Miracle-dz/UniFAR-FTUs huggingface dataset:
The Outcomes of Allogenic Bone Marrow Transplantation for BThalassemia Major Patients: Single-Centre Experience

Objectives: The Primary objective of this study is to assess the outcome of Allogenic Bone Marrow Transplantation for B- Thalassemia Major (B-TM) Patients at KFSHD, also to compare our local results with international outcomes, and to identify the complications of this modality of treatment in our cohort. Design: This is a retrospective descriptive study. It includes all patients’ less16 year transfusion-dependent, b-TM patients who underwent matched-related donor (MRD) allogeneic stem cell transplantation at KFSHD, pediatric hematology/ oncology department between January 2013 and December 2020. Total number of cohort is 21 patients. Methods: After obtaining the IRB approval, all the data and information were retrieved from the patients’ hard and electronic medical records and then computerized using a Microsoft Excel sheet. Computerized data exported to (SPSS) program updated version 24 (IBM Corp., Armonk, NY, USA) which was used for analysis of the data. Results: The results of the 21 patients with b-TM who underwent MRD allogenic BMT at KFSHD highlighted the role of MRD/BMT as curative therapy of b-TM with no increase in transplantation-related complications when compared with similar results. In our study, SOS rate was found to be 33.3%, GVHD was 23.8% but non-invasive infections were 76.2%, which is high but not influential. The overall survival (OS) was 90.5% and the disease-free survival (DFS) was 95.2%. Conclusion: Because of the perfect, promising figures of survival for stem cell transplantation in b-thalassemia major (b-TM) patients during the last decades and the noticeable improvement in the quality of their life after that, and since our study showed similar good comparable outcome and survival rates with other international centers, we strongly recommend early transplantation for b-TM if a suitable donor is available in an experienced BMT center. The overall survival (OS) was 90.5% and the disease-free survival (DFS) was 95.2%, which are comparable outcomes with other international similar centers.
PIMCOMP: A Universal Compilation Framework for Crossbar-based PIM DNN Accelerators

Crossbar-based PIM DNN accelerators can provide massively parallel in-situ operations. A specifically designed compiler is important to achieve high performance for a wide variety of DNN workloads. However, some key compilation issues such as parallelism considerations, weight replication selection, and array mapping methods have not been solved. In this work, we propose PIMCOMP - a universal compilation framework for NVM crossbar-based PIM DNN accelerators. PIMCOMP is built on an abstract PIM accelerator architecture, which is compatible with the widely used Crossbar/IMA/Tile/Chip hierarchy. On this basis, we propose four general compilation stages for crossbar-based PIM accelerators: node partitioning, weight replicating, core mapping, and dataflow scheduling. We design two compilation modes with different inter-layer pipeline granularities to support high-throughput and low-latency application scenarios, respectively. Our experimental results show that PIMCMOP yields improvements of 1.6× and 2.4× in throughput and latency, respectively, relative to PUMA.
HViT4Lung: Hybrid Vision Transformers Augmented by Transfer Learning to Enhance Lung Cancer Diagnosis

Lung cancer is the leading cause of mortality among other forms of cancer worldwide. Early and accurate recognition of lung nodules is crucial for the better life quality of patients. Although the chest Computed Tomography (CT) scan is the principal imaging procedure to evaluate and recognize lung cancer, the radiologists evaluation based on CT images is subjective and afflicted from a low accuracy compared to the post-surgery pathological tests. Computer Aided Diagnosis (CAD) has been proven to be beneficial in this context by increasing accuracy and minimizing expert involvement. Nevertheless, due to various factors including size and location inconsistency of nodules, the errorless detection of cancerous cases is still a challenge for CAD systems. Motivated by this fact, this paper presents a novel and effective method, called HViT4Lung (Hybrid Vision Transformers for Lung cancer detection), to enhance lung cancer diagnosis. The proposed deep learning-based hybrid framework combines Transformers and Convolution Neural Networks, augmented by transfer learning that extracts features from the chest CT images to detect lung nodules and predict their malignancy. The proposed pipeline of HViT4Lung is implemented with various blocks of deep learning and tested on a sample lung CT images dataset. The results of the proposed model are very promising compared to the other approaches in this field, achieving 99.20% training accuracy, 99.09% validation accuracy, and 99.09% testing accuracy for the classification of lung CT scans of 1190 images into three different classes of normal, benign, and malignant.
Determination of Optimal Battery and Renewable Capacity Investment for Hybrid Energy Storage Systems

Hybrid Energy Storage Systems (HESS) that combine renewable power sources with battery storage have gained increasing attention as a means of providing reliable and cost-effective renewable energy. However, determining the optimal battery, wind and solar capacity investments for these hybrid system remains a significant challenge. This study aims to address this challenge by proposing a comprehensive methodology that combines various factors, such as renewable forecasts, energy prices, investment costs, and battery charging/ discharging patterns, with mixed-integer linear programming model. The objective is to maximise the contribution margin available from the system configuration while matching the technical and financial criteria. The results show that the optimal investment capacity varies according to given scenarios of energy prices, wind conditions and costs. The proposed model can provide valuable insights for policymakers, investors, and energy stakeholders in promoting the development and deployment of HESS. This research also provides solutions for optimizing HESS investments and for optimal operation of battery systems.
A Study on Identifying, Categorizing and Reporting Usability Bugs and Challenges

To assess the quality, acceptability and user experience of interactive applications, usability is one of the most integral quality attributes. However, significant number of usability bugs are being experienced by the end users of interactive applications thus creating a gap between users and technology. To bridge this gap and to overcome those usability bugs and challenges, it is significantly important to clearly identify the those usability bugs along with the gaps and challenges causing them. This research study aims to identify the usability bugs being experienced by users of interactive applications and proposes a categorization taxonomy for the identified bugs. Moreover, to reach the cause of emergence of usability bugs, the study attempts to identify and report the usability gaps and challenges in existing usability practices. Furthermore, this study performs a mapping between the identified gaps and challenges in existing practices to that of the recommendations to address the identified gaps. Index Terms—Usability, interactive applications, usability bugs, user experience, user satisfaction, usability testing, user interface.
An Improved Depth Separable Convolutional Neural Networks for Efficient and Accurate Melanoma Detection

In recent years, Convolutional Neural Networks (CNNs) have achieved significant growth and outstanding success in the field of image classification. This paper explores the performance of a deep separable convolutional neural network optimized based on Google’s proposal, applied to the task of melanoma classification. In addition, several machine learning models, including k-nearest neighbors (K-NN), Gradient Boosting Decision Trees (GBDT), and Adaptive Boosting (AdaBoost), were constructed to evaluate the models from multiple dimensions for industrialisation. In addition to comparing model performance, this paper also innovatively introduces the concept of “model cost-effectiveness”, which provides reference recommendations for model selection in related field tasks considering the future deployment and application scenarios of the models. The experimental results show that the improved deep separable convolutional neural network we constructed performs more comprehensively and favorably on different indicators, while traditional learning models consume less computing and human resources.
Safe and Constrained Rendezvous, Proximity Operations, and Docking

This tutorial paper discusses the rising need for safe and constrained spacecraft Rendezvous, Proximity Operations, and Docking (RPOD). This class of problems brings with it i) a unique set of equations of motion, ii) a variety of constraints and objectives that are specialized to RPOD, and iii) a number of traditional and current Guidance, Navigation, and Control (GNC) considerations. There are strong connections between the work done in RPOD and a variety of other research domains that have synergistically aided in pushing forward the state-of-the-art. This tutorial paper discusses the above, provides an entry point into the field of spacecraft RPOD, and highlights a selection of open problems that still exist in the field.
Metaheuristic Approach for Optimizing Neural Networks Parameters in Battery State of Charge Estimation

To accurately estimate the battery state of charge (SOC), it is vital to improve the performance of a battery-powered system. This paper employs the recent proposed Evolutionary Mating Algorithm (EMA) for optimizing the weights and biases of Feed-Forward Neural Network (FNN) in estimating the state of charge (SOC) of Lithium-ion batteries. SOC estimation is the critical aspect in battery management system (BMS) to ensure the reliable operation of electric vehicles (EV) since there are no direct way to measure it. In addition, it is very nonlinear due to variation of charge/discharge currents and temperature. EMA is the recent evolutionary algorithm based on mating theory and environmental factor will be used in this paper to optimize the weights and biases of FNN on a common Li-ion battery, multiple data measurements, drive cycles and training repetitions. The performance of EMA will be compared with other algorithms to show the effectiveness of EMA in solving the SOC estimation problem. Findings of the study demonstrate the superiority of EMA in estimating the SOC of the batteries in terms of Root Mean Square Error (RMSE), mean Absolute Error (MAE) and Standard Deviation.
Thermal Comfort Control on Sustainable Building via Data-Driven Robust Model Predictive Control

While implementing renewable energy systems and model predictive control (MPC) could reduce non-renewable energy consumption, one challenge to building climate control using MPC is the weather forecast uncertainty. In this work, we propose a data-driven robust model predictive control (DDRMPC) framework to address climate control of a sustainable building with renewable hybrid energy systems under weather forecast uncertainty. The control and energy system configurations include heating, ventilation, and air conditioning, geothermal heat pump, photovoltaic panel, and electricity storage battery. Historical weather forecast and measurement data are gathered from the weather station to identify the forecast errors and for the use of uncertainty set construction. The data-driven uncertainty sets are constructed with multiple machine learning techniques, including principal component analysis with kernel density estimation, K-means clustering coupled with PCA and KDE, density-based spatial clustering of applications with noise, and the Dirichlet process mixture model. Lastly, a data-driven robust optimization problem is developed to obtain the optimal control inputs for a building with renewable energy systems. A case study on controlling a building with renewable energy systems located on the Cornell University campus is used to demonstrate the advantages of the proposed DDRMPC framework.
Unsupervised Vessel Segmentation Technique Using LTP and PCA

This paper presents a retinal vessels segmentation method than can be used by the ophthalmologists to diagnose different retinal diseases, such as thickening, thinning, or splitting. The proposed method consists of various interconnected steps. Initially, the G-channel is isolated from the input colored RGB image. Later, the Principal Component Analysis (PCA) is applied on the Green channel. Next, the Local Ternary Pattern (LTP) is used that feeds the processed image to the Morphological Filter Bank (MFB). The MFB uses various filters, such as spur, majority, and opening operations to segment the vessels image. Simulations are performed on the DRIVE and the STARE datasets. The proposed method yields mean accuracy of 94.07% and 93.45% on DRIVE and STARE datasets, respectively. Moreover, the proposed method is computationally less complex as compared to the few published methods.
Fast Stereo Visual Odometry with Point-line Features Using Improved EDLines Algorithm

Traditional point feature-based visual odometry (VO) makes it difficult to find reliable point features to estimate the camera pose in low-texture environments, resulting in a significant decrease in the positioning accuracy and robustness of the system. To address the above issues, we integrate line features into VO based on point features to improve the performance of the system in low-texture scenes. Specifically, we adopt an adaptive line feature extraction strategy based on the richness of scene texture information to solve the problem of difficulty in extracting sufficient point features in low-texture scenes while ensuring the real-time performance of the system. Then, we propose a line segment merging algorithm to improve the EDLines algorithm (LM-EDLines), making the extracted line segments more complete and improving the quality of line features. To reduce the positioning error of the system when the camera turns or changes speed sharply, we propose a motion model selection strategy based on error analysis. Finally, the experimental findings on the KITTI and EuRoC datasets demonstrate that the suggested technique outperforms previous state-of-the-art systems in terms of overall performance.
Simulation and Validation of a Precise 2RC Model of Lithium-Ion cell Incorporating Wire-bond Losses

Li-ion cell is a popular electrochemistry for energy storage devices in electric vehicles and portable electronics. This is mainly due to their relatively high energy-density and power-density compared to other types of battery chemistry. In a battery pack, several li-ion cells are interconnected in a series-parallel fashion to meet the current and voltage demands of the load. Interconnecting wires or wire-bonds add parasitic resistance to the current path, contributing to power loss. Therefore, it is essential to include wire-bond losses in the battery model to get an accurate prediction of battery performance. In this paper, the authors have developed a 2RC model of li-ion cells, taking into account the wire-bond losses. Simulation was performed in MATLAB Simulink and the simulated results were verified with hardware results obtained using a li-ion battery cycler.
Finite-Time Stability and Stabilization of Polynomial Systems

In this paper we consider the class of polynomial systems and we investigate on their finite-time stability properties. In this analysis, for the first time, finite-time stability is defined with respect to domains with polynomial bounds. A sufficient condition for finite-time stability is obtained, which can be solved recasting the feasibility problem in terms of SDP through SOS programming. Moreover, a nonlinear state-feedback control law is developed to stabilize the system in the finite-time notion. The effectiveness of the stabilizing control law is shown by a numerical example.
Online Social Robot Navigation in Indoor, Large and Crowded Environments

New robotics applications require robots to complete tasks in social spaces (i.e. environments shared with people), thus arising the necessity of enabling robots to operate in a socially acceptable manner. Some social spaces tend to be large and crowded (e.g. museums, shopping malls), which require robots to move around while showing appropriate social behaviors (e.g. not interfering with human's comfortable areas). Moving under such conditions is generally called social robot navigation, and there are different approaches to do so. Nonetheless, current approaches are mostly limited to navigate large and outdoor spaces, where both robots and people can easily avoid each other. Other approaches have been tested in indoor environments, however, the test environments tend to be small and largely empty. In this paper, we present an online social robot navigation framework, which allow robots to navigate indoor, large and crowded environments, while showing social behaviors. Our framework consists of 3 modules: 1) world modeling that incorporates a novel Social Heatmap (SH) to represent crowded areas, 2) multilayered path planning that uses sampling-based approaches, and 3) path following control. We extensively benchmark our approach against state-of-the-art approaches in challenging simulated scenarios, and $w$ e also demonstrate its feasibility with the Pepper robot in real-world trials.
A Model-Based Analysis of The Effect of Repeated Unilateral Low Stiffness Perturbations on Human Gait: Toward Robot-Assisted Rehabilitation

Human gait is quite complex, especially when considering the irregular and uncertain environments that humans are able to walk in. While unperturbed gait in a controlled environment is understood to a large degree, gait in more unique environments, such as asymmetric compliant terrain, is not understood to the same degree. In this study, we build upon a neuromuscular gait model and extend it to allow for walking on unilaterally compliant (soft) surfaces. This model is then compared to and verified by experimental human data. The model can successfully walk with step length trends similar to human data. Additionally, the model shows similar behaviors with respect to kinematics and muscle activity. We believe this work contributes significantly to a better understanding of the control of human gait and could lead to model-informed, patient-specific rehabilitation strategies that can advance the field of rehabilitation robotics, as well as the development of bio-inspired controllers for bipedal robots that would be able to traverse through dynamic and complaint terrains.
3D-DAT: 3D-Dataset Annotation Toolkit for Robotic Vision

Robots operating in the real world are expected to detect, classify, segment, and estimate the pose of objects to accomplish their task. Modern approaches using deep learning not only require large volumes of data but also pixel-accurate annotations in order to evaluate the performance and therefore safety of these algorithms. At present, publicly available tools for annotating data are scarce and those that are available rely on depth sensors, which excludes their use for transparent, metallic, and general non-Lambertian objects. To address this issue, we present a novel method for creating valuable datasets that can be used in these more difficult cases. Our key contribution is a purely RGB-based scene-level annotation approach that uses a neural radiance field-based method to automatically align objects. A set of user studies demonstrates the accuracy and speed of our approach over a purely manual or depth sensor assisted pipeline. We provide an open-source implementation of each component and a ROS-based recorder for capturing data with a eye-in-hand robot system. Code will be made available at https://github.com/markus-suchi/3D-DAT.
On the Lack of Robustness of Observers for Systems with Uncertain, Unstable Dynamics

We consider the robustness of state estimation for linear, time-invariant systems. Since state estimation is dual to full-state feedback, it may be expected that stability of the error dynamics depends continuously on perturbations of the dynamics matrix. This paper shows, however, that, if the system dynamics are unstable, then—regardless of how the filter gain is chosen—there always exist arbitrarily small perturbations of the system dynamics that give rise to unbounded state-estimation error. Since this phenomenon cannot occur in full-state feedback control, this result reveals a surprising break-down in the duality between estimation and control.
Fusion of Events and Frames using 8-DOF Warping Model for Robust Feature Tracking

Event cameras are asynchronous neuromorphic vision sensors with high temporal resolution and no motion blur, offering advantages over standard frame-based cameras especially in high-speed motions and high dynamic range conditions. However, event cameras are unable to capture the overall context of the scene, and produce different events for the same scenery depending on the direction of the motion, creating a challenge in data association. Standard camera, on the other hand, provides frames at a fixed rate that are independent of the motion direction, and are rich in context. In this paper, we present a robust feature tracking method that employs 8-DOF warping model in minimizing the difference between brightness increment patches from events and frames, exploiting the complementary nature of the two data types. Unlike previous works, the proposed method enables tracking of features under complex motions accompanying distortions. Extensive quantitative evaluation over publicly available datasets was performed where our method shows an improvement over state-of-the-art methods in robustness with greatly prolonged feature age and in accuracy for challenging scenarios.
L-C*: Visual-inertial Loose Coupling for Resilient and Lightweight Direct Visual Localization

This study presents a framework, L-C*, for resilient and lightweight direct visual localization, employing a loosely coupled fusion of visual and inertial data. Unlike indirect methods, direct visual localization facilitates accurate pose estimation on general color three-dimensional maps that are not tailored for visual localization. However, it suffers from temporal localization failures and high computational costs for real-time applications. For long-term and real-time visual localization, we developed an L-C* that incorporates direct visual localization C* in a visual-inertial loose coupling. By capturing ego-motion via visual-inertial odometry to interpolate global pose estimates, the framework allows for a significant reduction in the frequency of demanding global localization, thereby facilitating lightweight but reliable visual localization. In addition, forming a closed loop that feeds the latest pose estimate to the visual localization component as an initial guess for the next pose inference renders the system highly robust. A quantitative evaluation of a simulation dataset demonstrated the accuracy and efficiency of the proposed framework. Experiments using smartphone sensors also demonstrated the robustness and resiliency of L-C* in real-world situations.