77 research outputs found
A New Class of Multiple-rate Codes Based on Block Markov Superposition Transmission
Hadamard transform~(HT) as over the binary field provides a natural way to
implement multiple-rate codes~(referred to as {\em HT-coset codes}), where the
code length is fixed but the code dimension can be varied from
to by adjusting the set of frozen bits. The HT-coset codes, including
Reed-Muller~(RM) codes and polar codes as typical examples, can share a pair of
encoder and decoder with implementation complexity of order .
However, to guarantee that all codes with designated rates perform well,
HT-coset coding usually requires a sufficiently large code length, which in
turn causes difficulties in the determination of which bits are better for
being frozen. In this paper, we propose to transmit short HT-coset codes in the
so-called block Markov superposition transmission~(BMST) manner. At the
transmitter, signals are spatially coupled via superposition, resulting in long
codes. At the receiver, these coupled signals are recovered by a sliding-window
iterative soft successive cancellation decoding algorithm. Most importantly,
the performance around or below the bit-error-rate~(BER) of can be
predicted by a simple genie-aided lower bound. Both these bounds and simulation
results show that the BMST of short HT-coset codes performs well~(within one dB
away from the corresponding Shannon limits) in a wide range of code rates
Indocyanine Green-Loaded Polydopamine-Reduced Graphene Oxide Nanocomposites with Amplifying Photoacoustic and Photothermal Effects for Cancer Theranostics
Photoacoustic (PA) imaging and photothermal therapy (PTT) as light-induced theranostic platforms have been attracted much attention in recent years. However, the development of highly efficient and integrated phototheranostic nanoagents for amplifying PA imaging and PTT treatments poses great challenges. Here, we report a novel phototheranostic nanoagent using indocyanine green-loaded polydopamine-reduced graphene oxide nanocomposites (ICG-PDA-rGO) with amplifying PA and PTT effects for cancer theranostics. The results demonstrate that the PDA layer coating on the surface of rGO could effectively absorb a large number of ICG molecules, quench ICG's fluorescence, and enhance the PDA-rGO's optical absorption at 780 nm. The obtained ICG-PDA-rGO exhibits stronger PTT effect and higher PA contrast than that of pure GO and PDA-rGO. After PA imaging-guided PTT treatments, the tumors in 4T1 breast subcutaneous and orthotopic mice models are suppressed completely and no treatment-induced toxicity being observed. It illustrates that the ICG-PDA-rGO nanocomposites constitute a new class of theranostic nanomedicine for amplifying PA imaging and PTT treatments
Topological superconductor candidates PdBiTe and PdBiTe from a generic ab initio strategy
Superconducting topological metals (SCTMs) have recently emerged as a
promising platform of topological superconductivity (TSC) and Majorana zero
modes(MZMs) for quantum computation. Despite their importance in both
fundamental research and applications, SCTMs are very rare in nature. In
addition, some superconductors with topological electronic structures have been
reported recently, but a feasible program to determine their TSC properties is
still lacking. Here, we propose a new strategy to design SCTMs by intercalating
the superconducting units into the topological insulators. A program that
characterizes the superconducting BdG Chern number of 2D BdG Hamiltonian from
ab initio calculations is also developed. Following this strategy,
PdBiTe and PdBiTe are found to be experimentally synthesizable
and ideal SCTMs. Chiral TSC could be realized in such SCTMs by incorporating
topological surface states with Zeeman effect, which can be realized by an
external magnetic field or in proximity to ferromagnetic (FM) insulator. Our
strategy provides a new method for identifying the SCTMs and TSC candidates,
and the program makes it possible to design and modulate the TSC candidates
from ab initio calculations
A rare morphology of the cardiac fibroma in a child: a case report
Here we report a rare morphology of a cardiac fibroma in a child. A 2-year and 8-month-old toddler came for âchronic constipationâ and was found to have a heart murmur on cardiac auscultation. Further transthoracic echocardiography suggested âa strong echogenic mass in the left ventricular wall, with some part of âa string of beadsâ in shape extending into left ventricle outflow tractâ, which was atypical for either a tumor, thrombus or vegetation. The child underwent resection of the mass and mitral valvuloplasty. Pathological examination confirmed the mass as a cardiac fibroma
Intrachromosomal Looping Is Required for Activation of Endogenous Pluripotency Genes during Reprogramming
SummaryGeneration of induced pluripotent stem cells (iPSCs) by defined factors is an extremely inefficient process, because there is a strong epigenetic block preventing cells from achieving pluripotency. Here we report that virally expressed factors bound to the promoters of their target genes to the same extent in both iPSCs and unreprogrammed cells (URCs). However, expression of endogenous pluripotentcy genes was observed only in iPSCs. Comparison of local chromatin structure of the OCT4 locus revealed that there was a cohesin-complex-mediated intrachromosomal loop that juxtaposes a downstream enhancer to the geneâs promoter, enabling activation of endogenous stemness genes. None of these long-range interactions were observed in URCs. Knockdown of the cohesin-complex gene SMC1 by RNAi abolished the intrachromosomal interaction and affected pluripotency. These findings highlight the importance of the SMC1-orchestrated intrachromosomal loop as a critical epigenetic barrier to the induction of pluripotency
PyPose: A Library for Robot Learning with Physics-based Optimization
Deep learning has had remarkable success in robotic perception, but its
data-centric nature suffers when it comes to generalizing to ever-changing
environments. By contrast, physics-based optimization generalizes better, but
it does not perform as well in complicated tasks due to the lack of high-level
semantic information and the reliance on manual parametric tuning. To take
advantage of these two complementary worlds, we present PyPose: a
robotics-oriented, PyTorch-based library that combines deep perceptual models
with physics-based optimization techniques. Our design goal for PyPose is to
make it user-friendly, efficient, and interpretable with a tidy and
well-organized architecture. Using an imperative style interface, it can be
easily integrated into real-world robotic applications. Besides, it supports
parallel computing of any order gradients of Lie groups and Lie algebras and
-order optimizers, such as trust region methods. Experiments
show that PyPose achieves 3-20 speedup in computation compared to
state-of-the-art libraries. To boost future research, we provide concrete
examples across several fields of robotics, including SLAM, inertial
navigation, planning, and control
PyPose v0.6: The Imperative Programming Interface for Robotics
PyPose is an open-source library for robot learning. It combines a
learning-based approach with physics-based optimization, which enables seamless
end-to-end robot learning. It has been used in many tasks due to its
meticulously designed application programming interface (API) and efficient
implementation. From its initial launch in early 2022, PyPose has experienced
significant enhancements, incorporating a wide variety of new features into its
platform. To satisfy the growing demand for understanding and utilizing the
library and reduce the learning curve of new users, we present the fundamental
design principle of the imperative programming interface, and showcase the
flexible usage of diverse functionalities and modules using an extremely simple
Dubins car example. We also demonstrate that the PyPose can be easily used to
navigate a real quadruped robot with a few lines of code
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