MoE-AMC: Enhancing Automatic Modulation Classification Performance Using Mixture-of-Experts

Automatic Modulation Classification (AMC) plays a vital role in time series analysis, such as signal classification and identification within wireless communications. Deep learning-based AMC models have demonstrated significant potential in this domain. However, current AMC models inadequately consider the disparities in handling signals under conditions of low and high Signal-to-Noise Ratio (SNR), resulting in an unevenness in their performance. In this study, we propose MoE-AMC, a novel Mixture-of-Experts (MoE) based model specifically crafted to address AMC in a well-balanced manner across varying SNR conditions. Utilizing the MoE framework, MoE-AMC seamlessly combines the strengths of LSRM (a Transformer-based model) for handling low SNR signals and HSRM (a ResNet-based model) for high SNR signals. This integration empowers MoE-AMC to achieve leading performance in modulation classification, showcasing its efficacy in capturing distinctive signal features under diverse SNR scenarios. We conducted experiments using the RML2018.01a dataset, where MoE-AMC achieved an average classification accuracy of 71.76% across different SNR levels, surpassing the performance of previous SOTA models by nearly 10%. This study represents a pioneering application of MoE techniques in the realm of AMC, offering a promising avenue for elevating signal classification accuracy within wireless communication systems

Facile synthesis of chitosan-capped ZnS quantum dots as an eco-friendly fluorescence sensor for rapid determination of bisphenol A in water and plastic samples

This paper describes a novel eco-friendly fluorescence sensor for determination of bisphenol A (BPA) based on chitosan-capped ZnS quantum dots (QDs). By using safe and inexpensive materials, nontoxic ZnS QDs were synthesized via an environment-friendly method using chitosan as a capping agent. The as-prepared ZnS QDs exhibited characteristic absorption (absorbance edge at 310 nm) and emission (maxima at 430 nm) spectra with a relatively high fluorescence quantum yield of 11.8%. Quantitative detection of BPA was developed based on fluorescence quenching of chitosan-capped ZnS QDs with high sensitivity and selectivity. Under optimal conditions, the fluorescence response of ZnS QDs was linearly proportional to BPA concentration over a wide range from 0.50 to 300 mu g L-1 with a detection limit of 0.08 mu g L-1. Most of the potentially coexisting substances did not interfere with the BPA-induced quenching effect. The proposed analytical method for BPA was successfully applied to water and plastic real samples. The possible quenching mechanism is also discussed

Silk fibroin microneedle patches for the treatment of insomnia

As a patient-friendly technology, drug-loaded microneedles can deliver drugs through the skin into the body. This system has broad application prospects and is receiving wide attention. Based on the knowledge acquired in this work, we successfully developed a melatonin-loaded microneedle prepared from proline/melatonin/silk fibroin. The engineered microneedles’ morphological, physical, and chemical properties were characterized to investigate their structural transformation mechanism and transdermal drug-delivery capabilities. The results indicated that the crystal structure of silk fibroin in drug-loaded microneedles was mainly Silk I crystal structure, with a low dissolution rate and suitable swelling property. Melatonin-loaded microneedles showed high mechanical properties, and the breaking strength of a single needle was 1.2 N, which could easily be penetrated the skin. The drug release results in vitro revealed that the effective drug concentration was obtained quickly during the early delivery. The successful drug concentration was maintained through continuous release at the later stage. For in vivo experimentation, the Sprague Dawley (SD) rat model of insomnia was constructed. The outcome exhibited that the melatonin-loaded microneedle released the drug into the body through the skin and maintained a high blood concentration (over 5 ng/mL) for 4–6 h. The maximum blood concentration was above 10 ng/mL, and the peak time was 0.31 h. This system indicates that it achieved the purpose of mimicking physiological release and treating insomnia.This work was supported by National Natural Science Foundation of China (Grant No. 51973144), College Nature Science Research Project of Jiangsu Province, China (Grant No. 20KJA540002), PAPD, and Six Talent Peaks Project in Jiangsu Province (Grant No. SWYY-038).SCK is supported by the European Union Framework Programme for Research and Innovation HORIZON 2020 (Grant agreement no. 668983—FoReCaST) and the FCT-Portugal project BREAST-IT (PTDC/BTM-ORG/28168/2017)

Projection Convolutional Neural Networks for 1-bit CNNs via Discrete Back Propagation

The advancement of deep convolutional neural networks (DCNNs) has driven significant improvement in the accuracy of recognition systems for many computer vision tasks. However, their practical applications are often restricted in resource-constrained environments. In this paper, we introduce projection convolutional neural networks (PCNNs) with a discrete back propagation via projection (DBPP) to improve the performance of binarized neural networks (BNNs). The contributions of our paper include: 1) for the first time, the projection function is exploited to efficiently solve the discrete back propagation problem, which leads to a new highly compressed CNNs (termed PCNNs); 2) by exploiting multiple projections, we learn a set of diverse quantized kernels that compress the full-precision kernels in a more efficient way than those proposed previously; 3) PCNNs achieve the best classification performance compared to other state-of-the-art BNNs on the ImageNet and CIFAR datasets

Constructing Tree-based Index for Efficient and Effective Dense Retrieval

Recent studies have shown that Dense Retrieval (DR) techniques can significantly improve the performance of first-stage retrieval in IR systems. Despite its empirical effectiveness, the application of DR is still limited. In contrast to statistic retrieval models that rely on highly efficient inverted index solutions, DR models build dense embeddings that are difficult to be pre-processed with most existing search indexing systems. To avoid the expensive cost of brute-force search, the Approximate Nearest Neighbor (ANN) algorithm and corresponding indexes are widely applied to speed up the inference process of DR models. Unfortunately, while ANN can improve the efficiency of DR models, it usually comes with a significant price on retrieval performance. To solve this issue, we propose JTR, which stands for Joint optimization of TRee-based index and query encoding. Specifically, we design a new unified contrastive learning loss to train tree-based index and query encoder in an end-to-end manner. The tree-based negative sampling strategy is applied to make the tree have the maximum heap property, which supports the effectiveness of beam search well. Moreover, we treat the cluster assignment as an optimization problem to update the tree-based index that allows overlapped clustering. We evaluate JTR on numerous popular retrieval benchmarks. Experimental results show that JTR achieves better retrieval performance while retaining high system efficiency compared with widely-adopted baselines. It provides a potential solution to balance efficiency and effectiveness in neural retrieval system designs.Comment: 10 pages, accepted at SIGIR 202

Controlling Lateral Fano Interference Optical Force with Au-Ge2Sb2Te5 Hybrid Nanostructure

We numerically demonstrate that a pronounced dipole–quadrupole (DQ) Fano resonance (FR) induced lateral force can be exerted on a dielectric particle 80 nm in radius (Rsphere = 80 nm) that is placed 5 nm above an asymmetric bow-tie nanoantenna array based on Au/Ge2Sb2Te5 dual layers. The DQ-FR-induced lateral force achieves a broad tuning range in the mid-infrared region by changing the states of the Ge2Sb2Te5 dielectric layer between amorphous and crystalline and in turn pushes the nanoparticle sideways in the opposite direction for a given wavelength. The mechanism of lateral force reversal is revealed through optical singularity in the Poynting vector. A thermal–electric simulation is adopted to investigate the temporal change of the Ge2Sb2Te5 film’s temperature, which demonstrates the possibility of transiting the Ge2Sb2Te5 state by electrical heating. Our mechanism by tailoring the DQ-FR-induced lateral force presents clear advantages over the conventional nanoparticle manipulation techniques: it possesses a pronounced sideways force under a low incident light intensity of 10 mW/μm2, a fast switching time of 2.6 μs, and a large tunable wavelength range. It results in a better freedom in flexible nanomechanical control and may provide a new means of biomedical sensing and nano-optical conveyor belts

Reasoning over Hierarchical Question Decomposition Tree for Explainable Question Answering

Explainable question answering (XQA) aims to answer a given question and provide an explanation why the answer is selected. Existing XQA methods focus on reasoning on a single knowledge source, e.g., structured knowledge bases, unstructured corpora, etc. However, integrating information from heterogeneous knowledge sources is essential to answer complex questions. In this paper, we propose to leverage question decomposing for heterogeneous knowledge integration, by breaking down a complex question into simpler ones, and selecting the appropriate knowledge source for each sub-question. To facilitate reasoning, we propose a novel two-stage XQA framework, Reasoning over Hierarchical Question Decomposition Tree (RoHT). First, we build the Hierarchical Question Decomposition Tree (HQDT) to understand the semantics of a complex question; then, we conduct probabilistic reasoning over HQDT from root to leaves recursively, to aggregate heterogeneous knowledge at different tree levels and search for a best solution considering the decomposing and answering probabilities. The experiments on complex QA datasets KQA Pro and Musique show that our framework outperforms SOTA methods significantly, demonstrating the effectiveness of leveraging question decomposing for knowledge integration and our RoHT framework.Comment: has been accepted by ACL202

Road Traffic Law Adaptive Decision-making for Self-Driving Vehicles

Self-driving vehicles have their own intelligence to drive on open roads. However, vehicle managers, e.g., government or industrial companies, still need a way to tell these self-driving vehicles what behaviors are encouraged or forbidden. Unlike human drivers, current self-driving vehicles cannot understand the traffic laws, thus rely on the programmers manually writing the corresponding principles into the driving systems. It would be less efficient and hard to adapt some temporary traffic laws, especially when the vehicles use data-driven decision-making algorithms. Besides, current self-driving vehicle systems rarely take traffic law modification into consideration. This work aims to design a road traffic law adaptive decision-making method. The decision-making algorithm is designed based on reinforcement learning, in which the traffic rules are usually implicitly coded in deep neural networks. The main idea is to supply the adaptability to traffic laws of self-driving vehicles by a law-adaptive backup policy. In this work, the natural language-based traffic laws are first translated into a logical expression by the Linear Temporal Logic method. Then, the system will try to monitor in advance whether the self-driving vehicle may break the traffic laws by designing a long-term RL action space. Finally, a sample-based planning method will re-plan the trajectory when the vehicle may break the traffic rules. The method is validated in a Beijing Winter Olympic Lane scenario and an overtaking case, built in CARLA simulator. The results show that by adopting this method, the self-driving vehicles can comply with new issued or updated traffic laws effectively. This method helps self-driving vehicles governed by digital traffic laws, which is necessary for the wide adoption of autonomous driving