Exploring Disentangled Content Information for Face Forgery Detection

Convolutional neural network based face forgery detection methods have achieved remarkable results during training, but struggled to maintain comparable performance during testing. We observe that the detector is prone to focus more on content information than artifact traces, suggesting that the detector is sensitive to the intrinsic bias of the dataset, which leads to severe overfitting. Motivated by this key observation, we design an easily embeddable disentanglement framework for content information removal, and further propose a Content Consistency Constraint (C2C) and a Global Representation Contrastive Constraint (GRCC) to enhance the independence of disentangled features. Furthermore, we cleverly construct two unbalanced datasets to investigate the impact of the content bias. Extensive visualizations and experiments demonstrate that our framework can not only ignore the interference of content information, but also guide the detector to mine suspicious artifact traces and achieve competitive performance

Fast Switchable Ultrastrong Coupling Between Superconducting Artificial Atoms and Electromagnetic Fields

This thesis consists of two parts: the main part is a theoretical investigation of the ultrastrong coupling regime for atom-light coupling in superconducting circuits, and the second part is concerned with the development of a new high coherence flux qubit design. The spin-boson model describes the interaction between a quantum two-level system and a continuum of bosonic modes. When the interaction strength becomes comparable to the system frequency, the system enters the ultrastrong coupling (USC) regime, where the rotating wave approximation breaks and the system dynamics need to be described nonperturbatively. Recently, the ultrastrong coupling has been achieved on a device consisting of a superconducting flux qubit coupled to an electromagnetic continuum, with the coupling strength being verified in experiments by the standard transmission method. The first project in this thesis aims to measure the dynamics of the spin-boson model in a direct and controllable way when the coupling strength is in the USC regime. We propose three experiments to measure the coherence of the two-level system including the relaxation and dephasing, and the the renormalized tunneling frequencies in the ultrastrong coupling regime. The controllable measurements are realized with a fast-switchable ultrastrong coupling system consisting of a two-loop flux qubit galvanically coupled to an open transmission line, flux bias and driving lines, and readout circuits. The design and model of the full device are presented. We demonstrate that the three proposed experiments can be well implemented based on the simulations of qubit properties and coupling strengths. The second part of the thesis presents work on the design of a new type of capacitively shunted flux qubit. This work is motivated by improving qubit coherence time and anharmonicity, which is essential for speeding up qubit gates and enhancing the capability of quantum computing. It was demonstrated that adding shunting pads to flux qubits can drastically improve the coherence time and the reliability of qubit fabrication, but the CSFQ was shown with moderate anharmonicities. In this project, we present the a new design of CSFQ and its circuit model, which contains three Josephson junctions and three shunting pads. In experiments, the qubit spectroscopy matches well with the circuit model, which takes into account all the capacitances between the qubit and other circuits. The qubit is found to have both large anharmonicity A = ω₁₂ - ω₀₁ = 2π ⨯ 3.69 GHz, and high coherence with T₁ = 40 ± 5 µs. We also present experiments on the multilevel quantum control and multilevel relaxation measurement. We perform qutrit state tomography to reconstruct the full density matrix with the tomography fidelity reaching 99.2%. We are able to extract the time-dependent state populations for the relaxation process of a qutrit, and extract the exact transition rates between the three levels

Accelerating Stochastic Sequential Quadratic Programming for Equality Constrained Optimization using Predictive Variance Reduction

In this paper, we propose a stochastic method for solving equality constrained optimization problems that utilizes predictive variance reduction. Specifically, we develop a method based on the sequential quadratic programming paradigm that employs variance reduction in the gradient approximations. Under reasonable assumptions, we prove that a measure of first-order stationarity evaluated at the iterates generated by our proposed algorithm converges to zero in expectation from arbitrary starting points, for both constant and adaptive step size strategies. Finally, we demonstrate the practical performance of our proposed algorithm on constrained binary classification problems that arise in machine learning.Comment: 41 pages, 5 figures, 4 table

Simulation Design of a Tomato Picking Manipulator

Simulation is an important way to verify the feasibility of design parameters and schemes for robots. Through simulation, this paper analyzes the effectiveness of the design parameters selected for a tomato picking manipulator, and verifies the rationality of the manipulator in motion planning for tomato picking. Firstly, the basic parameters and workspace of the manipulator were determined based on the environment of a tomato greenhouse; the workspace of the lightweight manipulator was proved as suitable for the picking operation through MATLAB simulation. Next, the maximum theoretical torque of each joint of the manipulator was solved through analysis, the joint motors were selected reasonably, and SolidWorks simulation was performed to demonstrate the rationality of the material selected for the manipulator and the strength design of the joint connectors. After that, the trajectory control requirements of the manipulator in picking operation were determined in view of the operation environment, and the feasibility of trajectory planning was confirmed with MATLAB. Finally, a motion control system was designed for the manipulator, according to the end trajectory control requirements, followed by the manufacturing of a prototype. The prototype experiment shows that the proposed lightweight tomato picking manipulator boasts good kinematics performance, and basically meets the requirements of tomato picking operation: the manipulator takes an average of 21 s to pick a tomato, and achieves a success rate of 78.67%

An automated and intelligent microfluidic platform for microalgae detection and monitoring

Microalgae not only play a vital role in the ecosystem but also hold promising commercial applications. Conventional methods of detecting and monitoring microalgae rely on field sampling followed by transportation to the laboratory for manual analysis, which is both time-consuming and laborious. Although machine learning (ML) algorithms have been introduced for microalgae detection in the laboratory, no integrated platform approach has yet emerged to enable real-time, on-site sampling and analysing. To solve this problem, here, we develop an automated and intelligent microfluidic platform (AIMP) that can offer automated system control, intelligent data analysis, and user interaction, providing an economical and portable solution to alleviate the drawbacks of conventional methods for microalgae detection and monitoring. We demonstrate the feasibility of the AIMP by detecting and classifying four microalgal species (Cosmarium, Closterium, Micrasterias, and Haematococcus Pluvialis) that exhibit varying sizes (from a few to hundreds of microns) and morphologies. The trained microalgae species detection network (MSDN, based on YOLOv5 architecture) achieves a high overall mean average precision at 0.5 intersection-over-union ([email protected]) of 92.8%. Furthermore, the versatility of the AIMP is demonstrated by long-term monitoring of astaxanthin production from Haematococcus Pluvialis over a period of 30 days. The AIMP achieved 97.5% accuracy in the detection of Haematococcus Pluvialis and 96.3% in further classification based on astaxanthin accumulation. This study opens up a new path towards microalgae detection and monitoring using portable intelligent devices, providing new ideas to accelerate progress in the ecological studies and commercial exploitation of microalgae

Recent advances in non-optical microfluidic platforms for bioparticle detection

The effective analysis of the basic structure and functional information of bioparticles are of great significance for the early diagnosis of diseases. The synergism between microfluidics and particle manipulation/detection technologies offers enhanced system integration capability and test accuracy for the detection of various bioparticles. Most microfluidic detection platforms are based on optical strategies such as fluorescence, absorbance, and image recognition. Although optical microfluidic platforms have proven their capabilities in the practical clinical detection of bioparticles, shortcomings such as expensive components and whole bulky devices have limited their practicality in the development of point-of-care testing (POCT) systems to be used in remote and underdeveloped areas. Therefore, there is an urgent need to develop cost-effective non-optical microfluidic platforms for bioparticle detection that can act as alternatives to optical counterparts. In this review, we first briefly summarise passive and active methods for bioparticle manipulation in microfluidics. Then, we survey the latest progress in non-optical microfluidic strategies based on electrical, magnetic, and acoustic techniques for bioparticle detection. Finally, a perspective is offered, clarifying challenges faced by current non-optical platforms in developing practical POCT devices and clinical applications.</p

A study based on functional near-infrared spectroscopy: Cortical responses to music interventions in patients with myofascial pain syndrome

ObjectThis study measured cerebral blood oxygen changes in patients with myofascial pain syndrome (MPS) using functional near-infrared spectroscopy (fNIRS). The aim was to investigate the effect of music intervention on pain relief in MPS patients.Materials and methodsA total of 15 patients with MPS participated in this study. A self-controlled block task design was used to collect the oxy-hemoglobin ([HbO2]) and deoxy-hemoglobin ([HbR]) concentrations in the prefrontal cortex (PFC) and motor cortex using fNIRS. The cerebral cortex response and channel connectivity were further analyzed. In the experiment, the therapist was asked to apply compression of 3–4 kg/cm2 vertically using the thumb to induce pain. Soothing synthetic music with frequencies of 8–150 Hz and 50–70 dB was used as the audio for the music intervention.ResultCompared to the group without music intervention, the activation of brain regions showed a decreasing trend in the group with music intervention under the onset of pain. The results of paired t-tests showed that nine of the data were significantly different (p &lt; 0.05). It was also found that with music intervention, inter-channel connectivity was diminished. Besides, their dorsolateral prefrontal cortex (dlPFC) was significantly correlated with the anterior prefrontal cortex (aPFC) for pain response (r = 0.82), and weakly correlated with the premotor cortex (r = 0.40).ConclusionThis study combines objective assessment indicators and subjective scale assessments to demonstrate that appropriate music interventions can be effective in helping to relieve pain to some extent. The analgesic mechanisms between relevant brain regions under music intervention were explored in depth. New insights into effective analgesic methods and quantitative assessment of pain conditions are presented

Gradient Attention Balance Network: Mitigating Face Recognition Racial Bias via Gradient Attention

Although face recognition has made impressive progress in recent years, we ignore the racial bias of the recognition system when we pursue a high level of accuracy. Previous work found that for different races, face recognition networks focus on different facial regions, and the sensitive regions of darker-skinned people are much smaller. Based on this discovery, we propose a new de-bias method based on gradient attention, called Gradient Attention Balance Network (GABN). Specifically, we use the gradient attention map (GAM) of the face recognition network to track the sensitive facial regions and make the GAMs of different races tend to be consistent through adversarial learning. This method mitigates the bias by making the network focus on similar facial regions. In addition, we also use masks to erase the Top-N sensitive facial regions, forcing the network to allocate its attention to a larger facial region. This method expands the sensitive region of darker-skinned people and further reduces the gap between GAM of darker-skinned people and GAM of Caucasians. Extensive experiments show that GABN successfully mitigates racial bias in face recognition and learns more balanced performance for people of different races.Comment: Accepted by CVPR 2023 worksho

Fused filament fabrication of PVDF films for piezoelectric sensing and energy harvesting applications

Fused filament fabrication (FFF) of piezoelectric polymer polyvinylidene fluoride (PVDF) provides a simple manufacturing technique for the fabrication of lead-free piezoelectric devices compared to the traditional manufacturing methods, such as large-scale film extrusion and solution casting. Here, we investigate the effects of the stretching and poling parameters on the enhancement of piezoelectric performance of the printed PVDF films. The stretched and polarized PVDF films with dimensions of 40 × 20 × 0.06 mm (length × width × thickness) possess a piezoelectric charge coefficient (d33) of 7.29 pC N−1 and a fraction of β phase (Fβ) of 65% at a stretching ratio (R) of 4 after being polarized under an electric field of 30 V μm−1. The resulting d33 of the fabricated PVDF films has been substantially enhanced by ∼10–100 times higher than the related reported values of the FFF printed PVDF films. The fabricated PVDF films are capable of detecting compression (d33) and vibration (d31). By blowing four piezoelectric films connected in parallel for 3 min, the energy stored in the capacitor can make a LED blink. Our fabricated piezoelectric PVDF films could be used in the field of pressure sensing, vibration sensing and energy harvesting applications