arXiv subjects

Ning Li

Publications and source records attributed to Ning Li.

At least 127 records · Page 7Linked to original sources

arXiv2023

Matching the photocurrent of perovskite/organic tandem solar modules by varying the cell width

Photocurrent matching in conventional monolithic tandem solar cells is achieved by choosing semiconductors with complementary absorption spectra and by carefully adjusting the optical properties of the complete top and bottom stacks. However, for thin film photovoltaic technologies at the module level, another design variable significantly alleviates the task of photocurrent matching, namely the cell width, whose modification can be readily realized by the adjustment of the module layout. Herein we demonstrate this concept at the experimental level for the first time for a 2T-mechanically stacked perovskite (FAPbBr3)/organic (PM6:Y6:PCBM) tandem mini-module, an unprecedented approach for these emergent photovoltaic technologies fabricated in an independent manner. An excellent Isc matching is achieved by tuning the cell widths of the perovskite and organic modules to 7.22 mm (PCEPVKT-mod= 6.69%) and 3.19 mm (PCEOPV-mod= 12.46%), respectively, leading to a champion efficiency of 14.94% for the tandem module interconnected in series with an aperture area of 20.25 cm2. Rather than demonstrating high efficiencies at the level of small lab cells, our successful experimental proof-of-concept at the module level proves to be particularly useful to couple devices with non-complementary semiconductors, either in series or in parallel electrical connection, hence overcoming the limitations imposed by the monolithic structure.

cond-mat.mtrl-sci↗

arXiv2023

Radiative transition decay width of $ψ_2(3823)\rightarrowγχ_{c1}$ from lattice QCD

We present an exploratory $N_f=2$ lattice QCD study of $ψ_2(3823)\to γχ_{c1}$ at a pion mass $m_π\approx 350$~MeV. The related two-point and three-piont functions are calculated using the distillation method. The electromagnetic multipole form factor $\hat{V}(0)=2.083(11)$ for $J/ψ\toγη_c$ is consistent with previous lattice results, the form factors $\hat{E}_1(0)$, $\hat{M}_2(0)$ and $\hat{E}_3(0)$ for $Γ(χ_{c2}\toγJ/ψ)$ have the same hierarchy as that derived from experiments and the predicted decay width $Γ(χ_{c2}\toγJ/ψ)=368(5)~\text{keV}$ is in excellent agreement with the PDG value $374(10)~\text{keV}$ and previous lattice QCD results in the quenched approximation. The same strategy is applied to the study of the process $ψ_2(3823)\to γχ_{c1}$ and the partial decay width is predicted to be $337(27)~\text{keV}$. According to the BESIII constraints on the $ψ_2(3823)$ decay channels and some phenomenological results, we estimate the total width $Γ(ψ_2(3823))=520(100)~\text{keV}$.

hep-lat↗

arXiv2023

Machine learning for ultra high throughput screening of organic solar cells: Solving the needle in the hay stack problem

Over the last two decades the organic solar cell community has synthesised tens of thousands of novel polymers and small molecules in the search for an optimum light harvesting material. These materials were often crudely evaluated simply by measuring the current voltage curves in the light to obtain power conversion efficiencies (PCEs). Materials with low PCEs were quickly disregarded in the search for higher efficiencies. More complex measurements such as frequency/time domain characterisation that could explain why the material performed as it did were often not performed as they were too time consuming/complex. This limited feedback forced the field to advance using a more or less random walk of material development and has significantly slowed progress. Herein, we present a simple technique based on machine learning that can quickly and accurately extract recombination time constants and charge carrier mobilities as a function of light intensity simply from light/dark JV curves alone. This technique reduces the time to fully analyse a working cell from weeks to seconds and opens up the possibility of not only fully characterising new devices as they are fabricated, but also data mining historical data sets for promising materials the community has over looked.

physics.app-ph↗

arXiv2023

UMIFormer: Mining the Correlations between Similar Tokens for Multi-View 3D Reconstruction

In recent years, many video tasks have achieved breakthroughs by utilizing the vision transformer and establishing spatial-temporal decoupling for feature extraction. Although multi-view 3D reconstruction also faces multiple images as input, it cannot immediately inherit their success due to completely ambiguous associations between unstructured views. There is not usable prior relationship, which is similar to the temporally-coherence property in a video. To solve this problem, we propose a novel transformer network for Unstructured Multiple Images (UMIFormer). It exploits transformer blocks for decoupled intra-view encoding and designed blocks for token rectification that mine the correlation between similar tokens from different views to achieve decoupled inter-view encoding. Afterward, all tokens acquired from various branches are compressed into a fixed-size compact representation while preserving rich information for reconstruction by leveraging the similarities between tokens. We empirically demonstrate on ShapeNet and confirm that our decoupled learning method is adaptable for unstructured multiple images. Meanwhile, the experiments also verify our model outperforms existing SOTA methods by a large margin. Code will be available at https://github.com/GaryZhu1996/UMIFormer.

cs.CV↗

arXiv2023

Bogoliubov Corner Excitations in Conventional $s$-Wave Superfluids

Higher-order topological superconductors and superfluids have triggered a great deal of interest in recent years. While Majorana corner or hinge states have been studied intensively, whether superconductors and superfluids, being topological or trivial, host higher-order topological Bogoliubov excitations remains elusive. In this work, we propose that Bogoliubov corner excitations can be driven from a trivial conventional $s$-wave superfluid through mirror-symmetric local potentials. The topological Bogoliubov excited modes originate from the nontrivial Bogoliubov excitation bands. These modes are protected by mirror symmetry and robust against mirror-symmetric perturbations as long as the Bogoliubov energy gap remains open. Our work provides new insight into higher-order topological excitation states in superfluids and superconductors.

cond-mat.quant-gas↗

arXiv2023

AnomMAN: Detect Anomaly on Multi-view Attributed Networks

Anomaly detection on attributed networks is widely used in online shopping, financial transactions, communication networks, and so on. However, most existing works trying to detect anomalies on attributed networks only consider a single kind of interaction, so they cannot deal with various kinds of interactions on multi-view attributed networks. It remains a challenging task to jointly consider all different kinds of interactions and detect anomalous instances on multi-view attributed networks. In this paper, we propose a graph convolution-based framework, named AnomMAN, to detect Anomaly on Multi-view Attributed Networks. To jointly consider attributes and all kinds of interactions on multi-view attributed networks, we use the attention mechanism to define the importance of all views in networks. Since the low-pass characteristic of graph convolution operation filters out most high-frequency signals (aonmaly signals), it cannot be directly applied to anomaly detection tasks. AnomMAN introduces the graph auto-encoder module to turn the disadvantage of low-pass features into an advantage. According to experiments on real-world datasets, AnomMAN outperforms the state-of-the-art models and two variants of our proposed model.

cs.SI↗

arXiv2023

Light meson emissions of the selected charmonium-like states within compact tetraquark configurations

We adopt the quark pair creation model to investigate the light meson emissions of several charmonium-like states. The quark pair creation model is applied to the four-body systems, and we calculate the pion/kaon emissions of the $X(4700)$, $Z_c(4430)$, $Y(4230)$, $Y(4360)$, $Y(4390)$ and $Y(4660)$ within the compact tetraquark assumptions. It is found that the pion/kaon decay widths for the $X(4700)$ and $Z_c(4430)$ are relatively small, while the partial decay widths for the resonances $Y(4230)$, $Y(4360)$, $Y(4390)$ and $Y(4660)$ are significant. We expect that our exploration of these decay behaviors can provide useful information for future experimental searches and theoretical interpretations.

hep-ph↗

arXiv2023

Corner Detection Based on Multi-directional Gabor Filters with Multi-scales

Gabor wavelet is an essential tool for image analysis and computer vision tasks. Local structure tensors with multiple scales are widely used in local feature extraction. Our research indicates that the current corner detection method based on Gabor wavelets can not effectively apply to complex scenes. In this work, the capability of the Gabor function to discriminate the intensity changes of step edges, L-shaped corners, Y-shaped or T-shaped corners, X-shaped corners, and star-shaped corners are investigated. The properties of Gabor wavelets to suppress affine image transformation are investigated and obtained. Many properties for edges and corners were discovered, which prompted us to propose a new corner extraction method. To fully use the structural information from the tuned Gabor filters, a novel multi-directional structure tensor is constructed for corner detection, and a multi-scale corner measurement function is proposed to remove false candidate corners. Furthermore, we compare the proposed method with twelve current state-of-the-art methods, which exhibit optimal performance and practical application to 3D reconstruction with good application potential.

cs.CV↗

arXiv2023

Autonomous Exploration and Mapping for Mobile Robots via Cumulative Curriculum Reinforcement Learning

Deep reinforcement learning (DRL) has been widely applied in autonomous exploration and mapping tasks, but often struggles with the challenges of sampling efficiency, poor adaptability to unknown map sizes, and slow simulation speed. To speed up convergence, we combine curriculum learning (CL) with DRL, and first propose a Cumulative Curriculum Reinforcement Learning (CCRL) training framework to alleviate the issue of catastrophic forgetting faced by general CL. Besides, we present a novel state representation, which considers a local egocentric map and a global exploration map resized to the fixed dimension, so as to flexibly adapt to environments with various sizes and shapes. Additionally, for facilitating the fast training of DRL models, we develop a lightweight grid-based simulator, which can substantially accelerate simulation compared to popular robot simulation platforms such as Gazebo. Based on the customized simulator, comprehensive experiments have been conducted, and the results show that the CCRL framework not only mitigates the catastrophic forgetting problem, but also improves the sample efficiency and generalization of DRL models, compared to general CL as well as without a curriculum. Our code is available at https://github.com/BeamanLi/CCRL_Exploration.

cs.RO↗

arXiv2023

An Adaptive Plug-and-Play Network for Few-Shot Learning

Few-shot learning (FSL) requires a model to classify new samples after learning from only a few samples. While remarkable results are achieved in existing methods, the performance of embedding and metrics determines the upper limit of classification accuracy in FSL. The bottleneck is that deep networks and complex metrics tend to induce overfitting in FSL, making it difficult to further improve the performance. Towards this, we propose plug-and-play model-adaptive resizer (MAR) and adaptive similarity metric (ASM) without any other losses. MAR retains high-resolution details to alleviate the overfitting problem caused by data scarcity, and ASM decouples the relationship between different metrics and then fuses them into an advanced one. Extensive experiments show that the proposed method could boost existing methods on two standard dataset and a fine-grained datasets, and achieve state-of-the-art results on mini-ImageNet and tiered-ImageNet.

cs.CV↗

arXiv2023

Hardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based accelerators

Analog in-memory computing (AIMC) -- a promising approach for energy-efficient acceleration of deep learning workloads -- computes matrix-vector multiplications (MVMs) but only approximately, due to nonidealities that often are non-deterministic or nonlinear. This can adversely impact the achievable deep neural network (DNN) inference accuracy as compared to a conventional floating point (FP) implementation. While retraining has previously been suggested to improve robustness, prior work has explored only a few DNN topologies, using disparate and overly simplified AIMC hardware models. Here, we use hardware-aware (HWA) training to systematically examine the accuracy of AIMC for multiple common artificial intelligence (AI) workloads across multiple DNN topologies, and investigate sensitivity and robustness to a broad set of nonidealities. By introducing a new and highly realistic AIMC crossbar-model, we improve significantly on earlier retraining approaches. We show that many large-scale DNNs of various topologies, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, can in fact be successfully retrained to show iso-accuracy on AIMC. Our results further suggest that AIMC nonidealities that add noise to the inputs or outputs, not the weights, have the largest impact on DNN accuracy, and that RNNs are particularly robust to all nonidealities.

cs.LG↗

arXiv2023

Radiative Decay Width of $J/ψ\to γη_{(2)}$ from $N_f=2$ Lattice QCD

The large radiative production rate for pseudoscalar mesons in the $J/ψ$ radiative decay remains elusive. We present the first lattice QCD calculation of partial decay width of $J/ψ$ radiatively decaying into $η_{(2)}$, the $\mathrm{SU(2)}$ flavor singlet pseudoscalar meson, which confirms QCD $\mathrm{U_A(1)}$ anomaly enhancement to the coupling of gluons with flavor singlet pseudoscalar mesons. The lattice simulation is carried out using $N_f=2$ lattice QCD gauge configurations at the pion mass $m_π \approx 350$ MeV. In particular, the distillation method has been utilized to calculate light quark loops. The results are reported here with the mass $m_{η_{(2)}}= 718(8)$ MeV and the decay width $Γ(J/ψ\toγη_{(2)})=0.385(45)$ keV. By assuming the dominance of $\mathrm{U_A(1)}$ anomaly and flavor singlet-octet mixing angle $θ=-24.5^\circ$, the production rates for the physical $η$ and $η'$ in $J/ψ$ radiative decay are predicted to be $1.15(14)\times 10^{-3}$ and $4.49(53)\times 10^{-3}$, respectively, which agree well with the experimental measurement data. Our study manifests the potential of lattice QCD studies on the light hadron production in $J/ψ$ radiative decays.

hep-lat↗

arXiv2022

Multimodal Remote Sensing Image Registration Based on Adaptive Multi-scale PIIFD

In recent years, due to the wide application of multi-sensor vision systems, multimodal image acquisition technology has continued to develop, and the registration problem based on multimodal images has gradually emerged. Most of the existing multimodal image registration methods are only suitable for two modalities, and cannot uniformly register multiple modal image data. Therefore, this paper proposes a multimodal remote sensing image registration method based on adaptive multi-scale PIIFD(AM-PIIFD). This method extracts KAZE features, which can effectively retain edge feature information while filtering noise. Then adaptive multi-scale PIIFD is calculated for matching. Finally, the mismatch is removed through the consistency of the feature main direction, and the image alignment transformation is realized. The qualitative and quantitative comparisons with other three advanced methods shows that our method can achieve excellent performance in multimodal remote sensing image registration.

eess.IV↗

arXiv2022

GARNet: Global-Aware Multi-View 3D Reconstruction Network and the Cost-Performance Tradeoff

Deep learning technology has made great progress in multi-view 3D reconstruction tasks. At present, most mainstream solutions establish the mapping between views and shape of an object by assembling the networks of 2D encoder and 3D decoder as the basic structure while they adopt different approaches to obtain aggregation of features from several views. Among them, the methods using attention-based fusion perform better and more stable than the others, however, they still have an obvious shortcoming -- the strong independence of each view during predicting the weights for merging leads to a lack of adaption of the global state. In this paper, we propose a global-aware attention-based fusion approach that builds the correlation between each branch and the global to provide a comprehensive foundation for weights inference. In order to enhance the ability of the network, we introduce a novel loss function to supervise the shape overall and propose a dynamic two-stage training strategy that can effectively adapt to all reconstructors with attention-based fusion. Experiments on ShapeNet verify that our method outperforms existing SOTA methods while the amount of parameters is far less than the same type of algorithm, Pix2Vox++. Furthermore, we propose a view-reduction method based on maximizing diversity and discuss the cost-performance tradeoff of our model to achieve a better performance when facing heavy input amount and limited computational cost.

cs.CV↗

arXiv2022

Power-law density of states in organic solar cells revealed by the open-circuit voltage dependence of the ideality factor

The density of states (DOS) is fundamentally important for understanding physical processes in organic disordered semiconductors, yet hard to determine experimentally. We evaluated the DOS by considering recombination via tail states and using the temperature and open-circuit voltage ($V_\mathrm{oc}$) dependence of the ideality factor in organic solar cells. By performing Suns-$V_\mathrm{oc}$ measurements, we find that gaussian and exponential distributions describe the DOS only at a given quasi-Fermi level splitting. The DOS width increases linearly with the DOS depth, revealing the power-law DOS in these materials.

physics.app-ph↗

arXiv2022

Lexical semantics enhanced neural word embeddings

Current breakthroughs in natural language processing have benefited dramatically from neural language models, through which distributional semantics can leverage neural data representations to facilitate downstream applications. Since neural embeddings use context prediction on word co-occurrences to yield dense vectors, they are inevitably prone to capture more semantic association than semantic similarity. To improve vector space models in deriving semantic similarity, we post-process neural word embeddings through deep metric learning, through which we can inject lexical-semantic relations, including syn/antonymy and hypo/hypernymy, into a distributional space. We introduce hierarchy-fitting, a novel semantic specialization approach to modelling semantic similarity nuances inherently stored in the IS-A hierarchies. Hierarchy-fitting attains state-of-the-art results on the common- and rare-word benchmark datasets for deriving semantic similarity from neural word embeddings. It also incorporates an asymmetric distance function to specialize hypernymy's directionality explicitly, through which it significantly improves vanilla embeddings in multiple evaluation tasks of detecting hypernymy and directionality without negative impacts on semantic similarity judgement. The results demonstrate the efficacy of hierarchy-fitting in specializing neural embeddings with semantic relations in late fusion, potentially expanding its applicability to aggregating heterogeneous data and various knowledge resources for learning multimodal semantic spaces.

cs.CL↗

arXiv2022

Synonym Detection Using Syntactic Dependency And Neural Embeddings

Recent advances on the Vector Space Model have significantly improved some NLP applications such as neural machine translation and natural language generation. Although word co-occurrences in context have been widely used in counting-/predicting-based distributional models, the role of syntactic dependencies in deriving distributional semantics has not yet been thoroughly investigated. By comparing various Vector Space Models in detecting synonyms in TOEFL, we systematically study the salience of syntactic dependencies in accounting for distributional similarity. We separate syntactic dependencies into different groups according to their various grammatical roles and then use context-counting to construct their corresponding raw and SVD-compressed matrices. Moreover, using the same training hyperparameters and corpora, we study typical neural embeddings in the evaluation. We further study the effectiveness of injecting human-compiled semantic knowledge into neural embeddings on computing distributional similarity. Our results show that the syntactically conditioned contexts can interpret lexical semantics better than the unconditioned ones, whereas retrofitting neural embeddings with semantic knowledge can significantly improve synonym detection.

cs.CL↗

arXiv2022

Phonon transition across an isotopic interface

Natural materials usually consist of isotopic mixtures, for which different isotopic ratios can lead to distinct material properties such as thermal conductivity and nucleation process. However, the knowledge of isotopic interface remains largely unexplored mainly due to the challenges in isotopic identification and property measurement at an atomic scale. Here, by using monochromated electron energy-loss spectroscopy in a scanning transmission electron microscope, we reveal momentum-transfer-dependent lattice vibration behavior at an artificial h-10BN/h-11BN heterostructure with sub-unit-cell resolution. We find the vibrational energy changes across the isotopic interface gradually, featuring a wide transition regime, which suggests strong delocalization of the out-of-plane optical phonons at the interface. In addition, we identify phonons near the Brillouin zone center have a transition regime ~3.34 nm (10 atomic layers), whereas phonons at the Brillouin zone boundary transition in ~1.66 nm (5 atomic layers). We propose that the isotope-induced charge effect at the interface accounts for the distinct delocalization behavior. Moreover, intra-atomic layer variation of vibration energy is also sensitive to the momentum transfer, thus at the interface it depends on both of momentum transfer and mass change. The revealed lattice vibration behavior at an isotopic interface provides new insights to understand the isotopic effects on properties in natural materials.

cond-mat.mtrl-sci↗