Neutral top-pion and the rare top decays t→cliljt\to c l_{i} l_{j}

We study the rare top decays $t\to c l_{i} l_{j}(l=\tau,\mu,or e)$ in the framework of topcolor-assisted technicolor($TC2$) models. We find that the neutral top-pion $\pi_{t}^{0}$ can produce significant contributions to these processes via the flavor changing couplings $\pi_{t}^{0} \bar{t} c$ and $\pi_{t}^{0} l_{i} l_{j}$. For the $\pi_{t}^{0}$ mass $m_{\pi_{t}}=150 GeV$ and the parameter $\epsilon=0.08$, the branching ratio $Br$(t$\to c \tau \tau)$ can reach $7.1\times10^{-7}$. Taking into account the constraints of the present experimental limit of the process $\mu\to e \gamma$ on the free parameters of $TC2$ models, we find that the value of $Br$($t\to c \tau \mu$)$\approx$$Br$($t\to c \tau e$) is in the range of 1.8$\times10^{-10}\sim1.7\times10^{-8}$.Comment: To be published in Phys.

Does urbanization have spatial spillover effect on poverty reduction: empirical evidence from rural China

In light of a scarcity of research on the spatial effects of urbanization on poverty reduction, this study uses panel data on 30 provinces in China from 2009 to 2019 to construct a system of indices to assess poverty that spans the four dimensions of the economy, education, health, and living. We use the spatial autocorrelation test and the spatial Durbin model (SDM) to analyze the spatial effects of urbanization on poverty reduction in these different dimensions. The main conclusions are as follows: (a) China’s urbanization has the characteristics of spatial aggregation and a spatial spillover effect. (b) Different dimensions of poverty had the attributes of spatial agglomeration, and Moran’s index of a reduction in economic poverty was the highest. Under the SDM, the different dimensions of poverty also showed a significant positive spatial correlation. (c) Urbanization has a significant effect on poverty reduction along the dimensions of the economy, education, and living, but has little effect on reducing health poverty. It has a spatial spillover effect on poverty reduction in economic and living contexts. (d) There were spatial differences in the effect of urbanization on relieving economic and living-related poverty

Intramolecular hydrogen-bonding in aqueous carbohydrates as a cause or consequence of conformational preferences: a molecular dynamics study of cellobiose stereoisomers

It is often assumed that intramolecular hydrogen-bonding (H-bonding) exerts a significant influence on the conformational properties of aqueous (bio-)polymers. To discuss this statement, one should, however, distinguish between solvent-exposed and buried H-bonds, and between their respective roles in promoting stability (i.e., as a driving force) and specificity (for which the term steering force is introduced here). In this study, the role of solvent-exposed H-bonding in carbohydrates as a driving or steering force is probed using explicit-solvent molecular dynamics simulations with local elevation umbrella sampling in the simple context of cellobiose stereoisomers. More specifically, four β(1→4)-linked d-aldohexopyranose disaccharides are considered, which present a different stereochemisty of the potentially H-bonding groups neighboring the glycosidic linkage. Although the epimerization may largely alter the intramolecular trans-glycosidic H-bonding pattern, it is found to have only very limited influence on the Ramachandran free-energy map of the disaccharide, a loss of intramolecular H-bonding being merely compensated for by an enhancement of the interaction with the solvent molecules. This finding suggests that solvent-exposed trans-glycosidic H-bonding (and in particular the $$\hbox{HO}_3^{\prime}$$ →O5 H-bond) is not the cause of the 21-helical secondary structure characteristic of cellooligosaccharides, but rather the opportunistic consequence of a sterically and stereoelectronically dictated conformational preference. In other words, for these compounds, solvent-exposed H-bonding appears to represent a minor (possibly adverse) conformational driving as well as steering forc

FwdLLM: Efficient FedLLM using Forward Gradient

Large Language Models (LLMs) are transforming the landscape of mobile intelligence. Federated Learning (FL), a method to preserve user data privacy, is often employed in fine-tuning LLMs to downstream mobile tasks, an approach known as FedLLM. Though recent efforts have addressed the network issue induced by the vast model size, they have not practically mitigated vital challenges concerning integration with mobile devices, such as significant memory consumption and sluggish model convergence. In response to these challenges, this work introduces FwdLLM, an innovative FL protocol designed to enhance the FedLLM efficiency. The key idea of FwdLLM to employ backpropagation (BP)-free training methods, requiring devices only to execute ``perturbed inferences''. Consequently, FwdLLM delivers way better memory efficiency and time efficiency (expedited by mobile NPUs and an expanded array of participant devices). FwdLLM centers around three key designs: (1) it combines BP-free training with parameter-efficient training methods, an essential way to scale the approach to the LLM era; (2) it systematically and adaptively allocates computational loads across devices, striking a careful balance between convergence speed and accuracy; (3) it discriminatively samples perturbed predictions that are more valuable to model convergence. Comprehensive experiments with five LLMs and three NLP tasks illustrate FwdLLM's significant advantages over conventional methods, including up to three orders of magnitude faster convergence and a 14.6x reduction in memory footprint. Uniquely, FwdLLM paves the way for federated learning of billion-parameter LLMs such as LLaMA on COTS mobile devices -- a feat previously unattained.Comment: under revie

Federated NLP in Few-shot Scenarios

Natural language processing (NLP) sees rich mobile applications. To support various language understanding tasks, a foundation NLP model is often fine-tuned in a federated, privacy-preserving setting (FL). This process currently relies on at least hundreds of thousands of labeled training samples from mobile clients; yet mobile users often lack willingness or knowledge to label their data. Such an inadequacy of data labels is known as a few-shot scenario; it becomes the key blocker for mobile NLP applications. For the first time, this work investigates federated NLP in the few-shot scenario (FedFSL). By retrofitting algorithmic advances of pseudo labeling and prompt learning, we first establish a training pipeline that delivers competitive accuracy when only 0.05% (fewer than 100) of the training data is labeled and the remaining is unlabeled. To instantiate the workflow, we further present a system FFNLP, addressing the high execution cost with novel designs. (1) Curriculum pacing, which injects pseudo labels to the training workflow at a rate commensurate to the learning progress; (2) Representational diversity, a mechanism for selecting the most learnable data, only for which pseudo labels will be generated; (3) Co-planning of a model's training depth and layer capacity. Together, these designs reduce the training delay, client energy, and network traffic by up to 46.0$\times$, 41.2$\times$ and 3000.0$\times$, respectively. Through algorithm/system co-design, FFNLP demonstrates that FL can apply to challenging settings where most training samples are unlabeled

Towards Practical Few-shot Federated NLP

Transformer-based pre-trained models have emerged as the predominant solution for natural language processing (NLP). Fine-tuning such pre-trained models for downstream tasks often requires a considerable amount of labeled private data. In practice, private data is often distributed across heterogeneous mobile devices and may be prohibited from being uploaded. Moreover, well-curated labeled data is often scarce, presenting an additional challenge. To address these challenges, we first introduce a data generator for federated few-shot learning tasks, which encompasses the quantity and skewness of scarce labeled data in a realistic setting. Subsequently, we propose AUG-FedPrompt, a prompt-based federated learning system that exploits abundant unlabeled data for data augmentation. Our experiments indicate that AUG-FedPrompt can perform on par with full-set fine-tuning with a limited amount of labeled data. However, such competitive performance comes at a significant system cost.Comment: EuroSys23 worksho

Biomass Straw Based Activated Porous Carbon Materials for High-Performance Supercapacitors

Biomass straws are often regarding as agricultural waste, usually burned off in rural areas, which results in severe resource waste and air pollution. In this work, biomass-based porous carbon material with a lamellar microstructure is obtained via simple hydrothermal and subsequent KOH activation, the optimum activate process is determined by the proportion of activator. Scanning electron microscopy (SEM) and nitrogen adsorption techniques are conducted to investigate the physical properties of the materials. Cyclic voltammetry and constant current discharge/charge in the three-electrode system and symmetrical double-layer capacitors results indicate the best electrochemical performance of SCA-1.5 electrode material, with a capacity of 250.0 F g-1 at 1.0 A g-1. And notably, high recycling stability at a high cycling rate of 1.0 A g-1 after 18,000 cycles

Binder-free and carbon-free 3D porous air electrode for Li-O2 batteries with high efficiency, high capacity, and long life

Pt-Gd alloy polycrystalline thin film is deposited on 3D nickel foam by pulsed laser deposition method serving as a whole binder/carbon-free air electrode, showing great catalytic activity enhancement as an efficient bifunctional catalyst for the oxygen reduction and evolution reactions in lithium oxygen batteries. The porous structure can facilitate rapid O2 and electrolyte diffusion, as well as forming a continuous conductive network throughout the whole energy conversion process. It shows a favorable cycle performance in the full discharge/charge model, owing to the high catalytic activity of the Pt-Gd alloy composite and 3D porous nickel foam structure. Specially, excellent cycling performance under capacity limited mode is also demonstrated, in which the terminal discharge voltage is higher than 2.5 V and the terminal charge voltage is lower than 3.7 V after 100 cycles at a current density of 0.1 mA cm−2. Therefore, this electrocatalyst is a promising bifunctional electrocatalyst for lithium oxygen batteries and this depositing high-efficient electrocatalyst on porous substrate with polycrystalline thin film by pulsed laser deposition is also a promising technique in the future lithium oxygen batteries research