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

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

DiffPhysBA: Diffusion-based Physical Backdoor Attack against Person Re-Identification in Real-World

Person Re-Identification (ReID) systems pose a significant security risk from backdoor attacks, allowing adversaries to evade tracking or impersonate others. Beyond recognizing this issue, we investigate how backdoor attacks can be deployed in real-world scenarios, where a ReID model is typically trained on data collected in the digital domain and then deployed in a physical environment. This attack scenario requires an attack flow that embeds backdoor triggers in the digital domain realistically enough to also activate the buried backdoor in person ReID models in the physical domain. This paper realizes this attack flow by leveraging a diffusion model to generate realistic accessories on pedestrian images (e.g., bags, hats, etc.) as backdoor triggers. However, the noticeable domain gap between the triggers generated by the off-the-shelf diffusion model and their physical counterparts results in a low attack success rate. Therefore, we introduce a novel diffusion-based physical backdoor attack (DiffPhysBA) method that adopts a training-free similarity-guided sampling process to enhance the resemblance between generated and physical triggers. Consequently, DiffPhysBA can generate realistic attributes as semantic-level triggers in the digital domain and provides higher physical ASR compared to the direct paste method by 25.6% on the real-world test set. Through evaluations on newly proposed real-world and synthetic ReID test sets, DiffPhysBA demonstrates an impressive success rate exceeding 90% in both the digital and physical domains. Notably, it excels in digital stealth metrics and can effectively evade state-of-the-art defense methods

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

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

A Real-Time Signal-Based Wavelet Long Short-Term Memory Method for Length-of-Stay Prediction for the Intensive Care Unit: Development and Evaluation Study

Background: Efficient allocation of health care resources is essential for long-term hospital operation. Effective intensive care unit (ICU) management is essential for alleviating the financial strain on health care systems. Accurate prediction of length-of-stay in ICUs is vital for optimizing capacity planning and resource allocation, with the challenge of achieving early, real-time predictions. Objective: This study aimed to develop a predictive model, namely wavelet long short-term memory model (WT-LSTM), for ICU length-of-stay using only real-time vital sign data. The model is designed for urgent care settings where demographic and historical patient data or laboratory results may be unavailable; the model leverages real-time inputs to deliver early and accurate ICU length-of-stay predictions. Methods: The proposed model integrates discrete wavelet transformation and long short-term memory (LSTM) neural networks to filter noise from patients’ vital sign series and improve length-of-stay prediction accuracy. Model performance was evaluated using the electronic ICU database, focusing on 10 common ICU admission diagnoses in the database. Results: The results demonstrate that WT-LSTM consistently outperforms baseline models, including linear regression, LSTM, and bidirectional long short-term memory, in predicting ICU length-of-stay using vital sign data, achieving significant improvements in mean square error. Specifically, the wavelet transformation component of the model enhances the overall performance of WT-LSTM. Removing this component results in an average decrease of 3.3% in mean square error; such a phenomenon is particularly pronounced in specific patient cohorts. The model’s adaptability is highlighted through real-time predictions using only 3-hour, 6-hour, 12-hour, and 24-hour input data. Using only 3 hours of input data, the WT-LSTM model delivers competitive results across the 10 most common ICU admission diagnoses, often outperforming Acute Physiology and Chronic Health Evaluation IV, the leading ICU outcome prediction system currently implemented in clinical practice. WT-LSTM effectively captures patterns from vital signs recorded during the initial hours of a patient’s ICU stay, making it a promising tool for early prediction and resource optimization in the ICU. Conclusions: Our proposed WT-LSTM model, based on real-time vital sign data, offers a promising solution for ICU length-of-stay prediction. Its high accuracy and early prediction capabilities hold significant potential for enhancing clinical practice, optimizing resource allocation, and supporting critical clinical and administrative decisions in ICU management.This article is published as Jiang Y, Li Q, Zhang W., A Real-Time Signal-Based Wavelet Long Short-Term Memory Method for Length-of-Stay Prediction for the Intensive Care Unit: Development and Evaluation Study. JMIR AI 2025;4:e71247. https://doi.org/10.2196/7124