Visible-Light-Promoted Radical Cascade Cyclization of 2-Vinyl Benzimidazoles: Access to Benzo[4,5]imidazo[1,2-b]isoquinolin- 11(6H)-ones.

A visible-light-enabled photoredox radical cascade cyclization of 2-vinyl benzimidazole derivatives is developed. This chemistry is applicable to a wide range of N-aroyl 2-vinyl benzimidazoles as acceptors, and halo compounds, including alkyl halides, acyl chlorides and sulfonyl chlorides, as radical precursors. The Langlois reagent also serves as an effective partner in this photocatalytic oxidative cascade process. This protocol provides a robust alternative for rendering highly functionalized benzo[4,5]imidazo[1,2-b]isoquinolin-11(6H)-ones.

Intrinsically Stretchable Organic Thermoelectric Polymers Enabled by Incorporating Fused-Ring Conjugated Breakers.

While research on organic thermoelectric polymers is making significant progress in recent years, realization of a single polymer material possessing both thermoelectric properties and stretchability for the next generation of self-powered wearable electronics is a challenging task and remains an area yet to be explored. A new molecular engineering concept of "conjugated breaker" is employed to impart stretchability to a highly crystalline diketopyrrolepyrrole (DPP)-based polymer. A hexacyclic diindenothieno[2,3-b]thiophene (DITT) unit, with two 4-octyloxyphenyl groups substituted at the tetrahedral sp3-carbon bridges, is selected to function as the conjugated breaker that can sterically hinder intermolecular packing to reduce polymers' crystallinity. A series of donor-acceptor random copolymers is thus developed via polymerizing the crystalline DPP units with the DITT conjugated breakers. By controlling the monomeric DPP/DITT ratios, DITT30 reaches the optimal balance of crystalline/amorphous regions, exhibiting an exceptional power factor (PF) value up to 12.5 µW m-1 K-2 after FeCl3-doping; while, simultaneously displaying the capability to withstand strains exceeding 100%. More significantly, the doped DITT30 film possesses excellent mechanical endurance, retaining 80% of its initial PF value after 200 cycles of stretching/releasing at a strain of 50%. This research marks a pioneering achievement in creating intrinsically stretchable polymers with exceptional thermoelectric properties.

Electrical Double-Layer Transistors Comprising Block Copolymer Electrolytes for Low-Power-Consumption Photodetectors.

Electrical double-layer transistors (EDLTs) have received extensive research attention owing to their exciting advantages of low working voltage, high biocompatibility, and sensitive interfacial properties in ultrasensitive portable sensing applications. Therefore, it is of great interest to reduce photodetectors' operating voltage and power consumption by utilizing photo-EDLT. In this study, a series of block copolymers (BCPs) of poly(4-vinylpyridine)-block-poly(ethylene oxide) (P4VP-b-PEO) with different compositions were applied to formulate polyelectrolyte with indigo carmine salt in EDLT. Accordingly, PEO conduces ion conduction in the BCP electrolyte and enhances the carrier transport capability in the semiconducting channel; P4VP boosts the photocurrent by providing charge-trapping sites during light illumination. In addition, the severe aggregation of PEO is mitigated by forming a BCP structure with P4VP, enhancing the stability and photoresponse of the photo-EDLT. By optimizing the BCP composition, EDLT comprising P4VP16k-b-PEO5k and indigo carmine provides the highest specific detectivity of 2.1 × 107 Jones, along with ultralow power consumptions of 0.59 nW under 450 nm light illumination and 0.32 pW under dark state. The results indicate that photo-EDLT comprising the BCP electrolyte is a practical approach to reducing phototransistors' operating voltage and power consumption.

A Lesion-Fusion Neural Network for Multi-View Diabetic Retinopathy Grading.

As the most common complication of diabetes, diabetic retinopathy (DR) is one of the main causes of irreversible blindness. Automatic DR grading plays a crucial role in early diagnosis and intervention, reducing the risk of vision loss in people with diabetes. In these years, various deep-learning approaches for DR grading have been proposed. Most previous DR grading models are trained using the dataset of single-field fundus images, but the entire retina cannot be fully visualized in a single field of view. There are also problems of scattered location and great differences in the appearance of lesions in fundus images. To address the limitations caused by incomplete fundus features, and the difficulty in obtaining lesion information. This work introduces a novel multi-view DR grading framework, which solves the problem of incomplete fundus features by jointly learning fundus images from multiple fields of view. Furthermore, the proposed model combines multi-view inputs such as fundus images and lesion snapshots. It utilizes heterogeneous convolution blocks (HCB) and scalable self-attention classes (SSAC), which enhance the ability of the model to obtain lesion information. The experimental results show that our proposed method performs better than the benchmark methods on the large-scale dataset.

Engineered coiled-coil HIF1α protein domain mimic.

The development of targeted anti-cancer therapeutics offers the potential for increased efficacy of drugs and diagnostics. Utilizing modalities agnostic to tumor type, such as the hypoxic tumor microenvironment (TME), may assist in the development of universal tumor targeting agents. The hypoxia-inducible factor (HIF), in particular HIF1, plays a key role in tumor adaptation to hypoxia, and inhibiting its interaction with p300 has been shown to provide therapeutic potential. Using a multivalent assembled protein (MAP) approach based on the self-assembly of the cartilage oligomeric matrix protein coiled-coil (COMPcc) domain fused to the critical residues of the C-terminal transactivation domain (C-TAD) of the α subunit of HIF1 (HIF1α), we generate HIF1α-MAP (H-MAP). The resulting H-MAP demonstrates picomolar binding affinity to p300, the ability to downregulate hypoxia-inducible genes, and in vivo tumor targeting capability.

A pH-Sensitive Stretchable Zwitterionic Hydrogel with Bipolar Thermoelectricity.

Amid growing interest in using body heat for electricity in wearables, creating stretchable devices poses a major challenge. Herein, a hydrogel composed of two core constituents, namely the negatively-charged 2-acrylamido-2-methylpropanesulfonic acid and the zwitterionic (ZI) sulfobetaine acrylamide, is engineered into a double-network hydrogel. This results in a significant enhancement in mechanical properties, with tensile stress and strain of up to 470.3 kPa and 106.6%, respectively. Moreover, the ZI nature of the polymer enables the fabrication of a device with polar thermoelectric properties by modulating the pH. Thus, the ionic Seebeck coefficient (Si ) of the ZI hydrogel ranges from -32.6 to 31.7 mV K-1 as the pH is varied from 1 to 14, giving substantial figure of merit (ZTi ) values of 3.8 and 3.6, respectively. Moreover, a prototype stretchable ionic thermoelectric supercapacitor incorporating the ZI hydrogel exhibits notable power densities of 1.8 and 0.9 mW m-2 at pH 1 and 14, respectively. Thus, the present work paves the way for the utilization of pH-sensitive, stretchable ZI hydrogels for thermoelectric applications, with a specific focus on harvesting low-grade waste heat within the temperature range of 25-40 °C.

Cardiologist-level interpretable knowledge-fused deep neural network for automatic arrhythmia diagnosis.

BACKGROUND: Long-term monitoring of Electrocardiogram (ECG) recordings is crucial to diagnose arrhythmias. Clinicians can find it challenging to diagnose arrhythmias, and this is a particular issue in more remote and underdeveloped areas. The development of digital ECG and AI methods could assist clinicians who need to diagnose arrhythmias outside of the hospital setting. METHODS: We constructed a large-scale Chinese ECG benchmark dataset using data from 272,753 patients collected from January 2017 to December 2021. The dataset contains ECG recordings from all common arrhythmias present in the Chinese population. Several experienced cardiologists from Shanghai First People's Hospital labeled the dataset. We then developed a deep learning-based multi-label interpretable diagnostic model from the ECG recordings. We utilized Accuracy, F1 score and AUC-ROC to compare the performance of our model with that of the cardiologists, as well as with six comparison models, using testing and hidden data sets. RESULTS: The results show that our approach achieves an F1 score of 83.51%, an average AUC ROC score of 0.977, and 93.74% mean accuracy for 6 common arrhythmias. Results from the hidden dataset demonstrate the performance of our approach exceeds that of cardiologists. Our approach also highlights the diagnostic process. CONCLUSIONS: Our diagnosis system has superior diagnostic performance over that of clinicians. It also has the potential to help clinicians rapidly identify abnormal regions on ECG recordings, thus improving efficiency and accuracy of clinical ECG diagnosis in China. This approach could therefore potentially improve the productivity of out-of-hospital ECG diagnosis and provides a promising prospect for telemedicine.

Arrhythmia, also known as an irregular heartbeat, is a common cardiovascular disease. Sometimes the presence of an arrhythmia can increase the risk of more serious heart conditions. Long-term monitoring of the heartbeat enables arrhythmia to be more easily diagnosed. To accurately detect arrhythmia, we developed a computational model that was able to detect six common types of arrhythmias from readings of the heart rate obtained using a device connected to a mobile phone. We showed that our model could diagnose these arrhythmias in over 270,000 people living in China. Our diagnostic system could enable arrhythmias to be diagnosed more easily outside of hospitals and therefore improve access to healthcare, particularly for those in remote settings.

Reversible Molecular Conformation Transitions of Smectic Liquid Crystals for Light/Bias-Gated Transistor Memory.

In recent years, organic photonic field-effect transistors have made remarkable progress with the rapid development of conjugated polycrystalline materials. Liquid crystals, with their smooth surface, defined layer thickness, and crystalline structures, are commonly used for these advantages. In this work, a series of smectic liquid crystalline molecules, 2,9-didecyl-dinaphtho-thienothiophene (C10-DNTT), 2,7-didecyl-benzothieno-benzothiopene (C10-BTBT), 3,9-didecyl-dinaphtho-thiophene (C10-DNT), and didecyl-sexithiophene (C10-6T), have been used in photonic transistor memory, functioning as both hole-transport channels and electron traps to investigate systematically the reasons and mechanisms behind the memory behavior of smectic liquid crystals. After thermal annealing, C10-BTBT and C10-6T/C10-DNTT are homeotropically aligned from the smectic A and smectic X phases, respectively. The 3D-ordered structure of these smectic-aligned crystals contributed to efficient photowriting and electrical erasing processes. Among them, the device performance of C10-BTBT was particularly significant, with a memory window of 21 V. The memory ratio could reach 1.5 × 106 and maintain a memory ratio of over 3 orders after 10,000 s, contributing to its smectic A structure. Through the research, we confirmed the memory and light/bias-gated behaviors of these smectic liquid crystalline molecules, attributing them to reversible molecular conformation transitions and the inherent structural inhomogeneity inside the polycrystalline channel layer.

Bicyclopentadithiophene-Based Organic Semiconductor for Stable and High-Performance Perovskite Solar Cells Exceeding 22.

Well-performing organic-inorganic halide perovskites are susceptible to poor efficiency and instability due to their various defects at the interphases, grain boundaries (GBs), and surfaces. In this study, an in situ method is utilized for effectively passivating the under-coordinated Pb2+ defects of perovskite with new non-fullerene acceptors (NFAs) (INXBCDT; X = H, Cl, and Br) through their carbonyl and cyano functional groups during the antisolvent dripping process. It reveals that the bicyclopentadithiophene (BCDT) core with highly electron-withdrawing end-capping groups passivates GBs and boosts perovskite grain growth. This effective defect passivation decreases the trap density to increase the carrier recombination lifetime of the perovskite film. As a result, bromo-substituted dicyanomethylene indanone (INBr)-end-capped BCDT (INBrBCDT-b8; 3a)-passivated devices exhibit the highest power conversion efficiency (PCE) of 22.20% (vs those of 18.09% obtained for perovskite films without passivation) upon an optimized film preparation process. Note that devices treated with more soluble 2-ethylhexyl-substituted compounds (1a, 2a, and 3a) exhibit higher PCE than those treated with less soluble octyl-substituted compounds (1b, 2b, and 3b). It is also worth noting that BCDT is a cost-effective six-ring core that is easier to synthesize with a higher yield and therefore much cheaper than those with highly fused-ring cores. In addition, a long-term stability test in a glovebox for 1500 h reveals that the perovskite solar cells (PSCs) based on a perovskite absorber treated with compound 3a maintain ∼90% of their initial PCE. This is the first example of the simplest high-conjugation additive for perovskite film to achieve a PCE greater than 22% of the corresponding lead-based PSCs.

Enhanced Electrical Conductivity and Mechanical Properties of Stretchable Thermoelectric Generators Formed by Doped Semiconducting Polymer/Elastomer Blends.

Recent research efforts have concentrated on the development of flexible and stretchable thermoelectric (TE) materials. However, significant challenges have emerged, including increased resistance and reduced electrical conductivity when subjected to strain. To address these issues, rigid semiconducting polymers and elastic insulating polymers have been incorporated and nanoconfinement effects have been exploited to enhance the charge mobility. Herein, a feasible approach is presented for fabricating stretchable TE materials by using a doped semiconducting polymer blend consisting of either poly(3-hexylthiophene) (P3HT) or poly(3,6-dithiophen-2-yl-2,5-di(2-decyltetradecyl)-pyrrolo[3,4-c]pyrrole-1,4-dione-alt-thienylenevinylene-2,5-yl) (PDVT-10) as the rigid polymer with styrene-ethylene-butylene-styrene (SEBS) as the elastic polymer. In particular, the blend composition is optimized to achieve a continuous network structure with SEBS, thereby improving the stretchability. The optimized polymer films exhibit well-ordered microstructural aggregates, indicative of good miscibility with FeCl3 and enhanced doping efficiency. Notably, a lower activation energy and higher charge-carrier concentration contribute to an improved electrical conductivity under high tensile strain, with a maximum output power of 1.39 nW at a ΔT of 22.4 K. These findings offer valuable insights and serve as guidelines for the development of stretchable p-n junction thermoelectric generators based on doped semiconducting polymer blends with potential applications in wearable electronics and energy harvesting.

Diselenophene-Dithioalkylthiophene Based Quinoidal Small Molecules for Ambipolar Organic Field Effect Transistors.

This work presents a series of novel quinoidal organic semiconductors based on diselenophene-dithioalkylthiophene (DSpDST) conjugated cores with various side-chain lengths (-thiohexyl, -thiodecyl, and -thiotetradecyl, designated DSpDSTQ-6, DSpDSTQ-10, and DSpDSTQ-14, respectively). The purpose of this research is to develop solution-processable organic semiconductors using dicyanomethylene end-capped organic small molecules for organic field effect transistors (OFETs) application. The physical, electrochemical, and electrical properties of these new DSpDSTQs are systematically studied, along with their performance in OFETs and thin film morphologies. Additionally, the molecular structures of DSpDSTQ are determined through density functional theory (DFT) calculations and single-crystal X-ray diffraction analysis. The results reveal the presence of intramolecular S (alkyl)···Se (selenophene) interactions, which result in a planar SR-containing DSpDSTQ core, thereby promoting extended π-orbital interactions and efficient charge transport in the OFETs. Moreover, the influence of thioalkyl side chain length on surface morphologies and microstructures is investigated. Remarkably, the compound with the shortest thioalkyl chain, DSpDSTQ-6, demonstrates ambipolar carrier transport with the highest electron and hole mobilities of 0.334 and 0.463 cm2 V-1 s-1 , respectively. These findings highlight the excellence of ambipolar characteristics of solution-processable OFETs based on DSpDSTQs even under ambient conditions.

Synergistic Interactions in Sequential Process Doping of Polymer/Single-Walled Carbon Nanotube Nanocomposites for Enhanced n-Type Thermoelectric Performance.

This study focuses on the fabrication of nanocomposite thermoelectric devices by blending either a naphthalene-diimide (NDI)-based conjugated polymer (NDI-T1 or NDI-T2), or an isoindigo (IID)-based conjugated polymer (IID-T2), with single-walled carbon nanotubes (SWCNTs). This is followed by sequential process doping method with the small molecule 4-(2,3-dihydro-1,3-dimethyl-1H-benzimidazol-2-yl)-N,N-dimethylbenzenamine (N-DMBI) to provide the nanocomposite with n-type thermoelectric properties. Experiments in which the concentrations of the N-DMBI dopant are varied demonstrate the successful conversion of all three polymer/SWCNT nanocomposites from p-type to n-type behavior. Comprehensive spectroscopic, microstructural, and morphological analyses of the pristine polymers and the various N-DMBI-doped polymer/SWCNT nanocomposites are performed in order to gain insights into the effects of various interactions between the polymers and SWCNTs on the doping outcomes. Among the obtained nanocomposites, the NDI-T1/SWCNT exhibits the highest n-type Seebeck coefficient and power factor of -57.7 µV K-1 and 240.6 µW m-1 K-2 , respectively. However, because the undoped NDI-T2/SWCNT exhibits a slightly higher p-type performance, an integral p-n thermoelectric generator is fabricated using the doped and undoped NDI-T2/SWCNT nanocomposite. This device is shown to provide an output power of 27.2 nW at a temperature difference of 20 K.

Computational Prediction of Coiled-Coil Protein Gelation Dynamics and Structure.

Protein hydrogels represent an important and growing biomaterial for a multitude of applications, including diagnostics and drug delivery. We have previously explored the ability to engineer the thermoresponsive supramolecular assembly of coiled-coil proteins into hydrogels with varying gelation properties, where we have defined important parameters in the coiled-coil hydrogel design. Using Rosetta energy scores and Poisson-Boltzmann electrostatic energies, we iterate a computational design strategy to predict the gelation of coiled-coil proteins while simultaneously exploring five new coiled-coil protein hydrogel sequences. Provided this library, we explore the impact of in silico energies on structure and gelation kinetics, where we also reveal a range of blue autofluorescence that enables hydrogel disassembly and recovery. As a result of this library, we identify the new coiled-coil hydrogel sequence, Q5, capable of gelation within 24 h at 4 °C, a more than 2-fold increase over that of our previous iteration Q2. The fast gelation time of Q5 enables the assessment of structural transition in real time using small-angle X-ray scattering (SAXS) that is correlated to coarse-grained and atomistic molecular dynamics simulations revealing the supramolecular assembling behavior of coiled-coils toward nanofiber assembly and gelation. This work represents the first system of hydrogels with predictable self-assembly, autofluorescent capability, and a molecular model of coiled-coil fiber formation.

Protein-Engineered Fibers For Drug Encapsulation Traceable via 19F Magnetic Resonance.

Theranostic materials research is experiencing rapid growth driven by the interest in integrating both therapeutic and diagnostic modalities. These materials offer the unique capability to not only provide treatment but also track the progression of a disease. However, to create an ideal theranostic biomaterial without compromising drug encapsulation, diagnostic imaging must be optimized for improved sensitivity and spatial localization. Herein, we create a protein-engineered fluorinated coiled-coil fiber, Q2TFL, capable of improved sensitivity to 19F magnetic resonance spectroscopy (MRS) detection. Leveraging residue-specific noncanonical amino acid incorporation of trifluoroleucine (TFL) into the coiled-coil, Q2, which self-assembles into nanofibers, we generate Q2TFL. We demonstrate that fluorination results in a greater increase in thermostability and 19F magnetic resonance detection compared to the nonfluorinated parent, Q2. Q2TFL also exhibits linear ratiometric 19F MRS thermoresponsiveness, allowing it to act as a temperature probe. Furthermore, we explore the ability of Q2TFL to encapsulate the anti-inflammatory small molecule, curcumin (CCM), and its impact on the coiled-coil structure. Q2TFL also provides hyposignal contrast in 1H MRI, echogenic signal with high-frequency ultrasound and sensitive detection by 19F MRS in vivo illustrating fluorination of coiled-coils for supramolecular assembly and their use with 1H MRI, 19F MRS and high frequency ultrasound as multimodal theranostic agents.

Foregroundness-Aware Task Disentanglement and Self-Paced Curriculum Learning for Domain Adaptive Object Detection.

Unsupervised domain adaptive object detection (UDA-OD) is a challenging problem since it needs to locate and recognize objects while maintaining the generalization ability across domains. Most existing UDA-OD methods directly integrate the adaptive modules into the detectors. This integration procedure can significantly sacrifice the detection performances, though it enhances the generalization ability. To solve this problem, we propose an effective framework, named foregroundness-aware task disentanglement and self-paced curriculum adaptation (FA-TDCA), to disentangle the UDA-OD task into four independent subtasks of source detector pretraining, classification adaptation, location adaptation, and target detector training. The disentanglement can transfer the knowledge effectively while maintaining the detection performance of our model. In addition, we propose a new metric, i.e., foregroundness, and use it to evaluate the confidence of the location result. We use both foregroundness and classification confidence to assess the label quality of the proposals. For effective knowledge transfer across domains, we utilize a self-paced curriculum learning paradigm to train adaptors and gradually improve the quality of the pseudolabels associated with the target samples. Experiment results indicate that our method achieves state-of-the-art results on four cross-domain object detection tasks.

Tunable Thermoelectric Performance of the Nanocomposites Formed by Diketopyrrolopyrrole/Isoindigo-Based Donor-Acceptor Random Conjugated Copolymers and Carbon Nanotubes.

This paper presents the development of thermoelectric properties in nanocomposites comprising donor-acceptor random conjugated copolymers and single-walled carbon nanotubes (SWCNTs). The composition of the conjugated polymers, specifically the ratio of diketopyrrolopyrrole (DPP) to isoindigo (IID), is manipulated to design a series of random conjugated copolymers (DPP0, DPP5, DPP10, DPP30, DPP50, DPP90, DPP95, and DPP100). The objective is to improve the dispersion of SWCNTs into smaller bundles, leading to enhanced thermoelectric properties of the polymer/SWCNT nanocomposite. This dispersion strategy promotes an interconnected conducting network, which plays a critical role in optimizing the thermoelectric performance. Accordingly, the effects of morphologies on the thermoelectric properties of the nanocomposites are systematically investigated. The DPP95/SWCNT nanocomposite exhibits the strongest interaction, resulting in the highest power factor (PF) of 711.1 µW m-1 K-2, derived from the high electrical conductivity of 1690 S cm-1 and Seebeck coefficient of 64.8 µV K-1. The prototype flexible thermoelectric generators assembled with a DPP95/SWCNT film achieve a maximum power output of 20.4 µW m-2 at a temperature difference of 29.3 K. These findings highlight the potential of manipulating the composition of random conjugated copolymers and incorporating SWCNTs to efficiently harvest low-grade waste heat in wearable thermoelectric devices.

Mixed Ionic-Electronic Conducting Hydrogels with Carboxylated Carbon Nanotubes for High Performance Wearable Thermoelectric Harvesters.

Mixed ionic-electronic conducting (MIEC) thermoelectric (TE) materials offer higher ionic conductivity and ionic Seebeck coefficient compared to those of purely ionic-conducting TE materials. These characteristics make them suitable for direct use in thermoelectric generators (TEGs) as the charge carriers can be effectively transported from one electrode to the other via the external circuit. In the present study, MIEC hydrogels are fabricated via the chemical cross-linking of polyacrylamide (PAAM) and polydopamine (PDA) to form a double network. In addition, electrically conducting carboxylated carbon nanotubes (CNT-COOH) are dispersed evenly within the hydrogel via sonication and interaction with the PDA. Moreover, the electrical properties of the hydrogel are further improved via the in situ polymerization of polyaniline (PANI). The presence of CNT-COOH facilitates the ionic conductivity and enhances the ionic Seebeck coefficient via ionic-electronic interactions between sodium ions and carboxyl groups on CNT-COOH, which can be observed in X-ray photoelectron spectroscopy results, thereby promoting the charge transport properties. As a result, the optimum device exhibits a remarkable ionic conductivity of 175.3 mS cm-1 and a high ionic Seebeck coefficient of 18.6 mV K-1, giving an ionic power factor (PFi) of 6.06 mW m-1 K-2 with a correspondingly impressive ionic figure of merit (ZTi) of 2.65. These values represent significant achievements within the field of gel-state organic TE materials. Finally, a wearable module is fabricated by embedding the PAAM/PDA/CNT-COOH/PANI hydrogel into a poly(dimethylsiloxane) mold. This configuration yields a high power density of 171.4 mW m-2, thus highlighting the considerable potential for manufacturing TEGs for wearable devices capable of harnessing waste heat.

Balance guided incomplete multi-view spectral clustering.

There is a large volume of incomplete multi-view data in the real-world. How to partition these incomplete multi-view data is an urgent realistic problem since almost all of the conventional multi-view clustering methods are inapplicable to cases with missing views. In this paper, a novel graph learning-based incomplete multi-view clustering (IMVC) method is proposed to address this issue. Different from existing works, our method aims at learning a common consensus graph from all incomplete views and obtaining a clustering indicator matrix in a unified framework. To achieve a stable clustering result, a relaxed spectral clustering model is introduced to obtain a probability consensus representation with all positive elements that reflect the data clustering result. Considering the different contributions of views to the clustering task, a weighted multi-view learning mechanism is introduced to automatically balance the effects of different views in model optimization. In this way, the intrinsic information of the incomplete multi-view data can be fully exploited. The experiments on several incomplete multi-view datasets show that our method outperforms the compared state-of-the-art clustering methods, which demonstrates the effectiveness of our method for IMVC.

Information Recovery-Driven Deep Incomplete Multiview Clustering Network.

Incomplete multiview clustering (IMC) is a hot and emerging topic. It is well known that unavoidable data incompleteness greatly weakens the effective information of multiview data. To date, existing IMC methods usually bypass unavailable views according to prior missing information, which is considered a second-best scheme based on evasion. Other methods that attempt to recover missing information are mostly applicable to specific two-view datasets. To handle these problems, in this article, we propose an information-recovery-driven-deep IMC network, termed as RecFormer. Concretely, a two-stage autoencoder network with self-attention structure is built to synchronously extract high-level semantic representations of multiple views and recover the missing data. Besides, we develop a recurrent graph reconstruction mechanism that cleverly leverages the restored views to promote representation learning and further data reconstruction. Visualization of recovery results are given and sufficient experimental results confirm that our RecFormer has obvious advantages over other top methods.