Dextran hydrogel preparation and applications in biomedical engineering

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

The BCS Pairing Instability in the Thermodynamic Limit

The superconducting pairing instability---as determined by a divergence of the two-particle susceptibility---is obtained in the mean field (BCS) approximation in the thermodynamic limit. The usual practice is to examine this property for a finite lattice. We illustrate that, while the conclusions remain unchanged, the technical features are very different in the thermodynamic limit and conform more closely with the usual treatment of phase transitions encountered in, for example, the mean-field paramagnetic-ferromagnetic transition. Furthermore, by going to the extreme dilute limit, one can distinguish three dimensions from one and two dimensions, in which a pairing instability occurs even for two particles.Comment: 4 pages + references, 4 figure

I-WAS: a Data Augmentation Method with GPT-2 for Simile Detection

Simile detection is a valuable task for many natural language processing (NLP)-based applications, particularly in the field of literature. However, existing research on simile detection often relies on corpora that are limited in size and do not adequately represent the full range of simile forms. To address this issue, we propose a simile data augmentation method based on \textbf{W}ord replacement And Sentence completion using the GPT-2 language model. Our iterative process called I-WAS, is designed to improve the quality of the augmented sentences. To better evaluate the performance of our method in real-world applications, we have compiled a corpus containing a more diverse set of simile forms for experimentation. Our experimental results demonstrate the effectiveness of our proposed data augmentation method for simile detection.Comment: 15 pages, 1 figur

A Pre-training Based Personalized Dialogue Generation Model with Persona-sparse Data

Endowing dialogue systems with personas is essential to deliver more human-like conversations. However, this problem is still far from well explored due to the difficulties of both embodying personalities in natural languages and the persona sparsity issue observed in most dialogue corpora. This paper proposes a pre-training based personalized dialogue model that can generate coherent responses using persona-sparse dialogue data. In this method, a pre-trained language model is used to initialize an encoder and decoder, and personal attribute embeddings are devised to model richer dialogue contexts by encoding speakers' personas together with dialogue histories. Further, to incorporate the target persona in the decoding process and to balance its contribution, an attention routing structure is devised in the decoder to merge features extracted from the target persona and dialogue contexts using dynamically predicted weights. Our model can utilize persona-sparse dialogues in a unified manner during the training process, and can also control the amount of persona-related features to exhibit during the inference process. Both automatic and manual evaluation demonstrates that the proposed model outperforms state-of-the-art methods for generating more coherent and persona consistent responses with persona-sparse data.Comment: Long paper accepted at AAAI 202

Neighborhood-based Hard Negative Mining for Sequential Recommendation

Negative sampling plays a crucial role in training successful sequential recommendation models. Instead of merely employing random negative sample selection, numerous strategies have been proposed to mine informative negative samples to enhance training and performance. However, few of these approaches utilize structural information. In this work, we observe that as training progresses, the distributions of node-pair similarities in different groups with varying degrees of neighborhood overlap change significantly, suggesting that item pairs in distinct groups may possess different negative relationships. Motivated by this observation, we propose a Graph-based Negative sampling approach based on Neighborhood Overlap (GNNO) to exploit structural information hidden in user behaviors for negative mining. GNNO first constructs a global weighted item transition graph using training sequences. Subsequently, it mines hard negative samples based on the degree of overlap with the target item on the graph. Furthermore, GNNO employs curriculum learning to control the hardness of negative samples, progressing from easy to difficult. Extensive experiments on three Amazon benchmarks demonstrate GNNO's effectiveness in consistently enhancing the performance of various state-of-the-art models and surpassing existing negative sampling strategies. The code will be released at \url{https://github.com/floatSDSDS/GNNO}

Combined PD-1 blockade and GITR triggering induce a potent antitumor immunity in murine cancer models and synergizes with chemotherapeutic drugs

BACKGROUND: The coinhibitory receptor Programmed Death-1 (PD-1) inhibits effector functions of activated T cells and prevents autoimmunity, however, cancer hijack this pathway to escape from immune attack. The costimulatory receptor glucocorticoid-induced TNFR related protein (GITR) is up-regulated on activated T cells and increases their proliferation, activation and cytokine production. We hypothesize that concomitant PD-1 blockade and GITR triggering would synergistically improve the effector functions of tumor-infiltrating T cells and increase the antitumor immunity. In present study, we evaluated the antitumor effects and mechanisms of combined PD-1 blockade and GITR triggering in a clinically highly relevant murine ID8 ovarian cancer model. METHODS: Mice with 7 days-established peritoneal ID8 ovarian cancer were treated intraperitoneally (i.p.) with either control, anti-PD-1, anti-GITR or anti-PD-1/GITR monoclonal antibody (mAb) and their survival was evaluated; the phenotype and function of tumor-associated immune cells in peritoneal cavity of treated mice was analyzed by flow cytometry, and systemic antigen-specific immune response was evaluated by ELISA and cytotoxicity assay. RESULTS: Combined anti-PD-1/GITR mAb treatment remarkably inhibited peritoneal ID8 tumor growth with 20% of mice tumor free 90 days after tumor challenge while treatment with either anti-PD-1 or anti-GITR mAb alone exhibited little antitumor effect. The durable antitumor effect was associated with a memory immune response and conferred by CD4(+) cells and CD8(+) T cells. The treatment of anti-PD-1/GITR mAb increased the frequencies of interferon-γ-producing effector T cells and decreased immunosuppressive regulatory T cells and myeloid-derived suppressor cells, shifting an immunosuppressive tumor milieu to an immunostimulatory state in peritoneal cavity. In addition, combined treatment of anti-PD-1/GITR mAb mounted an antigen-specific immune response as evidenced by antigen-specific IFN-γ production and cytolytic activity of spleen cells from treated mice. More importantly, combined treatment of anti-PD-1/GITR mAb and chemotherapeutic drugs (cisplatin or paclitaxel) further increased the antitumor efficacy with 80% of mice obtaining tumor-free long-term survival in murine ID8 ovarian cancer and 4 T1 breast cancer models. CONCLUSIONS: Combined anti-PD-1/GITR mAb treatment induces a potent antitumor immunity, which can be further promoted by chemotherapeutic drugs. A combined strategy of anti-PD-1/GITR mAb plus cisplatin or paclitaxel should be considered translation into clinic

Sudowoodo: a Chinese Lyric Imitation System with Source Lyrics

Lyrics generation is a well-known application in natural language generation research, with several previous studies focusing on generating accurate lyrics using precise control such as keywords, rhymes, etc. However, lyrics imitation, which involves writing new lyrics by imitating the style and content of the source lyrics, remains a challenging task due to the lack of a parallel corpus. In this paper, we introduce \textbf{\textit{Sudowoodo}}, a Chinese lyrics imitation system that can generate new lyrics based on the text of source lyrics. To address the issue of lacking a parallel training corpus for lyrics imitation, we propose a novel framework to construct a parallel corpus based on a keyword-based lyrics model from source lyrics. Then the pairs \textit{(new lyrics, source lyrics)} are used to train the lyrics imitation model. During the inference process, we utilize a post-processing module to filter and rank the generated lyrics, selecting the highest-quality ones. We incorporated audio information and aligned the lyrics with the audio to form the songs as a bonus. The human evaluation results show that our framework can perform better lyric imitation. Meanwhile, the \textit{Sudowoodo} system and demo video of the system is available at \href{https://Sudowoodo.apps-hp.danlu.netease.com/}{Sudowoodo} and \href{https://youtu.be/u5BBT_j1L5M}{https://youtu.be/u5BBT\_j1L5M}.Comment: 7 pages,3 figures, submit to emnlp 2023 demo trac