OpenRaiser/Intern-Atlas huggingface dataset:
Maniposynth: Bimodal Tangible Functional Programming
Venue: arXiv cs.PL 2022
Year: 2022

Traditionally, writing code is a non-graphical, abstract, and linear process. Not everyone is comfortable with this way of thinking at all times. Can programming be transformed into a graphical, concrete, non-linear activity? While nodes-and-wires and blocks-based programming environments do leverage graphical direct manipulation, users perform their manipulations on abstract syntax tree elements, which are still abstract. Is it possible to be more concrete - could users instead directly manipulate live program values to create their program? We present a system, Maniposynth, that reimagines functional programming as a non-linear workflow where program expressions are spread on a 2D canvas. The live results of those expressions are continuously displayed and available for direct manipulation. The non-linear canvas liberates users to work out-of-order, and the live values can be interacted with via drag-and-drop. Incomplete programs are gracefully handled via hole expressions, which allow Maniposynth to offer program synthesis. Throughout the workflow, the program is valid OCaml code which the user may inspect and edit in their preferred text editor at any time. With Maniposynth's direct manipulation features, we created 38 programs drawn from a functional data structures course. We additionally hired two professional OCaml developers to implement a subset of these programs. We report on these experiences and discuss to what degree Maniposynth meets its goals of providing a non-linear, concrete, graphical programming workflow.
Security and Privacy vulnerabilities of 5G/6G and WiFi 6: Survey and Research Directions from a Coexistence Perspective
Venue: arXiv cs.NI 2022
Year: 2022

Spectrum scarcity has been a major concern for achieving the desired quality of experience (QoE) in next-generation (5G/6G and beyond) networks supporting a massive volume of mobile and IoT devices with low-latency and seamless connectivity. Hence, spectrum sharing systems have been considered as a major enabler for next-generation wireless networks in meeting QoE demands. While most current coexistence solutions and standards focus on performance improvement and QoE optimization, the emerging security challenges of such network environments have been ignored in the literature. The security framework of standalone networks (either 5G or WiFi) assumes the ownership of entire network resources from spectrum to core functions. Hence, all accesses to the network shall be authenticated and authorized within the intra-network security system and is deemed illegal otherwise. However, coexistence network environments can lead to unprecedented security vulnerabilities and breaches as the standalone networks shall tolerate unknown and out-of-network accesses, specifically in the medium access. In this paper, for the first time in literature, we review some of the critical and emerging security vulnerabilities in the 5G/WiFi coexistence network environment which have not been observed previously in standalone networks. Specifically, independent medium access control (MAC) protocols and the resulting hidden node issues can result in exploitation such as service blocking, deployment of rogue base-stations, and eavesdropping attacks. We study potential vulnerabilities in the perspective of physical layer authentication, network access security, and cross-layer authentication mechanisms. This study opens a new direction of research in the analysis and design of a security framework that can address the unique challenges of coexistence networks.
"Diversity and Uncertainty in Moderation" are the Key to Data Selection for Multilingual Few-shot Transfer
Venue: arXiv cs.CL 2022
Year: 2022

Few-shot transfer often shows substantial gain over zero-shot transfer~\cite{lauscher2020zero}, which is a practically useful trade-off between fully supervised and unsupervised learning approaches for multilingual pretrained model-based systems. This paper explores various strategies for selecting data for annotation that can result in a better few-shot transfer. The proposed approaches rely on multiple measures such as data entropy using $n$-gram language model, predictive entropy, and gradient embedding. We propose a loss embedding method for sequence labeling tasks, which induces diversity and uncertainty sampling similar to gradient embedding. The proposed data selection strategies are evaluated and compared for POS tagging, NER, and NLI tasks for up to 20 languages. Our experiments show that the gradient and loss embedding-based strategies consistently outperform random data selection baselines, with gains varying with the initial performance of the zero-shot transfer. Furthermore, the proposed method shows similar trends in improvement even when the model is fine-tuned using a lower proportion of the original task-specific labeled training data for zero-shot transfer.
A Causal Approach to Detecting Multivariate Time-series Anomalies and Root Causes
Venue: arXiv cs.LG 2022
Year: 2022

Detecting anomalies and the corresponding root causes in multivariate time series plays an important role in monitoring the behaviors of various real-world systems, e.g., IT system operations or manufacturing industry. Previous anomaly detection approaches model the joint distribution without considering the underlying mechanism of multivariate time series, making them computationally hungry and hard to identify root causes. In this paper, we formulate the anomaly detection problem from a causal perspective and view anomalies as instances that do not follow the regular causal mechanism to generate the multivariate data. We then propose a causality-based framework for detecting anomalies and root causes. It first learns the causal structure from data and then infers whether an instance is an anomaly relative to the local causal mechanism whose conditional distribution can be directly estimated from data. In light of the modularity property of causal systems (the causal processes to generate different variables are irrelevant modules), the original problem is divided into a series of separate, simpler, and low-dimensional anomaly detection problems so that where an anomaly happens (root causes) can be directly identified. We evaluate our approach with both simulated and public datasets as well as a case study on real-world AIOps applications, showing its efficacy, robustness, and practical feasibility.
A note on Linear Bottleneck networks and their Transition to Multilinearity
Venue: arXiv cs.LG 2022
Year: 2022

Randomly initialized wide neural networks transition to linear functions of weights as the width grows, in a ball of radius $O(1)$ around initialization. A necessary condition for this result is that all layers of the network are wide enough, i.e., all widths tend to infinity. However, the transition to linearity breaks down when this infinite width assumption is violated. In this work we show that linear networks with a bottleneck layer learn bilinear functions of the weights, in a ball of radius $O(1)$ around initialization. In general, for $B-1$ bottleneck layers, the network is a degree $B$ multilinear function of weights. Importantly, the degree only depends on the number of bottlenecks and not the total depth of the network.
Prefix-free parsing for building large tunnelled Wheeler graphs
Venue: arXiv cs.DS 2022
Year: 2022

We propose a new technique for creating a space-efficient index for large repetitive text collections, such as pangenomic databases containing sequences of many individuals from the same species. We combine two recent techniques from this area: Wheeler graphs (Gagie et al., 2017) and prefix-free parsing (PFP, Boucher et al., 2019). Wheeler graphs (WGs) are a general framework encompassing several indexes based on the Burrows-Wheeler transform (BWT), such as the FM-index. Wheeler graphs admit a succinct representation which can be further compacted by employing the idea of tunnelling, which exploits redundancies in the form of parallel, equally-labelled paths called blocks that can be merged into a single path. The problem of finding the optimal set of blocks for tunnelling, i.e. the one that minimizes the size of the resulting WG, is known to be NP-complete and remains the most computationally challenging part of the tunnelling process. To find an adequate set of blocks in less time, we propose a new method based on the prefix-free parsing (PFP). The idea of PFP is to divide the input text into phrases of roughly equal sizes that overlap by a fixed number of characters. The original text is represented by a sequence of phrase ranks (the parse) and a list of all used phrases (the dictionary). In repetitive texts, the PFP of the text is generally much shorter than the original. To speed up the block selection for tunnelling, we apply the PFP to obtain the parse and the dictionary of the text, tunnel the WG of the parse using existing heuristics and subsequently use this tunnelled parse to construct a compact WG of the original text. Compared with constructing a WG from the original text without PFP, our method is much faster and uses less memory on collections of pangenomic sequences. Therefore, our method enables the use of WGs as a pangenomic reference for real-world datasets.
Detecting and Recovering Adversarial Examples from Extracting Non-robust and Highly Predictive Adversarial Perturbations
Venue: arXiv cs.CV 2022
Year: 2022

Deep neural networks (DNNs) have been shown to be vulnerable against adversarial examples (AEs) which are maliciously designed to fool target models. The normal examples (NEs) added with imperceptible adversarial perturbation, can be a security threat to DNNs. Although the existing AEs detection methods have achieved a high accuracy, they failed to exploit the information of the AEs detected. Thus, based on high-dimension perturbation extraction, we propose a model-free AEs detection method, the whole process of which is free from querying the victim model. Research shows that DNNs are sensitive to the high-dimension features. The adversarial perturbation hiding in the adversarial example belongs to the high-dimension feature which is highly predictive and non-robust. DNNs learn more details from high-dimension data than others. In our method, the perturbation extractor can extract the adversarial perturbation from AEs as high-dimension feature, then the trained AEs discriminator determines whether the input is an AE. Experimental results show that the proposed method can not only detect the adversarial examples with high accuracy, but also detect the specific category of the AEs. Meanwhile, the extracted perturbation can be used to recover the AEs to NEs.
Optimizing Character Animations using Online Crowdsourcing
Venue: arXiv cs.HC 2022
Year: 2022

This paper presents a novel approach for exploring diverse and expressive motions that are physically correct and interactive. The approach combining user participation in with the animation development process using crowdsourcing to remove the need for data-driven libraries while address aesthetic limitations. A core challenge for character animation solutions that do not use pre-recorded data is they are constrained to specific actions or appear unnatural and out of place (compared to real-life movements). Character movements are very subjective to human perception (easily identify underlying unnatural or strange patterns with simple actions, such as walking or climbing). We present an approach that leverage's crowdsourcing to reduce these uncanny artifacts within generated character animations. Crowdsourcing animations is an uncommon practice due to the complexities of having multiple people working in parallel on a single animation. A web-based solution for analysis and animation is presented in this paper. It allows users to optimize and evaluate complicated character animation mechanism conveniently on-line. The context of this paper introduces a simple animation system, which is integrated into a web-based solution (JavaScript/HTML5). Since Web browser are commonly available on computers, the presented application is easy to use on any platform from any location (easy to maintain and share). Our system combines the expressive power of web pages for visualising content on-the-fly with a fully fledged interactive (physics-based) animation solution that includes a rich set of libraries.
MatPIM: Accelerating Matrix Operations with Memristive Stateful Logic
Venue: arXiv cs.AR 2022
Year: 2022

The emerging memristive Memory Processing Unit (mMPU) overcomes the memory wall through memristive devices that unite storage and logic for real processing-in-memory (PIM) systems. At the core of the mMPU is stateful logic, which is accelerated with memristive partitions to enable logic with massive inherent parallelism within crossbar arrays. This paper vastly accelerates the fundamental operations of matrix-vector multiplication and convolution in the mMPU, with either full-precision or binary elements. These proposed algorithms establish an efficient foundation for large-scale mMPU applications such as neural-networks, image processing, and numerical methods. We overcome the inherent asymmetry limitation in the previous in-memory full-precision matrix-vector multiplication solutions by utilizing techniques from block matrix multiplication and reduction. We present the first fast in-memory binary matrix-vector multiplication algorithm by utilizing memristive partitions with a tree-based popcount reduction (39x faster than previous work). For convolution, we present a novel in-memory input-parallel concept which we utilize for a full-precision algorithm that overcomes the asymmetry limitation in convolution, while also improving latency (2x faster than previous work), and the first fast binary algorithm (12x faster than previous work).
LiDAR-as-Camera for End-to-End Driving
Venue: arXiv cs.AI 2022
Year: 2022

The core task of any autonomous driving system is to transform sensory inputs into driving commands. In end-to-end driving, this is achieved via a neural network, with one or multiple cameras as the most commonly used input and low-level driving command, e.g. steering angle, as output. However, depth-sensing has been shown in simulation to make the end-to-end driving task easier. On a real car, combining depth and visual information can be challenging, due to the difficulty of obtaining good spatial and temporal alignment of the sensors. To alleviate alignment problems, Ouster LiDARs can output surround-view LiDAR-images with depth, intensity, and ambient radiation channels. These measurements originate from the same sensor, rendering them perfectly aligned in time and space. We demonstrate that such LiDAR-images are sufficient for the real-car road-following task and perform at least equally to camera-based models in the tested conditions, with the difference increasing when needing to generalize to new weather conditions. In the second direction of study, we reveal that the temporal smoothness of off-policy prediction sequences correlates equally well with actual on-policy driving ability as the commonly used mean absolute error.
Graph-Time Convolutional Neural Networks: Architecture and Theoretical Analysis
Venue: arXiv cs.LG 2022
Year: 2022

Devising and analyzing learning models for spatiotemporal network data is of importance for tasks including forecasting, anomaly detection, and multi-agent coordination, among others. Graph Convolutional Neural Networks (GCNNs) are an established approach to learn from time-invariant network data. The graph convolution operation offers a principled approach to aggregate multiresolution information. However, extending the convolution principled learning and respective analysis to the spatiotemporal domain is challenging because spatiotemporal data have more intrinsic dependencies. Hence, a higher flexibility to capture jointly the spatial and the temporal dependencies is required to learn meaningful higher-order representations. Here, we leverage product graphs to represent the spatiotemporal dependencies in the data and introduce Graph-Time Convolutional Neural Networks (GTCNNs) as a principled architecture to aid learning. The proposed approach can work with any type of product graph and we also introduce a parametric product graph to learn also the spatiotemporal coupling. The convolution principle further allows a similar mathematical tractability as for GCNNs. In particular, the stability result shows GTCNNs are stable to spatial perturbations but there is an implicit trade-off between discriminability and robustness; i.e., the more complex the model, the less stable. Extensive numerical results on benchmark datasets corroborate our findings and show the GTCNN compares favorably with state-of-the-art solutions. We anticipate the GTCNN to be a starting point for more sophisticated models that achieve good performance but are also fundamentally grounded.
Neural Network Assisted Depth Map Packing for Compression Using Standard Hardware Video Codecs
Venue: arXiv cs.MM 2022
Year: 2022

Depth maps are needed by various graphics rendering and processing operations. Depth map streaming is often necessary when such operations are performed in a distributed system and it requires in most cases fast performing compression, which is why video codecs are often used. Hardware implementations of standard video codecs enable relatively high resolution and framerate combinations, even on resource constrained devices, but unfortunately those implementations do not currently support RGB+depth extensions. However, they can be used for depth compression by first packing the depth maps into RGB or YUV frames. We investigate depth map compression using a combination of depth map packing followed by encoding with a standard video codec. We show that the precision at which depth maps are packed has a large and nontrivial impact on the resulting error caused by the combination of the packing scheme and lossy compression when bitrate is constrained. Consequently, we propose a variable precision packing scheme assisted by a neural network model that predicts the optimal precision for each depth map given a bitrate constraint. We demonstrate that the model yields near optimal predictions and that it can be integrated into a game engine with very low overhead using modern hardware.
Data-Efficient Learning via Minimizing Hyperspherical Energy
Venue: arXiv cs.LG 2022
Year: 2022

Deep learning on large-scale data is dominant nowadays. The unprecedented scale of data has been arguably one of the most important driving forces for the success of deep learning. However, there still exist scenarios where collecting data or labels could be extremely expensive, e.g., medical imaging and robotics. To fill up this gap, this paper considers the problem of data-efficient learning from scratch using a small amount of representative data. First, we characterize this problem by active learning on homeomorphic tubes of spherical manifolds. This naturally generates feasible hypothesis class. With homologous topological properties, we identify an important connection -- finding tube manifolds is equivalent to minimizing hyperspherical energy (MHE) in physical geometry. Inspired by this connection, we propose a MHE-based active learning (MHEAL) algorithm, and provide comprehensive theoretical guarantees for MHEAL, covering convergence and generalization analysis. Finally, we demonstrate the empirical performance of MHEAL in a wide range of applications on data-efficient learning, including deep clustering, distribution matching, version space sampling and deep active learning.
Secure Heterogeneous Multi-Robot Collaboration and Docking with Hyperledger Fabric Blockchain
Venue: arXiv cs.RO 2022
Year: 2022

In recent years, multi-robot systems have received increasing attention from both industry and academia. Besides the need of accurate and robust estimation of relative localization, security and trust in the system are essential to enable wider adoption. In this paper, we propose a framework using Hyperledger Fabric for multi-robot collaboration in industrial applications. We rely on blockchain identities for the interaction of ground and aerial robots, and use smart contracts for collaborative decision making. The use of ultra-wideband (UWB) localization for both autonomous navigation and robot collaboration extends our previous work in Fabric-based fleet management. We focus on an inventory management application which uses a ground robot and an aerial robot to inspect a warehouse-like environment and store information about the found objects in the blockchain. We measure the impact of adding the blockchain layer, analyze the transaction commit latency and compare the resource utilization of blockchain-related processes to the already running data processing modules.
Neural Rendering for Stereo 3D Reconstruction of Deformable Tissues in Robotic Surgery
Venue: arXiv cs.CV 2022
Year: 2022

Reconstruction of the soft tissues in robotic surgery from endoscopic stereo videos is important for many applications such as intra-operative navigation and image-guided robotic surgery automation. Previous works on this task mainly rely on SLAM-based approaches, which struggle to handle complex surgical scenes. Inspired by recent progress in neural rendering, we present a novel framework for deformable tissue reconstruction from binocular captures in robotic surgery under the single-viewpoint setting. Our framework adopts dynamic neural radiance fields to represent deformable surgical scenes in MLPs and optimize shapes and deformations in a learning-based manner. In addition to non-rigid deformations, tool occlusion and poor 3D clues from a single viewpoint are also particular challenges in soft tissue reconstruction. To overcome these difficulties, we present a series of strategies of tool mask-guided ray casting, stereo depth-cueing ray marching and stereo depth-supervised optimization. With experiments on DaVinci robotic surgery videos, our method significantly outperforms the current state-of-the-art reconstruction method for handling various complex non-rigid deformations. To our best knowledge, this is the first work leveraging neural rendering for surgical scene 3D reconstruction with remarkable potential demonstrated. Code is available at: https://github.com/med-air/EndoNeRF.
Self-SuperFlow: Self-supervised Scene Flow Prediction in Stereo Sequences
Venue: arXiv cs.CV 2022
Year: 2022

In recent years, deep neural networks showed their exceeding capabilities in addressing many computer vision tasks including scene flow prediction. However, most of the advances are dependent on the availability of a vast amount of dense per pixel ground truth annotations, which are very difficult to obtain for real life scenarios. Therefore, synthetic data is often relied upon for supervision, resulting in a representation gap between the training and test data. Even though a great quantity of unlabeled real world data is available, there is a huge lack in self-supervised methods for scene flow prediction. Hence, we explore the extension of a self-supervised loss based on the Census transform and occlusion-aware bidirectional displacements for the problem of scene flow prediction. Regarding the KITTI scene flow benchmark, our method outperforms the corresponding supervised pre-training of the same network and shows improved generalization capabilities while achieving much faster convergence.