Iteratively Optimized Patch Label Inference Network for Automatic Pavement Disease Detection

We present a novel deep learning framework named the Iteratively Optimized Patch Label Inference Network (IOPLIN) for automatically detecting various pavement diseases that are not solely limited to specific ones, such as cracks and potholes. IOPLIN can be iteratively trained with only the image label via the Expectation-Maximization Inspired Patch Label Distillation (EMIPLD) strategy, and accomplish this task well by inferring the labels of patches from the pavement images. IOPLIN enjoys many desirable properties over the state-of-the-art single branch CNN models such as GoogLeNet and EfficientNet. It is able to handle images in different resolutions, and sufficiently utilize image information particularly for the high-resolution ones, since IOPLIN extracts the visual features from unrevised image patches instead of the resized entire image. Moreover, it can roughly localize the pavement distress without using any prior localization information in the training phase. In order to better evaluate the effectiveness of our method in practice, we construct a large-scale Bituminous Pavement Disease Detection dataset named CQU-BPDD consisting of 60,059 high-resolution pavement images, which are acquired from different areas at different times. Extensive results on this dataset demonstrate the superiority of IOPLIN over the state-of-the-art image classification approaches in automatic pavement disease detection. The source codes of IOPLIN are released on \url{https://github.com/DearCaat/ioplin}.Comment: Revision on IEEE Trans on IT

Badanie ścieżki handlu uprawnieniami do emisji dwutlenku węgla w Chinach w kontekście tzw. podwójnego węgla

With the continuous development of the global economy, the rapid deterioration of the global ecological environment has caused a huge impact on the future development of the world. In order to solve the problem of global warming and enhance the self-development capacity of all countries, based on the concept of sustainable development, China has set the ambitious goal of dual carbon. To this end, China is actively promoting the establishment of a national carbon emissions trading system.In response to low price competitiveness, such as nonstandard trading system, the influence of the development of the carbon emissions trading system in the future, should not only attach importance to enrich and strengthen the basic function of the carbon market, also continue to carbon pricing system and in-depth reform of the fiscal and taxation system, clear up the thoughts to the carbon market trading rules, is on its relevant rights and obligations, firmly adhere to steadily promote carbon market links between countries. Currently, China’s carbon emission trading is still in its infancy, and its effect is still limited in specific practice. Meanwhile, carbon emission trading markets in developed countries such as the United States and the United Kingdom have begun to implement carbon tariffs and other means to maintain their carbon borders. Therefore, the construction of carbon emission trading is necessary for development, but also for the sustainable development of the country.The lag of China’s carbon emission market leads to the worsening of the problem of carbon excess emissions of industries in the regions not covered, and the increased economic burden caused by the carbon barriers of other countries in foreign trade. Of course, this requires China take the path of sustainable development to continue to strengthen the system construction of carbon emission rights and promote the further optimization of their functions.Wraz z ciągłym rozwojem światowej gospodarki, gwałtowne pogarszanie się globalnego środowiska ekologicznego wywiera ogromny wpływ na przyszły rozwój świata. Aby rozwiązać problem globalnego ocieplenia i zwiększyć zdolność do samorozwoju wszystkich krajów, w oparciu o koncepcję zrównoważonego rozwoju, Chiny postawiły sobie ambitny cel podwójnego węgla. W tym celu Chiny aktywnie promują ustanowienie krajowego systemu handlu uprawnieniami do emisji dwutlenku węgla. W odpowiedzi na niską konkurencyjność cenową, taką jak niestandardowy system handlu, wpływ rozwoju systemu handlu uprawnieniami do emisji dwutlenku węgla w przyszłości powinien nie tylko wzmacniać podstawową funkcję rynku uprawnieniami do emisji dwutlenku węgla, ale także sprzyjać kontynuacji systemu ustalania cen emisji uprawnień do emisji dwutlenku węgla oraz dogłębneie reformy systemu fiskalnego i podatkowego, wyjaśnieniu zasad handlu uprawnieniami do emisji dwutlenku węgla, jego odpowiednich praw i obowiązków oraz stanowczo opowiadać się za stałym promowaniem powiązań między krajami na rynku uprawnień do emisji dwutlenku węgla. Obecnie handel emisjami dwutlenku węgla w Chinach jest wciąż w powijakach, a jego efekt jest nadal ograniczony. Tymczasem rynki handlu emisjami dwutlenku węgla w krajach rozwiniętych, takich jak Stany Zjednoczone i Wielka Brytania, zaczęły wdrażać taryfy węglowe i inne środki utrzymania swoich granic węglowych. Dlatego budowa handlu emisjami dwutlenku węgla jest konieczna dla rozwoju, ale także dla zrównoważonego rozwoju kraju. Opóźnienie rynku emisji dwutlenku węgla w Chinach prowadzi do pogłębienia problemu nadprodukcji węgla przez przemysł i zwiększonego obciążenia ekonomicznego spowodowane go barierami węglowymi innych krajów w handlu zagranicznym. Oczywiście wymaga to od Chin wejścia na ścieżkę zrównoważonego rozwoju, dalszego wzmacniania budowy systemu uprawnień do emisji dwutlenku węgla oraz promowania dalszej optymalizacji ich funkcji

Your Room is not Private: Gradient Inversion Attack on Reinforcement Learning

The prominence of embodied Artificial Intelligence (AI), which empowers robots to navigate, perceive, and engage within virtual environments, has attracted significant attention, owing to the remarkable advancements in computer vision and large language models. Privacy emerges as a pivotal concern within the realm of embodied AI, as the robot accesses substantial personal information. However, the issue of privacy leakage in embodied AI tasks, particularly in relation to reinforcement learning algorithms, has not received adequate consideration in research. This paper aims to address this gap by proposing an attack on the value-based algorithm and the gradient-based algorithm, utilizing gradient inversion to reconstruct states, actions, and supervision signals. The choice of using gradients for the attack is motivated by the fact that commonly employed federated learning techniques solely utilize gradients computed based on private user data to optimize models, without storing or transmitting the data to public servers. Nevertheless, these gradients contain sufficient information to potentially expose private data. To validate our approach, we conduct experiments on the AI2THOR simulator and evaluate our algorithm on active perception, a prevalent task in embodied AI. The experimental results demonstrate the effectiveness of our method in successfully reconstructing all information from the data across 120 room layouts.Comment: 7 pages, 4 figures, 2 table

轮式移动机器人瞬态模型鲁棒自适应同步终端滑模编队控制 (Robust adaptive synchronized formation control for the transient model of wheeled mobile robots with terminal sliding mode)

In the cooperative formation of wheeled mobile robots, the problem how to guarantee that mobile robots can track their own trajectories while synchronizing motions with others puts forward higher requirements on the design of control algorithms. A robust adaptive synchronized control with terminal sliding mode based on the algebraic graph theory is developed to solve this problem. Firstly, the nonlinear kinematics transient model of wheeled mobile robot is introduced. This model avoids the problem of multi-input coupling mutual interference in general kinematics model. Then, the synchronized control algorithm is designed according to the cross-coupling errors to realize the motion synchronization, and the external disturbance of the system is suppressed by the robust control. The adaptive law ensures the real-time adjustment of the switching gain. The stability analysis is carried out by using the Lyapunov method, which proves the convergence of the system tracking errors. Finally, the effectiveness of the designed algorithm is verified by MATLAB simulation

A Review of the U.S. Supreme Court decision in Endrew F. v. Douglas County School District (2017) : Implications for Academic Achievement for Students with Disabilities.

本稿は，学校が障害のある子どもに保障すべき教育成果の水準について，米国連邦最高裁判所が示した画期的な判決（Endrew F. v. Douglas County SD , 2017）の論点を捉えることにより，今後のインクルーシブ教育のあり方の検討に必要な基礎資料を提供することを目的とした。米国では障害者教育法(IDEA)により，障害のある子どもに対する「無償で適切な公教育」（FAPE）が保障されている。従来，FAPEが求める教育成果の水準に関しては，1980年代の最高裁判所判決（Board of Educ. v. Rowley , 1982）が大きな影響を与えてきた。つまり，FAPEの要求は「最小限を満たすもの」であれば足りると解釈されてきた。しかしながら，Endrew 裁判によって，実質的な意味のある教育成果が求められることが判示された。今後，本裁判を契機に，障害のある子どもに対する教育の成果がこれまで以上に大きな議論となることも予想できる。The purpose of this brief note was to understand the whole context of the "Endrew F. v. Douglas County School District " (2017) in the U.S. Supreme Court. The Individuals with Disabilities Education Act (IDEA) requires to guarantee a free appropriate public education (FAPE) for students with disabilities. In the "Board of Education of the Hendrick Hudson Central School District v. Rowley " (1982), the U.S. Supreme Court was rejected requirement to maximize educational potential of student. This decision has been quoted for a long time in the lower courts. However, new standard in the Andrew case was judged. We provides a summary of important legal contents