SplitAMC: Split Learning for Robust Automatic Modulation Classification

Automatic modulation classification (AMC) is a technology that identifies a modulation scheme without prior signal information and plays a vital role in various applications, including cognitive radio and link adaptation. With the development of deep learning (DL), DL-based AMC methods have emerged, while most of them focus on reducing computational complexity in a centralized structure. This centralized learning-based AMC (CentAMC) violates data privacy in the aspect of direct transmission of client-side raw data. Federated learning-based AMC (FedeAMC) can bypass this issue by exchanging model parameters, but causes large resultant latency and client-side computational load. Moreover, both CentAMC and FedeAMC are vulnerable to large-scale noise occured in the wireless channel between the client and the server. To this end, we develop a novel AMC method based on a split learning (SL) framework, coined SplitAMC, that can achieve high accuracy even in poor channel conditions, while guaranteeing data privacy and low latency. In SplitAMC, each client can benefit from data privacy leakage by exchanging smashed data and its gradient instead of raw data, and has robustness to noise with the help of high scale of smashed data. Numerical evaluations validate that SplitAMC outperforms CentAMC and FedeAMC in terms of accuracy for all SNRs as well as latency.Comment: to be presented at IEEE VTC2023-Sprin

Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data

On-device machine learning (ML) enables the training process to exploit a massive amount of user-generated private data samples. To enjoy this benefit, inter-device communication overhead should be minimized. With this end, we propose federated distillation (FD), a distributed model training algorithm whose communication payload size is much smaller than a benchmark scheme, federated learning (FL), particularly when the model size is large. Moreover, user-generated data samples are likely to become non-IID across devices, which commonly degrades the performance compared to the case with an IID dataset. To cope with this, we propose federated augmentation (FAug), where each device collectively trains a generative model, and thereby augments its local data towards yielding an IID dataset. Empirical studies demonstrate that FD with FAug yields around 26x less communication overhead while achieving 95-98% test accuracy compared to FL.Comment: presented at the 32nd Conference on Neural Information Processing Systems (NIPS 2018), 2nd Workshop on Machine Learning on the Phone and other Consumer Devices (MLPCD 2), Montr\'eal, Canad

Recent advances in label-free imaging and quantification techniques for the study of lipid droplets in cells

Lipid droplets (LDs), once considered mere storage depots for lipids, have gained recognition for their intricate roles in cellular processes, including metabolism, membrane trafficking, and disease states like obesity and cancer. This review explores label-free imaging techniques' applications in LD research. We discuss holotomography and vibrational spectroscopic microscopy, emphasizing their potential for studying LDs without molecular labels, and we highlight the growing integration of artificial intelligence. Clinical applications in disease diagnosis and therapy are also considered

Multiple Phases of Chondrocyte Enlargement Underlie Differences in Skeletal Proportions

Even a casual pass through the great halls of mammals in the world’s natural history museums highlights the wide diversity of skeletal proportions that allow us to distinguish between species even when reduced to their calcified components. Similarly each individual is comprised of a variety of bones of differing lengths. The largest contribution to the lengthening of a skeletal element, and to the differential elongation of elements, comes from a dramatic increase in the volume of hypertrophic chondrocytes in the growth plate as they undergo terminal differentiation1–7. Despite this recognized importance, the mechanisms of chondrocyte volume enlargement have remained a mystery8–11. Here we use quantitative phase microscopy12 to show that chondrocytes undergo three distinct phases of volume increase, including a phase of massive cell swelling in which the cellular dry mass is significantly diluted. In light of the tight fluid regulatory mechanisms known to control volume in many cell types13, this stands as a remarkable mechanism for increasing cell size and regulating growth rate. It is, however, the duration of the final phase of volume enlargement by proportional dry mass increase at low density that varies most between rapidly and slowly elongating growth plates. Moreover, we find that this third phase is locally regulated through an Insulin-like Growth Factor-dependent mechanism. This study provides a framework for understanding how skeletal size is regulated and for exploring how cells sense, modify, and establish a volume set point

Experimental Study: Enhancing Voice Spoofing Detection Models with wav2vec 2.0

Conventional spoofing detection systems have heavily relied on the use of handcrafted features derived from speech data. However, a notable shift has recently emerged towards the direct utilization of raw speech waveforms, as demonstrated by methods like SincNet filters. This shift underscores the demand for more sophisticated audio sample features. Moreover, the success of deep learning models, particularly those utilizing large pretrained wav2vec 2.0 as a featurization front-end, highlights the importance of refined feature encoders. In response, this research assessed the representational capability of wav2vec 2.0 as an audio feature extractor, modifying the size of its pretrained Transformer layers through two key adjustments: (1) selecting a subset of layers starting from the leftmost one and (2) fine-tuning a portion of the selected layers from the rightmost one. We complemented this analysis with five spoofing detection back-end models, with a primary focus on AASIST, enabling us to pinpoint the optimal configuration for the selection and fine-tuning process. In contrast to conventional handcrafted features, our investigation identified several spoofing detection systems that achieve state-of-the-art performance in the ASVspoof 2019 LA dataset. This comprehensive exploration offers valuable insights into feature selection strategies, advancing the field of spoofing detection.Comment: 5 page

Improved phase sensitivity in spectral domain phase microscopy using line-field illumination and self phase-referencing

Abstract: We report a quantitative phase microscope based on spectral domain optical coherence tomography and line-field illumination. The line illumination allows self phase-referencing method to reject common-mode phase noise. The quantitative phase microscope also features a separate reference arm, permitting the use of high numerical aperture (NA > 1) microscope objectives for high resolution phase measurement at multiple points along the line of illumination. We demonstrate that the path-length sensitivity of the instrument can be as good as 41 / pm Hz , which makes it suitable for nanometer scale study of cell motility. We present the detection of natural motions of cell surface and two-dimensional surface profiling of a HeLa cell

Ultraviolet refractometry using field-based light scattering spectroscopy

Accurate refractive index measurement in the deep ultraviolet (UV) range is important for the separate quantification of biomolecules such as proteins and DNA in biology. This task is demanding and has not been fully exploited so far. Here we report a new method of measuring refractive index using field-based light scattering spectroscopy, which is applicable to any wavelength range and suitable for both solutions and homogenous objects with well-defined shape such as microspheres. The angular scattering distribution of single microspheres immersed in homogeneous media is measured over the wavelength range 260 to 315 nm using quantitative phase microscopy. By least square fitting the observed scattering distribution with Mie scattering theory, the refractive index of either the sphere or the immersion medium can be determined provided that one is known a priori. Using this method, we have measured the refractive index dispersion of SiO2 spheres and bovine serum albumin (BSA) solutions in the deep UV region. Specific refractive index increments of BSA are also extracted. Typical accuracy of the present refractive index technique is ≤0.003. The precision of refractive index measurements is ≤0.002 and that of specific refractive index increment determination is ≤0.01 mL/g.Hamamatsu CorporationNational Science Foundation (DBI-0754339)National Center for Research Resources of the National Institutes of Health (P41-RR02594-18