Showing papers by "Hong Kong University of Science and Technology published in 2021"

Advances and open problems in federated learning

Abstract: The term Federated Learning was coined as recently as 2016 to describe a machine learning setting where multiple entities collaborate in solving a machine learning problem, under the coordination of a central server or service provider. Each client’s raw data is stored locally and not exchanged or transferred; instead, focused updates intended for immediate aggregation are used to achieve the learning objective. Since then, the topic has gathered much interest across many different disciplines and the realization that solving many of these interdisciplinary problems likely requires not just machine learning but techniques from distributed optimization, cryptography, security, differential privacy, fairness, compressed sensing, systems, information theory, statistics, and more. This monograph has contributions from leading experts across the disciplines, who describe the latest state-of-the art from their perspective. These contributions have been carefully curated into a comprehensive treatment that enables the reader to understand the work that has been done and get pointers to where effort is required to solve many of the problems before Federated Learning can become a reality in practical systems. Researchers working in the area of distributed systems will find this monograph an enlightening read that may inspire them to work on the many challenging issues that are outlined. This monograph will get the reader up to speed quickly and easily on what is likely to become an increasingly important topic: Federated Learning.

Non-fullerene acceptors with branched side chains and improved molecular packing to exceed 18% efficiency in organic solar cells

Abstract: Molecular design of non-fullerene acceptors is of vital importance for high-efficiency organic solar cells. The branched alkyl chain modification is often regarded as a counter-intuitive approach, as it may introduce an undesirable steric hindrance that reduces charge transport in non-fullerene acceptors. Here we show the design and synthesis of a highly efficient non-fullerene acceptor family by substituting the beta position of the thiophene unit on a Y6-based dithienothiophen[3,2-b]-pyrrolobenzothiadiazole core with branched alkyl chains. It was found that such a modification to a different alkyl chain length could completely change the molecular packing behaviour of non-fullerene acceptors, leading to improved structural order and charge transport in thin films. An unprecedented efficiency of 18.32% (certified value of 17.9%) with a fill factor of 81.5% is achieved for single-junction organic solar cells. This work reveals the importance of the branched alkyl chain topology in tuning the molecular packing and blend morphology, which leads to improved organic photovoltaic performance. Molecular design of acceptor and donor molecules has enabled major progress in organic photovoltaics. Li et al. show that branched alkyl chains in non-fullerene acceptors allow favourable morphology in the active layer, enabling a certified device efficiency of 17.9%.

An evidence review of face masks against COVID-19.

Abstract: The science around the use of masks by the public to impede COVID-19 transmission is advancing rapidly. In this narrative review, we develop an analytical framework to examine mask usage, synthesizing the relevant literature to inform multiple areas: population impact, transmission characteristics, source control, wearer protection, sociological considerations, and implementation considerations. A primary route of transmission of COVID-19 is via respiratory particles, and it is known to be transmissible from presymptomatic, paucisymptomatic, and asymptomatic individuals. Reducing disease spread requires two things: limiting contacts of infected individuals via physical distancing and other measures and reducing the transmission probability per contact. The preponderance of evidence indicates that mask wearing reduces transmissibility per contact by reducing transmission of infected respiratory particles in both laboratory and clinical contexts. Public mask wearing is most effective at reducing spread of the virus when compliance is high. Given the current shortages of medical masks, we recommend the adoption of public cloth mask wearing, as an effective form of source control, in conjunction with existing hygiene, distancing, and contact tracing strategies. Because many respiratory particles become smaller due to evaporation, we recommend increasing focus on a previously overlooked aspect of mask usage: mask wearing by infectious people ("source control") with benefits at the population level, rather than only mask wearing by susceptible people, such as health care workers, with focus on individual outcomes. We recommend that public officials and governments strongly encourage the use of widespread face masks in public, including the use of appropriate regulation.

RepVGG: Making VGG-style ConvNets Great Again.

Abstract: We present a simple but powerful architecture of convolutional neural network, which has a VGG-like inference-time body composed of nothing but a stack of 3x3 convolution and ReLU, while the training-time model has a multi-branch topology. Such decoupling of the training-time and inference-time architecture is realized by a structural re-parameterization technique so that the model is named RepVGG. On ImageNet, RepVGG reaches over 80% top-1 accuracy, which is the first time for a plain model, to the best of our knowledge. On NVIDIA 1080Ti GPU, RepVGG models run 83% faster than ResNet-50 or 101% faster than ResNet-101 with higher accuracy and show favorable accuracy-speed trade-off compared to the state-of-the-art models like EfficientNet and RegNet. The code and trained models are available at this https URL.

Increase in suicide following an initial decline during the COVID-19 pandemic in Japan.

Abstract: There is increasing concern that the coronavirus disease 2019 (COVID-19) pandemic could harm psychological health and exacerbate suicide risk. Here, based on month-level records of suicides covering the entire Japanese population in 1,848 administrative units, we assessed whether suicide mortality changed during the pandemic. Using difference-in-difference estimation, we found that monthly suicide rates declined by 14% during the first 5 months of the pandemic (February to June 2020). This could be due to a number of complex reasons, including the government's generous subsidies, reduced working hours and school closure. By contrast, monthly suicide rates increased by 16% during the second wave (July to October 2020), with a larger increase among females (37%) and children and adolescents (49%). Although adverse impacts of the COVID-19 pandemic may remain in the long term, its modifiers (such as government subsidies) may not be sustained. Thus, effective suicide prevention-particularly among vulnerable populations-should be an important public health consideration.

All One Needs to Know about Metaverse: A Complete Survey on Technological Singularity, Virtual Ecosystem, and Research Agenda

Abstract: Since the popularisation of the Internet in the 1990s, the cyberspace has kept evolving. We have created various computer-mediated virtual environments including social networks, video conferencing, virtual 3D worlds (e.g., VR Chat), augmented reality applications (e.g., Pokemon Go), and Non-Fungible Token Games (e.g., Upland). Such virtual environments, albeit non-perpetual and unconnected, have bought us various degrees of digital transformation. The term `metaverse' has been coined to further facilitate the digital transformation in every aspect of our physical lives. At the core of the metaverse stands the vision of an immersive Internet as a gigantic, unified, persistent, and shared realm. While the metaverse may seem futuristic, catalysed by emerging technologies such as Extended Reality, 5G, and Artificial Intelligence, the digital `big bang' of our cyberspace is not far away. This survey paper presents the first effort to offer a comprehensive framework that examines the latest metaverse development under the dimensions of state-of-the-art technologies and metaverse ecosystems, and illustrates the possibility of the digital `big bang'. First, technologies are the enablers that drive the transition from the current Internet to the metaverse. We thus examine eight enabling technologies rigorously - Extended Reality, User Interactivity (Human-Computer Interaction), Artificial Intelligence, Blockchain, Computer Vision, IoT and Robotics, Edge and Cloud computing, and Future Mobile Networks. In terms of applications, the metaverse ecosystem allows human users to live and play within a self-sustaining, persistent, and shared realm. Therefore, we discuss six user-centric factors -- Avatar, Content Creation, Virtual Economy, Social Acceptability, Security and Privacy, and Trust and Accountability. Finally, we propose a concrete research agenda for the development of the metaverse.

A high-energy and long-cycling lithium-sulfur pouch cell via a macroporous catalytic cathode with double-end binding sites.

Abstract: Lithium–sulfur batteries are attractive alternatives to lithium-ion batteries because of their high theoretical specific energy and natural abundance of sulfur. However, the practical specific energy and cycle life of Li–S pouch cells are significantly limited by the use of thin sulfur electrodes, flooded electrolytes and Li metal degradation. Here we propose a cathode design concept to achieve good Li–S pouch cell performances. The cathode is composed of uniformly embedded ZnS nanoparticles and Co–N–C single-atom catalyst to form double-end binding sites inside a highly oriented macroporous host, which can effectively immobilize and catalytically convert polysulfide intermediates during cycling, thus eliminating the shuttle effect and lithium metal corrosion. The ordered macropores enhance ionic transport under high sulfur loading by forming sufficient triple-phase boundaries between catalyst, conductive support and electrolyte. This design prevents the formation of inactive sulfur (dead sulfur). Our cathode structure shows improved performances in a pouch cell configuration under high sulfur loading and lean electrolyte operation. A 1-A-h-level pouch cell with only 100% lithium excess can deliver a cell specific energy of >300 W h kg−1 with a Coulombic efficiency >95% for 80 cycles. The shuttling effect in Li–S batteries can be drastically suppressed by using a single-atom Co catalyst and polar ZnS nanoparticles embedded in a macroporous conductive matrix as a cathode. Using this strategy, Li–S pouch cells show stable cycling and high energy performances.

SecureBoost: A Lossless Federated Learning Framework

Abstract: The protection of user privacy is an important concern in machine learning, as evidenced by the rolling out of the General Data Protection Regulation (GDPR) in the European Union (EU) in May 2018 The GDPR is designed to give users more control over their personal data, which motivates us to explore machine learning frameworks for data sharing that do not violate user privacy To meet this goal, in this paper, we propose a novel lossless privacy-preserving tree-boosting system known as SecureBoost in the setting of federated learning This federated-learning system allows the learning process to be jointly conducted over multiple parties with partially common user samples but different feature sets, which corresponds to a vertically partitioned data set An advantage of SecureBoost is that it provides the same level of accuracy as the non privacy-preserving approach while at the same time, reveals no information of each private data provider We formally prove that the SecureBoost framework is as accurate as other non-federated gradient tree-boosting algorithms that concentrate data in one place In addition, we describe information leakage during the protocol execution and propose ways to provably reduce it

RepVGG: Making VGG-style ConvNets Great Again

Abstract: We present a simple but powerful architecture of convolutional neural network, which has a VGG-like inference-time body composed of nothing but a stack of 3 × 3 convolution and ReLU, while the training-time model has a multi-branch topology. Such decoupling of the training-time and inference-time architecture is realized by a structural re-parameterization technique so that the model is named RepVGG. On ImageNet, RepVGG reaches over 80% top-1 accuracy, which is the first time for a plain model, to the best of our knowledge. On NVIDIA 1080Ti GPU, RepVGG models run 83% faster than ResNet-50 or 101% faster than ResNet-101 with higher accuracy and show favorable accuracy-speed trade-off compared to the state-of-the-art models like EfficientNet and RegNet. The code and trained models are available at https://github.com/megvii-model/RepVGG.

A Survey on Multi-Task Learning

Abstract: Multi-Task Learning (MTL) is a learning paradigm in machine learning and its aim is to leverage useful information contained in multiple related tasks to help improve the generalization performance of all the tasks In this paper, we give a survey for MTL from the perspective of algorithmic modeling, applications, and theoretical analyses For algorithmic modeling, we give a definition of MTL and then classify different MTL algorithms into five categories, including feature learning approach, low-rank approach, task clustering approach, task relation learning approach, and decomposition approach as well as discussing the characteristics of each approach In order to improve the performance of learning tasks further, MTL can be combined with other learning paradigms including semi-supervised learning, active learning, unsupervised learning, reinforcement learning, multi-view learning, and graphical models When the number of tasks is large or the data dimensionality is high, we review online, parallel, and distributed MTL models as well as dimensionality reduction and feature hashing to reveal their computational and storage advantages Many real-world applications use MTL to boost their performance and we review representative works Finally, we present theoretical analyses and discuss several future directions for MTL

Approaching 18% efficiency of ternary organic photovoltaics with wide bandgap polymer donor and well compatible Y6 : Y6-1O as acceptor.

Abstract: A series of ternary organic photovoltaics (OPVs) are fabricated with one wide bandgap polymer D18-Cl as donor, and well compatible Y6 and Y6-1O as acceptor. The open-circuit-voltage (VOC ) of ternary OPVs is monotonously increased along with the incorporation of Y6-1O, indicating that the alloy state should be formed between Y6 and Y6-1O due to their excellent compatibility. The energy loss can be minimized by incorporating Y6-1O, leading to the VOC improvement of ternary OPVs. By finely adjusting the Y6-1O content, a power conversion efficiency of 17.91% is achieved in the optimal ternary OPVs with 30 wt% Y6-1O in acceptors, resulting from synchronously improved short-circuit-current density (JSC ) of 25.87 mA cm-2, fill factor (FF) of 76.92% and VOC of 0.900 V in comparison with those of D18-Cl : Y6 binary OPVs. The JSC and FF improvement of ternary OPVs should be ascribed to comprehensively optimal photon harvesting, exciton dissociation and charge transport in ternary active layers. The more efficient charge separation and transport process in ternary active layers can be confirmed by the magneto-photocurrent and impedance spectroscopy experimental results, respectively. This work provides new insight into constructing highly efficient ternary OPVs with well compatible Y6 and its derivative as acceptor.

NIR‑II AIEgens: A Win–Win Integration towards Bioapplications

Abstract: Fluorescence imaging in the second near-infrared (NIR-II) window facilitated by aggregation-induced emission luminogens (AIEgens) is an emerging research field. NIR-II AIEgens overcome limitations imposed by penetration depth and fluorescence efficiency, offering high-performance imaging with enhanced precision. Some reported NIR-II AIEgens demonstrate capabilities for fluorescence and photoacoustic bimodal imaging, and fluorescence imaging guided photothermal therapy, which not only improves diagnosis accuracy but provides an efficient theranostic platform to accelerate preclinical translation as well. This minireview summarizes recent efforts on exploiting NIR-II AIEgens with regard to molecular design strategies and bioapplications, and puts forward current challenges and promising prospects. This timely sketch should benefit the further exploitation of diverse and multifunctional NIR-II AIEgens for a wide array of applications.

Involution: Inverting the Inherence of Convolution for Visual Recognition

Abstract: Convolution has been the core ingredient of modern neural networks, triggering the surge of deep learning in vision. In this work, we rethink the inherent principles of standard convolution for vision tasks, specifically spatial-agnostic and channel-specific. Instead, we present a novel atomic operation for deep neural networks by inverting the aforementioned design principles of convolution, coined as involution. We additionally demystify the recent popular self-attention operator and subsume it into our involution family as an over-complicated instantiation. The proposed involution operator could be leveraged as fundamental bricks to build the new generation of neural networks for visual recognition, powering different deep learning models on several prevalent benchmarks, including ImageNet classification, COCO detection and segmentation, together with Cityscapes segmentation. Our involution-based models improve the performance of convolutional baselines using ResNet-50 by up to 1.6% top-1 accuracy, 2.5% and 2.4% bounding box AP, and 4.7% mean IoU absolutely while compressing the computational cost to 66%, 65%, 72%, and 57% on the above benchmarks, respectively. Code and pre-trained models for all the tasks are available at https://github.com/d-li14/involution.

Redirecting dynamic surface restructuring of a layered transition metal oxide catalyst for superior water oxidation

Abstract: Rationally manipulating the in situ formed catalytically active surface of catalysts remains a tremendous challenge for a highly efficient water electrolysis. Here we present a cationic redox-tuning method to modulate in situ catalyst leaching and to redirect the dynamic surface restructuring of layered LiCoO2–xClx (x = 0, 0.1 or 0.2), for the electrochemical oxygen evolution reaction (OER). Chlorine doping lowered the potential to trigger in situ cobalt oxidation and lithium leaching, which induced the surface of LiCoO1.8Cl0.2 to transform into a self-terminated amorphous (oxy)hydroxide phase during the OER. In contrast, Cl-free LiCoO2 required higher electrochemical potentials to initiate the in situ surface reconstruction to spinel-type Li1±xCo2O4 and longer cycles to stabilize it. Surface-restructured LiCoO1.8Cl0.2 outperformed many state-of-the-art OER catalysts and demonstrated remarkable stability. This work makes a stride in modulating surface restructuring and in designing superior OER electrocatalysts via manipulating the in situ catalyst leaching. Rationally manipulating the in-situ-formed catalytically active surface of catalysts is a challenging but promising endeavour. Now, the surface of LiCoO2 during water oxidation is engineered by Cl doping via a cationic redox-tuning method that modulates in situ leaching and redirects the dynamic surface restructuring.

Recent Advances in Electrocatalysts for Proton Exchange Membrane Fuel Cells and Alkaline Membrane Fuel Cells.

Abstract: The rapid progress of proton exchange membrane fuel cells (PEMFCs) and alkaline exchange membrane fuel cells (AMFCs) has boosted the hydrogen economy concept via diverse energy applications in the past decades. For a holistic understanding of the development status of PEMFCs and AMFCs, recent advancements in electrocatalyst design and catalyst layer optimization, along with cell performance in terms of activity and durability in PEMFCs and AMFCs, are summarized here. The activity, stability, and fuel cell performance of different types of electrocatalysts for both oxygen reduction reaction and hydrogen oxidation reaction are discussed and compared. Research directions on the further development of active, stable, and low-cost electrocatalysts to meet the ultimate commercialization of PEMFCs and AMFCs are also discussed.

Secure Federated Matrix Factorization

Abstract: To protect user privacy and meet law regulations, federated (machine) learning is obtaining vast interests in recent years. The key principle of federated learning is training a machine learning model without needing to know each user’s personal raw private data. In this article, we propose a secure matrix factorization framework under the federated learning setting, called FedMF. First, we design a user-level distributed matrix factorization framework where the model can be learned when each user only uploads the gradient information (instead of the raw preference data) to the server. While gradient information seems secure, we prove that it could still leak users’ raw data. To this end, we enhance the distributed matrix factorization framework with homomorphic encryption. We implement the prototype of FedMF and test it with a real movie rating dataset. Results verify the feasibility of FedMF. We also discuss the challenges for applying FedMF in practice for future research.

Recent advances of AIE light-up probes for photodynamic therapy

Abstract: As a new non-invasive treatment method, photodynamic therapy (PDT) has attracted great attention in biomedical applications. The advantages of possessing fluorescence for photosensitizers have made it possible to combine imaging and diagnosis together with PDT. The unique features of aggregation-induced emission (AIE) fluorogens provide new opportunities for facile design of light-up probes with high signal-to-noise ratios and improved theranostic accuracy and efficacy for image-guided PDT. In this review, we summarize the recent advances of AIE light-up probes for PDT. The strategies and principles to design AIE photosensitizers and light-up probes are firstly introduced. The application of AIE light-up probes in photodynamic antitumor and antibacterial applications is further elaborated in detail, from binding/targeting-mediated, reaction-mediated, and external stimuli-mediated light-up aspects. The challenges and future perspectives of AIE light-up probes in the PDT field are also presented with the hope to encourage more promising developments of AIE materials for phototheranostic applications and translational research.

Frontiers in circularly polarized luminescence: molecular design, self-assembly, nanomaterials, and applications

Abstract: The research in circularly polarized luminescence has attracted wide interest in recent years. Efforts on one side are directed toward the development of chiral materials with both high luminescence efficiency and dissymmetry factors, and on the other side, are focused on the exploitations of these materials in optoelectronic applications. This review summarizes the recent frontiers (mostly within five years) in the research in circularly polarized luminescence, including the development of chiral emissive materials based on organic small molecules, compounds with aggregation-induced emissions, supramolecular assemblies, liquid crystals and liquids, polymers, metal-ligand coordination complexes and assemblies, metal clusters, inorganic nanomaterials, and photon upconversion systems. In addition, recent applications of related materials in organic light-emitting devices, circularly polarized light detectors, and organic lasers and displays are also discussed.

Roadmap of Spin–Orbit Torques

Abstract: Spin–orbit torque (SOT) is an emerging technology that enables the efficient manipulation of spintronic devices. The initial processes of interest in SOTs involved electric fields, spin–orbit coupling, conduction electron spins, and magnetization. More recently, interest has grown to include a variety of other processes that include phonons, magnons, or heat. Over the past decade, many materials have been explored to achieve a larger SOT efficiency. Recently, holistic design to maximize the performance of SOT devices has extended material research from a nonmagnetic layer to a magnetic layer. The rapid development of SOT has spurred a variety of SOT-based applications. In this article, we first review the theories of SOTs by introducing the various mechanisms thought to generate or control SOTs, such as the spin Hall effect, the Rashba-Edelstein effect, the orbital Hall effect, thermal gradients, magnons, and strain effects. Then, we discuss the materials that enable these effects, including metals, metallic alloys, topological insulators, 2-D materials, and complex oxides. We also discuss the important roles in SOT devices of different types of magnetic layers, such as magnetic insulators, antiferromagnets, and ferrimagnets. Afterward, we discuss device applications utilizing SOTs. We discuss and compare three- and two-terminal SOT-magnetoresistive random access memories (MRAMs); we mention various schemes to eliminate the need for an external field. We provide technological application considerations for SOT-MRAM and give perspectives on SOT-based neuromorphic devices and circuits. In addition to SOT-MRAM, we present SOT-based spintronic terahertz generators, nano-oscillators, and domain-wall and skyrmion racetrack memories. This article aims to achieve a comprehensive review of SOT theory, materials, and applications, guiding future SOT development in both the academic and industrial sectors.

High Performance of Simple Organic Phosphorescence Host-Guest Materials and their Application in Time-Resolved Bioimaging.

Abstract: The study of purely organic room-temperature phosphorescence (RTP) has drawn increasing attention because of its considerable theoretical research and practical application value. Currently, organic RTP materials with both high efficiency (ΦP > 20%) and a long lifetime (τP > 10 s) in air are still scarce due to the lack of related design guidance. Here, a new strategy to increase the phosphorescence performance of organic materials by integrating the RTP host and RTP guest in one doping system to form a triplet exciplex, is reported. With these materials, the high-contrast labeling of tumors in living mice and encrypted patterns in thermal printing are both successfully realized by taking advantage of both the long afterglow time (up to 25 min in aqueous media) and high phosphorescence efficiency (43%).

FFB6D: A Full Flow Bidirectional Fusion Network for 6D Pose Estimation

Abstract: In this work, we present FFB6D, a Full Flow Bidirectional fusion network designed for 6D pose estimation from a single RGBD image. Our key insight is that appearance information in the RGB image and geometry information from the depth image are two complementary data sources, and it still remains unknown how to fully leverage them. Towards this end, we propose FFB6D, which learns to combine appearance and geometry information for representation learning as well as output representation selection. Specifically, at the representation learning stage, we build bidirectional fusion modules in the full flow of the two networks, where fusion is applied to each encoding and decoding layer. In this way, the two networks can leverage local and global complementary in-formation from the other one to obtain better representations. Moreover, at the output representation stage, we designed a simple but effective 3D keypoints selection algorithm considering the texture and geometry information of objects, which simplifies keypoint localization for precise pose estimation. Experimental results show that our method outperforms the state-of-the-art by large margins on several benchmarks. Code and video are available at https://github.com/ethnhe/FFB6D.git.

High-Performance Blue Perovskite Light-Emitting Diodes Enabled by Efficient Energy Transfer between Coupled Quasi-2D Perovskite Layers.

Abstract: While there has been extensive investigation into modulating quasi-2D perovskite compositions in light-emitting diodes (LEDs) for promoting their electroluminescence, very few reports have studied approaches involving enhancement of the energy transfer between quasi-2D perovskite layers of the film, which plays very important role for achieving high-performance perovskite LEDs (PeLEDs). In this work, a bifunctional ligand of 4-(2-aminoethyl)benzoic acid (ABA) cation is strategically introduced into the perovskite to diminish the weak van der Waals gap between individual perovskite layers for promoting coupled quasi-2D perovskite layers. In particular, the strengthened interaction between coupled quasi-2D perovskite layers favors an efficient energy transfer in the perovskite films. The introduced ABA can also simultaneously passivate the perovskite defects by reducing metallic Pb for less nonradiative recombination loss. Benefiting from the advanced properties of ABA incorporated perovskites, highly efficient blue PeLEDs with external quantum efficiency of 10.11% and a very long operational stability of 81.3 min, among the best performing blue quasi-2D PeLEDs, are achieved. Consequently, this work contributes an effective approach for high-performance and stable blue PeLEDs toward practical applications.

AoI-Minimal Trajectory Planning and Data Collection in UAV-Assisted Wireless Powered IoT Networks

Abstract: This article investigates the unmanned aerial vehicle (UAV)-assisted wireless powered Internet-of-Things system, where a UAV takes off from a data center, flies to each of the ground sensor nodes (SNs) in order to transfer energy and collect data from the SNs, and then returns to the data center. For such a system, an optimization problem is formulated to minimize the average Age of Information (AoI) of the data collected from all ground SNs. Since the average AoI depends on the UAV’s trajectory, the time required for energy harvesting (EH) and data collection for each SN, these factors need to be optimized jointly. Moreover, instead of the traditional linear EH model, we employ a nonlinear model because the behavior of the EH circuits is nonlinear by nature. To solve this nonconvex problem, we propose to decompose it into two subproblems, i.e., a joint energy transfer and data collection time allocation problem and a UAV’s trajectory planning problem. For the first subproblem, we prove that it is convex and give an optimal solution by using Karush–Kuhn–Tucker (KKT) conditions. This solution is used as the input for the second subproblem, and we solve optimally it by designing dynamic programming (DP) and ant colony (AC) heuristic algorithms. The simulation results show that the DP-based algorithm obtains the minimal average AoI of the system, and the AC-based heuristic finds solutions with near-optimal average AoI. The results also reveal that the average AoI increases as the flying altitude of the UAV increases and linearly with the size of the collected data at each ground SN.

Stimuli-Responsive AIEgens

Abstract: The unique advantages and the exciting application prospects of AIEgens have triggered booming developments in this area in recent years. Among them, stimuli-responsive AIEgens have received particular attention and impressive progress, and they have been demonstrated to show tremendous potential in many fields from physical chemistry to materials science and to biology and medicine. Here, the recent achievements of stimuli-responsive AIEgens in terms of seven most representative types of stimuli including force, light, polarity, temperature, electricity, ion, and pH, are summarized. Based on typical examples, it is illustrated how each type of systems realize the desired stimuli-responsive performance for various applications. The key work principles behind them are ultimately deciphered and figured out to offer new insights and guidelines for the design and engineering of the next-generation stimuli-responsive luminescent materials for more broad applications.

Fine-tuning of side-chain orientations on nonfullerene acceptors enables organic solar cells with 17.7% efficiency

Abstract: Side-chain engineering has been shown to be an important strategy to optimize Y-series nonfullerene acceptors (NFAs). Most previous reports were focusing on changing the branching positions and size of the alkyl side chains on Y6. In this paper, we investigate the influence of the orientation of side chains on the properties of NFAs and the performance of the organic solar cells (OSCs). Three isomeric NFAs named o-BTP-PhC6, m-BTP-PhC6, and p-BTP-PhC6 are designed by changing the substitution positions and thus orientations of the side chains attached to the central core. Our studies show that the optimal side-chain orientation can be achieved by the meta-positioned hexylphenyl group (of the m-BTP-PhC6 molecule), which introduces significant beneficial effects on optical absorption, intermolecular packing and phase separation of the NFAs. By pairing a donor polymer PTQ10 with m-BTP-PhC6, device efficiencies of 17.7% can be achieved, which is among the best values for PTQ10-based nonfullerene OSC devices so far. These results reveal that regulating side-chain orientations of Y-series NFAs is a promising strategy to achieve favorable morphology, and high charge mobility and solar cell performances.