Bulk-deformed potentials for toric Fano surfaces, wall-crossing and period

We provide an inductive algorithm to compute the bulk-deformed potentials for toric Fano surfaces via wall-crossing techniques and a tropical-holomorphic correspondence theorem for holomorphic discs. As an application of the correspondence theorem, we also prove a big quantum period theorem for toric Fano surfaces which relates the log descendant Gromov-Witten invariants with the oscillatory integrals of the bulk-deformed potentials.Comment: 44 pages, 9 figures, comments are welcom

Functional Genomic Studies of Soybean Defenses against Pests and Soybean Meal Improvement

Soybean [Glycine max (L.) Merr.] is an important crop worldwide. It has been widely consumed for protein, oil and other soy products. To develop soybean cultivars with greater resistance against pests and improved meal quality, it is important to elucidate the molecular bases of these traits. This dissertation aims to investigate the biochemical and biological functions of soybean genes from four gene families, which are hypothesized to be associated with soybean defense against pests and soybean meal quality. There are three specific objectives in this dissertation. The first one is to determine the function of components in the salicylic acid (SA) signaling pathway in soybean resistance against soybean cyst nematode (Heterodera glycines, SCN). The second one is to determine whether insect herbivory induce the emission of volatiles from soybean, and if so, how these volatiles are biosynthesized. The third objective is to identify and characterize soybean mannanase genes that can be used for the improvement of soybean meal quality. The soybean genome has been fully sequenced, which provides opportunities for cross-species comparison of gene families of interest and identification of candidate genes in soybean. The cloned cDNAs of putative genes were expressed in Escherichia coli to produce recombinant enzymes. Through biochemical assays, these proteins were proved to be soybean salicylic acid methyltransferase (GmSAMT1), methyl salicylate esterase (GmSABP2-1), α[alpha]-farnesene synthase (GmTPS1) and E-β[beta]-caryophyllene synthase (GmTPS2), and endo-β[beta]-mannanase (GmMAN1). Through a transgenic hairy root system harboring overexpression of GmSAMT1 and GmSABP2-1, both of these two genes were evaluated for their biological function related to resistance against SCN. The results showed that the over-expression of GmSAMT1 and GmSABP2-1 in the susceptible soybean background lead to enhanced resistance against SCN. Among four putative soybean mannanase genes, one gene was cloned and characterized. GmMAN1 showed the endo-β[beta]-mannanase hydrolyse activity and can hydrolyze cell walls isolated from soybean seeds. In summary, using comparative and functional genomics, a number of genes involved in soybean defense and meal quality were isolated and characterized. This study provides novel knowledge and molecular tools for the genetic improvement of soybean for enhanced resistance and improved meal quality

Effect of chloroprocaine combined with morphine on analgesia, adverse reactions and dynamic changes in inflammation in patients receiving TURP

Purpose: To investigate the influence of chloroprocaine combined with morphine on the analgesic effects, adverse reactions and inflammation factors in patients receiving transurethral resection of the prostate (TURP).Methods: A total of 80 patients with benign prostatic hyperplasia (BPH) in the Fourth Medical Center of Chinese PLA General Hospital, Beijing 100048, China, were divided into morphine group and combination-therapy group (morphine combined with chloroprocaine). Pain index, changes in inflammatory factors and incidence of adverse reactions in the two groups of patients were assessed.Results: The morphine group and combination-therapy group showed basic profile prior to the treatments. Visual Analogue Scale (VAS) scores before operation and 6 h after operation in the morphine group were similar to those in the combination-therapy group, but the scores at 12, 24 and 48 h after operation in the combination-therapy group were significantly lower than those in the morphine group. Similarly, the combination-therapy group showed lower levels of substance P (SP) and bradykinin (BK) at 12, 24 and 48 h after operation than the morphine group (p < 0.05). Both groups exhibited similar levels of serum inflammatory factors before the operation, but the levels decreased in the combination-therapy group when compared with those in the morphine group after operation (p < 0.05). The combination-therapy group also showed a lower incidence of adverse reactions than the morphine group.Conclusion: Chloroprocaine combined with morphine effectively ameliorates postoperative pain, lowers secretion of tumor necrosis factor-alpha (TNF-α) and interleukin-10 (IL-10), and decreases the incidence of postoperative adverse reactions, thus affording a high level of safety after operation

Blending-target Domain Adaptation by Adversarial Meta-Adaptation Networks

(Unsupervised) Domain Adaptation (DA) seeks for classifying target instances when solely provided with source labeled and target unlabeled examples for training. Learning domain-invariant features helps to achieve this goal, whereas it underpins unlabeled samples drawn from a single or multiple explicit target domains (Multi-target DA). In this paper, we consider a more realistic transfer scenario: our target domain is comprised of multiple sub-targets implicitly blended with each other, so that learners could not identify which sub-target each unlabeled sample belongs to. This Blending-target Domain Adaptation (BTDA) scenario commonly appears in practice and threatens the validities of most existing DA algorithms, due to the presence of domain gaps and categorical misalignments among these hidden sub-targets. To reap the transfer performance gains in this new scenario, we propose Adversarial Meta-Adaptation Network (AMEAN). AMEAN entails two adversarial transfer learning processes. The first is a conventional adversarial transfer to bridge our source and mixed target domains. To circumvent the intra-target category misalignment, the second process presents as ``learning to adapt'': It deploys an unsupervised meta-learner receiving target data and their ongoing feature-learning feedbacks, to discover target clusters as our ``meta-sub-target'' domains. These meta-sub-targets auto-design our meta-sub-target DA loss, which empirically eliminates the implicit category mismatching in our mixed target. We evaluate AMEAN and a variety of DA algorithms in three benchmarks under the BTDA setup. Empirical results show that BTDA is a quite challenging transfer setup for most existing DA algorithms, yet AMEAN significantly outperforms these state-of-the-art baselines and effectively restrains the negative transfer effects in BTDA.Comment: CVPR-19 (oral). Code is available at http://github.com/zjy526223908/BTD

PRIVACY DASHBOARD

A computing device may display a graphical user interface (GUI) of a privacy dashboard showing permission usage of sensor data (e.g., location, microphone, camera, etc.) and content provider data (e.g., files, media, contacts, calendar, short message service (SMS), call log, etc.) by applications for a given time period (e.g., the past 24 hours, the past 7 days, etc.). The privacy dashboard may include graphs, lists, and other visual indicators for displaying information about the permission usage per sensor and/or per application in a clear, concise, and comprehensible way. In the privacy dashboard, the computing device may include certain types of privacy information, such as sensor data usage, within the initial screen while omitting other types of privacy information, such as content provider data usage, from the initial screen. The computing device may display information about other types of privacy information in response to a user input (e.g., touch input). In some examples, the privacy dashboard may enable a user to see detailed information about permission usage of an application and manage permissions for each application via the privacy dashboard. As such, the privacy dashboard may improve user awareness and comprehension of data access by increasing transparency with respect to permission usage by applications

Random attractors for stochastic delay wave equations on Rn with linear memory nad nonlinear damping

A non-autonomous stochastic delay wave equation with linear memory and nonlinear damping driven by additive white noise is considered on the unbounded domain Rn. We establish the existence and uniqueness of a random attractor A that is compact in C([−h, 0]; H1 (Rn)) × C([−h, 0]; L2 (Rn)) × L2 µ(R+; H1 (Rn)) with 1 6 n 6 3

DreamEditor: Text-Driven 3D Scene Editing with Neural Fields

Neural fields have achieved impressive advancements in view synthesis and scene reconstruction. However, editing these neural fields remains challenging due to the implicit encoding of geometry and texture information. In this paper, we propose DreamEditor, a novel framework that enables users to perform controlled editing of neural fields using text prompts. By representing scenes as mesh-based neural fields, DreamEditor allows localized editing within specific regions. DreamEditor utilizes the text encoder of a pretrained text-to-Image diffusion model to automatically identify the regions to be edited based on the semantics of the text prompts. Subsequently, DreamEditor optimizes the editing region and aligns its geometry and texture with the text prompts through score distillation sampling [29]. Extensive experiments have demonstrated that DreamEditor can accurately edit neural fields of real-world scenes according to the given text prompts while ensuring consistency in irrelevant areas. DreamEditor generates highly realistic textures and geometry, significantly surpassing previous works in both quantitative and qualitative evaluations

Coupled effects of climate variability and land use pattern on surface water quality: An elasticity perspective and watershed health indicators.

Understanding the coupled effects of climate variability and land use on riverine nitrogen is essential for watershed management. The climate-water relationships for ammonium (NH4-N) and nitrate (NO3-N) were determined by an elasticity approach and then the watershed health index was estimated using the reliability, resilience, and vulnerability framework. These methods were applied to an in-situ monitoring dataset of N concentrations measured during 2010-2017 from nine sub-watersheds in the Jiulong River Watershed, China. The results showed that temperature and precipitation elasticity of NH4-N and NO3-N changed substantially among various land use patterns. The N concentrations were highly sensitive to extreme climate conditions, particularly at urban and agricultural sub-watersheds. The measure of risk indicators revealed that the watershed health index varied from good health to unhealthy status. Linear regression analysis was used to analyze the interactions among watershed characteristics, climate elasticity, and watershed health. Cropland and population had strong positive correlations with climate elasticity of NO3-N. Forest and elevation had strong negative associations with climate elasticity of NO3-N. Watershed health significantly declined with increasing proportion of cropland and population density. This study demonstrated that human-impacted watersheds were less healthy to unhealthy and tend to be more sensitive to climate variability than natural watersheds, which is useful for efforts aimed at improving watershed management