SongRewriter: A Chinese Song Rewriting System with Controllable Content and Rhyme Scheme

Although lyrics generation has achieved significant progress in recent years, it has limited practical applications because the generated lyrics cannot be performed without composing compatible melodies. In this work, we bridge this practical gap by proposing a song rewriting system which rewrites the lyrics of an existing song such that the generated lyrics are compatible with the rhythm of the existing melody and thus singable. In particular, we propose SongRewriter, a controllable Chinese lyric generation and editing system which assists users without prior knowledge of melody composition. The system is trained by a randomized multi-level masking strategy which produces a unified model for generating entirely new lyrics or editing a few fragments. To improve the controllabiliy of the generation process, we further incorporate a keyword prompt to control the lexical choices of the content and propose novel decoding constraints and a vowel modeling task to enable flexible end and internal rhyme schemes. While prior rhyming metrics are mainly for rap lyrics, we propose three novel rhyming evaluation metrics for song lyrics. Both automatic and human evaluations show that the proposed model performs better than the state-of-the-art models in both contents and rhyming quality. Our code and models implemented in MindSpore Lite tool will be available

ZerNet: Convolutional Neural Networks on Arbitrary Surfaces via Zernike Local Tangent Space Estimation

In this paper, we propose a novel formulation to extend CNNs to two-dimensional (2D) manifolds using orthogonal basis functions, called Zernike polynomials. In many areas, geometric features play a key role in understanding scientific phenomena. Thus, an ability to codify geometric features into a mathematical quantity can be critical. Recently, convolutional neural networks (CNNs) have demonstrated the promising capability of extracting and codifying features from visual information. However, the progress has been concentrated in computer vision applications where there exists an inherent grid-like structure. In contrast, many geometry processing problems are defined on curved surfaces, and the generalization of CNNs is not quite trivial. The difficulties are rooted in the lack of key ingredients such as the canonical grid-like representation, the notion of consistent orientation, and a compatible local topology across the domain. In this paper, we prove that the convolution of two functions can be represented as a simple dot product between Zernike polynomial coefficients; and the rotation of a convolution kernel is essentially a set of 2-by-2 rotation matrices applied to the coefficients. As such, the key contribution of this work resides in a concise but rigorous mathematical generalization of the CNN building blocks

Orbital Expansion Variational Quantum Eigensolver: Enabling Efficient Simulation of Molecules with Shallow Quantum Circuit

In the noisy-intermediate-scale-quantum era, Variational Quantum Eigensolver (VQE) is a promising method to study ground state properties in quantum chemistry, materials science, and condensed physics. However, general quantum eigensolvers are lack of systematical improvability, and achieve rigorous convergence is generally hard in practice, especially in solving strong-correlated systems. Here, we propose an Orbital Expansion VQE~(OE-VQE) framework to construct an efficient convergence path. The path starts from a highly correlated compact active space and rapidly expands and converges to the ground state, enabling simulating ground states with much shallower quantum circuits. We benchmark the OE-VQE on a series of typical molecules including H$_{6}$-chain, H$_{10}$-ring and N$_2$, and the simulation results show that proposed convergence paths dramatically enhance the performance of general quantum eigensolvers.Comment: Wu et al 2023 Quantum Sci. Techno

Age-related sensitivity and pathological differences in infections by 2009 pandemic influenza A (H1N1) virus

<p>Abstract</p> <p>Background</p> <p>The highly pandemic 2009 influenza A H1N1 virus infection showed distinguished skewed age distribution with majority of infection and death in children and young adults. Although previous exposure to related antigen has been proposed as an explanation, the mechanism of age protection is still unknown.</p> <p>Methods</p> <p>In this study, murine model of different ages were inoculated intranasally with H1N1 (A/Beijing/501/09) virus and the susceptibility and pathological response to 2009 H1N1 infection were investigated.</p> <p>Results</p> <p>Our results showed that the younger mice had higher mortality rate when infected with the same dose of virus and the lethal dose increased with age. Immunohistochemical staining of H1N1 antigens in mice lung indicated infection was in the lower respiratory tract. Most bronchial and bronchiolar epithelial cells in 4-week mice were infected while only a minor percentage of those cells in 6-month and 1-year old mice did. The young mice developed much more severe lung lesions and had higher virus load in lung than the two older groups of mice while older mice formed more inducible bronchus-associated lymphoid tissue in their lungs and more severe damage in spleen.</p> <p>Conclusions</p> <p>These results suggest that young individuals are more sensitive to H1N1 infection and have less protective immune responses than older adults. The age factor should be considered when studying the pathogenesis and transmission of influenza virus and formulating strategies on vaccination and treatment.</p

M4LE: A Multi-Ability Multi-Range Multi-Task Multi-Domain Long-Context Evaluation Benchmark for Large Language Models

Managing long sequences has become an important and necessary feature for large language models (LLMs). However, it is still an open question of how to comprehensively and systematically evaluate the long-sequence capability of LLMs. One of the reasons is that conventional and widely-used benchmarks mainly consist of short sequences. In this paper, we propose M4LE, a Multi-ability, Multi-range, Multi-task, Multi-domain benchmark for Long-context Evaluation. M4LE is based on a diverse NLP task pool comprising 36 NLP datasets, 11 task types and 12 domains. To alleviate the scarcity of tasks with naturally long sequences and incorporate multiple-ability assessment, we propose an automatic approach (but with negligible human annotations) to convert short-sequence tasks into a unified long-sequence scenario where LLMs have to identify single or multiple relevant spans in long contexts based on explicit or semantic hints. Specifically, the scenario includes five different types of abilities: (1) explicit single-span; (2) semantic single-span; (3) explicit multiple-span; (4) semantic multiple-span; and (5) global context understanding. The resulting samples in M4LE are evenly distributed from 1k to 8k input length. We conducted a systematic evaluation on 11 well-established LLMs, especially those optimized for long-sequence inputs. Our results reveal that: 1) Current LLMs struggle to understand long context, particularly when tasks require multiple-span attention. 2) Semantic retrieval task is more difficult for competent LLMs. 3) Models fine-tuned on longer text with position interpolation have comparable performance to those using Neural Tangent Kernel (NTK) aware scaling methods without fine-tuning. We make our benchmark publicly available to encourage future research in this challenging area.Comment: Code and data are available at https://github.com/KwanWaiChung/M4L

A novel approach to inhibit HIV-1 infection and enhance lysis of HIV by a targeted activator of complement

<p>Abstract</p> <p>Background</p> <p>The complement system is one of the most potent weapons of innate immunity. It is not only a mechanism for direct protection against invading pathogens but it also interacts with the adaptive immunity to optimize the pathogen-specific humoral and cellular defense cascades in the body. Complement-mediated lysis of HIV is inefficient but the presence of HIV particles results in complement activation by the generation of many C3-fragments, such as C3dg and C3d. It has been demonstrated that activation of complement can enhance HIV infection through the binding of special complement receptor type 2 expression on the surface of mature B cells and follicular dendritic cells.</p> <p>Presentation of the hypothesis</p> <p>Previous studies have proven that the complement-mediated antibody-dependent enhancement of HIV infection is mediated by the association of complement receptor type 2 bound to the C3 fragment and deposited on the surface of HIV virions. Thus, we hypothesize that a new activator of complement, consisting of a target domain (C3-binding region of complement receptor type 2) linked to a complement-activating human IgG1 Fc domain (CR2-Fc), can target and amplify complement deposition on HIV virions and enhance the efficiency of HIV lysis.</p> <p>Testing the hypothesis</p> <p>Our hypothesis was tested using cell-free HIV-1 virions cultivated <it>in vitro </it>and assessment of virus opsonization was performed by incubating appropriate dilutions of virus with medium containing normal human serum and purified CR2-Fc proteins. As a control group, viruses were incubated with normal human serum under the same conditions. Virus neutralization assays were used to estimate the degree of CR2-Fc-enhanced lysis of HIV compared to untreated virus.</p> <p>Implications of the hypothesis</p> <p>The targeted complement activator, CR2-Fc, can be used as a novel approach to HIV therapy by abrogating the complement-enhanced HIV infection of cells.</p

A new therapeutic strategy for lung tissue injury induced by influenza with CR2 targeting complement inhibitior

<p>Abstract</p> <p>Background</p> <p>Influenza is a respiratory disease that seriously threatens human health. In fact, influenza virus itself does not make critical contribution to mortality induced by influenza, but "cytokine storm" produced by the excessive immune response triggered by the virus can result in inflammatory reaction of lung tissues and fatal lung tissue injury, and thus increase influenza mortality. Therefore, besides antiviral drugs, immunosuppression drugs should also be included in infection treatment.</p> <p>Presentation of the hypothesis</p> <p>Complement is the center of inflammatory reaction. If complement system is over activated, the body will have strong inflammatory reaction or tissue injury, resulting in pathological process. Many studies have proved that, inflammatory injury of lung tissues caused by influenza virus is closely related to complement activation. Therefore, inhibiting complement activation can significantly reduce inflammatory injury in lung tissues. As complement is both a physiological defense and pathological damage medium, systematic inhibition may result in side effects including infection. Therefore, we design targeting complement inhibitors for complement activation sites, i.e. with CR2 as targeting vector, complement inhibitors like CD59 and Crry are targeted to inflammatory sites to specially inhibit the complement activation in local injury, thus local inflammatory reaction is inhibited.</p> <p>Testing the hypothesis</p> <p>CR2-CD59 and CR2-Crry targeting complement inhibitors are fusion-expressed, and their biological activity is examined via in <it>vivo </it>and in vitro tests. CR2 targeting complement inhibitors are used to treat mouse influenza viral pneumonia model, with PBS treatment group as the control. The survival and lung tissue injury of the mice is observed and the effect of CR2 targeting complement inhibitors on pneumonia induced by influenza virus is evaluated.</p> <p>Implications of the hypothesis</p> <p>CR2 targeting complement inhibitors are expected to be ideal drugs for viral pneumonia.</p