Optimizing Quantum Programs against Decoherence: Delaying Qubits into Quantum Superposition

Quantum computing technology has reached a second renaissance in the last decade. However, in the NISQ era pointed out by John Preskill in 2018, quantum noise and decoherence, which affect the accuracy and execution effect of quantum programs, cannot be ignored and corrected by the near future NISQ computers. In order to let users more easily write quantum programs, the compiler and runtime system should consider underlying quantum hardware features such as decoherence. To address the challenges posed by decoherence, in this paper, we propose and prototype QLifeReducer to minimize the qubit lifetime in the input OpenQASM program by delaying qubits into quantum superposition. QLifeReducer includes three core modules, i.e.,the parser, parallelism analyzer and transformer. It introduces the layered bundle format to express the quantum program, where a set of parallelizable quantum operations is packaged into a bundle. We evaluate quantum programs before and after transformed by QLifeReducer on both real IBM Q 5 Tenerife and the self-developed simulator. The experimental results show that QLifeReducer reduces the error rate of a quantum program when executed on IBMQ 5 Tenerife by 11%; and can reduce the longest qubit lifetime as well as average qubit lifetime by more than 20% on most quantum workloads.Comment: To appear in TASE2019 - the 13th International Symposium on Theoretical Aspects of Software Engineering (submitted on Jan 25, 2019, and this is camera-ready version

A Knowledge Graph Construction Approach for Legal Domain

Considering that the existing domain knowledge graphs have difficulty in updating data in a timely manner and cannot make use of knowledge sufficiently in the construction process, this paper proposes a legal domain knowledge graph construction approach based on \u27China Judgments Online\u27 in order to manage the cases\u27 knowledge contained in it. The construction process is divided into two steps. First, we extract the classification relationships of the cases from structured data. Then, we obtain attribute knowledge of cases from semi-structured data and unstructured data through a relationship extraction model based on an improved cross-entropy loss function. The triples describing knowledge of cases are stored through Neo4j. The accuracy of the proposed approach is verified through experiments and we construct a legal domain knowledge graph which contains more than 4K classification relationships and 12K attribute knowledge to prove its validity

Baichuan 2: Open Large-scale Language Models

Large language models (LLMs) have demonstrated remarkable performance on a variety of natural language tasks based on just a few examples of natural language instructions, reducing the need for extensive feature engineering. However, most powerful LLMs are closed-source or limited in their capability for languages other than English. In this technical report, we present Baichuan 2, a series of large-scale multilingual language models containing 7 billion and 13 billion parameters, trained from scratch, on 2.6 trillion tokens. Baichuan 2 matches or outperforms other open-source models of similar size on public benchmarks like MMLU, CMMLU, GSM8K, and HumanEval. Furthermore, Baichuan 2 excels in vertical domains such as medicine and law. We will release all pre-training model checkpoints to benefit the research community in better understanding the training dynamics of Baichuan 2.Comment: Baichuan 2 technical report. Github: https://github.com/baichuan-inc/Baichuan

Threat categories of Vatica mangachapoi should be reassessed

IntroductionAsian tropical rainforests have the highest rates of degradation in the world. Consequently, a large decline in Chinese Vatica mangachapoi (a keystone species) had led to its listing in the category of “vulnerable” species by IUCN. However, its current status after decades of conservation efforts remains unknown.MethodsHere, we evaluate the current status of Chinese V. mangachapoi.Results and DiscussionWe found that its population is now dispersed in 14 protected areas, the largest being a coastal forest that contains 96.84% of all the Chinese V. mangachapoi. Compared to their historic records, the age of this forest was estimated at ≤ 70 years. The mono-culturing of V. mangachapoi in this forest, since 1960, has replaced all the older trees, resulting in its extremely high (91%) relative abundance, and an extensively low (only 44) tree species richness. Further, these V. mangachapoi trees now suffer from vine strangulations and severe Amauroderma perplexum infections: 18.5% of V. mangachapoi have died and 75% are at a high risk, thereby creating a threat of its extinction. Although, the other 13 protected areas have a higher tree species richness (152–451), a lower (6.1–25%) relative abundance of V. mangachapoi, and they neither suffer from vine strangulation or disease infections, they contribute to only 3.16% of total Chinese population of this species. Therefore, an immediate revision of threat status of this species in IUCN, from vulnerable to endangered, is warranted. Further, a change in planting patterns, from monocultures to mix-plantations of native species, is needed to promote biodiversity and restrict other biotic challenges so that this species is not extinct

Research progress on root canal irrigation disinfection drugs

Endodontic infection control is crucial to successful root canal treatment. Irrigation is the key step in endodontic procedures, and the application of root canal irrigation and disinfection medications play an important role. How to enhance antibacterial effects and functions in removing tissues while maintaining biocompatibility is a hot topic in endodontics. Currently, insights to address this issue can be split into two categories: one, the modification or combination of conventional endodontic irrigation solutions, and two, the development of novel endodontic irrigation solutions with new technologies and materials, for instance, nanomaterials and natural exacts. However, conventional endodontic irrigation solutions, such as sodium hypochlorite and chlorhexidine, are still the first choice in clinical practice. Most novel endodontic irrigation solutions remain at the pre-clinical laboratory stage. Clinical research and relevant data are required to determine whether various methods can improve endodontic irrigation. From basic research to clinical application is the direction for advancing to the next stage. The present article focuses on research progress on endodontic irrigation, especially concerning its antibacterial mechanism, characteristics and efficacy, to provide a reference for future clinical translation

Integrated numerical simulation and quality attributes of soybean protein isolate extrusion under different screw speeds and combinations

A numerical simulation of the fluid-dynamic parameters (shear rate distribution, shear viscosity distribution and residence time) inside the barrel combined with extrudate properties, is a potential novel approach for investigating molten soybean protein isolate (SPI) under different screw speeds and combinations. Through finite element simulation, computer fluid dynamics and particle tracking simulation analysis, it was found that increasing the screw speed can increase the shear rate, decreased the shear viscosity of the SPI fluid, and reduced the RDT, thereby promoting the dispersion degree. The maximum shear rate and minimum shear viscosity were generated at the screw flight flanks, and the fluid underwent an alternate shearing force in the barrel. A small axial channel width can significantly promote the fluidity of molten proteins. In conclusion, SPI extrudates with a homogenous structure, smooth surface, and favourable colour and textual profile were produced at a relatively high screw speed (140 rpm)

Aerosolization Behaviour of Fungi and Its Potential Health Effects during the Composting of Animal Manure

Compost is an important source of airborne fungi that can adversely affect occupational health. However, the aerosol behavior of fungi and their underlying factors in composting facilities are poorly understood. We collected samples from compost piles and the surrounding air during the composting of animal manure and analyzed the aerosolization behavior of fungi and its potential health effects based on the fungal composition and abundance in two media using high-throughput sequencing and ddPCR. There were differences in fungal diversity and richness between the air and composting piles. Ascomycota and Basidiomycota were the two primary fungal phyla in both media. The dominant fungal genera in composting piles were Aspergillus, Thermomyces, and Alternaria, while the dominant airborne fungal genes were Alternaria, Cladosporium, and Sporobolomyces. Although the communities of total fungal genera and pathogenic/allergenic genera were different in the two media, fungal abundance in composting piles was significantly correlated with abundance in air. According to the analysis on fungal composition, a total of 69.10% of the fungal genera and 91.30% of pathogenic/allergenic genera might escape from composting pile into the air. A total of 77 (26.64%) of the fungal genera and six (20%) of pathogenic/allergenic genera were likely to aerosolize. The influence of physicochemical parameters and heavy metals on the aerosol behavior of fungal genera, including pathogenic/allergenic genera, varied among the fungal genera. These results increase our understanding of fungal escape during composting and highlight the importance of aerosolization behavior for predicting the airborne fungal composition and corresponding human health risks in compost facilities