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Pre-trained Language Models (PLMs) have shown impressive results in various Natural Language Generation (NLG) tasks, such as powering chatbots and generating stories. However, an ethical concern arises due to their potential to produce verbatim copies of paragraphs from their training data. This is problematic as PLMs are trained on corpora constructed by human authors. As such, there is a pressing need for research to promote the generation of original content by these models. In this study, we introduce a unique "self-plagiarism" contrastive decoding strategy, aimed at boosting the originality of text produced by PLMs. Our method entails modifying prompts in LLMs to develop an amateur model and a professional model. Specifically, the amateur model is urged to plagiarize using three plagiarism templates we have designed, while the professional model maintains its standard language model status. This strategy employs prompts to stimulate the model's capacity to identify non-original candidate token combinations and subsequently impose penalties. The application of this strategy is integrated prior to the model's final layer, ensuring smooth integration with most existing PLMs (T5, GPT, LLaMA) without necessitating further adjustments. Implementing our strategy, we observe a significant decline in non-original sequences comprised of more than three words in the academic AASC dataset and the story-based ROCStories dataset., Comment: 16 pages, 8 figures. The paper has been accepted by ACL 2024 (Findings), with Kaixin Lan and Tao Fang contributing equally, and Derek F. Wong serving as the corresponding author

Let $\mathcal{F}$ be a set of graphs. The planar Tur\'an number, $ex_{\mathcal{P}}(n,\mathcal{F})$, is the maximum number of edges in an $n$-vertex planar graph which does not contain any member of $\mathcal{F}$ as a subgraph. In this paper, we give upper bounds of $ex_{\mathcal{P}}(n,\{K_4,\Theta_5\})\leqslant25/11(n-2)$. We also give constructions which show the bounds are tight for infinitely many graphs.

Proprietary Large Language Models (LLMs), such as ChatGPT, have garnered significant attention due to their exceptional capabilities in handling a diverse range of tasks. Recent studies demonstrate that open-sourced smaller foundational models, such as 7B-size LLaMA, can also display remarkable proficiency in tackling diverse tasks when fine-tuned using instruction-driven data. In this work, we investigate a practical problem setting where the primary focus is on one or a few particular tasks rather than general-purpose instruction following, and explore whether LLMs can be beneficial and further improved for such targeted scenarios. We choose the writing-assistant scenario as the testbed, which includes seven writing tasks. We collect training data for these tasks, reframe them in an instruction-following format, and subsequently refine the LLM, specifically LLaMA, via instruction tuning. Experimental results show that fine-tuning LLaMA on writing instruction data significantly improves its ability on writing tasks. We also conduct more experiments and analyses to offer insights for future work on effectively fine-tuning LLaMA for specific scenarios. Finally, we initiate a discussion regarding the necessity of employing LLMs for only one targeted task, taking into account the efforts required for tuning and the resources consumed during deployment.

Given a graph $T$ and a family of graphs $\mathcal{H}$. The generalized Tur\'an number of $\mathcal{H}$ is the maximum number of copies of $T$ in an $\mathcal{H}$-free graph on $n$ vertices, denoted by $ex(n, T, \mathcal{H})$. Let $ex(n, T, \mathcal{H})$ denote the maximum number of copies of $T$ in an $n$-vertex $\mathcal{H}$-free graph. Recently, Alon and Frankl (arXiv2210.15076) determined the exact values of $\rm{ex}(n, \{K_{r+1}, M_{s+1}\})$, where $K_{r+1}$ and $M_{s+1}$ are complete graph on $r + 1$ vertices and matching of size $s + 1$, respectively. Ma and Hou (arXiv2301.05625) gave the generalized version of Alon and Frankl's Theorem, which determine the exact values of $ex(n, K_r, \{K_{k+1}, M_{s+1}\})$. Zhang determined the exact values of $ex(n, K_r, \mathcal{L}_{n, s})$, where $\mathcal{L}_{n, s}$ be the family of all linear forests of order $n$ with $s$ edges. Inspired by the work of Zhang and Ma, in this paper, we determined the exact number of $ex(n, \{K_{r+1}, \mathcal{L}_{n, s}\})$., Comment: 8 pages

ChatGPT, a large-scale language model based on the advanced GPT-3.5 architecture, has shown remarkable potential in various Natural Language Processing (NLP) tasks. However, there is currently a dearth of comprehensive study exploring its potential in the area of Grammatical Error Correction (GEC). To showcase its capabilities in GEC, we design zero-shot chain-of-thought (CoT) and few-shot CoT settings using in-context learning for ChatGPT. Our evaluation involves assessing ChatGPT's performance on five official test sets in three different languages, along with three document-level GEC test sets in English. Our experimental results and human evaluations demonstrate that ChatGPT has excellent error detection capabilities and can freely correct errors to make the corrected sentences very fluent, possibly due to its over-correction tendencies and not adhering to the principle of minimal edits. Additionally, its performance in non-English and low-resource settings highlights its potential in multilingual GEC tasks. However, further analysis of various types of errors at the document-level has shown that ChatGPT cannot effectively correct agreement, coreference, tense errors across sentences, and cross-sentence boundary errors.

2D MoS2 attracts increasing attention for its application in flexible electronics and photonic devices. For 2D material optoelectronic devices, light absorption of the molecularly thin 2D absorber would be one of the key limiting factors in device efficiency, and conventional photon management techniques are not necessarily compatible with them. In this paper, we show two semimetal composite nanostructures for synergistic photon management and strain-induced band gap engineering of 2D MoS2: (1) pseudo-periodic Sn nanodots, (2) conductive SnOx (x<1) core-shell nanoneedle structures. Without sophisticated nanolithography, both nanostructures are self-assembled from physical vapor deposition. 2D MoS2 achieves up to >15x enhancement in absorption at {\lambda}=650-950 nm under Sn nanodots, and 20-30x at {\lambda}=700-900 nm under SnOx (x<1) nanoneedles, both spanning from visible to near infrared regime. Enhanced absorption in MoS2 results from strong near field enhancement and reduced MoS2 band gap due to the tensile strain induced by the Sn nanostructures, as confirmed by Raman and photoluminescence spectroscopy. Especially, we demonstrate that up to 3.5% biaxial tensile strain is introduced to 2D MoS2 using conductive nanoneedle-structured SnOx (x<1), which reduces the band gap by ~0.35 eV to further enhance light absorption at longer wavelengths. To the best of our knowledge, this is the first demonstration of a synergistic triple-functional photon management, stressor, and conductive electrode layer on 2D MoS2. Such synergistic photon management and band gap engineering approach for extended spectral response can be further applied to other 2D materials for future 2D photonic devices.

The Tur\'an number of a graph $H$, denoted by $ex(n, H)$, is the maximum number of edges in any graph on $n$ vertices containing no $H$ as a subgraph. Let $P_{\ell}$ denote the path on $\ell$ vertices, $S_{\ell-1}$ denote the star on $\ell$ vertices and $k_1P_{\ell}\cup k_2S_{\ell-1}$ denote the path-star forest with disjoint union of $k_1$ copies of $P_{\ell}$ and $k_2$ copies of $S_{\ell-1}$. In 2013, Lidick\'y et al. first considered the Tur\'an number of $k_1P_4\cup k_2S_3$ for sufficiently large $n$. In 2022, Zhang and Wang raised a conjecture about the Tur\'an number of $k_1P_{2\ell}\cup k_2S_{2\ell-1}$. In this paper, we determine the Tur\'an numbers of $P_{\ell}\cup kS_{\ell-1}$, $k_1P_{2\ell}\cup k_2S_{2\ell-1}$, $2P_5\cup kS_4$ for $n$ appropriately large, which implies the conjecture of Zhang and Wang. The corresponding extremal graphs are also completely characterized., Comment: 13 pages

The Tur\'an number of a graph $H$, denoted by $ex(n, H)$, is the maximum number of edges in any graph on $n$ vertices containing no $H$ as a subgraph. Let $P_k$ denote the path on $k$ vertices, $S_k$ denote the star on $k+1$ vertices and $k_1P_{2l}\cup k_2S_{2l-1}$ denote the path-star forest with disjoint union of $k_1$ copies of $P_{2l}s$ and $k_2$ copies of $S_{2l-1}s$. In 2019, Lan et al. determined the Tur\'an numbers of $kS_l$ and $k_1P_4\cup k_2S_3$. In 2022, Zhang et al. determined the Tur\'an numbers of $k_1P_6\cup k_2S_5$ and raised a conjecture of the Tur\'an numbers of $k_1P_{2l}\cup k_2S_{2l-1}$, where $k_1\geqslant 1$ and $l\geqslant 2$. In this paper, we study the hypothesis and determine the Tur\'an numbers of $k_1P_{2l}\cup k_2S_{2l-1}$ when $n$ is sufficiently large., Comment: We need to continue to validate our results to ensure that they are completely correct

The odd-ballooning of a graph $G$, denoted by $G_q$, is the graph obtained from replacing each edge in $G$ by a odd cycle of the same size where the new vertices of the odd cycles are all different. In 2002, Erd\"os et al. determined the extremal graphs of $k$-fan. In 2016, Hou et al. determined extremal graphs of the odd-ballooning of stars for $q\geqslant 5$. In 2020, Zhu et al. determined extremal graphs of the odd-ballooning of paths for $q\geqslant 3$. In this article, we use progressive induction lemma of Simonovits to determine the extremal graphs of both odd-ballooning of stars and odd-ballooning of paths for $q\geqslant 3$., Comment: This version of the article has some bugs and we would like to withdraw it

During the era of global warming and highly urbanized development, extreme and high impact weather as well as air pollution incidents influence everyday life and might even cause the incalculable loss of life and property. Although with the vast development of numerical simulation of atmosphere, there still exists substantial forecast biases objectively. To predict extreme weather, severe air pollution, and abrupt climate change accurately, the numerical atmospheric model requires not only to simulate meteorology and atmospheric compositions and their impacts simultaneously involving many sophisticated physical and chemical processes but also at high spatiotemporal resolution. Global atmospheric simulation of meteorology and atmospheric compositions simultaneously at spatial resolutions of a few kilometers remains challenging due to its intensive computational and input/output (I/O) requirement. Through multi-dimension-parallelism structuring, aggressive and finer-grained optimizing, manual vectorizing, and parallelized I/O fragmenting, an integrated Atmospheric Model Across Scales (iAMAS) was established on the new Sunway supercomputer platform to significantly increase the computational efficiency and reduce the I/O cost. The global 3-km atmospheric simulation for meteorology with online integrated aerosol feedbacks with iAMAS was scaled to 39,000,000 processor cores and achieved the speed of 0.82 simulation day per hour (SDPH) with routine I/O, which enables us to perform 5-day global weather forecast at 3-km horizontal resolution with online natural aerosol impacts. The results demonstrate the promising future that the increasing of spatial resolution to a few kilometers with online integrated aerosol impacts may significantly improve the global weather forecast.