Genetic analysis and QTL mapping of aroma volatile compounds in the apple progeny ‘Fuji’ × ‘Cripps Pink’

Aroma is an essential trait for apple fruit quality, but the understanding of biochemical mechanisms underlying aroma formation is still limited. To better characterize and assess the genetic potential for improving aroma quality for breeding, many efforts have been paid to map quantitative trait loci (QTLs) using a saturated molecular linkage map. In the present study, aroma profiles in ripe fruit of F1 population between ‘Fuji’ and ‘Cripps Pink’ were evaluated by gas chromatography-mass spectrometry (GC-MS) over 2019 and 2020 years, and the genetics of volatile compounds were dissected. In total, 38 volatile compounds were identified in ‘Fuji’ × ‘Cripps Pink’ population, including 23 esters, 3 alcohols, 7 aldehydes and 5 others. With the combination of aroma phenotypic data and constructed genetic linkage map, 87 QTLs were detected for 15 volatile compounds on 14 linkage groups (LGs). Among them, a set of QTLs associated with ester production identified and confirmed on LG 6. A candidate gene MdAAT6 in the QTL mapping interval was detected. Over-expression of MdAAT6 in tomato and apple fruits showed significantly higher esters accumulation compared to the control, indicating it was critical for the ester production. Our results give light on the mode of inheritance of the apple volatilome and provide new insights for apple flavor improvement in the future

SPAN: A Stochastic Projected Approximate Newton Method

Second-order optimization methods have desirable convergence properties. However, the exact Newton method requires expensive computation for the Hessian and its inverse. In this paper, we propose SPAN, a novel approximate and fast Newton method. SPAN computes the inverse of the Hessian matrix via low-rank approximation and stochastic Hessian-vector products. Our experiments on multiple benchmark datasets demonstrate that SPAN outperforms existing first-order and second-order optimization methods in terms of the convergence wall-clock time. Furthermore, we provide a theoretical analysis of the per-iteration complexity, the approximation error, and the convergence rate. Both the theoretical analysis and experimental results show that our proposed method achieves a better trade-off between the convergence rate and the per-iteration efficiency.Comment: Appeared in the AAAI 2020, 25 pages, 6 figure

Self-Organized Time Crystal in Driven-Dissipative Quantum System

Continuous time crystals (CTCs) are characterized by sustained oscillations that break the time translation symmetry. Since the ruling out of equilibrium CTCs by no-go theorems, the emergence of such dynamical phases has been observed in various driven-dissipative quantum platforms. The current understanding of CTCs is mainly based on mean-field (MF) theories, which fail to address the problem of whether the long-range time crystalline order exists in noisy, spatially extended systems without the protection of all-to-all couplings. Here, we propose a new kind of CTC realized in a quantum contact model through self-organized bistability (SOB). The exotic CTCs stem from the interplay between collective dissipation induced by the first-order absorbing phase transitions (APTs) and slow constant driving provided by an incoherent pump. The stability of such oscillatory phases in finite dimensions under the action of intrinsic quantum fluctuations is scrutinized by the functional renormalization group method and numerical simulations. Occurring at the edge of quantum synchronization, the CTC phase exhibits an inherent period and amplitude with a coherence time diverging with system size, thus also constituting a boundary time crystal (BTC). Our results serve as a solid route towards self-protected CTCs in strongly interacting open systems.Comment: 15 pages, 7 figure

IMPUS: Image Morphing with Perceptually-Uniform Sampling Using Diffusion Models

We present a diffusion-based image morphing approach with perceptually-uniform sampling (IMPUS) that produces smooth, direct, and realistic interpolations given an image pair. A latent diffusion model has distinct conditional distributions and data embeddings for each of the two images, especially when they are from different classes. To bridge this gap, we interpolate in the locally linear and continuous text embedding space and Gaussian latent space. We first optimize the endpoint text embeddings and then map the images to the latent space using a probability flow ODE. Unlike existing work that takes an indirect morphing path, we show that the model adaptation yields a direct path and suppresses ghosting artifacts in the interpolated images. To achieve this, we propose an adaptive bottleneck constraint based on a novel relative perceptual path diversity score that automatically controls the bottleneck size and balances the diversity along the path with its directness. We also propose a perceptually-uniform sampling technique that enables visually smooth changes between the interpolated images. Extensive experiments validate that our IMPUS can achieve smooth, direct, and realistic image morphing and be applied to other image generation tasks

SOTOPIA: Interactive Evaluation for Social Intelligence in Language Agents

Humans are social beings; we pursue social goals in our daily interactions, which is a crucial aspect of social intelligence. Yet, AI systems' abilities in this realm remain elusive. We present SOTOPIA, an open-ended environment to simulate complex social interactions between artificial agents and evaluate their social intelligence. In our environment, agents role-play and interact under a wide variety of scenarios; they coordinate, collaborate, exchange, and compete with each other to achieve complex social goals. We simulate the role-play interaction between LLM-based agents and humans within this task space and evaluate their performance with a holistic evaluation framework called SOTOPIA-Eval. With SOTOPIA, we find significant differences between these models in terms of their social intelligence, and we identify a subset of SOTOPIA scenarios, SOTOPIA-hard, that is generally challenging for all models. We find that on this subset, GPT-4 achieves a significantly lower goal completion rate than humans and struggles to exhibit social commonsense reasoning and strategic communication skills. These findings demonstrate SOTOPIA's promise as a general platform for research on evaluating and improving social intelligence in artificial agents.Comment: Preprint, 43 pages. The first two authors contribute equall

Chemical Constituents and Digestion-Promoting Effect of Maojian Green Tea

In this study, the digestion-promoting function of an aqueous extract from Maojian green tea extract (MJ-GTE) was evaluated by small intestinal motility in mice as well as body mass, body mass gain, food intake, food utilization rate, gastric pepsin activity, and gastric pepsin excretion in rats. The chemical composition of MJ-GTE was then systematically analyzed using metabolomics based on ultra-high performance liquid chromatography-quadrupole electrostatic orbitrap mass spectrometry (UPLC-Q-Exactive/MS). The results of animal experiments showed that the intestinal propulsion ratio of ink in the high-dose MJ-GTE group (0.83 g/(kg·d)) was significantly increased compared with the model group (P < 0.05), and gastric pepsin excretion in the medium-dose MJ-GTE group (0.21 g/(kg·d)) was significantly increased compared with the negative control group (deionized water) (P < 0.05), which collectively indicated that MJ-GTE has a digestion-promoting effect. The metabolomics analysis identified 98 compounds, among which, flavones (apigenin and luteolin, 0.14–0.77 mg/g), flavanones (naringenin and eriodictyol, 0.49–1.49 mg/g), flavone-7-O-glycosides (0.57–9.07 mg/g), and flavanone-7-O-glycosides (4.49–38.98 mg/g) were the major components in MJ-GTE. This study will provide a theoretical basis for the promotion and development of Maojian green tea and related products in the future

WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing

Self-supervised learning (SSL) achieves great success in speech recognition, while limited exploration has been attempted for other speech processing tasks. As speech signal contains multi-faceted information including speaker identity, paralinguistics, spoken content, etc., learning universal representations for all speech tasks is challenging. To tackle the problem, we propose a new pre-trained model, WavLM, to solve full-stack downstream speech tasks. WavLM jointly learns masked speech prediction and denoising in pre-training. By this means, WavLM does not only keep the speech content modeling capability by the masked speech prediction, but also improves the potential to non-ASR tasks by the speech denoising. In addition, WavLM employs gated relative position bias for the Transformer structure to better capture the sequence ordering of input speech. We also scale up the training dataset from 60k hours to 94k hours. WavLM Large achieves state-of-the-art performance on the SUPERB benchmark, and brings significant improvements for various speech processing tasks on their representative benchmarks. The code and pre-trained models are available at https://aka.ms/wavlm.Comment: Submitted to the Journal of Selected Topics in Signal Processing (JSTSP

Designing Artificial Two-Dimensional Landscapes via Room-Temperature Atomic-Layer Substitution

Manipulating materials with atomic-scale precision is essential for the development of next-generation material design toolbox. Tremendous efforts have been made to advance the compositional, structural, and spatial accuracy of material deposition and patterning. The family of 2D materials provides an ideal platform to realize atomic-level material architectures. The wide and rich physics of these materials have led to fabrication of heterostructures, superlattices, and twisted structures with breakthrough discoveries and applications. Here, we report a novel atomic-scale material design tool that selectively breaks and forms chemical bonds of 2D materials at room temperature, called atomic-layer substitution (ALS), through which we can substitute the top layer chalcogen atoms within the 3-atom-thick transition-metal dichalcogenides using arbitrary patterns. Flipping the layer via transfer allows us to perform the same procedure on the other side, yielding programmable in-plane multi-heterostructures with different out-of-plane crystal symmetry and electric polarization. First-principle calculations elucidate how the ALS process is overall exothermic in energy and only has a small reaction barrier, facilitating the reaction to occur at room temperature. Optical characterizations confirm the fidelity of this design approach, while TEM shows the direct evidence of Janus structure and suggests the atomic transition at the interface of designed heterostructure. Finally, transport and Kelvin probe measurements on MoXY (X,Y=S,Se; X and Y corresponding to the bottom and top layers) lateral multi-heterostructures reveal the surface potential and dipole orientation of each region, and the barrier height between them. Our approach for designing artificial 2D landscape down to a single layer of atoms can lead to unique electronic, photonic and mechanical properties previously not found in nature