Direct Solving the Many-Electron Schr\"odinger Equation with a Language Model

The many-electron Schr\"odinger equation is solved straightforwardly with a Transformer-based neural-network architecture (QiankunNet), which requires no external training data and significantly improves the accuracy and efficiency of first-principles calculations compared to previous Fermionic ansatz. The intricate quantum correlations are effectively captured by incorporating the attention mechanism into our methodology. Additionally, the batched sampling strategy is used to significantly improve the sampling accuracy and efficiency. Furthermore, a pre-training stage which incorporates the truncated configuration interaction solution into the variational ansatz, ensuring high expressiveness and further improving computational efficiency. QiankunNet demonstrates the power of the Transformer-based language model in achieving unprecedented efficiency in quantum chemistry calculations, which opens new avenues to chemical discovery and has the potential to solve the large-scale Schr\"odinger equation with modest computational cost

Disentangled Variational Auto-encoder Enhanced by Counterfactual Data for Debiasing Recommendation

Recommender system always suffers from various recommendation biases, seriously hindering its development. In this light, a series of debias methods have been proposed in the recommender system, especially for two most common biases, i.e., popularity bias and amplified subjective bias. However, exsisting debias methods usually concentrate on correcting a single bias. Such single-functionality debiases neglect the bias-coupling issue in which the recommended items are collectively attributed to multiple biases. Besides, previous work cannot tackle the lacking supervised signals brought by sparse data, yet which has become a commonplace in the recommender system. In this work, we introduce a disentangled debias variational auto-encoder framework(DB-VAE) to address the single-functionality issue as well as a counterfactual data enhancement method to mitigate the adverse effect due to the data sparsity. In specific, DB-VAE first extracts two types of extreme items only affected by a single bias based on the collier theory, which are respectively employed to learn the latent representation of corresponding biases, thereby realizing the bias decoupling. In this way, the exact unbiased user representation can be learned by these decoupled bias representations. Furthermore, the data generation module employs Pearl's framework to produce massive counterfactual data, making up the lacking supervised signals due to the sparse data. Extensive experiments on three real-world datasets demonstrate the effectiveness of our proposed model. Besides, the counterfactual data can further improve DB-VAE, especially on the dataset with low sparsity

NNQS-Transformer: an Efficient and Scalable Neural Network Quantum States Approach for Ab initio Quantum Chemistry

Neural network quantum state (NNQS) has emerged as a promising candidate for quantum many-body problems, but its practical applications are often hindered by the high cost of sampling and local energy calculation. We develop a high-performance NNQS method for \textit{ab initio} electronic structure calculations. The major innovations include: (1) A transformer based architecture as the quantum wave function ansatz; (2) A data-centric parallelization scheme for the variational Monte Carlo (VMC) algorithm which preserves data locality and well adapts for different computing architectures; (3) A parallel batch sampling strategy which reduces the sampling cost and achieves good load balance; (4) A parallel local energy evaluation scheme which is both memory and computationally efficient; (5) Study of real chemical systems demonstrates both the superior accuracy of our method compared to state-of-the-art and the strong and weak scalability for large molecular systems with up to $120$ spin orbitals.Comment: Accepted by SC'2

Evaluation of MicroRNA 125b as a potential biomarker for postmenopausal osteoporosis

Purpose: To identify significant dysregulated miRNAs in postmenopausal  osteoporosis in Chinese women and to test whether any of these miRNAs have diagnostic potential as circulatory biomarkers for postmenopausal osteoporosis.Methods: Thirty osteoporotic patients and 30 non-osteoporotic healthy individuals were recruited, and blood and bone tissue samples were collected from them. miRNA expression profiling and quantitative real-time polymerase chain reaction  (qRT-PCR) were used to identify and substantiate dysregulated miRNAs in blood sera and bone tissue from osteoporotic patients. Receiver operating characteristic curve (ROC) analysis was carried out to assess the diagnostic potential of significantly dysregulated miRNAs.Results: Based on profiling and qRT-PCR, miR-125b, miR-30 and miR-5914 were significantly upregulated in the blood sera and bone tissues of patients with postmenopausal osteoporosis. In all the experiments carried out, miR-125b showed the highest levels of upregulation both in the blood sera and bone tissue compared to other upregulated miRNAs in osteoporotic patients. ROC analysis indicate that the AUC of miR-125b was the highest amongst the upregulated miRNAs.Conclusion: miR-125b is the highest significantly upregulated miRNA in  postmenopausal osteoporosis. Furthermore, circulating miR-125b has the potential of a non-invasive biomarker for postmenopausal osteoporosis.Keywords: Postmenopausal osteoporosis, Profiling, Up-regulation, miR-125b,  Biomarke

Differentiable matrix product states for simulating variational quantum computational chemistry

Quantum Computing is believed to be the ultimate solution for quantum chemistry problems. Before the advent of large-scale, fully fault-tolerant quantum computers, the variational quantum eigensolver (VQE) is a promising heuristic quantum algorithm to solve real world quantum chemistry problems on near-term noisy quantum computers. Here we propose a highly parallelizable classical simulator for VQE based on the matrix product state representation of quantum state, which significantly extend the simulation range of the existing simulators. Our simulator seamlessly integrates the quantum circuit evolution into the classical auto-differentiation framework, thus the gradients could be computed efficiently similar to the classical deep neural network, with a scaling that is independent of the number of variational parameters. As applications, we use our simulator to study commonly used small molecules such as HF, HCl, LiH and H$_2$O, as well as larger molecules CO$_2$, BeH$_2$ and H$_4$ with up to $40$ qubits. The favorable scaling of our simulator against the number of qubits and the number of parameters could make it an ideal testing ground for near-term quantum algorithms and a perfect benchmarking baseline for oncoming large scale VQE experiments on noisy quantum computers

Polarization aberrations in high-numerical-aperture lens systems and their effects on vectorial-information sensing

The importance of polarization aberrations has been recognized and studied in numerous optical systems and related applications. It is known that polarization aberrations are particularly crucial in certain photogrammetry and microscopy techniques that are related to vectorial information—such as polarization imaging, stimulated emission depletion microscopy, and structured illumination microscopy. Hence, a reduction in polarization aberrations would be beneficial to different types of optical imaging/sensing techniques with enhanced vectorial information. In this work, we first analyzed the intrinsic polarization aberrations induced by a high-NA lens theoretically and experimentally. The aberrations of depolarization, diattenuation, and linear retardance were studied in detail using the Mueller matrix polar-decomposition method. Based on an analysis of the results, we proposed strategies to compensate the polarization aberrations induced by high-NA lenses for hardware-based solutions. The preliminary imaging results obtained using a Mueller matrix polarimeter equipped with multiple coated aspheric lenses for polarization-aberration reduction confirmed that the conclusions and strategies proposed in this study had the potential to provide more precise polarization information of the targets for applications spanning across classical optics, remote sensing, biomedical imaging, photogrammetry, and vectorial optical-information extraction

Electrocaloric effect in ferroelectric ceramics with point defects

The electrocaloric effect has drawn much attention due to its potential application in cooling devices. A negative electrocaloric effect is predicted to be induced in defect-doped ferroelectrics by computational results [A. Grunebohm and T. Nishimatsu, Phys. Rev. B 93, 134101 (2016) and Ma et al., Phys. Rev. B 94, 094113 (2016)], but it need to be confirmed by experimental results. In this work, we prepared a 1mol. % Mn-doped Pb(Zr0.2,Ti0.8)O3 ceramics (Pb((Zr0.2,Ti0.8)0.99,Mn0.01)O3), and the electrocaloric effect of the defect-containing ferroelectric ceramics has been investigated by both direct and indirect methods. The indirect method shows a similar negative electrocaloric effect signal as the computational results predicted, while the direct method gives a positive electrocaloric effect. The absence of the negative electrocaloric effect obtained by the direct method may originate from: (a) the unavailability and the improper prediction of the Maxwell relation, (b) an improper assumption of fixed defects in the computational models, and (c) the offset of heat loss due to the application of a large electric field. In addition, we find a giant positive electrocaloric effect of 0.55K at room temperature in the aged ceramics where no phase transition takes place. We attribute this abnormal electrocaloric effect to the restoration force of the defect dipoles. Our results not only provide insights into the origin of the negative electrocaloric effect, but also offer opportunities for the design of electrocaloric materials

Research Progress in Preparation and Application of Marine Polysaccharide-based Probiotics Microcapsules

Probiotics have a variety of probiotic effects. However, in the process of processing, storage or digestion, it is easy to reduce its activity due to the influence of external adverse environment. The use of microcapsule technology can play a good role in the protection of probiotics and reduce or avoid the impact of adverse environment. As a wall material of probiotic microcapsules, marine polysaccharides can not only boost the stress resistance and stability of probiotics, improve the sensory characteristics of probiotic products, but also enhance the therapeutic effect with probiotics. This paper analyzes the species and characteristics of marine polysaccharides from different sources. It also summarizes the preparation methods of marine polysaccharide-based probiotic microcapsules, and expounds the application of marine polysaccharide-based probiotic microcapsules in food industry, biomedicine, aquaculture feed and other fields. It is expected to provide some references for the research direction of probiotic microcapsules and the high-value utilization of marine resources

The Protective Roles of Dietary Selenium Yeast and Tea Polyphenols on Growth Performance and Ammonia Tolerance of Juvenile Wuchang Bream (Megalobrama amblycephala)

In order to investigate protective roles of dietary selenium yeast (SY) and tea polyphenols (TPs) on growth of juvenile Wuchang bream (Megalobrama amblycephala) and its resistance under ammonia stress, juvenile Wuchang bream were randomly assigned into four groups: a control group fed basal diets and three treatment groups fed basal diets supplemented with 0.50 mg/kg SY, 50 mg/kg TPs and a combination of 0.50 mg/kg SY and 50 mg/kg TPs, respectively. After 60 days of feeding, growth parameters of Wuchang bream were measured along with serum hormones and the transcription of growth axis-related genes. Then fish were exposed to ammonia stress of 22.5 mg/L total ammonia nitrogen. Hepatic oxidative damage parameters, antioxidant responses and ultrastructure were evaluated before ammonia exposure (0 h) and at 3, 6, 12, 24, and 48 h after ammonia exposure. Results show that before ammonia exposure, the growth parameters, serum GH and IGF-1 levels as well as the growth axis-related gene expression (gh, ghr2 and igf-1) for the SY and combination groups were higher than those determined for the fish on the control diet. In contrast, the administration of TP alone didn’t have significant effects on the growth parameters and growth-related hormones. After ammonia exposure, compared with the control, remarkable increases in the activity and mRNA expression of hepatic antioxidant enzymes (glutathione peroxidase and catalase superoxide dismutase) in three treatment groups were observed along with decreases of hepatic malondialdehyde and protein carbonylation levels, indicating that the single and combined supplementation of SY and TPs could enhance antioxidant capacity to alleviate oxidative stress and damage by ammonia. Consistent with this finding, alterations of the liver ultrastructure in three treatment groups were less severe and faster recovery than in the control group after ammonia exposure. In conclusion, a basal diet supplemented with the combination of 0.50 mg/kg SY and 50 mg/kg TPs could has very beneficial effects on the whole aspects of the growth and ammonia resistance in Wuchang bream juveniles