DeepMapping2: Self-Supervised Large-Scale LiDAR Map Optimization

LiDAR mapping is important yet challenging in self-driving and mobile robotics. To tackle such a global point cloud registration problem, DeepMapping converts the complex map estimation into a self-supervised training of simple deep networks. Despite its broad convergence range on small datasets, DeepMapping still cannot produce satisfactory results on large-scale datasets with thousands of frames. This is due to the lack of loop closures and exact cross-frame point correspondences, and the slow convergence of its global localization network. We propose DeepMapping2 by adding two novel techniques to address these issues: (1) organization of training batch based on map topology from loop closing, and (2) self-supervised local-to-global point consistency loss leveraging pairwise registration. Our experiments and ablation studies on public datasets (KITTI, NCLT, and Nebula) demonstrate the effectiveness of our method. Our code will be released

Genome-wide identification and expression profile analysis of the Hsp20 gene family in Barley (Hordeum vulgare L.)

In plants, heat shock proteins (Hsps) play important roles in response to diverse stresses. Hsp20 is the major family of Hsps, but their role remains poorly understood in barley (Hordeum vulgare L.). To reveal the mechanisms of barley Hsp20s (HvHsp20s) response to stress conditions, we performed a comprehensive genome-wide analysis of the HvHsp20 gene family using bioinformatics-based methods. In total, 38 putative HvHsp20s were identified in barley and grouped into four subfamilies (C, CP, PX, and MT) based on predicted subcellular localization and their phylogenetic relationships. A sequence analysis indicated that most HvHsp20 genes have no intron or one with a relatively short length. In addition, the same group of HvHsp20 proteins in the phylogenetic tree shared similar gene structure and motifs, indicating that they were highly conserved and might have similar function. Based on RNA-seq data analysis, we showed that the transcript levels of HvHsp20 genes could be induced largely by abiotic and biotic stresses such as heat, salt, and powdery mildew. Three HvHsp20 genes, HORVU7Hr1G036540, HORVU7Hr1G036470, and HORVU3Hr1G007500, were up-regulated under biotic and abiotic stresses, suggesting their potential roles in mediating the response of barley plants to environment stresses. These results provide valuable information for further understanding the complex mechanisms of HvHsp20 gene family in barley

Congestion Control Based on Multiple Model Adaptive Control

The congestion controller based on the multiple model adaptive control is designed for the network congestion in TCP/AQM network. As the conventional congestion control is sensitive to the variable network condition, the adaptive control method is adopted in our congestion control. The multiple model adaptive control is introduced in this paper based on the weight calculation instead of the parameter estimation in past adaptive control. The model set is composed by the dynamic model based on the fluid flow. And three “local” congestion controllers are nonlinear output feedback controller based on variable RTT, H2 output feedback controller, and proportional-integral controller, respectively. Ns-2 simulation results in section 4 indicate that the proposed algorithm restrains the congestion in variable network condition and maintains a high throughput together with a low packet drop ratio

COMPARISON OF SOME BIOMECHANICS PARAMETERS OF BREASTSTROKE SWIMMERS IN FLUME AND SWIMMING POOL

The purpose of this study was to compare some parameters of breaststroke swimmers in a swimming pool with those for breaststroke swimming in the flume, to search whether there is some difference between two test circumstances of swimming pool and flume in technical parameters. Four male breaststroke swimmers aged between16 and 18 years were studied. Subjects were required to swim in a 25m pool for best or familiar stroke length and tried to decrease stroke rate, and performed at three minute intervals at speeds ranging from 70% to 100% of the best performance of individuals. Subjects were familiarized to flume swimming on the day prior to be tested, then swam at the same speed based upon conversion from pool in swimming flume. According to testing we found that stroke rate, stroke length and efficiency index for pool and swimming flume at corresponding speeds were similar. Of course, there was as expected significant difference in the stroke rate and stroke length used between subjects to swim at the various speeds

Countertraveling waves in rotating Rayleigh-Bénard convection

Linear and nonlinear counter-traveling waves in a fluid-filled annular cylinder with realistic no-slip boundary conditions uniformly heated from below and rotating about a vertical axis are investigated. When the gap of the annular cylinder is moderate, there exist two three-dimensional traveling waves driven by convective instabilities: a retrograde mode localized near the outer sidewall and a prograde mode adjacent to the inner sidewall with a different wave number, frequency and critical Rayleigh number. It is found that the retrogradely propagating mode is always more unstable and is marked by a larger azimuthal wave number. When the Rayleigh number is sufficiently large, both the counter-traveling modes can be excited and nonlinearly interacting, leading to an unusual nonlinear phenomenon in rotating Rayleigh-Bénard convection

MIMOSA: Multi-constraint Molecule Sampling for Molecule Optimization

Molecule optimization is a fundamental task for accelerating drug discovery, with the goal of generating new valid molecules that maximize multiple drug properties while maintaining similarity to the input molecule. Existing generative models and reinforcement learning approaches made initial success, but still face difficulties in simultaneously optimizing multiple drug properties. To address such challenges, we propose the MultI-constraint MOlecule SAmpling (MIMOSA) approach, a sampling framework to use input molecule as an initial guess and sample molecules from the target distribution. MIMOSA first pretrains two property agnostic graph neural networks (GNNs) for molecule topology and substructure-type prediction, where a substructure can be either atom or single ring. For each iteration, MIMOSA uses the GNNs' prediction and employs three basic substructure operations (add, replace, delete) to generate new molecules and associated weights. The weights can encode multiple constraints including similarity and drug property constraints, upon which we select promising molecules for next iteration. MIMOSA enables flexible encoding of multiple property- and similarity-constraints and can efficiently generate new molecules that satisfy various property constraints and achieved up to 49.6% relative improvement over the best baseline in terms of success rate.Comment: Accepted by AAAI 202

VideoMamba: State Space Model for Efficient Video Understanding

Addressing the dual challenges of local redundancy and global dependencies in video understanding, this work innovatively adapts the Mamba to the video domain. The proposed VideoMamba overcomes the limitations of existing 3D convolution neural networks and video transformers. Its linear-complexity operator enables efficient long-term modeling, which is crucial for high-resolution long video understanding. Extensive evaluations reveal VideoMamba's four core abilities: (1) Scalability in the visual domain without extensive dataset pretraining, thanks to a novel self-distillation technique; (2) Sensitivity for recognizing short-term actions even with fine-grained motion differences; (3) Superiority in long-term video understanding, showcasing significant advancements over traditional feature-based models; and (4) Compatibility with other modalities, demonstrating robustness in multi-modal contexts. Through these distinct advantages, VideoMamba sets a new benchmark for video understanding, offering a scalable and efficient solution for comprehensive video understanding. All the code and models are available at https://github.com/OpenGVLab/VideoMamba.Comment: 19 Pages, 7 Figures, 8 Table

Towards Explainable Artificial Intelligence (XAI): A Data Mining Perspective

Given the complexity and lack of transparency in deep neural networks (DNNs), extensive efforts have been made to make these systems more interpretable or explain their behaviors in accessible terms. Unlike most reviews, which focus on algorithmic and model-centric perspectives, this work takes a "data-centric" view, examining how data collection, processing, and analysis contribute to explainable AI (XAI). We categorize existing work into three categories subject to their purposes: interpretations of deep models, referring to feature attributions and reasoning processes that correlate data points with model outputs; influences of training data, examining the impact of training data nuances, such as data valuation and sample anomalies, on decision-making processes; and insights of domain knowledge, discovering latent patterns and fostering new knowledge from data and models to advance social values and scientific discovery. Specifically, we distill XAI methodologies into data mining operations on training and testing data across modalities, such as images, text, and tabular data, as well as on training logs, checkpoints, models and other DNN behavior descriptors. In this way, our study offers a comprehensive, data-centric examination of XAI from a lens of data mining methods and applications