Human Detection in Realistic Through-the-Wall Environments using Raw Radar ADC Data and Parametric Neural Networks

The radar signal processing algorithm is one of the core components in through-wall radar human detection technology. Traditional algorithms (e.g., DFT and matched filtering) struggle to adaptively handle low signal-to-noise ratio echo signals in challenging and dynamic real-world through-wall application environments, which becomes a major bottleneck in the system. In this paper, we introduce an end-to-end through-wall radar human detection network (TWP-CNN), which takes raw radar Analog-to-Digital Converter (ADC) signals without any preprocessing as input. We replace the conventional radar signal processing flow with the proposed DFT-based adaptive feature extraction (DAFE) module. This module employs learnable parameterized 3D complex convolution layers to extract superior feature representations from ADC signals, which is beyond the limitation of traditional preprocessing methods. Additionally, by embedding phase information from radar data within the network and employing multi-task learning, a more accurate detection is achieved. Finally, due to the absence of through-wall radar datasets containing raw ADC data, we gathered a realistic through-wall (RTW) dataset using our in-house developed through-wall radar system. We trained and validated our proposed method on this dataset to confirm its effectiveness and superiority in real through-wall detection scenarios.

A Closed-loop Brain-Machine Interface SoC Featuring a 0.2$μ$J/class Multiplexer Based Neural Network

This work presents the first fabricated electrophysiology-optogenetic closed-loop bidirectional brain-machine interface (CL-BBMI) system-on-chip (SoC) with electrical neural signal recording, on-chip sleep staging and optogenetic stimulation. The first multiplexer with static assignment based table lookup solution (MUXnet) for multiplier-free NN processor was proposed. A state-of-the-art average accuracy of 82.4% was achieved with an energy consumption of only 0.2$\mu$J/class in sleep staging task.

DISCO: Distributed Inference with Sparse Communications

Deep neural networks (DNNs) have great potential to solve many real-world problems, but they usually require an extensive amount of computation and memory. It is of great difficulty to deploy a large DNN model to a single resource-limited device with small memory capacity. Distributed computing is a common approach to reduce single-node memory consumption and to accelerate the inference of DNN models. In this paper, we explore the "within-layer model parallelism", which distributes the inference of each layer into multiple nodes. In this way, the memory requirement can be distributed to many nodes, making it possible to use several edge devices to infer a large DNN model. Due to the dependency within each layer, data communications between nodes during this parallel inference can be a bottleneck when the communication bandwidth is limited. We propose a framework to train DNN models for Distributed Inference with Sparse Communications (DISCO). We convert the problem of selecting which subset of data to transmit between nodes into a model optimization problem, and derive models with both computation and communication reduction when each layer is inferred on multiple nodes. We show the benefit of the DISCO framework on a variety of CV tasks such as image classification, object detection, semantic segmentation, and image super resolution. The corresponding models include important DNN building blocks such as convolutions and transformers. For example, each layer of a ResNet-50 model can be distributively inferred across two nodes with five times less data communications, almost half overall computations and half memory requirement for a single node, and achieve comparable accuracy to the original ResNet-50 model. This also results in 4.7 times overall inference speedup.

The MABe22 Benchmarks for Representation Learning of Multi-Agent Behavior

Real-world behavior is often shaped by complex interactions between multiple agents. To scalably study multi-agent behavior, advances in unsupervised and self-supervised learning have enabled a variety of different behavioral representations to be learned from trajectory data. To date, there does not exist a unified set of benchmarks that can enable comparing methods quantitatively and systematically across a broad set of behavior analysis settings. We aim to address this by introducing a large-scale, multi-agent trajectory dataset from real-world behavioral neuroscience experiments that covers a range of behavior analysis tasks. Our dataset consists of trajectory data from common model organisms, with 9.6 million frames of mouse data and 4.4 million frames of fly data, in a variety of experimental settings, such as different strains, lengths of interaction, and optogenetic stimulation. A subset of the frames also consist of expert-annotated behavior labels. Improvements on our dataset corresponds to behavioral representations that work across multiple organisms and is able to capture differences for common behavior analysis tasks.

Characterizing Performance Bugs in Deep Learning Systems

Deep learning (DL) has been increasingly applied to a variety of domains. The programming paradigm shift from traditional systems to DL systems poses unique challenges in engineering DL systems. Performance is one of the challenges, and performance bugs(PBs) in DL systems can cause severe consequences such as excessive resource consumption and financial loss. While bugs in DL systems have been extensively investigated, PBs in DL systems have hardly been explored. To bridge this gap, we present the first comprehensive study to characterize symptoms, root causes, and introducing and exposing stages of PBs in DL systems developed in TensorFLow and Keras, with a total of 238 PBs collected from 225 StackOverflow posts. Our findings shed light on the implications on developing high performance DL systems, and detecting and localizing PBs in DL systems. We also build the first benchmark of 56 PBs in DL systems, and assess the capability of existing approaches in tackling them. Moreover, we develop a static checker DeepPerf to detect three types of PBs, and identify 488 new PBs in 130 GitHub projects.62 and 18 of them have been respectively confirmed and fixed by developers.

RelationRS: Relationship Representation Network for Object Detection in Aerial Images

Object detection is a basic and important task in the field of aerial image processing and has gained much attention in computer vision. However, previous aerial image object detection approaches have insufficient use of scene semantic information between different regions of large-scale aerial images. In addition, complex background and scale changes make it difficult to improve detection accuracy. To address these issues, we propose a relationship representation network for object detection in aerial images (RelationRS): 1) Firstly, multi-scale features are fused and enhanced by a dual relationship module (DRM) with conditional convolution. The dual relationship module learns the potential relationship between features of different scales and learns the relationship between different scenes from different patches in a same iteration. In addition, the dual relationship module dynamically generates parameters to guide the fusion of multi-scale features. 2) Secondly, The bridging visual representations module (BVR) is introduced into the field of aerial images to improve the object detection effect in images with complex backgrounds. Experiments with a publicly available object detection dataset for aerial images demonstrate that the proposed RelationRS achieves a state-of-the-art detection performance.

Point Cloud Pre-training by Mixing and Disentangling

The annotation for large-scale point clouds is still time-consuming and unavailable for many real-world tasks. Point cloud pre-training is one potential solution for obtaining a scalable model for fast adaptation. Therefore, in this paper, we investigate a new self-supervised learning approach, called Mixing and Disentangling (MD), for point cloud pre-training. As the name implies, we explore how to separate the original point cloud from the mixed point cloud, and leverage this challenging task as a pretext optimization objective for model training. Considering the limited training data in the original dataset, which is much less than prevailing ImageNet, the mixing process can efficiently generate more high-quality samples. We build one baseline network to verify our intuition, which simply contains two modules, encoder and decoder. Given a mixed point cloud, the encoder is first pre-trained to extract the semantic embedding. Then an instance-adaptive decoder is harnessed to disentangle the point clouds according to the embedding. Albeit simple, the encoder is inherently able to capture the point cloud keypoints after training and can be fast adapted to downstream tasks including classification and segmentation by the pre-training and fine-tuning paradigm. Extensive experiments on two datasets show that the encoder + ours (MD) significantly surpasses that of the encoder trained from scratch and converges quickly. In ablation studies, we further study the effect of each component and discuss the advantages of the proposed self-supervised learning strategy. We hope this self-supervised learning attempt on point clouds can pave the way for reducing the deeply-learned model dependence on large-scale labeled data and saving a lot of annotation costs in the future.

TBNet:Two-Stream Boundary-aware Network for Generic Image Manipulation Localization

Finding tampered regions in images is a hot research topic in machine learning and computer vision. Although many image manipulation location algorithms have been proposed, most of them only focus on the RGB images with different color spaces, and the frequency information that contains the potential tampering clues is often ignored. In this work, a novel end-to-end two-stream boundary-aware network (abbreviated as TBNet) is proposed for generic image manipulation localization in which the RGB stream, the frequency stream, and the boundary artifact location are explored in a unified framework. Specifically, we first design an adaptive frequency selection module (AFS) to adaptively select the appropriate frequency to mine inconsistent statistics and eliminate the interference of redundant statistics. Then, an adaptive cross-attention fusion module (ACF) is proposed to adaptively fuse the RGB feature and the frequency feature. Finally, the boundary artifact location network (BAL) is designed to locate the boundary artifacts for which the parameters are jointly updated by the outputs of the ACF, and its results are further fed into the decoder. Thus, the parameters of the RGB stream, the frequency stream, and the boundary artifact location network are jointly optimized, and their latent complementary relationships are fully mined. The results of extensive experiments performed on four public benchmarks of the image manipulation localization task, namely, CASIA1.0, COVER, Carvalho, and In-The-Wild, demonstrate that the proposed TBNet can significantly outperform state-of-the-art generic image manipulation localization methods in terms of both MCC and F1.

PhysiNet: A Combination of Physics-based Model and Neural Network Model for Digital Twins

As the real-time digital counterpart of a physical system or process, digital twins are utilized for system simulation and optimization. Neural networks are one way to build a digital twins model by using data especially when a physics-based model is not accurate or even not available. However, for a newly designed system, it takes time to accumulate enough data for neural network moded and only an approximate physics-based model is available. To take advantage of both models, this paper proposed a model that combines the physics-based model and the neural network model to improve the prediction accuracy for the whole life cycle of a system. The proposed model was able to automatically combine the models and boost their prediction performance. Experiments showed that the proposed hybrid model outperformed both the physics-based model and the neural network model.