DeepASL: Enabling Ubiquitous and Non-Intrusive Word and Sentence-Level Sign Language Translation

There is an undeniable communication barrier between deaf people and people with normal hearing ability. Although innovations in sign language translation technology aim to tear down this communication barrier, the majority of existing sign language translation systems are either intrusive or constrained by resolution or ambient lighting conditions. Moreover, these existing systems can only perform single-sign ASL translation rather than sentence-level translation, making them much less useful in daily-life communication scenarios. In this work, we fill this critical gap by presenting DeepASL, a transformative deep learning-based sign language translation technology that enables ubiquitous and non-intrusive American Sign Language (ASL) translation at both word and sentence levels. DeepASL uses infrared light as its sensing mechanism to non-intrusively capture the ASL signs. It incorporates a novel hierarchical bidirectional deep recurrent neural network (HB-RNN) and a probabilistic framework based on Connectionist Temporal Classification (CTC) for word-level and sentence-level ASL translation respectively. To evaluate its performance, we have collected 7,306 samples from 11 participants, covering 56 commonly used ASL words and 100 ASL sentences. DeepASL achieves an average 94.5% word-level translation accuracy and an average 8.2% word error rate on translating unseen ASL sentences. Given its promising performance, we believe DeepASL represents a significant step towards breaking the communication barrier between deaf people and hearing majority, and thus has the significant potential to fundamentally change deaf people's lives

Applicability of UAV-based optical imagery and classification algorithms for detecting pine wilt disease at different infection stages

As a quarantine disease with a rapid spread tendency in the context of climate change, accurate detection and location of pine wilt disease (PWD) at different infection stages is critical for maintaining forest health and being highly productivity. In recent years, unmanned aerial vehicle (UAV)-based optical remote-sensing images have provided new instruments for timely and accurate PWD monitoring. Numerous corresponding analysis algorithms have been proposed for UAV-based image classification, but their applicability of detecting different PWD infection stages has not yet been evaluated under a uniform conditions and criteria. This research aims to systematically assess the performance of multi-source images for detecting different PWD infection stages, analyze effective classification algorithms, and further analyze the validity of thermal images for early detection of PWD. In this study, PWD infection was divided into four stages: healthy, chlorosis, red and gray, and UAV-based hyperspectral (HSI), multispectral (MSI), and MSI with a thermal band (MSI&TIR) datasets were used as the data sources. Spectral analysis, support vector machine (SVM), random forest (RF), two- and three-dimensional convolutional network (2D- and 3D-CNN) algorithms were applied to these datasets to compare their classification abilities. The results were as follows: (I) The classification accuracy of the healthy, red, and gray stages using the MSI dataset was close to that obtained when using the MSI&TIR dataset with the same algorithms, whereas the HSI dataset displayed no obvious advantages. (II) The RF and 3D-CNN algorithms were the most accurate for all datasets (RF: overall accuracy = 94.26%, 3D-CNN: overall accuracy = 93.31%), while the spectral analysis method is also valid for the MSI&TIR dataset. (III) Thermal band displayed significant potential in detection of the chlorosis stage, and the MSI&TIR dataset displayed the best performance for detection of all infection stages. Considering this, we suggest that the MSI&TIR dataset can essentially satisfy PWD identification requirements at various stages, and the RF algorithm provides the best choice, especially in actual forest investigations. In addition, the performance of thermal imaging in the early monitoring of PWD is worthy of further investigation. These findings are expected to provide insight into future research and actual surveys regarding the selection of both remote sensing datasets and data analysis algorithms for detection requirements of different PWD infection stages to detect the disease earlier and prevent losses

Assessing real-world safety concerns of Sacituzumab govitecan: a disproportionality analysis using spontaneous reports in the FDA adverse event reporting system

AimThe aim of this study was to identify potential safety concerns associated with Sacituzumab Govitecan (SG), an antibody-drug conjugate targeting trophoblastic cell-surface antigen-2, by analyzing real-world safety data from the largest publicly available worldwide pharmacovigilance database.MethodsAll data obtained from the FDA Adverse Event Reporting System (FAERS) database from the second quarter of 2020 to the fourth quarter of 2022 underwent disproportionality analysis and Bayesian analysis to detect and assess the adverse event signals of SG, considering statistical significance when the lower limit of the 95% CI >1, based on at least 3 reports.ResultsTotal of 1072 cases were included. The main safety signals were blood and lymphatic system disorders [ROR(95CI)=7.23 (6.43-8.14)], gastrointestinal disorders [ROR(95CI)=2.01 (1.81-2.22)], and relative infection adverse events, such as neutropenic sepsis [ROR(95CI)=46.02 (27.15-77.99)] and neutropenic colitis [ROR(95CI)=188.02 (120.09-294.37)]. We also noted unexpected serious safety signals, including large intestine perforation [ROR(95CI)=10.77 (3.47-33.45)] and hepatic failure [ROR(95CI)=3.87 (1.45-10.31)], as well as a high signal for pneumonitis [ROR(95CI)=9.93 (5.75-17.12)]. Additionally, age sub-group analysis revealed that geriatric patients (>65 years old) were at an increased risk of neutropenic colitis [ROR(95CI)=282.05 (116.36-683.66)], neutropenic sepsis [ROR(95CI)=101.11 (41.83-244.43)], acute kidney injury [ROR(95CI)=3.29 (1.36-7.94)], and atrial fibrillation [ROR(95CI)=6.91 (2.86-16.69)].ConclusionThis study provides crucial real-world safety data on SG, complementing existing clinical trial information. Practitioners should identify contributing factors, employ monitoring and intervention strategies, and focus on adverse events like neutropenic sepsis, large intestine perforation, and hepatic failure. Further prospective studies are needed to address these safety concerns for a comprehensive understanding and effective management of associated risks

Hydrogeochemical Characteristics and Groundwater Quality Evaluation Based on Multivariate Statistical Analysis

Hydrogeochemical research and water quality evaluation are an important part of groundwater development and management projects in Dehui City, Jilin Province, China. We collected 217 groundwater samples in the study area and used two multivariate statistical methods, hierarchical cluster analysis and principal component analysis to classify groundwater; combined graphical method, piper diagram, and Gibbs diagram to characterize groundwater chemical types and distinguish the water chemical control mechanism; and fuzzy comprehensive evaluation method to evaluate groundwater quality. Three major categories have been identified. Most of the groundwater in the study area is Ca-HCO3 type water. The water chemistry control mechanism is determined to be based on water-rock interaction and less evaporation. From east to west in the study area, the total dissolved solids (TDS) gradually increased, and water quality gradually deteriorated. In the whole region, 79.26% of the groundwater is suitable for drinking. With Yinma River at the boundary, the water quality in the eastern part is excellent, while that in the southwest is poor. After appropriate treatment, it can be used in industry and agriculture. The excess NO3− is mainly affected by human activities. The unique geological conditions of the Songnen Plain result in an excess amount of Fe3+ and Mn2+ in some areas. This study determined the chemical characteristics of groundwater in the study area and distinguished water quality levels. The results will be helpful for the development and management of groundwater resources

Cascaded Shape Space Pruning for Robust Facial Landmark Detection

In this paper, we propose a novel cascaded face shape space pruning algorithm for robust facial landmark detec-tion. Through progressively excluding the incorrect candi-date shapes, our algorithm can accurately and efficiently achieve the globally optimal shape configuration. Specif-ically, individual landmark detectors are firstly applied to eliminate wrong candidates for each landmark. Then, the candidate shape space is further pruned by jointly remov-ing incorrect shape configurations. To achieve this purpose, a discriminative structure classifier is designed to assess the candidate shape configurations. Based on the learned discriminative structure classifier, an efficient shape space pruning strategy is proposed to quickly reject most incorrect candidate shapes while preserve the true shape. The pro-posed algorithm is carefully evaluated on a large set of real world face images. In addition, comparison results on the publicly available BioID and LFW face databases demon-strate that our algorithm outperforms some state-of-the-art algorithms. 1

Pose Normalization Using Generic 3D Face Model as a Priori for Pose-Insensitive Face Recognition

Abstract. Abrupt performance degradation caused by face pose variations has been one of the bottlenecks for practical face recognition applications. This paper presents a practical pose normalization technique by using a generic 3D face model as a priori. The 3D face model greatly facilitates the setup of the correspondence between non-frontal and frontal face images, which can be exploited as a priori to transform a non-frontal face image, with known pose but very sparse correspondence with the generic face model, into a frontal one by warping techniques. Our experiments have shown that the proposed method can greatly improve the recognition performance of the current face recognition methods without pose normalization.

Pose Estimation Based on Gaussian Error Models

Abstract. In this paper, a new method is presented to estimate the 3D pose of facial image based on statistical Gaussian error models. The basic idea is that the pose angle can be computed by the orthogonal projection computation if the specific 3D shape vector of the given person is known. In our algorithm, Gaussian probability density function is used to model the distributions of the 3D shape vector as well as the errors between the orthogonal projection computation and the weak perspective projection. By using the prior knowledge of the errors distribution, the most likely 3D shape vector can be referred by the labeled 2D landmarks in the given facial image according to the maximum posterior probability theory. Refining the error term, thus the pose parameters can be estimated by the transformed orthogonal projection formula. Experimental results on real images are presented to give the objective evaluation.