CROUCHING TIGER CELLO CONCERTO - A MELDING OF FORM AND CONTENT FOR THE CONCERT STAGE

Tan Dun’s Crouching Tiger Concerto for Amplified Cello and Orchestra is not only one of the most frequently performed cello concerto of the recent past; it also demonstrates Tan’s masterful synthesis of artistic forms from the Chinese and the Western art music traditions with visual media that extends beyond the concert-hall. The music for this concerto was initially composed as part of the score for Ang Lee’s film Crouching Tiger, Hidden Dragon, itself a landmark blend of Chinese cinema with Western technique. The score broke boundaries, combining Western orchestral music with traditional Chinese instruments and thematic material. This melding of a wide variety of influences is typical of Tan’s œuvre and reveals the depth of his personal experience; his works include references to childhood experiences in the Hunan province, soundscapes suggested by his many years of struggle in New York City, and instrumentations that reflect his interest in environmentalism. Performing the Crouching Tiger Concerto can be a challenging undertaking. Each movement expresses musical ideas both Chinese and Western, while simultaneously mirroring the emotions of the film clips that Tan selected for display behind the performance. This paper will explore these connections, suggesting ways in which an aspiring performer can bring out the most important details of each section of the concerto. It will also give suggestions for navigating some of the unique technical challenges of the solo cello part; glissandi, use of a guitar pick, and amplification. The music of Crouching Tiger, Hidden Dragon had an indelible impact on me as a young child, single-handedly cementing my future as cellist, and I am glad, twenty years later, to be able use my experiences learning this piece to help others who are approaching it for the first time

Nonlinear Hall effect as a signature of electronic phase separation in the semimetallic ferromagnet EuB6

This work reports a study of the nonlinear Hall Effect (HE) in the semimetallic ferromagnet EuB6. A distinct switch in its Hall resistivity slope is observed in the paramagnetic phase, which occurs at a single critical magnetization over a wide temperature range. The observation is interpreted as the point of percolation for entities of a more conducting and magnetically ordered phase in a less ordered background. With an increasing applied magnetic field, the conducting regions either increase in number or expand beyond the percolation limit, hence increasing the global conductivity and effective carrier density. An empirical two-component model expression provides excellent scaling and a quantitative fit to the HE data and may be applicable to other correlated electron systems.Comment: 15 Pages, 4 Figures. Accepted for publication in Phys. Rev. Let

Super-NeRF: View-consistent Detail Generation for NeRF super-resolution

The neural radiance field (NeRF) achieved remarkable success in modeling 3D scenes and synthesizing high-fidelity novel views. However, existing NeRF-based methods focus more on the make full use of the image resolution to generate novel views, but less considering the generation of details under the limited input resolution. In analogy to the extensive usage of image super-resolution, NeRF super-resolution is an effective way to generate the high-resolution implicit representation of 3D scenes and holds great potential applications. Up to now, such an important topic is still under-explored. In this paper, we propose a NeRF super-resolution method, named Super-NeRF, to generate high-resolution NeRF from only low-resolution inputs. Given multi-view low-resolution images, Super-NeRF constructs a consistency-controlling super-resolution module to generate view-consistent high-resolution details for NeRF. Specifically, an optimizable latent code is introduced for each low-resolution input image to control the 2D super-resolution images to converge to the view-consistent output. The latent codes of each low-resolution image are optimized synergistically with the target Super-NeRF representation to fully utilize the view consistency constraint inherent in NeRF construction. We verify the effectiveness of Super-NeRF on synthetic, real-world, and AI-generated NeRF datasets. Super-NeRF achieves state-of-the-art NeRF super-resolution performance on high-resolution detail generation and cross-view consistency

opML: Optimistic Machine Learning on Blockchain

The integration of machine learning with blockchain technology has witnessed increasing interest, driven by the vision of decentralized, secure, and transparent AI services. In this context, we introduce opML (Optimistic Machine Learning on chain), an innovative approach that empowers blockchain systems to conduct AI model inference. opML lies a interactive fraud proof protocol, reminiscent of the optimistic rollup systems. This mechanism ensures decentralized and verifiable consensus for ML services, enhancing trust and transparency. Unlike zkML (Zero-Knowledge Machine Learning), opML offers cost-efficient and highly efficient ML services, with minimal participation requirements. Remarkably, opML enables the execution of extensive language models, such as 7B-LLaMA, on standard PCs without GPUs, significantly expanding accessibility. By combining the capabilities of blockchain and AI through opML, we embark on a transformative journey toward accessible, secure, and efficient on-chain machine learning

ImmersiveNeRF: Hybrid Radiance Fields for Unbounded Immersive Light Field Reconstruction

This paper proposes a hybrid radiance field representation for unbounded immersive light field reconstruction which supports high-quality rendering and aggressive view extrapolation. The key idea is to first formally separate the foreground and the background and then adaptively balance learning of them during the training process. To fulfill this goal, we represent the foreground and background as two separate radiance fields with two different spatial mapping strategies. We further propose an adaptive sampling strategy and a segmentation regularizer for more clear segmentation and robust convergence. Finally, we contribute a novel immersive light field dataset, named THUImmersive, with the potential to achieve much larger space 6DoF immersive rendering effects compared with existing datasets, by capturing multiple neighboring viewpoints for the same scene, to stimulate the research and AR/VR applications in the immersive light field domain. Extensive experiments demonstrate the strong performance of our method for unbounded immersive light field reconstruction

Learning One-Shot 4D Head Avatar Synthesis using Synthetic Data

Existing one-shot 4D head synthesis methods usually learn from monocular videos with the aid of 3DMM reconstruction, yet the latter is evenly challenging which restricts them from reasonable 4D head synthesis. We present a method to learn one-shot 4D head synthesis via large-scale synthetic data. The key is to first learn a part-wise 4D generative model from monocular images via adversarial learning, to synthesize multi-view images of diverse identities and full motions as training data; then leverage a transformer-based animatable triplane reconstructor to learn 4D head reconstruction using the synthetic data. A novel learning strategy is enforced to enhance the generalizability to real images by disentangling the learning process of 3D reconstruction and reenactment. Experiments demonstrate our superiority over the prior art.Comment: Project page: https://yudeng.github.io/Portrait4D

opp/ai: Optimistic Privacy-Preserving AI on Blockchain

The convergence of Artificial Intelligence (AI) and blockchain technology is reshaping the digital world, offering decentralized, secure, and efficient AI services on blockchain platforms. Despite the promise, the high computational demands of AI on blockchain raise significant privacy and efficiency concerns. The Optimistic Privacy-Preserving AI (opp/ai) framework is introduced as a pioneering solution to these issues, striking a balance between privacy protection and computational efficiency. The framework integrates Zero-Knowledge Machine Learning (zkML) for privacy with Optimistic Machine Learning (opML) for efficiency, creating a hybrid model tailored for blockchain AI services. This study presents the opp/ai framework, delves into the privacy features of zkML, and assesses the framework's performance and adaptability across different scenarios

Inscuteable and Staufen Mediate Asymmetric Localization and Segregation of prosperoRNA during Drosophila Neuroblast Cell Divisions

AbstractWhen neuroblasts divide, inscuteable acts to coordinate protein localization and mitotic spindle orientation, ensuring that asymmetrically localized determinants like Prospero partition into one progeny. staufen encodes a dsRNA-binding protein implicated in mRNA transport in oocytes. We demonstrate that prospero RNA is also asymmetrically localized and partitioned during neuroblast cell divisions, a process requiring both inscuteable and staufen. Inscuteable and Staufen interact and colocalize with prospero RNA on the apical cortex of interphase neuroblasts. Staufen binds prospero RNA in its 3′UTR. Our findings suggest that Inscuteable nucleates an apical complex and is required for protein localization, spindle orientation, and RNA localization. Stau, as one component of this complex, is required only for RNA localization. Hence staufen also acts zygotically, downstream of inscuteable, to effect aspects of neuroblast asymmetry

A Detailed Audio-Text Data Simulation Pipeline using Single-Event Sounds

Recently, there has been an increasing focus on audio-text cross-modal learning. However, most of the existing audio-text datasets contain only simple descriptions of sound events. Compared with classification labels, the advantages of such descriptions are significantly limited. In this paper, we first analyze the detailed information that human descriptions of audio may contain beyond sound event labels. Based on the analysis, we propose an automatic pipeline for curating audio-text pairs with rich details. Leveraging the property that sounds can be mixed and concatenated in the time domain, we control details in four aspects: temporal relationship, loudness, speaker identity, and occurrence number, in simulating audio mixtures. Corresponding details are transformed into captions by large language models. Audio-text pairs with rich details in text descriptions are thereby obtained. We validate the effectiveness of our pipeline with a small amount of simulated data, demonstrating that the simulated data enables models to learn detailed audio captioning