We well know that photon concept is self-contradictory because we assume that it is a particle with wave properties. This contradiction insensibly spoils our subconscious thinking. It is shown in the article that phot...We well know that photon concept is self-contradictory because we assume that it is a particle with wave properties. This contradiction insensibly spoils our subconscious thinking. It is shown in the article that photon has no 4-coordinates for work within atomic quantum electrodynamics (QED). This implies that actually photon is not a particle. I draw attention that QED is the most precise theory developed by humankind. It is noticed that terms photon and electromagnetic field in practice are in use as synonyms. These results validate the title of the article and exempt us from contradictions within quantum mechanics.展开更多
12001 Transformational Logic of Numbers of PurseEquivalence and its Error. Xiao Mingyao: 1(3). 1980pp 190--197Multiplier or counter --type adder can be usually used torealize the multiplication of two multidigit figur...12001 Transformational Logic of Numbers of PurseEquivalence and its Error. Xiao Mingyao: 1(3). 1980pp 190--197Multiplier or counter --type adder can be usually used torealize the multiplication of two multidigit figures. Thispaper Introduces a Simple method, namely. the展开更多
Lipids play an important role in plants due to their abundance and their extensive participation in many metabolic processes.Genes involved in lipid metabolism have been extensively studied in Arabidopsis and other pl...Lipids play an important role in plants due to their abundance and their extensive participation in many metabolic processes.Genes involved in lipid metabolism have been extensively studied in Arabidopsis and other plant species.In this study,a total of 1003 maize lipid-related genes were cloned and annotated,including 42 genes with experimental validation,732 genes with full-length cDNA and protein sequences in public databases and 229 newly cloned genes.Ninety-seven maize lipid-related genes with tissue-preferential expression were discovered by in silico gene expression profiling based on 1984483 maize Expressed Sequence Tags collected from 182 cDNA libraries.Meanwhile,70 QTL clusters for maize kernel oil were identified,covering 34.5%of the maize genome.Fifty-nine(84%)QTL clusters co-located with at least one lipid-related gene,and the total number of these genes amounted to 147.Interestingly,thirteen genes with kernel-preferential expression profiles fell within QTL clusters for maize kernel oil content.All the maize lipid-related genes identified here may provide good targets for maize kernel oil QTL cloning and thus help us to better understand the molecular mechanism of maize kernel oil accumulation.展开更多
Quantitative investment(abbreviated as“quant”in this paper)is an interdisciplinary field combining financial engineering,computer science,mathematics,statistics,etc.Quant has become one of the mainstream investment ...Quantitative investment(abbreviated as“quant”in this paper)is an interdisciplinary field combining financial engineering,computer science,mathematics,statistics,etc.Quant has become one of the mainstream investment methodologies over the past decades,and has experienced three generations:quant 1.0,trading by mathematical modeling to discover mis-priced assets in markets;quant 2.0,shifting the quant research pipeline from small“strategy workshops”to large“alpha factories”;quant 3.0,applying deep learning techniques to discover complex nonlinear pricing rules.Despite its advantage in prediction,deep learning relies on extremely large data volume and labor-intensive tuning of“black-box”neural network models.To address these limitations,in this paper,we introduce quant 4.0 and provide an engineering perspective for next-generation quant.Quant 4.0 has three key differentiating components.First,automated artificial intelligence(AI)changes the quant pipeline from traditional hand-crafted modeling to state-of-the-art automated modeling and employs the philosophy of“algorithm produces algorithm,model builds model,and eventually AI creates AI.”Second,explainable AI develops new techniques to better understand and interpret investment decisions made by machine learning black boxes,and explains complicated and hidden risk exposures.Third,knowledge-driven AI supplements data-driven AI such as deep learning and incorporates prior knowledge into modeling to improve investment decisions,in particular for quantitative value investing.Putting all these together,we discuss how to build a system that practices the quant 4.0 concept.We also discuss the application of large language models in quantitative finance.Finally,we propose 10 challenging research problems for quant technology,and discuss potential solutions,research directions,and future trends.展开更多
It is well known that the finite forcing companion T<sup>f</sup> is, in general, hyperarithmetic over T<sub>?</sub> . We obtain T<sup>f</sup> is T<sub>?</sub>-decidable ...It is well known that the finite forcing companion T<sup>f</sup> is, in general, hyperarithmetic over T<sub>?</sub> . We obtain T<sup>f</sup> is T<sub>?</sub>-decidable if it ∑<sub>1</sub><sup>T<sub>?</sub></sup>. The way of computing, one of the achievements of the 19th century mathematics, was found, by which for each positive primitive formula ψ, a single quantifier-free（Q-free） formula ψ* is equivalent to Res<sub>ψ</sub> mod T, where T is the theory of fields. A. Robinson developed a model-theoretic approach, showing the exist-展开更多
文摘目的:探讨mDixon‐Quant联合血细胞参数评估直肠癌(rectal cancer,RC)脉管侵犯(lymphovascular invasion,LVI)、淋巴结转移(lymph node metastasis,LNM)的价值。方法:回顾性收集2022年11月至2024年6月华北理工大学附属医院经病理证实62例RC患者的临床和MRI资料,计算各血细胞参数比值,测量病灶mDixon‐Quant参数脂肪分数(fat fraction,FF)、R2^(*)值、T2^(*)值并比较各组差异。进行多因素Logistic回归分析寻找LVI、LNM相关风险因素,计算受试者工作特征曲线下面积(area under the curve,AUC)评价预测效能。平滑曲线拟合及Spearman相关性分析用于评价影像学和血细胞参数之间的关系。结果:LVI阳性组R2^(*)值、中性粒细胞/淋巴细胞值(neutrophil to lymphocyte ratio,NLR)、系统免疫炎症指数(systemic immuneinflammation index,SII)、全身炎症反应指数(systemic inflammatory response index,SIRI)高于阴性组;LNM阳性组R2^(*)值、FF值、NLR、血小板/淋巴细胞值(platelet to lymphocyte ratio,PLR)、SII、SIRI高于阴性组,以上差异均有统计学意义(P<0.05)。R2^(*)、SII是LVI的独立风险因素,R2^(*)、SII及R2^(*)+SII的AUC分别为0.752、0.802及0.883。R2^(*)、FF和SII是LNM的独立风险因素,R2^(*)、FF、SII、R2^(*)+FF及R2^(*)+FF+SII的AUC分别为0.733、0.702、0.778、0.825及0.857。相关性分析显示FF与NLR、单核细胞/淋巴细胞值(monocyte to lymphocyte ratio,MLR)、SII、SIRI呈正相关(r=0.534、0.451、0.353、0.468,均P<0.05)。结论:mDixonQuant及血细胞参数均可有效评估RC LVI、LNM状态。此外,FF与多个血细胞参数之间存在相关性,提示了癌症的异常脂质代谢及炎症反应在RC发展中的作用,为临床个体化治疗方案的制定提供新思路。
文摘We well know that photon concept is self-contradictory because we assume that it is a particle with wave properties. This contradiction insensibly spoils our subconscious thinking. It is shown in the article that photon has no 4-coordinates for work within atomic quantum electrodynamics (QED). This implies that actually photon is not a particle. I draw attention that QED is the most precise theory developed by humankind. It is noticed that terms photon and electromagnetic field in practice are in use as synonyms. These results validate the title of the article and exempt us from contradictions within quantum mechanics.
文摘12001 Transformational Logic of Numbers of PurseEquivalence and its Error. Xiao Mingyao: 1(3). 1980pp 190--197Multiplier or counter --type adder can be usually used torealize the multiplication of two multidigit figures. Thispaper Introduces a Simple method, namely. the
基金supported by the National HiTech Research and Development Program of China(Grant Nos.2006AA100103,2006AA10Z183)the College Science Research and Business Plan Project sponsored by the Education Committee of Peking City
文摘Lipids play an important role in plants due to their abundance and their extensive participation in many metabolic processes.Genes involved in lipid metabolism have been extensively studied in Arabidopsis and other plant species.In this study,a total of 1003 maize lipid-related genes were cloned and annotated,including 42 genes with experimental validation,732 genes with full-length cDNA and protein sequences in public databases and 229 newly cloned genes.Ninety-seven maize lipid-related genes with tissue-preferential expression were discovered by in silico gene expression profiling based on 1984483 maize Expressed Sequence Tags collected from 182 cDNA libraries.Meanwhile,70 QTL clusters for maize kernel oil were identified,covering 34.5%of the maize genome.Fifty-nine(84%)QTL clusters co-located with at least one lipid-related gene,and the total number of these genes amounted to 147.Interestingly,thirteen genes with kernel-preferential expression profiles fell within QTL clusters for maize kernel oil content.All the maize lipid-related genes identified here may provide good targets for maize kernel oil QTL cloning and thus help us to better understand the molecular mechanism of maize kernel oil accumulation.
文摘Quantitative investment(abbreviated as“quant”in this paper)is an interdisciplinary field combining financial engineering,computer science,mathematics,statistics,etc.Quant has become one of the mainstream investment methodologies over the past decades,and has experienced three generations:quant 1.0,trading by mathematical modeling to discover mis-priced assets in markets;quant 2.0,shifting the quant research pipeline from small“strategy workshops”to large“alpha factories”;quant 3.0,applying deep learning techniques to discover complex nonlinear pricing rules.Despite its advantage in prediction,deep learning relies on extremely large data volume and labor-intensive tuning of“black-box”neural network models.To address these limitations,in this paper,we introduce quant 4.0 and provide an engineering perspective for next-generation quant.Quant 4.0 has three key differentiating components.First,automated artificial intelligence(AI)changes the quant pipeline from traditional hand-crafted modeling to state-of-the-art automated modeling and employs the philosophy of“algorithm produces algorithm,model builds model,and eventually AI creates AI.”Second,explainable AI develops new techniques to better understand and interpret investment decisions made by machine learning black boxes,and explains complicated and hidden risk exposures.Third,knowledge-driven AI supplements data-driven AI such as deep learning and incorporates prior knowledge into modeling to improve investment decisions,in particular for quantitative value investing.Putting all these together,we discuss how to build a system that practices the quant 4.0 concept.We also discuss the application of large language models in quantitative finance.Finally,we propose 10 challenging research problems for quant technology,and discuss potential solutions,research directions,and future trends.
文摘It is well known that the finite forcing companion T<sup>f</sup> is, in general, hyperarithmetic over T<sub>?</sub> . We obtain T<sup>f</sup> is T<sub>?</sub>-decidable if it ∑<sub>1</sub><sup>T<sub>?</sub></sup>. The way of computing, one of the achievements of the 19th century mathematics, was found, by which for each positive primitive formula ψ, a single quantifier-free（Q-free） formula ψ* is equivalent to Res<sub>ψ</sub> mod T, where T is the theory of fields. A. Robinson developed a model-theoretic approach, showing the exist-
