Enhanced coherent light-matter interaction and room-temperature quantum yield of plasmonic resonances engineered by a chiral exceptional point

Strong dissipation of plasmonic resonances is detrimental to quantum manipulation. To enhance the quantum coherence, we propose to tailor the local density of states (LDOS) of plasmonic resonances by integrating with a photonic cavity operating at a chiral exceptional point (CEP), where the phase of light field can offer a new degree of freedom to flexibly manipulate the quantum states. A quantized few-mode theory is employed to reveal that the LDOS of the proposed hybrid cavity can evolve into sub-Lorentzian lineshape, with order-of-magnitude linewidth narrowing and additionally a maximum of eightfold enhancement compared to the usual plasmonic-photonic cavity without CEP. This results in the enhanced coherent light-matter interaction accompanied by the reduced dissipation of polaritonic states. Furthermore, a scattering theory based on eigenmode decomposition is present to elucidate two mechanisms responsible for the significant improvement of quantum yield at CEP, the reduction of plasmonic absorption by the Fano interference and the enhancement of cavity radiation through the superscattering. Importantly, we find the latter allows achieving a near-unity quantum yield at room temperature; in return, high quantum yield is beneficial to experimentally verify the enhanced LDOS at CEP by measuring the fluorescence lifetime of a quantum emitter. Therefore, our work demonstrates that the plasmonic resonances in CEP-engineered environment can serve as a promising platform for exploring the quantum states control by virtue of the non-Hermiticity of open optical resonators and building the high-performance quantum devices for sensing, spectroscopy, quantum information processing and quantum computing.Comment: 20 pages,9 figure

Adaptive predefined-time robust control for nonlinear time-delay systems with different power Hamiltonian functions

The article studies $H_\infty$ control as well as adaptive robust control issues on the predefined time of nonlinear time-delay systems with different power Hamiltonian functions. First, for such Hamiltonian systems with external disturbance and delay phenomenon, we construct the appropriate Lyapunov function and Hamiltonian function of different powers. Then, a predefined-time $H_\infty$ control approach is presented to stabilize the systems within a predefined time. Furthermore, when considering nonlinear Hamiltonian system with unidentified disturbance, parameter uncertainty and delay, we devise a predefined-time adaptive robust strategy to ensure that the systems reach equilibrium within one predefined time and have better resistance to disturbance and uncertainty. Finally, the validity of the results is verified with a river pollution control system example

Single-Cell Multimodal Prediction via Transformers

The recent development of multimodal single-cell technology has made the possibility of acquiring multiple omics data from individual cells, thereby enabling a deeper understanding of cellular states and dynamics. Nevertheless, the proliferation of multimodal single-cell data also introduces tremendous challenges in modeling the complex interactions among different modalities. The recently advanced methods focus on constructing static interaction graphs and applying graph neural networks (GNNs) to learn from multimodal data. However, such static graphs can be suboptimal as they do not take advantage of the downstream task information; meanwhile GNNs also have some inherent limitations when deeply stacking GNN layers. To tackle these issues, in this work, we investigate how to leverage transformers for multimodal single-cell data in an end-to-end manner while exploiting downstream task information. In particular, we propose a scMoFormer framework which can readily incorporate external domain knowledge and model the interactions within each modality and cross modalities. Extensive experiments demonstrate that scMoFormer achieves superior performance on various benchmark datasets. Remarkably, scMoFormer won a Kaggle silver medal with the rank of 24/1221 (Top 2%) without ensemble in a NeurIPS 2022 competition. Our implementation is publicly available at Github.Comment: CIKM 202

Graph Positional and Structural Encoder

Positional and structural encodings (PSE) enable better identifiability of nodes within a graph, as in general graphs lack a canonical node ordering. This renders PSEs essential tools for empowering modern GNNs, and in particular graph Transformers. However, designing PSEs that work optimally for a variety of graph prediction tasks is a challenging and unsolved problem. Here, we present the graph positional and structural encoder (GPSE), a first-ever attempt to train a graph encoder that captures rich PSE representations for augmenting any GNN. GPSE can effectively learn a common latent representation for multiple PSEs, and is highly transferable. The encoder trained on a particular graph dataset can be used effectively on datasets drawn from significantly different distributions and even modalities. We show that across a wide range of benchmarks, GPSE-enhanced models can significantly improve the performance in certain tasks, while performing on par with those that employ explicitly computed PSEs in other cases. Our results pave the way for the development of large pre-trained models for extracting graph positional and structural information and highlight their potential as a viable alternative to explicitly computed PSEs as well as to existing self-supervised pre-training approaches

Whole brain radiotherapy plus simultaneous in-field boost with image guided intensity-modulated radiotherapy for brain metastases of non-small cell lung cancer

BACKGROUND: Whole brain radiotherapy (WBRT) plus sequential focal radiation boost is a commonly used therapeutic strategy for patients with brain metastases. However, recent reports on WBRT plus simultaneous in-field boost (SIB) also showed promising outcomes. The objective of present study is to retrospectively evaluate the efficacy and toxicities of WBRT plus SIB with image guided intensity-modulated radiotherapy (IG-IMRT) for inoperable brain metastases of NSCLC. METHODS: Twenty-nine NSCLC patients with 87 inoperable brain metastases were included in this retrospective study. All patients received WBRT at a dose of 40 Gy/20 f, and SIB boost with IG-IMRT at a dose of 20 Gy/5 f concurrent with WBRT in the fourth week. Prior to each fraction of IG-IMRT boost, on-line positioning verification and correction were used to ensure that the set-up errors were within 2 mm by cone beam computed tomography in all patients. RESULTS: The one-year intracranial control rate, local brain failure rate, and distant brain failure rate were 62.9%, 13.8%, and 19.2%, respectively. The two-year intracranial control rate, local brain failure rate, and distant brain failure rate were 42.5%, 30.9%, and 36.4%, respectively. Both median intracranial progression-free survival and median survival were 10 months. Six-month, one-year, and two-year survival rates were 65.5%, 41.4%, and 13.8%, corresponding to 62.1%, 41.4%, and 10.3% of intracranial progression-free survival rates. Patients with Score Index for Radiosurgery in Brain Metastases (SIR) >5, number of intracranial lesions <3, and history of EGFR-TKI treatment had better survival. Three lesions (3.45%) demonstrated radiation necrosis after radiotherapy. Grades 2 and 3 cognitive impairment with grade 2 radiation leukoencephalopathy were observed in 4 (13.8%) and 4 (13.8%) patients. No dosimetric parameters were found to be associated with these late toxicities. Patients received EGFR-TKI treatment had higher incidence of grades 2–3 cognitive impairment with grade 2 leukoencephalopathy. CONCLUSIONS: WBRT plus SIB with IG-IMRT is a tolerable and effective treatment for NSCLC patients with inoperable brain metastases. However, the results of present study need to be examined by the prospective investigations

Deep Learning in Single-Cell Analysis

Single-cell technologies are revolutionizing the entire field of biology. The large volumes of data generated by single-cell technologies are high-dimensional, sparse, heterogeneous, and have complicated dependency structures, making analyses using conventional machine learning approaches challenging and impractical. In tackling these challenges, deep learning often demonstrates superior performance compared to traditional machine learning methods. In this work, we give a comprehensive survey on deep learning in single-cell analysis. We first introduce background on single-cell technologies and their development, as well as fundamental concepts of deep learning including the most popular deep architectures. We present an overview of the single-cell analytic pipeline pursued in research applications while noting divergences due to data sources or specific applications. We then review seven popular tasks spanning through different stages of the single-cell analysis pipeline, including multimodal integration, imputation, clustering, spatial domain identification, cell-type deconvolution, cell segmentation, and cell-type annotation. Under each task, we describe the most recent developments in classical and deep learning methods and discuss their advantages and disadvantages. Deep learning tools and benchmark datasets are also summarized for each task. Finally, we discuss the future directions and the most recent challenges. This survey will serve as a reference for biologists and computer scientists, encouraging collaborations.Comment: 77 pages, 11 figures, 15 tables, deep learning, single-cell analysi

A genome-wide association study identifies GRK5 and RASGRP1 as type 2 diabetes loci in Chinese Hans.

Substantial progress has been made in identification of type 2 diabetes (T2D) risk loci in the past few years, but our understanding of the genetic basis of T2D in ethnically diverse populations remains limited. We performed a genome-wide association study and a replication study in Chinese Hans comprising 8,569 T2D case subjects and 8,923 control subjects in total, from which 10 single nucleotide polymorphisms were selected for further follow-up in a de novo replication sample of 3,410 T2D case and 3,412 control subjects and an in silico replication sample of 6,952 T2D case and 11,865 control subjects. Besides confirming seven established T2D loci (CDKAL1, CDKN2A/B, KCNQ1, CDC123, GLIS3, HNF1B, and DUSP9) at genome-wide significance, we identified two novel T2D loci, including G-protein-coupled receptor kinase 5 (GRK5) (rs10886471: P = 7.1 × 10(-9)) and RASGRP1 (rs7403531: P = 3.9 × 10(-9)), of which the association signal at GRK5 seems to be specific to East Asians. In nondiabetic individuals, the T2D risk-increasing allele of RASGRP1-rs7403531 was also associated with higher HbA(1c) and lower homeostasis model assessment of β-cell function (P = 0.03 and 0.0209, respectively), whereas the T2D risk-increasing allele of GRK5-rs10886471 was also associated with higher fasting insulin (P = 0.0169) but not with fasting glucose. Our findings not only provide new insights into the pathophysiology of T2D, but may also shed light on the ethnic differences in T2D susceptibility

Design, optimization and simulation of trad­able mobility credits

Road traffic congestion is a critical problem affecting urban mobility worldwide and its severity continues to increase, causing significant costs at the individual, environmental, economic, and societal levels. While a significant agenda has been put forward on the transport supply side, mostly driven by vehicle technology (automation and electrification), demand shifts are often considered a hard-to-reach but effective means to reduce the social and environmental costs associated with transport. Demand management has thus become an increasingly important focus of the policy agenda in many metropolitan areas. Congestion pricing as a demand management instrument has been widely investigated in both theory and practice motivated by its potential gains in social welfare. Nevertheless, congestion pricing often receives political and social resistance as it is perceived as a tax and in some contexts, can be vertically inequitable. An alternative market-based solution called a tradable credit scheme (TCS) has been receiving attention in recent years. In a typical TCS system, a regulator predetermines a total quota of credits available for the area and period of interest and distributes these credits to all potential travelers. The credits can be bought and sold in a free market at a price determined by credit demand and supply. Consequently, a tradable credit scheme has mainly three potential advantages over congestion pricing (without revenue redistribution): (i) TCS is revenue neutral as there is no monetary transfer to or from the regulator; (ii) TCS can be more equitable than congestion pricing since the inconvenience caused by the limited use of vehicles is compensated by selling extra credits; (iii) TCS has been shown to yield efficiency gains under uncertainty when congestion is significant. The first two features of TCS could help address the long-standing issue of public opposition to congestion pricing. This PhD study extends the growing body of literature in TCS with new area-based TCS designs, the flexible modeling and assessment of TCS via agent-based simulations, and the development of TCS optimization frameworks using machine learning techniques. This thesis is divided into three parts: i) Part I presents two studies on the formulation and application of Bayesian Optimization (BO) to the second-best design of tariff schemes for congestion pricing (and used in the design of tariff schemes for tradable mobility credits in Part II), ii) Part II includes two studies proposing different trading mechanisms (peer-to-regulator and peer-to-peer) for trip- and area-based TCS for the management of urban networks, and iii) Part III proposes a detailed and flexible simulation framework for assessing the impact of different TCS designs under realistic scenarios by extending a state-of-the-art activity-driven agent-based simulation platform (SimMobility). Part I deals with the development of two BO frameworks for congestion pricing optimization problems with different perspectives on utilizing problem-specific information for efficiency improvement. In the first study, we propose a BO formulation with problem-specific dropout strategies which can learn the relationship between the tariffs (decision variables) and social welfare (objective function) within a few iterations even under high-dimensional tariff structures. In the second study, we further develop a contextual BO framework where the BO scheme is embedded within the day-to-day dynamic model by using temporal contextual information. We numerically demonstrate that the framework utilizes a significantly smaller number of simulation evaluations (ten-fold reduction) than the standard BO approach. This framework can also incorporate context-specific demand and supply information which can be of value to policymakers when evaluating optimal tariff design schemes under a wide range of scenarios in a computationally tractable manner. We further show that distance-based tariff schemes yield significant welfare gains relative to area-based schemes and highlight that the design of the distance-based tariff scheme can significantly affect distributional impacts: a suitably designed two-part tariff structure can partially offset the relatively large welfare losses of travelers with longer commute distances while maintaining overall welfare. Part II focuses on the design and properties of TCS when applied to trip-based Macroscopic Fundamental Diagram (MFD) models considering the dynamics of the credit price. We propose an area-based TCS with time- and distance-based credit tariffs and incorporate this TCS into a day-to-day modeling framework with heterogeneous travelers. Analytically, we present conditions for the existence of the market and network equilibrium and the uniqueness of the credit price. Numerical results validate the analytical properties (including convergence and credit price uniqueness and its inverse proportionality with the credit allocation), demonstrate that the proposed TCS yields identical social welfare as congestion pricing while maintaining revenue neutrality, and show the superiority of a trip-based TCS to a trip-agnostic area-based TCS. Here, all travelers are assumed to trade directly with the regulator (peer-to-regulator, P2R), where, in the short-term, budget neutrality of credits cannot be guaranteed. Instead, these issues can be partially addressed by a peer-to-peer (P2P) trading market design where the transactions of credits happen between travelers. Although most existing TCS research adopts the assumption of P2P trading, the underlying mechanism that achieves market clearing (in terms of matching sellers and buyers and pricing of credits) is typically not elaborated. Thus, in Part II's second study, we investigate two different P2P trading paradigms that define the rules of matching selling and buying orders, market price adjustment, and individual bidding format. Numerical results show that all proposed P2P trading paradigms lead to a near identical equilibrium in terms of social welfare gains, departure flows, and credit price as that obtained from P2R schemes, while the P2P trading mechanisms are able to ensure the budget neutrality of credits as well as revenue neutrality of the regulator during the day-to-day process. Most TCS studies to date use simple demand and supply models for assessment. A TCS however may affect several mobility related dimensions of decision making at the individual level and interactions of users, influencing demand patterns, traffic flows, and network performance. Agent-based simulation is a common technique to capture these complex interactions and conduct analysis and evaluation of different TCSs. In Part III, we propose a flexible framework with a modular and extensible implementation in the state-of-the-art urban simulator SimMobility for the detailed simulation of the operation of a TCS system. Demand is modeled through an activity-based model and a within-day departure and route choice model sensitive to individual TCS, account budgets and heterogeneous preferences. The transportation supply is now represented by a mesoscopic network model and is extended with a TCS controller for handling all credit transactions within the simulation. This proposed framework allows for the simulation of a variety of TCS design schemes and is tested in a prototypical urban setting where theoretical TCS properties are then assessed. Another contribution of this work is that the developed scalable, operational, flexible, and open-source simulation platform is delivered as part of the PhD project. In summary, this PhD study contributes to the body of literature in mobility demand management, namely in tradable credit schemes, covering the topics of machine-learning based optimization, market design for TCS, and the design and assessment using more realistic demand and supply models. This thesis provides promising simulation-based optimization approaches for the optimal design of tariffs, enabling the efficient design of demand management instruments. The findings of this thesis also bring insights into the properties of the area-based TCS as well as key modeling and implementation frameworks for the design of future TCS including trading behaviors, credit allocation, expiration, trading patterns, price adjustment, and complex behavioral changes.Doctor of Philosoph