) emitter, the household is one key sector to pilot integrated energy-carbon-water (ECW) management. This study developed an integrated framework to explore China’s provincial household ECW nexus as well as their drivers from the years 2000 through 2016. The absolute amount and growth rate of household energy use (HEU), household CO2 emissions (HCE), and household water use (HWU) were abstracted to reveal the dynamic characteristics of the household ECW nexus. Efficiency advance, income growth, urbanization, family size, and household number were defined to explain the changes in the household ECW nexus. This study revealed that there is a huge regional heterogeneity in China’s household ECW nexus. Developed regions such as Zhejiang, Jiangsu, Guangdong, and Shanghai are the most important household ECW nexus nodes with larger amounts and growth rates of household ECW. Income growth overwhelmingly increases ECW, while efficiency advance effectively curbs its growth. Comparatively, household number, family size, and urbanization have small effects. Therefore, implementing differentiated management and focusing on the synergy of socioeconomic factors are the keys to achieving integrated household ECW management. And the analytical framework can be used to analyze ECW nexus from a sector, city, or country perspective
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