Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhancing EOG Artifact Removal from EEG Through Independent Component, Band Power, and Pearson’s Correlation

Eye movements introduce artifacts in EEG recordings, thus hindering accurate analysis. To detect and remove electrooculogram (EOG) components from EEG data, we commonly use independent component analysis (ICA). However, existing methods may be inconsistent, thus necessitating better EOG artifact removal. Our study presents an improved EOG artifact removal method that employs band power analysis and Pearson’s correlation coefficient (PCC). This method enhances accuracy and efficiency in identification and removal of EOG artifacts from EEG data. We applied the fast Fourier transform to compute band power (1–2 Hz) for each ICA-derived component. A threshold of 106 μV2 identified EOG components with excessive band power. Subsequently, PCC values quantified the impact of each component on EEG channels. This approach accurately pinpointed EOG components for removal. Our proposed method significantly improved EEG signal quality. Combining band power and PCC analysis refined EOG component identification, thus facilitating targeted artifact removal. This method can potentially enhance EEG studies, particularly in eye movement-rich scenarios.
scNMF-Impute: imputation for single-cell RNA-seq data based on nonnegative matrix factorization

Single-cell RNA sequencing (scRNA-seq) data are collected at an unheard-of rate thanks to the advancement of high-throughput sequencing technologies. However, due to the limitations of current technology, scRNA-seq is sometimes unable to capture the expressed genes, resulting in a large number of zero counts (also known as dropout events) in the data. These dropout events can cause data loss in the gene expression matrix and severely hampers the accuracy of downstream analysis. To address this problem, in this paper, we propose a new imputation method called scNMF-impute. The scNMF-impute method imputes the dropout events and performs dimensionality reduction under the framework of nonnegative matrix factorization (NMF). To effectively identify the location of the dropout and recover the value of the dropout, we explicitly model the dropout events as a matrix. Therefore, the gene expression matrix without dropout is represented as the sum of the original data matrix and the dropout matrix. In addition, to reduce the influence of dropout on factorization, we introduce the similarity information between genes into the NMF model. The introduction of gene similarity information can ensure the accurate recovery of data structures obscured by dropout events in the gene expression matrix. We conducted extensive experiments on simulated datasets and real scRNA-seq datasets to verify the effectiveness of scNMF-impute and other state-of-the-art methods. The results show that scNMF-impute can accurately calculate missing data and restore true gene expression, thus improving the accuracy of existing clustering methods and obtaining more accurate cell clustering results.
An Optimization-Based Method for Transient Stability Assessment

The paper proposes an optimization-based method for the transient stability assessment of lossy multi-machine power systems. To achieve this objective, a global control Lyapunov function candidate including an auxiliary state is introduced. On this basis, a new excitation control law is proposed. This control law is well-defined provided that an ‘index’ matrix remains non-singular along the closed-loop trajectories. Such a matrix plays a key role in the formulation of an optimization problem, which allows calculating the so-called critical value associated to the introduced Lyapunov function. This permits a direct assessment of transient stability property of the considered post-fault power system. To illustrate the effectiveness of such an optimization-based method, a case study on the model of a three-machine system is presented.
A Factual Aware Two-Stage Model for Medical Dialogue Summarization

Medical dialogue summarization (MDS) is commonly known as generating electronic health records (EHR) from doctor-patient dialogues to relieve doctors from trivial recordings. Because of their excellent performance on summarization tasks, it is advisable to employ pre-trained language models (PLM) for MDS. However, most of these models are not designed to handle such lengthy dialogues and struggle with domain-specific characteristics. To address this problem, we propose a two-stage summarization model that first constructs compact contexts by selecting salient utterances and then generates EHRs with delexicalization and lexicalization. A REINFORCE algorithm with a multiple reward strategy is employed to connect the two modules to increase faithfulness and adaptively control the length of the extracted context. We implement our model using publicly available PLMs without changing their nature, and we pre-train each module separately before alternately fine-tuning them with the reinforcement objective. Extensive experiments on two public datasets show that our proposed model significantly outperforms state-of-the-art comparison models w.r. t. ROUGE score and terminology matching rate.
Plume‐ridge interaction via melt channelization at Galápagos and other near‐ridge hotspot provinces

The interaction of mantle plume driven flow with upwelling flow due to a nearby mid‐ocean ridge occurs for many mantle plumes including Galápagos and Iceland. This interaction is typified by trace element and isotopic signatures demonstrating the “contamination” of normal ridge composition by relatively enriched plume material. However, another common signature of plume‐ridge interaction is volcanic lineaments linking ridges and nearby plumes, perhaps most conspicuously the Wolf‐Darwin lineament (WDL) at Galápagos and the Rodrigues Ridge (RR) at La Réunion. These enigmatic features remain unexplained. Plume‐ridge interaction is commonly modeled in terms of interaction between solid‐state plume flow and divergent ridge flow, but such models do not likely lead to the kind of solid‐state flow channelization that might explain narrow features such as the WDL and RR. Likewise, models involving tapping of anomalously hot and/or fertile asthenosphere between the plume and ridge due to lithospheric faulting appear to be inconsistent with a variety of evidence. We propose an alternative model in which the lineaments are the surface expressions of localized melt channels in the asthenosphere formed due to instabilities in a two‐phase partially molten system. A thermodynamic analysis shows that given the magma fluxes inferred to be associated with structures such as WDL and RR, these melt channels can be maintained over plume‐ridge distances up to ∼1000 km. These results suggest that plume‐ridge interaction in general, possibly including transport of plume‐derived material along ridge axes (e.g., Iceland), may involve transport in high‐melt‐fraction channels, as opposed to just solid‐state mantle flow.
Towards Tactile Sensing Of The Epidural Needle Into The Spinal Column

The accurate placement of a needle into the spinal column is critical for spinal anesthesia, spinal taps, and other spinal procedures. Currently, the insertion of the needle is guided by visual and palpation feedback, which can be limited in accuracy and reliability. This study presents a novel approach to providing tactile feedback during needle insertion into the spinal column. This study aims to investigate the effectiveness of providing feedback during the insertion of a needle into the epidural column. The study uses a force-sensing resistor that is placed at the base of the needle. As the needle is inserted into the spinal column, the sensors measure the resistance and force encountered by the needle. These measurements are transmitted to a computer system that processes the data and generates real-time graphical feedback. The system was tested on a phantom model that simulates the spinal column. The results showed that the tactile feedback provided by the system improved the accuracy of needle placement and fewer tries at needle insertion were needed. The proposed tactile feedback system has the potential to improve the accuracy and safety of needle placement during spinal procedures.
Undetectable Attacks on Boolean Networks

In this paper, driven by the ever-increasing cyber-security threats, we study the undetectable attack problems for Boolean networks (BNs), which model distributed systems with a limited capacity of storage and bandwidth of communication. Given a consistent monitor, undetectable attacks are formalized for BNs as those do not yield an output sequence out of the nominal output sequence set. By the graph-theoretic approach, undetectable attacks are characterized by a reachability problem of a directed cycle in the augmented transition graph. On the other hand, the algebraic approach also derives a necessary and sufficient criterion for undetectable attacks by testing the existence of the nonzero elements in the constructed matrix. While all these derived results are only computationally efficient for relatively small-size BNs. The detection of attack signals is indeed NP-hard. In other words, there is no polynomial-time algorithm to check the detectability of an attack signal or an attack node set unless NP=P.
Identifying Main and Epistasis Effects of Genetic Variations on Neuroimaging Phenotypes Using Effective Feature Interaction Learning

Brain imaging genetics investigates the complex relationships between genetic variations and brain imaging quantitative traits (QTs). However, existing approaches primarily focus on the main effects of genetic variations, potentially neglecting the crucial role of epistasis that explains the missing heritability of brain disorders. Unfortunately, this issue has been largely unexploited. In this paper, to fill this gap, we present Multi-Task feature interaction-aware Sparse Canonical Correlation Analysis (MTfiSCCA) to identify disease-related main effect and epistasis of risk genetic factors on multimodal neuroimaging phenotypes simultaneously. To ensure stability and interpretation, we use innovative sparsity-inducing penalties to identify biomarkers that make significant contributions. Additionally, we develop an efficient optimization algorithm to solve the proposed method, which converges to a local optimum. Experimental results on the Alzheimer’s disease neuroimaging initiative (ADNI) dataset show that our MTfiSCCA method achieves higher canonical correlation coefficients (CCC) and better feature selection subsets such as disease-related biomarkers compared to the state-of-the-art methods. Furthermore, MTfiSCCA reveals interpretable epistasis among genetic variations implicated in AD, offering novel insights into the underlying pathogenic mechanisms of brain disorders such as Alzheimer’s disease (AD).
A 0.57 mm2 Platform with 70.7% Efficient 4 mA 3.2 V Charge Pump and a Current-Input Ramp ADC for Implantable Optical Sensing of Tumors

Continuous and real-time in vivo monitoring of solid tumors using implanted sensors would be transformative for precision health in cancer research and treatment. Optical techniques such as spectroscopy offer a path towards realizing such treatments, but require additional miniaturization efforts. To address this need, we have designed and prototyped an integrated circuit (IC) for implantable in-vivo optical sensing of tumors. Our prototype, fabricated in 65nm CMOS technology, occupies a 0.57mm2 active area and comprises a high-efficiency cross-coupled charge pump and two current-input ramp analog-to-digital converters (ADCs). The charge pump, with a peak efficiency of 70.7%, delivers 12.8 mW to power the off-chip light sources necessary for spectroscopy. We validate our approach through measurements on a tissue simulating phantom and demonstrate the prototypes suitability for in vivo optical sensing.
An Adaptive Extremum Seeking Scheme for Non-Convex Optimisation

The paper presents an extremum-seeking scheme in which the dither is adaptively tuned to deal with non-convex cost functions. The adaptation law decreases the dither when local cost function trends are easily visible from output data. Contrarily, when the cost function does not have a dominant trend, the dither is increased to enrich the output data. This adaptive scheme can give advantages in practical applications when a conservatively large dither implies unnecessary high energy to optimise a cost function corrupted by non-uniform state-dependent disturbances. Numerical comparisons confirm the superior performance of the proposed solution.
EdtClust: A fast homologous protein sequences clustering method based on edit distance

Homology among protein sequences plays a crucial role in evolutionary and comparative biology, with a key step involving retrieving homologous sequences of a query or clustering homologous protein sequences within a dataset. However, the growing size of known protein sequences due to advances in sequencing technology has made homologous protein sequence clustering an increasingly challenging task. Current feasible solutions employ locality-sensitive hashing to achieve linear time complexity for sequence clustering. Nonetheless, existing methods do not directly apply this hashing technique based on the foundational concept of edit distance, which precisely defines homology. To address this gap, this paper introduces EdtClust, which aims to design a specific instance of locality-sensitive hashing for edit distance, enhancing clustering efficiency while maintaining a foundation in homology’s essence. EdtClust comprises two core steps: first, utilizing the gapped k-mer embedding framework, it transforms the metric space derived from protein sequences and edit distances into a novel metric space containing gapped k-mer sets and L1 distances. Subsequently, a locality-sensitive hashing technique based on the Cauchy distribution is implemented to expedite clustering in this new metric space. Through rigorous theoretical analysis, EdtClust demonstrates enhanced precision and efficiency, while also ensuring a simple and swift implementation. Experimental results using real protein sequences validate that EdtClust can identify more homologous sequence pairs in a shorter time with reduced memory consumption compared to state-of-the-art algorithms like Linclust and ALFATClust, thereby confirming its substantial potential in the field of computational biology.
The transformation of seabed seismic

Seabed seismic surveys have been part of the hydrocarbon exploration industry for many decades. Initial implementations deployed cables which were populated with hydrophones and generally used for shallow water and transition zone projects. The cables were directly connected to a recording vessel to power the in-sea hardware, manage the spread and record the sensor measurements. Barr et al. in the 1980s implemented a technique, first postulated in 1954 by Haggerty and Backus, which uses a dual component measurement (pressure and vertical particle velocity) to eliminate the receiver side ghost, extending the reach of the technique into deeper waters. The potential of the seabed technique was further extended to make a measurement of the shear reflectivity through the addition of horizontal particle velocity components (four-component recording or 4-C). In the late 1990s the industry started experimenting with buried seafloor cables as part of permanent seismic installations to assist in production monitoring of the reservoir. As well as ocean bottom cable (OBC) systems, it is possible to make a seabed seismic measurement with an array of ocean bottom nodes (OBN). An ocean bottom node is an autonomous recording device with a self-contained recording system, clock and battery. As there is no connection with the surface, there is no limitation on length of the receiver line, no downtime due to telemetry/power line failures and no lost time associated with moving of the recording vessel. Ocean bottom nodes have been in use for many decades, but their use had primarily been limited to long-offset refraction surveys for studies of earth tectonics. The majority of the early node technologies were developed in academia and were neither industrially engineered nor designed with a focus on operational efficiency. After early development by Statoil in the 1990s, the first seismic reflection ocean bottom node survey was acquired in 2004 over Pemex’s Cantarell Field in the Gulf of Mexico. Ocean bottom nodes, when used for hydrocarbon exploration, were initially deployed by remotely operated vehicles (ROVs), but more recently ropes or wires have also been employed.
Implementation of Model Pruning Methods for Resnet

The field of intelligent manufacturing is developing rapidly, with the number of network edge devices and the data generated growing rapidly. Technology of intelligent edge monitoring for surface defects has emerged, but the resources of edge devices are limited, making it difficult to deploy deep neural network models. This article proposes to use model pruning method to compress the network structure in response to this issue and implements the proposed method on ResNet network. The model reduces the number of parameters to 1/5 of the original while ensuring detection accuracy, and improves inference speed by 50%, ensuring the realtime detection of edge convolutional neural networks and improving the efficiency of varistor defect classification.
Human Perception Mechanism based CNN-RNN Framework for Dimensional Emotion Recognition

In this paper, we propose human perception mechanism based CNN-RNN based approach to recognize dimensional emotion using Aff-Wild dataset. The proposed model recognizes dimensional expression through two steps. First, motivated by the human facial expression perception mechanism, the patches were extracted based on facial key points. Second, the facial expression was classified based on CNN-RNN model using extracted patches. The mean concordance correlation coefficient (CCC) of the proposed model is 0.322, indicating superior performance compared to the original CNN-RNN model, which achieved a mean CCC of 0.283.
An unsupervised deep learning framework for gene regulatory network inference from single-cell expression data

Recent advances in single-cell RNA sequencing (scRNA-seq) technology provides unprecedented opportunities for reconstruction gene regulation networks (GRNs). At present, many different models have been proposed to infer GRN from a large number of RNA-seq data, but most deep learning models use a priori gene regulatory network to infer potential GRNs. It is a challenge to reconstruct GRNs from scRNA-seq data due to the noise and sparsity introduced by the dropout effect. Here, we propose GAALink, a novel unsupervised deep learning method. It first constructs the gene similarity matrix and then refines it by threshold value. It then learns feature representations of genes through a graphical attention autoencoder that propagates information across genes with different weights. Finally, we use gene feature expression for matrix completion such that the GRNs are reconstructed. Compared with seven existing GRNs reconstruction methods, GAALink achieves more accurate performance on seven scRNA-seq dataset with four ground truth networks. GAALink can provide a useful tool for inferring GRNs for scRNA-seq expression data.
Initialization-Free Distributed Constrained Optimization Algorithms with a Pre-Specified Time

The distributed constrained optimization problem over an undirected communication topology is investigated in this study. It focuses on addressing a global coupled equality constraint that applies to all agents. To tackle this problem, a distributed approach with arbitrary initialization is developed by virtue of the aperiodic sampling control idea and the consensus-based multi-agent system(MAS) technology. This approach is developed to address constrained optimization problems within a pre-specified time. In addition, this predefined time is freely defined by users and irrelevant to the initial states, control coefficients, and network structure of systems. The Lyapunov stability theory completes the convergence proof of the developed method. Then, the developed method is extended to handle distributed nonlinear constrained optimization problems. Finally, The availability of two developed methods is demonstrated through two simulation examples.
Adaptive Anti-swing Control for Clasping Operations in Quadrotors with Cable-suspended Payload

Crucial phases in aerial transportation and delivery of suspended payloads are the clasping and unclasping of the payload to the cable. During these phases, along with the uncertainties in the quadrotor and in the environment, the inevitable payload swings induced by the human interaction or by other external interaction will create additional state-dependent uncertainties; such uncertainties pose a significant challenge in terms of control. If they continue unabated, these uncertainties can cause safety hazard for the quadrotor, the payload and, most importantly, for the human operating the clasping/unclasping tasks. As the state-of-the-art adaptive controllers cannot tackle such uncertainties or considers them as bounded terms, this paper presents an adaptive anti-swing controller where all uncertainties are taken in a state-dependent form. This choice is made to better capture uncertain clasping and unclasping operations of the suspended payload. The closed-loop stability is studied analytically and the real-time experiments confirm significant performance improvements for the proposed scheme over the state of the art.
Investigating the Generalizability of Physiological Characteristics of Anxiety

Recent works have demonstrated the effectiveness of machine learning (ML) techniques in detecting anxiety and stress using physiological signals, but it is unclear whether ML models are learning physiological features specific to stress. To address this ambiguity, we evaluated the generalizability of physiological features that have been shown to be correlated with anxiety and stress to high-arousal emotions. Specifically, we examine features extracted from electrocardiogram (ECG) and electrodermal (EDA) signals from the following three datasets: Anxiety Phases Dataset (APD), Wearable Stress and Affect Detection (WESAD), and the Continuously Annotated Signals of Emotion (CASE) dataset. We aim to understand whether these features are specific to anxiety or general to other high-arousal emotions through a statistical regression analysis, in addition to a within-corpus, cross-corpus, and leave-one-corpus-out cross-validation across instances of stress and arousal. We used the following classifiers: Support Vector Machines, LightGBM, Random Forest, XGBoost, and an ensemble of the aforementioned models. We found that models trained on an arousal dataset perform relatively well on a previously unseen stress dataset, and vice versa. Our experimental results suggest that the evaluated models may be identifying emotional arousal instead of stress. This work is the first cross-corpus evaluation across stress and arousal from ECG and EDA signals, contributing new findings about the generalizability of stress detection.