One Network, Many Masks: Towards More Parameter-Efficient Transfer Learning

Fine-tuning pre-trained language models for multiple tasks tends to be expensive in terms of storage. To mitigate this, parameter-efficient transfer learning (PETL) methods have been proposed to address this issue, but they still require a significant number of parameters and storage when being applied to broader ranges of tasks. To achieve even greater storage reduction, we propose PROPETL, a novel method that enables efficient sharing of a single PETL module which we call prototype network (e.g., adapter, LoRA, and prefix-tuning) across layers and tasks. We then learn binary masks to select different sub-networks from the shared prototype network and apply them as PETL modules into different layers. We find that the binary masks can determine crucial information from the network, which is often ignored in previous studies. Our work can also be seen as a type of pruning method, where we find that overparameterization also exists in the seemingly small PETL modules. We evaluate PROPETL on various downstream tasks and show that it can outperform other PETL methods with approximately 10% of the parameter storage required by the latter.Comment: Accepted by ACL 202

Integrated Multi-Omics Perspective to Strengthen the Understanding of Salt Tolerance in Rice

Salt stress is one of the major constraints to rice cultivation worldwide. Thus, the development of salt-tolerant rice cultivars becomes a hotspot of current rice breeding. Achieving this goal depends in part on understanding how rice responds to salt stress and uncovering the molecular mechanism underlying this trait. Over the past decade, great efforts have been made to understand the mechanism of salt tolerance in rice through genomics, transcriptomics, proteomics, metabolomics, and epigenetics. However, there are few reviews on this aspect. Therefore, we review the research progress of omics related to salt tolerance in rice and discuss how these advances will promote the innovations of salt-tolerant rice breeding. In the future, we expect that the integration of multi-omics salt tolerance data can accelerate the solution of the response mechanism of rice to salt stress, and lay a molecular foundation for precise breeding of salt tolerance

A Regional Time-of-Use Electricity Price Based Optimal Charging Strategy for Electrical Vehicles

With the popularization of electric vehicles (EVs), the out-of-order charging behaviors of large numbers of EVs will bring new challenges to the safe and economic operation of power systems. This paper studies an optimal charging strategy for EVs. For that a typical urban zone is divided into four regions, a regional time-of-use (RTOU) electricity price model is proposed to guide EVs when and where to charge considering spatial and temporal characteristics. In light of the elastic coefficient, the user response to the RTOU electricity price is analyzed, and also a bilayer optimization charging strategy including regional-layer and node-layer models is suggested to schedule the EVs. On the one hand, the regional layer model is designed to coordinate the EVs located in different time and space. On the other hand, the node layer model is built to schedule the EVs to charge in certain nodes. According to the simulations of an IEEE 33-bus distribution network, the performance of the proposed optimal charging strategy is verified. The results demonstrate that the proposed bilayer optimization strategy can effectively decrease the charging cost of users, mitigate the peak-valley load difference and the network loss. Besides, the RTOU electricity price shows better performance than the time-of-use (TOU) electricity price

A Regional Time-of-Use Electricity Price Based Optimal Charging Strategy for Electrical Vehicles

With the popularization of electric vehicles (EVs), the out-of-order charging behaviors of large numbers of EVs will bring new challenges to the safe and economic operation of power systems. This paper studies an optimal charging strategy for EVs. For that a typical urban zone is divided into four regions, a regional time-of-use (RTOU) electricity price model is proposed to guide EVs when and where to charge considering spatial and temporal characteristics. In light of the elastic coefficient, the user response to the RTOU electricity price is analyzed, and also a bilayer optimization charging strategy including regional-layer and node-layer models is suggested to schedule the EVs. On the one hand, the regional layer model is designed to coordinate the EVs located in different time and space. On the other hand, the node layer model is built to schedule the EVs to charge in certain nodes. According to the simulations of an IEEE 33-bus distribution network, the performance of the proposed optimal charging strategy is verified. The results demonstrate that the proposed bilayer optimization strategy can effectively decrease the charging cost of users, mitigate the peak-valley load difference and the network loss. Besides, the RTOU electricity price shows better performance than the time-of-use (TOU) electricity price

A Regional Time-of-Use Electricity Price Based Optimal Charging Strategy for Electrical Vehicles

With the popularization of electric vehicles (EVs), the out-of-order charging behaviors of large numbers of EVs will bring new challenges to the safe and economic operation of power systems. This paper studies an optimal charging strategy for EVs. For that a typical urban zone is divided into four regions, a regional time-of-use (RTOU) electricity price model is proposed to guide EVs when and where to charge considering spatial and temporal characteristics. In light of the elastic coefficient, the user response to the RTOU electricity price is analyzed, and also a bilayer optimization charging strategy including regional-layer and node-layer models is suggested to schedule the EVs. On the one hand, the regional layer model is designed to coordinate the EVs located in different time and space. On the other hand, the node layer model is built to schedule the EVs to charge in certain nodes. According to the simulations of an IEEE 33-bus distribution network, the performance of the proposed optimal charging strategy is verified. The results demonstrate that the proposed bilayer optimization strategy can effectively decrease the charging cost of users, mitigate the peak-valley load difference and the network loss. Besides, the RTOU electricity price shows better performance than the time-of-use (TOU) electricity price

Amorphous Ge/C Composite Sponges: Synthesis and Application in a High-Rate Anode for Lithium Ion Batteries

A Ge/C spongelike composite is prepared by the facile and scalable single-step pyrolysis of the GeO_<i>x</i>/ethylenediamine gel process, which has a feature with three-dimensional interconnected pore structures and is hybridized with nitrogen-doped carbon. A detailed investigation shows that the pore in the sponge is formed for the departure of the gaseous products at the evaluated temperature. As an anode for lithium ion batteries, the obtained composite exhibits superior specific capacity in excess of 1016 mA h g^–1 at 100 mA g^–1 after 100 cycles. Moreover, the amorphous Ge/C sponge electrode also has a good rate capacity and stable cycling performance. The obtained amorphous Ge/C sponges are a good candidate anode for next-generation lithium ion batteries

Association of body composition and physical activity with pain and function in knee osteoarthritis patients: a cross-sectional study

Objective The objective of this study is to delineate disparities between patients with knee osteoarthritis (KOA) based on obesity status, investigate the interplay among body composition, physical activity and knee pain/function in patients with KOA and conduct subgroup analyses focusing on those with KOA and obesity.Design Cross-sectional study.Setting Residents of eight communities in Shijiazhuang, Hebei Province, China, were surveyed from March 2021 to November 2021.Participants 178 patients with symptomatic KOA aged 40 years or older were included.Main outcomes and measures The primary outcome measure was knee pain, assessed using the Western Ontario and McMaster Universities Osteoarthritis Index-pain (WOMAC-P) scale. Secondary outcome measures included function, evaluated through the WOMAC-function (WOMAC-F) scale and the Five-Time-Sit-to-Stand Test (FTSST). Data analysis involved t-tests, Wilcoxon rank-sum tests, χ2 tests, linear and logistical regression analysis.Results Participants (n=178) were 41–80 years of age (median: 65, P25–P75: 58–70), and 82% were female. Obese patients (n=103) had worse knee pain and self-reported function (p&lt;0.05). In general patients with KOA, body fat mass was positively associated with bilateral knee pain (β=1.21 (95% CI 0.03 to 0.15)), WOMAC-P scores (β=0.25 (95% CI 0.23 to 1.22)), WOMAC-F scores (β=0.28 (95% CI 0.35 to 1.29)) and FTSST (β=0.19 (95% CI 0.03 to 0.42)), moderate-intensity to low-intensity physical activity was negatively associated with bilateral knee pain (β=−0.80 (95% CI −0.10 to –0.01)) and Skeletal Muscle Index (SMI) was negatively associated with WOMAC-F scores (β=−0.16 (95% CI −0.66 to –0.03)). In patients with KOA and obesity, SMI was negatively associated with FTSST (β=−0.30 (95% CI −3.94 to –0.00)).Conclusion Patients with KOA and obesity had worse knee pain and self-reported function compared with non-obese patients. Greater fat mass, lower muscle mass and lower moderate-intensity to low-intensity physical activity were associated with increased knee pain and poor self-reported function. More skeletal muscle mass was associated with the improvement of objective function

A Computational Method of Defining Potential Biomarkers based on Differential Sub-Networks

Analyzing omics data from a network-based perspective can facilitate biomarker discovery. To improve disease diagnosis and identify prospective information indicating the onset of complex disease, a computational method for identifying potential biomarkers based on differential sub-networks (PBDSN) is developed. In PB-DSN, Pearson correlation coefficient (PCC) is used to measure the relationship between feature ratios and to infer potential networks. A differential sub-network is extracted to identify crucial information for discriminating different groups and indicating the emergence of complex diseases. Subsequently, PB-DSN defines potential biomarkers based on the topological analysis of these differential sub-networks. In this study, PB-DSN is applied to handle a static genomics dataset of small, round blue cell tumors and a time-series metabolomics dataset of hepatocellular carcinoma. PB-DSN is compared with support vector machine-recursive feature elimination, multivariate empirical Bayes statistics, analyzing time-series data based on dynamic networks, molecular networks based on PCC, PinnacleZ, graph-based iterative group analysis, KeyPathwayMiner and BioNet. The better performance of PB-DSN not only demonstrates its effectiveness for the identification of discriminative features that facilitate disease classification, but also shows its potential for the identification of warning signals