352 research outputs found
Observation of higher-order topological states on a quantum computer
Programmable quantum simulators such as superconducting quantum processors
and ultracold atomic lattices represent rapidly developing emergent technology
that may one day qualitatively outperform existing classical computers. Yet,
apart from a few breakthroughs, the range of viable computational applications
with current-day noisy intermediate-scale quantum (NISQ) devices is still
significantly limited by gate errors, quantum decoherence, and the number of
high-quality qubits. In this work, we develop an approach that places NISQ
hardware as a particularly suitable platform for simulating multi-dimensional
condensed matter systems, including lattices beyond three dimensions which are
difficult to realize or probe in other settings. By fully exploiting the
exponentially large Hilbert space of a quantum chain, we encoded a
high-dimensional model in terms of non-local many-body interactions that can
further be systematically transcribed into quantum gates. We demonstrate the
power of our approach by realizing, on IBM transmon-based quantum computers,
higher-order topological states in up to four dimensions, which are exotic
phases that have never been realized in any quantum setting. With the aid of
in-house circuit compression and error mitigation techniques, we measured the
topological state dynamics and their protected mid-gap spectra to a high degree
of accuracy, as benchmarked by reference exact diagonalization data. The time
and memory needed with our approach scale favorably with system size and
dimensionality compared to exact diagonalization on classical computers.Comment: 21 pages, 8 figures in main text; 4 pages, 2 tables in supplementary
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A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection
Time series are the primary data type used to record dynamic system
measurements and generated in great volume by both physical sensors and online
processes (virtual sensors). Time series analytics is therefore crucial to
unlocking the wealth of information implicit in available data. With the recent
advancements in graph neural networks (GNNs), there has been a surge in
GNN-based approaches for time series analysis. Approaches can explicitly model
inter-temporal and inter-variable relationships, which traditional and other
deep neural network-based methods struggle to do. In this survey, we provide a
comprehensive review of graph neural networks for time series analysis
(GNN4TS), encompassing four fundamental dimensions: Forecasting,
classification, anomaly detection, and imputation. Our aim is to guide
designers and practitioners to understand, build applications, and advance
research of GNN4TS. At first, we provide a comprehensive task-oriented taxonomy
of GNN4TS. Then, we present and discuss representative research works and,
finally, discuss mainstream applications of GNN4TS. A comprehensive discussion
of potential future research directions completes the survey. This survey, for
the first time, brings together a vast array of knowledge on GNN-based time
series research, highlighting both the foundations, practical applications, and
opportunities of graph neural networks for time series analysis.Comment: 27 pages, 6 figures, 5 table
A Mechanism for the Polarity Formation of Chemoreceptors at the Growth Cone Membrane for Gradient Amplification during Directional Sensing
Accurate response to external directional signals is essential for many physiological functions such as chemotaxis or axonal guidance. It relies on the detection and amplification of gradients of chemical cues, which, in eukaryotic cells, involves the asymmetric relocalization of signaling molecules. How molecular events coordinate to induce a polarity at the cell level remains however poorly understood, particularly for nerve chemotaxis. Here, we propose a model, inspired by single-molecule experiments, for the membrane dynamics of GABA chemoreceptors in nerve growth cones (GCs) during directional sensing. In our model, transient interactions between the receptors and the microtubules, coupled to GABA-induced signaling, provide a positive-feedback loop that leads to redistribution of the receptors towards the gradient source. Using numerical simulations with parameters derived from experiments, we find that the kinetics of polarization and the steady-state polarized distribution of GABA receptors are in remarkable agreement with experimental observations. Furthermore, we make predictions on the properties of the GC seen as a sensing, amplification and filtering module. In particular, the growth cone acts as a low-pass filter with a time constant ∼10 minutes determined by the Brownian diffusion of chemoreceptors in the membrane. This filtering makes the gradient amplification resistent to rapid fluctuations of the external signals, a beneficial feature to enhance the accuracy of neuronal wiring. Since the model is based on minimal assumptions on the receptor/cytoskeleton interactions, its validity extends to polarity formation beyond the case of GABA gradient sensing. Altogether, it constitutes an original positive-feedback mechanism by which cells can dynamically adapt their internal organization to external signals
Association analyses of East Asian individuals and trans-ancestry analyses with European individuals reveal new loci associated with cholesterol and triglyceride levels
Large-scale meta-analyses of genome-wide association studies (GWAS) have identified >175 loci associated with fasting cholesterol levels, including total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and triglycerides (TG). With differences in linkage disequilibrium (LD) structure and allele frequencies between ancestry groups, studies in additional large samples may detect new associations. We conducted staged GWAS meta-analyses in up to 69,414 East Asian individuals from 24 studies with participants from Japan, the Philippines, Korea, China, Singapore, and Taiwan. These meta-analyses identified (P < 5 × 10-8) three novel loci associated with HDL-C near CD163-APOBEC1 (P = 7.4 × 10-9), NCOA2 (P = 1.6 × 10-8), and NID2-PTGDR (P = 4.2 × 10-8), and one novel locus associated with TG near WDR11-FGFR2 (P = 2.7 × 10-10). Conditional analyses identified a second signal near CD163-APOBEC1. We then combined results from the East Asian meta-analysis with association results from up to 187,365 European individuals from the Global Lipids Genetics Consortium in a trans-ancestry meta-analysis. This analysis identified (log10Bayes Factor ≥6.1) eight additional novel lipid loci. Among the twelve total loci identified, the index variants at eight loci have demonstrated at least nominal significance with other metabolic traits in prior studies, and two loci exhibited coincident eQTLs (P < 1 × 10-5) in subcutaneous adipose tissue for BPTF and PDGFC. Taken together, these analyses identified multiple novel lipid loci, providing new potential therapeutic targets
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