Theoretical and experimental design of an alternative system to 2 x 2 MIMO for LTE over 60 km directly modulated RoF link

Relay nodes (RN) are used as an important structure to extend the coverage of the Third Generation Partnership Program’s Long Term Evolution (3GPP-LTE). The promising technology as the interface between eNodeB (eNB) and RN is radio-over-fibre (RoF), due to its longer span transmission capability. In this paper, we propose an alternative technique to 2×2 multiple-input and multiple-output (MIMO) in LTE structure for transmission over 60 km directly modulated RoF link by introducing frequency division multiplexing (FDM) for orthogonal FDM (OFDM). The system is demonstrated theoretically and experimentally. In the baseband, quadrature phase shift keying (QPSK), 16-quadrature amplitude modulation (QAM) and 64-QAM are considered as the single carrier modulations (SCM) according to the LTE standard. The system degradation pattern is identical between the theoretical and experimental system, thus proving the accuracy of the theoretical system design. The real time QPSK, 16-QAM and 64-QAM system achieved an average EVM of 5.84%, 5.90% and 5.97%, respectively for 2 GHz and 2.6 GHz bands. These resultant EVMs are below the 8% 3GPP-LTE EVM requirement

Interpreting the 3 TeV WHWH Resonance as a W′W' boson

Motivated by a local $3.2-3.4$ sigma resonance in $WH$ and $ZH$ in the ATLAS Run 2 data, we attempt to interpret the excess in terms of a $W'$ boson in a $SU(2)_1 \times SU(2)_2 \times U(1)_X$ model. We stretch the deviation from the alignment limit of the Equivalence Theorem, so as to maximize $WH$ production while keeping the $WZ$ production rate below the experimental limit. We found a viable though small region of parameter space that satisfies all existing constraints on $W' \to jj, t \bar b, WZ$, as well as the precision Higgs data. The cross section of $W' \to WH$ that we obtain is about $5-6$ fb.Comment: 14 pages, 2 figure

EPA: Easy Prompt Augmentation on Large Language Models via Multiple Sources and Multiple Targets

Large language models (LLMs) have shown promising performance on various NLP tasks via task prompting. And their performance can be further improved by appending task demonstrations to the head of the prompt. And usually, a better performance can be achieved with more demonstrations. However, asking the users to write the demonstrations can be cumbersome. As a simple yet cost-effective workaround, this paper proposes a novel method called EPA (\textbf{E}asy \textbf{P}rompt \textbf{A}ugmentation)\footnote{While this paper considers augmenting prompts via demonstrations, we name it EPA as the name EDA is already taken by a well-known NLP method \citep{wei-zou-2019-eda}.} that effectively minimizes user efforts in writing demonstrations while improving the model performance at the same time. EPA achieves these goals by automatically augmenting the demonstrations with multiple sources/targets, where each of them paraphrases each other. This is well motivated as augmenting data via paraphrasing effectively improves neural language models. EPA thus employs paraphrasing as an augmentation method for in-context learning. Extensive experiments indicate that EPA effectively improves both NLU and NLG tasks, covering from natural language inference to machine translation in translating tens of languages.\footnote{Code and data will be released upon publication.

PCC: Paraphrasing with Bottom-k Sampling and Cyclic Learning for Curriculum Data Augmentation

Curriculum Data Augmentation (CDA) improves neural models by presenting synthetic data with increasing difficulties from easy to hard. However, traditional CDA simply treats the ratio of word perturbation as the difficulty measure and goes through the curriculums only once. This paper presents \textbf{PCC}: \textbf{P}araphrasing with Bottom-k Sampling and \textbf{C}yclic Learning for \textbf{C}urriculum Data Augmentation, a novel CDA framework via paraphrasing, which exploits the textual paraphrase similarity as the curriculum difficulty measure. We propose a curriculum-aware paraphrase generation module composed of three units: a paraphrase candidate generator with bottom-k sampling, a filtering mechanism and a difficulty measure. We also propose a cyclic learning strategy that passes through the curriculums multiple times. The bottom-k sampling is proposed to generate super-hard instances for the later curriculums. Experimental results on few-shot text classification as well as dialogue generation indicate that PCC surpasses competitive baselines. Human evaluation and extensive case studies indicate that bottom-k sampling effectively generates super-hard instances, and PCC significantly improves the baseline dialogue agent

Efficient recovery of lithium as Li2CO3 and cobalt as Co3O4 from spent lithium-ion batteries after leaching with p-toluene sulfonic acid

Rechargeable lithium-ion batteries (LIBs) have been widely used in consumer electronics and electric vehicles. In terms of environmental restrictions and circular economy, proper treatment of spent LIBs is of great significance for achieving sustainable development. In this study, organic p-toluene sulfonic acid (PTSA) was employed to recycle valuable Li and Co elements from the spent LIBs for production of battery raw materials (e.g. Li2CO3 and Co3O4). Operation parameters such as PTSA concentration, hydrogen peroxide (H2O2) concentration, solid-toliquid ratio, leaching temperature and leaching time, were systematically investigated. Under the optimal conditions (0.9 vol% H2O2, 1.5 mol L 1 PTSA, 30 g L 1 solid-to-liquid ratio, 80 ◦C, and 60 min), the leaching efficiencies of commercial LiCoO2 could reach ~100% and 99% for Li and Co, respectively, while the corresponding values were about 95% and 93% for the spent LiCoO2. In addition, the selective precipitation of Co-rich compounds in cooled leachate allowed an effective separation of Co from the mixture. The high recovery yield of Co3O4 and Li2CO3 demonstrated the great potential of the PTSA-assisted leaching strategy in hydrometallurgical recycling of the spent LIBs for practical applications. Overall, this proposed recovery process is simple, efficient, and environmentally friendly and is of vital importance for rational treatment of spent LIBs