The simplest massive S-matrix: from minimal coupling to Black Holes

In this paper, we explore the physics of electromagnetically and gravitationally coupled massive higher spin states from the on-shell point of view. Starting with the three-point amplitude, we focus on the simplest amplitude which is characterized by matching to minimal coupling in the UV. In the IR such amplitude leads to g = 2 for arbitrary charged spin states, and the best high energy behavior for a given spin. We proceed to construct the (gravitational) Compton amplitude for generic spins. We find that the leading deformation away from minimal coupling, in the gravitation sector, will lead to inconsistent factorizations and are thus forbidden. As the corresponding deformation in the gauge sector encodes the anomalous magnetic dipole moment, this leads to the prediction that for systems with gauge2 =gravity relations, such as perturbative string theory, all charged states must have g = 2. It is then natural to ask for generic spin, what is the theory that yields such minimal coupling. By matching to the one body effective action, remarkably we verify that for large spins, the answer is Kerr black holes. This identification is then an on-shell avatar of the no hair theorem. Finally using this identification as well as the newly constructed Compton amplitudes, we proceed to compute the spin dependent pieces for the classical potential at 2PM order up to degree four in spin operator of either black holes.Comment: 78 pages 4 figures V2. Improved discussion on the computation procedure of the classical potential, the issue of polynomial ambiguities and update on references. We've also included the complete list of new results involving up to degree four in spin operators of either black hole v3 Typos corrected, published versio

Adversarial Fine-tuning using Generated Respiratory Sound to Address Class Imbalance

Deep generative models have emerged as a promising approach in the medical image domain to address data scarcity. However, their use for sequential data like respiratory sounds is less explored. In this work, we propose a straightforward approach to augment imbalanced respiratory sound data using an audio diffusion model as a conditional neural vocoder. We also demonstrate a simple yet effective adversarial fine-tuning method to align features between the synthetic and real respiratory sound samples to improve respiratory sound classification performance. Our experimental results on the ICBHI dataset demonstrate that the proposed adversarial fine-tuning is effective, while only using the conventional augmentation method shows performance degradation. Moreover, our method outperforms the baseline by 2.24% on the ICBHI Score and improves the accuracy of the minority classes up to 26.58%. For the supplementary material, we provide the code at https://github.com/kaen2891/adversarial_fine-tuning_using_generated_respiratory_sound.Comment: accepted in NeurIPS 2023 Workshop on Deep Generative Models for Health (DGM4H

Self-supervised debiasing using low rank regularization

Spurious correlations can cause strong biases in deep neural networks, impairing generalization ability. While most existing debiasing methods require full supervision on either spurious attributes or target labels, training a debiased model from a limited amount of both annotations is still an open question. To address this issue, we investigate an interesting phenomenon using the spectral analysis of latent representations: spuriously correlated attributes make neural networks inductively biased towards encoding lower effective rank representations. We also show that a rank regularization can amplify this bias in a way that encourages highly correlated features. Leveraging these findings, we propose a self-supervised debiasing framework potentially compatible with unlabeled samples. Specifically, we first pretrain a biased encoder in a self-supervised manner with the rank regularization, serving as a semantic bottleneck to enforce the encoder to learn the spuriously correlated attributes. This biased encoder is then used to discover and upweight bias-conflicting samples in a downstream task, serving as a boosting to effectively debias the main model. Remarkably, the proposed debiasing framework significantly improves the generalization performance of self-supervised learning baselines and, in some cases, even outperforms state-of-the-art supervised debiasing approaches

Stethoscope-guided Supervised Contrastive Learning for Cross-domain Adaptation on Respiratory Sound Classification

Despite the remarkable advances in deep learning technology, achieving satisfactory performance in lung sound classification remains a challenge due to the scarcity of available data. Moreover, the respiratory sound samples are collected from a variety of electronic stethoscopes, which could potentially introduce biases into the trained models. When a significant distribution shift occurs within the test dataset or in a practical scenario, it can substantially decrease the performance. To tackle this issue, we introduce cross-domain adaptation techniques, which transfer the knowledge from a source domain to a distinct target domain. In particular, by considering different stethoscope types as individual domains, we propose a novel stethoscope-guided supervised contrastive learning approach. This method can mitigate any domain-related disparities and thus enables the model to distinguish respiratory sounds of the recording variation of the stethoscope. The experimental results on the ICBHI dataset demonstrate that the proposed methods are effective in reducing the domain dependency and achieving the ICBHI Score of 61.71%, which is a significant improvement of 2.16% over the baseline.Comment: accepted to ICASSP 202

Silicon germanium photo-blocking layers for a-IGZO based industrial display

Amorphous indium- gallium-zinc oxide (a-IGZO) has been intensively studied for the application to active matrix flat-panel display because of its superior electrical and optical properties. However, the characteristics of a-IGZO were found to be very sensitive to external circumstance such as light illumination, which dramatically degrades the device performance and stability practically required for display applications. Here, we suggest the use for silicon-germanium (Si-Ge) films grown plasmaenhanced chemical vapour deposition (PECVD) as photo-blocking layers in the a-IGZO thin film transistors (TFTs). The charge mobility and threshold voltage (V-th) of the TFTs depend on the thickness of the Si-Ge films and dielectric buffer layers (SiNX), which were carefully optimized to be similar to 200 nm and similar to 300 nm, respectively. As a result, even after 1,000 s illumination time, the V-th and electron mobility of the TFTs remain unchanged, which was enabled by the photo-blocking effect of the Si-Ge layers for a-IGZO films. Considering the simple fabrication process by PECVD with outstanding scalability, we expect that this method can be widely applied to TFT devices that are sensitive to light illumination.

Diagnosis in a Preclinical Model of Bladder Pain Syndrome Using a Au/ZnO Nanorod-based SERS Substrate

To evaluate the feasibility of ZnO nanorod-based surface enhanced Raman scattering (SERS) diagnostics for disease models, particularly for interstitial cystitis/bladder pain syndrome (IC/BPS), ZnO-based SERS sensing chips were developed and applied to an animal disease model. ZnO nanorods were grown to form nano-sized porous structures and coated with gold to facilitate size-selective biomarker detection. Raman spectra were acquired on a surface enhanced Raman substrate from the urine in a rat model of IC/BPS and analyzed using a statistical analysis method called principal component analysis (PCA). The nanorods grown after the ZnO seed deposition were 30 to 50 nm in diameter and 500 to 600 nm in length. A volume of gold corresponding to a thin film thickness of 100 nm was deposited on the grown nanorod structure. Raman spectroscopic signals were measured in the scattered region for nanometer biomarker detection to indicate IC/BPS. The Raman peaks for the control group and IC/BPS group are observed at 641, 683, 723, 873, 1002, 1030, and 1355 cm(-1),which corresponded to various bonding types and compounds. The PCA results are plotted in 2D and 3D. The Raman signals and statistical analyses obtained from the nano-sized biomarkers of intractable inflammatory diseases demonstrate the possibility of an early diagnosis

Results from Over One Year of Follow-Up for Absorbable Mesh Insertion in Partial Mastectomy

∙ The authors have no financial conflicts of interest. © Copyright: Yonsei University College of Medicine 2011 This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial Licens