OpenRaiser/Intern-Atlas huggingface dataset:
Randomized Approximation Schemes for the Tutte Polynomial and Random Clustering in Subdense and Superdense Graphs
Venue: arXiv cs.DS 2022
Year: 2022

Extending the work of Alon, Frieze abnd Welsh, we show that there are randomized polynomial time approximation schemes for computing the Tutte polynomial in subdense graphs with an minimal node degree of $Ω\left ( \frac{n}{\sqrt{\log n}}\right )$ . The same holds for the partition function $Z$ in the random cluster model with uniform edge probabilities and for the associated distribution $λ(A),\: A \subseteq E$ whenever the underlying graph $G=(V,E)$ is $c\cdot\frac{n}{\sqrt{\log (n)}}$-subdense. In the superdense case with node degrees $n-o(n)$, we show that the Tutte polynomial $T_G(x,y)$ is asymptotically equal to $Q=(x-1)(y-1)$. Moreover, we briefly discuss the problem of approximating $Z$ in the case of $(α, β)$-power law graphs.
Generating Regular Hyperbolic Honeycombs
Venue: arXiv cs.CG 2022
Year: 2022

Geodesic regular tree structures are essential to combat numerical precision issues that arise while working with large-scale computational hyperbolic geometry and have applications in algorithms based on distances in such tessellations. We present a method of generating and applying such structures to the tessellations of 3-dimensional hyperbolic space.
Delay-aware Robust Control for Safe Autonomous Driving and Racing
Venue: arXiv cs.RO 2022
Year: 2022

Delays endanger safety of autonomous systems operating in a rapidly changing environment, such as nondeterministic surrounding traffic participants in autonomous driving and high-speed racing. Unfortunately, delays are typically not considered during the conventional controller design or learning-enabled controller training phases prior to deployment in the physical world. In this paper, the computation delay from nonlinear optimization for motion planning and control, as well as other unavoidable delays caused by actuators, are addressed systematically and unifiedly. To deal with all these delays, in our framework: 1) we propose a new filtering approach with no prior knowledge of dynamics and disturbance distribution to adaptively and safely estimate the time-variant computation delay; 2) we model actuation dynamics for steering delay; 3) all the constrained optimization is realized in a robust tube model predictive controller. For the application merits, we demonstrate that our approach is suitable for both autonomous driving and autonomous racing. Our approach is a novel design for a standalone delay compensation controller. In addition, in the case that a learning-enabled controller assuming no delay works as a primary controller, our approach serves as the primary controller's safety guard.
Minimum color spanning circle of imprecise points
Venue: arXiv cs.CG 2022
Year: 2022

Let $\cal R$ be a set of $n$ colored imprecise points, where each point is colored by one of $k$ colors. Each imprecise point is specified by a unit disk in which the point lies. We study the problem of computing the smallest and the largest possible minimum color spanning circle, among all possible choices of points inside their corresponding disks. We present an $O(nk\log n)$ time algorithm to compute a smallest minimum color spanning circle. Regarding the largest minimum color spanning circle, we show that the problem is NP-Hard and present a $\frac{1}{3}$-factor approximation algorithm. We improve the approximation factor to $\frac{1}{2}$ for the case where no two disks of distinct color intersect.
Reducing Certified Regression to Certified Classification for General Poisoning Attacks
Venue: arXiv cs.LG 2022
Year: 2022

Adversarial training instances can severely distort a model's behavior. This work investigates certified regression defenses, which provide guaranteed limits on how much a regressor's prediction may change under a poisoning attack. Our key insight is that certified regression reduces to voting-based certified classification when using median as a model's primary decision function. Coupling our reduction with existing certified classifiers, we propose six new regressors provably-robust to poisoning attacks. To the extent of our knowledge, this is the first work that certifies the robustness of individual regression predictions without any assumptions about the data distribution and model architecture. We also show that the assumptions made by existing state-of-the-art certified classifiers are often overly pessimistic. We introduce a tighter analysis of model robustness, which in many cases results in significantly improved certified guarantees. Lastly, we empirically demonstrate our approaches' effectiveness on both regression and classification data, where the accuracy of up to 50% of test predictions can be guaranteed under 1% training set corruption and up to 30% of predictions under 4% corruption. Our source code is available at https://github.com/ZaydH/certified-regression.
CUAHN-VIO: Content-and-Uncertainty-Aware Homography Network for Visual-Inertial Odometry
Venue: arXiv cs.RO 2022
Year: 2022

Learning-based visual ego-motion estimation is promising yet not ready for navigating agile mobile robots in the real world. In this article, we propose CUAHN-VIO, a robust and efficient monocular visual-inertial odometry (VIO) designed for micro aerial vehicles (MAVs) equipped with a downward-facing camera. The vision frontend is a content-and-uncertainty-aware homography network (CUAHN) that is robust to non-homography image content and failure cases of network prediction. It not only predicts the homography transformation but also estimates its uncertainty. The training is self-supervised, so that it does not require ground truth that is often difficult to obtain. The network has good generalization that enables "plug-and-play" deployment in new environments without fine-tuning. A lightweight extended Kalman filter (EKF) serves as the VIO backend and utilizes the mean prediction and variance estimation from the network for visual measurement updates. CUAHN-VIO is evaluated on a high-speed public dataset and shows rivaling accuracy to state-of-the-art (SOTA) VIO approaches. Thanks to the robustness to motion blur, low network inference time (~23ms), and stable processing latency (~26ms), CUAHN-VIO successfully runs onboard an Nvidia Jetson TX2 embedded processor to navigate a fast autonomous MAV.
Prior-Aware Synthetic Data to the Rescue: Animal Pose Estimation with Very Limited Real Data
Venue: arXiv cs.CV 2022
Year: 2022

Accurately annotated image datasets are essential components for studying animal behaviors from their poses. Compared to the number of species we know and may exist, the existing labeled pose datasets cover only a small portion of them, while building comprehensive large-scale datasets is prohibitively expensive. Here, we present a very data efficient strategy targeted for pose estimation in quadrupeds that requires only a small amount of real images from the target animal. It is confirmed that fine-tuning a backbone network with pretrained weights on generic image datasets such as ImageNet can mitigate the high demand for target animal pose data and shorten the training time by learning the the prior knowledge of object segmentation and keypoint estimation in advance. However, when faced with serious data scarcity (i.e., $<10^2$ real images), the model performance stays unsatisfactory, particularly for limbs with considerable flexibility and several comparable parts. We therefore introduce a prior-aware synthetic animal data generation pipeline called PASyn to augment the animal pose data essential for robust pose estimation. PASyn generates a probabilistically-valid synthetic pose dataset, SynAP, through training a variational generative model on several animated 3D animal models. In addition, a style transfer strategy is utilized to blend the synthetic animal image into the real backgrounds. We evaluate the improvement made by our approach with three popular backbone networks and test their pose estimation accuracy on publicly available animal pose images as well as collected from real animals in a zoo.
Combining keyphrase extraction and lexical diversity to characterize ideas in publication titles
Venue: arXiv cs.CL 2022
Year: 2022

Beyond bibliometrics, there is interest in characterizing the evolution of the number of ideas in scientific papers. A common approach for investigating this involves analyzing the titles of publications to detect vocabulary changes over time. With the notion that phrases, or more specifically keyphrases, represent concepts, lexical diversity metrics are applied to phrased versions of the titles. Thus changes in lexical diversity are treated as indicators of shifts, and possibly expansion, of research. Therefore, optimizing detection of keyphrases is an important aspect of this process. Rather than just one, we propose to use multiple phrase detection models with the goal to produce a more comprehensive set of keyphrases from the source corpora. Another potential advantage to this approach is that the union and difference of these sets may provide automated techniques for identifying and omitting non-specific phrases. We compare the performance of several phrase detection models, analyze the keyphrase sets output of each, and calculate lexical diversity of corpora variants incorporating keyphrases from each model, using four common lexical diversity metrics.
Performance Study of Time Series Databases
Venue: arXiv cs.DB 2022
Year: 2022

The growth of big-data sectors such as the Internet of Things (IoT) generates enormous volumes of data. As IoT devices generate a vast volume of time-series data, the Time Series Database (TSDB) popularity has grown alongside the rise of IoT. Time series databases are developed to manage and analyze huge amounts of time series data. However, it is not easy to choose the best one from them. The most popular benchmarks compare the performance of different databases to each other but use random or synthetic data that applies to only one domain. As a result, these benchmarks may not always accurately represent real-world performance. It is required to comprehensively compare the performance of time series databases with real datasets. The experiment shows significant performance differences for data injection time and query execution time when comparing real and synthetic datasets. The results are reported and analyzed.
ZEUS: An Experimental Toolkit for Evaluating Congestion Control Algorithms in 5G Environments
Venue: arXiv cs.NI 2022
Year: 2022

As global cellular networks converge to 5G, one question lingers: Are we ready for the 5G challenge? A growing concern surrounds how well do existing congestion control algorithms perform in diverse 5G networks. Given that 5G networks are not yet widely deployed, assessing the performance of existing congestion control algorithms in realistic 5G settings presents several challenges. Moreover, existing network simulation and emulation environments are also not ideally suited to address the unique challenges of 5G network environments. Therefore, building a simple and easily accessible platform becomes crucial to allow testing and comparison of congestion control algorithms under different testing conditions. This paper makes two main contributions. First, we present Zeus, an open-source testbed that emulates 5G channels to evaluate congestion control algorithms in a repeatable and reproducible manner. Second, we assess and characterize ten of today's state-of-the-art congestion control algorithms under different 5G environments and show the difficulties of these solutions to achieve high performance under highly varying 5G channel conditions. In addition, we also utilize the recently proposed harm metric to characterize the detriment done by such algorithms to TCP Cubic cross traffic in 5G environments.
Spacecraft depth completion based on the gray image and the sparse depth map
Venue: arXiv cs.CV 2022
Year: 2022

Perceiving the three-dimensional (3D) structure of the spacecraft is a prerequisite for successfully executing many on-orbit space missions, and it can provide critical input for many downstream vision algorithms. In this paper, we propose to sense the 3D structure of spacecraft using light detection and ranging sensor (LIDAR) and a monocular camera. To this end, Spacecraft Depth Completion Network (SDCNet) is proposed to recover the dense depth map based on gray image and sparse depth map. Specifically, SDCNet decomposes the object-level spacecraft depth completion task into foreground segmentation subtask and foreground depth completion subtask, which segments the spacecraft region first and then performs depth completion on the segmented foreground area. In this way, the background interference to foreground spacecraft depth completion is effectively avoided. Moreover, an attention-based feature fusion module is also proposed to aggregate the complementary information between different inputs, which deduces the correlation between different features along the channel and the spatial dimension sequentially. Besides, four metrics are also proposed to evaluate object-level depth completion performance, which can more intuitively reflect the quality of spacecraft depth completion results. Finally, a large-scale satellite depth completion dataset is constructed for training and testing spacecraft depth completion algorithms. Empirical experiments on the dataset demonstrate the effectiveness of the proposed SDCNet, which achieves 0.25m mean absolute error of interest and 0.759m mean absolute truncation error, surpassing state-of-the-art methods by a large margin. The spacecraft pose estimation experiment is also conducted based on the depth completion results, and the experimental results indicate that the predicted dense depth map could meet the needs of downstream vision tasks.
CAIR: Fast and Lightweight Multi-Scale Color Attention Network for Instagram Filter Removal
Venue: arXiv cs.CV 2022
Year: 2022

Image restoration is an important and challenging task in computer vision. Reverting a filtered image to its original image is helpful in various computer vision tasks. We employ a nonlinear activation function free network (NAFNet) for a fast and lightweight model and add a color attention module that extracts useful color information for better accuracy. We propose an accurate, fast, lightweight network with multi-scale and color attention for Instagram filter removal (CAIR). Experiment results show that the proposed CAIR outperforms existing Instagram filter removal networks in fast and lightweight ways, about 11$\times$ faster and 2.4$\times$ lighter while exceeding 3.69 dB PSNR on IFFI dataset. CAIR can successfully remove the Instagram filter with high quality and restore color information in qualitative results. The source code and pretrained weights are available at \url{https://github.com/HnV-Lab/CAIR}.
Intelligent Perception System for Vehicle-Road Cooperation
Venue: arXiv cs.RO 2022
Year: 2022

With the development of autonomous driving, the improvement of autonomous driving technology for individual vehicles has reached the bottleneck. The advancement of vehicle-road cooperation autonomous driving technology can expand the vehicle's perception range, supplement the perception blind area and improve the perception accuracy, to promote the development of autonomous driving technology and achieve vehicle-road integration. This project mainly uses lidar to develop data fusion schemes to realize the sharing and combination of vehicle and road equipment data and achieve the detection and tracking of dynamic targets. At the same time, some test scenarios for the vehicle-road cooperative system were designed and used to test our vehicle-road cooperative awareness system, which proved the advantages of vehicle-road cooperative autonomous driving over single-vehicle autonomous driving.
Deep Open-Set Recognition for Silicon Wafer Production Monitoring
Venue: arXiv cs.CV 2022
Year: 2022

The chips contained in any electronic device are manufactured over circular silicon wafers, which are monitored by inspection machines at different production stages. Inspection machines detect and locate any defect within the wafer and return a Wafer Defect Map (WDM), i.e., a list of the coordinates where defects lie, which can be considered a huge, sparse, and binary image. In normal conditions, wafers exhibit a small number of randomly distributed defects, while defects grouped in specific patterns might indicate known or novel categories of failures in the production line. Needless to say, a primary concern of semiconductor industries is to identify these patterns and intervene as soon as possible to restore normal production conditions. Here we address WDM monitoring as an open-set recognition problem to accurately classify WDM in known categories and promptly detect novel patterns. In particular, we propose a comprehensive pipeline for wafer monitoring based on a Submanifold Sparse Convolutional Network, a deep architecture designed to process sparse data at an arbitrary resolution, which is trained on the known classes. To detect novelties, we define an outlier detector based on a Gaussian Mixture Model fitted on the latent representation of the classifier. Our experiments on a real dataset of WDMs show that directly processing full-resolution WDMs by Submanifold Sparse Convolutions yields superior classification performance on known classes than traditional Convolutional Neural Networks, which require a preliminary binning to reduce the size of the binary images representing WDMs. Moreover, our solution outperforms state-of-the-art open-set recognition solutions in detecting novelties.
On some properties of medians, percentiles, baselines, and thresholds in empirical bibliometric analysis
Venue: arXiv cs.DL 2022
Year: 2022

One of the most useful and correct methodological approaches in bibliometrics is ranking. In the context of highly skewed bibliometric distributions and severe distortions caused by outliers, it is often the preferable way of analysis. Ranking methodology strictly implies that "oranges should be compared with oranges, apples with apples". We should make a "like with like" comparison. Ranks in different fields show how a unit under study is compared to others in its field. But do we always apply an "apples approach" appropriately? Is median really a 50%, quartile a 25%, 10th percentile a 10%? The paper considers theoretical definitions of such terms compared to their real sense in the course of bibliometric research. It is found that in many empirical cases quartiles are not quarters, medians are not halves, world baselines are not unity, and integer thresholds lead to inequality of performance evaluation in different science fields.
On Temporal Isolation Assessment in Virtualized Railway Signaling as a Service Systems
Venue: arXiv cs.DC 2022
Year: 2022

Railway signaling systems provide numerous critical functions at different safety level, to correctly implement the entire transport ecosystem. Today, we are witnessing the increasing use of the cloud and virtualization technologies in such mixed-criticality systems, with the main goal of reducing costs, improving reliability, while providing orchestration capabilities. Unfortunately, virtualization includes several issues for assessing temporal isolation, which is critical for safety-related standards like EN50128. In this short paper, we envision leveraging the real-time flavor of a general-purpose hypervisor, like Xen, to build the Railway Signaling as a Service (RSaaS) systems of the future. We provide a preliminary background, highlighting the need for a systematic evaluation of the temporal isolation to demonstrate the feasibility of using general-purpose hypervisors in the safety-critical context for certification purposes.
Solving the Capsulation Attack against Backdoor-based Deep Neural Network Watermarks by Reversing Triggers
Venue: arXiv cs.CR 2022
Year: 2022

Backdoor-based watermarking schemes were proposed to protect the intellectual property of artificial intelligence models, especially deep neural networks, under the black-box setting. Compared with ordinary backdoors, backdoor-based watermarks need to digitally incorporate the owner's identity, which fact adds extra requirements to the trigger generation and verification programs. Moreover, these concerns produce additional security risks after the watermarking scheme has been published for as a forensics tool or the owner's evidence has been eavesdropped on. This paper proposes the capsulation attack, an efficient method that can invalidate most established backdoor-based watermarking schemes without sacrificing the pirated model's functionality. By encapsulating the deep neural network with a rule-based or Bayes filter, an adversary can block ownership probing and reject the ownership verification. We propose a metric, CAScore, to measure a backdoor-based watermarking scheme's security against the capsulation attack. This paper also proposes a new backdoor-based deep neural network watermarking scheme that is secure against the capsulation attack by reversing the encoding process and randomizing the exposure of triggers.
On the Fixed-Parameter Tractability of Capacitated Clustering
Venue: arXiv cs.DS 2022
Year: 2022

We study the complexity of the classic capacitated k-median and k-means problems parameterized by the number of centers, k. These problems are notoriously difficult since the best known approximation bound for high dimensional Euclidean space and general metric space is $Θ(\log k)$ and it remains a major open problem whether a constant factor exists. We show that there exists a $(3+ε)$-approximation algorithm for the capacitated k-median and a $(9+ε)$-approximation algorithm for the capacitated k-means problem in general metric spaces whose running times are $f(ε,k) n^{O(1)}$. For Euclidean inputs of arbitrary dimension, we give a $(1+ε)$-approximation algorithm for both problems with a similar running time. This is a significant improvement over the $(7+ε)$-approximation of Adamczyk et al. for k-median in general metric spaces and the $(69+ε)$-approximation of Xu et al. for Euclidean k-means.
FAKD: Feature Augmented Knowledge Distillation for Semantic Segmentation
Venue: arXiv cs.CV 2022
Year: 2022

In this work, we explore data augmentations for knowledge distillation on semantic segmentation. To avoid over-fitting to the noise in the teacher network, a large number of training examples is essential for knowledge distillation. Imagelevel argumentation techniques like flipping, translation or rotation are widely used in previous knowledge distillation framework. Inspired by the recent progress on semantic directions on feature-space, we propose to include augmentations in feature space for efficient distillation. Specifically, given a semantic direction, an infinite number of augmentations can be obtained for the student in the feature space. Furthermore, the analysis shows that those augmentations can be optimized simultaneously by minimizing an upper bound for the losses defined by augmentations. Based on the observation, a new algorithm is developed for knowledge distillation in semantic segmentation. Extensive experiments on four semantic segmentation benchmarks demonstrate that the proposed method can boost the performance of current knowledge distillation methods without any significant overhead. Code is available at: https://github.com/jianlong-yuan/FAKD.