CILIATE: Towards Fairer Class-based Incremental Learning by Dataset and Training Refinement

Due to the model aging problem, Deep Neural Networks (DNNs) need updates to adjust them to new data distributions. The common practice leverages incremental learning (IL), e.g., Class-based Incremental Learning (CIL) that updates output labels, to update the model with new data and a limited number of old data. This avoids heavyweight training (from scratch) using conventional methods and saves storage space by reducing the number of old data to store. But it also leads to poor performance in fairness. In this paper, we show that CIL suffers both dataset and algorithm bias problems, and existing solutions can only partially solve the problem. We propose a novel framework, CILIATE, that fixes both dataset and algorithm bias in CIL. It features a novel differential analysis guided dataset and training refinement process that identifies unique and important samples overlooked by existing CIL and enforces the model to learn from them. Through this process, CILIATE improves the fairness of CIL by 17.03%, 22.46%, and 31.79% compared to state-of-the-art methods, iCaRL, BiC, and WA, respectively, based on our evaluation on three popular datasets and widely used ResNet models

FairNeuron: Improving Deep Neural Network Fairness with Adversary Games on Selective Neurons

With Deep Neural Network (DNN) being integrated into a growing number of critical systems with far-reaching impacts on society, there are increasing concerns on their ethical performance, such as fairness. Unfortunately, model fairness and accuracy in many cases are contradictory goals to optimize. To solve this issue, there has been a number of work trying to improve model fairness by using an adversarial game in model level. This approach introduces an adversary that evaluates the fairness of a model besides its prediction accuracy on the main task, and performs joint-optimization to achieve a balanced result. In this paper, we noticed that when performing backward propagation based training, such contradictory phenomenon has shown on individual neuron level. Based on this observation, we propose FairNeuron, a DNN model automatic repairing tool, to mitigate fairness concerns and balance the accuracy-fairness trade-off without introducing another model. It works on detecting neurons with contradictory optimization directions from accuracy and fairness training goals, and achieving a trade-off by selective dropout. Comparing with state-of-the-art methods, our approach is lightweight, making it scalable and more efficient. Our evaluation on 3 datasets shows that FairNeuron can effectively improve all models' fairness while maintaining a stable utility

A Wall-Associated Kinase Gene CaWAKL20 From Pepper Negatively Modulates Plant Thermotolerance by Reducing the Expression of ABA-Responsive Genes

Heat stress has become a major threat to crop production due to global warming; however, the mechanisms underlying plant high-temperature sensing are not well known. In plants, the membrane-anchored receptor-like kinases (RLKs) relay environmental signals into the cytoplasm. In a previous study, we isolated a wall-associated RLK-like (WAKL) gene CaWAKL20 from pepper (Capsicum annuum L.). Here, the amino acid sequence of CaWAKL20 was characterized and found to consist of conserved domains of WAK/WAKL family, including an extracellular region containing a GUB-WAK binding domain and a degenerated EGF2-like domain; a transmembrane region; and an intercellular region with an STKc catalytic domain. Moreover, CaWAKL20 transcription was inhibited by heat stress, whereas it was induced by both ABA and H2O2 treatments. Silencing of CaWAKL20 enhanced pepper thermotolerance, while overexpression decreased Arabidopsis thermotolerance. Additionally, Arabidopsis lines overexpressing CaWAKL20 showed less sensitivity to ABA during seed germination and root growth. Finally, the survival rate of Arabidopsis seedlings under heat stress treatment was enhanced by ABA pre-treatment, while it was compromised by the overexpression of CaWAKL20. Furthermore, the heat-induced expression of several ABA-responsive genes and some key regulator genes for thermotolerance was decreased in Arabidopsis CaWAKL20-overexpression lines. These results suggest that CaWAKL20 negatively modulates plant thermotolerance by reducing the expression of ABA-responsive genes, laying a foundation for further investigation into the functional mechanisms of WAKs/WAKLs in plants undergoing environmental stresses

GLM-130B: An Open Bilingual Pre-trained Model

We introduce GLM-130B, a bilingual (English and Chinese) pre-trained language model with 130 billion parameters. It is an attempt to open-source a 100B-scale model at least as good as GPT-3 (davinci) and unveil how models of such a scale can be successfully pre-trained. Over the course of this effort, we face numerous unexpected technical and engineering challenges, particularly on loss spikes and divergence. In this paper, we introduce the training process of GLM-130B including its design choices, training strategies for both efficiency and stability, and engineering efforts. The resultant GLM-130B model offers significant outperformance over GPT-3 175B (davinci) on a wide range of popular English benchmarks while the performance advantage is not observed in OPT-175B and BLOOM-176B. It also consistently and significantly outperforms ERNIE TITAN 3.0 260B -- the largest Chinese language model -- across related benchmarks. Finally, we leverage a unique scaling property of GLM-130B to reach INT4 quantization without post training, with almost no performance loss, making it the first among 100B-scale models and more importantly, allowing its effective inference on 4$\times$RTX 3090 (24G) or 8$\times$RTX 2080 Ti (11G) GPUs, the most affordable GPUs required for using 100B-scale models. The GLM-130B model weights are publicly accessible and its code, training logs, related toolkit, and lessons learned are open-sourced at \url{https://github.com/THUDM/GLM-130B/}.Comment: Accepted to ICLR 202

Characterization of BiP Genes from Pepper (Capsicum annuum L.) and the Role of CaBiP1 in Response to Endoplasmic Reticulum and Multiple Abiotic Stresses

Adverse environmental conditions have a detrimental impact on crop growth and development, and cause protein denaturation or misfolding. The binding protein (BiP) plays an important protective role by alleviating endoplasmic reticulum (ER) stress induced by misfolded proteins. In this study, we characterized three BiP genes (CaBiP1, CaBiP2, and CaBiP3) in pepper, an economically important vegetable and spice species. The role of CaBiP1 in plant tolerance to ER stress and adverse environmental conditions (including heat, salinity, osmotic and drought stress) were investigated. All the expected functional and signaling domains were detected in three BiP proteins, but the motifs and exon-intron distribution differed slightly in CaBiP3. CaBiP1 and CaBiP2 were constitutively expressed in all the tested tissues under both normal and stressed conditions, whereas CaBiP3 was mainly expressed following stress. Silencing of CaBiP1 reduced pepper tolerance to ER stress and various environment stresses, and was accompanied by increased H2O2 accumulation, MDA content, relative electric leakage (REL), water loss rate, and a reduction in soluble protein content and relative water content (RWC) in the leaves. Conversely, overexpression of CaBiP1 in Arabidopsis enhanced tolerance to ER stress and multiple environment stresses, as demonstrated by an increase in germination rate, root length, survival rate, RWC, the unfolded protein response (UPR) pathway, and a decrease in water loss rate. Our results suggest that CaBiP1 may contribute to plant tolerance to abiotic stresses by reducing ROS accumulation, increasing the water-retention ability, and stimulating UPR pathways and expression of stress-related genes

The use of deep learning integrating image recognition in language analysis technology in secondary school education

Abstract This work aims to investigate the application of advanced deep learning algorithms and image recognition technologies to enhance language analysis tools in secondary education, with the goal of providing educators with more effective resources and support. Based on artificial intelligence, this work integrates data mining techniques related to deep learning to analyze and study language behavior in secondary school education. Initially, a framework for analyzing language behavior in secondary school education is constructed. This involves evaluating the current state of language behavior, establishing a framework based on evaluation comments, and defining indicators for analyzing language behavior in online secondary school education. Subsequently, data mining technology and image and character recognition technology are employed to conduct data mining for online courses in secondary schools, encompassing the processing of teaching video images and character recognition. Finally, an experiment is designed to validate the proposed framework for analyzing language behavior in secondary school education. The results indicate specific differences among the grouped evaluation scores for each analysis indicator. The significance p values for the online classroom discourse’s speaking rate, speech intelligibility, average sentence length, and content similarity are −0.56, −0.71, −0.71, and −0.74, respectively. The aim is to identify the most effective teaching behaviors for learners and enhance the support for online course instruction

High-Resolution Image Processing of Probe-Based Confocal Laser Endomicroscopy Based on Multistage Neural Networks and Cross-Channel Attention Module

Probe-based confocal laser endomicroscopy (pCLE) is a subcellular in vivo imaging technique that generates diagnostic images revealing malignant structural modifications in epithelial tissues. In the clinical diagnosis of probe confocal laser endomicroscopy (pCLE), the image background generally has the problems of dynamic blur or information loss, which is not conducive to achieving high-resolution and clear pCLE imaging. In recent years, deep learning technology has achieved remarkable results in image deblurring. For the task of recovering high-resolution pCLE images, the current methods still suffer from the following drawbacks: it is difficult to choose a strategy to make CNN converge at a deeper level and mainstream methods cannot handle the complex balance between spatial details and high-level feature information well when reconstructing clear images. In order to solve the problem, we propose a new cross-channel attention, multistage, high-resolution pCLE image deblurring structure. This methodology improves the supervised attention mechanism, enhances the ability of feature extraction and fusion capabilities, and improves the quality of image deblurring by adding cross-channel attention module (CAM) into the multistage neural networks’ architecture. The experimental results show that the average peak signal-to-noise ratio (PSNR) of the proposed model on the dataset is as high as 29.643 dB, and the structural similarity (SSIM) reaches 0.855. This method is superior to the prior algorithms in the visualization of recovered images, and the edge and texture details of the restored pCLE images are clearer

Field test study for evaluation of vibration control capacity of cracked mass concrete layer

Passive control is typically implemented to regulate ground vibration in advanced ultra-precision and large-scale synchrotron radiation facilities. This paper presents the field test to evaluate the influence of cracks on the vibration control capacity of a mass concrete layer used as a passive control method of an advanced synchrotron radiation facility. Simplified finite element model (FEM) analysis is utilized to simulate the cracks and analyze the key factor that influences the vibration control capacity of the mass concrete layer. In the field test, cracks are artificially formed by creating cuts in two orthometric directions on the surface of a 3-m-thick concrete layer. Measurement points and a vibrator capable of generating simple harmonic excitation (1–100 Hz) are positioned along a straight line. Velocity signals vertical to the ground are obtained to study the vibration attenuation in the vertical direction. Test results indicate that the velocity signals on the concrete layer are sensitive to the cracks; the vibration control ability of the concrete layer is slightly affected by the depth, length and location of the cracks in the frequency band of 1–100 Hz. The proposed simplified FEMs can reflect the cracked concrete layer’s vibration control ability from 1 to 100 Hz. Numerical study results indicate that the thickness of the concrete layer rather than the density or elastic modulus is the key factor in vibration control ability

A Study of the Fluid Intake, Hydration Status, and Health Effects among Pregnant Women in Their Second Trimester in China: A Cross-Sectional Study

The fluid intake and hydration status during pregnancy may influence the health outcomes of both the mother and the fetus. However, there are few studies related to this. The aim of the present study was to investigate fluid intake behaviors among pregnant women in their second trimester, to evaluate their hydration status and pregnancy complications, and to further explore the association of fluid intake and the amniotic fluid index (AFI). Participants’ total fluid intake (TFI) levels were determined using a 7-day 24 h fluid intake questionnaire. The levels of water intake from food were not recorded or measured. Morning urine samples were collected, and both urine osmolality levels and urine specific gravity (USG) were tested to evaluate their hydration status. Fasting blood samples were also collected and measured for osmolality and complete blood count (CBC). A total of 324 participants completed the study. They were divided into four groups based on quartiles of TFI, including participants with lower (LFI1 and LFI2) and higher (HFI1 and HFI2) fluid intake levels. The median TFI was 1485 mL, and the median values of the four groups with different TFI levels were 1348, 1449, 1530, and 1609 mL, respectively. Only 3.4% of the participants attained the recommended value following an adequate water intake (1.7 L) level for pregnant women in China. Plain water was the main TFI resource (78.8~100.00%), and differences in the plain water intake levels among the four groups were evident (χ2 = 222.027, p 1 to HFI2 group, and significant differences in the urine osmolality levels among the four groups were evident (p 1 group to 0.0% in the HFI2 group (χ2 = 131.241, p 2 = 58.386, all p p < 0.05). A large proportion of the participants had insufficient TFIs during the second trimester of pregnancy, and a proportion of the participants were dehydrated. The preliminary analysis showed that the AFI was correlated with the TFI during the second trimester of pregnancy. A sufficient TFI is necessary for pregnant women to improve their hydration status and may have effects on their health. The results can provide appropriate scientific references for the development of beneficial recommendations concerning adequate water intake levels for pregnant women in China

GNSS Data Processing and Validation of the Altimeter Zenith Wet Delay around the Wanshan Calibration Site

The Wanshan calibration site (WSCS) is the first in-situ field for calibration and validation (Cal/Val) of HY-2 satellite series in China. It was built in December, 2018 and began business operation in 2020. In order to define an accurate datum for Cal/Val of altimeters, the permanent GNSS station (PGS) data of the WSCS observed on Zhiwan (ZWAN) and Wailingding (WLDD) islands were processed using GAMIT/GLOBK software in a regional solution, combined with 61 GNSS stations distributed nearby, collected from the GNSS Research Center, Wuhan University (GRC). The Hector software was used to analyze the trend of North (N), East (E), and Up (U) directions using six different noise models with criteria of maximum likelihood estimation (MLE), Akaike Information Criteria (AIC), and the Bayesian Information Criteria (BIC). We found that the favorite noise models were white noise plus generalized Gauss&ndash;Markov noise (WN + GGM), followed by generalized Gauss&ndash;Markov noise (GGM). Then, we compared the PGS velocities of each direction with the Scripps Orbit and Permanent Array Center (SOPAC) output parameters and found that there was good agreement between them. The PGSs in the WSCS had velocities in the N, E, and U directions of &minus;10.20 &plusmn; 0.39 mm/year, 31.09 &plusmn; 0.36 mm/year, and &minus;2.24 &plusmn; 0.66 mm/year for WLDD, and &minus;10.85 &plusmn; 0.38 mm/year, 30.67 &plusmn; 0.30 mm/year, and &minus;3.81 &plusmn; 0.66 mm/year for ZWAN, respectively. The accurate datum was defined for Cal/Val of altimeters for WSCS as a professional in-situ site. Moreover, the zenith wet delay (ZWD) of the coastal PGSs in the regional and sub-regional solutions was calculated and used to validate the microwave radiometers (MWRs) of Jason-3, Haiyang-2B (HY-2B), and Haiyang-2C (HY-2C). A sub-regional PGS solution was processed using 19 continuous operational reference stations (CORS) of Hong Kong Geodetic Survey Services to derive the ZWD and validate the MWRs of the altimeters. The ZWD of the PGSs were compared with the radiosonde-derived data in the regional and sub-regional solutions. The difference between them was &minus;7.72~2.79 mm with an RMS of 14.53~18.62 mm, which showed good consistency between the two. Then, the PGSs&rsquo; ZWD was used to validate the MWRs. To reduce the land contamination of the MWR, we determined validation distances of 6~30 km, 16~28 km, and 18~30 km for Jason-3, HY-2B, and HY-2C, respectively. The ZWD differences between PGSs and the Jason-3, HY-2B, and HY-2C altimeters were &minus;2.30 &plusmn; 16.13 mm, 9.22 &plusmn; 22.73 mm, and &minus;3.02 &plusmn; 22.07 mm, respectively