arXiv subjects

Yijun Yu

Publications and source records attributed to Yijun Yu.

At least 37 records · Page 2Linked to original sources

arXiv2025

Resolving Structural Origins for Superconductivity in Strain-Engineered La$_3$Ni$_2$O$_7$ Thin Films

The discovery of high-temperature superconductivity in bulk La$_3$Ni$_2$O$_7$ under high hydrostatic pressure and, more recently, biaxial compression in epitaxial thin films has ignited significant interest in understanding the interplay between atomic and electronic structure in these compounds. Subtle changes in the nickel-oxygen bonding environment are thought to be key drivers for stabilizing superconductivity, but specific details of which bonds and which modifications are most relevant remains so far unresolved. While direct, atomic-scale structural characterization under hydrostatic pressure is beyond current experimental capabilities, static stabilization of strained La$_3$Ni$_2$O$_7$ films provides a platform well-suited to investigation with new picometer-resolution electron microscopy methods. Here, we use multislice electron ptychography to directly measure the atomic-scale structural evolution of La$_3$Ni$_2$O$_7$ thin films across a wide range of biaxial strains tuned via substrate. By resolving both the cation and oxygen sublattices, we study strain-dependent evolution of atomic bonds, providing the opportunity to isolate and disentangle the effects of specific structural motifs for stabilizing superconductivity. We identify the lifting of crystalline symmetry through modification of the nickel-oxygen octahedral distortions under compressive strain as a key structural ingredient for superconductivity. Rather than previously supposed $c$-axis compression, our results highlight the importance of in-plane biaxial compression in superconducting thin films, which suggests an alternative -- possibly cuprate-like -- understanding of the electronic structure. Identifying local regions of inhomogeneous oxygen stoichiometry and high internal strain near crystalline defects, we suggest potential pathways for improving the sharpness and temperature of the superconducting transition.

cond-mat.supr-con↗

arXiv2024

Network Intent Decomposition and Optimization for Energy-Aware Radio Access Network

With recent advancements in the sixth generation (6G) communication technologies, more vertical industries have encountered diverse network services. How to reduce energy consumption is critical to meet the expectation of the quality of diverse network services. In particular, the number of base stations in 6G is huge with coupled adjustable network parameters. However, the problem is complex with multiple network objectives and parameters. Network intents are difficult to map to individual network elements and require enhanced automation capabilities. In this paper, we present a network intent decomposition and optimization mechanism in an energy-aware radio access network scenario. By characterizing the intent ontology with a standard template, we present a generic network intent representation framework. Then we propose a novel intent modeling method using Knowledge Acquisition in automated Specification language, which can model the network ontology. To clarify the number and types of network objectives and energy-saving operations, we develop a Softgoal Interdependency Graph-based network intent decomposition model, and thus, a network intent decomposition algorithm is presented. Simulation results demonstrate that the proposed algorithm outperforms without conflict analysis in intent decomposition time. Moreover, we design a deep Q-network-assisted intent optimization scheme to validate the performance gain.

cs.NI↗

arXiv2024

Decomposition Model Assisted Energy-Saving Design in Radio Access Network

The continuous emergence of novel services and massive connections involve huge energy consumption towards ultra-dense radio access networks. Moreover, there exist much more number of controllable parameters that can be adjusted to reduce the energy consumption from a network-wide perspective. However, a network-level energy-saving intent usually contains multiple network objectives and constraints. Therefore, it is critical to decompose a network-level energy-saving intent into multiple levels of configurated operations from a top-down refinement perspective. In this work, we utilize a softgoal interdependency graph decomposition model to assist energy-saving scheme design. Meanwhile, we propose an energy-saving approach based on deep Q-network, which achieve a better trade-off among the energy consumption, the throughput, and the first packet delay. In addition, we illustrate how the decomposition model can assist in making energy-saving decisions. Evaluation results demonstrate the performance gain of the proposed scheme in accelerating the model training process.

cs.NI↗

arXiv2024

Millimeter-scale freestanding superconducting infinite-layer nickelate membranes

Progress in the study of infinite-layer nickelates has always been highly linked to materials advances. In particular, the recent development of superconductivity via hole-doping was predicated on the controlled synthesis of Ni in a very high oxidation state, and subsequent topotactic reduction to a very low oxidation state, currently limited to epitaxial thin films. Here we demonstrate a process to combine these steps with a heterostructure which includes an epitaxial soluble buffer layer, enabling the release of freestanding membranes of (Nd,Sr)NiO2 encapsulated in SrTiO3, which serves as a protective layer. The membranes have comparable structural and electronic properties to that of optimized thin films, and range in lateral dimensions from millimeters to ~100 micron fragments, depending on the degree of strain released with respect to the initial substrate. The changes in the superconducting transition temperature associated with membrane release are quite similar to those reported for substrate and pressure variations, suggestive of a common underlying mechanism. These membranes structures should provide a versatile platform for a range of experimental studies and devices free from substrate constraints.

cond-mat.mtrl-sci↗

arXiv2023

Unleashing the Power of Clippy in Real-World Rust Projects

Clippy lints are considered as essential tools for Rust developers, as they can be configured as gate-keeping rules for a Rust project during continuous integration. Despite their availability, little was known about practical application and cost-effectiveness of the lints in reducing code quality issues. In this study, we embark on a comprehensive analysis to unveil the true impact of Clippy lints in the Rust development landscape. The study is structured around three interrelated components, each contributing to the overall effectiveness of Clippy. Firstly, we conduct a comprehensive analysis of Clippy lints in all idiomatic crates-io Rust projects with an average warning density of 21/KLOC. The analysis identifies the most cost-effective lint fixes, offering valuable opportunities for optimizing code quality. Secondly, we actively engage Rust developers through a user survey to garner invaluable feedback on their experiences with Clippy. User insights shed light on two crucial concerns: the prevalence of false positives in warnings and the need for auto-fix support for most warnings. Thirdly, building upon these findings, we engineer three innovative automated refactoring techniques to effectively fix the four most frequent Clippy lints. As a result, the warning density in Rosetta benchmarks has significantly decreased from 195/KLOC to an impressive 18/KLOC, already lower than the average density of the crates-io Rust projects. These results demonstrate tangible benefit and impact of our efforts in enhancing the overall code quality and maintainability for Rust developers.

cs.SE↗

arXiv2023

Ownership guided C to Rust translation

Dubbed a safer C, Rust is a modern programming language that combines memory safety and low-level control. This interesting combination has made Rust very popular among developers and there is a growing trend of migrating legacy codebases (very often in C) to Rust. In this paper, we present a C to Rust translation approach centred around static ownership analysis. We design a suite of analyses that infer ownership models of C pointers and automatically translate the pointers into safe Rust equivalents. The resulting tool, Crown, scales to real-world codebases (half a million lines of code in less than 10 seconds) and achieves a high conversion rate.

cs.PL↗

arXiv2022

Metal to Mott Insulator Transition in Two-dimensional 1T-TaSe$_2$

When electron-electron interaction dominates over other electronic energy scales, exotic, collective phenomena often emerge out of seemingly ordinary matter. The strongly correlated phenomena, such as quantum spin liquid and unconventional superconductivity, represent a major research frontier and a constant source of inspiration. Central to strongly correlated physics is the concept of Mott insulator, from which various other correlated phases derive. The advent of two-dimensional (2D) materials brings unprecedented opportunities to the study of strongly correlated physics in the 2D limit. In particular, the enhanced correlation and extreme tunability of 2D materials enables exploring strongly correlated systems across uncharted parameter space. Here, we discover an intriguing metal to Mott insulator transition in 1T-TaSe$_2$ as the material is thinned down to atomic thicknesses. Specifically, we discover, for the first time, that the bulk metallicity of 1T-TaSe$_2$ arises from a band crossing Fermi level. Reducing the dimensionality effectively quenches the kinetic energy of the initially itinerant electrons and drives the material into a Mott insulating state. The dimensionality-driven Metal to Mott insulator transition resolves the long-standing dichotomy between metallic bulk and insulating surface of 1T-TaSe$_2$. Our results additionally establish 1T-TaSe$_2$ as an ideal variable system for exploring various strongly correlated phenomena.

cond-mat.str-el↗

arXiv2022

Energy-bounded Learning for Robust Models of Code

In programming, learning code representations has a variety of applications, including code classification, code search, comment generation, bug prediction, and so on. Various representations of code in terms of tokens, syntax trees, dependency graphs, code navigation paths, or a combination of their variants have been proposed, however, existing vanilla learning techniques have a major limitation in robustness, i.e., it is easy for the models to make incorrect predictions when the inputs are altered in a subtle way. To enhance the robustness, existing approaches focus on recognizing adversarial samples rather than on the valid samples that fall outside a given distribution, which we refer to as out-of-distribution (OOD) samples. Recognizing such OOD samples is the novel problem investigated in this paper. To this end, we propose to first augment the in=distribution datasets with out-of-distribution samples such that, when trained together, they will enhance the model's robustness. We propose the use of an energy-bounded learning objective function to assign a higher score to in-distribution samples and a lower score to out-of-distribution samples in order to incorporate such out-of-distribution samples into the training process of source code models. In terms of OOD detection and adversarial samples detection, our evaluation results demonstrate a greater robustness for existing source code models to become more accurate at recognizing OOD data while being more resistant to adversarial attacks at the same time. Furthermore, the proposed energy-bounded score outperforms all existing OOD detection scores by a large margin, including the softmax confidence score, the Mahalanobis score, and ODIN.

cs.LG↗

arXiv2022

Character of the "normal state" of the nickelate superconductors

The occurrence of superconductivity in proximity to various strongly correlated phases of matter has drawn extensive focus on their normal state properties, to develop an understanding of the state from which superconductivity emerges. The recent finding of superconductivity in layered nickelates raises similar interests. However, transport measurements of doped infinite-layer nickelate thin films have been hampered by materials limitations of these metastable compounds - in particular, a relatively high density of extended defects. Here, by moving to a substrate (LaAlO$_{3}$)$_{0.3}$(Sr$_{2}$TaAlO$_{6}$)$_{0.7}$ which better stabilizes the growth and reduction conditions, we can synthesize the doping series of Nd$_{1-x}$Sr$_{x}$NiO$_{2}$ essentially free from extended defects. This enables the first examination of the 'intrinsic' temperature and doping dependent evolution of the transport properties. The normal state resistivity exhibits a low-temperature upturn in the underdoped regime, linear behavior near optimal doping, and quadratic temperature dependence for overdoping. This is strikingly similar to the copper oxides, despite key distinctions - namely the absence of an insulating parent compound, multiband electronic structure, and a Mott-Hubbard orbital alignment rather than the charge-transfer insulator of the copper oxides. These results suggest an underlying universality in the emergent electronic properties of both superconducting families.

cond-mat.supr-con↗

arXiv2021

Self-Supervised Contrastive Learning for Code Retrieval and Summarization via Semantic-Preserving Transformations

We propose Corder, a self-supervised contrastive learning framework for source code model. Corder is designed to alleviate the need of labeled data for code retrieval and code summarization tasks. The pre-trained model of Corder can be used in two ways: (1) it can produce vector representation of code which can be applied to code retrieval tasks that do not have labeled data; (2) it can be used in a fine-tuning process for tasks that might still require label data such as code summarization. The key innovation is that we train the source code model by asking it to recognize similar and dissimilar code snippets through a contrastive learning objective. To do so, we use a set of semantic-preserving transformation operators to generate code snippets that are syntactically diverse but semantically equivalent. Through extensive experiments, we have shown that the code models pretrained by Corder substantially outperform the other baselines for code-to-code retrieval, text-to-code retrieval, and code-to-text summarization tasks.

cs.SE↗

arXiv2021

Quantum Cognitively Motivated Decision Fusion for Video Sentiment Analysis

Video sentiment analysis as a decision-making process is inherently complex, involving the fusion of decisions from multiple modalities and the so-caused cognitive biases. Inspired by recent advances in quantum cognition, we show that the sentiment judgment from one modality could be incompatible with the judgment from another, i.e., the order matters and they cannot be jointly measured to produce a final decision. Thus the cognitive process exhibits "quantum-like" biases that cannot be captured by classical probability theories. Accordingly, we propose a fundamentally new, quantum cognitively motivated fusion strategy for predicting sentiment judgments. In particular, we formulate utterances as quantum superposition states of positive and negative sentiment judgments, and uni-modal classifiers as mutually incompatible observables, on a complex-valued Hilbert space with positive-operator valued measures. Experiments on two benchmarking datasets illustrate that our model significantly outperforms various existing decision level and a range of state-of-the-art content-level fusion approaches. The results also show that the concept of incompatibility allows effective handling of all combination patterns, including those extreme cases that are wrongly predicted by all uni-modal classifiers.

cs.CL↗

arXiv2021

A Feature Table approach to decomposing monolithic applications into microservices

Microservice architecture refers to the use of numerous small-scale and independently deployed services, instead of encapsulating all functions into one monolith. It has been a challenge in software engineering to decompose a monolithic system into smaller parts. In this paper, we propose the Feature Table approach, a structured approach to service decomposition based on the correlation between functional features and microservices: (1) we defined the concept of {\em Feature Cards} and 12 instances of such cards; (2) we formulated {\em Decomposition Rules} to decompose monolithic applications; (3) we designed the {\em Feature Table Analysis Tool} to provide semi-automatic analysis for identification of microservices; and (4) we formulated {\em Mapping Rules} to help developers implement microservice candidates. We performed a case study on Cargo Tracking System to validate our microservice-oriented decomposition approach. Cargo Tracking System is a typical case that has been decomposed by other related methods (dataflow-driven approach, Service Cutter, and API Analysis). Through comparison with the related methods in terms of specific coupling and cohesion metrics, the results show that the proposed Feature Table approach can deliver more reasonable microservice candidates, which are feasible in implementation with semi-automatic support.

cs.SE↗

arXiv2021

On the Generalizability of Neural Program Models with respect to Semantic-Preserving Program Transformations

With the prevalence of publicly available source code repositories to train deep neural network models, neural program models can do well in source code analysis tasks such as predicting method names in given programs that cannot be easily done by traditional program analysis techniques. Although such neural program models have been tested on various existing datasets, the extent to which they generalize to unforeseen source code is largely unknown. Since it is very challenging to test neural program models on all unforeseen programs, in this paper, we propose to evaluate the generalizability of neural program models with respect to semantic-preserving transformations: a generalizable neural program model should perform equally well on programs that are of the same semantics but of different lexical appearances and syntactical structures. We compare the results of various neural program models for the method name prediction task on programs before and after automated semantic-preserving transformations. We use three Java datasets of different sizes and three state-of-the-art neural network models for code, namely code2vec, code2seq, and GGNN, to build nine such neural program models for evaluation. Our results show that even with small semantically preserving changes to the programs, these neural program models often fail to generalize their performance. Our results also suggest that neural program models based on data and control dependencies in programs generalize better than neural program models based only on abstract syntax trees. On the positive side, we observe that as the size of the training dataset grows and diversifies the generalizability of correct predictions produced by the neural program models can be improved too. Our results on the generalizability of neural program models provide insights to measure their limitations and provide a stepping stone for their improvement.

cs.SE↗

arXiv2020

InferCode: Self-Supervised Learning of Code Representations by Predicting Subtrees

Building deep learning models on source code has found many successful software engineering applications, such as code search, code comment generation, bug detection, code migration, and so on. Current learning techniques, however, have a major drawback that these models are mostly trained on datasets labeled for particular downstream tasks, and code representations may not be suitable for other tasks. While some techniques produce representations from unlabeled code, they are far from satisfactory when applied to downstream tasks. Although certain techniques generate representations from unlabeled code when applied to downstream tasks they are far from satisfactory. This paper proposes InferCode to overcome the limitation by adapting the self-supervised learning mechanism to build source code model. The key novelty lies in training code representations by predicting automatically identified subtrees from the context of the ASTs. Subtrees in ASTs are treated with InferCode as the labels for training code representations without any human labeling effort or the overhead of expensive graph construction, and the trained representations are no longer tied to any specific downstream tasks or code units. We trained an InferCode model instance using the Tree-based CNN as the encoder of a large set of Java code and applied it to downstream unsupervised tasks such as code clustering, code clone detection, cross-language code search or reused under a transfer learning scheme to continue training the model weights for supervised tasks such as code classification and method name prediction. Compared to previous code learning techniques applied to the same downstream tasks, such as Code2Vec, Code2Seq, ASTNN, higher performance results are achieved using our pre-trained InferCode model with a significant margin for most tasks including those involving different programming languages.

cs.SE↗

arXiv2020

TreeCaps: Tree-Based Capsule Networks for Source Code Processing

Recently program learning techniques have been proposed to process source code based on syntactical structures (e.g., Abstract Syntax Trees) and/or semantic information (e.g., Dependency Graphs). Although graphs may be better at capturing various viewpoints of code semantics than trees, constructing graph inputs from code needs static code semantic analysis that may not be accurate and introduces noise during learning. Although syntax trees are precisely defined according to the language grammar and easier to construct and process than graphs, previous tree-based learning techniques have not been able to learn semantic information from trees to achieve better accuracy than graph-based techniques. We propose a new learning technique, named TreeCaps, by fusing together capsule networks with tree-based convolutional neural networks, to achieve learning accuracy higher than existing graph-based techniques while it is based only on trees. TreeCaps introduces novel variable-to-static routing algorithms into the capsule networks to compensate for the loss of previous routing algorithms. Aside from accuracy, we also find that TreeCaps is the most robust to withstand those semantic-preserving program transformations that change code syntax without modifying the semantics. Evaluated on a large number of Java and C/C++ programs, TreeCaps models outperform prior deep learning models of program source code, in terms of both accuracy and robustness for program comprehension tasks such as code functionality classification and function name prediction

cs.SE↗

arXiv2019

SAR: Learning Cross-Language API Mappings with Little Knowledge

To save manual effort, developers often translate programs from one programming language to another, instead of implementing it from scratch. Translating application program interfaces (APIs) used in one language to functionally equivalent ones available in another language is an important aspect of program translation. Existing approaches facilitate the translation by automatically identifying the API mappings across programming languages. However, all these approaches still require large amount of manual effort in preparing parallel program corpora, ranging from pairs of APIs, to manually identified code in different languages that are considered as functionally equivalent. To minimize the manual effort in identifying parallel program corpora and API mappings, this paper aims at an automated approach to map APIs across languages with much less knowledge a priori needed than other existing approaches. The approach is based on an realization of the notion of domain adaption combined with code embedding, which can better align two vector spaces: taking as input large sets of programs, our approach first generates numeric vector representations of the programs, especially the APIs used in each language, and it adapts generative adversarial networks (GAN) to align the vectors from the spaces of two languages. For a better alignment, we initialize the GAN with parameters derived from optional API mapping seeds that can be identified accurately with a simple automatic signature-based matching heuristic. Then the cross-language API mappings can be identified via nearest-neighbors queries in the aligned vector spaces.

cs.LG↗

arXiv2019

Mapping Dynamical Magnetic Responses of Ultra-thin Micron-size Superconducting Films using Nitrogen-vacancy Centers in Diamond

Two-dimensional superconductors have attracted growing interest because of their scientific novelty, structural tunability, and useful properties. Studies of their magnetic responses, however, are often hampered by difficulties to grow large-size samples of high quality and uniformity. We report here an imaging method that employed NV- centers in diamond as sensor capable of mapping out the microwave magnetic field distribution on an ultrathin superconducting film of micron size. Measurements on a 33nm-thick film and a 125nm-thick bulk-like film of $Bi_2Sr_2CaCu_2O_{8+δ}$ revealed that the ac Meissner effect (or repulsion of ac magnetic field) set in at 78K and 91K, respectively; the latter was the superconducting transition temperature (Tc) of both films. The unusual ac magnetic response of the thin film presumably was due to thermally excited vortex-antivortex diffusive motion in the film. Spatial resolution of our ac magnetometer was limited by optical diffraction and the noise level was at 14 $μT/Hz^{1/2}$. The technique could be extended with better detection sensitivity to extract local ac conductivity/susceptibility of ultrathin or monolayer superconducting samples as well as ac magnetic responses of other two-dimensional exotic thin films of limited lateral size.

cond-mat.supr-con↗

arXiv2019

Magnetic-field-induced quantized anomalous Hall effect in intrinsic magnetic topological insulator MnBi$_2$Te$_4$

In a magnetic topological insulator, nontrivial band topology conspires with magnetic order to produce exotic states of matter that are best exemplified by quantum anomalous Hall (QAH) insulators and axion insulators. Up till now, such magnetic topological insulators are obtained by doping topological insulators with magnetic atoms. The random magnetic dopants, however, inevitably introduce disorders that hinder further exploration of quantum effects in the material. Here, we resolve this dilemma by probing quantum transport in MnBi$_2$Te$_4$ thin flake - a topological insulator with intrinsic magnetic order. In this layered van der Waals crystal, the ferromagnetic layers couple anti-parallel to each other, so MnBi$_2$Te$_4$ is an antiferromagnet. A magnetic field, however, aligns all the layers and induces an interlayer ferromagnetic order; we show that a quantized anomalous Hall response emerges in atomically thin MnBi$_2$Te$_4$ under a moderate magnetic field. MnBi$_2$Te$_4$ therefore becomes the first intrinsic magnetic topological insulator exhibiting quantized anomalous Hall effect. The result establishes MnBi$_2$Te$_4$ as an ideal arena for further exploring various topological phenomena.

cond-mat.mtrl-sci↗