Unstable Galaxy Models

The dynamics of collisionless galaxy can be described by the Vlasov-Poisson system. By the Jean's theorem, all the spherically symmetric steady galaxy models are given by a distribution of {\Phi}(E,L), where E is the particle energy and L the angular momentum. In a celebrated Doremus-Feix-Baumann Theorem, the galaxy model {\Phi}(E,L) is stable if the distribution {\Phi} is monotonically decreasing with respect to the particle energy E. On the other hand, the stability of {\Phi}(E,L) remains largely open otherwise. Based on a recent abstract instability criterion of Guo-Lin, we constuct examples of unstable galaxy models of f(E,L) and f(E) in which f fails to be monotone in E

Ladder: A software to label images, detect objects and deploy models recurrently for object detection

Object Detection (OD) is a computer vision technology that can locate and classify objects in images and videos, which has the potential to significantly improve efficiency in precision agriculture. To simplify OD application process, we developed Ladder - a software that provides users with a friendly graphic user interface (GUI) that allows for efficient labelling of training datasets, training OD models, and deploying the trained model. Ladder was designed with an interactive recurrent framework that leverages predictions from a pre-trained OD model as the initial image labeling. After adding human labels, the newly labeled images can be added into the training data to retrain the OD model. With the same GUI, users can also deploy well-trained OD models by loading the model weight file to detect new images. We used Ladder to develop a deep learning model to access wheat stripe rust in RGB (red, green, blue) images taken by an Unmanned Aerial Vehicle (UAV). Ladder employs OD to directly evaluate different severity levels of wheat stripe rust in field images, eliminating the need for photo stitching process for UAVs-based images. The accuracy for low, medium and high severity scores were 72%, 50% and 80%, respectively. This case demonstrates how Ladder empowers OD in precision agriculture and crop breeding.Comment: 5 pages, 2 figure

ANALYSIS OF FREEDOM AND THOUGHT IN DESCARTES’ PHILOSOPHY AND ITS INFLUENCE

The search for truth and certainty is a major preoccupationwith all Western philosophy. This has its most famousattempt in the philosophy of Descartes. This paperstudies Descartes’ pure philosophy considered as a purefirst philosophy. Descartes regards thinking as his coreexistence, and thinking is a freedom that I can truly grasp.Descartes is sometimes criticized for offering only a defenseof the primacy of the freedom of thought in opposition to thefreedom of action. This paper will show that Descartes doesnot oppose practical philosophy but intends to seek a metaphilosophywhich supports practical philosophy, using thefreedom of thinking as the foundation. In short, Descartesjust wants to find a way of verifying truth prior to anypolitical, cultural, traditional, moral, or religious factors.His influence is visible in the subsequent philosophies andphilosophers who place the human beings at the center ofphilosophy

An Investigation of Cyberinfrastructure Adoption in University Libraries

This study aims to understand factors that affect university libraries’ adoption of cyberinfrastructure for big data sharing and reuse. A cyberinfrastructure adoption model which contains 10 factors has been developed based on the technology-organization-environment (TOE) framework and the literature regarding tradeoffs of applying cyberinfrastructure. This paper describes the proposed cyberinfrastructure adoption model and explains the survey in-struments. The next steps of the study are also presented

Selection of a stealthy and harmful attack function in discrete event systems

In this paper we consider the problem of joint state estimation under attack in partially-observed discrete event systems. An operator observes the evolution of the plant to evaluate its current states. The attacker may tamper with the sensor readings received by the operator inserting dummy events or erasing real events that have occurred in the plant with the goal of preventing the operator from computing the correct state estimation. An attack function is said to be harmful if the state estimation consistent with the correct observation and the state estimation consistent with the corrupted observation satisfy a given misleading relation. On the basis of an automaton called joint estimator, we show how to compute a supremal stealthy joint subestimator that allows the attacker to remain stealthy, no matter what the future evolution of the plant is. Finally, we show how to select a stealthy and harmful attack function based on such a subestimator

Facial Emotion Recognition with Noisy Multi-task Annotations

Human emotions can be inferred from facial expressions. However, the annotations of facial expressions are often highly noisy in common emotion coding models, including categorical and dimensional ones. To reduce human labelling effort on multi-task labels, we introduce a new problem of facial emotion recognition with noisy multi-task annotations. For this new problem, we suggest a formulation from the point of joint distribution match view, which aims at learning more reliable correlations among raw facial images and multi-task labels, resulting in the reduction of noise influence. In our formulation, we exploit a new method to enable the emotion prediction and the joint distribution learning in a unified adversarial learning game. Evaluation throughout extensive experiments studies the real setups of the suggested new problem, as well as the clear superiority of the proposed method over the state-of-the-art competing methods on either the synthetic noisy labeled CIFAR-10 or practical noisy multi-task labeled RAF and AffectNet. The code is available at https://github.com/sanweiliti/noisyFER.Comment: Accepted by 2021 WACV, camera-ready version with appendi

Disentangling Spatial and Temporal Learning for Efficient Image-to-Video Transfer Learning

Recently, large-scale pre-trained language-image models like CLIP have shown extraordinary capabilities for understanding spatial contents, but naively transferring such models to video recognition still suffers from unsatisfactory temporal modeling capabilities. Existing methods insert tunable structures into or in parallel with the pre-trained model, which either requires back-propagation through the whole pre-trained model and is thus resource-demanding, or is limited by the temporal reasoning capability of the pre-trained structure. In this work, we present DiST, which disentangles the learning of spatial and temporal aspects of videos. Specifically, DiST uses a dual-encoder structure, where a pre-trained foundation model acts as the spatial encoder, and a lightweight network is introduced as the temporal encoder. An integration branch is inserted between the encoders to fuse spatio-temporal information. The disentangled spatial and temporal learning in DiST is highly efficient because it avoids the back-propagation of massive pre-trained parameters. Meanwhile, we empirically show that disentangled learning with an extra network for integration benefits both spatial and temporal understanding. Extensive experiments on five benchmarks show that DiST delivers better performance than existing state-of-the-art methods by convincing gaps. When pre-training on the large-scale Kinetics-710, we achieve 89.7% on Kinetics-400 with a frozen ViT-L model, which verifies the scalability of DiST. Codes and models can be found in https://github.com/alibaba-mmai-research/DiST.Comment: ICCV2023. Code: https://github.com/alibaba-mmai-research/DiS