Hartree-Fock-Bogoliubov Theory of Dipolar Fermi Gases

We construct a fully self-consistent Hartree-Fock-Bogoliubov theory that describes a spinless Fermi gas with long-range interaction. We apply this theory to a system of uniform dipolar fermionic polar molecules, which has attracted much attention recently, due to rapid experimental progress in achieving such systems. By calculating the anisotropic superfluid order parameter, and the critical temperature $T_{c}$, we show that, "hign $T_c$" superfluid can be achieved with a quite modest value of interaction strength for polar molecules. In addition, we also show that the presence of the Fock exchange interaction enhances superfluid pairing.Comment: 4.1 pages, 4 figure

Perspective: Ferromagnetic Liquids

Mechanical jamming of nanoparticles at liquid–liquid interfaces has evolved into a versatile approach to structure liquids with solid-state properties. Ferromagnetic liquids obtain their physical and magnetic properties, including a remanent magnetization that distinguishes them from ferrofluids, from the jamming of magnetic nanoparticles assembled at the interface between two distinct liquids to minimize surface tension. This perspective provides an overview of recent progress and discusses future directions, challenges and potential applications of jamming magnetic nanoparticles with regard to 3D nano-magnetism. We address the formation and characterization of curved magnetic geometries, and spin frustration between dipole-coupled nanostructures, and advance our understanding of particle jamming at liquid–liquid interfaces

Ab initio uncertainty quantification in scattering analysis of microscopy

Estimating parameters from data is a fundamental problem in physics, customarily done by minimizing a loss function between a model and observed statistics. In scattering-based analysis, researchers often employ their domain expertise to select a specific range of wavevectors for analysis, a choice that can vary depending on the specific case. We introduce another paradigm that defines a probabilistic generative model from the beginning of data processing and propagates the uncertainty for parameter estimation, termed ab initio uncertainty quantification (AIUQ). As an illustrative example, we demonstrate this approach with differential dynamic microscopy (DDM) that extracts dynamical information through Fourier analysis at a selected range of wavevectors. We first show that DDM is equivalent to fitting a temporal variogram in the reciprocal space using a latent factor model as the generative model. Then we derive the maximum marginal likelihood estimator, which optimally weighs information at all wavevectors, therefore eliminating the need to select the range of wavevectors. Furthermore, we substantially reduce the computational cost by utilizing the generalized Schur algorithm for Toeplitz covariances without approximation. Simulated studies validate that AIUQ significantly improves estimation accuracy and enables model selection with automated analysis. The utility of AIUQ is also demonstrated by three distinct sets of experiments: first in an isotropic Newtonian fluid, pushing limits of optically dense systems compared to multiple particle tracking; next in a system undergoing a sol-gel transition, automating the determination of gelling points and critical exponent; and lastly, in discerning anisotropic diffusive behavior of colloids in a liquid crystal. These outcomes collectively underscore AIUQ's versatility to capture system dynamics in an efficient and automated manner

Ontology-aware Learning and Evaluation for Audio Tagging

This study defines a new evaluation metric for audio tagging tasks to overcome the limitation of the conventional mean average precision (mAP) metric, which treats different kinds of sound as independent classes without considering their relations. Also, due to the ambiguities in sound labeling, the labels in the training and evaluation set are not guaranteed to be accurate and exhaustive, which poses challenges for robust evaluation with mAP. The proposed metric, ontology-aware mean average precision (OmAP) addresses the weaknesses of mAP by utilizing the AudioSet ontology information during the evaluation. Specifically, we reweight the false positive events in the model prediction based on the ontology graph distance to the target classes. The OmAP measure also provides more insights into model performance by evaluations with different coarse-grained levels in the ontology graph. We conduct human evaluations and demonstrate that OmAP is more consistent with human perception than mAP. To further verify the importance of utilizing the ontology information, we also propose a novel loss function (OBCE) that reweights binary cross entropy (BCE) loss based on the ontology distance. Our experiment shows that OBCE can improve both mAP and OmAP metrics on the AudioSet tagging task.Comment: Submitted to ICASSP 2023. The code is open-sourced at https://github.com/haoheliu/ontology-aware-audio-taggin

Perspective: Ferromagnetic liquids

Mechanical jamming of nanoparticles at liquid-liquid interfaces has evolved into a versatile approach to structure liquids with solid-state properties. Ferromagnetic liquids obtain their physical and magnetic properties, including a remanent magnetization that distinguishes them from ferrofluids, from the jamming of magnetic nanoparticles assembled at the interface between two distinct liquids to minimize surface tension. This perspective provides an overview of recent progress and discusses future directions, challenges and potential applications of jamming magnetic nanoparticles with regard to 3D nano-magnetism. We address the formation and characterization of curved magnetic geometries, and spin frustration between dipole-coupled nanostructures, and advance our understanding of particle jamming at liquid-liquid interfaces

Leveraging Pre-trained AudioLDM for Text to Sound Generation: A Benchmark Study

Deep neural networks have recently achieved breakthroughs in sound generation with text prompts. Despite their promising performance, current text-to-sound generation models face issues on small-scale datasets (e.g., overfitting), significantly limiting their performance. In this paper, we investigate the use of pre-trained AudioLDM, the state-of-the-art model for text-to-audio generation, as the backbone for sound generation. Our study demonstrates the advantages of using pre-trained models for text-to-sound generation, especially in data-scarcity scenarios. In addition, experiments show that different training strategies (e.g., training conditions) may affect the performance of AudioLDM on datasets of different scales. To facilitate future studies, we also evaluate various text-to-sound generation systems on several frequently used datasets under the same evaluation protocols, which allow fair comparisons and benchmarking of these methods on the common ground.Comment: EUSIPCO 202

Segment-level Metric Learning for Few-shot Bioacoustic Event Detection

Few-shot bioacoustic event detection is a task that detects the occurrence time of a novel sound given a few examples. Previous methods employ metric learning to build a latent space with the labeled part of different sound classes, also known as positive events. In this study, we propose a segment-level few-shot learning framework that utilizes both the positive and negative events during model optimization. Training with negative events, which are larger in volume than positive events, can increase the generalization ability of the model. In addition, we use transductive inference on the validation set during training for better adaptation to novel classes. We conduct ablation studies on our proposed method with different setups on input features, training data, and hyper-parameters. Our final system achieves an F-measure of 62.73 on the DCASE 2022 challenge task 5 (DCASE2022-T5) validation set, outperforming the performance of the baseline prototypical network 34.02 by a large margin. Using the proposed method, our submitted system ranks 2nd in DCASE2022-T5. The code of this paper is fully open-sourced at https://github.com/haoheliu/DCASE_2022_Task_5.Comment: 2nd place in the DCASE 2022 Challenge Task 5. Submitted to the DCASE 2022 worksho

Origin and tuning of the magnetocaloric effect for the magnetic refrigerant MnFe(P1-xGex)

Neutron diffraction and magnetization measurements of the magneto refrigerant Mn1+yFe1-yP1-xGex reveal that the ferromagnetic and paramagnetic phases correspond to two very distinct crystal structures, with the magnetic entropy change as a function of magnetic field or temperature being directly controlled by the phase fraction of this first-order transition. By tuning the physical properties of this system we have achieved a maximum magnetic entropy change exceeding 74 J/Kg K for both increasing and decreasing field, more than twice the value of the previous record.Comment: 6 Figures. One tabl

Text-Driven Foley Sound Generation With Latent Diffusion Model

Foley sound generation aims to synthesise the background sound for multimedia content. Previous models usually employ a large development set with labels as input (e.g., single numbers or one-hot vector). In this work, we propose a diffusion model based system for Foley sound generation with text conditions. To alleviate the data scarcity issue, our model is initially pre-trained with large-scale datasets and fine-tuned to this task via transfer learning using the contrastive language-audio pertaining (CLAP) technique. We have observed that the feature embedding extracted by the text encoder can significantly affect the performance of the generation model. Hence, we introduce a trainable layer after the encoder to improve the text embedding produced by the encoder. In addition, we further refine the generated waveform by generating multiple candidate audio clips simultaneously and selecting the best one, which is determined in terms of the similarity score between the embedding of the candidate clips and the embedding of the target text label. Using the proposed method, our system ranks ${1}^{st}$ among the systems submitted to DCASE Challenge 2023 Task 7. The results of the ablation studies illustrate that the proposed techniques significantly improve sound generation performance. The codes for implementing the proposed system are available online.Comment: Submit to DCASE-workshop 2023. arXiv admin note: text overlap with arXiv:2305.1590

Adapting Language-Audio Models as Few-Shot Audio Learners

We presented the Treff adapter, a training-efficient adapter for CLAP, to boost zero-shot classification performance by making use of a small set of labelled data. Specifically, we designed CALM to retrieve the probability distribution of text-audio clips over classes using a set of audio-label pairs and combined it with CLAP's zero-shot classification results. Furthermore, we designed a training-free version of the Treff adapter by using CALM as a cosine similarity measure. Experiments showed that the proposed Treff adapter is comparable and even better than fully-supervised methods and adaptation methods in low-shot and data-abundant scenarios. While the Treff adapter shows that combining large-scale pretraining and rapid learning of domain-specific knowledge is non-trivial for obtaining generic representations for few-shot learning, it is still limited to audio classification tasks. In the future, we will explore how to use audio-language models in diverse audio domains