Search

Data-serialization libraries are essential tools in software development, responsible for converting between programmable data structures and data persistence formats. Among them, JSON is the most popular choice for exchanging data between different systems and programming languages, while JSON libraries serve as the programming toolkit for this task. Despite their widespread use, bugs in JSON libraries can cause severe issues such as data inconsistencies and security vulnerabilities. Unit test generation techniques are widely adopted to identify bugs in various libraries. However, there is limited systematic testing effort specifically for exposing bugs within JSON libraries in industrial practice. In this paper, we propose JSONTestGen, an approach leveraging large language models (LLMs) to generate unit tests for fastjson2, a popular open source JSON library from Alibaba. Pre-trained on billions of open-source text and code corpora, LLMs have demonstrated remarkable abilities in programming tasks. Based on historical bug-triggering unit tests, we utilize LLMs to generate more diverse test cases by incorporating JSON domain-specific mutation rules. To systematically and efficiently identify potential bugs, we adopt differential testing on the results of the generated unit tests. Our evaluation shows that JSONTestGen outperforms existing test generation tools in unknown defect detection. With JSONTestGen, we found 34 real bugs in fastjson2, 30 of which have already been fixed, including 12 non-crashing bugs. While manual inspection reveals that LLM-generated tests can be erroneous, particularly with self-contradictory assertions, we demonstrate that LLMs have the potential for classifying false-positive test failures. This suggests a promising direction for improved test oracle automation in the future.

We present a comprehensive theoretical investigation of the strain-modulated excitonic properties of uni-axially strained transition-metal dichalcogenide monolayers (TMD-MLs) by solving the Bethe-Salpeter equation (BSE) established on the basis of first principles. We show that imposing an uni-axial strain onto a MoS_$2$ monolayers leads to the diversified band dispersions of the bright exciton (BX), gray exciton (GX), and dark exciton (DX) states, as a consequence of the competitive interplay between strain-induced valley drift (VD) and momentum-dependent electron-hole exchange interaction (EHEI). While the band dispersions of BX doublet in the light-accessible small reciprocal area remain almost unchanged against strain, the band dispersion of DX is reshaped by an increasing uni-axial strain from a parabola to a Mexican-hat-like profile, featured with unusual sign-reversal of the heavy effective mass and strain-activated brightness. In contrast, the effective mass of GX is drastically lightened by uni-axial strain and remains always positive. We show that the strain-diversified exciton band dispersions leads to the distinct exciton diffusivities and angle-resolved optical patterns of BX, GX, and DX in a strained TMD-ML, suggesting the feasibility of {\it spatially} resolving spinallowed and -forbidden excitons in exciton transport experiments and angle-resolved optical spectroscopies., Comment: 18pages, 5 figures

In this work, we report a pronounced light upconversion in few-layer transition metal dichalcogenides. Our joint theory-experiment study attributes the upconversion photoluminescence to a resonant exciton-exciton annihilation involving a pair of dark excitons with opposite momenta, followed by the spontaneous emission of upconverted bright excitons, which can have a high upconversion efficiency. Additionally, the upconversion photoluminescence is generic in MoS2, MoSe2, WS2, and WSe2, showing a high tuneability from green to ultraviolet light.

Three-dimensional Dirac semimetals (DSMs) have been shown to exhibit one-dimensional hinge modes which are termed the higher-order hinge Fermi-arc (HOFA) states. They are the topological consequences of Dirac points. Superconducting states from Dirac semimetals can inherit the Dirac points to form nodal Dirac superconducting states, raising a question of whether there exists a topological superconducting bulk-hinge correspondence similar to DSMs. In this work, we discuss the nodal superconducting states from half-filled DSMs respecting non-magnetic (Type-II) Shubnikov space group (SSG) $P6/mmm1'$. We find that the BdG Dirac points can lead to higher-order topological Dirac superconducting (HOTDSC) states instead of the expected higher-order Majorana-arc (HOMA) states. The HOTDSC states can be regarded as a crossing between the HOFAs in normal states and the BdG shadow states. We demonstrate that HOTDSC states can be indicated by relative topologies of BdG Dirac points by utilizing the theory of magnetic topological quantum chemistry (MTQC).

Knowledge Tracing (KT) aims to determine whether students will respond correctly to the next question, which is a crucial task in intelligent tutoring systems (ITS). In educational KT scenarios, transductive ID-based methods often face severe data sparsity and cold start problems, where interactions between individual students and questions are sparse, and new questions and concepts consistently arrive in the database. In addition, existing KT models only implicitly consider the correlation between concepts and questions, lacking direct modeling of the more complex relationships in the heterogeneous graph of concepts and questions. In this paper, we propose a Structure-aware Inductive Knowledge Tracing model with large language model (dubbed SINKT), which, for the first time, introduces large language models (LLMs) and realizes inductive knowledge tracing. Firstly, SINKT utilizes LLMs to introduce structural relationships between concepts and constructs a heterogeneous graph for concepts and questions. Secondly, by encoding concepts and questions with LLMs, SINKT incorporates semantic information to aid prediction. Finally, SINKT predicts the student's response to the target question by interacting with the student's knowledge state and the question representation. Experiments on four real-world datasets demonstrate that SINKT achieves state-of-the-art performance among 12 existing transductive KT models. Additionally, we explore the performance of SINKT on the inductive KT task and provide insights into various modules.

In quantum computing, the connectivity of qubits placed on two-dimensional chips limits the scalability and functionality of solid-state quantum computers. This paper presents two approaches to constructing complex quantum networks from simple qubit arrays, specifically grid lattices. The first approach utilizes a subset of qubits as tunable couplers, effectively yielding a range of non-trivial graph-based Hamiltonians. The second approach employs dynamic graph engineering by periodically activating and deactivating couplers, enabling the creation of effective quantum walks with longer-range couplings. Numerical simulations verify the effective dynamics of these approaches. In terms of these two approaches, we explore implementing various graphs, including cubes and fullerenes, etc, on two-dimensional lattices. These techniques facilitate the realization of analog quantum simulation, particularly continuous-time quantum walks discussed in detail in this manuscript, for different computational tasks on superconducting quantum chips despite their inherent low dimensional simple architecture., Comment: 9 pages, 12 figures

In this paper, we introduce a new quantum circuit synthesis (QCS) framework, Qsyn, for developers to research, develop, test, experiment, and then contribute their QCS algorithms and tools to the framework. Our framework is more developer-friendly than other modern QCS frameworks in three aspects: (1) We design a rich command-line interface so that developers can easily design various testing scenarios and flexibly conduct experiments on their algorithms. (2) We offer detailed access to many data representations on different abstract levels of quantum circuits so that developers can optimize their algorithms to the extreme. (3) We define a rigid developing flow and environment so that developers can ensure their development qualities with the best modern software engineering practices. We illustrate the friendliness of our framework with a showcase of developing a T-Count Optimization algorithm and demonstrate our performance superiority with fair comparisons to other modern QCS frameworks.

We present PAODING, a toolkit to debloat pretrained neural network models through the lens of data-free pruning. To preserve the model fidelity, PAODING adopts an iterative process, which dynamically measures the effect of deleting a neuron to identify candidates that have the least impact to the output layer. Our evaluation shows that PAODING can significantly reduce the model size, generalize on different datasets and models, and meanwhile preserve the model fidelity in terms of test accuracy and adversarial robustness. PAODING is publicly available on PyPI via https://pypi.org/project/paoding-dl., Comment: 3 pages

A light beam can be spatially structured in the complex amplitude to possess orbital angular momentum (OAM), which introduces a new degree of freedom alongside the intrinsic spin angular momentum (SAM) associated with circular polarization. Moreover, super-imposing two twisted lights with distinct SAM and OAM produces a vector vortex beam (VVB) in non-separable states where not only complex amplitude but also polarization are spatially structured and entangled with each other. In addition to the non-separability, the SAM and OAM in a VVB are intrinsically coupled by the optical spin-orbit interaction and constitute the profound spin-orbit physics in photonics. In this work, we present a comprehensive theoretical investigation, implemented on the first-principles base, of the intriguing light-matter interaction between VVBs and WSe$_{2}$ monolayers (WSe$_{2}$-MLs), one of the best-known and promising two-dimensional (2D) materials in optoelectronics dictated by excitons, encompassing bright exciton (BX) as well as various dark excitons (DXs). One of the key findings of our study is the substantial enhancement of the photo-excitation of gray excitons (GXs), a type of spin-forbidden dark exciton, in a WSe$_2$-ML through the utilization of a twisted light that possesses a longitudinal field associated with the optical spin-orbit interaction. Our research demonstrates that a spin-orbit-coupled VVB surprisingly allows for the imprinting of the carried optical information onto gray excitons in 2D materials, which is robust against the decoherence mechanisms in materials. This observation suggests a promising method for deciphering the transferred angular momentum from structured lights to excitons.

Machine learning in medical imaging often faces a fundamental dilemma, namely, the small sample size problem. Many recent studies suggest using multi-domain data pooled from different acquisition sites/centers to improve statistical power. However, medical images from different sites cannot be easily shared to build large datasets for model training due to privacy protection reasons. As a promising solution, federated learning, which enables collaborative training of machine learning models based on data from different sites without cross-site data sharing, has attracted considerable attention recently. In this paper, we conduct a comprehensive survey of the recent development of federated learning methods in medical image analysis. In this survey, we first introduce the background knowledge of federated learning for dealing with privacy protection and collaborative learning issues in medical imaging. We then present a comprehensive review of recent advances in federated learning methods for medical image analysis. Specifically, existing methods are categorized based on three critical aspects of a federated learning system, including client end, server end, and communication techniques. In each category, we summarize the existing federated learning methods according to specific research problems in medical image analysis and also provide insights into the motivations of different approaches. In addition, we provide a review of existing benchmark medical imaging datasets and software platforms for current federated learning research. We also conduct an experimental study to empirically evaluate typical federated learning methods for medical image analysis. This survey can help to better understand the current research status, challenges, and potential research opportunities in this promising research field., Comment: 35 pages, 6 figures

Charge density wave (CDW) resulted from a small distortion in the lattice is able to create new orders beyond the original lattice. In 2H-NbSe2, one of the layered transition metal dichalcogenides (TMD), the 3x3 charge order appears in two-dimensional (2D) layers. Although CDW is usually described by a sine wave, the spatial distribution within a 2D layer has never been systematically visualized. Here by using scanning tunneling microscopy (STM) and density functional theory (DFT), we have monitored the evolution of 3x3 CDW along c-axis and realized a nearly tomography scan of CDW of the topmost layer. The results show that the strength of 3x3 charge order varies while increasing the tunneling current. The 3x3 charge order is relatively strong at the outermost Se level and decreases while probing in between Se and Nb levels. Interestingly, the 3x3 charge order gets strong again as reaching Nb level but along with a phase shift. We further calculated the orbital charge distributions and found that both CDW intensity modulation and phase shift are strongly correlated with the distribution of Se p orbitals and Nb d orbitals., Comment: 12 pages, 4 figures

In this work, we present a comprehensive theoretical and computational investigation of exciton fine structures of WSe$_2$-monolayers, one of the best known two-dimensional (2D) transition-metal dichalcogenides (TMD's), in various dielectric-layer environments by solving the first-principles-based Bethe-Salpeter equation. While the physical and electronic properties of atomically thin nano-materials are normally sensitive to the variation of surrounding environment, our studies reveal that the influence of dielectric environment on the exciton fine structures of TMD-ML's is surprisingly limited. We point out that the non-locality of Coulomb screening plays a key role to suppress the factor of dielectric environment and drastically shrink the fine structure splittings between bright exciton (BX) states and various dark exciton (DX) states of TMD-ML's. The intriguing non-locality of screening in 2D materials can be manifested by the measurable {\it non-linear} correlation between the BX-DX splittings and exciton binding energies with varying the surrounding dielectric environments. The revealed environment-insensitive exciton fine structures of TMD-ML's suggest the robustness of prospective dark-exciton-based opto-electronics against the inevitable variation of inhomogeneous dielectric environment., Comment: 29 pages, 4 figures

Efficient detection of single optical centers in solids is essential for quantum information processing, sensing, and single-photon generation applications. In this work, we use radio-frequency (RF) reflectometry to electrically detect the photoionization induced by a single Er$^{3+}$ ion in Si. The high bandwidth and sensitivity of the RF reflectometry provide sub-100-ns time resolution for the photoionization detection. With this technique, the optically excited state lifetime of a single Er$^{3+}$ ion in a Si nano-transistor is measured for the first time to be 0.49 $\pm$ 0.04 $\mu$s. Our results demonstrate an efficient approach for detecting a charge state change induced by Er excitation and relaxation. This approach could be used for fast readout of other single optical centers in solids and is attractive for large-scale integrated optical quantum systems thanks to the multi-channel RF reflectometry demonstrated with frequency multiplexing techniques.

Domain adaptation (DA) is an important technique for modern machine learning-based medical data analysis, which aims at reducing distribution differences between different medical datasets. A proper domain adaptation method can significantly enhance the statistical power by pooling data acquired from multiple sites/centers. To this end, we have developed the Domain Adaptation Toolbox for Medical data analysis (DomainATM) - an open-source software package designed for fast facilitation and easy customization of domain adaptation methods for medical data analysis. The DomainATM is implemented in MATLAB with a user-friendly graphical interface, and it consists of a collection of popular data adaptation algorithms that have been extensively applied to medical image analysis and computer vision. With DomainATM, researchers are able to facilitate fast feature-level and image-level adaptation, visualization and performance evaluation of different adaptation methods for medical data analysis. More importantly, the DomainATM enables the users to develop and test their own adaptation methods through scripting, greatly enhancing its utility and extensibility. An overview characteristic and usage of DomainATM is presented and illustrated with three example experiments, demonstrating its effectiveness, simplicity, and flexibility. The software, source code, and manual are available online., Comment: 10 pages, 13 figures

Subjective cognitive decline (SCD) is a preclinical stage of Alzheimer's disease (AD) which occurs even before mild cognitive impairment (MCI). Progressive SCD will convert to MCI with the potential of further evolving to AD. Therefore, early identification of progressive SCD with neuroimaging techniques (e.g., structural MRI) is of great clinical value for early intervention of AD. However, existing MRI-based machine/deep learning methods usually suffer the small-sample-size problem which poses a great challenge to related neuroimaging analysis. The central question we aim to tackle in this paper is how to leverage related domains (e.g., AD/NC) to assist the progression prediction of SCD. Meanwhile, we are concerned about which brain areas are more closely linked to the identification of progressive SCD. To this end, we propose an attention-guided autoencoder model for efficient cross-domain adaptation which facilitates the knowledge transfer from AD to SCD. The proposed model is composed of four key components: 1) a feature encoding module for learning shared subspace representations of different domains, 2) an attention module for automatically locating discriminative brain regions of interest defined in brain atlases, 3) a decoding module for reconstructing the original input, 4) a classification module for identification of brain diseases. Through joint training of these four modules, domain invariant features can be learned. Meanwhile, the brain disease related regions can be highlighted by the attention mechanism. Extensive experiments on the publicly available ADNI dataset and a private CLAS dataset have demonstrated the effectiveness of the proposed method. The proposed model is straightforward to train and test with only 5-10 seconds on CPUs and is suitable for medical tasks with small datasets., Comment: 10 pages, 12 figures

We present a comprehensive theoretical investigation of the photo-generated excitons in transition-metal dichalcogenide monolayers (TMD-ML's) by Laguerre-Gaussian beams, a celebrated kind of twisted lights (TL's) carrying quantized orbital angular momenta (OAM). We show that the photo-excitation of TL incident to a TMD-ML leads to the formation of spatially localized exciton wave packets, constituted by the superposition of finite-momentum exciton states determined by the intriguing interplay between the multiple degrees of freedom of the optical and excitonic subsystems. Consequently, the TL-induced exciton wave packets yield profound directional photo-luminescences whose polar-angle-dependences are encoded by the transferred optical OAM and azimuthal angle part, despite OAM-irrelevant, optically resolves the exchange-split longitudinal and transverse exciton bands. Interestingly, the application of linearly polarized TL onto a valley-excitonic system mimics an exciton multiplexer allowing for selectively detecting the individual valley-mixed exciton bands, which are normally hardly measured spectrally., Comment: 33 pages, 10 figures

High degree of valley polarization of optically active excitons in transition-metal dichalcogenide monolayers (TMD-MLs) is vital in valley-based photonic applications but known to be likely spoiled by the intrinsic electron-hole exchange interactions. In this study, we present a theoretical investigation of the valley and optical properties of finite-momentum dark excitons in WSe$_2$-MLs by solving the density-functional-theory(DFT)-based Bethe-Salpeter equation (BSE) under the guidance of symmetry analysis. %We reveal that, in general, finite-momentum excitons are actually well immune from the exchange-induced valley depolarization, except for those with specific exciton momenta coincident with the $3\sigma_v$ and $3C_2'$ symmetries in the $D_{3h}$ point group of TMD-MLs. We reveal that, unlike the bright exciton inevitably subjected to electron-hole exchange interaction, inter-valley finite-momentum dark excitons in WSe$_2$-MLs are well immune from the exchange-induced valley depolarization and inherently highly valley-polarized under the enforcement of the crystal symmetries. More importantly, the superior valley polarizations of the inter-valley dark excitons in WSe$_2$-MLs are shown almost fully transferable to the optical polarization in the phonon-assisted photo-luminescences because of the native suppression of exchange-induced depolarization in the second-order optical processes. The analysis of phonon-assisted photo-luminescences accounts for the recently observed brightness, high degree of optical polarization and long lifetime of the inter-valley dark exciton states in tungsten-based TMD-MLs., Comment: 14 pages, 3 figures

Chronological age of healthy people is able to be predicted accurately using deep neural networks from neuroimaging data, and the predicted brain age could serve as a biomarker for detecting aging-related diseases. In this paper, a novel 3D convolutional network, called two-stage-age-network (TSAN), is proposed to estimate brain age from T1-weighted MRI data. Compared with existing methods, TSAN has the following improvements. First, TSAN uses a two-stage cascade network architecture, where the first-stage network estimates a rough brain age, then the second-stage network estimates the brain age more accurately from the discretized brain age by the first-stage network. Second, to our knowledge, TSAN is the first work to apply novel ranking losses in brain age estimation, together with the traditional mean square error (MSE) loss. Third, densely connected paths are used to combine feature maps with different scales. The experiments with $6586$ MRIs showed that TSAN could provide accurate brain age estimation, yielding mean absolute error (MAE) of $2.428$ and Pearson's correlation coefficient (PCC) of $0.985$, between the estimated and chronological ages. Furthermore, using the brain age gap between brain age and chronological age as a biomarker, Alzheimer's disease (AD) and Mild Cognitive Impairment (MCI) can be distinguished from healthy control (HC) subjects by support vector machine (SVM). Classification AUC in AD/HC and MCI/HC was $0.904$ and $0.823$, respectively. It showed that brain age gap is an effective biomarker associated with risk of dementia, and has potential for early-stage dementia risk screening. The codes and trained models have been released on GitHub: https://github.com/Milan-BUAA/TSAN-brain-age-estimation., Comment: Accepted by IEEE transactions on Medical Imaging, 13 pages, 6 figures

We propose a three-terminal structure to probe robust signatures of Majorana zero modes. This structure consists of a quantum dot coupled to the normal metal, s-wave superconducting and Majorana Y-junction leads. The zero-bias differential conductance at zero temperature of the normal-metal lead peaks at $2e^{2}/h$, which will be deflected after Majorana braiding. This quantized conductance can entirely arise from the Majorana-induced crossed Andreev reflection, protected by the energy gap of the superconducting lead. We find that the effect of thermal broadening is significantly suppressed when the dot is on resonance. In the case that the energy level of the quantum dot is much larger than the superconducting gap, tunneling processes are dominated by Majorana-induced crossed Andreev reflection. Particularly, a novel kind of crossed Andreev reflection equivalent to the splitting of charge quanta $3e$ occurs after Majorana braiding., Comment: 15 pages, 8 figures, accepted for publication in Physical Review B

Mild cognitive impairment (MCI) conversion prediction, i.e., identifying MCI patients of high risks converting to Alzheimer's disease (AD), is essential for preventing or slowing the progression of AD. Although previous studies have shown that the fusion of multi-modal data can effectively improve the prediction accuracy, their applications are largely restricted by the limited availability or high cost of multi-modal data. Building an effective prediction model using only magnetic resonance imaging (MRI) remains a challenging research topic. In this work, we propose a multi-modal multi-instance distillation scheme, which aims to distill the knowledge learned from multi-modal data to an MRI-based network for MCI conversion prediction. In contrast to existing distillation algorithms, the proposed multi-instance probabilities demonstrate a superior capability of representing the complicated atrophy distributions, and can guide the MRI-based network to better explore the input MRI. To our best knowledge, this is the first study that attempts to improve an MRI-based prediction model by leveraging extra supervision distilled from multi-modal information. Experiments demonstrate the advantage of our framework, suggesting its potentials in the data-limited clinical settings.

Machine learning techniques used in computer-aided medical image analysis usually suffer from the domain shift problem caused by different distributions between source/reference data and target data. As a promising solution, domain adaptation has attracted considerable attention in recent years. The aim of this paper is to survey the recent advances of domain adaptation methods in medical image analysis. We first present the motivation of introducing domain adaptation techniques to tackle domain heterogeneity issues for medical image analysis. Then we provide a review of recent domain adaptation models in various medical image analysis tasks. We categorize the existing methods into shallow and deep models, and each of them is further divided into supervised, semi-supervised and unsupervised methods. We also provide a brief summary of the benchmark medical image datasets that support current domain adaptation research. This survey will enable researchers to gain a better understanding of the current status, challenges., Comment: 15 pages, 7 figures