Dish-TS: A General Paradigm for Alleviating Distribution Shift in Time Series Forecasting

The distribution shift in Time Series Forecasting (TSF), indicating series distribution changes over time, largely hinders the performance of TSF models. Existing works towards distribution shift in time series are mostly limited in the quantification of distribution and, more importantly, overlook the potential shift between lookback and horizon windows. To address above challenges, we systematically summarize the distribution shift in TSF into two categories. Regarding lookback windows as input-space and horizon windows as output-space, there exist (i) intra-space shift, that the distribution within the input-space keeps shifted over time, and (ii) inter-space shift, that the distribution is shifted between input-space and output-space. Then we introduce, Dish-TS, a general neural paradigm for alleviating distribution shift in TSF. Specifically, for better distribution estimation, we propose the coefficient net (CONET), which can be any neural architectures, to map input sequences into learnable distribution coefficients. To relieve intra-space and inter-space shift, we organize Dish-TS as a Dual-CONET framework to separately learn the distribution of input- and output-space, which naturally captures the distribution difference of two spaces. In addition, we introduce a more effective training strategy for intractable CONET learning. Finally, we conduct extensive experiments on several datasets coupled with different state-of-the-art forecasting models. Experimental results show Dish-TS consistently boosts them with a more than 20% average improvement. Code is available.Comment: Accepted by AAAI 202

Actin capping protein regulates postsynaptic spine development through CPI-motif interactions

Dendritic spines are small actin-rich protrusions essential for the formation of functional circuits in the mammalian brain. During development, spines begin as dynamic filopodia-like protrusions that are then replaced by relatively stable spines containing an expanded head. Remodeling of the actin cytoskeleton plays a key role in the formation and modification of spine morphology, however many of the underlying regulatory mechanisms remain unclear. Capping protein (CP) is a major actin regulating protein that caps the barbed ends of actin filaments, and promotes the formation of dense branched actin networks. Knockdown of CP impairs the formation of mature spines, leading to an increase in the number of filopodia-like protrusions and defects in synaptic transmission. Here, we show that CP promotes the stabilization of dendritic protrusions, leading to the formation of stable mature spines. However, the localization and function of CP in dendritic spines requires interactions with proteins containing a capping protein interaction (CPI) motif. We found that the CPI motif-containing protein Twinfilin-1 (Twf1) also localizes to spines where it plays a role in CP spine enrichment. The knockdown of Twf1 leads to an increase in the density of filopodia-like protrusions and a decrease in the stability of dendritic protrusions, similar to CP knockdown. Finally, we show that CP directly interacts with Shank and regulates its spine accumulation. These results suggest that spatiotemporal regulation of CP in spines not only controls the actin dynamics underlying the formation of stable postsynaptic spine structures, but also plays an important role in the assembly of the postsynaptic apparatus underlying synaptic function

Boosting Urban Traffic Speed Prediction via Integrating Implicit Spatial Correlations

Urban traffic speed prediction aims to estimate the future traffic speed for improving the urban transportation services. Enormous efforts have been made on exploiting spatial correlations and temporal dependencies of traffic speed evolving patterns by leveraging explicit spatial relations (geographical proximity) through pre-defined geographical structures ({\it e.g.}, region grids or road networks). While achieving promising results, current traffic speed prediction methods still suffer from ignoring implicit spatial correlations (interactions), which cannot be captured by grid/graph convolutions. To tackle the challenge, we propose a generic model for enabling the current traffic speed prediction methods to preserve implicit spatial correlations. Specifically, we first develop a Dual-Transformer architecture, including a Spatial Transformer and a Temporal Transformer. The Spatial Transformer automatically learns the implicit spatial correlations across the road segments beyond the boundary of geographical structures, while the Temporal Transformer aims to capture the dynamic changing patterns of the implicit spatial correlations. Then, to further integrate both explicit and implicit spatial correlations, we propose a distillation-style learning framework, in which the existing traffic speed prediction methods are considered as the teacher model, and the proposed Dual-Transformer architectures are considered as the student model. The extensive experiments over three real-world datasets indicate significant improvements of our proposed framework over the existing methods

WGIT*: Workspace-Guided Informed Tree for Motion Planning in Restricted Environments

The motion planning of robots faces formidable challenges in restricted environments, particularly in the aspects of rapidly searching feasible solutions and converging towards optimal solutions. This paper proposes Workspace-guided Informed Tree (WGIT*) to improve planning efficiency and ensure high-quality solutions in restricted environments. Specifically, WGIT* preprocesses the workspace by constructing a hierarchical structure to obtain critical restricted regions and connectivity information sequentially. The refined workspace information guides the sampling and exploration of WGIT*, increasing the sample density in restricted areas and prioritizing the search tree exploration in promising directions, respectively. Furthermore, WGIT* utilizes gradually enriched configuration space information as feedback to rectify the guidance from the workspace and balance the information of the two spaces, which leads to efficient convergence toward the optimal solution. The theoretical analysis highlights the valuable properties of the proposed WGIT*. Finally, a series of simulations and experiments verify the ability of WGIT* to quickly find initial solutions and converge towards optimal solutions

WGIT*: Workspace-Guided Informed Tree for Motion Planning in Restricted Environments

The motion planning of robots faces formidable challenges in restricted environments, particularly in the aspects of rapidly searching feasible solutions and converging towards optimal solutions. This paper proposes Workspace-guided Informed Tree (WGIT*) to improve planning efficiency and ensure high-quality solutions in restricted environments. Specifically, WGIT* preprocesses the workspace by constructing a hierarchical structure to obtain critical restricted regions and connectivity information sequentially. The refined workspace information guides the sampling and exploration of WGIT*, increasing the sample density in restricted areas and prioritizing the search tree exploration in promising directions, respectively. Furthermore, WGIT* utilizes gradually enriched configuration space information as feedback to rectify the guidance from the workspace and balance the information of the two spaces, which leads to efficient convergence toward the optimal solution. The theoretical analysis highlights the valuable properties of the proposed WGIT*. Finally, a series of simulations and experiments verify the ability of WGIT* to quickly find initial solutions and converge towards optimal solutions

De novo mutations in ARID1B associated with both syndromic and non-syndromic short stature

Supplementary data 2. (JPEG 378 kb

A novel compound heterozygous mutation of the CLCN7 gene is associated with autosomal recessive osteopetrosis

Osteopetrosis is a genetic condition of the skeleton characterized by increased bone density caused by osteoclast formation and function defects. Osteopetrosis is inherited in the form of autosomal dominant and autosomal recessive manner. We report autosomal recessive osteopetrosis (ARO; OMIM 611490) in a Chinese case with a history of scarce leukocytosis, vision and hearing loss, frequent seizures, and severe intellectual and motor disability. Whole-exome sequencing (WES) followed by Sanger sequencing revealed novel compound heterozygous mutations in the chloride channel 7 (CLCN7) gene [c.982-1G > C and c.1208G > A (p. Arg403Gln)] in the affected individual, and subsequent familial segregation showed that each parent had transmitted a mutation. Our results confirmed that mutations in the CLCN7 gene caused ARO in a Chinese family. Additionally, our study expanded the clinical and allelic spectrum of the CLCN7 gene and enhanced the applications of WES technology in determining the etiology of prenatal diagnoses in fetuses with ultrasound anomalies

An Empirical Study on Campus Dwelling Environment Quality in Beijing and Its Influencing Factors

Combining with the current dwelling environmental assessment system, this article reviews the domestic and foreign theoretical documents, and tries to construct an evaluation model based on the influencing factors of campus dwelling environment quality in Beijing, including natural landscape, amenities and cultural environment. The research indicates that the campus dwelling environment quality is linearly related with and can be effectively predicted by these three factors. It also shows that the regression coefficient of cultural environment is the highest among the three; but most interviewees didn’t appraise their campus dwelling environment quality high. Therefore, colleges in Beijing need to improve especially in the following three aspects – gas power system (natural landscape), population density (amenities) and manager quality (cultural environment) – to make the campus dwelling environment clean pleasant and eco-friendly.Key words: Universities in Beijing; Dwelling environment; Natural landscape; Amenities; Cultural environmen