High-performance quantum entanglement generation via cascaded second-order nonlinear processes

In this paper, we demonstrate the generation of high-performance entangled photon-pairs in different degrees of freedom from a single piece of fiber pigtailed periodically poled LiNbO$_3$ (PPLN) waveguide. We utilize cascaded second-order nonlinear optical processes, i.e. second-harmonic generation (SHG) and spontaneous parametric down conversion (SPDC), to generate photon-pairs. Previously, the performance of the photon pairs is contaminated by Raman noise photons from the fiber pigtails. Here by integrating the PPLN waveguide with noise rejecting filters, we obtain a coincidence-to-accidental ratio (CAR) higher than 52,600 with photon-pair generation and detection rate of 52.3 kHz and 3.5 kHz, respectively. Energy-time, frequency-bin and time-bin entanglement is prepared by coherently superposing correlated two-photon states in these degrees of freedom, respectively. The energy-time entangled two-photon states achieve the maximum value of CHSH-Bell inequality of S=2.708$\pm$0.024 with a two-photon interference visibility of 95.74$\pm$0.86%. The frequency-bin entangled two-photon states achieve fidelity of 97.56$\pm$1.79% with a spatial quantum beating visibility of 96.85$\pm$2.46%. The time-bin entangled two-photon states achieve the maximum value of CHSH-Bell inequality of S=2.595$\pm$0.037 and quantum tomographic fidelity of 89.07$\pm$4.35%. Our results provide a potential candidate for quantum light source in quantum photonics.Comment: 29 pages,7 figure

Artificial intelligence for advanced functional materials: exploring current and future directions

This perspective addresses the topic of harnessing the tools of artificial intelligence (AI) for boosting innovation in functional materials design and engineering as well as discovering new materials for targeted applications in energy storage, biomedicine, composites, nanoelectronics or quantum technologies. It gives a current view of experts in the field, insisting on challenges and opportunities provided by the development of large materials databases, novel schemes for implementing AI into materials production and characterization as well as progress in the quest of simulating physical and chemical properties of realistic atomic models reaching the trillion atoms scale and with near ab initio accuracy

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Effects of perioperative dextrose infusion on preventing postoperative nausea and vomiting in patients undergoing laparoscopic surgery: a meta-analysis of randomized controlled trials

ObjectiveThe aim of this study was to systematically examine the literature and assess the effects of perioperative dextrose infusion on the prevention of postoperative nausea and vomiting (PONV) in patients following laparoscopic surgery under general anesthesia.MethodsWe conducted a systematic review and meta-analysis of randomized controlled trials (RCTs). Studies were eligible for inclusion if they evaluated the prevention of PONV with perioperative intravenous dextrose. Studies listed in PUBMED, Web of Science, and EMBASE databases published up to December 2020 were identified. Data were extracted and analyzed independently using a fixed-effects or random-effects model according to the heterogeneity.ResultsSix RCTs involving 526 patients were included. Our results showed that perioperative dextrose infusion not only reduced the incidence of PONV (risk ratio [RR] = 0.61, 95% confidence interval [CI]: 0.39–0.95; I2 = 59%) but also decreased the requirement for antiemetics compared with the control (RR = 0.53, 95% CI: 0.42–0.66; I2 = 32%). Furthermore, perioperative glucose infusion did not increase blood glucose levels compared with the control (mean difference [95% CI] = 74.55 [−20.64 to 169.73] mg/dL; I2 = 100%).ConclusionOur study reveals that perioperative dextrose infusion may reduce the risk of PONV after laparoscopic surgery. However, additional population-based RCTs are needed to confirm this finding.</jats:sec

Improving Chinese Named Entity Recognition by Interactive Fusion of Contextual Representation and Glyph Representation

Named entity recognition (NER) is a fundamental task in natural language processing. In Chinese NER, additional resources such as lexicons, syntactic features and knowledge graphs are usually introduced to improve the recognition performance of the model. However, Chinese characters evolved from pictographs, and their glyphs contain rich semantic information, which is often ignored. Therefore, in order to make full use of the semantic information contained in Chinese character glyphs, we propose a Chinese NER model that combines character contextual representation and glyph representation, named CGR-NER (Character–Glyph Representation for NER). First, CGR-NER uses the large-scale pre-trained language model to dynamically generate contextual semantic representations of characters. Secondly, a hybrid neural network combining a three-dimensional convolutional neural network (3DCNN) and bi-directional long short-term memory network (BiLSTM) is designed to extract the semantic information contained in a Chinese character glyph, the potential word formation knowledge between adjacent glyphs and the contextual semantic and global dependency features of the glyph sequence. Thirdly, an interactive fusion method with a crossmodal attention and gate mechanism is proposed to fuse the contextual representation and glyph representation from different models dynamically. The experimental results show that our proposed model achieves 82.97% and 70.70% F1 scores on the OntoNotes 4 and Weibo datasets. Multiple ablation studies also verify the advantages and effectiveness of our proposed model

Improving Chinese Named Entity Recognition by Interactive Fusion of Contextual Representation and Glyph Representation

Named entity recognition (NER) is a fundamental task in natural language processing. In Chinese NER, additional resources such as lexicons, syntactic features and knowledge graphs are usually introduced to improve the recognition performance of the model. However, Chinese characters evolved from pictographs, and their glyphs contain rich semantic information, which is often ignored. Therefore, in order to make full use of the semantic information contained in Chinese character glyphs, we propose a Chinese NER model that combines character contextual representation and glyph representation, named CGR-NER (Character–Glyph Representation for NER). First, CGR-NER uses the large-scale pre-trained language model to dynamically generate contextual semantic representations of characters. Secondly, a hybrid neural network combining a three-dimensional convolutional neural network (3DCNN) and bi-directional long short-term memory network (BiLSTM) is designed to extract the semantic information contained in a Chinese character glyph, the potential word formation knowledge between adjacent glyphs and the contextual semantic and global dependency features of the glyph sequence. Thirdly, an interactive fusion method with a crossmodal attention and gate mechanism is proposed to fuse the contextual representation and glyph representation from different models dynamically. The experimental results show that our proposed model achieves 82.97% and 70.70% F1 scores on the OntoNotes 4 and Weibo datasets. Multiple ablation studies also verify the advantages and effectiveness of our proposed model.</jats:p