Quantum nonlinear interferometers (QNIs) can measure the infrared physical quantities of a sample by detecting visible photons. A QNI with Michelson geometry based on the spontaneous parametric down-conversion in a second-order nonlinear crystal is studied systematically. A simplified theoretical model of the QNI is presented. The interference visibility, coherence length, equal-inclination interference, and equal-thickness interference for the QNI are demonstrated theoretically and experimentally. As an application example of the QNI, the refractive index and the angle between two surfaces of a BBO crystal are measured using equal-inclination interference and equal-thickness interference.
Adversarial training (AT) has been demonstrated as one of the most promising defense methods against various adversarial attacks. To our knowledge, existing AT-based methods usually train with the locally most adversarial perturbed points and treat all the perturbed points equally, which may lead to considerably weaker adversarial robust generalization on test data. In this work, we introduce a new adversarial training framework that considers the diversity as well as characteristics of the perturbed points in the vicinity of benign samples. To realize the framework, we propose a Regional Adversarial Training (RAT) defense method that first utilizes the attack path generated by the typical iterative attack method of projected gradient descent (PGD), and constructs an adversarial region based on the attack path. Then, RAT samples diverse perturbed training points efficiently inside this region, and utilizes a distance-aware label smoothing mechanism to capture our intuition that perturbed points at different locations should have different impact on the model performance. Extensive experiments on several benchmark datasets show that RAT consistently makes significant improvement on standard adversarial training (SAT), and exhibits better robust generalization.
Neurofeedback games are an effective and playful approach to enhance certain social and attentional capabilities in children with autism, which are promising to become widely accessible along with the commercialization of mobile EEG modules. However, little industry-based experiences are shared, regarding how to better design neurofeedback games to fine-tune their playability and user experiences for autistic children. In this paper, we review the experiences we gained from industry practice, in which a series of mobile EEG neurofeedback games have been developed for preschool autistic children. We briefly describe our design and development in a one-year collaboration with a special education center involving a group of stakeholders: children with autism and their caregivers and parents. We then summarize four concrete implications we learnt concerning the design of game characters, game narratives, as well as gameplay elements, which aim to support future work in creating better neurofeedback games for preschool children with autism.
Nowadays, analysis of Transparent Environmental Microorganism Images (T-EM images) in the field of computer vision has gradually become a new and interesting spot. This paper compares different deep learning classification performance for the problem that T-EM images are challenging to analyze. We crop the T-EM images into 8 * 8 and 224 * 224 pixel patches in the same proportion and then divide the two different pixel patches into foreground and background according to ground truth. We also use four convolutional neural networks and a novel ViT network model to compare the foreground and background classification experiments. We conclude that ViT performs the worst in classifying 8 * 8 pixel patches, but it outperforms most convolutional neural networks in classifying 224 * 224 pixel patches.
A new fluid-driven soft robot hand in this study uses the idea of the bionics and has the anthropomorphic form, which is oriented to the flexible grasp function. The soft robot hand is composed of a new kind of multi-freedom soft finger and soft palm, which realizes the characteristic grasping function of forehand and backhand. Combined with the fine fluid control system, the soft hand can realize flexible grasping under high pressure, so as to realize flexible grasping operation for different types of target objects in the underwater environment. The soft robot hand was controlled based on water hydraulic platform, Finally, the soft robot hand and the fine fluid control system were connected to form the underwater soft robot hand experiment platform.
Despite the enormous success of neural networks, they are still hard to interpret and often overfit when applied to low-sample-size (LSS) datasets. To tackle these obstacles, we propose a framework for training locally sparse neural networks where the local sparsity is learned via a sample-specific gating mechanism that identifies the subset of most relevant features for each measurement. The sample-specific sparsity is predicted via a textit{gating} network, which is trained in tandem with the textit{prediction} network. By learning these subsets and weights of a prediction model, we obtain an interpretable neural network that can handle LSS data and can remove nuisance variables, which are irrelevant for the supervised learning task. Using both synthetic and real-world datasets, we demonstrate that our method outperforms state-of-the-art models when predicting the target function with far fewer features per instance.
Traffic anomaly detection has played a crucial role in Intelligent Transportation System (ITS). The main challenges of this task lie in the highly diversified anomaly scenes and variational lighting conditions. Although much work has managed to identify the anomaly in homogenous weather and scene, few resolved to cope with complex ones. In this paper, we proposed a dual-modality modularized methodology for the robust detection of abnormal vehicles. We introduced an integrated anomaly detection framework comprising the following modules: background modeling, vehicle tracking with detection, mask construction, Region of Interest (ROI) backtracking, and dual-modality tracing. Concretely, we employed background modeling to filter the motion information and left the static information for later vehicle detection. For the vehicle detection and tracking module, we adopted YOLOv5 and multi-scale tracking to localize the anomalies. Besides, we utilized the frame difference and tracking results to identify the road and obtain the mask. In addition, we introduced multiple similarity estimation metrics to refine the anomaly period via backtracking. Finally, we proposed a dual-modality bilateral tracing module to refine the time further. The experiments conducted on the Track 4 testset of the NVIDIA 2021 AI City Challenge yielded a result of 0.9302 F1-Score and 3.4039 root mean square error (RMSE), indicating the effectiveness of our framework.
Microcavity lasers based on erbium-doped lithium niobate on insulator (LNOI), which are key devices for LNOI integrated photonics, have attracted much attention recently. In this Letter, we report the realization of a C-band single-mode laser using Vernier effect in two coupled Erbium-doped LNOI microrings with different radii under the pump of a 980-nm continuous laser. The laser, operating stably over a large range of pumping power, has a pump threshold of ~200 {mu}W and a side-mode suppression ratio exceeding 26 dB. The high-performance LNOI single-mode laser will promote the development of lithium niobate integrated photonics.
We apply moment methods to obtaining an approximate analytical solution to Knudsen layers. Based on the hyperbolic regularized moment system for the Boltzmann equation with the Shakhov collision model, we derive a linearized hyperbolic moment system to model the scenario with the Knudsen layer vicinity to a solid wall with Maxwell boundary condition. We find that the reduced system is in an even-odd parity form that the reduced system proves to be well-posed under all accommodation coefficients. We show that the system may capture the temperature jump coefficient and the thermal Knudsen layer well with only a few moments. With the increasing number of moments used, qualitative convergence of the approximate solution is observed.
Clinical trials are considered as the golden standard for medical device validation. However, many sacrifices have to be made during the design and conduction of the trials due to cost considerations and partial information, which may compromise the significance of the trial results. In this paper, we proposed a model-based pre-clinical trial framework using statistical model checking. Physiological models represent disease mechanism, which enable automated adjudication of simulation results. Sampling of the patient parameters and hypothesis testing are performed by statistical model checker. The framework enables a broader range of hypothesis to be tested with guaranteed statistical significance, which are useful complements to the clinical trials. We demonstrated our framework with a pre-clinical trial on implantable cardioverter defibrillators.
