Reviewerly/exHarmony huggingface dataset:
Optimization of detection algorithm based on YOLOV5

Aiming at the problems that the current YOLOV5 target detection algorithm has many parameters, complex network structure, and high configuration required for training models, CNS-YOLO proposes a lightweight algorithm based on YOLOV5 (CNS-YOLO). First of all, this paper uses the ConvNeXt structure to optimize the neck network of YOLOV5, and secondly, this paper uses Shufflenetv2 to improve the backbone structure of YOLOV5, and reconstructs the feature extraction network and feature fusion network. Through the improvement of these two structures, the problem of too many parameters of YOLOV5 is optimized, and finally the group convolution and attention mechanism are added to further increase the ability to extract information and suppress background noise, thereby improving the detection speed and detection accuracy of the algorithm. The results of the RSOD data set show that the mAP@0.5 of the CNS-YOLO network has increased by 2.3 percentage points compared to before the improvement, and the FLOPs have decreased by 12.8G compared to the before improvement; the generated model file has decreased by 10.6M compared to before. In the case of reducing the number of model parameters, mAP@0.5 is still improved, indicating that the algorithm has achieved good improvements in all aspects and improved the effect of target detection.
Preprocessing high-definition images: interpretable feature extraction with pre-trained StyleGAN

The interpretable image classifier VAE-FNN can extract high-level features for classification from complex image information and provide explanations that are consistent with human intuition. However, due to the insufficient reconstruction ability of VAE, there are still challenges in feature extraction and interpretable classification for highdefinition images. An image preprocessing method is proposed in this paper and a model named E2GAN that can extract low-dimensional interpretable features from high-definition images is constructed. The model is based on a pre-trained StyleGAN generator, and two mapping networks are trained, one for extracting the low-dimensional compressed encoding of the input image and the other for restoring it to the matrix representation required by the StyleGAN generator, which effectively improves the quality of feature extraction and image reconstruction. A discriminator is introduced to perform adversarial training with the mapping network, further improving the realism of the reconstructed image. The training algorithm of the E2GAN model is designed, and a decoupling loss for the low-dimensional encoding is added to further improve its semantic interpretability. Experiments on the CelebA-HQ dataset show that the E2GAN model can extract low-dimensional, semantically informative features from high-definition images, which can be used to train high-precision and interpretable fuzzy neural network classifiers.
Semantic image synthesis with vision graph neural network

Semantic image synthesis aims to generate realistic images from semantic label maps. Current generative adversarial network models still struggle with understanding visual information with irregular topological structures because they rely on convolutional neural networks, which interpret semantic label map as grid structures. In this work, we propose a Vision GNN Generative Adversarial Network (VG-GAN). In our redesigned generator, the input semantic label map is embedded in patches and then converted into a graph structure. We use graph neural networks on the constructed graph to learn the complex interrelationships in the graph structure. Experimental results show that our proposed method generates images with irregular and complex objects that appear more realistic and perform better than state-of-the-art methods.
TransUNet for image forgery localization

Image tampering can easily be used in illegal activities such as false propaganda, fake news and falsifying evidence in court which may have a negative impact on society. Therefore, we need to constantly update and improve the image tampering detection technology. TransUNet is an efficient model for medical image segmentation. This paper modified and migrated TransUNet which to image forgery localization, and conducted experiments on standard datasets to demonstrate the effectiveness of TransUNet. The findings shows that the proposed framework is superior to several existing forged area localization techniques.
Reservoir concatenation and the spectrum distribution of concatenated reservoir state matrices

Reservoir computing, one of the state-of-the-art machine learning architectures, processes time-series data generated by dynamical systems. Nevertheless, we have realized that reservoir computing with the conventional single-reservoir structure suffers from capacity saturation. This leads to performance stagnation in practice. Therefore, we propose an extended reservoir computing architecture called reservoir concatenation to further delay such stagnation. Not only do we provide training error analysis and test error comparison of reservoir concatenation, but we also propose a crucial measure, which is the trace associated with a reservoir state matrix, that explains the level of responsiveness to reservoir concatenation. Two reservoir dynamics are compared in detail, one by using the echo state network and the other by using a synchronization model called an explosive Kuramoto model. The distinct eigenvalue distributions of the reservoir state matrices from the two models are well reflected in the trace values that are shown to account for the different reservoir capacity behaviors, determining the different levels of responsiveness.
The impact of artificial intelligence systems in micropalaeontology

The discipline of micropalaeontology, fundamental in Geology, has witnessed substantial technological advancements in recent decades, aided by the exploitation of Artificial Intelligence (AI) systems to facilitate microfossil identification. This perspective paper explores the transformative role of AI in micropalaeontology, particularly in species identification, and its potential to help with the interpretation of microfossil assemblages. While it is argued that AI cannot fully replicate the expertise of a micropalaeontologist, an abundance of scientific studies shows the promising success of AI becoming adept at accurately identifying microfossil species.
Ensemble transport smoothing. Part II: Nonlinear updates

Smoothing is a specialized form of Bayesian inference for state-space models that characterizes the posterior distribution of a collection of states given an associated sequence of observations. Ramgraber et al. (2022) proposes a general framework for transport-based ensemble smoothing, which includes linear Kalman-type smoothers as special cases. Here, we build on this foundation to realize and demonstrate nonlinear backward ensemble transport smoothers. We discuss parameterization and regularization of the associated transport maps, and then examine the performance of these smoothers for nonlinear and chaotic dynamical systems that exhibit non-Gaussian behavior. In these settings, our nonlinear transport smoothers yield lower estimation error than conventional linear smoothers and state-of-the-art iterative ensemble Kalman smoothers, for comparable numbers of model evaluations.
Intelligent Vehicle Systematic Design Based on Arduino and Raspberry Pi

Abstract The intelligent vehicle designed in this paper can realize functions, such as safety detection, visual identification, remote control and manipulator grasping, and so on. Arduino MEGA is used as the main control board to send signal messages to drive vehicles. Wi-Fi module is used to receive messages to remote control vehicles. The ultrasonic and infrared module is used to realize object detection around vehicles. To realize complex route movement, raspberry pie is used for visual recognition and path planning. Data is sent to Arduino for judgment in real time. Finally, it is verified that the design effectively improves the path-planning ability and obstacle-avoidance function in a sample vehicle.
Correlation measures of a quantum state and information characteristics of a quantum channel

We discuss the interconnections between basic correlation measures of a bipartite quantum state and basic information characteristics of a quantum channel, focusing on the benefits of these interconnections for solving specific problems concerning the characteristics of both types. We describe properties of the (unoptimized and optimized) quantum discord in infinite-dimensional bipartite systems. In particular, using the generalized Koashi–Winter relation, a simple condition is obtained that guarantees that a state with zero quantum discord is quantum-classical. Two possible definitions of the quantum discord for states with infinite one-way classical correlation are proposed and analysed. The generalized versions of Koashi–Winter and Xi–Lu–Wang–Li relations are used to obtain advanced continuity bounds for the Holevo information at the outputs of a channel and its complementary channel (as functions of a channel for a given ensemble of input states), for the Holevo capacity and the unregularized private capacity of a quantum channel depending either on the input dimension or on the input energy bound. We also discuss the properties of quantum channels which are “doppelgangers” of the monotonicity of the quantum discord and the entropy reduction of a local measurement under quantum channels acting on an unmeasured subsystem.
PROTECTION OF POLIGRAPHIC PRODUCTS USING DIGITAL WATERMARKS

The article proposes algorithms for steganographic embedding and extraction of digital watermarks (DWM)&#x0D; embedded in the digital original of printed products in order to protect copyrights to them. The built-in digital&#x0D; image is resistant to distortions introduced into images during printing and scanning and is not visually noticeable.&#x0D; The resistance of the extraction algorithm to geometric distortions is achieved through the use of an algorithm&#x0D; for restoring the original geometry of the image by comparing the special points of the distorted copy of the container&#x0D; image and the original image. The results of testing the robustness of the algorithm both to simulated distortions&#x0D; of the printing and scanning channel, and to distortions introduced into images when using real printing and scanning&#x0D; devices are presented. The impact of the embedding process on image quality is also considered.
Interpretable Disease Prediction from Clinical Text by Leveraging Pattern Disentanglement

For artificial intelligence (AI) systems to be adopted in high stake human-oriented applications, they must be able to make complex decisions in an understandable and interpretable manner. While AI systems today have grown leaps and bounds in predictive power using larger datasets with more complex architectures, existing models remain ineffective at generating interpretable insights in the clinical setting. In this paper, we address the challenge of discovering interpretable insights from the clinical text for disease prediction. For this purpose, we apply the clinical notes from the electronic health records (EHR) available in the Medical Information Mart of Intensive Care III (MIMIC-III) dataset, which are labeled with the international classification of diseases (ICD9) codes. Our proposed algorithm combines interpretable text-based features with a novel pattern discovery and disentanglement algorithm. Specifically, our approach encompasses the following: (1) uncovering strong association patterns between clinical notes and diseases, (2) surpassing baseline clustering algorithms in effectively distinguishing between disease clusters, and (3) demonstrating comparable performance to baseline supervised methods in predicting diseases. Our results validate the model’s capability to strike a balance between interpretability and outcome prediction accuracy. By unveiling insightful patterns between clinical notes and diseases, our approach upholds a reasonable level of diagnostic accuracy.Clinical relevance—This paper proposes a novel all-in-one clinical Natural Language Processing (NLP) knowledge base, which can be applied in healthcare systems to discover interpretable insights from the clinical text for predicting medical conditions.
SiaKey: A Method for Improving Few-shot Learning with Clinical Domain Information

Supervised Natural Language Processing (NLP) models can achieve high accuracy, but they often require a significant amount of annotated data for training, which can be expensive and time-consuming. This is especially true for clinical NLP, where annotations on large-scale electronic health records (EHRs) and online posts necessitate specialists with clinical expertise. On the other hand, fine-tuning Pretrained Language Models (PLMs) may yield poor performance due to limited training data. Few-Shot Learning (FSL) methods offer a promising solution as they can significantly improve clinical NLP with only a small amount of labeled data. In this paper, we introduce a novel FSL technique named SiaKey, which utilizes Siamese Networks and integrates Keyphrases Extraction and Domain Knowledge, for the task of online post classification. This task is challenging since online posts typically contain a greater amount of irrelevant information compared to traditional EHRs. By incorporating Keyphrases using domain knowledge, we extract essential information and reduce distractions, enhancing the classification process. To evaluate SiaKey’s performance, we conducted tests with 5, 10, 15, and 20-shot learning on health-related online post-classification tasks. The results of our experiments demonstrate SiaKey’s effectiveness in capturing text features, showcasing its superior performance compared to BioBERT on similar FSL tasks.
Towards Accurate and Clinically Meaningful Summarization of Electronic Health Record Notes: A Guided Approach

Clinicians are often under time pressure when they review patients’ electronic health records (EHR), therefore, there are great benefits to providing clinicians high-quality summarizations of patients’ EHR. However, existing summarization algorithms fall short in certain key aspects, such as focusing on pertinent information that is clinically significant, and adhering to a structured template that aligns with the formats clinicians are accustomed to. In this paper, we present a novel approach to summarize EHR notes using a guided summarization model. Our model integrates a structured template developed with a clinical domain expert, a Named Entity Recognition (NER) model and sentence classification model for guidance extraction, and a fact-checking metric for evaluating the generated summaries. We trained our model on a large de-identified EHR dataset. The results demonstrate that our guidance, which includes Chief Complaint (CC), NER, guidance from the History of Present Illness (HPI) section, and guidance from the Medical Decision Making (MDM) section, can significantly improve the performance of the models in generating accurate and clinically meaningful summaries. The Gsum (CNN) model with all the guidance aforementioned achieved the highest F1 score of 46.4, demonstrating the effectiveness of introducing precise and informative guidance to models from the general domain when the training data on the clinical domain is prohibitively sensitive and expensive. This work contributes to the ongoing efforts to automate the summarization of EHR notes, with the ultimate goal of improving healthcare delivery and patient outcomes.
Temporal Phenotype Matrix Engineering for Electronic Health Records – Enhancing Coronary Artery Disease Prediction

Nowadays, most studies still aggregate electronic health records (EHRs) into one record per patient for analysis and model development without considering temporal information, which is valuable for disease progression and outcome prediction. However, EHRs often exhibit sparsity and irregularity due to their inherent nature, and data preprocessing is needed to extract temporal information in EHRs. It is crucial to consider that imputation and aggregation techniques used during EHRs preprocessing can introduce artificial and unrealistic data, potentially leading to the loss of critical information. In this study, we proposed a temporal phenotype matrix engineering approach with auxiliary data layers (ADL) to extract important hidden information from EHRs. Our proposed approach was applied to the early prediction of coronary artery disease (CAD), one of the leading causes of death worldwide. We evaluated the performance of the long short term memory network (LSTM), convolutional neural network (CNN), and temporal convolution network (TCN) models on the CAD prediction task. Upon applying our proposed matrix engineering technique with ADL, we observed a substantial improvement, with an AUROC (area under the receiver operating characteristic) score of 0.919 ± 0.006 (a 10% increase, compared to when no ADL was included, 0.831 ± 0.011) in CNN model. In conclusion, this study highlights the benefits of the proposed temporal phenotype matrix engineering approach with ADL to address the sparsity and irregularity inherent in EHRs data.Clinical Relevance—Our findings underscore the potential of the proposed temporal phenotype matrix engineering approach with ADL for enhancing the early prediction of CAD, thereby contributing to improved patient outcomes and reduced mortality rates.
SASL-JTAG: A Light-Weight Dependable JTAG

This paper addresses the performance imbalance issue of IoT systems and proposes a lightweight JTAG authentication scheme for enhancing the dependability of IoT devices. We propose a one-time authentication protocol, called SAS-L(Simple And Secure Light authentication), to improve the dependability of the JTAG, referred to as SASL-JTAG. The architecture of the SASLJTAG authentication is designed and implemented on an FPGA to verify its functionality and evaluate hardware overhead. SASL-JTAG offers a cost-effective and scalable solution for securing JTAG systems in IoT devices while ensuring high reliability.
On the resilience of representative and novel data formats in CNNs

In recent years, a wide range of data type representations have been employed for training and storing the parameters of Deep Neural Networks (DNNs). The decision to employ a particular data type over another is influenced by various requirements, including the desire to enhance training accuracy or reduce data size to minimize memory usage, energy and power consumption. However, opting for one data type over another inevitably impacts the reliability of the model. This work studies the impact of different data representations on the reliability of LeNet-5, a popular Convolutional Neural Network (CNN) used for image classification tasks. An investigation is performed to evaluate the efficacy of the Average Bit-Flip Distance (ABFD) in predicting the criticality of bit positions in the data representation. The data type under analysis are FP32, POSIT32, POSIT16 and INT8. Together with the widely adopted metrics, this work proposes a new metric, called Soft SDC-n, to measure the percentage of faults that cause a change in the order of the top-n output elements. Experimental results shows that POSIT is not as reliable as FP32, while indicating that the most reliable data type is INT8. Furthermore, the same results confirm the presence of a relationship between the ABFD and the criticality of a bit in all the data representations under analysis.
Fast max-affine regression via stochastic gradient descent

We consider regression of the max-affine model that combines multiple affine models via the max function. The max-affine model ubiquitously arises in applications such as multiclass classification and auction problems. It also generalizes the forward model in phase retrieval and rectifier linear unit activation function. We present a non-asymptotic convergence analysis of mini-batch stochastic gradient descent (SGD) for max-affine regression when the model is observed at random locations following the sub-Gaussianity with anti-concentration. Under these assumptions, a suitably initialized SGD converges linearly to the ground truth. Due to its low per-iteration cost, SGD converges faster than alternating minimization and gradient descent in run time. Our numerical results corroborate the presented theoretical results.
Quantum Key Distribution with State Replacement

Quantum key distribution protocols have been studied for almost four decades, and have already seen real world applications. Such implementations often operate in naturally noisy environments (e.g., sensor thermal noise, sun radiation, etc.) with known or measurable noise levels. However, existing security proofs implicitly attribute any channel noise to a possible eavesdropper, an overly pessimistic assumption that can adversely effect the guaranteed protocol key-rate. In this paper, we analyze general prepare-and-measure QKD schemes, and show how to improve the bounds on the key-rate under the assumption of an independent state replacement noise at the receiver. Taking BB84 as a concrete example, our results theoretically guarantee a higher key-rate, as a function of the minimal possible replacement probability. In practice, for some parameter-regimes, this guarantees a higher tolerable QBER.
Learning Source Coding for General Alphabets and Finite State Machines

This paper develops bounds for learning lossless source coding under the PAC (probably approximately correct) framework. The paper considers both iid sources and sources generated by finite state machines (FSM).
A Differential Dynamic Programming-based Approach for Balancing Energy and Time Optimality in Motion Planning

Optimal motion planning that simultaneously considers energy and time efficiency is crucial for a wide range of applications from industrial manufacturing to autonomous vehicle navigation. This paper introduces a novel perspective on discrete-time systems which recognizes the time interval size as an additional aspect of the control variable that is conventionally sought to be determined. By incorporating ideas from Dynamic Differential Programming (DDP), our method, called BO-DDP (Balancing energy and time Optimality via DDP), enables an adjustable trade-off between energy and time optimality. DDP leverages quadratic approximation of dynamics and cost function, ensuring accurate information capture and a high convergence rate. To address the challenge of high computational complexity in obtaining related derivatives, we introduce a Taylor series-based numerical scheme for simultaneous forward integration and differentiation. Extensive simulation experiments on two scenarios, including autonomous car navigation and quadcopter flight, demonstrate the practicality and effectiveness of our algorithm.