An On-demand Photonic Ising Machine with Simplified Hamiltonian Calculation by Phase-encoding and Intensity Detection

Photonic Ising machine is a new paradigm of optical computing, which is based on the characteristics of light wave propagation, parallel processing and low loss transmission. Thus, the process of solving the combinatorial optimization problems can be accelerated through photonic/optoelectronic devices. In this work, we have proposed and demonstrated the so-called Phase-Encoding and Intensity Detection Ising Annealer (PEIDIA) to solve arbitrary Ising problems on demand. The PEIDIA is based on the simulated annealing algorithm and requires only one step of optical linear transformation with simplified Hamiltonian calculation. With PEIDIA, the Ising spins are encoded on the phase term of the optical field and only intensity detection is required during the solving process. As a proof of principle, several 20 and 30-dimensional Ising problems have been solved with high ground state probability

Detection of leaf structures in close-range hyperspectral images using morphological fusion

Close-range hyperspectral images are a promising source of information in plant biology, in particular, for in vivo study of physiological changes. In this study, we investigate how data fusion can improve the detection of leaf elements by combining pixel reflectance and morphological information. The detection of image regions associated to the leaf structures is the first step toward quantitative analysis on the physical effects that genetic manipulation, disease infections, and environmental conditions have in plants. We tested our fusion approach on Musa acuminata (banana) leaf images and compared its discriminant capability to similar techniques used in remote sensing. Experimental results demonstrate the efficiency of our fusion approach, with significant improvements over some conventional methods

KwaiYiiMath: Technical Report

Recent advancements in large language models (LLMs) have demonstrated remarkable abilities in handling a variety of natural language processing (NLP) downstream tasks, even on mathematical tasks requiring multi-step reasoning. In this report, we introduce the KwaiYiiMath which enhances the mathematical reasoning abilities of KwaiYiiBase1, by applying Supervised Fine-Tuning (SFT) and Reinforced Learning from Human Feedback (RLHF), including on both English and Chinese mathematical tasks. Meanwhile, we also constructed a small-scale Chinese primary school mathematics test set (named KMath), consisting of 188 examples to evaluate the correctness of the problem-solving process generated by the models. Empirical studies demonstrate that KwaiYiiMath can achieve state-of-the-art (SOTA) performance on GSM8k, CMath, and KMath compared with the similar size models, respectively.Comment: technical report. arXiv admin note: text overlap with arXiv:2306.16636 by other author

Screening biomarkers for Sjogren’s Syndrome by computer analysis and evaluating the expression correlations with the levels of immune cells

BackgroundSjögren’s syndrome (SS) is a systemic autoimmune disease that affects about 0.04-0.1% of the general population. SS diagnosis depends on symptoms, clinical signs, autoimmune serology, and even invasive histopathological examination. This study explored biomarkers for SS diagnosis.MethodsWe downloaded three datasets of SS patients’ and healthy pepole’s whole blood (GSE51092, GSE66795, and GSE140161) from the Gene Expression Omnibus (GEO) database. We used machine learning algorithm to mine possible diagnostic biomarkers for SS patients. Additionally, we assessed the biomarkers’ diagnostic value using the receiver operating characteristic (ROC) curve. Moreover, we confirmed the expression of the biomarkers through the reverse transcription quantitative polymerase chain reaction (RT-qPCR) using our own Chinese cohort. Eventually, the proportions of 22 immune cells in SS patients were calculated by CIBERSORT, and connections between the expression of the biomarkers and immune cell ratios were studied.ResultsWe obtained 43 DEGs that were mainly involved in immune-related pathways. Next, 11 candidate biomarkers were selected and validated by the validation cohort data set. Besides, the area under curves (AUC) of XAF1, STAT1, IFI27, HES4, TTC21A, and OTOF in the discovery and validation datasets were 0.903 and 0.877, respectively. Subsequently, eight genes, including HES4, IFI27, LY6E, OTOF, STAT1, TTC21A, XAF1, and ZCCHC2, were selected as prospective biomarkers and verified by RT-qPCR. Finally, we revealed the most relevant immune cells with the expression of HES4, IFI27, LY6E, OTOF, TTC21A, XAF1, and ZCCHC2.ConclusionIn this paper, we identified seven key biomarkers that have potential value for diagnosing Chinese SS patients

Nanocrystalline alternative rare earth-iron-boron permanent magnets without Nd, Pr, Tb and Dy: A review

The continuous increase in the output of Nd-Fe-B permanent magnets leads to the increasing consumption of rare earth (RE) metals of Nd, Pr, Tb and Dy. As a result, a serious unbalanced utilization of RE resource occurs. To reduce the use of these critical REs, high abundance REs of Y, La, Ce and even Gd and Ho have been proposed to partly substitute Pr and Nd in sintered magnets and rapid-quenched nanocrystalline magnetic powders. Recently, the cost-effective RE-Fe-B magnets without Nd, Pr, Tb and Dy have also been developed. This type of magnet is preliminarily designed to bridge the magnetic properties gap between hard ferrites and bonded Nd-Fe-B magnets, which shows the potential applications in voice coil motor, spindle motors and magnetic resonance imaging systems. This review summarizes the progress of RE-Fe-B-type magnets containing no Nd, Pr, Dy and Tb based on the world-wide investigations and our recent findings. The fundamentals of intrinsic magnetic properties of Y, La, Ce based RE2Fe14B phases are introduced first. The relationship between magnetic properties and microstructure of rapidly quenched and bulk RE-Fe-B magnets with nanocrystalline structure is then emphatically discussed. Hard magnetic properties and performance/cost ratio of various RE-Fe-B alloy systems are summarized and compared. Furthermore, the current discoveries and the existing challenges are discussed. Some potential strategies for the future research in this field are finally proposed

Emotional labor mediates the associations between self-consciousness and flow in dancers

Abstract Emotional labor has been a focal point in occupational well-being literature, but studies have long overlooked an important group of emotional laborers: performers. This research represents a pioneering effort to examine dancers’ adoption of emotional labor strategies, their antecedent of self-consciousness, and the outcome of flow experience. We explored these elements both in the traditional setting of stage dancing and in the novel context of online dance classes without on-site spectators during the COVID-19 pandemic. The results revealed that dancers employed all three common emotional labor strategies: surface acting, deep acting, and expression of naturally felt emotions, with deep acting being the most frequent. In the traditional setting, only the expression of naturally felt emotions mediated the positive effect of private self-consciousness and the negative effect of public self-consciousness on flow experience. In contrast, in the online setting, only private self-consciousness impacted flow through the mediation of deep acting and expression of naturally felt emotions. This exploratory study bridges dramaturgy-originated theories of emotional labor with empirical performing arts research, preliminarily advancing knowledge in the relevant fields of dance education, self-presentation, and flow studies

The complete mitochondrial genome of Monochamus alternatus alternatus (Coleoptera: Cerambycidae)

Monochamus alternatus alternatus is the major vector of pinewood nematode, Bursaphelenchus xylophilus, in Asia. The length of the complete mitochondria genome of M. alternatus alternatus was 15,880 bp with 21% GC content, including 39.7% A, 12.3% C, 8.7% G and 39.3% T. There were 13 protein-coding genes, 22 tRNAs, 2 rRNAs, and one AT-rich region. This study provides a useful genetic information for subsequent study of the differences between M. alternatus subspecies