A closed-form solution to estimate uncertainty in non-rigid structure from motion

Semi-Definite Programming (SDP) with low-rank prior has been widely applied in Non-Rigid Structure from Motion (NRSfM). Based on a low-rank constraint, it avoids the inherent ambiguity of basis number selection in conventional base-shape or base-trajectory methods. Despite the efficiency in deformable shape reconstruction, it remains unclear how to assess the uncertainty of the recovered shape from the SDP process. In this paper, we present a statistical inference on the element-wise uncertainty quantification of the estimated deforming 3D shape points in the case of the exact low-rank SDP problem. A closed-form uncertainty quantification method is proposed and tested. Moreover, we extend the exact low-rank uncertainty quantification to the approximate low-rank scenario with a numerical optimal rank selection method, which enables solving practical application in SDP based NRSfM scenario. The proposed method provides an independent module to the SDP method and only requires the statistic information of the input 2D tracked points. Extensive experiments prove that the output 3D points have identical normal distribution to the 2D trackings, the proposed method and quantify the uncertainty accurately, and supports that it has desirable effects on routinely SDP low-rank based NRSfM solver.Comment: 9 pages, 2 figure

Comparative transcriptomics of spotted seatrout (Cynoscion nebulosus) populations to cold and heat stress

Resilience to climate change depends on a species\u27 adaptive potential and phenotypic plasticity. The latter can enhance survival of individual organisms during short periods of extreme environmental perturbations, allowing genetic adaptation to take place over generations. Along the U.S. East Coast, estuarine‐dependent spotted seatrout (Cynoscion nebulosus) populations span a steep temperature gradient that provides an ideal opportunity to explore the molecular basis of phenotypic plasticity. Genetically distinct spotted seatrout sampled from a northern and a southern population were exposed to acute cold and heat stress (5 biological replicates in each treatment and control group), and their transcriptomic responses were compared using RNA‐sequencing (RNA‐seq). The southern population showed a larger transcriptomic response to acute cold stress, whereas the northern population showed a larger transcriptomic response to acute heat stress compared with their respective population controls. Shared transcripts showing significant differences in expression levels were predominantly enriched in pathways that included metabolism, transcriptional regulation, and immune response. In response to heat stress, only the northern population significantly upregulated genes in the apoptosis pathway, which could suggest greater vulnerability to future heat waves in this population as compared to the southern population. Genes showing population‐specific patterns of expression, including hpt, acot, hspa5, and hsc71, are candidates for future studies aiming to monitor intraspecific differences in temperature stress responses in spotted seatrout. Our findings contribute to the current understanding of phenotypic plasticity and provide a basis for predicting the response of a eurythermal fish species to future extreme temperatures

Convergence and Consistency Analysis for A 3D Invariant-EKF SLAM

In this paper, we investigate the convergence and consistency properties of an Invariant-Extended Kalman Filter (RI-EKF) based Simultaneous Localization and Mapping (SLAM) algorithm. Basic convergence properties of this algorithm are proven. These proofs do not require the restrictive assumption that the Jacobians of the motion and observation models need to be evaluated at the ground truth. It is also shown that the output of RI-EKF is invariant under any stochastic rigid body transformation in contrast to $\mathbb{SO}(3)$ based EKF SLAM algorithm ($\mathbb{SO}(3)$-EKF) that is only invariant under deterministic rigid body transformation. Implications of these invariance properties on the consistency of the estimator are also discussed. Monte Carlo simulation results demonstrate that RI-EKF outperforms $\mathbb{SO}(3)$-EKF, Robocentric-EKF and the "First Estimates Jacobian" EKF, for 3D point feature based SLAM

Be Your Own Teacher: Improve the Performance of Convolutional Neural Networks via Self Distillation

Convolutional neural networks have been widely deployed in various application scenarios. In order to extend the applications' boundaries to some accuracy-crucial domains, researchers have been investigating approaches to boost accuracy through either deeper or wider network structures, which brings with them the exponential increment of the computational and storage cost, delaying the responding time. In this paper, we propose a general training framework named self distillation, which notably enhances the performance (accuracy) of convolutional neural networks through shrinking the size of the network rather than aggrandizing it. Different from traditional knowledge distillation - a knowledge transformation methodology among networks, which forces student neural networks to approximate the softmax layer outputs of pre-trained teacher neural networks, the proposed self distillation framework distills knowledge within network itself. The networks are firstly divided into several sections. Then the knowledge in the deeper portion of the networks is squeezed into the shallow ones. Experiments further prove the generalization of the proposed self distillation framework: enhancement of accuracy at average level is 2.65%, varying from 0.61% in ResNeXt as minimum to 4.07% in VGG19 as maximum. In addition, it can also provide flexibility of depth-wise scalable inference on resource-limited edge devices.Our codes will be released on github soon.Comment: 10page

Plasticity in Standard and Maximum Aerobic Metabolic Rates in Two Populations of an Estuarine Dependent Teleost, Spotted Seatrout (Cynoscion nebulosus)

We studied the effects of metabolic cold adaptation (MCA) in two populations of a eurythermal species, spotted seatrout (Cynoscion nebulosus) along the U.S. East Coast. Fish were captured from their natural environment and acclimated at control temperatures 15 °C or 20 °C. Their oxygen consumption rates, a proxy for metabolic rates, were measured using intermittent flow respirometry during acute temperature decrease or increase (2.5 °C per hour). Mass-specific standard metabolic rates (SMR) were higher in fish from the northern population across an ecologically relevant temperature gradient (5 °C to 30 °C). SMR were up to 37% higher in the northern population at 25 °C and maximum metabolic rates (MMR) were up to 20% higher at 20 °C. We found evidence of active metabolic compensation in the southern population from 5 °C to 15 °C (Q10 \u3c 2), but not in the northern population. Taken together, our results indicate differences in metabolic plasticity between the northern and southern populations of spotted seatrout and provide a mechanistic basis for predicting population-specific responses to climate change

3D non-rigid SLAM in minimally invasive surgery

University of Technology Sydney. Faculty of Engineering and Information Technology.Aiming at reducing trauma and morbidity associated with large incisions in open surgery, minimally invasive surgery (MIS) has been widely acquired in clinical practice as a powerful tool enabling patients with less pain, shorter hospital stay, and fewer complications. However, MIS narrows the surgeon's field of view which confines visual information when implementing MIS. Therefore, a stereoscope or monocular scope is an essential tool for capturing and transmitting 2D images during the procedure. Although numbers of special sensors including laser, structured light, time-of-flight cameras have been applied or investigated in MIS, RGB scope is still widely applied in the intro-operative system because it is non-invasive and cheap to be installed. Thus it is an important topic to rebuild and visualize the latest deformed shape of soft-tissue surfaces to mitigate tissue damages from stereo or monocular scopes. This research aims at proposing innovative robocentric simultaneous localization and mapping (SLAM) algorithm for deformable dense reconstruction of soft-tissue surfaces using a sequence of images obtained from a stereoscope or monocular camera. In this paper, we try to solve the problem by introducing a warping field based on the embedded deformation (ED) nodes which makes full use of the 3D shapes recovered from consecutive pairs of stereo images by deforming the last updated model to the current live model. Our robocentric SLAM system (off-line and tested on stereo videos) can: (1) Incrementally build a live model by progressively fusing new observations with vivid accurate texture. (2) Estimate the deformed shape of the unobserved region with the principle As-Rigid-As-Possible. (3) Perform the dynamic model shape deformation. (4) Estimate the current relative pose between the soft-tissue and the scope. We further improve and optimize the proposed robocentric deformable SLAM algorithm to MIS-SLAM: a complete real-time large scale robocentric dense deformable SLAM system with stereoscope in MIS based on heterogeneous computing by making full use of CPU and GPU. Idled CPU is used to perform ORB-SLAM for providing robust global pose. Strategies are taken to integrate modules from CPU and GPU. We solve the key problem raised in previous work, that is, fast movement of scope and blurry images make the scope tracking fail. Benefiting from improved localization, MIS-SLAM can achieve large scale scope localizing and dense mapping in real-time. It transforms and deforms the current model and incrementally fuses new observation while keeping the vivid texture. In-vivo experiments conducted on publicly available datasets presented in the form of videos demonstrate the feasibility and practicality of MIS-SLAM for potential clinical purpose. In MIS-SLAM, however, it remains challenging to keep constant speed in deformation nodes parameter estimation when the model grows larger. In practice, the processing time grows rapidly in accordance with the expansion of the maps. Therefore, we propose an approach to decouple nodes of deformation graph in large scale robocentric dense deformable SLAM and keep the estimation time to be constant. We discover that only partial deformable nodes in the graph are connected to visible points. Based on this principle, the sparsity of the original Hessian matrix is utilized to split parameter estimation into two independent steps. With this new formulation, we achieve faster parameter estimation with amortized computation complexity reduced from (²) to closing (1). As a result, the computation cost barely increases as the map keeps growing. By our strategy, the bottleneck of limited computation in estimating deformation field in large scale environment has been overcome. The effectiveness is validated by experiments, featuring large scale deformation scenarios. In addition to robocentric SLAM, this thesis also aims at developing a general SLAM which estimates the scope poses correctly. An elaborate observability analysis is conducted on the ED graph. We demonstrate and prove that the ED graph widely used in such scenarios is unobservable and leads to multiple solutions unless suitable priors are provided. Example, as well as theoretical prove, are provided to show the ambiguity of ED graph and scope pose. Different from robocentric SLAM, in modelling non-rigid scenario with ED graph, motion priors of the deforming environment is essential to separate robot pose and deforming environment. The conclusion can be extrapolated to any free form deformation formulation. In guaranteeing the observability, this research proposes a preliminary deformable SLAM approach to estimate robot pose in complex environments that exhibits regular motion. A strategy that approximates deformed shape using a linear combination of several previous shapes is proposed to avoid the ambiguity in robot movement and rigid and non-rigid motions of the environment. Fisher information matrix rank analysis is performed to prove the effectiveness. Moreover, the proposed algorithm is validated using Monte Carlo simulations and real experiments. It is demonstrated that the new algorithm significantly outperforms conventional SLAM and ED based SLAM especially in scenarios where there is large deformation

Search for Selection: Genomic, Transcriptomic, and Phenotypic Investigations of Spotted Seatrout (Cynoscion nebulosus)

Climate change has resulted in both increased mean water temperature and higher frequencies of extreme water temperatures in coastal areas. These new thermal regimes exert strong selective pressure on the thermal physiology of coastal aquatic species. Phenotypic plasticity (the ability of one genotype to display multiple phenotypes) and local adaptation (increased fitness to local environment due to natural selection) dictate both short-term (from hours to days to weeks) and long-term (from years to decades) resilience of a species. To better predict how a species will respond to the negative impacts of climate change, one first needs to know the current levels of variation in plasticity and local adaptation. Marginal populations are especially critical for the persistence of a species, as those populations can harbor unique genetic variation and the interaction between plasticity and local adaptation determines the boundaries of future distributional ranges. This dissertation focuses on the northern marginal population of spotted seatrout (Cynoscion nebulosus), an estuarine-dependent fish, and compares them with those from the core region of the distribution to elucidate the physiological, transcriptomic and genetic mechanisms of plasticity and adaptation. I discovered significant differences between fish from different areas at all three levels of biological organization: Chapter 1 shows different whole-organism metabolic physiology of fish sampled from distinct populations and the northern population is consistent with cold-adaptation, given the pressure of natural selection from more severe and frequent winter kills in the region. Chapter 2 presents functional genetic evidence that the cold-adapted northern spotted seatrout are more vulnerable to heat stress than the warm-adapted southern spotted seatrout, suggesting that differential gene expression is contributing to observed differences in thermal tolerance. A liver transcriptome is de novo assembled and serves as a valuable resource for future genetic studies of spotted seatrout. Chapter 3 discovers signatures of selection based on over 15,000 genome-wide single nucleotide polymorphism (SNP) markers. The pattern of genetic variation is consistent with thermal adaptation along the US east coast. Genes involved in metabolic pathways and transcriptional regulation are the main targets of natural selection. In summary, spotted seatrout are relatively resilient to the thermal effects of climate change due to a wide range of metabolic plasticity and adaptive potential in climate-related genetic variation. Range expansion at the leading edge, however, is largely constrained by the species’ cold tolerance limit. The northern and southern population will likely respond to climate change differently and this should be taken into consideration in future conservation management of this species

Assessing for learning in middle school English language classrooms in China

As a growing economy and a fast-changing society, China has recognised the important role that education and the learner journey must play in that transformation. Assessment is a significant part of a student’s learning journey. It has the potential to engage students in the development of clear learning goals, to engage them in reflecting on their progress and performance, and to provide a basis for constructive feedback on how further progress can be made. In China, educational reforms featuring policies advocating formative assessment have sought to improve assessment practices in English language classrooms. Previous studies have explored teachers’ assessment practices and understandings in tertiary English education. However, assessment in Chinese secondary English classrooms, particularly those in middle schools, receives little attention. Local responses to the national assessment policies also receive inadequate investigations. This study addresses these issues. The study adopts a multiple-case study approach and investigates four English teachers and their classes in two middle schools in Shenzhen, China, in the context of the new ‘Zhongkao’ (senior high school entrance examination) reform in Shenzhen. Three research methods are chosen and findings triangulated. First, materials including textbook, teacher guidebook, and English Zhongkao exam paper are analysed using content analysis approach to understand the assessment content teachers work with. Second, each teacher’s assessment activities are explored through classroom observations over a unit of teaching. Third, teachers’ understanding of assessment and assessment policies is investigated through before- and after-observation interviews and analysed using thematic analysis approach. The data analysis reveals that the teachers adopt three types of assessment activities – oral assessment activities, written assessment activities, and student-assessed activities – with oral assessment activities being conducted the most frequently and student-assessed activities the least often. The teachers implement these assessment activities for various purposes, including assessing for instruction, learning, and maintaining discipline. Analysis of the assessment context demonstrates a wide range of factors inside and outside of classrooms influencing the teachers’ assessment activities and understandings. These include, first, teachers’ limited past academic and professional education regarding assessment, which poses a barrier for them in carrying out formative assessment practices; second, stakeholders’ test-result-oriented expectations, which provide a basis for teachers’ test-oriented aspirations for the future; third, teachers’ working environment, which has exerted an impact on teachers’ actions from three levels: the classroom level involves class size and students’ language level, the school level involves accountability pressure and available assessment support, and the policy level involves the Zhongkao and mandated textbook. The significance of the study is threefold. First, it contributes to the understanding of Chinese middle school English teachers’ assessment activities and understanding. Second, it has rich implications for the Zhongkao reform in Shenzhen regarding test design and washback on teachers. Third, it proposes a framework for understanding classroom assessment activities and teachers’ assessment beliefs from a contextual perspective, which may be adopted and adapted for assessment research in other contexts

Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery

This paper reports a CPU-level real-time stereo matching method for surgical images (10 Hz on 640 * 480 image with a single core of i5-9400). The proposed method is built on the fast ''dense inverse searching'' algorithm, which estimates the disparity of the stereo images. The overlapping image patches (arbitrary squared image segment) from the images at different scales are aligned based on the photometric consistency presumption. We propose a Bayesian framework to evaluate the probability of the optimized patch disparity at different scales. Moreover, we introduce a spatial Gaussian mixed probability distribution to address the pixel-wise probability within the patch. In-vivo and synthetic experiments show that our method can handle ambiguities resulted from the textureless surfaces and the photometric inconsistency caused by the Lambertian reflectance. Our Bayesian method correctly balances the probability of the patch for stereo images at different scales. Experiments indicate that the estimated depth has higher accuracy and fewer outliers than the baseline methods in the surgical scenario