Non-classical properties and generation schemes of superposition of multiple-photon-added two-mode squeezed vacuum state

In this paper, we study some non-classical properties and propose the generation schemes of the superposition of multiple-photon-added two-mode squeezed vacuum state (SMPA-TMSVS). Based on the   Wigner function, we clarify that this state is a non-Gaussian state, while the original two-mode squeezed vacuum state (TMSVS) is a Gaussian state. Besides, the SMPA-TMSVS is sum squeezing, as well as difference squeezing. In particular, the manifestation of the sum squeezing and the difference squeezing in the SMPA-TMSVS becomes more pronounced when increasing parameters r and e. In addition, by exploiting the schemes of photon-added superposition in the usual order, we give some schemes that the SMPA-TMSVS can be generated with the higher-order photon-added superposition by using some optical devices

Beyond Domain Adaptation: Unseen Domain Encapsulation via Universal Non-volume Preserving Models

Recognition across domains has recently become an active topic in the research community. However, it has been largely overlooked in the problem of recognition in new unseen domains. Under this condition, the delivered deep network models are unable to be updated, adapted or fine-tuned. Therefore, recent deep learning techniques, such as: domain adaptation, feature transferring, and fine-tuning, cannot be applied. This paper presents a novel Universal Non-volume Preserving approach to the problem of domain generalization in the context of deep learning. The proposed method can be easily incorporated with any other ConvNet framework within an end-to-end deep network design to improve the performance. On digit recognition, we benchmark on four popular digit recognition databases, i.e. MNIST, USPS, SVHN and MNIST-M. The proposed method is also experimented on face recognition on Extended Yale-B, CMU-PIE and CMU-MPIE databases and compared against other the state-of-the-art methods. In the problem of pedestrian detection, we empirically observe that the proposed method learns models that improve performance across a priori unknown data distributions

A Deep Learning Architecture with Spatio-Temporal Focusing for Detecting Respiratory Anomalies

This paper presents a deep learning system applied for detecting anomalies from respiratory sound recordings. Our system initially performs audio feature extraction using Continuous Wavelet transformation. This transformation converts the respiratory sound input into a two-dimensional spectrogram where both spectral and temporal features are presented. Then, our proposed deep learning architecture inspired by the Inception-residual-based backbone performs the spatial-temporal focusing and multi-head attention mechanism to classify respiratory anomalies. In this work, we evaluate our proposed models on the benchmark SPRSound (The Open-Source SJTU Paediatric Respiratory Sound) database proposed by the IEEE BioCAS 2023 challenge. As regards the Score computed by an average between the average score and harmonic score, our robust system has achieved Top-1 performance with Scores of 0.810, 0.667, 0.744, and 0.608 in Tasks 1-1, 1-2, 2-1, and 2-2, respectively.Comment: arXiv admin note: text overlap with arXiv:2303.0410

An Inception-Residual-Based Architecture with Multi-Objective Loss for Detecting Respiratory Anomalies

This paper presents a deep learning system applied for detecting anomalies from respiratory sound recordings. Initially, our system begins with audio feature extraction using Gammatone and Continuous Wavelet transformation. This step aims to transform the respiratory sound input into a two-dimensional spectrogram where both spectral and temporal features are presented. Then, our proposed system integrates Inception-residual-based backbone models combined with multi-head attention and multi-objective loss to classify respiratory anomalies. Instead of applying a simple concatenation approach by combining results from various spectrograms, we propose a Linear combination, which has the ability to regulate equally the contribution of each individual spectrogram throughout the training process. To evaluate the performance, we conducted experiments over the benchmark dataset of SPRSound (The Open-Source SJTU Paediatric Respiratory Sound) proposed by the IEEE BioCAS 2022 challenge. As regards the Score computed by an average between the average score and harmonic score, our proposed system gained significant improvements of 9.7%, 15.8%, 17.8%, and 16.1% in Task 1-1, Task 1-2, Task 2-1, and Task 2-2, respectively, compared to the challenge baseline system. Notably, we achieved the Top-1 performance in Task 2-1 and Task 2-2 with the highest Score of 74.5% and 53.9%, respectively

Driver Attention Tracking and Analysis

We propose a novel method to estimate a driver's points-of-gaze using a pair of ordinary cameras mounted on the windshield and dashboard of a car. This is a challenging problem due to the dynamics of traffic environments with 3D scenes of unknown depths. This problem is further complicated by the volatile distance between the driver and the camera system. To tackle these challenges, we develop a novel convolutional network that simultaneously analyzes the image of the scene and the image of the driver's face. This network has a camera calibration module that can compute an embedding vector that represents the spatial configuration between the driver and the camera system. This calibration module improves the overall network's performance, which can be jointly trained end to end. We also address the lack of annotated data for training and evaluation by introducing a large-scale driving dataset with point-of-gaze annotations. This is an in situ dataset of real driving sessions in an urban city, containing synchronized images of the driving scene as well as the face and gaze of the driver. Experiments on this dataset show that the proposed method outperforms various baseline methods, having the mean prediction error of 29.69 pixels, which is relatively small compared to the $1280{\times}720$ resolution of the scene camera

Investigation of anti-inflammatory lignans from the leaves of Symplocos sumuntia Buch-Ham ex D Don (Symplocaceae)

Purpose: To investigate the anti-inflammatory activity of Symplocos sumuntia Buch.-Ham. ex D. Don and identify the main secondary metabolites responsible for this effect.Methods: The in vitro anti-inflammatory activity of the plant extract and isolated compounds was determined in terms of the ability to inhibit the production of nitric oxide (NO), and expressions of iNOS and COX-2 proteins in RAW264.7 cells stimulated by lipopolysaccharide (LPS). Compounds were isolated and identified by spectroscopic methods.Results: The methanol extract of S. sumuntia leaves showed strong inhibitory effects on nitric oxide (NO) production and expression of iNOS and COX-2 in LPS-induced RAW264.7 cells. A phytochemical assay-guided fractionation of the methanol extract of S. sumuntia leaves led to the isolation of four lignans which are arctigenin (1), matairesinol (2), monomethylpinoresinol (3) and pinoresinol (4). These compounds were identified for the first time from S. sumuntia. All four compounds inhibited the production of nitric oxide (NO), with arctigenin showing the most potent activity with half-maximal inhibitory concentration (IC50) value of 4.08 μM.Conclusion: S. sumuntia is a promising source of anti-inflammatory agents, which may clarify to the therapeutic use of this plant in Vietamese traditional medicine.Keywords: Symplocos sumuntia, Symplocos caudata, Lignan, Arctigenin, Anti-inflammator

DyGLIP: A Dynamic Graph Model with Link Prediction for Accurate Multi-Camera Multiple Object Tracking

Multi-Camera Multiple Object Tracking (MC-MOT) is a significant computer vision problem due to its emerging applicability in several real-world applications. Despite a large number of existing works, solving the data association problem in any MC-MOT pipeline is arguably one of the most challenging tasks. Developing a robust MC-MOT system, however, is still highly challenging due to many practical issues such as inconsistent lighting conditions, varying object movement patterns, or the trajectory occlusions of the objects between the cameras. To address these problems, this work, therefore, proposes a new Dynamic Graph Model with Link Prediction (DyGLIP) approach to solve the data association task. Compared to existing methods, our new model offers several advantages, including better feature representations and the ability to recover from lost tracks during camera transitions. Moreover, our model works gracefully regardless of the overlapping ratios between the cameras. Experimental results show that we outperform existing MC-MOT algorithms by a large margin on several practical datasets. Notably, our model works favorably on online settings but can be extended to an incremental approach for large-scale datasets.Comment: accepted at CVPR 202