Spin-Orbit Interactions in Electronic Structure Quantum Monte Carlo

We develop generalization of the fixed-phase diffusion Monte Carlo method for Hamiltonians which explicitly depend on particle spins such as for spin-orbit interactions. The method is formulated in zero variance manner and is similar to treatment of nonlocal operators in commonly used static- spin calculations. Tests on atomic and molecular systems show that it is very accurate, on par with the fixed-node method. This opens electronic structure quantum Monte Carlo methods to a vast research area of quantum phenomena in which spin-related interactions play an important role.Comment: Version 3: Some text additions. Results and conclusions unchanged. 5 pages, 2 figure

Density-invariant Features for Distant Point Cloud Registration

Registration of distant outdoor LiDAR point clouds is crucial to extending the 3D vision of collaborative autonomous vehicles, and yet is challenging due to small overlapping area and a huge disparity between observed point densities. In this paper, we propose Group-wise Contrastive Learning (GCL) scheme to extract density-invariant geometric features to register distant outdoor LiDAR point clouds. We mark through theoretical analysis and experiments that, contrastive positives should be independent and identically distributed (i.i.d.), in order to train densityinvariant feature extractors. We propose upon the conclusion a simple yet effective training scheme to force the feature of multiple point clouds in the same spatial location (referred to as positive groups) to be similar, which naturally avoids the sampling bias introduced by a pair of point clouds to conform with the i.i.d. principle. The resulting fully-convolutional feature extractor is more powerful and density-invariant than state-of-the-art methods, improving the registration recall of distant scenarios on KITTI and nuScenes benchmarks by 40.9% and 26.9%, respectively. Code is available at https://github.com/liuQuan98/GCL.Comment: In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 202

MoGDE: Boosting Mobile Monocular 3D Object Detection with Ground Depth Estimation

Monocular 3D object detection (Mono3D) in mobile settings (e.g., on a vehicle, a drone, or a robot) is an important yet challenging task. Due to the near-far disparity phenomenon of monocular vision and the ever-changing camera pose, it is hard to acquire high detection accuracy, especially for far objects. Inspired by the insight that the depth of an object can be well determined according to the depth of the ground where it stands, in this paper, we propose a novel Mono3D framework, called MoGDE, which constantly estimates the corresponding ground depth of an image and then utilizes the estimated ground depth information to guide Mono3D. To this end, we utilize a pose detection network to estimate the pose of the camera and then construct a feature map portraying pixel-level ground depth according to the 3D-to-2D perspective geometry. Moreover, to improve Mono3D with the estimated ground depth, we design an RGB-D feature fusion network based on the transformer structure, where the long-range self-attention mechanism is utilized to effectively identify ground-contacting points and pin the corresponding ground depth to the image feature map. We conduct extensive experiments on the real-world KITTI dataset. The results demonstrate that MoGDE can effectively improve the Mono3D accuracy and robustness for both near and far objects. MoGDE yields the best performance compared with the state-of-the-art methods by a large margin and is ranked number one on the KITTI 3D benchmark.Comment: 36th Conference on Neural Information Processing Systems (NeurIPS), 2022. arXiv admin note: text overlap with arXiv:2303.1301

Efficient Adaptive Activation Rounding for Post-Training Quantization

Post-training quantization attracts increasing attention due to its convenience in deploying quantized neural networks. Although rounding-to-nearest remains the prevailing method for DNN quantization, prior research has demonstrated its suboptimal nature when applied to weight quantization. They propose optimizing weight rounding schemes by leveraging output error rather than the traditional weight quantization error. Our study reveals that similar rounding challenges also extend to activation quantization. Despite the easy generalization, the challenges lie in the dynamic nature of activation. Adaptive rounding is expected for varying activations and the method is subjected to runtime overhead. To tackle this, we propose the AQuant quantization framework with a novel perspective to reduce output error by adjusting rounding schemes of activations. Instead of using the constant rounding border 0.5 of the rounding-to-nearest operation, we make the border become a function w.r.t. the activation value to change the activation rounding by the adaptive border. To deal with the runtime overhead, we use a coarse-grained version of the border function. Finally, we introduce our framework to optimize the border function. Extensive experiments show that AQuant achieves notable improvements compared to state-of-the-art works and pushes the accuracy of ResNet-18 up to 60.31% under the 2-bit weight and activation quantization

Exploring automated formant analysis for comparative variationist study of Heritage Cantonese and English

We consider the possibility of Cantonese and English reciprocally influencing vowel space in Toronto’s Heritage Cantonese community by comparing Generation1 and Generation2 speakers in both languages. We predict more English-like patterns in Gen2 Cantonese (vs. Gen1) and more Cantonese-like patterns in Gen1 English (vs. Gen2). Methodological innovations include automated forced alignment and formant extraction for Cantonese -- methods increasingly used for English data but not frequently applied to other languages in sociolinguistics. Extension to additional languages provides testing grounds for sociolinguistic generalizations which have been based primarily on English, French and Spanish. FAVE (Rosenfelder et al. 2011) was used to force-align English transcripts to the corresponding .wav. Cantonese transcripts were force-aligned in ProsodyLab (Gorman et al. 2011), using unsupervised machine learning to train acoustic models, customizable for non-English data (unlike FAVE). FAVE was used to extract and normalize English formant measurements (F1, F2) at each vowel midpoint. A custom Praat script did the same for Cantonese. Data consists of ~40,000 measured vowels for each language: all stressed vowels produced by 10 speakers per language during a 1-hour interview. This paper focuses on ~9,000 tokens of /i/. Preliminary results from mixed-effects modeling: o Generation and sex are main effects in Cantonese for both F1 and F2, but only for F2 in English → As predicted; Gen1 speakers haven’t fully acquired social conditioning in English. Contra predictions, Gen2 sustains Gen1-like social conditioning in Cantonese. o As in Homeland Cantonese (Yue-Hashimoto 1972:158), Heritage Cantonese /i/ shows a centralizing effect of following velars; stronger in Gen1 than Gen2 → supports our hypothesis. Neither generation transfers this effect to English. o Without any human correction, the automatically extracted and measured data behaves much as expected → a promising avenue for further investigation. Comparisons to Toronto Anglo English (Boberg 2008, Roeder & Jarmasz 2010, Roeder 2012) will be reported