OpenRaiser/Intern-Atlas huggingface dataset:
Real-time Streaming Perception System for Autonomous Driving
Venue: arXiv cs.CV 2021
Year: 2021

Nowadays, plenty of deep learning technologies are being applied to all aspects of autonomous driving with promising results. Among them, object detection is the key to improve the ability of an autonomous agent to perceive its environment so that it can (re)act. However, previous vision-based object detectors cannot achieve satisfactory performance under real-time driving scenarios. To remedy this, we present the real-time steaming perception system in this paper, which is also the 2nd Place solution of Streaming Perception Challenge (Workshop on Autonomous Driving at CVPR 2021) for the detection-only track. Unlike traditional object detection challenges, which focus mainly on the absolute performance, streaming perception task requires achieving a balance of accuracy and latency, which is crucial for real-time autonomous driving. We adopt YOLOv5 as our basic framework, data augmentation, Bag-of-Freebies, and Transformer are adopted to improve streaming object detection performance with negligible extra inference cost. On the Argoverse-HD test set, our method achieves 33.2 streaming AP (34.6 streaming AP verified by the organizer) under the required hardware. Its performance significantly surpasses the fixed baseline of 13.6 (host team), demonstrating the potentiality of application.
From Multi-View to Hollow-3D: Hallucinated Hollow-3D R-CNN for 3D Object Detection
Venue: arXiv cs.CV 2021
Year: 2021

As an emerging data modal with precise distance sensing, LiDAR point clouds have been placed great expectations on 3D scene understanding. However, point clouds are always sparsely distributed in the 3D space, and with unstructured storage, which makes it difficult to represent them for effective 3D object detection. To this end, in this work, we regard point clouds as hollow-3D data and propose a new architecture, namely Hallucinated Hollow-3D R-CNN ($\text{H}^2$3D R-CNN), to address the problem of 3D object detection. In our approach, we first extract the multi-view features by sequentially projecting the point clouds into the perspective view and the bird-eye view. Then, we hallucinate the 3D representation by a novel bilaterally guided multi-view fusion block. Finally, the 3D objects are detected via a box refinement module with a novel Hierarchical Voxel RoI Pooling operation. The proposed $\text{H}^2$3D R-CNN provides a new angle to take full advantage of complementary information in the perspective view and the bird-eye view with an efficient framework. We evaluate our approach on the public KITTI Dataset and Waymo Open Dataset. Extensive experiments demonstrate the superiority of our method over the state-of-the-art algorithms with respect to both effectiveness and efficiency. The code will be made available at \url{https://github.com/djiajunustc/H-23D_R-CNN}.
Manipulating Identical Filter Redundancy for Efficient Pruning on Deep and Complicated CNN
Venue: arXiv cs.CV 2021
Year: 2021

The existence of redundancy in Convolutional Neural Networks (CNNs) enables us to remove some filters/channels with acceptable performance drops. However, the training objective of CNNs usually tends to minimize an accuracy-related loss function without any attention paid to the redundancy, making the redundancy distribute randomly on all the filters, such that removing any of them may trigger information loss and accuracy drop, necessitating a following finetuning step for recovery. In this paper, we propose to manipulate the redundancy during training to facilitate network pruning. To this end, we propose a novel Centripetal SGD (C-SGD) to make some filters identical, resulting in ideal redundancy patterns, as such filters become purely redundant due to their duplicates; hence removing them does not harm the network. As shown on CIFAR and ImageNet, C-SGD delivers better performance because the redundancy is better organized, compared to the existing methods. The efficiency also characterizes C-SGD because it is as fast as regular SGD, requires no finetuning, and can be conducted simultaneously on all the layers even in very deep CNNs. Besides, C-SGD can improve the accuracy of CNNs by first training a model with the same architecture but wider layers then squeezing it into the original width.
Medical Instrument Segmentation in 3D US by Hybrid Constrained Semi-Supervised Learning
Venue: arXiv cs.CV 2021
Year: 2021

Medical instrument segmentation in 3D ultrasound is essential for image-guided intervention. However, to train a successful deep neural network for instrument segmentation, a large number of labeled images are required, which is expensive and time-consuming to obtain. In this article, we propose a semi-supervised learning (SSL) framework for instrument segmentation in 3D US, which requires much less annotation effort than the existing methods. To achieve the SSL learning, a Dual-UNet is proposed to segment the instrument. The Dual-UNet leverages unlabeled data using a novel hybrid loss function, consisting of uncertainty and contextual constraints. Specifically, the uncertainty constraints leverage the uncertainty estimation of the predictions of the UNet, and therefore improve the unlabeled information for SSL training. In addition, contextual constraints exploit the contextual information of the training images, which are used as the complementary information for voxel-wise uncertainty estimation. Extensive experiments on multiple ex-vivo and in-vivo datasets show that our proposed method achieves Dice score of about 68.6%-69.1% and the inference time of about 1 sec. per volume. These results are better than the state-of-the-art SSL methods and the inference time is comparable to the supervised approaches.
PC4PM: A Tool for Privacy/Confidentiality Preservation in Process Mining
Venue: arXiv cs.CR 2021
Year: 2021

Process mining enables business owners to discover and analyze their actual processes using event data that are widely available in information systems. Event data contain detailed information which is incredibly valuable for providing insights. However, such detailed data often include highly confidential and private information. Thus, concerns of privacy and confidentiality in process mining are becoming increasingly relevant and new techniques are being introduced. To make the techniques easily accessible, new tools need to be developed to integrate the introduced techniques and direct users to appropriate solutions based on their needs. In this paper, we present a Python-based infrastructure implementing and integrating state-of-the-art privacy/confidentiality preservation techniques in process mining. Our tool provides an easy-to-use web-based user interface for privacy-preserving data publishing, risk analysis, and data utility analysis. The tool also provides a set of anonymization operations that can be utilized to support privacy/confidentiality preservation. The tool manages both standard XES event logs and non-standard event data. We also store and manage privacy metadata to track the changes made by privacy/confidentiality preservation techniques.
On Strong Observational Refinement and Forward Simulation
Venue: arXiv cs.DC 2021
Year: 2021

Hyperproperties are correctness conditions for labelled transition systems that are more expressive than traditional trace properties, with particular relevance to security. Recently, Attiya and Enea studied a notion of strong observational refinement that preserves all hyperproperties. They analyse the correspondence between forward simulation and strong observational refinement in a setting with finite traces only. We study this correspondence in a setting with both finite and infinite traces. In particular, we show that forward simulation does not preserve hyperliveness properties in this setting. We extend the forward simulation proof obligation with a progress condition, and prove that this progressive forward simulation does imply strong observational refinement.
Artist Similarity with Graph Neural Networks
Venue: arXiv cs.IR 2021
Year: 2021

Artist similarity plays an important role in organizing, understanding, and subsequently, facilitating discovery in large collections of music. In this paper, we present a hybrid approach to computing similarity between artists using graph neural networks trained with triplet loss. The novelty of using a graph neural network architecture is to combine the topology of a graph of artist connections with content features to embed artists into a vector space that encodes similarity. To evaluate the proposed method, we compile the new OLGA dataset, which contains artist similarities from AllMusic, together with content features from AcousticBrainz. With 17,673 artists, this is the largest academic artist similarity dataset that includes content-based features to date. Moreover, we also showcase the scalability of our approach by experimenting with a much larger proprietary dataset. Results show the superiority of the proposed approach over current state-of-the-art methods for music similarity. Finally, we hope that the OLGA dataset will facilitate research on data-driven models for artist similarity.
Assistance and Interdiction Problems on Interval Graphs
Venue: arXiv cs.DS 2021
Year: 2021

We introduce a novel framework of graph modifications specific to interval graphs. We study interdiction problems with respect to these graph modifications. Given a list of original intervals, each interval has a replacement interval such that either the replacement contains the original, or the original contains the replacement. The interdictor is allowed to replace up to $k$ original intervals with their replacements. Using this framework we also study the contrary of interdiction problems which we call assistance problems. We study these problems for the independence number, the clique number, shortest paths, and the scattering number. We obtain polynomial time algorithms for most of the studied problems. Via easy reductions, it follows that on interval graphs, the most vital nodes problem with respect to shortest path, independence number and Hamiltonicity can be solved in polynomial time.
DQ-SGD: Dynamic Quantization in SGD for Communication-Efficient Distributed Learning
Venue: arXiv cs.LG 2021
Year: 2021

Gradient quantization is an emerging technique in reducing communication costs in distributed learning. Existing gradient quantization algorithms often rely on engineering heuristics or empirical observations, lacking a systematic approach to dynamically quantize gradients. This paper addresses this issue by proposing a novel dynamically quantized SGD (DQ-SGD) framework, enabling us to dynamically adjust the quantization scheme for each gradient descent step by exploring the trade-off between communication cost and convergence error. We derive an upper bound, tight in some cases, of the convergence error for a restricted family of quantization schemes and loss functions. We design our DQ-SGD algorithm via minimizing the communication cost under the convergence error constraints. Finally, through extensive experiments on large-scale natural language processing and computer vision tasks on AG-News, CIFAR-10, and CIFAR-100 datasets, we demonstrate that our quantization scheme achieves better tradeoffs between the communication cost and learning performance than other state-of-the-art gradient quantization methods.
On the Quantum-like Contextuality of Ambiguous Phrases
Venue: arXiv cs.CL 2021
Year: 2021

Language is contextual as meanings of words are dependent on their contexts. Contextuality is, concomitantly, a well-defined concept in quantum mechanics where it is considered a major resource for quantum computations. We investigate whether natural language exhibits any of the quantum mechanics' contextual features. We show that meaning combinations in ambiguous phrases can be modelled in the sheaf-theoretic framework for quantum contextuality, where they can become possibilistically contextual. Using the framework of Contextuality-by-Default (CbD), we explore the probabilistic variants of these and show that CbD-contextuality is also possible.
SmartHand: Towards Embedded Smart Hands for Prosthetic and Robotic Applications
Venue: arXiv cs.RO 2021
Year: 2021

The sophisticated sense of touch of the human hand significantly contributes to our ability to safely, efficiently, and dexterously manipulate arbitrary objects in our environment. Robotic and prosthetic devices lack refined, tactile feedback from their end-effectors, leading to counterintuitive and complex control strategies. To address this lack, tactile sensors have been designed and developed, but they often offer an insufficient spatial and temporal resolution. This paper focuses on overcoming these issues by designing a smart embedded system, called SmartHand, enabling the acquisition and real-time processing of high-resolution tactile information from a hand-shaped multi-sensor array for prosthetic and robotic applications. We acquire a new tactile dataset consisting of 340,000 frames while interacting with 16 everyday objects and the empty hand, i.e., a total of 17 classes. The design of the embedded system minimizes response latency in classification, by deploying a small yet accurate convolutional neural network on a high-performance ARM Cortex-M7 microcontroller. Compared to related work, our model requires one order of magnitude less memory and 15.6x fewer computations while achieving similar inter-session accuracy and up to 98.86% and 99.83% top-1 and top-3 cross-validation accuracy, respectively. Experimental results show a total power consumption of 505mW and a latency of only 100ms.
A Novel Approach to Model the Kinematics of Human Fingers Based on an Elliptic Multi-Joint Configuration
Venue: arXiv cs.RO 2021
Year: 2021

In this paper, we present a novel kinematic model of the human phalanges based on the elliptical motion of their joints. The presence of the soft elastic tissues and the general anatomical structure of the hand joints highly affect the relative movement of the bones. Commonly used assumption of circular trajectories simplifies the designing process but leads to divergence with the actual hand behavior. The advantages of the proposed model are demonstrated through the comparison with the conventional revolute joint model. Conducted simulations and experiments validate designed forward and inverse kinematic algorithms. Obtained results show a high performance of the model in mimicking the human fingertip motion trajectory.
When Deep Learners Change Their Mind: Learning Dynamics for Active Learning
Venue: arXiv cs.LG 2021
Year: 2021

Active learning aims to select samples to be annotated that yield the largest performance improvement for the learning algorithm. Many methods approach this problem by measuring the informativeness of samples and do this based on the certainty of the network predictions for samples. However, it is well-known that neural networks are overly confident about their prediction and are therefore an untrustworthy source to assess sample informativeness. In this paper, we propose a new informativeness-based active learning method. Our measure is derived from the learning dynamics of a neural network. More precisely we track the label assignment of the unlabeled data pool during the training of the algorithm. We capture the learning dynamics with a metric called label-dispersion, which is low when the network consistently assigns the same label to the sample during the training of the network and high when the assigned label changes frequently. We show that label-dispersion is a promising predictor of the uncertainty of the network, and show on two benchmark datasets that an active learning algorithm based on label-dispersion obtains excellent results.
A Self-Adaptive IoT-based Approach for Improving the Decision Making of Active Surgical Robots in Hospitals
Venue: arXiv cs.RO 2021
Year: 2021

In recent years, surgical robots have become instrumental tools for assisting surgeons in performing complex surgical procedures in hospitals. Unlike conventional surgical methods, robotic systems help surgeons, for example, to perform minimally invasive surgical procedures while enhancing the precision and control of operations (e.g. tiny incisions, wound sutures, endoscopic suturing, among others). To this extent, it is essential to consider several factors that may influence the feasibility and decision making of employing robotic systems in surgical procedures. In this paper, we propose an IoT-based self-adaptive approach that uses multi-criteria decision analysis methods (MCDA) for enhancing the decision making of operations involving surgical robots. Throughout this paper, we present experimental validation results in utilizing MCDA as an effective strategy for enhancing the decisions of employing robotic systems in surgical procedures.
Co-Optimization of Design and Fabrication Plans for Carpentry: Supplemental Material
Venue: arXiv cs.GR 2021
Year: 2021

Past work on optimizing fabrication plans given a carpentry design can provide Pareto-optimal plans trading off between material waste, fabrication time, precision, and other considerations. However, when developing fabrication plans, experts rarely restrict to a single design, instead considering families of design variations, sometimes adjusting designs to simplify fabrication. Jointly exploring the design and fabrication plan spaces for each design is intractable using current techniques. We present a new approach to jointly optimize design and fabrication plans for carpentered objects. To make this bi-level optimization tractable, we adapt recent work from program synthesis based on equality graphs (e-graphs), which encode sets of equivalent programs. Our insight is that subproblems within our bi-level problem share significant substructures. By representing both designs and fabrication plans in a new bag of parts(BOP) e-graph, we amortize the cost of optimizing design components shared among multiple candidates. Even using BOP e-graphs, the optimization space grows quickly in practice. Hence, we also show how a feedback-guided search strategy dubbed Iterative Contraction and Expansion on E-graphs (ICEE) can keep the size of the e-graph manage-able and direct the search toward promising candidates. We illustrate the advantages of our pipeline through examples from the carpentry domain.
On the Efficacy of Small Self-Supervised Contrastive Models without Distillation Signals
Venue: arXiv cs.LG 2021
Year: 2021

It is a consensus that small models perform quite poorly under the paradigm of self-supervised contrastive learning. Existing methods usually adopt a large off-the-shelf model to transfer knowledge to the small one via distillation. Despite their effectiveness, distillation-based methods may not be suitable for some resource-restricted scenarios due to the huge computational expenses of deploying a large model. In this paper, we study the issue of training self-supervised small models without distillation signals. We first evaluate the representation spaces of the small models and make two non-negligible observations: (i) the small models can complete the pretext task without overfitting despite their limited capacity and (ii) they universally suffer the problem of over clustering. Then we verify multiple assumptions that are considered to alleviate the over-clustering phenomenon. Finally, we combine the validated techniques and improve the baseline performances of five small architectures with considerable margins, which indicates that training small self-supervised contrastive models is feasible even without distillation signals. The code is available at \textit{https://github.com/WOWNICE/ssl-small}.
Adaptive Approach Phase Guidance for a Hypersonic Glider via Reinforcement Meta Learning
Venue: arXiv cs.RO 2021
Year: 2021

We use Reinforcement Meta Learning to optimize an adaptive guidance system suitable for the approach phase of a gliding hypersonic vehicle. Adaptability is achieved by optimizing over a range of off-nominal flight conditions including perturbation of aerodynamic coefficient parameters, actuator failure scenarios, and sensor noise. The system maps observations directly to commanded bank angle and angle of attack rates. These observations include a velocity field tracking error formulated using parallel navigation, but adapted to work over long trajectories where the Earth's curvature must be taken into account. Minimizing the tracking error keeps the curved space line of sight to the target location aligned with the vehicle's velocity vector. The optimized guidance system will then induce trajectories that bring the vehicle to the target location with a high degree of accuracy at the designated terminal speed, while satisfying heating rate, load, and dynamic pressure constraints. We demonstrate the adaptability of the guidance system by testing over flight conditions that were not experienced during optimization. The guidance system's performance is then compared to that of a linear quadratic regulator tracking an optimal trajectory.
Local certification of graph decompositions and applications to minor-free classes
Venue: arXiv cs.DC 2021
Year: 2021

Local certification consists in assigning labels to the nodes of a network to certify that some given property is satisfied, in such a way that the labels can be checked locally. In the last few years, certification of graph classes received a considerable attention. The goal is to certify that a graph $G$ belongs to a given graph class~$\mathcal{G}$. Such certifications with labels of size $O(\log n)$ (where $n$ is the size of the network) exist for trees, planar graphs and graphs embedded on surfaces. Feuilloley et al. ask if this can be extended to any class of graphs defined by a finite set of forbidden minors. In this work, we develop new decomposition tools for graph certification, and apply them to show that for every small enough minor $H$, $H$-minor-free graphs can indeed be certified with labels of size $O(\log n)$. We also show matching lower bounds with a simple new proof technique.
Reactive Synthesis Modulo Theories Using Abstraction Refinement
Venue: arXiv cs.LO 2021
Year: 2021

Reactive synthesis builds a system from a specification given as a temporal logic formula. Traditionally, reactive synthesis is defined for systems with Boolean input and output variables. Recently, new theories and techniques have been proposed to extend reactive synthesis to data domains, which are required for more sophisticated programs. In particular, Temporal stream logic(TSL) (Finkbeiner et al. 2019) extends LTL with state variables, updates, and uninterpreted functions and was created for use in synthesis. We present a synthesis procedure for TSL(T), an extension of TSL with theories. Synthesis is performed using a counter-example guided synthesis loop and an LTL synthesis procedure. Our method translates TSL(T) specifications to LTL and extracts a system if synthesis is successful. Otherwise, it analyzes the counterstrategy for inconsistencies with the theory. If the counterstrategy is theory-consistent, it proves that the specification is unrealizable. Otherwise, we add temporal assumptions and Boolean predicates to the TSL(T) specification and start the next iteration of the the loop. We show that the synthesis problem for TSL (T) is undecidable. Nevertheless our method can successfully synthesize or show unrealizability of several non-Boolean examples.