Skilful nowcasting of extreme precipitation with NowcastNet.

Extreme precipitation is a considerable contributor to meteorological disasters and there is a great need to mitigate its socioeconomic effects through skilful nowcasting that has high resolution, long lead times and local details1-3. Current methods are subject to blur, dissipation, intensity or location errors, with physics-based numerical methods struggling to capture pivotal chaotic dynamics such as convective initiation4 and data-driven learning methods failing to obey intrinsic physical laws such as advective conservation5. We present NowcastNet, a nonlinear nowcasting model for extreme precipitation that unifies physical-evolution schemes and conditional-learning methods into a neural-network framework with end-to-end forecast error optimization. On the basis of radar observations from the USA and China, our model produces physically plausible precipitation nowcasts with sharp multiscale patterns over regions of 2,048 km × 2,048 km and with lead times of up to 3 h. In a systematic evaluation by 62 professional meteorologists from across China, our model ranks first in 71% of cases against the leading methods. NowcastNet provides skilful forecasts at light-to-heavy rain rates, particularly for extreme-precipitation events accompanied by advective or convective processes that were previously considered intractable.

High-Sensitive Uncooled Mid-Wave Infrared Detector Based on TiS3 Nanoribbon.

High-sensitive uncooled mid-wave infrared (MWIR) photodetection with fast speed is highly desired for biomedical imaging, optical communication, and night vision technology. Low-dimensional materials with low dark current and broadband photoresponse hold great promise for use in MWIR detection. Here, this study reports a high-performance MWIR photodetector based on a titanium trisulfide (TiS3) nanoribbon. This device demonstrates an ultra-broadband photoresponse ranging from the visible spectrum to the MWIR spectrum (405-4275 nm). In the MWIR spectral range, the photodetector achieves competitive high photoresponsivity (R) of 21.1 A W-1, and an impressive specific detectivity (D*) of 5.9 × 1010 cmHz1/2 W-1 in ambient air. Remarkably, the photoresponse speed in the MWIR with τr = 1.3 ms and τd = 1.5 ms is realized which is much faster than the thermal time constant of 15 ms. These findings pave the way for highly sensitive, room-temperature MWIR photodetectors with exceptionally fast response speed.

Reversible engineering of topological insulator surface state conductivity through optical excitation.

Despite the broadband response, limited optical absorption at a particular wavelength hinders the development of optoelectronics based on Dirac fermions. Heterostructures of graphene and various semiconductors have been explored for this purpose, while non-ideal interfaces often limit the performance. The topological insulator (TI) is a natural hybrid system, with the surface states hosting high-mobility Dirac fermions and the small-bandgap semiconducting bulk state strongly absorbing light. In this work, we show a large photocurrent response from a field effect transistor device based on intrinsic TI Sn-Bi1.1Sb0.9Te2S (Sn-BSTS). The photocurrent response is non-volatile and sensitively depends on the initial Fermi energy of the surface state, and it can be erased by controlling the gate voltage. Our observations can be explained with a remote photo-doping mechanism, in which the light excites the defects in the bulk and frees the localized carriers to the surface state. This photodoping modulates the surface state conductivity without compromising the mobility, and it also significantly modify the quantum Hall effect of the surface state. Our work thus illustrates a route to reversibly manipulate the surface states through optical excitation, shedding light into utilizing topological surface states for quantum optoelectronics.

Edge-Epitaxial Growth of InSe Nanowires toward High-Performance Photodetectors.

Semiconducting nanowires offer many opportunities for electronic and optoelectronic device applications due to their unique geometries and physical properties. However, it is challenging to synthesize semiconducting nanowires directly on a SiO2 /Si substrate due to lattice mismatch. Here, a catalysis-free approach is developed to achieve direct synthesis of long and straight InSe nanowires on SiO2 /Si substrates through edge-homoepitaxial growth. Parallel InSe nanowires are achieved further on SiO2 /Si substrates through controlling growth conditions. The underlying growth mechanism is attributed to a selenium self-driven vapor-liquid-solid process, which is distinct from the conventional metal-catalytic vapor-liquid-solid method widely used for growing Si and III-V nanowires. Furthermore, it is demonstrated that the as-grown InSe nanowire-based visible light photodetector simultaneously possesses an extraordinary photoresponsivity of 271 A W-1 , ultrahigh detectivity of 1.57 × 1014 Jones, and a fast response speed of microsecond scale. The excellent performance of the photodetector indicates that as-grown InSe nanowires are promising in future optoelectronic applications. More importantly, the proposed edge-homoepitaxial approach may open up a novel avenue for direct synthesis of semiconducting nanowire arrays on SiO2 /Si substrates.

Room-Temperature Single-Photon Detector Based on Single Nanowire.

Single-photon detectors that can resolve photon number play a key role in advanced quantum information technologies. Despite significant progress in improving conventional photon-counting detectors and developing novel device concepts, single-photon detectors that are capable of distinguishing incident photon number at room temperature are still very limited. We demonstrate a room-temperature photon-number-resolving detector by integrating a field-effect transistor configuration with core/shell-like nanowires. The shell serves as a photosensitive gate, shielding negative back-gated voltage, and leads to a persistent photocurrent. At room temperature, our detector is demonstrated to identify 1, 2, and 3 photon-number states with a confidence of >82%. The detection efficiency is determined to be 23%, and the dark count rate is 1.87 × 10-3 Hz. Importantly, benefiting from the anisotropic nature of 1D nanowires, the detector shows an intrinsic photon-polarization selection, which distinguishes itself from existing intensity single-photon detectors. The unique performance for the single-photon detectors based on single nanowire demonstrates the great potential for future single-photon detection applications.

Air-Stable In-Plane Anisotropic GeSe2 for Highly Polarization-Sensitive Photodetection in Short Wave Region.

In-plane anisotropic layered materials such as black phosphorus (BP) have emerged as an important class of two-dimensional (2D) materials that bring a new dimension to the properties of 2D materials, hence providing a wide range of opportunities for developing conceptually new device applications. However, all of recently reported anisotropic 2D materials are relatively narrow-bandgap semiconductors (<2 eV), and there has been no report about this type of materials with wide bandgap, restricting the relevant applications such as polarization-sensitive photodetection in short wave region. Here we present a new member of the family, germanium diselenide (GeSe2) with a wide bandgap of 2.74 eV, and systematically investigate the in-plane anisotropic structural, vibrational, electrical, and optical properties from theory to experiment. Photodetectors based on GeSe2 exhibit a highly polarization-sensitive photoresponse in short wave region due to the optical absorption anisotropy induced by in-plane anisotropy in crystal structure. Furthermore, exfoliated GeSe2 flakes show an outstanding stability in ambient air which originates from the high activation energy of oxygen chemisorption on GeSe2 (2.12 eV) through our theoretical calculations, about three times higher than that of BP (0.71 eV). Such unique in-plane anisotropy and wide bandgap, together with high air stability, make GeSe2 a promising candidate for future 2D optoelectronic applications in short wave region.

A Colloidal-Quantum-Dot Infrared Photodiode with High Photoconductive Gain.

The photodiode is a prevailing architecture for photodetection with the merits of fast response and low dark current. However, an ideal photodiode is also desired for both high responsivity and high external quantum efficiency (EQE), which may facilitate more applications. Here the photoconducting effect in a photodiode is discussed and an Au-PbS colloidal quantum dot (CQD)-indium tin oxide Schottky junction photodiode is fabricated. The long carrier lifetime and improved carrier mobility in tetrabutylammonium iodide-modified PbS CQDs cooperating with the proper band structure and an ultrashort channel in the diode enable the photodiode with high photoconductive gain, realizing an EQE of ≈400% and a responsivity (R) of 5.15 A W-1 while simultaneously achieving a response time of 110 µs and a specific detectivity of 1.96 × 1010 Jones under 1550 nm illumination. In addition, this CQD-based photodiode is stable, low cost, and compatible with complementary metal oxide semiconductor technology. All of these promise this device great potential in applications.

WSe2/Au vertical Schottky junction photodetector with low dark current and fast photoresponse.

Atomically thin two-dimensional materials including graphene, transition metal dichalcogenides, black phosphorus and so forth have been considered as promising channel medias for electronic and optoelectronic devices in the past few years. However, the poor photoresponse time and the large dark current are the two major issues which greatly block their applications. Here, we report a vertical Au-WSe2-ITO (indium tin oxide) Schottky junction photodetector with a broadband photoresponse from 550-950 nm and a stable photovoltaic responsivity of ∼0.1 A W-1. The fast photoresponse of ∼50 µs and low dark current of ∼1 pA are achieved at the vertical Au-WSe2-ITO photodetector. These results indicate that metal contact to a 2D material-based vertical Schottky junction can achieve an excellent photoelectric response.

Broadband Photovoltaic Detectors Based on an Atomically Thin Heterostructure.

van der Waals junctions of two-dimensional materials with an atomically sharp interface open up unprecedented opportunities to design and study functional heterostructures. Semiconducting transition metal dichalcogenides have shown tremendous potential for future applications due to their unique electronic properties and strong light-matter interaction. However, many important optoelectronic applications, such as broadband photodetection, are severely hindered by their limited spectral range and reduced light absorption. Here, we present a p-g-n heterostructure formed by sandwiching graphene with a gapless band structure and wide absorption spectrum in an atomically thin p-n junction to overcome these major limitations. We have successfully demonstrated a MoS2-graphene-WSe2 heterostructure for broadband photodetection in the visible to short-wavelength infrared range at room temperature that exhibits competitive device performance, including a specific detectivity of up to 10(11) Jones in the near-infrared region. Our results pave the way toward the implementation of atomically thin heterostructures for broadband and sensitive optoelectronic applications.

Characterization of graphene layers using super resolution polarization parameter indirect microscopic imaging.

We report on the development of super-resolution polarization (parameter) indirect microscopic imaging (PIMI) and its application to visualizing and quantifying graphene layer's morphological and structural features. The PIMI system was built by modifying a conventional optical microscopy such that the variation of the polarization status of incident light can be precisely controlled, imaging was subsequently acquired by analyzing the dependence of the optical intensity transmitted through (or reflected from) the samples on the incident light polarization status. Measurements on the thickness as well as other structural features of graphene samples which had been prepared by different methods were performed. The results which were highly consistent to those measured by Raman spectroscopy indicate that the PIMI system is capable of characterizing graphene's dimensional and structural features with super resolution.

One Fits Many: Class Confusion Loss for Versatile Domain Adaptation.

In the open world, various label sets and domain configurations give rise to a variety of Domain Adaptation (DA) setups, including closed-set, partial-set, open-set, and universal DA, as well as multi-source and multi-target DA. It is notable that existing DA methods are generally designed only for a specific setup, and may under-perform in setups they are not tailored to. This paper shifts the common paradigm of DA to Versatile Domain Adaptation (VDA), where one method can handle several different DA setups without any modification. Towards this goal, we first delve into a general inductive bias: class confusion, and then uncover that reducing such pairwise class confusion leads to significant transfer gains. With this insight, we propose one general class confusion loss (CC-Loss) to learn many setups. We estimate class confusion based only on classifier predictions and minimize the class confusion to enable accurate target predictions. Further, we improve the loss by enforcing the consistency of confusion matrices under different data augmentations to encourage its invariance to distribution perturbations. Experiments on 2D vision and 3D vision benchmarks show that the CC-Loss performs competitively in different mainstream DA setups. Code is available at https://github.com/thuml/Transfer-Learning-Library.

From Big to Small: Adaptive Learning to Partial-Set Domains.

Domain adaptation targets at knowledge acquisition and dissemination from a labeled source domain to an unlabeled target domain under distribution shift. Still, the common requirement of identical class space shared across domains hinders applications of domain adaptation to partial-set domains. Recent advances show that deep pre-trained models of large scale endow rich knowledge to tackle diverse downstream tasks of small scale. Thus, there is a strong incentive to adapt models from large-scale domains to small-scale domains. This paper introduces Partial Domain Adaptation (PDA), a learning paradigm that relaxes the identical class space assumption to that the source class space subsumes the target class space. First, we present a theoretical analysis of partial domain adaptation, which uncovers the importance of estimating the transferable probability of each class and each instance across domains. Then, we propose Selective Adversarial Network (SAN and SAN++) with a bi-level selection strategy and an adversarial adaptation mechanism. The bi-level selection strategy up-weighs each class and each instance simultaneously for source supervised training, target self-training, and source-target adversarial adaptation through the transferable probability estimated alternately by the model. Experiments on standard partial-set datasets and more challenging tasks with superclasses show that SAN++ outperforms several domain adaptation methods.

ModeRNN: Harnessing Spatiotemporal Mode Collapse in Unsupervised Predictive Learning.

Learning predictive models for unlabeled spatiotemporal data is challenging in part because visual dynamics can be highly entangled, especially in real scenes. In this paper, we refer to the multi-modal output distribution of predictive learning as spatiotemporal modes. We find an experimental phenomenon named spatiotemporal mode collapse (STMC) on most existing video prediction models, that is, features collapse into invalid representation subspaces due to the ambiguous understanding of mixed physical processes. We propose to quantify STMC and explore its solution for the first time in the context of unsupervised predictive learning. To this end, we present ModeRNN, a decoupling-aggregation framework that has a strong inductive bias of discovering the compositional structures of spatiotemporal modes between recurrent states. We first leverage a set of dynamic slots with independent parameters to extract individual building components of spatiotemporal modes. We then perform a weighted fusion of slot features to adaptively aggregate them into a unified hidden representation for recurrent updates. Through a series of experiments, we show high correlation between STMC and the fuzzy prediction results of future video frames. Besides, ModeRNN is shown to better mitigate STMC and achieve the state of the art on five video prediction datasets.

PredRNN: A Recurrent Neural Network for Spatiotemporal Predictive Learning.

The predictive learning of spatiotemporal sequences aims to generate future images by learning from the historical context, where the visual dynamics are believed to have modular structures that can be learned with compositional subsystems. This paper models these structures by presenting PredRNN, a new recurrent network, in which a pair of memory cells are explicitly decoupled, operate in nearly independent transition manners, and finally form unified representations of the complex environment. Concretely, besides the original memory cell of LSTM, this network is featured by a zigzag memory flow that propagates in both bottom-up and top-down directions across all layers, enabling the learned visual dynamics at different levels of RNNs to communicate. It also leverages a memory decoupling loss to keep the memory cells from learning redundant features. We further propose a new curriculum learning strategy to force PredRNN to learn long-term dynamics from context frames, which can be generalized to most sequence-to-sequence models. We provide detailed ablation studies to verify the effectiveness of each component. Our approach is shown to obtain highly competitive results on five datasets for both action-free and action-conditioned predictive learning scenarios.

Omni-Training: Bridging Pre-Training and Meta-Training for Few-Shot Learning.

Few-shot learning aims to fast adapt a deep model from a few examples. While pre-training and meta-training can create deep models powerful for few-shot generalization, we find that pre-training and meta-training focus respectively on cross-domain transferability and cross-task transferability, which restricts their data efficiency in the entangled settings of domain shift and task shift. We thus propose the Omni-Training framework to seamlessly bridge pre-training and meta-training for data-efficient few-shot learning. Our first contribution is a tri-flow Omni-Net architecture. Besides the joint representation flow, Omni-Net introduces two parallel flows for pre-training and meta-training, responsible for improving domain transferability and task transferability respectively. Omni-Net further coordinates the parallel flows by routing their representations via the joint-flow, enabling knowledge transfer across flows. Our second contribution is the Omni-Loss, which introduces a self-distillation strategy separately on the pre-training and meta-training objectives for boosting knowledge transfer throughout different training stages. Omni-Training is a general framework to accommodate many existing algorithms. Evaluations justify that our single framework consistently and clearly outperforms the individual state-of-the-art methods on both cross-task and cross-domain settings in a variety of classification, regression and reinforcement learning problems.

Enhanced Piezoelectricity and Thermal Stability of Electrostrain Performance in BiFeO3-Based Lead-Free Ceramics.

BiFeO3-based ceramics possess an advantage over large spontaneous polarization and high Curie temperature, and are thus widely explored in the field of high-temperature lead-free piezoelectrics and actuators. However, poor piezoelectricity/resistivity and thermal stability of electrostrain make them less competitive. To address this problem, (1 - x) (0.65BiFeO3-0.35BaTiO3)-xLa0.5Na0.5TiO3 (BF-BT-xLNT) systems are designed in this work. It is found that piezoelectricity is significantly improved with LNT addition, which is contributed by the phase boundary effect of rhombohedral and pseudocubic phase coexistence. The small-signal and large-signal piezoelectric coefficient (d33 and d33*) peaks at x = 0.02 with 97 pC/N and 303 pm/V, respectively. The relaxor property and resistivity are enhanced as well. This is verified by Rietveld refinement, dielectric/impedance spectroscopy and piezoelectric force microscopy (PFM) technique. Interestingly, a good thermal stability of electrostrain is obtained at x = 0.04 composition with fluctuation η = 31% (Smax'-SRTSRT×100%), in a wide temperature range of 25-180 °C, which is considered as a compromise of negative temperature dependent electrostrain for relaxors and the positive one for ferroelectric matrix. This work provides an implication for designing high-temperature piezoelectrics and stable electrostrain materials.

Ultrahigh-Sensitivity and Fast-Speed Solar-Blind Ultraviolet Photodetector Based on a Broken-Gap van der Waals Heterodiode.

Broad-bandgap semiconductor-based solar-blind ultraviolet (SBUV) photodetectors have attracted considerable research interest because of their broad applications in missile plume tracking, flame detectors, environmental monitoring, and optical communications due to their solar-blind nature and high sensitivity with low background radiation. Owing to its high light absorption coefficient, abundance, and wide tunable bandgap of 2-2.6 eV, tin disulfide (SnS2) has emerged as one of the most promising compounds for application in UV-visible optoelectronic devices. However, SnS2 UV detectors have some undesirable properties such as slow response speed, high current noise level, and low specific detectivity. This study reports a metal mirror-enhanced Ta0.01W0.99Se2/SnS2 (TWS) van der Waals heterodiode-based SBUV photodetector with an ultrahigh photoresponsivity (R) of ∼1.85 × 104 AW-1 and a fast speed with rising time (τr) of 3.3 µs and decay time (τd) of 3.4 µs. Notably, the TWS heterodiode device exhibits a significantly low noise equivalent power of ∼1.02 × 10-18 W Hz-1/2 and a high specific detectivity of ∼3.65 × 1014 cm Hz1/2 W-1. This study provides an alternative method for designing fast-speed SBUV photodetectors with enormous potential in applications.

A mixed-dimensional quasi-1D BiSeI nanowire-2D GaSe nanosheet p-n heterojunction for fast response optoelectronic devices.

Due to the unique combination configuration and the formation of a built-in electric field, mixed-dimensional heterojunctions present fruitful possibilities for improving the optoelectronic performances of low-dimensional optoelectronic devices. However, the response times of most photodetectors built from mixed-dimensional heterojunctions are within the millisecond range, limiting their applications in fast response optoelectronic devices. Herein, a mixed-dimensional BiSeI/GaSe van der Waals heterostructure is designed, which exhibits visible light detection ability and competitive photoresponsivity of 750 A W-1 and specific detectivity of 2.25 × 1012 Jones under 520 nm laser excitation. Excitingly, the device displays a very fast response time, e.g., the rise time and decay time under 520 nm laser excitation are 65 µs and 190 µs, respectively. Our findings provide a prospective approach to mixed-dimensional heterojunction photodetection devices with rapid switching capabilities.

A high-performance long-wave infrared photodetector based on a WSe2/PdSe2 broken-gap heterodiode.

Layered narrow bandgap quasi-two-dimensional (2D) transition metal dichalcogenides (TMDs) demonstrated excellent performance in long-wave infrared (LWIR) detection. However, the low light on/off ratio and specific detectivity (D*) due to the high dark current of the device fabricated using a single narrow bandgap material hindered its wide application. Herein, we report a type-III broken-gap band-alignment WSe2/PdSe2 van der Waals (vdW) heterostructure. The heterodiode device has a prominently low dark current and exhibits a high photoresponsivity (R) of 55.3 A W-1 and a high light on/off ratio >105 in the visible range. Notably, the WSe2/PdSe2 heterodiode shows an excellent uncooled LWIR response, with an R of ∼0.3 A W-1, a low noise equivalence power (NEP) of 4.5 × 10-11 W Hz-1/2, and a high D* of 1.8 × 108 cm Hz1/2 W-1. This work provides a new approach for designing high-performance room-temperature operational LWIR photodetectors.

Polarity Modulation Induced High Electrostrain Performance with Near-Zero Hysteresis in a (Sr0.7Bi0.2□0.1)TiO3-Based System.

High-precision piezo actuators necessitate dielectrics with high electrostrain performance with low hysteresis. Polarity-modulated (Sr0.7Bi0.2□0.1)TiO3-based ceramics exhibit extraordinarily discrete multiphase coexistence regions: (i) the relaxor phase coexistence (RPC) region with local weakly polar tetragonal (T) and pseudocubic (Pc) short-range polar nanodomains and (ii) the ferroelectric phase coexistence (FPC) region with T long-range domains and Pc nanodomains. The RPC composition features a specially high and pure electrostrain performance with near-zero hysteresis (S ∼ 0.185%, Q33 ∼ 0.038 m4·C-2), which is double those of conventional Pb(Mg1/3Nb2/3)O3-based ceramics. Particular interest is paid to the RPC and FPC with multiscale characterization to unravel local structure-performance relationships. Guided by piezoelectric force microscopy, scanning transmission electron microscopy, and phase-field simulations, the RPC composition with multiphase low-angle weakly polar nanodomains shows local structural heterogeneity and contributes to a flat local free energy profile and thus to nanodomain switching and superior electrostrain performance, in contrast to the FPC composition with a macroscopic domain that shows stark hysteresis. This work provides a paradigm to design high-precision actuator materials with large electrostrain and ultralow hysteresis, extending our knowledge of multiphase coexistence species in ferroelectrics.