Threshold Free Detection of Elliptical Landmarks Using Machine Learning

Elliptical shape detection is widely used in practical applications. Nearly all classical ellipse detection algorithms require some form of threshold, which can be a major cause of detection failure, especially in the challenging case of Moire Phase Tracking (MPT) target images. To meet the challenge, a threshold free detection algorithm for elliptical landmarks is proposed in this thesis. The proposed Aligned Gradient and Unaligned Gradient (AGUG) algorithm is a Support Vector Machine (SVM)-based classification algorithm, original features are extracted from the gradient information corresponding to the sampled pixels. with proper selection of features, the proposed algorithm has a high accuracy and a stronger robustness in blurring and contrast variation. The thesis confirms that the removal of thresholds in ellipse detection algorithm improves robustness

Route-based transportation network design

Given shipment demand and driving regulations, a consolidation carrier has to make decisions on how to route both shipments and drivers at minimal cost. The traditional way to formulate and solve these problems is through the use of two-step models. This thesis presents a heuristic algorithm to solve an integrated model that can provide superior solutions. The algorithm combines a slope scaling initialization phase and tabu search to find high-quality solutions. The performance of the proposed heuristic is benchmarked against a commercial solver and these results indicate that the proposed method is able to produce better quality solutions for the similar solution time

Bridge the Gap Between CV and NLP! An Optimization-based Textual Adversarial Attack Framework

Despite recent success on various tasks, deep learning techniques still perform poorly on adversarial examples with small perturbations. While optimization-based methods for adversarial attacks are well-explored in the field of computer vision, it is impractical to directly apply them in natural language processing due to the discrete nature of the text. To address the problem, we propose a unified framework to extend the existing optimization-based adversarial attack methods in the vision domain to craft textual adversarial samples. In this framework, continuously optimized perturbations are added to the embedding layer and amplified in the forward propagation process. Then the final perturbed latent representations are decoded with a masked language model head to obtain potential adversarial samples. In this paper, we instantiate our framework with an attack algorithm named Textual Projected Gradient Descent (T-PGD). We find our algorithm effective even using proxy gradient information. Therefore, we perform the more challenging transfer black-box attack and conduct comprehensive experiments to evaluate our attack algorithm with several models on three benchmark datasets. Experimental results demonstrate that our method achieves an overall better performance and produces more fluent and grammatical adversarial samples compared to strong baseline methods. All the code and data will be made public.Comment: Codes are available at: https://github.com/Phantivia/T-PG

Perceived stress of COVID-19 pandemic and problematic mobile phone use during quarantine conditions among Chinese adolescents: a mediated moderation model

The relation between perceived general stress and problematic mobile phone use (PMPU) has been well established. With the outbreak of the coronavirus disease (COVID-19), the present study was designed to examine the association between perceived stress of COVID-19 as a kind of event-related stress and PMPU, and the mechanisms underlying this relation. Participants were 724 adolescents ranging from 12 to 16 years old (M = 13.28, SD = 1.05) who completed four online questionnaires addressing perceived stress of COVID-19, search for meaning in life, escapism motivation, and PMPU. The results revealed that escapism motivation mediated the relationship between perceived stress of COVID-19 and PMPU. In addition, search for meaning in life played a moderating role between perceived stress of COVID-19 and escapism motivation. These findings extend the literature by addressing how and under what conditions perceived stress of COVID-19 can contribute to PMPU. We discussed the implications for developing targeted intervention programs aimed at reducing PMPU among adolescents

Probing Dark Energy with the Kunlun Dark Universe Survey Telescope

Dark energy is an important science driver of many upcoming large-scale surveys. With small, stable seeing and low thermal infrared background, Dome A, Antarctica, offers a unique opportunity for shedding light on fundamental questions about the universe. We show that a deep, high-resolution imaging survey of 10,000 square degrees in \emph{ugrizyJH} bands can provide competitive constraints on dark energy equation of state parameters using type Ia supernovae, baryon acoustic oscillations, and weak lensing techniques. Such a survey may be partially achieved with a coordinated effort of the Kunlun Dark Universe Survey Telescope (KDUST) in \emph{yJH} bands over 5000--10,000 deg$^2$ and the Large Synoptic Survey Telescope in \emph{ugrizy} bands over the same area. Moreover, the joint survey can take advantage of the high-resolution imaging at Dome A to further tighten the constraints on dark energy and to measure dark matter properties with strong lensing as well as galaxy--galaxy weak lensing.Comment: 9 pages, 6 figure

Maneuvering Star-Convex Extended Target Tracking Based on Modified Expected- Mode Augmentation Algorithm

In utilizing a variable-structure multiple-model (VSMM) algorithm for kinematic state estimation, the core step is the model set design. This study aims to refine the existing expected-mode augmentation (EMA) algorithm, a method of model set design. First, the OTSU algorithm is employed to determine an adaptive threshold, which in turn allows for a reasonable partition of the basic model set. Next, a subset of possible models is preserved, reactivating models adjacent to the one with the highest prediction probability, eliminating improbable models, and yielding an augmented expected mode. Additionally, the study leverages the translation properties of radial functions and inverse trigonometric function formulas to derive a maneuvering model for star- convex extended targets under uniformly accelerated conditions. In order to assess the effectiveness of the proposed algorithm and the validity of the established maneuvering model, simulation experiments were carried out in both fixed and random scenarios. The proposed algorithm demonstrates improved performance when compared to the interactive multiple-model algorithm and the unmodified EMA algorithm

SHP2 phosphatase as a novel therapeutic target for melanoma treatment

Melanoma ranks among the most aggressive and deadly human cancers. Although a number of targeted therapies are available, they are effective only in a subset of patients and the emergence of drug resistance often reduces durable responses. Thus there is an urgent need to identify new therapeutic targets and develop more potent pharmacological agents for melanoma treatment. Herein we report that SHP2 levels are frequently elevated in melanoma, and high SHP2 expression is significantly associated with more metastatic phenotype and poorer prognosis. We show that SHP2 promotes melanoma cell viability, motility, and anchorage-independent growth, through activation of both ERK1/2 and AKT signaling pathways. We demonstrate that SHP2 inhibitor 11a-1 effectively blocks SHP2-mediated ERK1/2 and AKT activation and attenuates melanoma cell viability, migration and colony formation. Most importantly, SHP2 inhibitor 11a-1 suppresses xenografted melanoma tumor growth, as a result of reduced tumor cell proliferation and enhanced tumor cell apoptosis. Taken together, our data reveal SHP2 as a novel target for melanoma and suggest SHP2 inhibitors as potential novel therapeutic agents for melanoma treatment