On Bismut Flat Manifolds

In this paper, we give a classification of all compact Hermitian manifolds with flat Bismut connection. We show that the torsion tensor of such a manifold must be parallel, thus the universal cover of such a manifold is a Lie group equipped with a bi-invariant metric and a compatible left invariant complex structure. In particular, isosceles Hopf surfaces are the only Bismut flat compact non-K\"ahler surfaces, while central Calabi-Eckmann threefolds are the only simply-connected compact Bismut flat threefolds.Comment: In this 3rd version, we add a lemma on Hermitian surfaces with flat Riemannian connection. References are updated and typos correcte

Results of the WMT19 metrics shared task: segment-level and strong MT systems pose big challenges

This paper presents the results of the WMT19 Metrics Shared Task. Participants were asked to score the outputs of the translations systems competing in the WMT19 News Translation Task with automatic metrics. 13 research groups submitted 24 metrics, 10 of which are reference-less "metrics" and constitute submissions to the joint task with WMT19 Quality Estimation Task, "QE as a Metric". In addition, we computed 11 baseline metrics, with 8 commonly applied baselines (BLEU, SentBLEU, NIST, WER, PER, TER, CDER, and chrF) and 3 reimplementations (chrF+, sacreBLEU-BLEU, and sacreBLEU-chrF). Metrics were evaluated on the system level, how well a given metric correlates with the WMT19 official manual ranking, and segment level, how well the metric correlates with human judgements of segment quality. This year, we use direct assessment (DA) as our only form of manual evaluation

Correlating Interlayer Spacing and Separation Capability of Graphene Oxide Membranes in Organic Solvents.

Membranes synthesized by stacking two-dimensional graphene oxide (GO) hold great promise for applications in organic solvent nanofiltration. However, the performance of a layer-stacked GO membrane in organic solvent nanofiltration can be significantly affected by its swelling and interlayer spacing, which have not been systematically characterized. In this study, the interlayer spacing of the layer-stacked GO membrane in different organic solvents was experimentally characterized by liquid-phase ellipsometry. To understand the swelling mechanism, the solubility parameters of GO were experimentally determined and used to mathematically predict the Hansen solubility distance between GO and solvents, which is found to be a good predictor for GO swelling and interlayer spacing. Solvents with a small solubility distance (e.g., dimethylformamide, N-methyl-2-pyrrolidone) tend to cause significant GO swelling, resulting in an interlayer spacing of up to 2.7 nm. Solvents with a solubility distance larger than 9.5 (e.g., ethanol, acetone, hexane, and toluene) only cause minor swelling and are thus able to maintain an interlayer spacing of around 1 nm. Correspondingly, GO membranes in solvents with a large solubility distance exhibit good separation performance, for example, rejection of more than 90% of the small organic dye molecules (e.g., rhodamine B and methylene blue) in ethanol and acetone. Additionally, solvents with a large solubility distance result in a high slip velocity in GO channels and thus high solvent flux through the GO membrane. In summary, the GO membrane performs better in solvents that are unlike GO, i.e., solvents with large solubility distance

Manifolds with positive orthogonal Ricci curvature

In this paper we study the class of compact K\"ahler manifolds with positive orthogonal Ricci curvature: $Ric^\perp>0$. First we illustrate examples of K\"ahler manifolds with $Ric^\perp>0$ on K\"ahler C-spaces, and construct ones on certain projectivized vector bundles. These examples show the abundance of K\"ahler manifolds which admit metrics of $Ric^\perp>0$. Secondly we prove some (algebraic) geometric consequences of the condition $Ric^\perp>0$ to illustrate that the condition is also quite restrictive. Finally this last point is made evident with a classification result in dimension three and a partial classification in dimension four

Adapter Learning in Pretrained Feature Extractor for Continual Learning of Diseases

Currently intelligent diagnosis systems lack the ability of continually learning to diagnose new diseases once deployed, under the condition of preserving old disease knowledge. In particular, updating an intelligent diagnosis system with training data of new diseases would cause catastrophic forgetting of old disease knowledge. To address the catastrophic forgetting issue, a novel adapter-based strategy is proposed to help effectively learn a set of new diseases at each round (or task) of continual learning, without changing the shared feature extractor. The learnable lightweight task-specific adapter(s) can be flexibly designed (e.g., two convolutional layers) and then added to the pretrained and fixed feature extractor. Together with a specially designed task-specific head which absorbs all previously learned old diseases as a single 'out-of-distribution' category, task-specific adapter(s) can help the pretrained feature extractor more effectively extract discriminative features between diseases. In addition, a simple yet effective fine-tuning is applied to collaboratively fine-tune multiple task-specific heads such that outputs from different heads are comparable and consequently the appropriate classifier head can be more accurately selected during model inference. Extensive empirical evaluations on three image datasets demonstrate the superior performance of the proposed method in continual learning of new diseases. The source code will be released publicly.Comment: 10 page

Quantitative and functional post-translational modification proteomics reveals that TREPH1 plays a role in plant thigmomorphogenesis

Plants can sense both intracellular and extracellular mechanical forces and can respond through morphological changes. The signaling components responsible for mechanotransduction of the touch response are largely unknown. Here, we performed a high-throughput SILIA (stable isotope labeling in Arabidopsis)-based quantitative phosphoproteomics analysis to profile changes in protein phosphorylation resulting from 40 seconds of force stimulation in Arabidopsis thaliana. Of the 24 touch-responsive phosphopeptides identified, many were derived from kinases, phosphatases, cytoskeleton proteins, membrane proteins and ion transporters. TOUCH-REGULATED PHOSPHOPROTEIN1 (TREPH1) and MAP KINASE KINASE 2 (MKK2) and/or MKK1 became rapidly phosphorylated in touch-stimulated plants. Both TREPH1 and MKK2 are required for touch-induced delayed flowering, a major component of thigmomorphogenesis. The treph1-1 and mkk2 mutants also exhibited defects in touch-inducible gene expression. A non-phosphorylatable site-specific isoform of TREPH1 (S625A) failed to restore touch-induced flowering delay of treph1-1, indicating the necessity of S625 for TREPH1 function and providing evidence consistent with the possible functional relevance of the touch-regulated TREPH1 phosphorylation. Bioinformatic analysis and biochemical subcellular fractionation of TREPH1 protein indicate that it is a soluble protein. Altogether, these findings identify new protein players in Arabidopsis thigmomorphogenesis regulation, suggesting that protein phosphorylation may play a critical role in plant force responses

Polyamine Function in Plants: Metabolism, Regulation on Development, and Roles in Abiotic Stress Responses

Polyamines (PAs) are low molecular weight aliphatic nitrogenous bases containing two or more amino groups. They are produced by organisms during metabolism and are present in almost all cells. Because they play important roles in diverse plant growth and developmental processes and in environmental stress responses, they are considered as a new kind of plant biostimulant. With the development of molecular biotechnology techniques, there is increasing evidence that PAs, whether applied exogenously or produced endogenously via genetic engineering, can positively affect plant growth, productivity, and stress tolerance. However, it is still not fully understood how PAs regulate plant growth and stress responses. In this review, we attempt to cover these information gaps and provide a comprehensive and critical assessment of the published literature on the relationships between PAs and plant flowering, embryo development, senescence, and responses to several (mainly abiotic) stresses. The aim of this review is to summarize how PAs improve plants' productivity, and to provide a basis for future research on the mechanism of action of PAs in plant growth and development. Future perspectives for PA research are also suggested

ADGym: Design Choices for Deep Anomaly Detection

Deep learning (DL) techniques have recently found success in anomaly detection (AD) across various fields such as finance, medical services, and cloud computing. However, most of the current research tends to view deep AD algorithms as a whole, without dissecting the contributions of individual design choices like loss functions and network architectures. This view tends to diminish the value of preliminary steps like data preprocessing, as more attention is given to newly designed loss functions, network architectures, and learning paradigms. In this paper, we aim to bridge this gap by asking two key questions: (i) Which design choices in deep AD methods are crucial for detecting anomalies? (ii) How can we automatically select the optimal design choices for a given AD dataset, instead of relying on generic, pre-existing solutions? To address these questions, we introduce ADGym, a platform specifically crafted for comprehensive evaluation and automatic selection of AD design elements in deep methods. Our extensive experiments reveal that relying solely on existing leading methods is not sufficient. In contrast, models developed using ADGym significantly surpass current state-of-the-art techniques.Comment: NeurIPS 2023. The first three authors contribute equally. Code available at https://github.com/Minqi824/ADGy

Timing Specific Requirement of microRNA Function is Essential for Embryonic and Postnatal Hippocampal Development

The adult hippocampus consists of the dentate gyrus (DG) and the CA1, CA2 and CA3 regions and is essential for learning and memory functions. During embryonic development, hippocampal neurons are derived from hippocampal neuroepithelial cells and dentate granular progenitors. The molecular mechanisms that control hippocampal progenitor proliferation and differentiation are not well understood. Here we show that noncoding microRNAs (miRNAs) are essential for early hippocampal development in mice. Conditionally ablating the RNAase III enzyme Dicer at different embryonic time points utilizing three Cre mouse lines causes abnormal hippocampal morphology and affects the number of hippocampal progenitors due to altered proliferation and increased apoptosis. Lack of miRNAs at earlier stages causes early differentiation of hippocampal neurons, in particular in the CA1 and DG regions. Lack of miRNAs at a later stage specifically affects neuronal production in the CA3 region. Our results reveal a timing requirement of miRNAs for the formation of specific hippocampal regions, with the CA1 and DG developmentally hindered by an early loss of miRNAs and the CA3 region to a late loss of miRNAs. Collectively, our studies indicate the importance of the Dicer-mediated miRNA pathway in hippocampal development and functions