Deep Learning of Phase Transitions for Quantum Spin Chains from Correlation Aspects

Using machine learning (ML) to recognize different phases of matter and to infer the entire phase diagram has proven to be an effective tool given a large dataset. In our previous proposals, we have successfully explored phase transitions for topological phases of matter at low dimensions either in a supervised or an unsupervised learning protocol with the assistance of quantum information related quantities. In this work, we adopt our previous ML procedures to study quantum phase transitions of magnetism systems such as the XY and XXZ spin chains by using spin-spin correlation functions as the input data. We find that our proposed approach not only maps out the phase diagrams with accurate phase boundaries, but also indicates some new features that have not observed before. In particular, we define so-called relevant correlation functions to some corresponding phases that can always distinguish between those and their neighbors. Based on the unsupervised learning protocol we proposed [Phys. Rev. B 104, 165108 (2021)], the reduced latent representations of the inputs combined with the clustering algorithm show the connectedness or disconnectedness between neighboring clusters (phases), just corresponding to the continuous or disrupt quantum phase transition, respectively.Comment: 18 pages, 21 figure

Human parvovirus B19 nonstructural protein NS1 enhanced the expression of cleavage of 70 kDa U1-snRNP autoantigen

<p>Abstract</p> <p>Background</p> <p>Human parvovirus B19 (B19) is known to induce apoptosis that has been associated with a variety of autoimmune disorders. Although we have previously reported that B19 non-structural protein (NS1) induces mitochondrial-dependent apoptosis in COS-7 cells, the precise mechanism of B19-NS1 in developing autoimmunity is still obscure.</p> <p>Methods</p> <p>To further examine the effect of B19-NS1 in presence of autoantigens, COS-7 cells were transfected with pEGFP, pEGFP-B19-NS1 and pEGFP-NS1K334E, a mutant form of B19-NS1, and detected the expressions of autoantigens by various autoantibodies against Sm, U1 small nuclear ribonucleoprotein (U1-snRNP), SSA/Ro, SSB/La, Scl-70, Jo-1, Ku, and centromere protein (CENP) A/B by using Immunoblotting.</p> <p>Results</p> <p>Significantly increased apoptosis was detected in COS-7 cells transfected with pEGFP-B19-NS1 compared to those transfected with pEGFP. Meanwhile, the apoptotic 70 kDa U1-snRNP protein in COS-7 cells transfected with pEGFP-B19-NS1 is cleaved by caspase-3 and converted into a specific 40 kDa product, which were recognized by anti-U1-snRNP autoantibody. In contrast, significantly decreased apoptosis and cleaved 40 kDa product were observed in COS-7 cells transfected with pEGFP-NS1K334E compared to those transfected with pEGFP-B19-NS1.</p> <p>Conclusions</p> <p>These findings suggested crucial association of B19-NS1 in development of autoimmunity by inducing apoptosis and specific cleavage of 70 kDa U1-snRNP.</p

Dynamic neural networks for real-time water level predictions of sewerage systems-covering gauged and ungauged sites

[[abstract]]In this research, we propose recurrent neural networks (RNNs) to build a relationship between rainfalls and water level patterns of an urban sewerage system based on historical torrential rain/storm events. The RNN allows signals to propagate in both forward and backward directions, which offers the network dynamic memories. Besides, the information at the current time-step with a feedback operation can yield a time-delay unit that provides internal input information at the next time-step to effectively deal with time-varying systems. The RNN is implemented at both gauged and ungauged sites for 5-, 10-, 15-, and 20-min-ahead water level predictions. The results show that the RNN is capable of learning the nonlinear sewerage system and producing satisfactory predictions at the gauged sites. Concerning the ungauged sites, there are no historical data of water level to support prediction. In order to overcome such problem, a set of synthetic data, generated from a storm water management model (SWMM) under cautious verification process of applicability based on the data from nearby gauging stations, are introduced as the learning target to the training procedure of the RNN and moreover evaluating the performance of the RNN at the ungauged sites. The results demonstrate that the potential role of the SWMM coupled with nearby rainfall and water level information can be of great use in enhancing the capability of the RNN at the ungauged sites. Hence we can conclude that the RNN is an effective and suitable model for successfully predicting the water levels at both gauged and ungauged sites in urban sewerage systems.[[incitationindex]]SCI[[booktype]]紙

Poincaré Plot of Fingertip Photoplethysmogram Pulse Amplitude Suitable to Assess Diabetes Status

Multiscale entropy (MSE), an estimate of the complexity of physiological signals has been used for assessing diabetes status. This method requires much computation effort. Our study aimed to examine the Poincaré plot, an easier method for computation to differentiate the diabetes status. We selected subjects and divided them into three groups including the non- diabetes (HbA1c ≤ 6.5%, n=22), diabetes with good control (6.5% &lt; HbA1c &lt; 8%, n=23), and diabetes with poor control (HbA1c ≥ 8%, n=17). Poincaré method used consecutive 250 data points of PPG pulse amplitudes from each subject’s right index fingertip. This method resulted in SSR, the standard deviation of the original photoplethysmogram (PPG) pulse amplitude (SD1) and the standard deviation of the interval 1 PPG pulse amplitude (SD2) ratio. The SSR in the three groups of non-diabetes, diabetes with good control and diabetes with poor control were 0.50, 0.28, and 0.23, respectively and differed between groups (P &lt; 0.05).  Our findings suggested that the Poincaré plot of right-hand PPG pulse amplitude may be convenient to evaluate diabetes status

Aging-Induced Dynamics for Statically Indeterminate System

Statically indeterminate systems are experimentally demonstrated to be in fact dynamical at the microscopic scale. Take the classic ladder-wall problem, for instance. Depending on the Young's modulus of the wall, it may take up to twenty minutes before its weight saturates. This finding is shown to be shared by other statically indeterminate systems, such as a granule silo and a beam with three support points. We believe that the aging effect is responsible for this surprising phenomenon because it can be correlated with the evolution of microscopic contact area with the wall and floor. Finally, a heuristic and simple method is introduced that can uniquely determine and analytically solve the saturated weight without invoking detailed material properties.Comment: 5 pages, 5 figure