Investigation of a Side-polished Fiber MZI and Its Sensing Performance

A novel all-fiber Mach–Zehnder interferometer (MZI), which consists of lateral core fusion splicing of a short section of side-polished single mode fiber (SMF) between two SMFs was proposed and demonstrated. A simple fiber side-polished platform was built to control the side polished depth through a microscope. The sensitivity of the fiber MZI structure to the surrounding refractive index (RI) can be greatly improved with the increase of the side-polished depth, but has no effect on the temperature sensitivity. The sensor with a polished depth of 44.2 μm measured RI sensitivity up to -118.0 nm/RIU (RI unit) in the RI range from 1.333 to 1.387, which agrees well with simulation results by using the beam propagation method (BPM). In addition, the fiber MZI structure also can achieve simultaneous measurement of both RI and temperature. These results show its potential for use in-line fiber type sensing application

Novel Microfiber Sensor and Its Biosensing Application for Detection of hCG Based on a Singlemode-Tapered Hollow Core-Singlemode Fiber Structure

A novel microfiber sensor is proposed and demonstrated based on a singlemode-tapered hollow core -singlemode (STHS) fiber structure. Experimentally a STHS with taper waist diameter of 26.5 μm has been fabricated and RI sensitivity of 816, 1601.86, and 4775.5 nm/RIU has been achieved with RI ranges from 1.3335 to 1.3395 , from 1.369 to 1.378, and from 1.409 to 1.4175 respectively, which agrees very well with simulated RI sensitivity of 885, 1517, and 4540 nm/RIU at RI ranges from 1.3335 to 1.337, from 1.37 to 1.374, and from 1.41 to 1.414 . The taper waist diameter has impact on both temperature and strain sensitivity of the sensor structure: (1) the smaller the waist diameter, the higher the temperature sensitivity, and experimentally 26.82 pm/°C has been achieved with a taper waist diameter of 21.4 μm; (2) as waist diameter decrease, strain sensitivity increase and 7.62 pm/με has been achieved with a taper diameter of 20.3 μm. The developed sensor was then functionalized for human chorionic gonadotropin (hCG) detection as an example for biosensing application. Experimentally for hCG concentration of 5 mIU/ml, the sensor has 0.5 nm wavelength shift, equivalent to limit of detection (LOD) of 0.6 mIU/ml by defining 3 times of the wavelength variation (0.06 nm) as measurement limit. The biosensor demonstrated relatively good reproducibility and specificity, which has potential for real medical diagnostics and other applications

N-[2-(2-Chloro­phen­yl)-2-hy­droxy­eth­yl]propan-2-aminium benzoate

In the title compound, C11H17ClNO+·C7H5O2 −, obtained by the reaction of chlorprenaline {or 1-(2-chlorophenyl)-2-[(1-methylethyl)amino]ethanol} and benzoic acid, the chlorprenaline is twisted moderately [C—C—C—C torsion angle = −76.00 (17)°] compared with related compounds. The mol­ecules as usual form dimers. In the crystal structure, the two components are connected by classical O—H⋯O and N—H⋯O hydrogen bonds

Towards Attack-tolerant Federated Learning via Critical Parameter Analysis

Federated learning is used to train a shared model in a decentralized way without clients sharing private data with each other. Federated learning systems are susceptible to poisoning attacks when malicious clients send false updates to the central server. Existing defense strategies are ineffective under non-IID data settings. This paper proposes a new defense strategy, FedCPA (Federated learning with Critical Parameter Analysis). Our attack-tolerant aggregation method is based on the observation that benign local models have similar sets of top-k and bottom-k critical parameters, whereas poisoned local models do not. Experiments with different attack scenarios on multiple datasets demonstrate that our model outperforms existing defense strategies in defending against poisoning attacks.Comment: ICCV'23 Accepte

FedDefender: Client-Side Attack-Tolerant Federated Learning

Federated learning enables learning from decentralized data sources without compromising privacy, which makes it a crucial technique. However, it is vulnerable to model poisoning attacks, where malicious clients interfere with the training process. Previous defense mechanisms have focused on the server-side by using careful model aggregation, but this may not be effective when the data is not identically distributed or when attackers can access the information of benign clients. In this paper, we propose a new defense mechanism that focuses on the client-side, called FedDefender, to help benign clients train robust local models and avoid the adverse impact of malicious model updates from attackers, even when a server-side defense cannot identify or remove adversaries. Our method consists of two main components: (1) attack-tolerant local meta update and (2) attack-tolerant global knowledge distillation. These components are used to find noise-resilient model parameters while accurately extracting knowledge from a potentially corrupted global model. Our client-side defense strategy has a flexible structure and can work in conjunction with any existing server-side strategies. Evaluations of real-world scenarios across multiple datasets show that the proposed method enhances the robustness of federated learning against model poisoning attacks.Comment: KDD'23 research track accepte