100 research outputs found
Conditional Local Convolution for Spatio-temporal Meteorological Forecasting
Spatio-temporal forecasting is challenging attributing to the high
nonlinearity in temporal dynamics as well as complex location-characterized
patterns in spatial domains, especially in fields like weather forecasting.
Graph convolutions are usually used for modeling the spatial dependency in
meteorology to handle the irregular distribution of sensors' spatial location.
In this work, a novel graph-based convolution for imitating the meteorological
flows is proposed to capture the local spatial patterns. Based on the
assumption of smoothness of location-characterized patterns, we propose
conditional local convolution whose shared kernel on nodes' local space is
approximated by feedforward networks, with local representations of coordinate
obtained by horizon maps into cylindrical-tangent space as its input. The
established united standard of local coordinate system preserves the
orientation on geography. We further propose the distance and orientation
scaling terms to reduce the impacts of irregular spatial distribution. The
convolution is embedded in a Recurrent Neural Network architecture to model the
temporal dynamics, leading to the Conditional Local Convolution Recurrent
Network (CLCRN). Our model is evaluated on real-world weather benchmark
datasets, achieving state-of-the-art performance with obvious improvements. We
conduct further analysis on local pattern visualization, model's framework
choice, advantages of horizon maps and etc.Comment: 14 page
Non-equispaced Fourier Neural Solvers for PDEs
Solving partial differential equations is difficult. Recently proposed neural
resolution-invariant models, despite their effectiveness and efficiency,
usually require equispaced spatial points of data. However, sampling in spatial
domain is sometimes inevitably non-equispaced in real-world systems, limiting
their applicability. In this paper, we propose a Non-equispaced Fourier PDE
Solver (\textsc{NFS}) with adaptive interpolation on resampled equispaced
points and a variant of Fourier Neural Operators as its components.
Experimental results on complex PDEs demonstrate its advantages in accuracy and
efficiency. Compared with the spatially-equispaced benchmark methods, it
achieves superior performance with improvements on MAE, and is able
to handle non-equispaced data with a tiny loss of accuracy. Besides, to our
best knowledge, \textsc{NFS} is the first ML-based method with mesh invariant
inference ability to successfully model turbulent flows in non-equispaced
scenarios, with a minor deviation of the error on unseen spatial points.Comment: 27 page
EVNet: An Explainable Deep Network for Dimension Reduction
Dimension reduction (DR) is commonly utilized to capture the intrinsic
structure and transform high-dimensional data into low-dimensional space while
retaining meaningful properties of the original data. It is used in various
applications, such as image recognition, single-cell sequencing analysis, and
biomarker discovery. However, contemporary parametric-free and parametric DR
techniques suffer from several significant shortcomings, such as the inability
to preserve global and local features and the pool generalization performance.
On the other hand, regarding explainability, it is crucial to comprehend the
embedding process, especially the contribution of each part to the embedding
process, while understanding how each feature affects the embedding results
that identify critical components and help diagnose the embedding process. To
address these problems, we have developed a deep neural network method called
EVNet, which provides not only excellent performance in structural
maintainability but also explainability to the DR therein. EVNet starts with
data augmentation and a manifold-based loss function to improve embedding
performance. The explanation is based on saliency maps and aims to examine the
trained EVNet parameters and contributions of components during the embedding
process. The proposed techniques are integrated with a visual interface to help
the user to adjust EVNet to achieve better DR performance and explainability.
The interactive visual interface makes it easier to illustrate the data
features, compare different DR techniques, and investigate DR. An in-depth
experimental comparison shows that EVNet consistently outperforms the
state-of-the-art methods in both performance measures and explainability.Comment: 18 pages, 15 figures, accepted by TVC
Double quantum dot with integrated charge sensor based on Ge/Si heterostructure nanowires
Coupled electron spins in semiconductor double quantum dots hold promise as
the basis for solid-state qubits. To date, most experiments have used III-V
materials, in which coherence is limited by hyperfine interactions. Ge/Si
heterostructure nanowires seem ideally suited to overcome this limitation: the
predominance of spin-zero nuclei suppresses the hyperfine interaction and
chemical synthesis creates a clean and defect-free system with highly
controllable properties. Here we present a top gate-defined double quantum dot
based on Ge/Si heterostructure nanowires with fully tunable coupling between
the dots and to the leads. We also demonstrate a novel approach to charge
sensing in a one-dimensional nanostructure by capacitively coupling the double
dot to a single dot on an adjacent nanowire. The double quantum dot and
integrated charge sensor serve as an essential building block required to form
a solid-state spin qubit free of nuclear spin.Comment: Related work at http://marcuslab.harvard.edu and
http://cmliris.harvard.ed
Challenges in QCD matter physics - The Compressed Baryonic Matter experiment at FAIR
Substantial experimental and theoretical efforts worldwide are devoted to
explore the phase diagram of strongly interacting matter. At LHC and top RHIC
energies, QCD matter is studied at very high temperatures and nearly vanishing
net-baryon densities. There is evidence that a Quark-Gluon-Plasma (QGP) was
created at experiments at RHIC and LHC. The transition from the QGP back to the
hadron gas is found to be a smooth cross over. For larger net-baryon densities
and lower temperatures, it is expected that the QCD phase diagram exhibits a
rich structure, such as a first-order phase transition between hadronic and
partonic matter which terminates in a critical point, or exotic phases like
quarkyonic matter. The discovery of these landmarks would be a breakthrough in
our understanding of the strong interaction and is therefore in the focus of
various high-energy heavy-ion research programs. The Compressed Baryonic Matter
(CBM) experiment at FAIR will play a unique role in the exploration of the QCD
phase diagram in the region of high net-baryon densities, because it is
designed to run at unprecedented interaction rates. High-rate operation is the
key prerequisite for high-precision measurements of multi-differential
observables and of rare diagnostic probes which are sensitive to the dense
phase of the nuclear fireball. The goal of the CBM experiment at SIS100
(sqrt(s_NN) = 2.7 - 4.9 GeV) is to discover fundamental properties of QCD
matter: the phase structure at large baryon-chemical potentials (mu_B > 500
MeV), effects of chiral symmetry, and the equation-of-state at high density as
it is expected to occur in the core of neutron stars. In this article, we
review the motivation for and the physics programme of CBM, including
activities before the start of data taking in 2022, in the context of the
worldwide efforts to explore high-density QCD matter.Comment: 15 pages, 11 figures. Published in European Physical Journal
Neuronal sensitivity to TDP-43 overexpression is dependent on timing of induction
Ubiquitin-immunoreactive neuronal inclusions composed of TAR DNA binding protein of 43 kDa (TDP-43) are a major pathological feature of frontotemporal lobar degeneration (FTLD-TDP). In vivo studies with TDP-43 knockout mice have suggested that TDP-43 plays a critical, although undefined role in development. In the current report, we generated transgenic mice that conditionally express wild-type human TDP-43 (hTDP-43) in the forebrain and established a paradigm to examine the sensitivity of neurons to TDP-43 overexpression at different developmental stages. Continuous TDP-43 expression during early neuronal development produced a complex phenotype, including aggregation of phospho-TDP-43, increased ubiquitin immunoreactivity, mitochondrial abnormalities, neurodegeneration and early lethality. In contrast, later induction of hTDP-43 in the forebrain of weaned mice prevented early death and mitochondrial abnormalities while yielding salient features of FTLD-TDP, including progressive neurodegeneration and ubiquitinated, phospho-TDP-43 neuronal cytoplasmic inclusions. These results suggest that neurons in the developing forebrain are extremely sensitive to TDP-43 overexpression and that timing of TDP-43 overexpression in transgenic mice must be considered when distinguishing normal roles of TDP-43, particularly as they relate to development, from its pathogenic role in FTLD-TDP and other TDP-43 proteinopathies. Finally, our adult induction of hTDP-43 strategy provides a mouse model that develops critical pathological features that are directly relevant for human TDP-43 proteinopathies
Noninvasive Prenatal Diagnosis of Fetal Trisomy 18 and Trisomy 13 by Maternal Plasma DNA Sequencing
Massively parallel sequencing of DNA molecules in the plasma of pregnant women has been shown to allow accurate and noninvasive prenatal detection of fetal trisomy 21. However, whether the sequencing approach is as accurate for the noninvasive prenatal diagnosis of trisomy 13 and 18 is unclear due to the lack of data from a large sample set. We studied 392 pregnancies, among which 25 involved a trisomy 13 fetus and 37 involved a trisomy 18 fetus, by massively parallel sequencing. By using our previously reported standard z-score approach, we demonstrated that this approach could identify 36.0% and 73.0% of trisomy 13 and 18 at specificities of 92.4% and 97.2%, respectively. We aimed to improve the detection of trisomy 13 and 18 by using a non-repeat-masked reference human genome instead of a repeat-masked one to increase the number of aligned sequence reads for each sample. We then applied a bioinformatics approach to correct GC content bias in the sequencing data. With these measures, we detected all (25 out of 25) trisomy 13 fetuses at a specificity of 98.9% (261 out of 264 non-trisomy 13 cases), and 91.9% (34 out of 37) of the trisomy 18 fetuses at 98.0% specificity (247 out of 252 non-trisomy 18 cases). These data indicate that with appropriate bioinformatics analysis, noninvasive prenatal diagnosis of trisomy 13 and trisomy 18 by maternal plasma DNA sequencing is achievable
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