Topological characterization of Lieb-Schultz-Mattis constraints and applications to symmetry-enriched quantum criticality

Lieb-Schultz-Mattis (LSM) theorems provide powerful constraints on the emergibility problem, i.e. whether a quantum phase or phase transition can emerge in a many-body system. We derive the topological partition functions that characterize the LSM constraints in spin systems with $G_s\times G_{int}$ symmetry, where $G_s$ is an arbitrary space group in one or two spatial dimensions, and $G_{int}$ is any internal symmetry whose projective representations are classified by $\mathbb{Z}_2^k$ with $k$ an integer. We then apply these results to study the emergibility of a class of exotic quantum critical states, including the well-known deconfined quantum critical point (DQCP), $U(1)$ Dirac spin liquid (DSL), and the recently proposed non-Lagrangian Stiefel liquid. These states can emerge as a consequence of the competition between a magnetic state and a non-magnetic state. We identify all possible realizations of these states on systems with $SO(3)\times \mathbb{Z}_2^T$ internal symmetry and either $p6m$ or $p4m$ lattice symmetry. Many interesting examples are discovered, including a DQCP adjacent to a ferromagnet, stable DSLs on square and honeycomb lattices, and a class of quantum critical spin-quadrupolar liquids of which the most relevant spinful fluctuations carry spin-$2$. In particular, there is a realization of spin-quadrupolar DSL that is beyond the usual parton construction. We further use our formalism to analyze the stability of these states under symmetry-breaking perturbations, such as spin-orbit coupling. As a concrete example, we find that a DSL can be stable in a recently proposed candidate material, NaYbO$_2$.Comment: 23 pages of main text + appendices + ancillary file

De novo genome assembly and population genomics of a shrub tree Barthea barthei (Hance) krass provide insights into the adaptive color variations

Flower color is a classic example of an ecologically important trait under selection in plants. Understanding the genetic mechanisms underlying shifts in flower color can provide key insights into ecological speciation. In this study, we investigated the genetic basis of flower color divergence in Barthea barthei, a shrub tree species exhibiting natural variation in flower color. We assembled a high-quality genome assembly for B. barthei with a contig N50 of 2.39 Mb and a scaffold N50 of 16.21 Mb. The assembly was annotated with 46,430 protein-coding genes and 1,560 non-coding RNAs. Genome synteny analysis revealed two recent tetraploidization events in B. barthei, estimated to have occurred at approximately 17 and 63 million years ago. These tetraploidization events resulted in massive duplicated gene content, with over 70% of genes retained in collinear blocks. Gene family members of the core regulators of the MBW complex were significantly expanded in B. barthei compared to Arabidopsis, suggesting that these duplications may have provided raw genetic material for the evolution of novel regulatory interactions and the diversification of anthocyanin pigmentation. Transcriptome profiling of B. barthei flowers revealed differential expression of 9 transcription factors related to anthocyanin biosynthesis between the two ecotypes. Six of these differentially expressed transcription factors were identified as high-confidence candidates for adaptive evolution based on positive selection signals. This study provides insights into the genetic basis of flower color divergence and the evolutionary mechanisms underlying ecological adaptation in plants

Towards an Understanding of Large Language Models in Software Engineering Tasks

Large Language Models (LLMs) have drawn widespread attention and research due to their astounding performance in tasks such as text generation and reasoning. Derivative products, like ChatGPT, have been extensively deployed and highly sought after. Meanwhile, the evaluation and optimization of LLMs in software engineering tasks, such as code generation, have become a research focus. However, there is still a lack of systematic research on the application and evaluation of LLMs in the field of software engineering. Therefore, this paper is the first to comprehensively investigate and collate the research and products combining LLMs with software engineering, aiming to answer two questions: (1) What are the current integrations of LLMs with software engineering? (2) Can LLMs effectively handle software engineering tasks? To find the answers, we have collected related literature as extensively as possible from seven mainstream databases, and selected 123 papers for analysis. We have categorized these papers in detail and reviewed the current research status of LLMs from the perspective of seven major software engineering tasks, hoping this will help researchers better grasp the research trends and address the issues when applying LLMs. Meanwhile, we have also organized and presented papers with evaluation content to reveal the performance and effectiveness of LLMs in various software engineering tasks, providing guidance for researchers and developers to optimize

Nested Event Extraction upon Pivot Element Recogniton

Nested Event Extraction (NEE) aims to extract complex event structures where an event contains other events as its arguments recursively. Nested events involve a kind of Pivot Elements (PEs) that simultaneously act as arguments of outer events and as triggers of inner events, and thus connect them into nested structures. This special characteristic of PEs brings challenges to existing NEE methods, as they cannot well cope with the dual identities of PEs. Therefore, this paper proposes a new model, called PerNee, which extracts nested events mainly based on recognizing PEs. Specifically, PerNee first recognizes the triggers of both inner and outer events and further recognizes the PEs via classifying the relation type between trigger pairs. In order to obtain better representations of triggers and arguments to further improve NEE performance, it incorporates the information of both event types and argument roles into PerNee through prompt learning. Since existing NEE datasets (e.g., Genia11) are limited to specific domains and contain a narrow range of event types with nested structures, we systematically categorize nested events in generic domain and construct a new NEE dataset, namely ACE2005-Nest. Experimental results demonstrate that PerNee consistently achieves state-of-the-art performance on ACE2005-Nest, Genia11 and Genia13

A high-precision bidirectional time-transfer system over a single fiber based on wavelength-division multiplexing and time-division multiplexing

In this paper, a high-precision bidirectional time-transfer system over a single fiber based on wavelength-division multiplexing and time-division multiplexing (SFWDM-TDM) is proposed, which combines the advantages of wavelength-division multiplexing and time-division multiplexing. It uses two dense wavelength-division channels to effectively suppress the problem of optical fiber reflection. At the same time, the time-division multiplexing method is used in combination with sampling and holding the time to complete the multi-user task. In hardware, we optimized the carrier processing and the high-precision time-delay control module of the SFWDM-TDM system to complete high-precision time-transfer equipment. In software and algorithm, the optical fiber time-interval measurement method and measurement times are optimized, and the SFWDM-TDM system reaches a synchronization accuracy of 8.9 ps at 1 s. Finally, a real-time detection mechanism with self-recovery ability is added to the system. This lays the foundation for a reliable, long-distance, high-precision, and multi-user mode optical fiber time- and frequency-transfer network