Learning Mutually Informed Representations for Characters and Subwords

Most pretrained language models rely on subword tokenization, which processes text as a sequence of subword tokens. However, different granularities of text, such as characters, subwords, and words, can contain different kinds of information. Previous studies have shown that incorporating multiple input granularities improves model generalization, yet very few of them outputs useful representations for each granularity. In this paper, we introduce the entanglement model, aiming to combine character and subword language models. Inspired by vision-language models, our model treats characters and subwords as separate modalities, and it generates mutually informed representations for both granularities as output. We evaluate our model on text classification, named entity recognition, and POS-tagging tasks. Notably, the entanglement model outperforms its backbone language models, particularly in the presence of noisy texts and low-resource languages. Furthermore, the entanglement model even outperforms larger pre-trained models on all English sequence labeling tasks and classification tasks. Our anonymized code is available at https://anonymous.4open.science/r/noisy-IE-A67

Application and Research Progress of Heat Pipe in Thermal Management of Lithium-Ion Battery

Lithium-ion batteries have the advantages of high energy density, high average output voltage, long service life, and environmental protection, and are widely used in the power system of new energy vehicles. However, during the working process of the battery, the working temperature is too high or too low, which will affect the charging and discharging performance, battery capacity and battery safety. As a result, a battery thermal management system (BTMS) is essential to maintain the proper ambient temperature of the working battery. Thermal management of power batteries is a key technology to ensure maximum battery safety and efficiency. This paper discusses the significance of thermal management technology in the development of new energy vehicles, introduces the main technical means of thermal management of lithium-ion batteries for vehicle, and focuses on the current state of research on the use of various types of heat pipes in lithium-ion batteries. Finally, the use of heat pipes in the thermal control of lithium-ion batteries is promising.Citation: Ning, Y., Tao, R., Luo, J., and Hu, Q. (2022). Application and Research Progress of Heat Pipe in Thermal Management of Lithium-Ion Battery. Trends in Renewable Energy, 8, 130-144. DOI: 10.17737/tre.2022.8.2.0014

Aggregated Feasible Region of Heterogeneous Demand-Side Flexible Resources---Part I: Theoretical Derivation of the Exact Model

In the first part of the two-part series, the model to describe the exact aggregated feasible region (AFR) of multiple types of demand-side resources is derived. Based on a discrete-time unified individual model of heterogeneous resources, the calculation of AFR is, in fact, a feasible region projection problem. Therefore, the Fourier-Motzkin Elimination (FME) method is used for derivation. By analyzing the redundancy of all possible constraints in the FME process, the mathematical expression and calculation method for the exact AFR is proposed. The number of constraints is linear with the number of resources and is exponential with the number of time intervals, respectively. The computational complexity has been dramatically simplified compared with the original FME. However, the number of constraints in the model is still exponential and cannot be simplified anymore. Hence, In Part II of this paper, several approximation methods are proposed and analyzed in detail.Comment: 10 page

Identity and function of an essential nitrogen ligand of the nitrogenase cofactor biosynthesis protein NifB.

NifB is a radical S-adenosyl-L-methionine (SAM) enzyme that is essential for nitrogenase cofactor assembly. Previously, a nitrogen ligand was shown to be involved in coupling a pair of [Fe4S4] clusters (designated K1 and K2) concomitant with carbide insertion into an [Fe8S9C] cofactor core (designated L) on NifB. However, the identity and function of this ligand remain elusive. Here, we use combined mutagenesis and pulse electron paramagnetic resonance analyses to establish histidine-43 of Methanosarcina acetivorans NifB (MaNifB) as the nitrogen ligand for K1. Biochemical and continuous wave electron paramagnetic resonance data demonstrate the inability of MaNifB to serve as a source for cofactor maturation upon substitution of histidine-43 with alanine; whereas x-ray absorption spectroscopy/extended x-ray fine structure experiments further suggest formation of an intermediate that lacks the cofactor core arrangement in this MaNifB variant. These results point to dual functions of histidine-43 in structurally assisting the proper coupling between K1 and K2 and concurrently facilitating carbide formation via deprotonation of the initial carbon radical

UV R-CNN: Stable and Efficient Dense Human Pose Estimation

Dense pose estimation is a dense 3D prediction task for instance-level human analysis, aiming to map human pixels from an RGB image to a 3D surface of the human body. Due to a large amount of surface point regression, the training process appears to be easy to collapse compared to other region-based human instance analyzing tasks. By analyzing the loss formulation of the existing dense pose estimation model, we introduce a novel point regression loss function, named Dense Points} loss to stable the training progress, and a new balanced loss weighting strategy to handle the multi-task losses. With the above novelties, we propose a brand new architecture, named UV R-CNN. Without auxiliary supervision and external knowledge from other tasks, UV R-CNN can handle many complicated issues in dense pose model training progress, achieving 65.0% $AP_{gps}$ and 66.1% $AP_{gpsm}$ on the DensePose-COCO validation subset with ResNet-50-FPN feature extractor, competitive among the state-of-the-art dense human pose estimation methods.Comment: 9pages, 4 figure

Structure of Precursor-Bound NifEN: A Nitrogenase FeMo Cofactor Maturase/Insertase

NifEN plays an essential role in the biosynthesis of the nitrogenase iron-molybdenum (FeMo) cofactor (M cluster). It is an α_2β_2 tetramer that is homologous to the catalytic molybdenum-iron (MoFe) protein (NifDK) component of nitrogenase. NifEN serves as a scaffold for the conversion of an iron-only precursor to a matured form of the M cluster before delivering the latter to its target location within NifDK. Here, we present the structure of the precursor-bound NifEN of Azotobacter vinelandii at 2.6 angstrom resolution. From a structural comparison of NifEN with des-M-cluster NifDK and holo NifDK, we propose similar pathways of cluster insertion for the homologous NifEN and NifDK proteins

The Antioxidative Role of Autophagy in Hearing Loss

Autophagy, a highly conserved cellular mechanism, plays an essential role in the development and pathology of many central and peripheral nervous system diseases. The auditory system, especially hair cells (HCs) and spiral ganglion neurons (SGNs) in the inner ear, are postmitotic cells, which are extremely reliant on cellular homeostasis and energy supply. Therefore, autophagy may be involved in contributing to and facilitating the normal function of inner ear cells. Recently, studies on hearing loss induced by ototoxic drugs, noise exposure and other factors have revealed that autophagy could serve in an antioxidative capacity and could possess the potential to treat sensorineural hearing loss (SNHL). Therefore, here we review previous studies concerning autophagy and SNHL to gain insight into the role of autophagic mechanisms in inner ear disorders

Structural Analysis of a Nitrogenase Iron Protein from Methanosarcina acetivorans: Implications for CO2 Capture by a Surface-Exposed [Fe4S4] Cluster.

Nitrogenase iron (Fe) proteins reduce CO2 to CO and/or hydrocarbons under ambient conditions. Here, we report a 2.4-Å crystal structure of the Fe protein from Methanosarcina acetivorans (MaNifH), which is generated in the presence of a reductant, dithionite, and an alternative CO2 source, bicarbonate. Structural analysis of this methanogen Fe protein species suggests that CO2 is possibly captured in an unactivated, linear conformation near the [Fe4S4] cluster of MaNifH by a conserved arginine (Arg) pair in a concerted and, possibly, asymmetric manner. Density functional theory calculations and mutational analyses provide further support for the capture of CO2 on MaNifH while suggesting a possible role of Arg in the initial coordination of CO2 via hydrogen bonding and electrostatic interactions. These results provide a useful framework for further mechanistic investigations of CO2 activation by a surface-exposed [Fe4S4] cluster, which may facilitate future development of FeS catalysts for ambient conversion of CO2 into valuable chemical commodities.IMPORTANCE This work reports the crystal structure of a previously uncharacterized Fe protein from a methanogenic organism, which provides important insights into the structural properties of the less-characterized, yet highly interesting archaeal nitrogenase enzymes. Moreover, the structure-derived implications for CO2 capture by a surface-exposed [Fe4S4] cluster point to the possibility of developing novel strategies for CO2 sequestration while providing the initial insights into the unique mechanism of FeS-based CO2 activation