Neural Diffeomorphic Non-uniform B-spline Flows

Normalizing flows have been successfully modeling a complex probability distribution as an invertible transformation of a simple base distribution. However, there are often applications that require more than invertibility. For instance, the computation of energies and forces in physics requires the second derivatives of the transformation to be well-defined and continuous. Smooth normalizing flows employ infinitely differentiable transformation, but with the price of slow non-analytic inverse transforms. In this work, we propose diffeomorphic non-uniform B-spline flows that are at least twice continuously differentiable while bi-Lipschitz continuous, enabling efficient parametrization while retaining analytic inverse transforms based on a sufficient condition for diffeomorphism. Firstly, we investigate the sufficient condition for Ck-2-diffeomorphic non-uniform kth-order B-spline transformations. Then, we derive an analytic inverse transformation of the non-uniform cubic B-spline transformation for neural diffeomorphic non-uniform B-spline flows. Lastly, we performed experiments on solving the force matching problem in Boltzmann generators, demonstrating that our C2-diffeomorphic non-uniform B-spline flows yielded solutions better than previous spline flows and faster than smooth normalizing flows. Our source code is publicly available at https://github.com/smhongok/Non-uniform-B-spline-Flow.Comment: Accepted to AAAI 202

The Pandemic, Ecological Justice, and Zhu Xi’s Philosophy

COVID-19 has brought many changes to society and encouraged mankind to reflect on its civilization. The pandemic has revealed that our health care systems and community solidarity are far more fragile than we believed. It made us rethink the solidarity of human civilization and community, and more fundamentally, reconsider the global ecosystem beyond human society. This paper claims that COVID-19 was an inevitable result of the anthropocentric perspective, and argues that it is necessary to change the perception to an ecological worldview and practice ecological justice in order to solve this situation. First, it analyses the ecological reasons for the regular outbreak of zoonotic diseases, including COVID-19, and then it examines Naess’s deep ecology with regard to a fundamental change of perception, but also finds several weaknesses in this. Third, this paper focuses on Zhu Xi’s philosophy in order to compensate for the weaknesses of deep ecology. It argues for the importance of human roles and obligations in relation to the safety and health of the environment based on his philosophy, and explains ecological justice by applying his social equality theory to ecology. Finally, it sheds new light on Zhu Xi’s theory of investigation of things (gewu 格物) as a practical way of implementing ecological justice

On Exact Inversion of DPM-Solvers

Diffusion probabilistic models (DPMs) are a key component in modern generative models. DPM-solvers have achieved reduced latency and enhanced quality significantly, but have posed challenges to find the exact inverse (i.e., finding the initial noise from the given image). Here we investigate the exact inversions for DPM-solvers and propose algorithms to perform them when samples are generated by the first-order as well as higher-order DPM-solvers. For each explicit denoising step in DPM-solvers, we formulated the inversions using implicit methods such as gradient descent or forward step method to ensure the robustness to large classifier-free guidance unlike the prior approach using fixed-point iteration. Experimental results demonstrated that our proposed exact inversion methods significantly reduced the error of both image and noise reconstructions, greatly enhanced the ability to distinguish invisible watermarks and well prevented unintended background changes consistently during image editing. Project page: \url{https://smhongok.github.io/inv-dpm.html}.Comment: 16 page

DFX: A Low-latency Multi-FPGA Appliance for Accelerating Transformer-based Text Generation

Transformer is a deep learning language model widely used for natural language processing (NLP) services in datacenters. Among transformer models, Generative Pre-trained Transformer (GPT) has achieved remarkable performance in text generation, or natural language generation (NLG), which needs the processing of a large input context in the summarization stage, followed by the generation stage that produces a single word at a time. The conventional platforms such as GPU are specialized for the parallel processing of large inputs in the summarization stage, but their performance significantly degrades in the generation stage due to its sequential characteristic. Therefore, an efficient hardware platform is required to address the high latency caused by the sequential characteristic of text generation. In this paper, we present DFX, a multi-FPGA acceleration appliance that executes GPT-2 model inference end-to-end with low latency and high throughput in both summarization and generation stages. DFX uses model parallelism and optimized dataflow that is model-and-hardware-aware for fast simultaneous workload execution among devices. Its compute cores operate on custom instructions and provide GPT-2 operations end-to-end. We implement the proposed hardware architecture on four Xilinx Alveo U280 FPGAs and utilize all of the channels of the high bandwidth memory (HBM) and the maximum number of compute resources for high hardware efficiency. DFX achieves 5.58x speedup and 3.99x energy efficiency over four NVIDIA V100 GPUs on the modern GPT-2 model. DFX is also 8.21x more cost-effective than the GPU appliance, suggesting that it is a promising solution for text generation workloads in cloud datacenters.Comment: Extension of HOTCHIPS 2022 and accepted in MICRO 202

Echocardiographic View Classification with Integrated Out-of-Distribution Detection for Enhanced Automatic Echocardiographic Analysis

In the rapidly evolving field of automatic echocardiographic analysis and interpretation, automatic view classification is a critical yet challenging task, owing to the inherent complexity and variability of echocardiographic data. This study presents ECHOcardiography VIew Classification with Out-of-Distribution dEtection (ECHO-VICODE), a novel deep learning-based framework that effectively addresses this challenge by training to classify 31 classes, surpassing previous studies and demonstrating its capacity to handle a wide range of echocardiographic views. Furthermore, ECHO-VICODE incorporates an integrated out-of-distribution (OOD) detection function, leveraging the relative Mahalanobis distance to effectively identify 'near-OOD' instances commonly encountered in echocardiographic data. Through extensive experimentation, we demonstrated the outstanding performance of ECHO-VICODE in terms of view classification and OOD detection, significantly reducing the potential for errors in echocardiographic analyses. This pioneering study significantly advances the domain of automated echocardiography analysis and exhibits promising prospects for substantial applications in extensive clinical research and practice

Optimization, Modification and Application of Gold Nanoparticles as the Substrates of Surface Enhanced Raman Spectroscopy

SER

Optimization, Modification and Applications of Gold Nanoparticles as the Substrates of Surface Enhanced Raman Spectroscopy

Surface enhanced Raman spectroscopy (SERS) is one of the techniques that overcomes the poor intensity of Raman scattering by utilizing the metal surface to enhance the Raman scattering. So far, silver (Ag), gold (Au) and copper (Cu) have been demonstrated to provide good enhancement for Raman signals. Many studies have proved that SERS is a powerful technique. However, the origin of the enhancement still needs clarification. More importantly, how to further improve the SERS enhancement through optimization of the SERS substrates and technique is a long and enduring challenge. Chapter 3 is dedicated to find out the optimal size of AuNPs. Au NPs with different sizes were synthesized from 17 nm to 80 nm. The SERS activities of AuNPs were tested using target molecules of 4-aminothiophenol and 4-nitrothiophenol. The experiments were performed under three different conditions: same number of AuNPs, same surface area of AuNPs, and same concentration of AuNPs. For the same number of AuNPs, it showed linear relationship between the enhancement factor and sizes of AuNPs. However, in case of same surface area and concentration, the maximum enhancement was achieved around 50 nm AuNPs. These results were identical for both molecules, which indicate that the conclusions might be also applicable to other analytes. More importantly, the highest SERS enhancement can be achieved with AuNPs of 50 nm while they introduce minimum toxicity to the biological samples. Once the optimal size of AuNPs was found, the work in chapter 4 was dedicated to find the way to improve the SERS enhancement of molecules that do not have strong affinity toward the surface of AuNPs. The affinity was improved by using 2-mercaptoethanol as a linking molecule (2MELM) since the thiol group of 2ME can be strongly adsorbed on the surface of AuNPs, and the other end of 2ME has a hydroxyl group that can induce intermolecular forces toward the molecules of interest. Three molecules were chosen as the target molecule (TM): benzoic acid, cyclohexanol and 1,3-cyclohexanediol. When the results were compared between the substrates with LM and without LM, the spectra of benzoic acid did not display any difference. However, the spectra of the other two TMs show higher enhancement with presence of LM than that without. The results from this simple modification method shine light on the possible characterization or detection of molecules that have low affinity toward the metal surface using SERS. SERS was also applied to study the intermolecular interaction in the field of Material Science. For the first part of the chapter 5, the interaction between vitamin B12 and metal organic frame (MOF) was studied. The results confirmed the encapsulation and also, strong interactions between the two components were observed from the shift of vibrational modes of both VB12 and Tb-MOF upon encapsulation. Second part of the chapter 5 was dedicated to study the functionalization of single wall carbon nanotubes in polymer network. The results confirm the successfully functionalized carbon nanotube. SERS was used to detect different biomarkers through collaborations and the results are shown in Chapter 6. The first part was neurotransmitters, dopamin, melatonin and serotonin. All of them showed low μM range as their detection limit. Other two parts are the priliminary results from studying caffeine and nicotine. The results were promsing exhibiting detection limits in low nM to sub nM range. None of these results were performed with optimized condition, therefore, when the conditions are optimized, it is very probable to have even lower detection limits

Optimal Size of Gold Nanoparticles for Surface-Enhanced Raman Spectroscopy under Different Conditions

Gold nanoparticles have been used as effective surface-enhanced Raman spectroscopy (SERS) substrates for decades. However, the origin of the enhancement and the effect of the size of nanoparticles still need clarification. Here, gold nanoparticles with different sizes from 17 to 80 nm were synthesized and characterized, and their SERS enhancement toward both 4-aminothiophenol and 4-nitrothiophenol was examined. For the same number of nanoparticles, the enhancement factor generated from the gold nanoparticles increases as the size of nanoparticles increases. Interestingly, when the concentration of gold or the total surface area of gold nanoparticles was kept the same, the optimal size of gold nanoparticles was found out to be around 50 nm when the enhancement factor reached a maximum. The same size effect was observed for both 4-aminothiophenol and 4-nitrothiophenol, which suggests that the conclusions drawn in this study might also be applicable to other adsorbates during SERS measurements

Optimal Size of Gold Nanoparticles for Surface-Enhanced Raman Spectroscopy under Different Conditions

Gold nanoparticles have been used as effective surface-enhanced Raman spectroscopy (SERS) substrates for decades. However, the origin of the enhancement and the effect of the size of nanoparticles still need clarification. Here, gold nanoparticles with different sizes from 17 to 80 nm were synthesized and characterized, and their SERS enhancement toward both 4-aminothiophenol and 4-nitrothiophenol was examined. For the same number of nanoparticles, the enhancement factor generated from the gold nanoparticles increases as the size of nanoparticles increases. Interestingly, when the concentration of gold or the total surface area of gold nanoparticles was kept the same, the optimal size of gold nanoparticles was found out to be around 50 nm when the enhancement factor reached a maximum. The same size effect was observed for both 4-aminothiophenol and 4-nitrothiophenol, which suggests that the conclusions drawn in this study might also be applicable to other adsorbates during SERS measurements