WFTNet: Exploiting Global and Local Periodicity in Long-term Time Series Forecasting

Recent CNN and Transformer-based models tried to utilize frequency and periodicity information for long-term time series forecasting. However, most existing work is based on Fourier transform, which cannot capture fine-grained and local frequency structure. In this paper, we propose a Wavelet-Fourier Transform Network (WFTNet) for long-term time series forecasting. WFTNet utilizes both Fourier and wavelet transforms to extract comprehensive temporal-frequency information from the signal, where Fourier transform captures the global periodic patterns and wavelet transform captures the local ones. Furthermore, we introduce a Periodicity-Weighted Coefficient (PWC) to adaptively balance the importance of global and local frequency patterns. Extensive experiments on various time series datasets show that WFTNet consistently outperforms other state-of-the-art baseline

Neuromatch Academy: a 3-week, online summer school in computational neuroscience

Neuromatch Academy (https://academy.neuromatch.io; (van Viegen et al., 2021)) was designed as an online summer school to cover the basics of computational neuroscience in three weeks. The materials cover dominant and emerging computational neuroscience tools, how they complement one another, and specifically focus on how they can help us to better understand how the brain functions. An original component of the materials is its focus on modeling choices, i.e. how do we choose the right approach, how do we build models, and how can we evaluate models to determine if they provide real (meaningful) insight. This meta-modeling component of the instructional materials asks what questions can be answered by different techniques, and how to apply them meaningfully to get insight about brain function

Low-Complexity Compressed Sensing-Aided Coherent Direction-of-Arrival Estimation for Large-Scale Lens Antenna Array

This paper delves into a novel compressed sensing (CS) strategy for estimating the directions of incoming signals in a coherent environment using a lens antenna array (LAA). In comparison to the well-known subspace-based algorithm family, CS techniques, such as the conventional orthogonal matching pursuit (COMP), can effectively address the direction-of-arrival (DoA) estimation problem requiring prior knowledge about the number of signals and offer lower complexity. However, they are susceptible to noise and can be adversely affected by multipath distortion. Leveraging the energy-concentrating property of an LAA, we first introduce the signal covariance matrix-based OMP (SCM-OMP) method that enhances the angular estimation performance, even in low-SNR regions. Subsequently, we propose the multiple sub-covariance matrices-based OMP (MSCM-OMP) to achieve a reduction in computational complexity. Simulation results demonstrate that the MSCM-OMP scheme also outperforms other high-resolution DoA estimation methods.</p

Integrated Multi-Omics Perspective to Strengthen the Understanding of Salt Tolerance in Rice

Salt stress is one of the major constraints to rice cultivation worldwide. Thus, the development of salt-tolerant rice cultivars becomes a hotspot of current rice breeding. Achieving this goal depends in part on understanding how rice responds to salt stress and uncovering the molecular mechanism underlying this trait. Over the past decade, great efforts have been made to understand the mechanism of salt tolerance in rice through genomics, transcriptomics, proteomics, metabolomics, and epigenetics. However, there are few reviews on this aspect. Therefore, we review the research progress of omics related to salt tolerance in rice and discuss how these advances will promote the innovations of salt-tolerant rice breeding. In the future, we expect that the integration of multi-omics salt tolerance data can accelerate the solution of the response mechanism of rice to salt stress, and lay a molecular foundation for precise breeding of salt tolerance

Controlled and Regioselective Ring-Opening Polymerization for Poly(disulfide)s by Anion-Binding Catalysis

Poly(disulfide)s are an emerging class of sulfur-containing polymers with applications in medicine, energy, and functional materials. However, the constituent dynamic covalent S−S bond is highly reactive in the presence of sulfide (RS−) anion, imposing a persistent challenge to control the polymerization. Here, we report an anion-binding approach to arrest the high reactivity of RS− chain end to control the synthesis of linear poly(disulfide)s, realizing a rapid, living ring-opening polymerization of 1,2-dithiolanes with narrow dispersity and high regioregularity (Mw /Mn ~ 1.1, Ps ~ 0.85). Mechanistic studies support the formation of a thiourea-base-sulfide ternary complex as the catalytically active species during the chain propagation. Theoretical analyses reveal a synergistic catalytic model where the catalyst preorganizes the protonated base and anionic chain end to establish spatial confinement over the bound monomer, effecting the observed regioselectivity. The catalytic system is amenable to monomers with various functional groups, and semicrystalline polymers are also obtained from lipoic acid derivatives by enhancing the regioregularity