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Large Language Models (LLMs) has made significant progress in a number of professional fields, including medicine, law, and finance. However, in traditional Chinese medicine (TCM), there are challenges such as the essential differences between theory and modern medicine, the lack of specialized corpus resources, and the fact that relying only on supervised fine-tuning may lead to overconfident predictions. To address these challenges, we propose a two-stage training approach that combines continuous pre-training and supervised fine-tuning. A notable contribution of our study is the processing of a 2GB corpus dedicated to TCM, constructing pre-training and instruction fine-tuning datasets for TCM, respectively. In addition, we have developed Qibo-Benchmark, a tool that evaluates the performance of LLM in the TCM on multiple dimensions, including subjective, objective, and three TCM NLP tasks. The medical LLM trained with our pipeline, named $\textbf{Qibo}$, exhibits significant performance boosts. Compared to the baselines, the average subjective win rate is 63%, the average objective accuracy improved by 23% to 58%, and the Rouge-L scores for the three TCM NLP tasks are 0.72, 0.61, and 0.55. Finally, we propose a pipline to apply Qibo to TCM consultation and demonstrate the model performance through the case study.

Electronically conductive protein-based materials could enable the creation of bioelectronic components and devices from sustainable and nontoxic materials, while also being well-suited to interface with biological systems, such as living cells, for biosensor applications. In addition, protein materials have other desirable properties such as inherent self-assembly and molecular recognition capabilities. However, as proteins are generally electrical insulators, the ability to render protein assemblies electronically conductive in a tailorable manner could usher in a plethora of useful materials. Here, we present an approach to fabricate electronically conductive protein nanowires by incorporating and aligning heme molecules in proximity along an ultrastable protein filament. The heme-incorporated protein nanowires demonstrated electron transfer over micrometer distances, with conductive atomic force microscopy showing individual nanowires having comparable conductance to naturally occurring bacterial nanowires. The heme-incorporated nanowires were also capable of harvesting energy from ambient humidity when deposited as multilayer films. Exposure of films to humidity produced electrical current, presumably through water molecules ionizing carboxy groups in the protein filament and creating an unbalanced total charge distribution that is enhanced by the presence of heme. A wide variety of other porphyrin molecules exist with varying electrochemical behaviors that could enable the electrical properties of protein assemblies to be tailored, paving the way to structurally- and electrically-defined protein-based bioelectronic devices., Comment: 39 pages, 5 main figures, 12 SI figures

The rapid advances in automation technologies, such as artificial intelligence (AI) and robotics, pose an increasing risk of automation for occupations, with a likely significant impact on the labour market. Recent social-economic studies suggest that nearly 50\% of occupations are at high risk of being automated in the next decade. However, the lack of granular data and empirically informed models have limited the accuracy of these studies and made it challenging to predict which jobs will be automated. In this paper, we study the automation risk of occupations by performing a classification task between automated and non-automated occupations. The available information is 910 occupations' task statements, skills and interactions categorised by Standard Occupational Classification (SOC). To fully utilize this information, we propose a graph-based semi-supervised classification method named \textbf{A}utomated \textbf{O}ccupation \textbf{C}lassification based on \textbf{G}raph \textbf{C}onvolutional \textbf{N}etworks (\textbf{AOC-GCN}) to identify the automated risk for occupations. This model integrates a heterogeneous graph to capture occupations' local and global contexts. The results show that our proposed method outperforms the baseline models by considering the information of both internal features of occupations and their external interactions. This study could help policymakers identify potential automated occupations and support individuals' decision-making before entering the job market., Comment: 15 pages, 6 figures

Inspired by our serendipitous discovery of six AGNs with varying broad-Halpha fluxes over years out of our searching for intermediate-mass black holes (IMBHs), we conduct a systematic investigation of changing-look (CL) and large-variability AGNs. We collect all the CL AGNs at z<0.15 and the reverberation mapped AGNs with strongly variable broad Halpha, and perform careful decomposition fittings to both their images and spectra. We find two observational facts: (1) The host galaxies of local CL and large-variability AGNs, mainly being Seyferts, are in the red (gas-poor) tail of the general Seyfert galaxy population. (2) In contrast, there is a significant trend that their more luminous counterparts namely CL and extremely variable quasars (CLQs and EVQs) are different: CLQs are generally in blue galaxies; in terms of the diagram of SFR and M* local CL Seyfert galaxies are located in the green valley, whereas CLQ hosts are in the star-forming main sequence. We propose explanations for those strongly variable Seyferts and quasars, respectively, under the thought that accretion disks broadly depend on nuclear fueling modes. Local large-variability and CL Seyferts are in nuclear famine mode, where cold-gas clumps can be formed stochastically in the fueling flow, and their episodic infall produces sharp peaks in the accretion-rate curve. CLQs and EVQs are in feast fueling mode, which may account for both their preference to blue galaxies and their variability pattern (high-amplitude tail of the continuous distribution). Lastly, we propose a new thinking: to search for IMBHs by optical variability in red galaxies., Comment: 37 pages(incl. Appendix), 10 figures, and 5 tables. Published in ApJ. (v2)Text changed to match the published version

This paper is a brief report to our submission to the VIPriors Action Recognition Challenge. Action recognition has attracted many researchers attention for its full application, but it is still challenging. In this paper, we study previous methods and propose our method. In our method, we are primarily making improvements on the SlowFast Network and fusing with TSM to make further breakthroughs. Also, we use a fast but effective way to extract motion features from videos by using residual frames as input. Better motion features can be extracted using residual frames with SlowFast, and the residual-frame-input path is an excellent supplement for existing RGB-frame-input models. And better performance obtained by combining 3D convolution(SlowFast) with 2D convolution(TSM). The above experiments were all trained from scratch on UCF101., Comment: ECCV2020,VIPriors Action Recognition Challenge

We present spectropolarimetric and spectrophotometric observations of the peculiar active galactic nucleus (AGN) SDSS J120300.19+162443.7 (hereafter J1203+1624) at $z=0.1656$. Its optical total flux spectra clearly show broad emission lines (BELs) in H$\alpha$ and H$\beta$. After removal of narrow emission lines (NELs), the full width at half maximum of the lorentzian BEL is $FWHM_{\rm BEL} \approx 1,270$ km s$^{-1}$, fulfilling the conventional definition of a narrow line Seyfert 1 (NLS1) galaxy. However, its NELs are unprecedentedly strong when compared to type 1AGNs. This, together with its large MIR excess ($g-W_4 = 13.172$ mag), implies that the observer and the narrow emission line region (NELR) might see a different ionization continuum. Our optical spectropolarimetry confirms its type 2 nature by detecting a polarized blue continuum and Balmer BELs ($FWHM_{\rm Polarized~BEL} \approx 1,183$ km s$^{-1}$), with a high polarization degree of $> 20$% in the blue wing. The mass and Eddington rate of the central black hole is estimated based on both transmitted and scattered AGN radiation, which is $M_{\bullet} < 2.9 \times 10^7 M_\odot$ and $L_{\rm bol}/L_{\rm Edd} > 1.5$. Severe extinction of the AGN emission also enables a clear view of the compact host galaxy. Discovery of J1203+1624 suggests that NLS1 follows AGN unfication scheme, studying of its analogs could blaze a new trail for exploring the connection between black hole growth and star formation in the host galaxy. Interesting features of J1203+1624 like the peculiar NELs, inflowing scattering clouds within sublimation radius are worth detailed follow-ups in the future., Comment: 9 pages, 6 figures