A Riemannian Primal-dual Algorithm Based on Proximal Operator and its Application in Metric Learning

In this paper, we consider optimizing a smooth, convex, lower semicontinuous function in Riemannian space with constraints. To solve the problem, we first convert it to a dual problem and then propose a general primal-dual algorithm to optimize the primal and dual variables iteratively. In each optimization iteration, we employ a proximal operator to search optimal solution in the primal space. We prove convergence of the proposed algorithm and show its non-asymptotic convergence rate. By utilizing the proposed primal-dual optimization technique, we propose a novel metric learning algorithm which learns an optimal feature transformation matrix in the Riemannian space of positive definite matrices. Preliminary experimental results on an optimal fund selection problem in fund of funds (FOF) management for quantitative investment showed its efficacy.Comment: 8 pages, 2 figures, published as a conference paper in 2019 International Joint Conference on Neural Networks (IJCNN

Asymmetric Flow Control in a Slab Mold through a New Type of Electromagnetic Field Arrangement

This research aims to investigate the control effect of asymmetric flow in a slab mold using a novel magnetic field arrangement: freestanding adjustable combination electromagnetic brake (FAC-EMBr). Three scenarios (submerged entry nozzle moves to the narrow face, wide face of the slab mold, and rotates 10°) were studied using three-dimensional numerical simulation. The results show that the magnetic field generated by the FAC-EMBr system can effectively cover three key zones in mold and that the magnetic flux density in the zone cover by a vertical magnetic pole can be adjusted according to the actual flow condition. The FAC-EMBr can effectively improve the asymmetric flow in a mold and near the narrow surface caused by the asymmetric arrangement of the nozzle and can effectively inhibit the occurrence of the flow deviation phenomenon and stabilize the steel/slag interface fluctuation. At the same time, FAC-EMBr has obvious inhibition effects on the surface velocity and can optimize the asymmetric distribution of the surface velocity and the upper reflux velocity caused by the asymmetric arrangement of the nozzle. This study can provide theoretical evidence for the development and utilization of a new electromagnetic brake technology

A Graph Regularized Point Process Model For Event Propagation Sequence

Point process is the dominant paradigm for modeling event sequences occurring at irregular intervals. In this paper we aim at modeling latent dynamics of event propagation in graph, where the event sequence propagates in a directed weighted graph whose nodes represent event marks (e.g., event types). Most existing works have only considered encoding sequential event history into event representation and ignored the information from the latent graph structure. Besides they also suffer from poor model explainability, i.e., failing to uncover causal influence across a wide variety of nodes. To address these problems, we propose a Graph Regularized Point Process (GRPP) that can be decomposed into: 1) a graph propagation model that characterizes the event interactions across nodes with neighbors and inductively learns node representations; 2) a temporal attentive intensity model, whose excitation and time decay factors of past events on the current event are constructed via the contextualization of the node embedding. Moreover, by applying a graph regularization method, GRPP provides model interpretability by uncovering influence strengths between nodes. Numerical experiments on various datasets show that GRPP outperforms existing models on both the propagation time and node prediction by notable margins.Comment: IJCNN 202

End-to-End Modeling Hierarchical Time Series Using Autoregressive Transformer and Conditional Normalizing Flow based Reconciliation

Multivariate time series forecasting with hierarchical structure is pervasive in real-world applications, demanding not only predicting each level of the hierarchy, but also reconciling all forecasts to ensure coherency, i.e., the forecasts should satisfy the hierarchical aggregation constraints. Moreover, the disparities of statistical characteristics between levels can be huge, worsened by non-Gaussian distributions and non-linear correlations. To this extent, we propose a novel end-to-end hierarchical time series forecasting model, based on conditioned normalizing flow-based autoregressive transformer reconciliation, to represent complex data distribution while simultaneously reconciling the forecasts to ensure coherency. Unlike other state-of-the-art methods, we achieve the forecasting and reconciliation simultaneously without requiring any explicit post-processing step. In addition, by harnessing the power of deep model, we do not rely on any assumption such as unbiased estimates or Gaussian distribution. Our evaluation experiments are conducted on four real-world hierarchical datasets from different industrial domains (three public ones and a dataset from the application servers of Alipay's data center) and the preliminary results demonstrate efficacy of our proposed method

SLOTH: Structured Learning and Task-based Optimization for Time Series Forecasting on Hierarchies

Multivariate time series forecasting with hierarchical structure is widely used in real-world applications, e.g., sales predictions for the geographical hierarchy formed by cities, states, and countries. The hierarchical time series (HTS) forecasting includes two sub-tasks, i.e., forecasting and reconciliation. In the previous works, hierarchical information is only integrated in the reconciliation step to maintain coherency, but not in forecasting step for accuracy improvement. In this paper, we propose two novel tree-based feature integration mechanisms, i.e., top-down convolution and bottom-up attention to leverage the information of the hierarchical structure to improve the forecasting performance. Moreover, unlike most previous reconciliation methods which either rely on strong assumptions or focus on coherent constraints only,we utilize deep neural optimization networks, which not only achieve coherency without any assumptions, but also allow more flexible and realistic constraints to achieve task-based targets, e.g., lower under-estimation penalty and meaningful decision-making loss to facilitate the subsequent downstream tasks. Experiments on real-world datasets demonstrate that our tree-based feature integration mechanism achieves superior performances on hierarchical forecasting tasks compared to the state-of-the-art methods, and our neural optimization networks can be applied to real-world tasks effectively without any additional effort under coherence and task-based constraint

10.13% Efficiency All-Polymer Solar Cells Enabled by Improving the Optical Absorption of Polymer Acceptors

The limited light absorption capacity for most polymer acceptors hinders the improvement of the power conversion efficiency (PCE) of all-polymer solar cells (all-PSCs). Herein, by simultaneously increasing the conjugation of the acceptor unit and enhancing the electron-donating ability of the donor unit, a novel narrow-bandgap polymer acceptor PF3-DTCO based on an A–D–A-structured acceptor unit ITIC16 and a carbon–oxygen (C–O)-bridged donor unit DTCO is developed. The extended conjugation of the acceptor units from IDIC16 to ITIC16 results in a red-shifted absorption spectrum and improved absorption coefficient without significant reduction of the lowest unoccupied molecular orbital energy level. Moreover, in addition to further broadening the absorption spectrum by the enhanced intramolecular charge transfer effect, the introduction of C–O bridges into the donor unit improves the absorption coefficient and electron mobility, as well as optimizes the morphology and molecular order of active layers. As a result, the PF3-DTCO achieves a higher PCE of 10.13% with a higher short-circuit current density (Jsc) of 15.75 mA cm−2 in all-PSCs compared with its original polymer acceptor PF2-DTC (PCE = 8.95% and Jsc = 13.82 mA cm−2). Herein, a promising method is provided to construct high-performance polymer acceptors with excellent optical absorption for efficient all-PSCs

Clinical Performance Evaluation of VersaTrek 528 Blood Culture System in a Chinese Tertiary Hospital

Background: The aim of this study was to evaluate the clinical performance of VersaTrek 528 compared to BACTEC FX 400 blood culture (BC) systems.Materials and Methods: Simulated and clinically obtained BCs were used in the study. Confirmed bacterial species (n = 78), including 43 Gram-positives, 30 Gram-negatives, and 5 Candida albicans strains, were each inoculated into BC bottles. Clinically obtained BCs were subdivided into two groups, A and B. In group A were 72 BC sets (pair: aerobic and anaerobic) in which a set inoculated with 5 ml blood was processed in the VersaTrek BC system, whilst the one inoculated with 10 ml blood was processed in the FX BC system. In group B, 76 BC sets (pairs) corresponding to 152 VersaTrek bottles and 152 FX bottles were inoculated with the same volume (10 ml) of blood, and processed in each system.Results: In the simulated BC study, 90% (63/70) of the VersaTrek aerobic bottles were positive, which was higher than that of FX 400 (59/70, 84%), but was not statistically significant (P = 0.423). In contrast, FX 400 anaerobic bottles had a higher positive rate than the other BC system (84 vs. 77%), although it was statistically insignificant (P = 0.267). Time to detection of organisms in the two BCs was comparable for both aerobic (P = 0.131) and anaerobic bottles (P = 0.104). In clinical BCs of group A, FX BC system had slightly higher positive rates for both aerobic (11.1 vs. 9.7%, P = 0.312) and anaerobic (8.3 vs. 6.9%, P = 0.375) bottles. However, the difference was not statistically significant. In group B, VersaTrek aerobic bottles had a higher positive rate compared to the other BC system (10.5 vs. 5.2%, P = 0.063). In terms of positive rates of sub-studies A and B, VersaTrek and FX BC systems were comparable.Conclusion: There was no significant difference between the two BC systems in the detection of bacteria and fungi in simulated BCs. In clinical BCs, the performance of the VersaTrek BC system, with inoculation of 5 or 10 ml patient’s blood, was comparable to the FX system with inoculation of 10 ml patient’s blood

Synaptic Neurotransmission Depression in Ventral Tegmental Dopamine Neurons and Cannabinoid-Associated Addictive Learning

Drug addiction is an association of compulsive drug use with long-term associative learning/memory. Multiple forms of learning/memory are primarily subserved by activity- or experience-dependent synaptic long-term potentiation (LTP) and long-term depression (LTD). Recent studies suggest LTP expression in locally activated glutamate synapses onto dopamine neurons (local Glu-DA synapses) of the midbrain ventral tegmental area (VTA) following a single or chronic exposure to many drugs of abuse, whereas a single exposure to cannabinoid did not significantly affect synaptic plasticity at these synapses. It is unknown whether chronic exposure of cannabis (marijuana or cannabinoids), the most commonly used illicit drug worldwide, induce LTP or LTD at these synapses. More importantly, whether such alterations in VTA synaptic plasticity causatively contribute to drug addictive behavior has not previously been addressed. Here we show in rats that chronic cannabinoid exposure activates VTA cannabinoid CB1 receptors to induce transient neurotransmission depression at VTA local Glu-DA synapses through activation of NMDA receptors and subsequent endocytosis of AMPA receptor GluR2 subunits. A GluR2-derived peptide blocks cannabinoid-induced VTA synaptic depression and conditioned place preference, i.e., learning to associate drug exposure with environmental cues. These data not only provide the first evidence, to our knowledge, that NMDA receptor-dependent synaptic depression at VTA dopamine circuitry requires GluR2 endocytosis, but also suggest an essential contribution of such synaptic depression to cannabinoid-associated addictive learning, in addition to pointing to novel pharmacological strategies for the treatment of cannabis addiction

AIDA directly connects sympathetic innervation to adaptive thermogenesis by UCP1

AIDA最早是由林圣彩教授团队首先鉴定和命名的。2007年林圣彩教授团队与孟安明院士团队合作发现AIDA在斑马鱼体轴发育中的功能(Rui, 2007)。2018年，林圣彩教授团队首次发现了AIDA在哺乳动物中的功能，即AIDA介导的内质网降解途径通过降解脂肪合成途径中的关键酶，而限制膳食脂肪在肠道的吸收这一内在抵御肥胖(Luo, 2018)。而本次成果揭示了AIDA在棕色脂肪组织中特定的功能。这些工作将AIDA引入了脂质应激代谢的重要环节，包括脂质吸收和依赖于脂质的产热过程。该论文的共同第一作者为生命科学学院博士生史猛和硕士生黄晓羽，林圣彩教授和林舒勇教授则为共同通讯作者。【Abstract】The sympathetic nervous system–catecholamine–uncoupling protein 1 (UCP1) axis plays an essential role in non-shivering adaptive thermogenesis. However, whether there exists a direct effector that physically connects catecholamine signalling to UCP1 in response to acute cold is unknown. Here we report that outer mitochondrial membrane-located AIDA is phosphorylated at S161 by the catecholamine-activated protein kinase A (PKA). Phosphorylated AIDA translocates to the intermembrane space, where it binds to and activates the uncoupling activity of UCP1 by promoting cysteine oxidation of UCP1.Adipocyte-specific depletion of AIDA abrogates UCP1-dependent thermogenesis, resulting in hypothermia during acute cold exposure. Re-expression of S161A-AIDA, unlike wild-type AIDA, fails to restore the acute cold response in Aida-knockout mice.The PKA–AIDA–UCP1 axis is highly conserved in mammals, including hibernators. Denervation of the sympathetic postganglionic fibres abolishes cold-induced AIDA-dependent thermogenesis. These findings uncover a direct mechanistic link between sympathetic input and UCP1-mediated adaptive thermogenesis.We thank Y. Li, E. Gnaiger, T. Kuwaki, J. R. B. Lighton, E. T. Chouchani and D. Jiang for technical instruction; X. Li and X.-D. Jiang (Core Facility of Biomedical, Xiamen University) for raising the p-S161-AIDA antibody; the Xiamen University Laboratory Animal Center for the mouse in vitro fertilization service and all the other members of S.C.L. laboratory for their technical assistance. This work was supported by grants from the National Key Research and Development Project of China (grant no. 2016YFA0502001) and the National Natural Science Foundation of China (grant nos 31822027, 31871168, 31690101, 91854208 and 82088102), the Fundamental Research Funds for the Central Universities (grant nos 20720190084 and 20720200069), Project ‘111’ sponsored by the State Bureau of Foreign Experts and Ministry of Education of China (grant no. BP2018017), the Youth Innovation Fund of Xiamen (grant no. 3502Z20206028), the Natural Science Foundation of Fujian Province of China (grant no. 2017J01364) and XMU Training Program of Innovation and Entrepreneurship for Undergraduates (grant no. 2019×0666). 该工作得到了厦门大学实验动物中心和生物医学学部仪器平台的重要协助和国家重点研究和发展项目，国家自然科学基金，厦门大学校长基金等的支持