Polaron dynamics with a multitude of Davydov D2_2 trial states

We propose an extension to the Davydov D$_2$ Ansatz in the dynamics study of the Holstein molecular crystal model with diagonal and off-diagonal exciton-phonon coupling using the Dirac-Frenkel time-dependent variational principle. The new trial state by the name of the "multi-D$_2$ Ansatz" is a linear combination of Davydov D$_2$ trial states, and its validity is carefully examined by quantifying how faithfully it follows the Schr\"odinger equation. Considerable improvements in accuracy have been demonstrated in comparison with the usual Davydov trial states, i.e., the single D$_1$ and D$_2$ Ans\"atze. With an increase in the number of the Davydov D$_2$ trial states in the multi-D$_2$ Ansatz, deviation from the exact Schr\"odinger dynamics is gradually diminished, leading to a numerically exact solution to the Schr\"odinger equation.Comment: 14 pages, 15 figure

Cluster Attack: Query-based Adversarial Attacks on Graphs with Graph-Dependent Priors

While deep neural networks have achieved great success in graph analysis, recent work has shown that they are vulnerable to adversarial attacks. Compared with adversarial attacks on image classification, performing adversarial attacks on graphs is more challenging because of the discrete and non-differential nature of the adjacent matrix for a graph. In this work, we propose Cluster Attack -- a Graph Injection Attack (GIA) on node classification, which injects fake nodes into the original graph to degenerate the performance of graph neural networks (GNNs) on certain victim nodes while affecting the other nodes as little as possible. We demonstrate that a GIA problem can be equivalently formulated as a graph clustering problem; thus, the discrete optimization problem of the adjacency matrix can be solved in the context of graph clustering. In particular, we propose to measure the similarity between victim nodes by a metric of Adversarial Vulnerability, which is related to how the victim nodes will be affected by the injected fake node, and to cluster the victim nodes accordingly. Our attack is performed in a practical and unnoticeable query-based black-box manner with only a few nodes on the graphs that can be accessed. Theoretical analysis and extensive experiments demonstrate the effectiveness of our method by fooling the node classifiers with only a small number of queries.Comment: IJCAI 2022 (Long Presentation

Ammonia and Greenhouse Gases Concentrations and Emissions of a Naturally Ventilated Laying Hen House in Northeast China

This study quantifies concentrations and emission rates (ER) of ammonia (NH3) and greenhouse gases (GHG) including carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) from a naturally ventilated cage layer (Hy-Line brown strain) house with daily manure removal, located in northeast China during four seasons of one year, with each monitoring episode lasting five consecutive days. Gaseous concentrations of background and exhaust air were measured using an infrared photoacoustic multi-gas monitor with a multi-channel sampler. Building ventilation rate (VR) was determined by CO2 mass balance using literature metabolic rate data for modern laying hens. Both gas concentrations and ER showed considerable diurnal and seasonal variations. Annual mean (±SD) ER of NH3, CO2, CH4, and N2O for the monitored layer house were, in mg d-1 bird-1, 129 ±40.3, 78,250 ±15,384, 112 ±56.5, and 9.4 ±2.5, respectively, or in g d-1 AU-1 (AU = 500 kg live body weight), 33.4 ±11.4, 19,975 ±3,071, 29.2 ±15.2, and 2.5 ±0.7, respectively. Ammonia ER from the current study was within the ranges of values reported for high-rise houses with annual manure removal and manure-belt houses with daily manure removal. Results of the study contribute to improving ammonia and GHG emissions inventory for animal feeding operations in China and worldwide

Bi-level Physics-Informed Neural Networks for PDE Constrained Optimization using Broyden's Hypergradients

Deep learning based approaches like Physics-informed neural networks (PINNs) and DeepONets have shown promise on solving PDE constrained optimization (PDECO) problems. However, existing methods are insufficient to handle those PDE constraints that have a complicated or nonlinear dependency on optimization targets. In this paper, we present a novel bi-level optimization framework to resolve the challenge by decoupling the optimization of the targets and constraints. For the inner loop optimization, we adopt PINNs to solve the PDE constraints only. For the outer loop, we design a novel method by using Broyden's method based on the Implicit Function Theorem (IFT), which is efficient and accurate for approximating hypergradients. We further present theoretical explanations and error analysis of the hypergradients computation. Extensive experiments on multiple large-scale and nonlinear PDE constrained optimization problems demonstrate that our method achieves state-of-the-art results compared with strong baselines

Carbon and Nitrogen Budget of Commercial Cage-Grown Broilers

Understanding the fate of carbon (C) and nitrogen (N) inputs and outputs from commercial broiler operations is important for increasing the flock C and N efficiency and reducing gaseous emissions. This study was conducted to determine the C and N balance for broiler chickens grown in cages from day old to 42-d market age. Concentrations of ammonia (NH3), methane (CH4), carbon dioxide (CO2) and nitrous oxide (N2O) gases in three side-by-side mechanically ventilated caged broiler houses were measured using an infrared photoacoustic multi-gas monitor coupled with a multi-channel sampler system. Bird manure was removed out of the houses daily. Ventilation rate (VR) was determined by continuously monitoring the building static pressure and operational status of the exhaust fans whose performance was determined in-situ with a fan assessment numeration system (FANS). At the end of 42-d growth period, the N recovery relative to the total feed N intake was 148.2±3.78 g N·bird-1 (mean±SD) 58.6±2.20% in live birds, 34.5±1.42% in manure, and 3.14±0.60% in TAN emissions, with the amount of N2O emitted being negligible. The C input recovery relative to the total feed C intake was 1,738±33.4 g C·bird-1,31.3±1.17% in live birds, 22.5±0.11% in manure, 41.4±3.47% in CO2-C emissions, and 0.27±0.01% in CH4-C emissions. The C and N accumulation of the market broilers was, respectively, 544 g·bird-1 and 86.9 g·bird-1, whereas the manure C and N accumulation was, respectively, 390 g C·bird-1 and 51.1 g N·bird-1. Total TAN emissions over the 42-d growth period averaged 4.65±0.84 g·bird-1. The total emission of CH4-C and CO2-C for the same period was 4.72±0.12 g·bird-1 and 718.9±47.5 g·bird-1, respectively

A Unified Hard-Constraint Framework for Solving Geometrically Complex PDEs

We present a unified hard-constraint framework for solving geometrically complex PDEs with neural networks, where the most commonly used Dirichlet, Neumann, and Robin boundary conditions (BCs) are considered. Specifically, we first introduce the "extra fields" from the mixed finite element method to reformulate the PDEs so as to equivalently transform the three types of BCs into linear forms. Based on the reformulation, we derive the general solutions of the BCs analytically, which are employed to construct an ansatz that automatically satisfies the BCs. With such a framework, we can train the neural networks without adding extra loss terms and thus efficiently handle geometrically complex PDEs, alleviating the unbalanced competition between the loss terms corresponding to the BCs and PDEs. We theoretically demonstrate that the "extra fields" can stabilize the training process. Experimental results on real-world geometrically complex PDEs showcase the effectiveness of our method compared with state-of-the-art baselines.Comment: 10 pages, 6 figures, NeurIPS 202

Equivariant Energy-Guided SDE for Inverse Molecular Design

Inverse molecular design is critical in material science and drug discovery, where the generated molecules should satisfy certain desirable properties. In this paper, we propose equivariant energy-guided stochastic differential equations (EEGSDE), a flexible framework for controllable 3D molecule generation under the guidance of an energy function in diffusion models. Formally, we show that EEGSDE naturally exploits the geometric symmetry in 3D molecular conformation, as long as the energy function is invariant to orthogonal transformations. Empirically, under the guidance of designed energy functions, EEGSDE significantly improves the baseline on QM9, in inverse molecular design targeted to quantum properties and molecular structures. Furthermore, EEGSDE is able to generate molecules with multiple target properties by combining the corresponding energy functions linearly

Task Aware Dreamer for Task Generalization in Reinforcement Learning

A long-standing goal of reinforcement learning is to acquire agents that can learn on training tasks and generalize well on unseen tasks that may share a similar dynamic but with different reward functions. A general challenge is to quantitatively measure the similarities between these different tasks, which is vital for analyzing the task distribution and further designing algorithms with stronger generalization. To address this, we present a novel metric named Task Distribution Relevance (TDR) via optimal Q functions of different tasks to capture the relevance of the task distribution quantitatively. In the case of tasks with a high TDR, i.e., the tasks differ significantly, we show that the Markovian policies cannot differentiate them, leading to poor performance. Based on this insight, we encode all historical information into policies for distinguishing different tasks and propose Task Aware Dreamer (TAD), which extends world models into our reward-informed world models to capture invariant latent features over different tasks. In TAD, we calculate the corresponding variational lower bound of the data log-likelihood, including a novel term to distinguish different tasks via states, to optimize reward-informed world models. Extensive experiments in both image-based control tasks and state-based control tasks demonstrate that TAD can significantly improve the performance of handling different tasks simultaneously, especially for those with high TDR, and demonstrate a strong generalization ability to unseen tasks

WavJourney: Compositional Audio Creation with Large Language Models

Large Language Models (LLMs) have shown great promise in integrating diverse expert models to tackle intricate language and vision tasks. Despite their significance in advancing the field of Artificial Intelligence Generated Content (AIGC), their potential in intelligent audio content creation remains unexplored. In this work, we tackle the problem of creating audio content with storylines encompassing speech, music, and sound effects, guided by text instructions. We present WavJourney, a system that leverages LLMs to connect various audio models for audio content generation. Given a text description of an auditory scene, WavJourney first prompts LLMs to generate a structured script dedicated to audio storytelling. The audio script incorporates diverse audio elements, organized based on their spatio-temporal relationships. As a conceptual representation of audio, the audio script provides an interactive and interpretable rationale for human engagement. Afterward, the audio script is fed into a script compiler, converting it into a computer program. Each line of the program calls a task-specific audio generation model or computational operation function (e.g., concatenate, mix). The computer program is then executed to obtain an explainable solution for audio generation. We demonstrate the practicality of WavJourney across diverse real-world scenarios, including science fiction, education, and radio play. The explainable and interactive design of WavJourney fosters human-machine co-creation in multi-round dialogues, enhancing creative control and adaptability in audio production. WavJourney audiolizes the human imagination, opening up new avenues for creativity in multimedia content creation.Comment: Project Page: https://audio-agi.github.io/WavJourney_demopage

Ultra-Strong Long-Chain Polyamide Elastomers With Programmable Supramolecular Interactions and Oriented Crystalline Microstructures

Polyamides are one of the most important polymers. Long-chain aliphatic polyamides could bridge the gap between traditional polyamides and polyethylenes. Here we report an approach to preparing sustainable ultra-strong elastomers from biomass-derived long-chain polyamides by thiol-ene addition copolymerization with diamide diene monomers. The pendant polar hydroxyl and non-polar butyrate groups between amides allow controlled programming of supramolecular hydrogen bonding and facile tuning of crystallization of polymer chains. The presence of thioether groups on the main chain can further induce metal–ligand coordination (cuprous-thioether). Unidirectional step-cycle tensile deformation has been applied to these polyamides and significantly enhances tensile strength to over 210 MPa while maintaining elasticity. Uniaxial deformation leads to a rearrangement and alignment of crystalline microstructures, which is responsible for the mechanical enhancement. These chromophore-free polyamides are observed with strong luminescence ascribed to the effect of aggregation-induced emission (AIE), originating from the formation of amide clusters with restricted molecular motions