Suan Zao Ren Tang as an Original Treatment for Sleep Difficulty in Climacteric Women: A Prospective Clinical Observation

Little scientific evidence supports the efficacy of herbal medicines in the treatment of women with sleep difficulty during the climacteric period. The purpose of this study is to evaluate the efficacy and safety of Suan Zao Ren Tang (SZRT) in reducing the impact of sleep disturbance on climacteric women, as measured by Pittsburg sleep quality index (PSQI) and the World Health Organization quality of life (WHOQOL). Sixty-seven climacteric women with sleep difficulty intending to treat received SZRT at a rate of 4.0 g, thrice daily for four weeks (MRS < 16, n = 34; MRS ≥ 16, n = 33). After taking into account potential confounding factors, the mean PSQI total scores had fallen from 13.0 (±2.9) to 9.0 (±3.2) (95% confidence interval −4.93, −3.10). Further analyses showed that SZRT produced superior benefit of daytime dysfunction in women with severe menopausal symptoms (MRS ≥ 16). There were three of the withdrawals involved treatment-related adverse events (stomachache, diarrhea, and dizziness). Excluding women with a past history of stomachache, diarrhea, or dizziness, four weeks of therapy with SZRT appears to be a relatively safe and effective short-term therapeutic option in improving daytime function of climacteric women with poor sleep quality

Enhancing Transformers without Self-supervised Learning: A Loss Landscape Perspective in Sequential Recommendation

Transformer and its variants are a powerful class of architectures for sequential recommendation, owing to their ability of capturing a user's dynamic interests from their past interactions. Despite their success, Transformer-based models often require the optimization of a large number of parameters, making them difficult to train from sparse data in sequential recommendation. To address the problem of data sparsity, previous studies have utilized self-supervised learning to enhance Transformers, such as pre-training embeddings from item attributes or contrastive data augmentations. However, these approaches encounter several training issues, including initialization sensitivity, manual data augmentations, and large batch-size memory bottlenecks. In this work, we investigate Transformers from the perspective of loss geometry, aiming to enhance the models' data efficiency and generalization in sequential recommendation. We observe that Transformers (e.g., SASRec) can converge to extremely sharp local minima if not adequately regularized. Inspired by the recent Sharpness-Aware Minimization (SAM), we propose SAMRec, which significantly improves the accuracy and robustness of sequential recommendation. SAMRec performs comparably to state-of-the-art self-supervised Transformers, such as S$^3$Rec and CL4SRec, without the need for pre-training or strong data augmentations

Bian Zheng Lun Zhi

Background. Limited scientific evidence supports the positive effects of traditional Chinese medicine (TCM) for treating dysmenorrhea. Thus, an observation period of 3 months could verify the ancient indication that TCM treatments effectively alleviate menstrual cramps in women with primary dysmenorrhea or endometriosis. Methods. A prospective, nonrandomized study (primary dysmenorrhea and endometriosis groups) was conducted in women with dysmenorrhea for more than three consecutive menstrual cycles. All patients received TCM prescriptions based on bian zheng lun zhi theory 14 days before menstruation for a period of 12 weeks. Pain intensity was evaluated using a 10-cm visual analogue scale and two validated questionnaires (the Menstrual Distress Questionnaire and the World Health Organization Quality of Life questionnaire). Results. Of the initial 70 intent-to-treat participants, the women with dysmenorrhea reported significant alleviation of cramps during menstruation after the 12-week TCM treatment. Mixed model analysis revealed that TCM prescriptions were more effective in alleviating fatigue, hot flashes, dizziness, painful breasts, excitement, and irritability in the primary dysmenorrhea group (N=36) than in the endometriosis group (N=34). Conclusion. TCM prescriptions based on syndrome differentiation theory might be a potentially viable choice for treating painful menstruation and premenstrual symptoms after ruling out endometriosis

TinyKG: Memory-Efficient Training Framework for Knowledge Graph Neural Recommender Systems

There has been an explosion of interest in designing various Knowledge Graph Neural Networks (KGNNs), which achieve state-of-the-art performance and provide great explainability for recommendation. The promising performance is mainly resulting from their capability of capturing high-order proximity messages over the knowledge graphs. However, training KGNNs at scale is challenging due to the high memory usage. In the forward pass, the automatic differentiation engines (\textsl{e.g.}, TensorFlow/PyTorch) generally need to cache all intermediate activation maps in order to compute gradients in the backward pass, which leads to a large GPU memory footprint. Existing work solves this problem by utilizing multi-GPU distributed frameworks. Nonetheless, this poses a practical challenge when seeking to deploy KGNNs in memory-constrained environments, especially for industry-scale graphs. Here we present TinyKG, a memory-efficient GPU-based training framework for KGNNs for the tasks of recommendation. Specifically, TinyKG uses exact activations in the forward pass while storing a quantized version of activations in the GPU buffers. During the backward pass, these low-precision activations are dequantized back to full-precision tensors, in order to compute gradients. To reduce the quantization errors, TinyKG applies a simple yet effective quantization algorithm to compress the activations, which ensures unbiasedness with low variance. As such, the training memory footprint of KGNNs is largely reduced with negligible accuracy loss. To evaluate the performance of our TinyKG, we conduct comprehensive experiments on real-world datasets. We found that our TinyKG with INT2 quantization aggressively reduces the memory footprint of activation maps with $7 \times$, only with $2\%$ loss in accuracy, allowing us to deploy KGNNs on memory-constrained devices

Hessian-aware Quantized Node Embeddings for Recommendation

Graph Neural Networks (GNNs) have achieved state-of-the-art performance in recommender systems. Nevertheless, the process of searching and ranking from a large item corpus usually requires high latency, which limits the widespread deployment of GNNs in industry-scale applications. To address this issue, many methods compress user/item representations into the binary embedding space to reduce space requirements and accelerate inference. Also, they use the Straight-through Estimator (STE) to prevent vanishing gradients during back-propagation. However, the STE often causes the gradient mismatch problem, leading to sub-optimal results. In this work, we present the Hessian-aware Quantized GNN (HQ-GNN) as an effective solution for discrete representations of users/items that enable fast retrieval. HQ-GNN is composed of two components: a GNN encoder for learning continuous node embeddings and a quantized module for compressing full-precision embeddings into low-bit ones. Consequently, HQ-GNN benefits from both lower memory requirements and faster inference speeds compared to vanilla GNNs. To address the gradient mismatch problem in STE, we further consider the quantized errors and its second-order derivatives for better stability. The experimental results on several large-scale datasets show that HQ-GNN achieves a good balance between latency and performance

Sharpness-Aware Graph Collaborative Filtering

Graph Neural Networks (GNNs) have achieved impressive performance in collaborative filtering. However, GNNs tend to yield inferior performance when the distributions of training and test data are not aligned well. Also, training GNNs requires optimizing non-convex neural networks with an abundance of local and global minima, which may differ widely in their performance at test time. Thus, it is essential to choose the minima carefully. Here we propose an effective training schema, called {gSAM}, under the principle that the \textit{flatter} minima has a better generalization ability than the \textit{sharper} ones. To achieve this goal, gSAM regularizes the flatness of the weight loss landscape by forming a bi-level optimization: the outer problem conducts the standard model training while the inner problem helps the model jump out of the sharp minima. Experimental results show the superiority of our gSAM

Endometriosis Patients in Taiwan: A Population-Based Study

Background. Traditional Chinese medicine (TCM), when given for symptom relief, has gained widespread popularity among women with endometriosis. The aim of this study was to analyze the utilization of TCM among women with endometriosis in Taiwan. Methods. The usage, frequency of service, and the Chinese herbal products prescribed for endometriosis, among endometriosis patients, were evaluated using a randomly sampled cohort of 1,000,000 beneficiaries recruited from the National Health Insurance Research Database. Results. Overall, 90.8% (N = 12, 788) of reproductive age women with endometriosis utilized TCM and 25.2% of them sought TCM with the intention of treating their endometriosis-related symptoms. Apart from the usage of either analgesics or more than one type of medical treatment, the odds of using TCM and Western medicine were similar in all types of conventional endometriosis treatment. However, endometriosis patients suffering from symptoms associated with endometriosis were more likely to seek TCM treatment than those with no symptoms. There were 21,056 TCM visits due to endometriosis and its related symptoms, of which more than 98% were treated with Chinese herbal products (CHPs). Conclusion. Gui-Zhi-Fu-Ling-Wan (Cinnamon Twig and Poria Pill) containing sedative and anti-inflammatory agents is the most commonly prescribed Chinese herbal formula mainly for the treatment of endometriosis-related symptomatic discomfort and the effects of these TCMs should be taken into account by healthcare providers

CFEVER: A Chinese Fact Extraction and VERification Dataset

We present CFEVER, a Chinese dataset designed for Fact Extraction and VERification. CFEVER comprises 30,012 manually created claims based on content in Chinese Wikipedia. Each claim in CFEVER is labeled as "Supports", "Refutes", or "Not Enough Info" to depict its degree of factualness. Similar to the FEVER dataset, claims in the "Supports" and "Refutes" categories are also annotated with corresponding evidence sentences sourced from single or multiple pages in Chinese Wikipedia. Our labeled dataset holds a Fleiss' kappa value of 0.7934 for five-way inter-annotator agreement. In addition, through the experiments with the state-of-the-art approaches developed on the FEVER dataset and a simple baseline for CFEVER, we demonstrate that our dataset is a new rigorous benchmark for factual extraction and verification, which can be further used for developing automated systems to alleviate human fact-checking efforts. CFEVER is available at https://ikmlab.github.io/CFEVER.Comment: AAAI-2

A novel randomly textured phosphor structure for highly efficient white light-emitting diodes

We have successfully demonstrated the enhanced luminous flux and lumen efficiency in white light-emitting diodes by the randomly textured phosphor structure. The textured phosphor structure was fabricated by a simple imprinting technique, which does not need an expensive dry-etching machine or a complex patterned definition. The textured phosphor structure increases luminous flux by 5.4% and 2.5% at a driving current of 120 mA, compared with the flat phosphor and half-spherical lens structures, respectively. The increment was due to the scattering of textured surface and also the phosphor particles, leading to the enhancement of utilization efficiency of blue light. Furthermore, the textured phosphor structure has a larger view angle at the full width at half maximum (87°) than the reference LEDs