Miracle-dz/UniFAR-FTUs huggingface dataset:
Active Genetic Learning with Evidential Uncertainty for Identifying Mushroom Toxicity

Mushroom's classification as edible or poisonous is an important problem that can have a direct impact on hu-man life. However, most of the existing works do not in-clude model uncertainty in their analysis and suffer from over-confidence issue. To solve this problem, we propose a learning framework, called deep active genetic with evi-dential uncertainty (DAG-EU), to model the uncertainty of the class probability to classify mushrooms. The framework selects the data points with high uncertainty and the most influencing features by using genetic algorithms. The ex-perimental results on the mushrooms dataset demonstrate that the proposed framework can improve the model classi-fication accuracy by 2.3% compared to the methods in the same domain. Moreover, it outperforms the other models from literature by 3.6%.
A Secured Scheme for Optimal Navigation of a Berthing Ship

The immense growth of the marine industry has led to increased maritime commerce in recent years. This has resulted in a rise in maritime activities and challenges in the security of communication between entities involved in a ship’s port navigation. In response to the challenges, the suggested security strategy in this work can provide vital security features such as authentication between communicating entities and important security services while consuming fewer resources. This technique also calculates the ideal distance between the berthing ship and the available parking site using their geographic coordinates to provide optimal navigation for a berthing ship. An external entity known as the trusted third party is in charge of calculating the optimum path, which works as a middleman in the communication process. The suggested technique is resistant to a variety of security attacks, these are Man-in-the-Middle attack, Replay attack, information leakage, and impersonation attack. This scheme also assures unlinkability and anonymity. The Automated Validation of Internet Security Protocols and Applications (AVISPA) software is utilized to formally verify the proposed scheme’s safety.
Spatial Considerations for Implementing Two Direct-to-Consumer Food Models in Two States

To open new markets, some farmers have adapted direct-to-consumer (DTC) models, such as Community Supported Agriculture (CSA), to reach new settings or audiences. We compared sociodemographic and geospatial contexts to farmers’ experience with one of two DTC innovations: a cost-offset CSA for low-income families and food boxes distributed through rural convenience stores. We geocoded addresses of thirteen farms and DTC pickup sites in two U.S. states (Vermont and Washington) and calculated road network distances from pickup to supermarket, farmers’ market, and farm. We compiled Census block-level demographic and transportation data, and compared it to postseason interviews to explore the effect of suitability of the pickup location; proximity to food retail; and potential farmer burden. Most pickup areas were heavily car-dependent, with low walkability and few public transportation options. Conventional sources of fresh produce were within six miles of most pickups, but farmers markets were further away. Despite modest profitability, both models were deemed worth pursuing, as they expanded farmers’ customer base. Farmers implementing the store-distributed food box were sensitive to market trends and customer needs in choosing pickup location. Farmers seemed more concerned with marketing in convenience store settings, and finding efficient ways to conduct recordkeeping than with delivery distances.
Walking and running cadence estimation using a single trunk-fixed accelerometer for daily physical activities assessment

Accurate assessment of the type, duration, and intensity of physical activity (PA) in daily life is considered very important because of the close relationship between PA level, health, and well-being. Therefore, the assessment of PA using lightweight wearable sensors has gained interest in recent years. In particular, the use of activity monitors could help to measure the health-related effects of specific PA interventions. Our study, named as Run4Vit, focuses on evaluating the acute and longterm effects of an eight-week running intervention on PA behaviour and vitality. To achieve this goal, we developed an algorithm to detect running and estimate instantaneous cadence using a single trunk-fixed accelerometer. Cadence was computed using time and frequency domain approaches. Validation was performed over a wide range of locomotion speeds using an open-source gait database. Across all subjects, the cadence estimation algorithms achieved a mean bias and precision of - 0.01 ± 0.69 steps/min for the temporal method and 0.02 ± 1.33 steps/min for the frequency method. The running detection algorithm demonstrated very good performance, with an accuracy of 98% and a precision superior to 99%. These algorithms could be used to extract metrics related to the multiple dimensions of PA, and provide reliable outcome measures for the Run4Vit longitudinal running intervention program. Clinical Relevance— This work aims at validating a multi-dimensional physical activity (PA) classification algorithm for assessing the acute and long-term effects of eight weeks running intervention on PA behaviours and vitality
Decadal‐scale variability in hazardous winds in northern Switzerland since end of the 19th century

The decadal‐scale variability in winter hazardous winds in northern Switzerland from 1871 to present is investigated in the Twentieth Century Reanalysis (20CR). Independent wind speed measurements taken at Zurich climate station show that the interannual and decadal variability in hazardous winds in northern Switzerland is realistically represented in the 20CR. Both time series exhibit pronounced decadal‐scale variability with periods between approximately 36 and 47 years. At these periodicities, the hazardous wind variability in northern Switzerland is positively correlated with the variability in the North Atlantic Oscillation, however the strength and statistical significance of their co‐variability varies over time.
Label Alignment Improves EEG-based Machine Learning-based Classification of Traumatic Brain Injury

Machine learning and deep learning algorithms have paved the way for improved analysis of biomedical data which has led to a better understanding of various biological conditions. However, one major hindrance to leveraging the potential of machine learning models is the requirement of huge datasets. In the biomedical domain, this becomes extremely difficult due to uncertainties in collecting high-quality data as well as, in the case of human subjects data, privacy. Further, when it comes to biomedical data, inter-subject variability has been a long-entrenched issue. The data obtained from different individuals will differ to a considerable extent that it becomes difficult to find population differences in small datasets. In this work, we investigate the use of label alignment techniques on an EEG-based Traumatic Brain Injury (TBI) classification task to overcome inter-subject variability, thereby increasing the classification accuracy. We show an increase in accuracy of around 6% in some cases as compared to our previous results. In the end, we also propose a methodology to incorporate TBI data from a different species (e.g., mice) after domain adaptation, which might further improve the performance by increasing the amount of training datasets available for the classification model.
Multi-Constrained Embedding for Accurate Community Detection on Undirected Networks

A Symmetric Non-negative Matrix Factorization (SNMF)-based network embedding model adopts a unique Latent Factor (LF) matrix for describing the symmetry of an undirected network, which reduces its representation ability to the target network and thus resulting in accuracy loss when performing community detection. To address this issue, this paper proposes a new undirected network embedding model, i.e., Alternating Direction Method of Multipliers (ADMM)-based, Modularity, Symmetry and Nonnegativity-constrained Embedding (AMSNE), which can be applicable to undirected, weighted or unweighted networks. It relies on two-fold ideas: a) Introducing the symmetry constraints into the model to correctly describe the symmetric of an undirected network without accuracy loss; and b) Adopting the ADMM principle to efficiently solve its constrained objective. Extensive experiments on eight real-world networks strongly evidence that the proposed AMSNE outperform several state-of-the-art models, making it suitable for real applications.
Examining the Impact of Sample Thickness Variations on the Hyperspectral Radiometric Responses of Flowing Blood

The hyperspectral reflectance and transmittance of flowing blood samples are employed in a wide range of biomedical applied research initiatives such as the detection and monitoring of hematological abnormalities. The success of these initiatives is tied to the correct interpretation of these radiometric quantities. This, in turn, requires a comprehensive understanding about their sensitivity to variations in the experimental conditions in which they have been obtained. In this paper, we aim to contribute to these efforts by systematically examining the effects of sample thickness variations on these quantities. More specifically, we employed controlled in silico experiments to assess these effects on samples with different biophysical characteristics, notably their hematocrit, hemolysis level and orientation of their constituent cells with respect to the flow direction. To ensure a high degree of fidelity in our experiments, we used a first-principles simulation framework supported by measured data. Our findings unveil distinct spectrally-dependent trends associated with reflectance and transmittance changes elicited by sample thickness variations.
A System-on-Board Integrated Multi-analyte PoC Biosensor for Combined Analysis of Saliva and Exhaled Breath

The need for oral health monitoring Point of Care (PoC) systems is ever growing. This is effectively highlighted by the ongoing COVID-19 pandemic where the lack of rapid PoC testing has placed an unsustainable burden on centralized laboratory testing. Urgent development has furthered pathogenic nucleic acid and antibody detection in oral samples throat swabs, but without corresponding advancements in biochemical monitoring through oral biosensing. We have recently reported two novel biosensor technologies for detection of high impact hormones: cortisol in saliva by organic electrolyte gated FETs (OEGFETs), and 8-isoprostane in exhaled breath condensate (EBC) using molecularly imprinted electroimpedance spectroscopy biosensors (MIP EIS). In this work, we report a first stage integration of the two biosensors - previously bench-top proven - with a miniaturized semi-hermetically sealed soft-fluidic enclosure, onto a low-power (<300 mW) customized printed circuit board. Our findings established comparable detection thresholds for the miniaturized board-based configuration and a lab-based test setup, and their ability to characterize, calibrate, and operate these small footprint biosensors. Testing with the 8-isoprostane EBC MIP EIS biosensors showed the system-on-board had an effective frequency range of 100-100kHz, comparable to lab bench impedance analyzers. Despite internal impedance increases of 210%, the expected data features are present in the impedance graphs collected with the PCB. The system-on-board experiments using OEGFET aptasensor showed a predictable behavior and comparable sensor detection range and resolution using unadulterated supernatant and serial dilutions of cortisol over a range of $273 \mu\mathrm{M}$ to 2.73pM. The portable, multi-analyte oral biosensor is a promising prototype for future packaging and clinical validation.
Class-distinctiveness-based frequency band selection on the Riemannian manifold for oscillatory activity-based BCIs: preliminary results

Considering user-specific settings is known to enhance Brain-Computer Interface (BCI) performances. In particular, the optimal frequency band for oscillatory activity classification is highly user-dependent and many frequency band selection methods have been developed in the past two decades. However, it is not well studied whether those conventional methods can be efficiently applied to the Riemannian BCIs, a recent family of BCI systems that utilize the non-Euclidean nature of the data unlike conventional BCI pipelines. In this paper, we proposed a novel frequency band selection method working on the Riemannian manifold. The frequency band is selected considering the class distinctiveness as quantified based on the inter-class distance and the intra-class variance on the manifold. An advantage of this method is that the frequency bandwidth can be adjusted for each individual without intensive optimization steps. In a comparative experiment using a public dataset of motor imagery-based BCI, our method showed a substantial improvement in average accuracy over both a fixed broad frequency band and a popular conventional frequency band selection method. In particular, our method substantially improved performances for subjects with initially low accuracies. This preliminary result suggests the importance of developing new user-specific setting algorithms considering the manifold properties, rather than directly applying methods developed prior to the rise of the Riemannian BCIs.
A Supervised Contrastive Learning-based Analysis of rs-tMRI Data Captures Gender Differences in Nonlinear Functional Network Coupling*

Many studies in neuroscience have focused on interpreting brain activity using functional connectivity (FC). The most widely used approach for measuring FC is based on linear correlation (e.g., the Pearson correlation), where the temporal cofluctuations between functional brain regions are computed. However, such approaches ignore nonlinear dependencies among regions that might carry distinctive information across groups of subjects. In this study, we offer a deep learning-based approach that also captures nonlinear temporal relationships between brain networks. Our approach consists of two main parts: an encoder that learns domain-specific embeddings of time courses estimated from independent component analysis (ICA) and a similarity metric that measures the similarities between the embeddings. We call such similarities as nonlinear functional relationships between networks. Our findings on a large dataset (including above 11k normal control subjects) suggest that male subjects exhibit stronger nonlinear network-network relationships than female subjects in most cases. Furthermore, we observe that, unlike FC, our approach could capture some intra-network relationships, especially between cognitive control and visual networks, which are significantly different between males and females, suggesting that our approach can provide a complementary interpretation of the functional brain activity to FC.
Xception-ResNet Autoencoder for Pneumothorax Segmentation

Computer vision has made a significant advance in the medical imaging. Pneumothorax is a severe ailment that can be fatal if the patient does not receive proper care. The main way for diagnosing Pneumothorax is by examining the Chest X-ray by a specialist. The urge of experienced radiologists to anticipate whether someone is suffering from pneumothorax or not by examining chest X-rays is indisputable. Such a facility is not available to everyone. Furthermore, in some circumstances, quick diagnosis is required. In this paper We present a deep learning based image segmentation model capable of predicting Pneumothorax cases by localizing it in chest x-ray exam to help the doctor in making this critical choice. Deep Learning has demonstrated its value in multiple domains, outperforming several state-of-the-arts methods. We seek to overcome this challenge by leveraging deep learning capabilities. We used U-Net architecture with Xception as the encoder and ResNet as a decoder. We obtained encouraging findings, and U-Net works exceptionally well in medical imaging. Our work is listed with in as semantic segmentation. With 77.8 (±0.15), our technique obtains a good outcome in terms of Intersection over Union.
Improved Grip Force Prediction Using a Loss Function that Penalizes Reward Related Neural Information

Neural activity in the sensorimotor cortices has been previously shown to correlate with kinematics, kinetics, and non-sensorimotor variables, such as reward. In this work, we compare the grip force offline Brain Machine Interface (BMI) prediction performance, of a simple artificial neural network (ANN), under two loss functions: the standard mean squared error (MSE) and a modified reward penalized mean squared error (RP_MSE), which penalizes for correlation between reward and grip force. Our results show that the ANN performs significantly better under the RP_MSE loss function in three brain regions: dorsal premotor cortex (PMd), primary motor cortex (M1) and the primary somatosensory cortex (S1) by approximately 6%.
A Comparative Study on the Potential of Unsupervised Deep Learning-based Feature Selection in Radiomics

In Radiomics, deep learning-based systems for medical image analysis play an increasing role. However, due to the better explainability, feature-based systems are still preferred, especially by physicians. Often, high-dimensional data and low sample size pose different challenges (e.g. increased risk of overfitting) to machine learning systems. By removing irrelevant and redundant features from the data, feature selection is an effective way of pre-processing. The research in this study is focused on unsupervised deep learning-based methods for feature selection. Five recently proposed algorithms are compared regarding their applicability and efficiency on seven data sets in three different sample applications. It was found that deep learning-based feature selection leads to improved classification results compared to conventional methods, especially for small feature subsets. Clinical Relevance - The exploration of distinctive features and the ability to rank their importance without the need for outcome information is a potential field of application for unsupervised feature selection methods. Especially in multiparametric radiology, the number of features is increasing. The identification of new potential biomarkers is important both for treatment and prevention.
Integer Convex Minimization in Low Dimensions

In this dissertation we discuss several approaches to solve integer and mixedinteger convex minimization problems. That is, we try to minimize a convex function over a convex set with the additional constraint that a small number variables must be integral. The thesis consists of four parts. In the first part we apply the Mirror-Descent Method from continuous convex optimization to the mixed-integer setting. The main feature of this method is that the number of iterations is independent of the dimension, however, this method relies on a strong oracle, the so called improvement oracle. We present an efficient realization of such an oracle for the case when only two variables are required to be integral. The second part contains two alternative, short, and geometrically motivated proofs of the well known result that minimizing a convex function over the integral points of a bounded convex set is polynomial in fixed dimension. In particular, we present an oracle-polynomial algorithm that is based on a mixed-integer linear optimization oracle. Then, in the third part, we extend the Method of Centers of Gravity to the integer and mixed-integer setting. The crucial step consists in replacing the points of center of gravity by more general center-points, allowing us to use measures other than the volume. We introduce the concepts of center-points and approximate center-points. For special instances we prove properties of the (approximate) center-points. In the integer setting and when the dimension is fixed, we present an algorithm to compute approximate center-points. Furthermore, we establish optimality certificates for (mixed-) integer minimization problems based on lattice free polyhedra and we present a algorithm based on center-points that terminates with such an optimality certificate. In the last part we consider a special class of, not necessarily convex, optimization problems in variable dimension. We aim to optimize f(Wx) over a set P ∩ Z, where f is a non-linear function, P ⊂ R is a polyhedron and W ∈ Zd×n. The dimension n may vary, however, we assume that the dimension d is fixed. We obtain an efficient transformation from the latter class of problems to integer linear problems. The core result is a representation
Classification of Human Balance Recovery Strategies Through Kinematic Motor Synergy Analysis

A key problem in human balance recovery lies in understanding the mechanism of balance behavior with redundant bio-mechanical motors. Motor synergy has been known as an efficient tool to analyze characteristics of motion behavior and reconstruct control command. In this paper, motor synergy analysis for different control strategies is proposed to analyze different balance motion coordination for various levels of pushing force, and understand the coordination of human multiple joints regarding balance recovery. The spatial synergy of specific joint angles for different pushing force levels exerted on the subject's back is computed with the principal component analysis (PCA) to evaluate the adaptive balance motion response patterns and illustrate the improvement of balance robustness through the switch of joint coordination. Therefore, the switch of postural coordination over multiple joints in balance recovery movements was analyzed to better understand the mechanism of balance strategy generation in this study.
Residual Multilayer Perceptrons for Genotype-Guided Recurrence Prediction of Non-Small Cell Lung Cancer

Non-small cell lung cancer (NSCLC) is a malignant tumor with high morbidity and mortality, with a high recurrence rate after surgery, which directly affects the life and health of patients. Recently, many studies are based on Computed Tomography (CT) images. They are cheap but have low accuracy. In contrast, the use of gene expression data to predict the recurrence of NSCLC has high accuracy. However, the acquisition of gene data is expensive and invasive, and cannot meet the recurrence prediction requirement of all patients. In this paper, we proposed a low-cost, high-accuracy residual multilayer perceptrons (ResMLP) recurrence prediction method. First, several proposed ResMLP modules are applied to construct a deep regression estimation model. Then, we build a mapping function of mixed features (handcrafted features and deep features) and gene data via this model. Finally, the recurrence prediction task is realized, by utilizing the gene estimation data obtained from the regression model to learn the information representation related to recurrence. The experimental results show that the proposed method has strong generalization ability and can reach 86.38% prediction accuracy. Clinical Relevance- This study improved the preoperative recurrence of NSCLC prediction accuracy from 78.61% by the conventional method to 86.38% by our proposed method using only the CT image.
Identification of electromigration failure mechanism dominated in FCBGA applications

In this paper, electro-migration (EM) reliability of Sn-3.0Ag-0.5Cu (SAC305) solder balls was studied. Large current coefficient n and activation energy Ea in Black’s equation were extracted by multi-level accelerated lifetime tests (ALTs). Liquid-solid (L-S) failure mode, rather than solid-solid (S-S) failure mode, is the dominant EM failure mechanism of SAC305 solder balls and was confirmed by failure analysis (FA). A step-by-step degradation process of SAC305 solder balls induced by L-S failure mode is also described.