Advances in stabilisation of hybrid stochastic differential equations by delay feedback control

A novel approach to design the feedback control based on past states is proposed for hybrid stochastic differential equations (HSDEs). This new theorem builds up the connection between the delay feedback control and the control function without delay terms, which enables one to construct the delay feedback control using the existing results on stabilities of HSDEs. Methods to find the upper bound of the length of the time delay are also investigated. Numerical simulations are presented to demonstrate the new theorem

Preserving Specificity in Federated Graph Learning for fMRI-based Neurological Disorder Identification

Resting-state functional magnetic resonance imaging (rs-fMRI) offers a non-invasive approach to examining abnormal brain connectivity associated with brain disorders. Graph neural network (GNN) gains popularity in fMRI representation learning and brain disorder analysis with powerful graph representation capabilities. Training a general GNN often necessitates a large-scale dataset from multiple imaging centers/sites, but centralizing multi-site data generally faces inherent challenges related to data privacy, security, and storage burden. Federated Learning (FL) enables collaborative model training without centralized multi-site fMRI data. Unfortunately, previous FL approaches for fMRI analysis often ignore site-specificity, including demographic factors such as age, gender, and education level. To this end, we propose a specificity-aware federated graph learning (SFGL) framework for rs-fMRI analysis and automated brain disorder identification, with a server and multiple clients/sites for federated model aggregation and prediction. At each client, our model consists of a shared and a personalized branch, where parameters of the shared branch are sent to the server while those of the personalized branch remain local. This can facilitate knowledge sharing among sites and also helps preserve site specificity. In the shared branch, we employ a spatio-temporal attention graph isomorphism network to learn dynamic fMRI representations. In the personalized branch, we integrate vectorized demographic information (i.e., age, gender, and education years) and functional connectivity networks to preserve site-specific characteristics. Representations generated by the two branches are then fused for classification. Experimental results on two fMRI datasets with a total of 1,218 subjects suggest that SFGL outperforms several state-of-the-art approaches

Theoretical analysis of the non-reciprocal phase shifts due to birefringence and topology in fiber ring interferometers

The non-reciprocal phase shifts in fiber ring interferometers due to the fiber birefringence and the path topology are investigated for the first time. It is shown that the resultant birefringence of the fiber, which is the combination of the linear birefringence intrinsic to the fiber and the circular birefringence induced by the twisting in the fiber coiling, is not reciprocal for both rays in the bidirectional propagation due to the path topology confined by the coiled fiber. Our model indicates that the performance of fiber ring interferometers periodically depends on both the linear and the circular birefringence of the coiled fiber, and the bias error can be reduced by the typical fabrication process of the fiber ring interferometers

Analytical estimation of stress-induced birefringence in panda-type polarization-maintaining fibers

An analytical model for estimating the stress -induced birefringence in true Panda-type polarization -maintaining fibers with imperfect geometry has been developed in this letter. The developed model is simpler and more accurate compared to conventional sophisticated and asymptotic formulas in reported works. Our model provides a clear and simple solution to demonstrate the periodic dependence of the birefringence on the misalignment angle between the two stress-applying parts, and the monotonic dependence on the geometric parameters of stress-applying parts. Our work also reveals the important role of the misalignment angle between the two stress-applying parts in practical Panda-type fibers

Bias error and its thermal drift due to fiber birefringence in interferometric fiber-optic gyroscopes

Polarization-maintaining fibers (PMFs) with intrinsic highly stress-induced birefringence (SIB) are widely employed in interferometric fiber-optic gyroscopes (IFOGs). The performance of which is limited by the refractive index and its thermal fluctuations induced by the temperature variations. The SIB contributes to the refractive index variously along with the temperature. However, the bias error and its thermal drift arising from the SIB in PMFs are never considered. In this paper, we present theoretical analysis on high-performance IFOGs considering the effects of the SIB and its thermal fluctuation incorporated into the early model. The numerical analysis of the proposed model shows that the accuracy of IFOG using PMFs is better than single-mode fibers (SMFs) by a factor of 2,and the high performance with ultimate sensitivity of IFOGs is achievable by the special design of PMFs which depends not only on the pure Shupe effect but also on the effects from intrinsic SIB and its temperature sensitivity

A Comparison of Pattern of Pregnancy Loss in Women with Infertility Undergoing IVF and Women with Unexplained Recurrent Miscarriages Who Conceive Spontaneously

Objective. Women with infertility and recurrent miscarriages may have an overlapping etiology. The aim of this study was to compare the pregnancy loss in pregnancies after IVF treatment with spontaneous pregnancies in women with recurrent miscarriages and to assess differences related to cause of infertility. Methods. The outcome from 1220 IVF pregnancies (Group I) was compared with 611 spontaneous pregnancies (Group II) in women with recurrent miscarriages. Subgroup analysis was performed in Group I based on cause of infertility: tubal factor (392 pregnancies); male factor (610 pregnancies); and unexplained infertility (218 pregnancies). Results. The clinical pregnancy loss rate in Group I (14.3%) was significantly lower than that of Group II (25.8%, p<0.001) and this was independent of the cause of infertility. However the timing of pregnancy loss was similar between Groups I and II. The clinical pregnancy loss rate in Group I was similar in different causes of infertility. Conclusions. The clinical pregnancy loss rate following IVF treatment is lower than that of women with unexplained recurrent miscarriages who conceived spontaneously. This difference persists whether the infertility is secondary to tubal factors, male factors, or unexplained cause

Multi-view 3D Face Reconstruction Based on Flame

At present, face 3D reconstruction has broad application prospects in various fields, but the research on it is still in the development stage. In this paper, we hope to achieve better face 3D reconstruction quality by combining multi-view training framework with face parametric model Flame, propose a multi-view training and testing model MFNet (Multi-view Flame Network). We build a self-supervised training framework and implement constraints such as multi-view optical flow loss function and face landmark loss, and finally obtain a complete MFNet. We propose innovative implementations of multi-view optical flow loss and the covisible mask. We test our model on AFLW and facescape datasets and also take pictures of our faces to reconstruct 3D faces while simulating actual scenarios as much as possible, which achieves good results. Our work mainly addresses the problem of combining parametric models of faces with multi-view face 3D reconstruction and explores the implementation of a Flame based multi-view training and testing framework for contributing to the field of face 3D reconstruction

Improving Knowledge-aware Dialogue Generation via Knowledge Base Question Answering

Neural network models usually suffer from the challenge of incorporating commonsense knowledge into the open-domain dialogue systems. In this paper, we propose a novel knowledge-aware dialogue generation model (called TransDG), which transfers question representation and knowledge matching abilities from knowledge base question answering (KBQA) task to facilitate the utterance understanding and factual knowledge selection for dialogue generation. In addition, we propose a response guiding attention and a multi-step decoding strategy to steer our model to focus on relevant features for response generation. Experiments on two benchmark datasets demonstrate that our model has robust superiority over compared methods in generating informative and fluent dialogues. Our code is available at https://github.com/siat-nlp/TransDG.Comment: Accepted by AAAI-202