Characterization of sea surface temperature and air-sea heat flux anomalies associated with mesoscale eddies in the South China Sea

Author Posting. © American Geophysical Union, 2020. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research-Oceans 125(4), (2020): e2019JC015470, doi:10.1029/2019JC015470.This study is to quantify the effects of mesoscale eddies on air‐sea heat fluxes and related air‐sea variables in the South China Sea. Using satellite observations of sea surface temperature (SST) and sea surface height anomaly and a high‐resolution air‐sea heat flux product for the 16‐year period from 2000 to 2015, we conducted the composite patterns of air‐sea fluxes and variables associated with anticyclonic eddies (AEs) and cyclonic eddies (CEs). It is found that the SST‐sea surface height correlations over eddies are not always positive. Only 56% of AEs are corresponded with positive SST anomalies (SSTA), that is, SST+ AEs, and 58% of CEs with negative SSTA, that is, SST− CEs. The percentage of these eddies increases with eddy amplitude and shows slight seasonal variations, higher in winter and lower in summer. Composites of SSTA, air‐sea variables, and fluxes are constructed over all eddies, including both SST+ eddies and SST− eddies. All composites show asymmetric patterns, showing that the centers (where the extrema are located) of the fluxes and variables shift westward and poleward (equatorward) relative to the AEs (CEs) cores. Besides, composites of latent heat flux (LHF), sensible heat flux (SHF), and air temperature show monopole patterns, while composites of wind speed and specific humidity show dipole patterns. For SST+ AEs, the coupling strength is 39.6 ± 6.5 W/m2 (7.2 ± 1.7 W/m2) per degree increase of SSTA for LHF (SHF). For SST− CEs, the coupling strength is 39.0 ± 2.0 W/m2 (9.0 ± 0.96 W/m2) per degree decrease of SSTA for LHF (SHF).This research was conducted while Y. Liu was a visiting graduate student at Woods Hole Oceanographic Institution (WHOI). She sincerely thanks the WHOI Academic Programs Office for hosting her visit and is grateful to the support from China Scholarship Council (CSC). This study was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant XDA19060101), the Key R & D project of Shandong Province (Grant 2019JZZY010102), the Key deployment project of Center for Ocean Mega‐Science, CAS (Grant COMS2019R02), the CAS Program (Grant Y9KY04101L), and the National Natural Science Foundation of China (Grant 41776183 and 41906157). Dr. Xiangze Jin is acknowledged for providing the OAFluxHR analysis and for his programming support and guidance to this study. Heat flux data used in this paper can be downloaded (from https://figshare.com/articles/Eddy‐induced_heat_flux_in_the_South_China_Sea/11949735). AVISO SSH data are downloaded from the website (http://www.aviso.altimetry.fr), OISST from the ftp://eclipse.ncdc.noaa.gov/ site, and OAFluxHR analysis will be available from the project website (http://oaflux.whoi.edu).2020-09-1

Rubik's Optical Neural Networks: Multi-task Learning with Physics-aware Rotation Architecture

Recently, there are increasing efforts on advancing optical neural networks (ONNs), which bring significant advantages for machine learning (ML) in terms of power efficiency, parallelism, and computational speed. With the considerable benefits in computation speed and energy efficiency, there are significant interests in leveraging ONNs into medical sensing, security screening, drug detection, and autonomous driving. However, due to the challenge of implementing reconfigurability, deploying multi-task learning (MTL) algorithms on ONNs requires re-building and duplicating the physical diffractive systems, which significantly degrades the energy and cost efficiency in practical application scenarios. This work presents a novel ONNs architecture, namely, \textit{RubikONNs}, which utilizes the physical properties of optical systems to encode multiple feed-forward functions by physically rotating the hardware similarly to rotating a \textit{Rubik's Cube}. To optimize MTL performance on RubikONNs, two domain-specific physics-aware training algorithms \textit{RotAgg} and \textit{RotSeq} are proposed. Our experimental results demonstrate more than 4$\times$ improvements in energy and cost efficiency with marginal accuracy degradation compared to the state-of-the-art approaches.Comment: To appear at 32nd International Joint Conference on Artificial Intelligence (IJCAI'23

Norovirus GII.17: The Emergence and Global Prevalence of a Novel Variant

A rare norovirus (NoV) genotype GII.17 has recently emerged and rapidly became predominant in most East Asian countries in the winters of 2014–2015. In this study, we report the diversity of NoV GII.17 in detail; a total of 646 GII.17 sequences obtained during 1978–2015 were analyzed and subjected to meta-analysis. At least five major recombinant GII.17 clusters were identified. Each recombinant variant group appeared to have emerged following the time order: GII.P4-GII.17 (1978–1990), GII.P16-GII.17 (2001–2004), GII.P13-GII.17 (2004–2010), GII.Pe-GII.17 (2012–2015) and GII.P3-GII.17 (2011–2015). The newly emerged GII.P3-GII.17 variant, which exhibited significant sequence and structure variations, is evolving toward a unique lineage. Our results indicate that circulation of GII.17 appears to change every 3–5 years due to replacement by a newly emerged variant and that the evolution of GII.17 is sequentially promoted by inter-genotype recombination, which contributes to the exchange between non-GII.17 and GII.17 RdRp genes and drives the evolution of GII.17 capsid genes

RESPECT: Reinforcement Learning based Edge Scheduling on Pipelined Coral Edge TPUs

Deep neural networks (DNNs) have substantial computational and memory requirements, and the compilation of its computational graphs has a great impact on the performance of resource-constrained (e.g., computation, I/O, and memory-bound) edge computing systems. While efficient execution of their computational graph requires an effective scheduling algorithm, generating the optimal scheduling solution is a challenging NP-hard problem. Furthermore, the complexity of scheduling DNN computational graphs will further increase on pipelined multi-core systems considering memory communication cost, as well as the increasing size of DNNs. Using the synthetic graph for the training dataset, this work presents a reinforcement learning (RL) based scheduling framework RESPECT, which learns the behaviors of optimal optimization algorithms and generates near-optimal scheduling results with short solving runtime overhead. Our framework has demonstrated up to $\sim2.5\times$ real-world on-chip inference runtime speedups over the commercial compiler with ten popular ImageNet models deployed on the physical Coral Edge TPUs system. Moreover, compared to the exact optimization methods, the proposed RL scheduling improves the scheduling optimization runtime by up to 683$\times$ speedups compared to the commercial compiler and matches the exact optimal solutions with up to 930$\times$ speedups. Finally, we perform a comprehensive generalizability test, which demonstrates RESPECT successfully imitates optimal solving behaviors from small synthetic graphs to large real-world DNNs computational graphs.Comment: 6 pages, ACM/IEEE Design Automation Conference (DAC'23

Origin Distribution Visualization of Floating Population and Determinants Analysis: A Case study of Yiwu City

AbstractBased on registered individual floating population data from 2005 to 2008 of Yiwu, the phenomena that population floating to Yiwu City from 34 province and 91 counties in Jiangxi provinces is analyzed. The study aims at analyzing the “pull” forces of Yiwu City and developing migration models for understanding determinants factors of population migration/floating into Yiwu City from other areas in China. The spatial layout of Yiwu's pull forces is proved as a V-shaped pattern consisting of the two axes by using explorative spatial data analysis and map visualization method. The migration models with (model 3) or without (model 2) migration stock are presented and estimated using standard linear regression model, spatial error model as well as spatial lag model at the county scale in Jiangxi province. Based on the likelihood statistics, the AIC and the Moran's I statistics of residuals, the model with migration stock provides an improved fit over the model without migration stock. The correlation between migration ratio and man land ratio is significant at the 0.5 level according to estimates of model 3 and spatial version of model 2. All the three estimates of model 2 and the OLS results of model 3 confirm the distance-decay effect while results from the spatial version of model 3 failed to support the distance rule in population floating. Contrary to the previous studies at the provincial level, the correlation between per capital net income of rural labor forces and migration ratio is not significant according to the three versions of the two models due to the small disparities of income within the counties in Jiangxi. Examination of specification tests in spatial version of model 3 indicates that there is less significant spatial error dependence in the spatial lag models than spatial lag dependence in the error models, further suggesting a preference for the lag model. Model 2 does not suggest any preference for choosing spatial error model and spatial lag model

A thesis on algorithmic trading

Algorithmic trading is one of the most phenomenal changes in the financial industry in the past decade. While the impacts are significant, the microstructure of algorithmic trading remains unknown.By using Diff-in-Diff analysis, this paper shows that for low price securities, algorithmic trading activities are more active than high price securities. Besides, algorithm trading per se may also trigger significant price impact. As a result, algorithmic order execution has to be dynamically adapted to real-time market environments. This makes dynamic programming (DP) the most natural approach. This paper builds a optimal order execution model using dynamic programming. It works with the mean-variance utilities of Almgren and Chriss (J. Risk, 3, 2000) to effectively express risk aversion of a typical trader. The new framework is demonstrated through building one particular style called MV-MVP, i.e., the mean-variance (MV) objective formulated upon the state variables of moneyness and volume participation (MVP). The MV-MVP style generalizes the VWAP strategy by facilitating dynamic reactions to moneyness and by embodying the popular street practice of trading aggressively or passively while in the money. Simulated dynamic trading paths illustrates the MV-MVP style oscillates around the VWAP strategy

Verilog-to-PyG -- A Framework for Graph Learning and Augmentation on RTL Designs

The complexity of modern hardware designs necessitates advanced methodologies for optimizing and analyzing modern digital systems. In recent times, machine learning (ML) methodologies have emerged as potent instruments for assessing design quality-of-results at the Register-Transfer Level (RTL) or Boolean level, aiming to expedite design exploration of advanced RTL configurations. In this presentation, we introduce an innovative open-source framework that translates RTL designs into graph representation foundations, which can be seamlessly integrated with the PyTorch Geometric graph learning platform. Furthermore, the Verilog-to-PyG (V2PYG) framework is compatible with the open-source Electronic Design Automation (EDA) toolchain OpenROAD, facilitating the collection of labeled datasets in an utterly open-source manner. Additionally, we will present novel RTL data augmentation methods (incorporated in our framework) that enable functional equivalent design augmentation for the construction of an extensive graph-based RTL design database. Lastly, we will showcase several using cases of V2PYG with detailed scripting examples. V2PYG can be found at \url{https://yu-maryland.github.io/Verilog-to-PyG/}.Comment: 8 pages, International Conference on Computer-Aided Design (ICCAD'23

CCAAT/Enhancer Binding Protein-delta (C/EBP-delta) regulates cell growth, migration and differentiation

<p>Abstract</p> <p>Background</p> <p>CCAAT/enhancer binding protein-delta (C/EBP-delta) is a member of the highly conserved C/EBP family of basic region leucine zipper transcription factors. C/EBP family members regulate cell growth and differentiation and "loss of function" alterations in C/EBPs have been reported in a variety of human cancers. C/EBP-delta gene expression is upregulated by G₀growth arrest, IL-6 family cytokines and endotoxin treatments. C/EBP-delta exhibits properties of a tumor suppressor gene, including reduced expression and promoter methylation-induced silencing in transformed cell lines and primary tumors. In addition, C/EBP-delta gene expression is repressed by c-Myc, an oncogene that is over-expressed in a wide range of human cancers. "ChIP-chip" studies demonstrated that C/EBP-delta functions as a transcriptional activator of target genes that function in intracellular signal transduction, transcription, DNA binding/repair, cell cycle control, cell adhesion, and apoptosis. Despite progress in determining the biochemical functions of C/EBP-delta, the specific cellular defects that are induced by C/EBP-delta "loss of function" alterations are poorly understood. This study investigated the impact of C/EBP-delta "loss of function" alterations on growth arrest, migration/invasion and differentiation in nontransformed mouse mammary epithelial cells (MECs) and primary mouse embryo fibroblasts (MEFs).</p> <p>Results</p> <p>C/EBP-delta siRNA transfected MECs exhibited ~90% reduction in C/EBP-delta mRNA and protein levels. C/EBP-delta siRNA treatment resulted in defective growth arrest as demonstrated by persistently elevated BrdU labeling, ³H-thymidine incorporation and cyclin D1 levels in response to growth arrest treatments. C/EBP-delta siRNA treatment also resulted in increased migration/invasion and defective differentiation. C/EBP-delta knockout MEFs exhibited defective growth arrest and increased proliferation/migration. Re-introduction of C/EBP-delta expression restored the growth arrest response of C/EBP-delta knockout MEFs. Finally, deletion of the C/EBP-delta DNA binding domain or the C/EBP-delta bZIP domain resulted in the loss of C/EBP-delta growth inhibition in clonogenic assays.</p> <p>Conclusions</p> <p>This study demonstrates that C/EBP-delta functions in the regulation of critical cell fate determining programs such as growth arrest, migration, and differentiation. These results support the tumor suppressor function of C/EBP-delta and identify potential mechanisms in which "loss of function" alterations in C/EBP-delta could promote cell transformation and tumorigenesis.</p

Myc interacts with Max and Miz1 to repress C/EBPδ promoter activity and gene expression

<p>Abstract</p> <p>Background</p> <p>"Loss of function" alterations in CCAAT/Enhancer Binding Proteinδ (C/EBPδ) have been reported in a number of human cancers including breast, prostate and cervical cancer, hepatocellular carcinoma and acute myeloid leukemia. C/EBPδ gene transcription is induced during cellular quiescence and repressed during active cell cycle progression. C/EBPδ exhibits tumor suppressor gene properties including reduced expression in cancer cell lines and tumors and promoter methylation silencing.</p> <p>We previously reported that C/EBPδ expression is inversely correlated with c-Myc (Myc) expression. Aberrant Myc expression is common in cancer and transcriptional repression is a major mechanism of Myc oncogenesis. A number of tumor suppressor genes are targets of Myc transcriptional repression including C/EBPα, p15^{<it>INK</it>4}, p21^{<it>CIP</it>1}, p27^{<it>KIP</it>1}and p57^{<it>KIP</it>2}. This study investigated the mechanisms underlying Myc repression of C/EBPδ expression.</p> <p>Results</p> <p>Myc represses C/EBPδ promoter activity in nontransformed mammary epithelial cells in a dose-dependent manner that requires Myc Box II, Basic Region and HLH/LZ domains. Chromatin Immunoprecipitation (ChIP) assays demonstrate that Myc, Miz1 and Max are associated with the C/EBPδ promoter in proliferating cells, when C/EBPδ expression is repressed. EMSAs demonstrate that Miz1 binds to a 30 bp region (-100 to -70) of the C/EBPδ promoter which contains a putative transcription initiator (Inr) element. Miz1 functions exclusively as a repressor of C/EBPδ promoter activity. Miz1 siRNA expression or expression of a Miz1 binding deficient Myc (MycV394D) construct reduces Myc repression of C/EBPδ promoter activity. Max siRNA expression, or expression of a Myc construct lacking the HLH/LZ (Max interacting) region, also reduces Myc repression of C/EBPδ promoter activity. Miz1 and Max siRNA treatments attenuate Myc repression of endogenous C/EBPδ expression. Myc Box II interacting proteins RuvBl1 (Pontin, TIP49) and RuvBl2 (Reptin, TIP48) enhances Myc repression of C/EBPδ promoter activity.</p> <p>Conclusion</p> <p>Myc represses C/EBPδ expression by associating with the C/EBPδ proximal promoter as a transient component of a repressive complex that includes Max and Miz1. RuvBl1 and RuvBl2 enhance Myc repression of C/EBPδ promoter activity. These results identify protein interactions that mediate Myc repression of C/EBPδ, and possibly other tumor suppressor genes, and suggest new therapeutic targets to block Myc transcriptional repression and oncogenic function.</p