Miracle-dz/UniFAR-FTUs huggingface dataset:
Data-Driven Synthesis of Safety Controllers for Partially-Observable Systems with Unknown Models

This paper is concerned with the formal synthesis of safety controllers for partially-observable discrete-time control systems with unknown mathematical models. Given a state estimator with unknown dynamics but a known upper bound on the estimation error, we propose a data-driven approach to compute controllers that render the partially-observable systems with unknown dynamics safe. Our proposed method is based on the construction of control barrier certificates, where we first formulate the barrier-based safety problem as a robust program (RP). The proposed RP is not tractable since the unknown model of the estimator appears in one of its constraints. To tackle this issue, we collect a set of data from the black-box system and its estimator and replace the original RP with a scenario program (SP). Due to the existence of a max-min constraint in the SP, we construct an analogous scenario program, denoted by SPα, in which the max-min constraint is replaced with a single inequality constraint. The control barrier certificates together with their corresponding controllers can then be computed by solving SPα via the collected data. By connecting the feasible solutions of SPα and SP, the safety of the partially-observable system equipped with the synthesized controller can be guaranteed with 100% confidence. We show the effectiveness of our results by synthesizing a safety controller for a partially-observable Van der Pol oscillator with unknown dynamics.
A Sparsity-driven tinyML Accelerator for Decoding Hand Kinematics in Brain-Computer Interfaces

Despite advances in understanding the mechanisms of movement disorders, controlling voluntary movements remains challenging, with limited treatment options. However, the integration of machine learning (ML) accelerators into the braincomputer interface (BCI) pipeline offers promising solutions by harnessing the capabilities of ML algorithms to decode intended motor actions accurately. While there have been numerous efforts to classify intended hand movements into discrete classes, the challenging problem of decoding continuous hand kinematics has seen limited research efforts. Our work focuses on tackling this challenge using RAMAN, an energy-efficient tinyML accelerator. We demonstrate the successful decoding of macaque hand kinematics from the MC_Maze dataset on the Efinix Ti60 FPGA with RAMAN architecture. Our approach achieves an R2-score of 0.91 across 108 different maze configurations with a significantly low memory footprint of 230 KB, a latency of 86.7 ms while consuming 52.23 mW of power at a 5 MHz clock rate and 5.78 ms latency with the peak power efficiency of 102.34 GOp/s/W at 75 MHz and 75% pruning.
Shape-aware contrastive deep supervision for esophageal tumor segmentation from CT scans

Accurate tumor segmentation is crucial for esophageal cancer radiotherapy treatment planning. The low contrast among the esophagus, tumors, and surrounding tissues, and irregular tumor shapes limit the performance of automatic segmentation methods. In this paper, we aim to exploit the irregular shapes of tumors to facilitate accurate segmentation. We propose a simple and pluggable shape-aware contrastive deep supervision network (SCDSNet) with shape-aware regularization and voxel-to-voxel contrastive deep supervision. Specifically, the shape-aware regularization with an uncertainty minimization strategy encourages the precise predictions of an additional shape-aware head. The voxel-to-voxel contrastive deep supervision enhances the multi-scale shape-tumor contrast for better voxel-to-voxel prediction of shapes. The proposed method is simple and highly pluggable, which can easily be extended to other frameworks. Further, we establish a large in-house dataset on esophageal cancer to validate the effectiveness of our proposed method. The quantitative and qualitative experimental results demonstrate the effectiveness of SCDSNet on the esophageal cancer dataset.
Road condition assessment by OBIA and feature selection techniques using very high-resolution WorldView-2 imagery

Abstract Accurate information on the conditions of road asphalt is necessary for economic development and transportation management. In this study, object-based image analysis (OBIA) rule-sets are proposed based on feature selection technique to extract road asphalt conditions (good and poor) using WorldView-2 (WV-2) satellite data. Different feature selection techniques, including support vector machine (SVM), random forest (RF) and chi-square (CHI) are evaluated to indicate the most effective algorithm to identify the best set of OBIA attributes (spatial, spectral, textural and colour). The chi-square algorithm outperformed SVM and RF techniques. The classification result based on CHI algorithm achieved an overall accuracy of 83.19% for the training image (first site). Furthermore, the proposed model was used to examine its performance in different areas; and it achieved accuracy levels of 83.44, 87.80 and 80.26% for the different selected areas. Therefore, the selected method can be potentially useful for detecting road conditions based on WV-2 images.
Logistics and Distribution Path Optimization Model Based on Hybrid Intelligent Algorithm

Logistics mode is crucial to goods-car combination logistics. Over 80% of India's logistics activities use finished vehicle logistics. Road transport completes India's logistics. Vehicle logistics lacks a transport method, therefore transport batches are small and repeat batches of considerable issues are rare. This means India's automotive logistics system has more cost-control room for improvement than Western countries'. Many modes of transportation can be used to cut costs, or the logistics and distribution paths can be optimised to avoid logistical flaws and make cost cuts easier. This article examines B company's vehicle logistics activity. Combine this with path optimisation to create the hybrid intelligent algorithm-based logistics and distribution path optimisation model. Finally, hybrid intelligent algorithm calculation technique verifies model and calculation method scientificity and validity. The paper ends here.
Closed-Loop Neighboring Extremal Optimal Control Using HJ Equation

This study introduces a method to obtain a neighboring extremal optimal control (NEOC) solution for a broad class of nonlinear systems with nonquadratic performance indices by investigating the variation to a known closed-loop optimal control law caused by small, known variations in the system parameters or in the performance index. The NEOC solution can formally be obtained by solving a linear partial differential equation similar to those arising in an iterative solution procedure for a nonlinear Hamilton-Jacobi equation. Motivated by numerical procedures for solving such an equation, we also propose a numerical algorithm based on the Galerkin algorithm that uses basis functions to solve the underlying Hamilton-Jacobi equation. This approach allows the determination of the minimum performance index as a function of both the system state and parameters and extends to allow the determination of the adjustment to an optimal control law given a small adjustment of parameters in the system or the performance index, effectively by computing the derivative of the law with respect to those parameters. The validity of the claims and theory is supported by numerical simulations.
Rehabilitation Following Anterior Cruciate Ligament and Posterolateral Corner Reconstruction with Medial and Lateral Meniscus Repairs in A High School Athlete: A Retrospective Case Report

Background and Purpose: Complex and extensive knee injury in athletes pose a unique challenge in rehabilitation. The purpose of this case report was to describe the rehabilitation of a high school athlete who suffered a sport contact injury damaging the anterior cruciate ligament (ACL), medial and lateral menisci, and postero-lateral knee compartment. Methods: The patient was a 17-year-old male athlete who underwent right ACL reconstruction, medial and lateral menisci repair, and posterolateral knee compartment reconstruction. Seven days postoperatively at initial evaluation he ambulated toe-touch weight bearing, using axillary crutches, with the knee immobilized by a brace locked at 0° extension. Passive knee range of motion (ROM) was 5° to 60°, with strength in available range graded 2/5 via manual muscle test (MMT). The patient was treated for 7 months for a total of 54 sessions. Physical therapy focused on passive and active ROM, therapeutic exercise (progressing from basic to sport specific), neuromuscular re-education, manual therapy, and modalities. Findings: At discharge passive ROM was +1-130°, active ROM 0° to 128°, and MMT of all R knee motions were 5/5. From 4 months to discharge isokinetic testing (60°/sec) of peak torque hamstrings to quadriceps ratio increased bilaterally (R LE: 43% to 61%; L LE: 57% to 72%). At discharge isokinetic knee extension peak torque-body mass ratio (180°/sec) was 66% right (R) lower extremity (LE) and 67% left (L) LE, all tibiofemoral stability tests were negative, and the patient returned to sport. Clinical Relevance: A 7-month progressive multi-modal rehabilitation plan was able to return a young athlete to sport following a complex and extensive knee injury. Conclu-sion: A progressive rehabilitation plan following extensive reconstructive repair of a complex knee injury was successful in returning an athlete to sport in a short 7-month timeframe.
A Study on the Factors Affecting Infants' Health-Related Issues and Child Mortality using Machine Learning

Child mortality and infant health-related issues remain significant challenges worldwide. Understanding the factors that influence these outcomes is crucial for implementing effective interventions and improving child health outcomes. In this study, we employ machine learning techniques to identify and analyze the key factors affecting infants' health-related issues and child mortality. Further, we identify several significant factors that influence infants' health-related issues and child mortality. These factors include maternal health indicators, access to healthcare services, socioeconomic status, environmental factors, and demographic characteristics. The machine learning models provide insights into the relative importance of these factors, enabling policymakers and healthcare professionals to prioritize interventions and allocate resources effectively. Additionally, we investigate the potential interaction effects among these factors and their impact on child health outcomes. This analysis helps in understanding the complex relationships and causal pathways involved in infants' health-related issues and child mortality. The findings of this study contribute to the existing knowledge by leveraging machine learning techniques to identify and analyze the factors affecting infants' health-related issues and child mortality. The insights gained from this research can inform evidence-based policies and interventions aimed at reducing child mortality rates and improving infant health outcomes globally. By addressing the underlying factors identified through this study, we can work towards achieving better health outcomes for infants and reducing the burden of child mortality worldwide.
Examining the impact of Artificial Intelligence methods on Intrusion Detection with the NSL-KDD dataset

This paper examines the prominent and challenging concern of ensuring Cybersecurity in contemporary information technology (IT) infrastructures. The rapid advancement of technology has given rise to a new breed of hackers who are constantly developing sophisticated and dangerous malware attacks. Consequently, the task of detecting and preventing these intrusions has become increasingly difficult. This study examines the challenges faced by current standard analytic techniques in detecting and mitigating potential dangers in the given setting. This paper presents a research study on the development of an intelligent intrusion detection system (IDS) using statistical analysis and autoencoder (AE) techniques. This paper proposes an Intrusion Detection System (IDS) that leverages data analytics, statistical methods, and advancements in artificial intelligence (AI) to extract enhanced and interconnected features. This research paper evaluates the validity of the suggested Intrusion Detection Systems (IDS) using the NSL-KDD database as a benchmark.
Navigating Sex-Specific Disease Dynamics in Incident Dementia

Dementia is among the leading causes of cognitive and functional loss and disability in older adults. Past studies suggested sex differences in health conditions and progression of cognitive decline. Existing studies on the temporal trajectory of health conditions for patient characterization after dementia diagnosis are scarce and ambiguous. Thus, there's limited and unclear research on how health conditions change over time after a dementia diagnosis. To this end, we aim to analyze the shift in medical conditions and examine sex-specific changes in patterns of chronic health conditions after dementia diagnosis. We centered our analysis on a 15-year window around the point of dementia diagnosis, encompassing the 5 years leading up to the diagnosis and the 10 years following it. We introduce (i) MedMet, a network metric to quantify the contribution of each medical condition, and (ii) growth and decay function for temporal trajectory analysis of medical conditions. Our experiments demonstrate that certain health conditions are more prevalent among females than males. Thus, our findings underscore the pressing need to examine differences between men and women, which could be important for healthcare utilization after a dementia diagnosis.
MEEG-Transformer: Transformer Network based on Multi-domain EEG for Emotion Recognition

Emotion recognition is a trending topic for research in the area of the brain computer interface (BCI). As an effective signal source, EEG(Electroencephalogram) is widely used in emotion recognition tasks, from which multiple features can be extracted in different domains, such as time domain and frequency domain. However, how to make full use of multiple domain features has become a challenge. In this study, we propose a transformer network for emotion recognition based on Multi-domain EEG features, named MEEG-Transformer. MEEG-Transformer can effectively capture the spatial information with the convolution layer, mine unique information within each domain, and explore the complementary information between features from different domains using self-attention mechanism. Specifically, we extract the features of time domain, frequency domain and wavelet domain respectively, construct the two-dimensional feature matrix of three domains based on the 10-20 system, and merge the three matrices into multi-domain EEG features. Using the DEAP dataset to perform experiments, the proposed model achieves 96.8% and 96.0% recognition accuracy in the arousal and valence dimensions respectively. It is indicated that the proposed method has a strong inspiration for emotion recognition tasks.
Emotion and Stress Recognition Utilizing Galvanic Skin Response and Wearable Technology: A Real-time Approach for Mental Health Care

In modern society, people are exposed to various stressors and negative emotions daily and they may cause mental and physical diseases such as depression, anxiety, high blood pressure, heart attacks, and stroke. Therefore, this paper delves into the potential of modern wearable technologies as a tool for real-time health monitoring. The advent of ubiquitous sensing has ushered in an era where physiological and behavioral measurements can be continuously recorded in daily life. One significant physiological marker is the Galvanic Skin Response (GSR), which exhibits noteworthy changes under different emotional states. We propose a machine learning-based emotion recognition framework. It includes a preprocessing stage that eliminates noise and extracts 87 features from the GSR data. To account for individual differences in physiological responses, we also introduce a novel normalization procedure per subject. Finally, a subset of dominant and discriminative features enhances the proposed framework’s performance. We conducted experiments on two datasets, the wearable stress and affect detection dataset (WESAD) for stress detection, and the multimodal MAHNOB-HCI dataset for emotion recognition. The results show that the Leave-One-Out method is capable of detecting stress with 97.03% accuracy. Moreover, the proposed method classifies arousal and valence with an accuracy of 82.20% and 82.57%, respectively.
The improved low cost grid connected EV charging station with PV and energy storage systems

Recently electric vehicles (EVs) have increasingly been used for transportation due to low cost operation and less carbon emission. However, major disadvantages of EVs are charging time, range and overloading the grid, and the latter may lead to instability in the grid when a vast number of EVs are simultaneously charged from the grid. One solution to this may be to charging times and to share amount of charging power through photovoltaic (PV) and energy storage (ES) systems with minimum cost. In this study, a 20 kW grid tied charging station with PV and ES systems is designed to charge EVs with minimum cost for the hourly changing electricity price through a variety of charge options such as grid to EV, ES to EV, PV to EV, EV to EV etc. This is achieved by optimizing charge start times and facilities using metaheuristic based computational algorithms. The proposed approach worked well and results obtained are encouraging and meaningful for the case study
Survey of Multimedia Information Mining Techniques Based on Deep Learning in the Context of Crime Domain

The rapid expansion of multimedia data has made multimedia data mining a crucial area of research in the field of crime. In the realm of crime, the significance of multimedia data mining lies in its ability to reveal hidden patterns, correlations, and knowledge within criminal activities, providing powerful support for crime investigation and prevention. For instance, through similarity search, multimedia data relevant to criminal suspects can be swiftly identified. By identifying associations and connections between criminal organizations, criminal networks can be discovered. Through entity resolution and classification, individuals, locations, and items involved in criminal events can be automatically identified. The automatic feature learning and large-scale data processing capabilities of deep learning make it closely intertwined with data mining. Therefore, this paper summarizes the applications of deep learning-based multimedia data mining techniques in the field of crime since 2018, along with the compilation of relevant crime datasets and evaluation metrics. Additionally, it outlines the challenges present in this domain and provides an outlook on future developments.
KDProg: A Knowledge distillation graph neural network for cancer prognosis prediction and analysis

Accurately predicting cancer prognosis remains challenging, owing to the combination of computational and practical challenges. This study proposes KDProg, a knowledge distillation-based graph learning framework for predicting cancer prognosis and exploring downstream tasks. The framework includes a novel feature distillation paradigm that compresses a multi-layer complex teacher model to a single-layer simple student by using the teacher model’s middle-layer feature representations and outputs as supervision information to improve the student model’s performance. In addition, instead of introducing a unified temperature hyperparameter, KDProg adopts a novel strategy to parameterize the distillation temperature and combine it with the Cox partial log-likelihood function. So the model can learn the appropriate temperature. Furthermore, considering multi-omics data of patients are often complex to obtain in practical cancer prognosis, this paper uses different input data for the teacher and student models, respectively. The input data for the teacher model are multi-omics data (mRNA, CNV, and DNA methylation), clinical data, and KEGG pathways. The input data for the student model are mRNA, clinical data, and KEGG pathways. Extensive experiments on 15 real-world datasets from TCGA demonstrated the effectiveness and efficiency of the proposed method in predicting cancer prognosis. The results suggest that the proposed model can guide clinical decision-making.
Predicting Tumor Mutation Burden of Lung Cancer Based on Residual Network

Medical studies have found that Tumor Mutation Burden (TMB) is positively correlated with the efficacy of immunotherapy for Non-Small Cell Lung Cancer (NSCLC), and TMB value can predict the efficacy of targeted therapy and chemotherapy. However, the calculation of TMB value mainly depends on the Whole Exon Sequencing (WES) technology, which is usually expensive and takes too much time. To deal with this problem, this paper explores the connection between TMB and digital pathology images, predicting TMB using common clinical digital slices. We proposes RCA-MSAG, a deep learning model based on the Residual Coordinate Attention (RCA) structure and Multi-Scale Attention Guidance (MSAG) module, enhancing TMB prediction accuracy. The model takes ResNet-50, integrating Coordinate Attention (CA) and MSAG to extract critical information from lung cancer pathology. Using The Cancer Genome Atlas (TCGA) dataset, the model achieves 96.2% accuracy, 96.4% precision, 96.2% recall, and a 96.3% F1 score, outperforming mainstream models. This model shows promise in aiding clinical diagnoses and guiding TMB predictions.
Quasi-Static Mechanical Manipulation as an Optimal Process

This work focuses on quasi-static manipulation of elastically-interconnected rigid bodies by an agent, e.g. a robot assembling mechanical parts. The whole system is seen as an underactuated control problem, where only certain degrees of freedom (e.g. robot end-effector) are directly controllable. Mechanical contact is regularized via nonlinear yet smooth elastic interaction giving rise to a smooth total potential energy. The squared-Hessian of such a potential is used as optimality measure and quasi-static mechanical manipulation is rephrased as optimal path planning on the manifold of mechanical equilibria. A simple example of an elastically-driven inverted pendulum is presented as a toy model. Numerical implementation as a minimum path on graphs is also described.