ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation

Score distillation sampling (SDS) has shown great promise in text-to-3D generation by distilling pretrained large-scale text-to-image diffusion models, but suffers from over-saturation, over-smoothing, and low-diversity problems. In this work, we propose to model the 3D parameter as a random variable instead of a constant as in SDS and present variational score distillation (VSD), a principled particle-based variational framework to explain and address the aforementioned issues in text-to-3D generation. We show that SDS is a special case of VSD and leads to poor samples with both small and large CFG weights. In comparison, VSD works well with various CFG weights as ancestral sampling from diffusion models and simultaneously improves the diversity and sample quality with a common CFG weight (i.e., $7.5$). We further present various improvements in the design space for text-to-3D such as distillation time schedule and density initialization, which are orthogonal to the distillation algorithm yet not well explored. Our overall approach, dubbed ProlificDreamer, can generate high rendering resolution (i.e., $512\times512$) and high-fidelity NeRF with rich structure and complex effects (e.g., smoke and drops). Further, initialized from NeRF, meshes fine-tuned by VSD are meticulously detailed and photo-realistic. Project page and codes: https://ml.cs.tsinghua.edu.cn/prolificdreamer/Comment: NeurIPS 2023 (Spotlight

Using Machine Learning and Natural Language Processing to Review and Classify the Medical Literature on Cancer Susceptibility Genes

PURPOSE: The medical literature relevant to germline genetics is growing exponentially. Clinicians need tools monitoring and prioritizing the literature to understand the clinical implications of the pathogenic genetic variants. We developed and evaluated two machine learning models to classify abstracts as relevant to the penetrance (risk of cancer for germline mutation carriers) or prevalence of germline genetic mutations. METHODS: We conducted literature searches in PubMed and retrieved paper titles and abstracts to create an annotated dataset for training and evaluating the two machine learning classification models. Our first model is a support vector machine (SVM) which learns a linear decision rule based on the bag-of-ngrams representation of each title and abstract. Our second model is a convolutional neural network (CNN) which learns a complex nonlinear decision rule based on the raw title and abstract. We evaluated the performance of the two models on the classification of papers as relevant to penetrance or prevalence. RESULTS: For penetrance classification, we annotated 3740 paper titles and abstracts and used 60% for training the model, 20% for tuning the model, and 20% for evaluating the model. The SVM model achieves 89.53% accuracy (percentage of papers that were correctly classified) while the CNN model achieves 88.95 % accuracy. For prevalence classification, we annotated 3753 paper titles and abstracts. The SVM model achieves 89.14% accuracy while the CNN model achieves 89.13 % accuracy. CONCLUSION: Our models achieve high accuracy in classifying abstracts as relevant to penetrance or prevalence. By facilitating literature review, this tool could help clinicians and researchers keep abreast of the burgeoning knowledge of gene-cancer associations and keep the knowledge bases for clinical decision support tools up to date

A Bi-Step Grounding Paradigm for Large Language Models in Recommendation Systems

As the focus on Large Language Models (LLMs) in the field of recommendation intensifies, the optimization of LLMs for recommendation purposes (referred to as LLM4Rec) assumes a crucial role in augmenting their effectiveness in providing recommendations. However, existing approaches for LLM4Rec often assess performance using restricted sets of candidates, which may not accurately reflect the models' overall ranking capabilities. In this paper, our objective is to investigate the comprehensive ranking capacity of LLMs and propose a two-step grounding framework known as BIGRec (Bi-step Grounding Paradigm for Recommendation). It initially grounds LLMs to the recommendation space by fine-tuning them to generate meaningful tokens for items and subsequently identifies appropriate actual items that correspond to the generated tokens. By conducting extensive experiments on two datasets, we substantiate the superior performance, capacity for handling few-shot scenarios, and versatility across multiple domains exhibited by BIGRec. Furthermore, we observe that the marginal benefits derived from increasing the quantity of training samples are modest for BIGRec, implying that LLMs possess the limited capability to assimilate statistical information, such as popularity and collaborative filtering, due to their robust semantic priors. These findings also underline the efficacy of integrating diverse statistical information into the LLM4Rec framework, thereby pointing towards a potential avenue for future research. Our code and data are available at https://github.com/SAI990323/Grounding4Rec.Comment: 17 page

Mixed methods to explore factors associated with the decline of patients in the methadone maintenance treatment program in Shanghai, China

BACKGROUND: This study was to characterize the Methadone Maintenance Treatment (MMT) in Shanghai, China, and to explore factors associated with the decline of patients in MMT during 2005-2016. METHODS: Both qualitative and quantitative methods were used in this study. Based on the data from Shanghai Centers for Disease Control (CDC), we described the changes in the number of patients who received MMT, and new enrollment each year from 2005 to 2016. Focus groups were conducted with 22 patients, and in-depth interviews were conducted with 9 service providers. RESULTS: Quantitative data demonstrate that the number of new enrollment began to decline in 2009, and the number of patients receiving MMT began to decline in 2012. The main reasons for dropout include (1) discontinuing medication due to unknown reasons (25%), (2) criminal activities other than drug-related crimes (20%), (3) relapse to heroin use (16%), and (4) physical disease (10%). Qualitative assessment results indicate that the major reasons for the decline of patients in MMT are as follows: (1) the increase of Amphetamine-type stimulants (ATS) use in recent years, (2) limited knowledge about MMT in both patients and MMT staff, (3) complicated enrollment criteria, and (4) discrimination against drug use. CONCLUSION: Various reasons to explain the decline of patients in MMT in Shanghai, China, were identified. Government agencies, service providers, and other stakeholders need to work together and overcome identified barriers to support MMT programs in China

A New Hybrid Neural Network Method for State-of-Health Estimation of Lithium-Ion Battery

Accurate estimation of lithium-ion battery state-of-health (SOH) is important for the safe operation of electric vehicles; however, in practical applications, the accuracy of SOH estimation is affected by uncertainty factors, including human operation, working conditions, etc. To accurately estimate the battery SOH, a hybrid neural network based on the dilated convolutional neural network and the bidirectional gated recurrent unit, namely dilated CNN-BiGRU, is proposed in this paper. The proposed data-driven method uses the voltage distribution and capacity changes in the extracted battery discharge curve to learn the serial data time dependence and correlation. This method can obtain more accurate temporal and spatial features of the original battery data, resulting higher accuracy and robustness. The effectiveness of dilated CNN-BiGRU for SOH estimation is verified on two publicly lithium-ion battery datasets, the NASA Battery Aging Dataset and Oxford Battery Degradation Dataset. The experimental results reveal that the proposed model outperforms the compared data-driven methods, e.g., CNN-series and RNN-series. Furthermore, the mean absolute error (MAE) and root mean square error (RMSE) are limited to within 1.9% and 3.3%, respectively, on the NASA Battery Aging Dataset

A New Hybrid Neural Network Method for State-of-Health Estimation of Lithium-Ion Battery

Accurate estimation of lithium-ion battery state-of-health (SOH) is important for the safe operation of electric vehicles; however, in practical applications, the accuracy of SOH estimation is affected by uncertainty factors, including human operation, working conditions, etc. To accurately estimate the battery SOH, a hybrid neural network based on the dilated convolutional neural network and the bidirectional gated recurrent unit, namely dilated CNN-BiGRU, is proposed in this paper. The proposed data-driven method uses the voltage distribution and capacity changes in the extracted battery discharge curve to learn the serial data time dependence and correlation. This method can obtain more accurate temporal and spatial features of the original battery data, resulting higher accuracy and robustness. The effectiveness of dilated CNN-BiGRU for SOH estimation is verified on two publicly lithium-ion battery datasets, the NASA Battery Aging Dataset and Oxford Battery Degradation Dataset. The experimental results reveal that the proposed model outperforms the compared data-driven methods, e.g., CNN-series and RNN-series. Furthermore, the mean absolute error (MAE) and root mean square error (RMSE) are limited to within 1.9% and 3.3%, respectively, on the NASA Battery Aging Dataset

Change of Rice Paddy and Its Impact on Human Well-Being from the Perspective of Land Surface Temperature in the Northeastern Sanjiang Plain of China

Large-scale and high-speed paddy land expansion has appeared in Northeast China since the 21st century, causing the change in land surface temperature. The lack of continuous investigation limits the exploration of discoveries in this region. To address this limitation, a collaborative approach that combined human–computer interaction technology, gravity center model and spatial analysis was established. It provided some new findings in spatiotemporal evolution, migration trajectory and surface cooling effect of the paddy field in Northeastern Sanjiang Plain, a center of paddy field planting in China. The results show that: (1) A sustained paddy expansion was monitored, with a total area ranging from 2564.58 km2 to 11430.94 km2, along with a rate of growth of 345.72% from 2000 to 2020. Correspondingly, its reclamation rate changed to 47.53% from 10.66%, showing the improved planting level of the paddy field. (2) Gravity center of paddy field continued to be revealed northward with a 5-year interval from 2000 to 2020. Migration distance of the straight line reached 23.94 km2, with the direction offset of 27.20° from east to north. (3) Throughout the growing season of crops, the land surface temperature of paddy field was 27.73°, 29.38°, 27.01°, 25.62° and 22.97° from May to October; and the cooling temperature effect of paddy field was investigated, with the reduced values of 0.61°, 0.79° and 1.10° in the low-, medium- and high-paddy field density regions from 2000 to 2020, respectively. Overall, these new findings in the cold temperate zone, high latitude region of the Northern Hemisphere, provided the reference for the investigation of paddy field monitoring and its environmental effects in China and other regions

Hierarchically structured porous materials:Synthesis strategies and applications in energy storage

To address the growing energy demands of sustainable development, it is crucial to develop new materials that can improve the efficiency of energy storage systems. Hierarchically structured porous materials have shown their great potential for energy storage applications owing to their large accessible space, high surface area, low density, excellent accommodation capability with volume and thermal variation, variable chemical compositions and well controlled and interconnected hierarchical porosity at different length scales. Porous hierarchy benefits electron and ion transport, and mass diffusion and exchange. The electrochemical behavior of hierarchically structured porous materials varies with different pore parameters. Understanding their relationship can lead to the defined and accurate design of highly efficient hierarchically structured porous materials to enhance further their energy storage performance. In this review, we take the characteristic parameters of the hierarchical pores as the survey object to summarize the recent progress on hierarchically structured porous materials for energy storage. This is the first of this kind exclusively to survey the performance of hierarchically structured porous materials from different porous characteristics. For those who are not familiar with hierarchically structured porous materials, a series of very significant synthesis strategies of hierarchically structured porous materials are firstly and briefly reviewed. This will be beneficial for those who want to quickly obtain useful reference information about the synthesis strategies of new hierarchically structured porous materials to improve their performance in energy storage. The effect of different organizational, structural and geometric parameters of porous hierarchy on their electrochemical behavior is then deeply discussed. We outline the existing problems and development challenges of hierarchically structured porous materials that need to be addressed in renewable energy applications. We hope that this review can stimulate strong intuition into the design and application of new hierarchically structured porous materials in energy storage and other fields

Prognosis of Lithium-Ion Batteries’ Remaining Useful Life Based on a Sequence-to-Sequence Model with Variational Mode Decomposition

The time-varying, dynamic, nonlinear, and other characteristics of lithium-ion batteries, as well as the capacity regeneration phenomenon, leads to the low accuracy of the traditional deep learning models in predicting the remaining useful life of lithium-ion batteries. This paper established a sequence-to-sequence model for remaining useful life prediction by combining the variational modal decomposition with bi-directional long short-term memory and Bayesian hyperparametric optimization. First, variational modal decomposition is used for noise reduction processing to maximize the retention of the original information of capacity degradation. Second, the capacity declining trend after noise reduction is modeled and predicted by the combination of bi-directional long-short term memory and temporal attention mechanism. In addition, a Bayesian optimizer is used to adaptively adjust the hyperparameters while training the model. Finally, the model was validated on NASA and CALCE data sets, and the results show that the model can accurately predict the future trend with only the initial 12% capacity data

Mitochondrial Mechanisms of Necroptosis in Liver Diseases

Cell death represents a basic biological paradigm that governs outcomes and long-term sequelae in almost every hepatic disease. Necroptosis is a common form of programmed cell death in the liver. Necroptosis can be activated by ligands of death receptors, which then interact with receptor-interactive protein kinases 1 (RIPK1). RIPK1 mediates receptor interacting receptor-interactive protein kinases 3 (RIPK3) and mixed lineage kinase domain-like protein (MLKL) and necrosome formation. Regarding the molecular mechanisms of mitochondrial-mediated necroptosis, the RIPK1/RIPK3/MLKL necrosome complex can enhance oxidative respiration and generate reactive oxygen species, which can be a crucial factor in the susceptibility of cells to necroptosis. The necrosome complex is also linked to mitochondrial components such as phosphoglycerate mutase family member 5 (PGAM5), metabolic enzymes in the mitochondrial matrix, mitochondrial permeability protein, and cyclophilin D. In this review, we focus on the role of mitochondria-mediated cell necroptosis in acute liver injury, chronic liver diseases, and hepatocellular carcinoma, and its possible translation into clinical applications