Exploring the association between sibling relationship quality, parenting styles, and theory-of-mind development in Chinese young adolescents: a preliminary analysis

Siblings’ influence has often been overshadowed globally by other family factors. This research gap is especially pronounced in China, largely due to the four-decade-long One-Child policy. To date, only one study by Hou et al. (2022) has examined the role of sibling relationship quality (SRQ) in the theory of mind (ToM) development among Chinese children. Since this study, along with previous research predominantly from Western cultures, has focused on preschoolers, the present study sought to employ a cross-sectional, multimethod, multi-informant correlational design to verify the relationship between Chinese young adolescents’ SRQ and ToM performance. Furthermore, this study incorporated parenting styles as a parental factor, which has been demonstrated to influence both SRQ and ToM. Consequently, the second aim of this research was to explore the parenting-ToM and parenting-SRQ correlations. Lastly, the study also assessed the associations between sibling structures (number of siblings, birth order, age gap, gender composition) and ToM. Thirty families (comprising 30 young adolescents aged 11-12 years, their 30 siblings, and 30 parents) participated in individual online research sessions, with one family per session. In each session, the SRQ was gauged through questionnaires completed by both children in the sibling pair, as well as by researcher observations during a cooperative drawing game called Etch-a-Sketch Online. The ToM skills of the young adolescents were evaluated using the performance-based task, Strange Stories. Parenting styles were explored through interviews. This study found that young adolescents’ self-rated sibling intimacy was negatively associated with cognitive ToM. In contrast, the parents’ strategy of referring to their own feelings when addressing social incidents with children, termed as Parent Emotions, was positively correlated with cognitive ToM scores. Moreover, when accounting for the Parent Emotions strategy, the self-rated sibling intimacy remained a significant predictor of cognitive ToM. Young adolescents with parents who often employed the Active Non-interference strategy (i.e., non-involvement based on trust in children) scored higher on emotional ToM tasks, while those with parents favouring Practical Solutions (i.e., taking direct action to solve conflicts without discussing with children) scored lower. Further, the study revealed that parents who frequently used Active Non-interference had children with more negatively observed SRQ by researchers. In terms of demographic information, all sibling structure variables showed no correlation with ToM abilities. However, the parents’ sibling status (with versus without siblings) was found to be related to their parenting styles. This study not only bridges the research gap but also offers insightful avenues for future research

Minimizing Co-Sleeping Disruptions: A design intervention

A recent survey indicates that 30% of adults in the United States sleep separately from their partners because of co-sleep disruptions (Rogojanski et al., 2013). However, research suggests that sleeping together is good for couple relationships (Holt-Lunstad, Smith, & Layton, 2010). While there are many products on the market designed to help individuals with sleep problems, there are few that address co-sleeping problems. This thesis addresses this gap in the market for co- sleeping products by exploring how a design intervention of bedroom furniture can help couples sleep together despite the disruptions to each other. This paper records the design process and the development of a functional, full-scale prototype. The resulting design is the “ECO-sleep system.” The product is effective because it minimizes disruptions caused by sound and light. However, it does not completely eliminate them. This paper provides conclusions about design potential and limitations, and recommendations for improving the design of the ECO Sleep System

From Interpolation to Extrapolation: Complete Length Generalization for Arithmetic Transformers

Since its introduction, the transformer model has demonstrated outstanding performance across various tasks. However, there are still unresolved issues regarding length generalization, particularly in algorithmic tasks. In this paper, we investigate the inherent capabilities of transformer models in learning arithmetic algorithms, such as addition and multiplication. Through experiments and attention analysis, we identify a number of crucial factors for achieving optimal length generalization. We show that transformer models are able to generalize to long lengths with the help of targeted attention biasing. We then introduce Attention Bias Calibration (ABC), a calibration stage that enables the model to automatically learn the proper attention biases, which we link to mechanisms in relative position encoding. We demonstrate that using ABC, the transformer model can achieve unprecedented perfect length generalization on certain arithmetic tasks

High-Latitude Ionospheric Field-Aligned Currents (FACs) Derived With Inverse and Assimilative Analysis of Iridium Magnetic Perturbation Data: New Insights Gained

The engineering-grade magnetic perturbation data from the Iridium satellite constellation, made available through the NSF-funded Active Magnetosphere and Planetary Electrodynamics Response Experiment (AMPERE) program, provides unprecedented global coverage for use in magnetosphere-ionospheric-thermosphere research. In this thesis, high-latitude field-aligned current (FAC) patterns are reconstructed from inverse and assimilative analysis of Iridium magnetic perturbation data. New scientific insights on dynamic FAC response to solar wind disturbances are gained from the analysis and FAC patterns and summarized here. Dominant modes of variability in FACs are associated with categories of solar wind drivers: (1) slow flow; (2) high-speed streams and (3) transient flow associated with coronal mass ejections (CMEs) and (4) all solar wind drivers combined. These are found using empirical orthogonal function (EOF) analysis. Correlations with the interplanetary magnetic field (IMF), solar wind plasma and solar wind-magnetosphere coupling are found for different solar wind drivers and EOFs. The two hemispheres show interhemispheric asymmetries in both spatial patterns and correlation with drivers. The EOFs are then used to construct a fixed background model and background error covariance for use in an optimal interpolation (OI) method. FAC patterns are reconstructed in two hemispheres with a 2-min cadence for two event studies with this procedure. Examples of intriguing FAC patterns under different IMF and solar wind conditions are presented. Interplanetary (IP) shocks, which are a category of strong solar wind disturbance, are studied using superposed epoch analysis. The geoeffectiveness of (1) nearly frontal shocks (NFS) where the IP shock hits the Earth head on and (2) highly inclined shocks (HIS) where the IP shock hits the Earth with a large inclination is examined in terms of the difference in high-latitude FACs response. Results derived from observations verify earlier findings from simulations that NFSs result in sharper increase in FACs and higher total currents. NFSs are in general stronger shocks than HISs in terms of solar wind parameters and therefore cause stronger FAC response. Noon, dusk and pre-noon regions are found to show the most notable difference in FAC response to the two groups of shocks, which can be valuable information for space weather application.</p

PanoVPR: Towards Unified Perspective-to-Equirectangular Visual Place Recognition via Sliding Windows across the Panoramic View

Visual place recognition has gained significant attention in recent years as a crucial technology in autonomous driving and robotics. Currently, the two main approaches are the perspective view retrieval (P2P) paradigm and the equirectangular image retrieval (E2E) paradigm. However, it is practical and natural to assume that users only have consumer-grade pinhole cameras to obtain query perspective images and retrieve them in panoramic database images from map providers. To address this, we propose \textit{PanoVPR}, a perspective-to-equirectangular (P2E) visual place recognition framework that employs sliding windows to eliminate feature truncation caused by hard cropping. Specifically, PanoVPR slides windows over the entire equirectangular image and computes feature descriptors for each window, which are then compared to determine place similarity. Notably, our unified framework enables direct transfer of the backbone from P2P methods without any modification, supporting not only CNNs but also Transformers. To facilitate training and evaluation, we derive the Pitts250k-P2E dataset from the Pitts250k and establish YQ360, latter is the first P2E visual place recognition dataset collected by a mobile robot platform aiming to simulate real-world task scenarios better. Extensive experiments demonstrate that PanoVPR achieves state-of-the-art performance and obtains 3.8% and 8.0% performance gain on Pitts250k-P2E and YQ360 compared to the previous best method, respectively. Code and datasets will be publicly available at https://github.com/zafirshi/PanoVPR.Comment: Accepted to ITSC 2023. Code and datasets will be made available at https://github.com/zafirshi/PanoVP

Soundify: Matching Sound Effects to Video

In the art of video editing, sound helps add character to an object and immerse the viewer within a space. Through formative interviews with professional editors (N=10), we found that the task of adding sounds to video can be challenging. This paper presents Soundify, a system that assists editors in matching sounds to video. Given a video, Soundify identifies matching sounds, synchronizes the sounds to the video, and dynamically adjusts panning and volume to create spatial audio. In a human evaluation study (N=889), we show that Soundify is capable of matching sounds to video out-of-the-box for a diverse range of audio categories. In a within-subjects expert study (N=12), we demonstrate the usefulness of Soundify in helping video editors match sounds to video with lighter workload, reduced task completion time, and improved usability.Comment: Full paper in UIST 2023; Short paper in NeurIPS 2021 ML4CD Workshop; Online demo: http://soundify.c

High-performance cVEP-BCI under minimal calibration

The ultimate goal of brain-computer interfaces (BCIs) based on visual modulation paradigms is to achieve high-speed performance without the burden of extensive calibration. Code-modulated visual evoked potential-based BCIs (cVEP-BCIs) modulated by broadband white noise (WN) offer various advantages, including increased communication speed, expanded encoding target capabilities, and enhanced coding flexibility. However, the complexity of the spatial-temporal patterns under broadband stimuli necessitates extensive calibration for effective target identification in cVEP-BCIs. Consequently, the information transfer rate (ITR) of cVEP-BCI under limited calibration usually stays around 100 bits per minute (bpm), significantly lagging behind state-of-the-art steady-state visual evoked potential-based BCIs (SSVEP-BCIs), which achieve rates above 200 bpm. To enhance the performance of cVEP-BCIs with minimal calibration, we devised an efficient calibration stage involving a brief single-target flickering, lasting less than a minute, to extract generalizable spatial-temporal patterns. Leveraging the calibration data, we developed two complementary methods to construct cVEP temporal patterns: the linear modeling method based on the stimulus sequence and the transfer learning techniques using cross-subject data. As a result, we achieved the highest ITR of 250 bpm under a minute of calibration, which has been shown to be comparable to the state-of-the-art SSVEP paradigms. In summary, our work significantly improved the cVEP performance under few-shot learning, which is expected to expand the practicality and usability of cVEP-BCIs.Comment: 35 pages, 5 figure

Single-Cell Transcriptome and Network Analyses Unveil Key Transcription Factors Regulating Mesophyll Cell Development in Maize

BACKGROUND: Maize mesophyll (M) cells play important roles in various biological processes such as photosynthesis II and secondary metabolism. Functional differentiation occurs during M-cell development, but the underlying mechanisms for regulating M-cell development are largely unknown. RESULTS: We conducted single-cell RNA sequencing (scRNA-seq) to profile transcripts in maize leaves. We then identified coregulated modules by analyzing the resulting pseudo-time-series data through gene regulatory network analyses. , , , and () families were highly expressed in the early stage, whereas () and families were highly expressed in the late stage of M-cell development. Construction of regulatory networks revealed that these transcript factor (TF) families, especially and , were the major players in the early and later stages of M-cell development, respectively. Integration of scRNA expression matrix with TF ChIP-seq and Hi-C further revealed regulatory interactions between these TFs and their targets. and were primarily expressed in the leaf bases and tips, respectively, and their targets were validated with protoplast-based ChIP-qPCR, with the binding sites of HSF1 being experimentally confirmed. CONCLUSIONS: Our study provides evidence that several TF families, with the involvement of epigenetic regulation, play vital roles in the regulation of M-cell development in maize

Early life malnutrition and risk of T2DM adulthood: evidence from the lower socioeconomic status of northwest Chinese population

ObjectiveThis study aimed to explore whether famine exposure during early life are associated with a high risk of Type 2 Diabetes Mellitus (T2DM) in adulthood and the role of socioeconomic status (SES) on this effect.Materials and methodsWe conducted a secondary data analysis based on data from a cross-sectional survey, collected 3,355 participants born between January 1, 1941 and December 31, 1966. Participants were categorized into four groups based on their date of birth, unexposed (individuals born in 1963–1966), infant exposed (individuals born in 1959–1962), childhood exposed (individuals born in 1949–1958), and adolescent exposed (born in 1941–1948). The association of famine exposure with T2DM risk in adults and conducted separately in plain area and mountain area was assessed using logistics regression model.Result22.35% of participants were diagnosed with T2DM, of which 43.47% were from the childhood famine-exposed group, representing the highest proportion among all subgroups (p &lt; 0.001). Participants exposed to famine during childhood and adolescence from the lower SES mountain areas showed a significantly higher prevalence of T2DM in adulthood than those from the plain areas (p &lt; 0.001). The adolescence stage exposed famine will increase the risk of T2DM in the mountain area (OR 2.46, 95% CI 1.61, 3.77).ConclusionNo strong evidence demonstrates that exposure to famine during the early life stage increases the risk of developing T2DM in adulthood. However, populations with lower SES are likely to be exposed to more risk factors for T2DM

CoBEV: Elevating Roadside 3D Object Detection with Depth and Height Complementarity

Roadside camera-driven 3D object detection is a crucial task in intelligent transportation systems, which extends the perception range beyond the limitations of vision-centric vehicles and enhances road safety. While previous studies have limitations in using only depth or height information, we find both depth and height matter and they are in fact complementary. The depth feature encompasses precise geometric cues, whereas the height feature is primarily focused on distinguishing between various categories of height intervals, essentially providing semantic context. This insight motivates the development of Complementary-BEV (CoBEV), a novel end-to-end monocular 3D object detection framework that integrates depth and height to construct robust BEV representations. In essence, CoBEV estimates each pixel's depth and height distribution and lifts the camera features into 3D space for lateral fusion using the newly proposed two-stage complementary feature selection (CFS) module. A BEV feature distillation framework is also seamlessly integrated to further enhance the detection accuracy from the prior knowledge of the fusion-modal CoBEV teacher. We conduct extensive experiments on the public 3D detection benchmarks of roadside camera-based DAIR-V2X-I and Rope3D, as well as the private Supremind-Road dataset, demonstrating that CoBEV not only achieves the accuracy of the new state-of-the-art, but also significantly advances the robustness of previous methods in challenging long-distance scenarios and noisy camera disturbance, and enhances generalization by a large margin in heterologous settings with drastic changes in scene and camera parameters. For the first time, the vehicle AP score of a camera model reaches 80% on DAIR-V2X-I in terms of easy mode. The source code will be made publicly available at https://github.com/MasterHow/CoBEV.Comment: The source code will be made publicly available at https://github.com/MasterHow/CoBE