Regularized Training and Tight Certification for Randomized Smoothed Classifier with Provable Robustness

Recently smoothing deep neural network based classifiers via isotropic Gaussian perturbation is shown to be an effective and scalable way to provide state-of-the-art probabilistic robustness guarantee against $\ell_2$ norm bounded adversarial perturbations. However, how to train a good base classifier that is accurate and robust when smoothed has not been fully investigated. In this work, we derive a new regularized risk, in which the regularizer can adaptively encourage the accuracy and robustness of the smoothed counterpart when training the base classifier. It is computationally efficient and can be implemented in parallel with other empirical defense methods. We discuss how to implement it under both standard (non-adversarial) and adversarial training scheme. At the same time, we also design a new certification algorithm, which can leverage the regularization effect to provide tighter robustness lower bound that holds with high probability. Our extensive experimentation demonstrates the effectiveness of the proposed training and certification approaches on CIFAR-10 and ImageNet datasets.Comment: AAAI202

ProgressLabeller: Visual Data Stream Annotation for Training Object-Centric 3D Perception

Visual perception tasks often require vast amounts of labelled data, including 3D poses and image space segmentation masks. The process of creating such training data sets can prove difficult or time-intensive to scale up to efficacy for general use. Consider the task of pose estimation for rigid objects. Deep neural network based approaches have shown good performance when trained on large, public datasets. However, adapting these networks for other novel objects, or fine-tuning existing models for different environments, requires significant time investment to generate newly labelled instances. Towards this end, we propose ProgressLabeller as a method for more efficiently generating large amounts of 6D pose training data from color images sequences for custom scenes in a scalable manner. ProgressLabeller is intended to also support transparent or translucent objects, for which the previous methods based on depth dense reconstruction will fail. We demonstrate the effectiveness of ProgressLabeller by rapidly create a dataset of over 1M samples with which we fine-tune a state-of-the-art pose estimation network in order to markedly improve the downstream robotic grasp success rates. ProgressLabeller is open-source at https://github.com/huijieZH/ProgressLabeller.Comment: IROS 2022 accepted paper; project page: https://progress.eecs.umich.edu/projects/progress-labeller

STTAR: A Traffic- and Thermal-Aware Adaptive Routing for 3D Network-on-Chip Systems

Since the three-dimensional Network on Chip (3D NoC) uses through-silicon via technology to connect the chips, each silicon layer is conducted through heterogeneous thermal, and 3D NoC system suffers from thermal problems. To alleviate the seriousness of the thermal problem, the distribution of data packets usually relies on traffic information or historical temperature information. However, thermal problems in 3D NoC cannot be solved only based on traffic or temperature information. Therefore, we propose a Score-Based Traffic- and Thermal-Aware Adaptive Routing (STTAR) that applies traffic load and temperature information to routing. First, the STTAR dynamically adjusts the input and output buffer lengths of each router with traffic load information to limit routing resources in overheated areas and control the rate of temperature rise. Second, STTAR adopts a scoring strategy based on temperature and the number of free slots in the buffer to avoid data packets being transmitted to high-temperature areas and congested areas and to improve the rationality of selecting routing output nodes. In our experiments, the proposed scoring Score-Based Traffic- and Thermal-Aware Adaptive Routing (STTAR) scheme can increase the throughput by about 14.98% to 47.90% and reduce the delay by about 10.80% to 35.36% compared with the previous works

TransNet: Transparent Object Manipulation Through Category-Level Pose Estimation

Transparent objects present multiple distinct challenges to visual perception systems. First, their lack of distinguishing visual features makes transparent objects harder to detect and localize than opaque objects. Even humans find certain transparent surfaces with little specular reflection or refraction, like glass doors, difficult to perceive. A second challenge is that depth sensors typically used for opaque object perception cannot obtain accurate depth measurements on transparent surfaces due to their unique reflective properties. Stemming from these challenges, we observe that transparent object instances within the same category, such as cups, look more similar to each other than to ordinary opaque objects of that same category. Given this observation, the present paper explores the possibility of category-level transparent object pose estimation rather than instance-level pose estimation. We propose \textit{\textbf{TransNet}}, a two-stage pipeline that estimates category-level transparent object pose using localized depth completion and surface normal estimation. TransNet is evaluated in terms of pose estimation accuracy on a large-scale transparent object dataset and compared to a state-of-the-art category-level pose estimation approach. Results from this comparison demonstrate that TransNet achieves improved pose estimation accuracy on transparent objects. Moreover, we use TransNet to build an autonomous transparent object manipulation system for robotic pick-and-place and pouring tasks

Robust Split Federated Learning for U-shaped Medical Image Networks

U-shaped networks are widely used in various medical image tasks, such as segmentation, restoration and reconstruction, but most of them usually rely on centralized learning and thus ignore privacy issues. To address the privacy concerns, federated learning (FL) and split learning (SL) have attracted increasing attention. However, it is hard for both FL and SL to balance the local computational cost, model privacy and parallel training simultaneously. To achieve this goal, in this paper, we propose Robust Split Federated Learning (RoS-FL) for U-shaped medical image networks, which is a novel hybrid learning paradigm of FL and SL. Previous works cannot preserve the data privacy, including the input, model parameters, label and output simultaneously. To effectively deal with all of them, we design a novel splitting method for U-shaped medical image networks, which splits the network into three parts hosted by different parties. Besides, the distributed learning methods usually suffer from a drift between local and global models caused by data heterogeneity. Based on this consideration, we propose a dynamic weight correction strategy (\textbf{DWCS}) to stabilize the training process and avoid model drift. Specifically, a weight correction loss is designed to quantify the drift between the models from two adjacent communication rounds. By minimizing this loss, a correction model is obtained. Then we treat the weighted sum of correction model and final round models as the result. The effectiveness of the proposed RoS-FL is supported by extensive experimental results on different tasks. Related codes will be released at https://github.com/Zi-YuanYang/RoS-FL.Comment: 11 pages, 5 figure

Observed deep energetic eddies by seamount wake

Despite numerous surface eddies are observed in the ocean, deep eddies (a type of eddies which have no footprints at the sea surface) are much less reported in the literature due to the scarcity of their observation. In this letter, from recently collected current and temperature data by mooring arrays, a deep energetic and baroclinic eddy is detected in the northwestern South China Sea (SCS) with its intensity, size, polarity and structure being characterized. It remarkably deepens isotherm at deep layers by the amplitude of ~120 m and induces a maximal velocity amplitude about 0.18 m/s, which is far larger than the median velocity (0.02 m/s). The deep eddy is generated in a wake when a steering flow in the upper layer passes a seamount, induced by a surface cyclonic eddy. More observations suggest that the deep eddy should not be an episode in the area. Deep eddies significantly increase the velocity intensity and enhance the mixing in the deep ocean, also have potential implication for deep-sea sediments transport

Episodic subduction patches in the western North Pacific identified from BGC-Argo float data

© The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Chen, S., Wells, M. L., Huang, R. X., Xue, H., Xi, J., & Chai, F. Episodic subduction patches in the western North Pacific identified from BGC-Argo float data. Biogeosciences, 18(19), (2021): 5539–5554, https://doi.org/10.5194/bg-18-5539-2021.Subduction associated with mesoscale eddies is an important but difficult-to-observe process that can efficiently export carbon and oxygen to the mesopelagic zone (100–1000 dbar). Using a novel BGC-Argo dataset covering the western North Pacific (20–50∘ N, 120–180∘ E), we identified imprints of episodic subduction using anomalies in dissolved oxygen and spicity, a water mass marker. These subduction patches were present in 4.0 % (288) of the total profiles (7120) between 2008 and 2019, situated mainly in the Kuroshio Extension region between March and August (70.6 %). Roughly 31 % and 42 % of the subduction patches were identified below the annual permanent pycnocline depth (300 m vs. 450 m) in the subpolar and subtropical regions, respectively. Around half (52 %) of these episodic events injected oxygen-enriched waters below the maximum annual permanent thermocline depth (450 dbar), with >20 % occurring deeper than 600 dbar. Subduction patches were detected during winter and spring when mixed layers are deep. The oxygen inventory within these subductions is estimated to be on the order of 64 to 152 g O2/m2. These mesoscale events would markedly increase oxygen ventilation as well as carbon removal in the region, both processes helping to support the nutritional and metabolic demands of mesopelagic organisms. Climate-driven patterns of increasing eddy kinetic energies in this region imply that the magnitude of these processes will grow in the future, meaning that these unexpectedly effective small-scale subduction processes need to be better constrained in global climate and biogeochemical models.This work was supported by the National Natural Science Foundation of China (NSFC) projects (grant nos. 41906159, 42030708, and 41730536), the Scientific Research Fund of the Second Institute of Oceanography MNR (grant no. 14283), and the Marine S&T Fund of Shandong Province for the Pilot National Laboratory for Marine Science and Technology (Qingdao) (grant no. 2018SDKJ0206)

Epidemiological study of scarlet fever in Shenyang City, China

AimsTo depict the Spatiotemporal epidemiological characteristics of the incidence of scarlet fever in Shenyang, China, in 2018 so as to provide the scientific basis for effective strategies of scarlet control and prevention.Methods Excel 2010 was used to demonstrate the temporal distribution at the month level and ArcGIS10.3 was used to demonstrate the spatial distribution at the district/county level. Moran’s autocorrelation coefficient was used to examine the spatial autocorrelation and the Getis-Ord statistic was used to determine the hot-spot areas of scarlet fever.Results A total of 2,314 scarlet fever cases were reported in Shenyang in 2018 with an annual incidence of 31.24 per 100,000. The incidence among males was higher than that amongfemales (X2=95.013, P≤0.001). A vast majority of the cases (96.89%) were among children aged 3 to 11 years. The highest incidence was 625.34/100,000 in children aged 5–9 years. There are two seasonal peaks occurred in June (Summer-peak) and in December (Winter-peak) in 2018. The incidence of scarlet fever in urban areas was significantly higher than that in rural areas(X2=514.115, P≤0.001). The incidence of scarlet fever was randomly distributed in Shenyang. There are hot-spots areas located in seven districts.ConclusionUrban areas are the hot spots of scarlet fever and joint prevention and control measures between districts should be applied. Children in the kindergartens and the primary school students are the main population of scarlet fever and the time distribution of scarlet fever is highly consistent with their school and vacation time. It is suggested that measure for prevention and control of scarlet fever in kindergartens and primary schools is the key to control the epidemic of scarlet fever