Bullion: A Column Store for Machine Learning

The past two decades have witnessed columnar storage revolutionizing data warehousing and analytics. However, the rapid growth of machine learning poses new challenges to this domain. This paper presents Bullion, a columnar storage system tailored for machine learning workloads. Bullion addresses the complexities of data compliance, optimizes the encoding of long sequence sparse features, efficiently manages wide-table projections, and introduces feature quantization in storage. By aligning with the evolving requirements of ML applications, Bullion extends columnar storage to various scenarios, from advertising and recommendation systems to the expanding realm of Generative AI. Preliminary experimental results and theoretical analysis demonstrate Bullion's superior performance in handling the unique demands of machine learning workloads compared to existing columnar storage solutions. Bullion significantly reduces I/O costs for deletion compliance, achieves substantial storage savings with its optimized encoding scheme for sparse features, and drastically improves metadata parsing speed for wide-table projections. These advancements position Bullion as a critical component in the future of machine learning infrastructure, enabling organizations to efficiently manage and process the massive volumes of data required for training and inference in modern AI applications.

ATFNet: Adaptive Time-Frequency Ensembled Network for Long-term Time Series Forecasting

The intricate nature of time series data analysis benefits greatly from the distinct advantages offered by time and frequency domain representations. While the time domain is superior in representing local dependencies, particularly in non-periodic series, the frequency domain excels in capturing global dependencies, making it ideal for series with evident periodic patterns. To capitalize on both of these strengths, we propose ATFNet, an innovative framework that combines a time domain module and a frequency domain module to concurrently capture local and global dependencies in time series data. Specifically, we introduce Dominant Harmonic Series Energy Weighting, a novel mechanism for dynamically adjusting the weights between the two modules based on the periodicity of the input time series. In the frequency domain module, we enhance the traditional Discrete Fourier Transform (DFT) with our Extended DFT, designed to address the challenge of discrete frequency misalignment. Additionally, our Complex-valued Spectrum Attention mechanism offers a novel approach to discern the intricate relationships between different frequency combinations. Extensive experiments across multiple real-world datasets demonstrate that our ATFNet framework outperforms current state-of-the-art methods in long-term time series forecasting.

NeuPAN: Direct Point Robot Navigation with End-to-End Model-based Learning

Navigating a nonholonomic robot in a cluttered environment requires extremely accurate perception and locomotion for collision avoidance. This paper presents NeuPAN: a real-time, highly-accurate, map-free, robot-agnostic, and environment-invariant robot navigation solution. Leveraging a tightly-coupled perception-locomotion framework, NeuPAN has two key innovations compared to existing approaches: 1) it directly maps raw points to a learned multi-frame distance space, avoiding error propagation from perception to control; 2) it is interpretable from an end-to-end model-based learning perspective, enabling provable convergence. The crux of NeuPAN is to solve a high-dimensional end-to-end mathematical model with various point-level constraints using the plug-and-play (PnP) proximal alternating-minimization network (PAN) with neurons in the loop. This allows NeuPAN to generate real-time, end-to-end, physically-interpretable motions directly from point clouds, which seamlessly integrates data- and knowledge-engines, where its network parameters are adjusted via back propagation. We evaluate NeuPAN on car-like robot, wheel-legged robot, and passenger autonomous vehicle, in both simulated and real-world environments. Experiments demonstrate that NeuPAN outperforms various benchmarks, in terms of accuracy, efficiency, robustness, and generalization capability across various environments, including the cluttered sandbox, office, corridor, and parking lot. We show that NeuPAN works well in unstructured environments with arbitrary-shape undetectable objects, making impassable ways passable.

Self-supervised Adaptive Weighting for Cooperative Perception in V2V Communications

Perception of the driving environment is critical for collision avoidance and route planning to ensure driving safety. Cooperative perception has been widely studied as an effective approach to addressing the shortcomings of single-vehicle perception. However, the practical limitations of vehicle-to-vehicle (V2V) communications have not been adequately investigated. In particular, current cooperative fusion models rely on supervised models and do not address dynamic performance degradation caused by arbitrary channel impairments. In this paper, a self-supervised adaptive weighting model is proposed for intermediate fusion to mitigate the adverse effects of channel distortion. The performance of cooperative perception is investigated in different system settings. Rician fading and imperfect channel state information (CSI) are also considered. Numerical results demonstrate that the proposed adaptive weighting algorithm significantly outperforms the benchmarks without weighting. Visualization examples validate that the proposed weighting algorithm can flexibly adapt to various channel conditions. Moreover, the adaptive weighting algorithm demonstrates good generalization to untrained channels and test datasets from different domains.

Cooperative Perception with Learning-Based V2V communications

Cooperative perception has been widely used in autonomous driving to alleviate the inherent limitation of single automated vehicle perception. To enable cooperation, vehicle-to-vehicle (V2V) communication plays an indispensable role. This work analyzes the performance of cooperative perception accounting for communications channel impairments. Different fusion methods and channel impairments are evaluated. A new late fusion scheme is proposed to leverage the robustness of intermediate features. In order to compress the data size incurred by cooperation, a convolution neural network-based autoencoder is adopted. Numerical results demonstrate that intermediate fusion is more robust to channel impairments than early fusion and late fusion, when the SNR is greater than 0 dB. Also, the proposed fusion scheme outperforms the conventional late fusion using detection outputs, and autoencoder provides a good compromise between detection accuracy and bandwidth usage.