Bounds on mutual information of mixture data for classification tasks

The data for many classification problems, such as pattern and speech recognition, follow mixture distributions. To quantify the optimum performance for classification tasks, the Shannon mutual information is a natural information-theoretic metric, as it is directly related to the probability of error. The mutual information between mixture data and the class label does not have an analytical expression, nor any efficient computational algorithms. We introduce a variational upper bound, a lower bound, and three estimators, all employing pair-wise divergences between mixture components. We compare the new bounds and estimators with Monte Carlo stochastic sampling and bounds derived from entropy bounds. To conclude, we evaluate the performance of the bounds and estimators through numerical simulations

Computational Methods for Photon-Counting and Photon- Processing Detectors

We present computational methods for attribute estimation of photon-counting and photon-processing detectors. We define a photon-processing detector as any imaging device that uses maximum-likelihood methods to estimate photon attributes, such as position, direction of propagation and energy. Estimated attributes are then stored at full precision in the memory of a computer. Accurate estimation of a large number of attributes for each collected photon does require considerable computational power. We show how mass-produced graphics processing units (GPUs) are viable parallel computing solutions capable of meeting the required computing needs of photon-counting and photon-processing detectors, while keeping overall costs affordable

Invertibility of Multi-Energy X-ray Transform

Purpose: The goal is to provide a sufficient condition on the invertibility of a multi-energy (ME) X-ray transform. The energy-dependent X-ray attenuation profiles can be represented by a set of coefficients using the Alvarez-Macovski (AM) method. An ME X-ray transform is a mapping from $N$ AM coefficients to $N$ noise-free energy-weighted measurements, where $N\geq2$. Methods: We apply a general invertibility theorem which tests whether the Jacobian of the mapping $J(\mathbf A)$ has zero values over the support of the mapping. The Jacobian of an arbitrary ME X-ray transform is an integration over all spectral measurements. A sufficient condition of $J(\mathbf A)\neq0$ for all $\mathbf A$ is that the integrand of $J(\mathbf A)$ is $\geq0$ (or $\leq0$) everywhere. Note that the trivial case of the integrand equals to zero everywhere is ignored. With symmetry, we simplified the integrand of the Jacobian into three factors that are determined by the total attenuation, the basis functions, and the energy-weighting functions, respectively. The factor related to total attenuation is always positive, hence the invertibility of the X-ray transform can be determined by testing the signs of the other two factors. Furthermore, we use the Cramer-Rao lower bound (CRLB) to characterize the noise-induced estimation uncertainty and provide a maximum-likelihood (ML) estimator. Conclusions: We have provided a framework to study the invertibility of an arbitrary ME X-ray transform and proved the global invertibility for four types of systems

Detection of MTAP Protein and Gene Expression in Non-small Cell Lung Cancer

Background and objective The abnormal expression of MTAP, a tumor suppressor gene, is found in a variety of tumor tissues. The aim of this study is to detect the expression of MTAP mRNA protein and the clinical significance for the therapy of non-small cell lung cancer tissue (NSCLC). Methods The expression of MTAP protein was detected by immunohistochemistry in 52 cases of NSCLC patients. The relative expression MTAP mRNA was detected by real-time quantitative PCR. Results The expression of MTAP protein in NSCLC tissue was significantly lower than that in paracarcinomous tissue and borderline lung tissue respectively (t=10.283, 10.940, P < 0.001). There was no significant difference among gender, age, smoking history, histology except differentiation (t=2.310, P=0.025). The MTAP mRNA relative expression in NSCLC tissue was significantly lower than that in paracarcinomous tissue (t=9.902, P < 0.001). Conclusion Downregulation of MTAP protein and gene expression is correlated to the oncogenesis and progression of NSCLC

Implementation of Integrated Performance Assessments (IPA) in Beginning Level Chinese Language Classes

Exploring possibilities for, and effects of, Integrated Performance Assessments (IPAs) in the Department of East Asian Languages and Literatures, this poster reports an action study using IPAs in an undergraduate beginning Mandarin program. The poster first features a review of IPAs, followed by an overview of curriculum redesign and IPA test reconstruction. The poster then presents a concrete model for IPAs in a Chinese 101 and 102 Beginning Mandarin. Successful teaching activities and assessment task samples will be presented. The effects of this adaptation are demonstrated using quantitative and qualitative data, including oral assessment videos, writing samples, supplementary listening and reading materials, rubrics for scoring, test scores, student self-reflections, and more. The data show that students (1) generally favored using IPAs, (2) took initiative to review the IPA rubrics and to reliably engage in filling out "can-do" checklists, (3) demonstrated a positive correlation between IPAs and traditional test scores. The poster concludes that IPAs can be equally successful, and can offer more, in university foreign language classes

Visual Semantic SLAM with Landmarks for Large-Scale Outdoor Environment

Semantic SLAM is an important field in autonomous driving and intelligent agents, which can enable robots to achieve high-level navigation tasks, obtain simple cognition or reasoning ability and achieve language-based human-robot-interaction. In this paper, we built a system to creat a semantic 3D map by combining 3D point cloud from ORB SLAM with semantic segmentation information from Convolutional Neural Network model PSPNet-101 for large-scale environments. Besides, a new dataset for KITTI sequences has been built, which contains the GPS information and labels of landmarks from Google Map in related streets of the sequences. Moreover, we find a way to associate the real-world landmark with point cloud map and built a topological map based on semantic map.Comment: Accepted by 2019 China Symposium on Cognitive Computing and Hybrid Intelligence(CCHI'19

Privacy-Aware UAV Flights through Self-Configuring Motion Planning

During flights, an unmanned aerial vehicle (UAV) may not be allowed to move across certain areas due to soft constraints such as privacy restrictions. Current methods on self-adaption focus mostly on motion planning such that the trajectory does not trespass predetermined restricted areas. When the environment is cluttered with uncertain obstacles, however, these motion planning algorithms are not flexible enough to find a trajectory that satisfies additional privacy-preserving requirements within a tight time budget during the flights. In this paper, we propose a privacy risk aware motion planning method through the reconfiguration of privacy-sensitive sensors. It minimises environmental impact by re-configuring the sensor during flight, while still guaranteeing the hard safety and energy constraints such as collision avoidance and timeliness. First, we formulate a model for assessing privacy risks of dynamically detected restricted areas. In case the UAV cannot find a feasible solution to satisfy both hard and soft constraints from the current configuration, our decision making method can then produce an optimal reconfiguration of the privacy-sensitive sensor with a more efficient trajectory. We evaluate the proposal through various simulations with different settings in a virtual environment and also validate the approach through real test flights on DJI Matrice 100 UAV