Spiking Neural Network for Ultra-low-latency and High-accurate Object Detection

Spiking Neural Networks (SNNs) have garnered widespread interest for their energy efficiency and brain-inspired event-driven properties. While recent methods like Spiking-YOLO have expanded the SNNs to more challenging object detection tasks, they often suffer from high latency and low detection accuracy, making them difficult to deploy on latency sensitive mobile platforms. Furthermore, the conversion method from Artificial Neural Networks (ANNs) to SNNs is hard to maintain the complete structure of the ANNs, resulting in poor feature representation and high conversion errors. To address these challenges, we propose two methods: timesteps compression and spike-time-dependent integrated (STDI) coding. The former reduces the timesteps required in ANN-SNN conversion by compressing information, while the latter sets a time-varying threshold to expand the information holding capacity. We also present a SNN-based ultra-low latency and high accurate object detection model (SUHD) that achieves state-of-the-art performance on nontrivial datasets like PASCAL VOC and MS COCO, with about remarkable 750x fewer timesteps and 30% mean average precision (mAP) improvement, compared to the Spiking-YOLO on MS COCO datasets. To the best of our knowledge, SUHD is the deepest spike-based object detection model to date that achieves ultra low timesteps to complete the lossless conversion.Comment: 14 pages, 10 figure

An Efficient and Extensible Zero-knowledge Proof Framework for Neural Networks

In recent years, cloud vendors have started to supply paid services for data analysis by providing interfaces of their well-trained neural network models. However, customers lack tools to verify whether outcomes supplied by cloud vendors are correct inferences from particular models, in the face of lazy or malicious vendors. The cryptographic primitive called zero-knowledge proof (ZKP) addresses this problem. It enables the outcomes to be verifiable without leaking information about the models. Unfortunately, existing ZKP schemes for neural networks have high computational overheads, especially for the non-linear layers in neural networks. In this paper, we propose an efficient and extensible ZKP framework for neural networks. Our work improves the performance of the proofs for non-linear layers. Compared to previous works relying on the technology of bit decomposition, we convert complex non-linear relations into range and exponent relations, which significantly reduces the number of constraints required to prove non-linear layers. Moreover, we adopt a modular design to make our framework compatible with more neural networks. Specifically, we propose two enhanced range and lookup proofs as basic blocks. They are efficient in proving the satisfaction of range and exponent relations. Then, we constrain the correct calculation of primitive non-linear operations using a small number of range and exponent relations. Finally, we build our ZKP framework from the primitive operations to the entire neural networks, offering the flexibility for expansion to various neural networks. We implement our ZKPs for convolutional and transformer neural networks. The evaluation results show that our work achieves over $168.6\times$ (up to $477.2\times$) speedup for separated non-linear layers and $41.4\times$ speedup for the entire ResNet-101 convolutional neural network, when compared with the state-of-the-art work, Mystique. In addition, our work can prove GPT-2, a transformer neural network with $117$ million parameters, in $287.1$ seconds, achieving $35.7\times$ speedup over ZKML, which is a state-of-the-art work supporting transformer neural networks

Initial Orbit Determination Method for Low Earth Orbit Objects Using Too-Short Arc Based on Bistatic Radar

The problem of initial orbit determination (IOD) for Low Earth Orbit (LEO) objects using bistatic radar too-short arc (TSA) observations is addressed. For TSA observations, the traditional IOD methods suffer low accuracy. For LEO objects with stable attitude, the high order kinematic parameters can be obtained from the time derivatives of the radar echo phase. In this paper, an analytical IOD method is presented using bistatic radar TSA observations, which contain the position measurements (bistatic range, azimuth angle, and elevation angle) and the high order kinematic measurements (bistatic velocity, acceleration, and jerk). As the undetermined target state variables constitute a complex system of equations that can only be solved iteratively, an auxiliary coordinate system based on the bistatic geometry is defined to help reduce the equations to one unary quartic equation. Further, the closed-form expressions of the orbital state are derived. The performance of the proposed method is evaluated using linearization approximations. Numerical simulations are carried out for several typical LEO observation scenarios to demonstrate the performance of the proposed method

Enhanced Seed Oil Production in Canola by Conditional Expression of Brassica napus LEAFY COTYLEDON1 and LEC1-LIKE in Developing Seeds1[W][OA]

The seed oil content in oilseed crops is a major selection trait to breeders. In Arabidopsis (Arabidopsis thaliana), LEAFY COTYLEDON1 (LEC1) and LEC1-LIKE (L1L) are key regulators of fatty acid biosynthesis. Overexpression of AtLEC1 and its orthologs in canola (Brassica napus), BnLEC1 and BnL1L, causes an increased fatty acid level in transgenic Arabidopsis plants, which, however, also show severe developmental abnormalities. Here, we use truncated napin A promoters, which retain the seed-specific expression pattern but with a reduced expression level, to drive the expression of BnLEC1 and BnL1L in transgenic canola. Conditional expression of BnLEC1 and BnL1L increases the seed oil content by 2% to 20% and has no detrimental effects on major agronomic traits. In the transgenic canola, expression of a subset of genes involved in fatty acid biosynthesis and glycolysis is up-regulated in developing seeds. Moreover, the BnLEC1 transgene enhances the expression of several genes involved in Suc synthesis and transport in developing seeds and the silique wall. Consistently, the accumulation of Suc and Fru is increased in developing seeds of the transgenic rapeseed, suggesting the increased carbon flux to fatty acid biosynthesis. These results demonstrate that BnLEC1 and BnL1L are reliable targets for genetic improvement of rapeseed in seed oil production