Vernier spectrometer using counter-propagating soliton microcombs

Acquisition of laser frequency with high resolution under continuous and abrupt tuning conditions is important for sensing, spectroscopy and communications. Here, a single microresonator provides rapid and broad-band measurement of frequencies across the optical C-band with a relative frequency precision comparable to conventional dual frequency comb systems. Dual-locked counter-propagating solitons having slightly different repetition rates are used to implement a Vernier spectrometer. Laser tuning rates as high as 10 THz/s, broadly step-tuned lasers, multi-line laser spectra and also molecular absorption lines are characterized using the device. Besides providing a considerable technical simplification through the dual-locked solitons and enhanced capability for measurement of arbitrarily tuned sources, this work reveals possibilities for chip-scale spectrometers that greatly exceed the performance of table-top grating and interferometer-based devices

Investigating the relevance of major signaling pathways in cancer survival using a biologically meaningful deep learning model

BACKGROUND: Survival analysis is an important part of cancer studies. In addition to the existing Cox proportional hazards model, deep learning models have recently been proposed in survival prediction, which directly integrates multi-omics data of a large number of genes using the fully connected dense deep neural network layers, which are hard to interpret. On the other hand, cancer signaling pathways are important and interpretable concepts that define the signaling cascades regulating cancer development and drug resistance. Thus, it is important to investigate potential associations between patient survival and individual signaling pathways, which can help domain experts to understand deep learning models making specific predictions. RESULTS: In this exploratory study, we proposed to investigate the relevance and influence of a set of core cancer signaling pathways in the survival analysis of cancer patients. Specifically, we built a simplified and partially biologically meaningful deep neural network, DeepSigSurvNet, for survival prediction. In the model, the gene expression and copy number data of 1967 genes from 46 major signaling pathways were integrated in the model. We applied the model to four types of cancer and investigated the influence of the 46 signaling pathways in the cancers. Interestingly, the interpretable analysis identified the distinct patterns of these signaling pathways, which are helpful in understanding the relevance of signaling pathways in terms of their application to the prediction of cancer patients\u27 survival time. These highly relevant signaling pathways, when combined with other essential signaling pathways inhibitors, can be novel targets for drug and drug combination prediction to improve cancer patients\u27 survival time. CONCLUSION: The proposed DeepSigSurvNet model can facilitate the understanding of the implications of signaling pathways on cancer patients\u27 survival by integrating multi-omics data and clinical factors

Survey on Visual Signal Coding and Processing with Generative Models: Technologies, Standards and Optimization

This paper provides a survey of the latest developments in visual signal coding and processing with generative models. Specifically, our focus is on presenting the advancement of generative models and their influence on research in the domain of visual signal coding and processing. This survey study begins with a brief introduction of well-established generative models, including the Variational Autoencoder (VAE) models, Generative Adversarial Network (GAN) models, Autoregressive (AR) models, Normalizing Flows and Diffusion models. The subsequent section of the paper explores the advancements in visual signal coding based on generative models, as well as the ongoing international standardization activities. In the realm of visual signal processing, our focus lies on the application and development of various generative models in the research of visual signal restoration. We also present the latest developments in generative visual signal synthesis and editing, along with visual signal quality assessment using generative models and quality assessment for generative models. The practical implementation of these studies is closely linked to the investigation of fast optimization. This paper additionally presents the latest advancements in fast optimization on visual signal coding and processing with generative models. We hope to advance this field by providing researchers and practitioners a comprehensive literature review on the topic of visual signal coding and processing with generative models

On the CR Nirenberg problem: density and multiplicity of solutions

We prove some results on the density and multiplicity of positive solutions to the prescribed Webster scalar curvature problem on the $(2n+1)$-dimensional standard unit CR sphere $(\mathbb{S} ^{2n+1},\theta_0)$. Specifically, we construct arbitrarily many multi-bump solutions via the variational gluing method. In particular, we show the Webster scalar curvature functions of contact forms conformal to $\theta_0$ are $C^{0}$-dense among bounded functions which are positive somewhere. Existence results of infinitely many positive solutions to the related equation $-\Delta_{\mathbb{H}} u=R(\xi) u^{(n+2) /n}$ on the Heisenberg group \Hn with $R(\xi)$ being asymptotically periodic with respect to left translation are also obtained. Our proofs make use of a refined analysis of bubbling behavior, gradient flow, Pohozaev identity, as well as blow up arguments

ローターの重複配置によるドローンの小型化とそれに向けた効率的な能動的外乱除去制御

筑波大学University of Tsukuba博士（工学）Doctor of Philosophy in Engineering2021この博士論文は、全文公表に適さないやむを得ない事由があり要約のみを公表していましたが、解消したため、令和6(2024)年2月15日に全文を公表しました。doctoral thesi

Using DeepSignalingFlow to mine signaling flows interpreting mechanism of synergy of cocktails

Complex signaling pathways are believed to be responsible for drug resistance. Drug combinations perturbing multiple signaling targets have the potential to reduce drug resistance. The large-scale multi-omic datasets and experimental drug combination synergistic score data are valuable resources to study mechanisms of synergy (MoS) to guide the development of precision drug combinations. However, signaling patterns of MoS are complex and remain unclear, and thus it is challenging to identify synergistic drug combinations in clinical. Herein, we proposed a novel integrative and interpretable graph AI model, DeepSignalingFlow, to uncover the MoS by integrating and mining multi-omic data. The major innovation is that we uncover MoS by modeling the signaling flow from multi-omic features of essential disease proteins to the drug targets, which has not been introduced by the existing models. The model performance was assessed utilizing four distinct drug combination synergy evaluation datasets, i.e., NCI ALMANAC, O\u27Neil, DrugComb, and DrugCombDB. The comparison results showed that the proposed model outperformed existing graph AI models in terms of synergy score prediction, and can interpret MoS using the core signaling flows. The code is publicly accessible via Github: https://github.com/FuhaiLiAiLab/DeepSignalingFlow