A Neural RDE-based model for solving path-dependent PDEs

The concept of the path-dependent partial differential equation (PPDE) was first introduced in the context of path-dependent derivatives in financial markets. Its semilinear form was later identified as a non-Markovian backward stochastic differential equation (BSDE). Compared to the classical PDE, the solution of a PPDE involves an infinite-dimensional spatial variable, making it challenging to approximate, if not impossible. In this paper, we propose a neural rough differential equation (NRDE)-based model to learn PPDEs, which effectively encodes the path information through the log-signature feature while capturing the fundamental dynamics. The proposed continuous-time model for the PPDE solution offers the benefits of efficient memory usage and the ability to scale with dimensionality. Several numerical experiments, provided to validate the performance of the proposed model in comparison to the strong baseline in the literature, are used to demonstrate its effectiveness

Lipidomic analysis to enhance the understanding of Chinese Hamster ovary cells

Chinese Hamster Ovary (CHO) cell lines are common hosts for the production of biotherapeutic proteins. Achieving high level of specific protein production by CHO cell lines remains a challenge. In order to address this issue, we are incorporating lipidomic analyses to study the role of lipids played in CHO-S cells. In our study, we have applied chromatography (TLC) methods for lipid analysis in terms of lipid polarity. For polar lipids, 2-D HPTLC (2-dimensional high performance TLC) was used instead of conventional 1D- TLC by virtue of its high separation capacity. The eluting solvent system was optimized for the 1st and 2nd dimension, respectively. Neutral lipids were separated on 1-D HPTLC with the optimal elution solvent of hexane-diethyl ether-acetic acid. The lipid spots on the TLC plates were stained by 0.2% of 2,7-dichlorofluorescein dissolved in ethanol solution and illuminated with UV. Multiple lipid standards were also run to correctly identify the lipid spots and the fluorescence of lipid spots was semi-quantitatively measured with ImageJ. By optimization of TLC conditions, the lipids of CHO-S cell line were separated successfully and the lipid contents were semi-quantified. From neutral lipids result, we observed high level of certain lipids in CHO-S cell lines. We will further investigate which lipid play a key role in various cell processes

Dynamic Sparse Training via Balancing the Exploration-Exploitation Trade-off

Over-parameterization of deep neural networks (DNNs) has shown high prediction accuracy for many applications. Although effective, the large number of parameters hinders its popularity on resource-limited devices and has an outsize environmental impact. Sparse training (using a fixed number of nonzero weights in each iteration) could significantly mitigate the training costs by reducing the model size. However, existing sparse training methods mainly use either random-based or greedy-based drop-and-grow strategies, resulting in local minimal and low accuracy. In this work, we consider the dynamic sparse training as a sparse connectivity search problem and design an exploitation and exploration acquisition function to escape from local optima and saddle points. We further design an acquisition function and provide the theoretical guarantees for the proposed method and clarify its convergence property. Experimental results show that sparse models (up to 98\% sparsity) obtained by our proposed method outperform the SOTA sparse training methods on a wide variety of deep learning tasks. On VGG-19 / CIFAR-100, ResNet-50 / CIFAR-10, ResNet-50 / CIFAR-100, our method has even higher accuracy than dense models. On ResNet-50 / ImageNet, the proposed method has up to 8.2\% accuracy improvement compared to SOTA sparse training methods

The FlySpeech Audio-Visual Speaker Diarization System for MISP Challenge 2022

This paper describes the FlySpeech speaker diarization system submitted to the second \textbf{M}ultimodal \textbf{I}nformation Based \textbf{S}peech \textbf{P}rocessing~(\textbf{MISP}) Challenge held in ICASSP 2022. We develop an end-to-end audio-visual speaker diarization~(AVSD) system, which consists of a lip encoder, a speaker encoder, and an audio-visual decoder. Specifically, to mitigate the degradation of diarization performance caused by separate training, we jointly train the speaker encoder and the audio-visual decoder. In addition, we leverage the large-data pretrained speaker extractor to initialize the speaker encoder

Evaluating Open-QA Evaluation

This study focuses on the evaluation of the Open Question Answering (Open-QA) task, which can directly estimate the factuality of large language models (LLMs). Current automatic evaluation methods have shown limitations, indicating that human evaluation still remains the most reliable approach. We introduce a new task, Evaluating QA Evaluation (QA-Eval) and the corresponding dataset EVOUNA, designed to assess the accuracy of AI-generated answers in relation to standard answers within Open-QA. Our evaluation of these methods utilizes human-annotated results to measure their performance. Specifically, the work investigates methods that show high correlation with human evaluations, deeming them more reliable. We also discuss the pitfalls of current methods and methods to improve LLM-based evaluators. We believe this new QA-Eval task and corresponding dataset EVOUNA will facilitate the development of more effective automatic evaluation tools and prove valuable for future research in this area. All resources are available at \url{https://github.com/wangcunxiang/QA-Eval} and it is under the Apache-2.0 License

Spherically Symmetric Noncommutative Spacetime via Exotic Atomic Transitions

In discussing non-commutative spacetime, the generally studied $\theta$-Poincare model is inconsistent with bound states. In this Letter, we develop the formalism and study the phenomenology of another model $\mathcal{B}_{\chi \hat{n}}$ by the twisted permutation algebra and extend the Pauli Exclusion Principle(PEP) into non-commutative spacetime. The model also implies time quantization and can avoid UV/IR mixing. Applying it to atomic systems, we show that the model with newly induced phase factors can cause exotic transitions consisting of three electrons in the 1S orbit of atoms. The transition rate is derived, and the upper bound of non-commutative parameter $\chi$ is thus set by utilizing data from the low-energy and low-background experiments, where strongest constraint $\chi\leq4.05\times10^{-30}$ eV$^{-1}$ at 90\% C.L. is given by XENONnT, with the time quanta $\Delta t\sim 2.67\times 10^{-45} s$, equivalent to twenty times smaller than the Planck time.Comment: 6 pages, 4 figure

Neurogenesis Dynamics-inspired Spiking Neural Network Training Acceleration

Biologically inspired Spiking Neural Networks (SNNs) have attracted significant attention for their ability to provide extremely energy-efficient machine intelligence through event-driven operation and sparse activities. As artificial intelligence (AI) becomes ever more democratized, there is an increasing need to execute SNN models on edge devices. Existing works adopt weight pruning to reduce SNN model size and accelerate inference. However, these methods mainly focus on how to obtain a sparse model for efficient inference, rather than training efficiency. To overcome these drawbacks, in this paper, we propose a Neurogenesis Dynamics-inspired Spiking Neural Network training acceleration framework, NDSNN. Our framework is computational efficient and trains a model from scratch with dynamic sparsity without sacrificing model fidelity. Specifically, we design a new drop-and-grow strategy with decreasing number of non-zero weights, to maintain extreme high sparsity and high accuracy. We evaluate NDSNN using VGG-16 and ResNet-19 on CIFAR-10, CIFAR-100 and TinyImageNet. Experimental results show that NDSNN achieves up to 20.52\% improvement in accuracy on Tiny-ImageNet using ResNet-19 (with a sparsity of 99\%) as compared to other SOTA methods (e.g., Lottery Ticket Hypothesis (LTH), SET-SNN, RigL-SNN). In addition, the training cost of NDSNN is only 40.89\% of the LTH training cost on ResNet-19 and 31.35\% of the LTH training cost on VGG-16 on CIFAR-10