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Autonomous driving relies on a huge volume of real-world data to be labeled to high precision. Alternative solutions seek to exploit driving simulators that can generate large amounts of labeled data with a plethora of content variations. However, the domain gap between the synthetic and real data remains, raising the following important question: What are the best ways to utilize a self-driving simulator for perception tasks? In this work, we build on top of recent advances in domain-adaptation theory, and from this perspective, propose ways to minimize the reality gap. We primarily focus on the use of labels in the synthetic domain alone. Our approach introduces both a principled way to learn neural-invariant representations and a theoretically inspired view on how to sample the data from the simulator. Our method is easy to implement in practice as it is agnostic of the network architecture and the choice of the simulator. We showcase our approach on the bird's-eye-view vehicle segmentation task with multi-sensor data (cameras, lidar) using an open-source simulator (CARLA), and evaluate the entire framework on a real-world dataset (nuScenes). Last but not least, we show what types of variations (e.g. weather conditions, number of assets, map design, and color diversity) matter to perception networks when trained with driving simulators, and which ones can be compensated for with our domain adaptation technique.

The first stage of tactile sensing is data acquisition using tactile sensors and the sensed data is transmitted to the central unit for neuromorphic computing. The memristive crossbars were proposed to use as synapses in neuromorphic computing but device intelligence at the sensor level are not investigated in literature. We propose the concept of Transistor Memristor Sensor (TMS)-crossbar by including sensor to memristor crossbar array configuration in the input layer of the neural network architecture. 2 possible cell configurations of TMS crossbar arrays: 1 Transistor 1 Memristor 1 Sensor (1T1M1S) and 2 Transistor 1 Memristor 1 Sensor (2T1M1S) are presented. We verified the proposed TMS-crossbar in the practical design of analog neural networks based Braille character recognition system. The proposed design is verified with SPICE simulations using circuit equivalent of FLX-A501 force sensor, TiO$_2$ memristors and low power 22nm high-k CMOS transistors. The proposed analog neuromorphic computing system presents a scalable solution and is possible to encode 125 symbols with good accuracy in comparison with other Braille character recognition systems in the literature. The benefits of analog implementation of the TMS crossbar arrays is substantiated with results of accuracy, area and power requirements in comparison with the binary counterparts.

This paper presents a novel strategy for autonomous teamed exploration of subterranean environments using legged and aerial robots. Tailored to the fact that subterranean settings, such as cave networks and underground mines, often involve complex, large-scale and multi-branched topologies, while wireless communication within them can be particularly challenging, this work is structured around the synergy of an onboard exploration path planner that allows for resilient long-term autonomy, and a multi-robot coordination framework. The onboard path planner is unified across legged and flying robots and enables navigation in environments with steep slopes, and diverse geometries. When a communication link is available, each robot of the team shares submaps to a centralized location where a multi-robot coordination framework identifies global frontiers of the exploration space to inform each system about where it should re-position to best continue its mission. The strategy is verified through a field deployment inside an underground mine in Switzerland using a legged and a flying robot collectively exploring for 45 min, as well as a longer simulation study with three systems.

Co-evolving time series appears in a multitude of applications such as environmental monitoring, financial analysis, and smart transportation. This paper aims to address the following challenges, including (C1) how to incorporate explicit relationship networks of the time series; (C2) how to model the implicit relationship of the temporal dynamics. We propose a novel model called Network of Tensor Time Series, which is comprised of two modules, including Tensor Graph Convolutional Network (TGCN) and Tensor Recurrent Neural Network (TRNN). TGCN tackles the first challenge by generalizing Graph Convolutional Network (GCN) for flat graphs to tensor graphs, which captures the synergy between multiple graphs associated with the tensors. TRNN leverages tensor decomposition to model the implicit relationships among co-evolving time series. The experimental results on five real-world datasets demonstrate the efficacy of the proposed method.

Deep learning has become the most widely used approach for cardiac image segmentation in recent years. In this paper, we provide a review of over 100 cardiac image segmentation papers using deep learning, which covers common imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound (US) and major anatomical structures of interest (ventricles, atria and vessels). In addition, a summary of publicly available cardiac image datasets and code repositories are included to provide a base for encouraging reproducible research. Finally, we discuss the challenges and limitations with current deep learning-based approaches (scarcity of labels, model generalizability across different domains, interpretability) and suggest potential directions for future research.

Ego hand gestures can be used as an interface in AR and VR environments. While the context of an image is important for tasks like scene understanding, object recognition, image caption generation and activity recognition, it plays a minimal role in ego hand gesture recognition. An ego hand gesture used for AR and VR environments conveys the same information regardless of the background. With this idea in mind, we present our work on ego hand gesture recognition that produces embeddings from RBG images with ego hands, which are simultaneously used for ego hand segmentation and ego gesture recognition. To this extent, we achieved better recognition accuracy (96.9%) compared to the state of the art (92.2%) on the biggest ego hand gesture dataset available publicly. We present a gesture recognition deep neural network which recognises ego hand gestures from videos (videos containing a single gesture) by generating and recognising embeddings of ego hands from image sequences of varying length. We introduce the concept of simultaneous segmentation and recognition applied to ego hand gestures, present the network architecture, the training procedure and the results compared to the state of the art on the EgoGesture dataset

We propose a novel technique to incorporate attention within convolutional neural networks using feature maps generated by a separate convolutional autoencoder. Our attention architecture is well suited for incorporation with deep convolutional networks. We evaluate our model on benchmark segmentation datasets in skin cancer segmentation and lung lesion segmentation. Results show highly competitive performance when compared with U-Net and it's residual variant.

Over the past decades, state-of-the-art medical image segmentation has heavily rested on signal processing paradigms, most notably registration-based label propagation and pair-wise patch comparison, which are generally slow despite a high segmentation accuracy. In recent years, deep learning has revolutionalized computer vision with many practices outperforming prior art, in particular the convolutional neural network (CNN) studies on image classification. Deep CNN has also started being applied to medical image segmentation lately, but generally involves long training and demanding memory requirements, achieving limited success. We propose a patch-based deep learning framework based on a revisit to the classic neural network model with substantial modernization, including the use of Rectified Linear Unit (ReLU) activation, dropout layers, 2.5D tri-planar patch multi-pathway settings. In a test application to hippocampus segmentation using 100 brain MR images from the ADNI database, our approach significantly outperformed prior art in terms of both segmentation accuracy and speed: scoring a median Dice score up to 90.98% on a near real-time performance (<1s).

Recently, generative adversarial networks (GANs) have shown promising performance in generating realistic images. However, they often struggle in learning complex underlying modalities in a given dataset, resulting in poor-quality generated images. To mitigate this problem, we present a novel approach called mixture of experts GAN (MEGAN), an ensemble approach of multiple generator networks. Each generator network in MEGAN specializes in generating images with a particular subset of modalities, e.g., an image class. Instead of incorporating a separate step of handcrafted clustering of multiple modalities, our proposed model is trained through an end-to-end learning of multiple generators via gating networks, which is responsible for choosing the appropriate generator network for a given condition. We adopt the categorical reparameterization trick for a categorical decision to be made in selecting a generator while maintaining the flow of the gradients. We demonstrate that individual generators learn different and salient subparts of the data and achieve a multiscale structural similarity (MS-SSIM) score of 0.2470 for CelebA and a competitive unsupervised inception score of 8.33 in CIFAR-10.

Recent advance in fluorescence microscopy enables acquisition of 3D image volumes with better quality and deeper penetration into tissue. Segmentation is a required step to characterize and analyze biological structures in the images. 3D segmentation using deep learning has achieved promising results in microscopy images. One issue is that deep learning techniques require a large set of groundtruth data which is impractical to annotate manually for microscopy volumes. This paper describes a 3D nuclei segmentation method using 3D convolutional neural networks. A set of synthetic volumes and the corresponding groundtruth volumes are generated automatically using a generative adversarial network. Segmentation results demonstrate that our proposed method is capable of segmenting nuclei successfully in 3D for various data sets.