SAGE-Based Algorithm for Direction-of-Arrival Estimation and Array Calibration

Most existing array processing algorithms are very sensitive to model uncertainties caused by the mutual coupling and sensor location error. To mitigate this problem, a novel method for direction-of-arrival (DOA) estimation and array calibration in the case of deterministic signals with unknown waveforms is presented in this paper. The analysis begins with a comprehensive perturbed array output model, and it is effective for various kinds of perturbations, such as mutual coupling and sensor location error. Based on this model, the Space Alternating Generalized Expectation-Maximization (SAGE) algorithm is applied to jointly estimate the DOA and array perturbation parameters, which simplifies the multidimensional search procedure required for finding maximum likelihood (ML) estimates. The proposed method inherits the characteristics of good convergence and high estimation precision of the SAGE algorithm. At the same time, it forms a unified framework for DOA and array perturbation parameters estimation in the presence of mutual coupling and sensor location error. The simulation results demonstrate the effectiveness of the algorithm

VENTURE CAPITAL AND CORPORATE INNOVATION INPUT FROM THE PERSPECTIVE OF SYNDICATED INVESTMENT

Using data for 341 enterprises listed on the Growth Enterprise Market (GEM) of the Shenzhen Stock Exchange and taking R&D expenditure as an indicator of innovation investment, this paper implements multiple linear regression to test whether venture capital promotes corporate innovation input. It also considers the relationship between the syndicated investment of venture capital and innovation input. The results showthat venture capital indeed promotes R&D in the invested enterprises. The innovation input of syndicated investment enterprises is significantly higher than that of sole investment enterprises. Under syndicated investment, the higher the number of syndicated investment members and the greater the heterogeneity of the shareholding ratio among the members, the higher is the innovation input. The reputation of the syndicated investment team, however, has no significant impact on innovation input.Using data for 341 enterprises listed on the Growth Enterprise Market (GEM) of the Shenzhen Stock Exchange and taking R&D expenditure as an indicator of innovation investment, this paper implements multiple linear regression to test whether venture capital promotes corporate innovation input. It also considers the relationship between the syndicated investment of venture capital and innovation input. The results showthat venture capital indeed promotes R&D in the invested enterprises. The innovation input of syndicated investment enterprises is significantly higher than that of sole investment enterprises. Under syndicated investment, the higher the number of syndicated investment members and the greater the heterogeneity of the shareholding ratio among the members, the higher is the innovation input. The reputation of the syndicated investment team, however, has no significant impact on innovation input

ICU Outcome Predictions Using Real-Time Signals with Wavelet-Transform-based Convolutional Neural Network

Intensive care units (ICUs) serve patients with life-threatening conditions. The limited ICU resources cause severe economic and healthcare burdens worldwide. It is critical to conduct ICU outcome predictions at an early stage and promote efficient use of ICU resources. However, all the current prediction methods have limitations such as unsatisfactory accuracy and depending on resource-demanding laboratory tests or expert domain knowledge. In this research, we design a wavelet-transformed-based convolutional neural network, WTCNN, which only requires patients’ vital sign series and information at ICU admission for real-time ICU outcome predictions. The model is evaluated using a large real-world ICU database and outperforms state-of-art baselines on both ICU mortality and length-of-stay prediction tasks. We conduct LIME for model interpretation and prescriptive analysis. Our work provides an efficient tool for ICU outcome predictions, allowing healthcare providers to take action promptly on patients at risk and reduce the negative impacts on patient outcomes

ICU Outcome Predictions Using Real-Time Signals with Wavelet-Transform-based Convolutional Neural Network

Intensive care units (ICUs) serve patients with life-threatening conditions. The limited ICU resources cause severe economic and healthcare burdens worldwide. It is critical to conduct ICU outcome predictions at an early stage and promote efficient use of ICU resources. However, all the current prediction methods have limitations such as unsatisfactory accuracy and depending on resource-demanding laboratory tests or expert domain knowledge. In this research, we design a wavelet-transformed-based convolutional neural network, WTCNN, which only requires patients’ vital sign series and information at ICU admission for real-time ICU outcome predictions. The model is evaluated using a large real-world ICU database and outperforms state-of-art baselines on both ICU mortality and length-of-stay prediction tasks. We conduct LIME for model interpretation and prescriptive analysis. Our work provides an efficient tool for ICU outcome predictions, allowing healthcare providers to take action promptly on patients at risk and reduce the negative impacts on patient outcomes

DOA estimation and mutual coupling calibration with the SAGE algorithm

AbstractIn this paper, a novel algorithm is presented for direction of arrival (DOA) estimation and array self-calibration in the presence of unknown mutual coupling. In order to highlight the relationship between the array output and mutual coupling coefficients, we present a novel model of the array output with the unknown mutual coupling coefficients. Based on this model, we use the space alternating generalized expectation-maximization (SAGE) algorithm to jointly estimate the DOA parameters and the mutual coupling coefficients. Unlike many existing counterparts, our method requires neither calibration sources nor initial calibration information. At the same time, our proposed method inherits the characteristics of good convergence and high estimation precision of the SAGE algorithm. By numerical experiments we demonstrate that our proposed method outperforms the existing method for DOA estimation and mutual coupling calibration

Interactive Physically-Based Simulation of Roadheader Robot

Roadheader is an engineering robot widely used in underground engineering and mining industry. Interactive dynamics simulation of roadheader is a fundamental problem in unmanned excavation and virtual reality training. However, current research is only based on traditional animation techniques or commercial game engines. There are few studies that apply real-time physical simulation of computer graphics to the field of roadheader robot. This paper aims to present an interactive physically-based simulation system of roadheader robot. To this end, an improved multibody simulation method based on generalized coordinates is proposed. First, our simulation method describes robot dynamics based on generalized coordinates. Compared to state-of-the-art methods, our method is more stable and accurate. Numerical simulation results showed that our method has significantly less error than the game engine in the same number of iterations. Second, we adopt the symplectic Euler integrator instead of the conventional fourth-order Runge-Kutta (RK4) method for dynamics iteration. Compared with other integrators, our method is more stable in energy drift during long-term simulation. The test results showed that our system achieved real-time interaction performance of 60 frames per second (fps). Furthermore, we propose a model format for geometric and robotics modeling of roadheaders to implement the system. Our interactive simulation system of roadheader meets the requirements of interactivity, accuracy and stability

De novo assembly of potential linear artificial chromosome constructs capped with expansive telomeric repeats

<p>Abstract</p> <p>Background</p> <p>Artificial chromosomes (ACs) are a promising next-generation vector for genetic engineering. The most common methods for developing AC constructs are to clone and combine centromeric DNA and telomeric DNA fragments into a single large DNA construct. The AC constructs developed from such methods will contain very short telomeric DNA fragments because telomeric repeats can not be stably maintained in <it>Escherichia coli</it>.</p> <p>Results</p> <p>We report a novel approach to assemble AC constructs that are capped with long telomeric DNA. We designed a plasmid vector that can be combined with a bacterial artificial chromosome (BAC) clone containing centromeric DNA sequences from a target plant species. The recombined clone can be used as the centromeric DNA backbone of the AC constructs. We also developed two plasmid vectors containing short arrays of plant telomeric DNA. These vectors can be used to generate expanded arrays of telomeric DNA up to several kilobases. The centromeric DNA backbone can be ligated with the telomeric DNA fragments to generate AC constructs consisting of a large centromeric DNA fragment capped with expansive telomeric DNA at both ends.</p> <p>Conclusions</p> <p>We successfully developed a procedure that circumvents the problem of cloning and maintaining long arrays of telomeric DNA sequences that are not stable in <it>E. coli</it>. Our procedure allows development of AC constructs in different eukaryotic species that are capped with long and designed sizes of telomeric DNA fragments.</p

Invisible Backdoor Attack with Dynamic Triggers against Person Re-identification

In recent years, person Re-identification (ReID) has rapidly progressed with wide real-world applications, but also poses significant risks of adversarial attacks. In this paper, we focus on the backdoor attack on deep ReID models. Existing backdoor attack methods follow an all-to-one/all attack scenario, where all the target classes in the test set have already been seen in the training set. However, ReID is a much more complex fine-grained open-set recognition problem, where the identities in the test set are not contained in the training set. Thus, previous backdoor attack methods for classification are not applicable for ReID. To ameliorate this issue, we propose a novel backdoor attack on deep ReID under a new all-to-unknown scenario, called Dynamic Triggers Invisible Backdoor Attack (DT-IBA). Instead of learning fixed triggers for the target classes from the training set, DT-IBA can dynamically generate new triggers for any unknown identities. Specifically, an identity hashing network is proposed to first extract target identity information from a reference image, which is then injected into the benign images by image steganography. We extensively validate the effectiveness and stealthiness of the proposed attack on benchmark datasets, and evaluate the effectiveness of several defense methods against our attack