Metric Learning for Projections Bias of Generalized Zero-shot Learning

Generalized zero-shot learning models (GZSL) aim to recognize samples from seen or unseen classes using only samples from seen classes as training data. During inference, GZSL methods are often biased towards seen classes due to the visibility of seen class samples during training. Most current GZSL methods try to learn an accurate projection function (from visual space to semantic space) to avoid bias and ensure the effectiveness of GZSL methods. However, during inference, the computation of distance will be important when we classify the projection of any sample into its nearest class since we may learn a biased projection function in the model. In our work, we attempt to learn a parameterized Mahalanobis distance within the framework of VAEGAN (Variational Autoencoder \& Generative Adversarial Networks), where the weight matrix depends on the network's output. In particular, we improved the network structure of VAEGAN to leverage the discriminative models of two branches to separately predict the seen samples and the unseen samples generated by this seen one. We proposed a new loss function with two branches to help us learn the optimized Mahalanobis distance representation. Comprehensive evaluation benchmarks on four datasets demonstrate the superiority of our method over the state-of-the-art counterparts. Our codes are available at https://anonymous.4open.science/r/111hxr.Comment: 9 pages, 2 figure

A Simple and Effective Baseline for Attentional Generative Adversarial Networks

Synthesising a text-to-image model of high-quality images by guiding the generative model through the Text description is an innovative and challenging task. In recent years, AttnGAN based on the Attention mechanism to guide GAN training has been proposed, SD-GAN, which adopts a self-distillation technique to improve the performance of the generator and the quality of image generation, and Stack-GAN++, which gradually improves the details and quality of the image by stacking multiple generators and discriminators. However, this series of improvements to GAN all have redundancy to a certain extent, which affects the generation performance and complexity to a certain extent. We use the popular simple and effective idea (1) to remove redundancy structure and improve the backbone network of AttnGAN. (2) to integrate and reconstruct multiple losses of DAMSM. Our improvements have significantly improved the model size and training efficiency while ensuring that the model's performance is unchanged and finally proposed our \textbf{SEAttnGAN}. Code is avalilable at https://github.com/jmyissb/SEAttnGAN.Comment: 12 pages, 3 figure

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

An Improved K-means Method with Density Distribution Analysis

In this paper, a novel K-means clustering algorithm is proposed. Before running the traditional Kmeans, the cluster centers should be randomly selected, which would influence the time cost and accuracy. To solve this problem, we utilize density distribution analysis in the traditional K-means. For a reasonable cluster, it should have a dense inside structure which means the points in the same cluster should tightly surround the center, while separated away from other cluster canters. Based on this assumption, two quantities are firstly introduced: the local density of cluster center ρi and its desperation degree δi, then some reasonable cluster centers candidates are selected from the original data. We performed our algorithm on three synthetic data and a real bank business data to evaluate its accuracy and efficiency. Comparing with Traditional K-means and K-means++, the results demonstrated that the improved method performs better

An Improved K-means Method with Density Distribution Analysis

In this paper, a novel K-means clustering algorithm is proposed. Before running the traditional Kmeans, the cluster centers should be randomly selected, which would influence the time cost and accuracy. To solve this problem, we utilize density distribution analysis in the traditional K-means. For a reasonable cluster, it should have a dense inside structure which means the points in the same cluster should tightly surround the center, while separated away from other cluster canters. Based on this assumption, two quantities are firstly introduced: the local density of cluster center ρi and its desperation degree δi, then some reasonable cluster centers candidates are selected from the original data. We performed our algorithm on three synthetic data and a real bank business data to evaluate its accuracy and efficiency. Comparing with Traditional K-means and K-means++, the results demonstrated that the improved method performs better

Experimental Study on Creep Behavior in Oedometer Tests of Reconstituted Soft Clays

International audienceThe creep behavior of soft clays is essential in evaluating the long-term deformation of foundations. Because this behavior of naturally deposited clays is very complex as a result of their structures, loading history, and physicochemical processes, in addition to the heavy workload and high cost for the sampling procedure of natural soft clays, oedometer creep tests were usually conducted on reconstituted clays, aiming to establish the fundamental work for the elastic-viscoplastic modeling (EVP) and the creep prediction of natural soft clays. In this study, based on a series of one-dimensional creep tests of reconstituted soft clays, the relationship between the creep coefficient (Ca) and the vertical effective stress (s0 v) and that between Ca and the ratio of vertical effective stress to preyielding stress (s0 v/s0 y) are presented in semilogarithmic planes. The results suggest that the Ca value increases with the vertical effective stress prior to reaching the threshold stress (2.17s0 y), and then Ca gradually decreases after it. The results (Ca vs. compression index Cc) show that the statistical Ca/Cc value of reconstituted clays ranges from 0.015 to 0.05, which is more than that of natural soft clays (0.03–0.05). This suggests that the reconstitution process is beneficial to restrain the creep of soils. Furthermore, it was found that the Ca/Cc value of reconstituted clays linearly decreased with the void ratio when vertical effective stress exceeded the threshold stress (2.17s0 y). Based on the data of this study and data from previous studies, a prediction equation for Ca after the threshold stress is proposed, showing that Ca is a function of the void ratio and the void ratio at the liquid limi

Modeling Disease Progression: Angiotensin II Indirectly Inhibits Nitric Oxide Production via ADMA Accumulation in Spontaneously Hypertensive Rats

Nitric oxide (NO) production impairment is involved in the onset and development of hypertension. Although NO production impairment in spontaneously hypertensive rat (SHR) has been reported in a variety of researches, the time course of this progressive procedure, as well as its relationship with asymmetric dimethylarginine (ADMA) and angiotensin II (Ang II), has not been quantified. The aim of this research is to establish a mechanism-based disease progression model to assess Ang II and ADMA’s inhibition of NO production in SHR’s disease progression with/without ramipril’s intervention. SHR were randomly divided into three groups: one disease group (n=8) and two treatment groups (n=8 for each group) ：standard treatment group (receiving ramipril 2mg/kg*day) and intensive treatment group (receiving ramipril 10mg/kg*day). ADMA, Ang II, NO and SBP were determined weekly. Intensive treatment with ramipril was found to have no further attenuation of plasma NO and ADMA than standard treatment beyond its significantly stronger antihypertensive effects. Four linked turnover models were developed to characterize the profiles of ADMA, Ang II, NO and SBP during hypertensive disease progression with/without ramipril intervention. Our model described Ang II and ADMA’s contribution to NO production impairment and their responses to ramipril treatment throughout the disease progression in SHR. Model simulations suggested that Ang II affected NO production mainly through inhibiting ADMA elimination rather than affecting nitric oxide synthase (NOS) directly

Non-Additive Effects of Environmental Factors on Growth and Physiology of Invasive <i>Solidago canadensis</i> and a Co-Occurring Native Species (<i>Artemisia argyi</i>)

Changes in environmental factors, such as temperature and UV, have significant impacts on the growth and development of both native and invasive plant species. However, few studies examine the combined effects of warming and enhanced UV on plant growth and performance in invasive species. Here, we investigated single and combined effects of warming and UV radiation on growth, leaf functional and photosynthesis traits, and nutrient content (i.e., total organic carbon, nitrogen and phosphorous) of invasive Solidago canadensis and its co-occurring native species, Artemisia argyi, when grown in culture racks in the greenhouse. The species were grown in monoculture and together in a mixed community, with and without warming, and with and without increased UV in a full factorial design. We found that growth in S. canadensis and A. argyi were inhibited and more affected by warming than UV-B radiation. Additionally, there were both antagonistic and synergistic interactions between warming and UV-B on growth and performance in both species. Overall, our results suggested that S. canadensis was more tolerant to elevated temperatures and high UV radiation compared to the native species. Therefore, substantial increases in temperature and UV-B may favour invasive S. canadensis over native A. argyi. Research focusing on the effects of a wider range of temperatures and UV levels is required to improve our understanding of the responses of these two species to greater environmental variability and the impacts of climate change