Miracle-dz/UniFAR-FTUs huggingface dataset:
Associations, Synagogues, and Congregations: Claiming a Place in Ancient Mediterranean Society

Ephesus, Galatia, Troas, and Pergamum are familiar names to readers of the New Testament. But what made this region such fertile ground for early synagogues and congregations of those who followed Christ? How did the earliest churches and synagogues organize themselves? How did other voluntary associations operate within the Roman empire? How did such organizations relate to the constraints of imperial religion? These are some of the questions that Philip Harland addresses in this stimulating look at first-century Roman Asia. He surveys the various forms of guilds and associations in the eastern Roman empire. Asia Minor is one of the primary regions of Paul's journeys described in Acts, and it provided the context for several New Testament books, especially the Pastoral Epistles, 1 Peter, and Revelation. The author's fresh look at ancient inscriptions reveals new insights about the formation, operation, and functions of congregations and synagogues within the larger framework of voluntary associations in the Roman world.
A History of Place: Using Phytolith Analysis to Discern Holocene Vegetation Change on Sanak Island, Western Gulf of Alaska

This study investigated a terrestrial climate proxy, phytoliths, as a complimentary approach to documenting the dynamics of present and past vegetation on Sanak Island, the largest island in a small island group in the eastern Aleutian archipelago, and as a new basis by which to interpret Holocene environmental variability in Alaska. A phytolith reference collection was established from 59 selected plant species of maritime tundra belonging to 27 families. The grass species and a sedge species produced abundant phytolith forms whereas the majority of dicotyledons in this study were trace producers of phytoliths. A paleoenvironmental reconstruction from fossil phytoliths recovered from a continuous lake sediment core indicates that sedges and cool season grasses were present on this landscape throughout most of the Holocene suggesting the long-term dominance of maritime climate on island vegetation. Adaptation and resilience of the modern vegetation on Sanak Island to a warming climate is suggested by the densities of silicified stomata recovered from six species of grasses, one species of fern, and one species of horsetail when compared to the paleoenvironmental reconstruction. The changes in stomata frequency in the plants on Sanak Island today may have connections to future changes in regional and global climate through the water cycle. Our changing climate is forcing ecosystems to migrate, acclimate or go extinct demonstrating that new insights into ecosystem responses to present and past environmental variation, and forecasting future ecological change are especially relevant today for ecologic and economic sustainability. The electronic version of this Dissertation is at OhioLink EDT Center, www.ohiolink. edu/etd.
Evaluating Performance of Intrusion Detection Systems under Different Configurations in SDN

Software Defined Networks (SDN) have been proposed as a possible development for next generation networking technology. In a SDN, Virtual Network Functions (VNFs) are used to replace the functions of traditional middleboxes. All of these VNFs can be controlled from a centralized controller, which comes with its own security concerns. However, there are many benefits that come from having a centralized controller as traffic can be easily controlled from a single point. This makes it interesting to further study security when it concerns SDN. This work looks to test intrusion detection system (IDS) performance under different configurations in a SDN in order to make security in SDN more robust. In this work, we take two IDSs, Snort and Suricata, and test their performance under different configurations and traffic loads. We test four different configurations: single, chain, parallel, and cross. Our findings seem to suggest that the cross configuration has the best performance of these IDS configurations.
Use of Open-source Software in Mechatronics and Robotics Engineering Education – Part II: Controller Implementation

This paper is the second part of a two-part study on promoting the use of Open-Source Software (OSS) in Mechatronics and Robotics Engineering (MRE) education. Part I demonstrated the capabilities and limitations of several popular OSS, namely, Python, Java, Modelica, and GNU Octave, in model simulation and analysis of dynamic systems, through a DC motor example. The DC motor was chosen as a representative of a large class of dynamic systems described by linear differential equations. The perceptions of MRE community members about the OSS and their applications, gathered through an online survey, were also presented in Part I. In this paper, another fundamental pillar of MRE systems development, i.e., controller implementation, is considered. To this end, the OSS above, along with Gazebo, are used to simulate the closed-loop trajectory tracking performance of a 2-DOF robot manipulator, controlled by a PID controller. Robot manipulators represent a broader category of dynamic systems, which are described by nonlinear differential equations. Furthermore, PID controllers are one of the most versatile closed-loop control methodologies which have been established as the industry standard. Showcasing the implementation of this important category of controllers through OSS can promote their use in a wide range of MRE problems and projects. This paper also provides an overview of the potentials, limitations, and challenges regarding the use of each of the above OSS in solving the aforementioned problems. The OSS are increasingly being adapted as industry standard; furthermore, their numerous bene-ﬁts pose them as a viable option to complement traditional higher education courses along with facilitating online and remote education. Therefore, this two-part paper and the various problems showcased and solved therein aim to pave the way towards further utilization of the OSS in MRE higher education, reaping the wide range of their beneﬁts, and preparing the students for the future workforce. Full scripts of the codes summarized and discussed in this paper, along with Matlab scripts included to enable comparison, are made freely available on the Github repository of this paper.
Depth Camera Noise Modeling

Depth camera is a field-of-view (FoV) based distance sensor and has been widely used in commercial entertainments such as augmented reality, industrial field for object 3D modeling, as well as intelligence vehicle obstacle avoidance. No doubt, the depth camera measurement accuracy definitely affects the associated application performance and therefore the noise behavior should be properly modeled. The measurement noise of the depth cameras depends on various factors, which can be difficult to model in practice. In this short note, three different depth noise models are presented based on the pin-hole model of the camera. The goal is to match the practical depth camera noise distributions as close as possible and to provide a simulation sketch for further noise analysis and possible improvement of the depth measurements.
Prediction of Head and Neck Radiotherapy Toxicity Using a Deformable 3D CNN on Longitudinal Daily CBCT Acquisitions

Adaptive radiotherapy is a growing field of study in cancer treatment due to it’s objective in sparing healthy tissue. The standard of care in several institutions includes longitudinal cone-beam computed tomography (CBCT) acquisitions to monitor changes, but have yet to be used to improve tumor control while managing side-effects. The aim of this study is to demonstrate the clinical value of pre-treatment CBCT acquired daily during radiation therapy treatment for head and neck cancers for the downstream task of predicting severe toxicity occurrence: reactive feeding tube (NG), hospitalization and radionecrosis. For this, we propose a deformable 3D classification pipeline that includes a component analyzing the Jacobian matrix of the deformation between planning CT and longitudinal CBCT, as well as clinical data. The model is based on a multi-branch 3D residual convolutional neural network, while the CT to CBCT registration is based on a pair of VoxelMorph architectures. Accuracies of 85.8% and 75.3% was found for radionecrosis and hospitalization, respectively, with similar performance as early as after the first week of treatment. For NG tube risk, performance improves with increasing the timing of the CBCT fraction, reaching 83.1% after the 5th week of treatment.
Attention in a Little Network is All You Need to Go Green

The widely adopted practice to improve performance of deep neural networks is by increasing its depth and the number of parameters. Restraining their energy consumption is yet another community expectation. However, energy consumption is dependent on the number of operation and the memory involved during computation which increase with depth and number of parameters. It seems to be a conflicting goal to achieve high performance at low energy consumption. This phenomena can be observed with UNeXt which consumes significantly less energy on account of lesser number of parameters but fails to achieve adequate performance. In this paper, we crack the code by introducing attention mechanism within a network with lesser number of parameters in order to compensate its reduced information capacity, by fixating the attention to relevant regions per layer. The Energy efficient, Lightweight, and computationally Thin Network (ELiTNet) is thus proposed for semantic segmentation tasks, and demonstrated for semantic segmentation of retinal arteries and optic disc in digital color fundus images. Experiments compare it against SUMNet, U-Net, UNeXt and ResUNet++ architectures on five publicly available datasets including HRF, DRIVE, AMD, IDRiD, and REFUGE. We demonstrate that the proposed method consumes 2.2× lower energy, while featuring 155.2× fewer parameters, and 41.22× lesser GFlop in comparison with U-Net while maintaining an F1-score of 97.22 % and Jaccard Index of 94.74 % on IDRiD dataset. Source codes are available at Github1.
Nonlinear Three-Phase Reluctance Synchronous Machine Modeling With Extended Torque Equation

Reluctance synchronous machines (RSMs) are characterized by high nonlinearities, cross-coupling effects and high torque ripple. This paper presents a nonlinear machine model of three-phase RSMs based on a nonlinear electromagnetic model including iron losses and a novel (extended) torque equation. The $\gamma$-component is used to consider asymmetries. The novel torque model provides a precise approximation of the machine’s torque ripple. Iron losses are covered as well. The nonlinear machine model with extended torque equation is implemented based on current, angle and speed dependent flux linkage and iron loss maps. Finally, the nonlinear and extended machine model is validated by comparing with state-of-the-art results obtained by finite-element analysis (FEA).
Extracting 10-m Resolution Photovoltaic Landcover Using A Slightly Modified U-Net and Sentinel-2 Images

Centralized photovoltaic (PV) power plants, as the main form of solar energy utilization, has been world-widely constructing in recent two decades, which makes photovoltaic landcover (PVL) becoming an important landcover type. Accurately mapping PVL provides basis for both the estimation of grid-connected PV power capacity and the optimization of spatial layouts of PV power plants. However, mapping PVL at large-scale is still facing challenges due to the complexity of landcover with PV panels, and the differences of PV panel sizes and arrangement patterns. This study aims to apply a slightly modified U-Net model for precisely extracting PVL from 10-m resolution Sentine1-2 (S2) images. Firstly, combining visual interpretation and historical data of PV power plants, we selected 16 sample sites and 4 test sites for study, the sample sites are scattered in China, while the test sites are typical of three centralized PVL types. Secondly, the S2 images in 2022 in these 20 sites were labeled and cropped into chips with 64 * 64 pixels. Thirdly, the modified model was trained and validated using the sample dataset with four schemas of channel combinations. Finally, the trained model was adopted for mapping PVL in the 4 test sites. The validating results show that the slightly modified U-Net achieves excellent performance with the mean intersection over union (MIoU) of the four schemas all above 97%. Visually interpreting the testing results also demonstrates that the modified model competes to mapping PVL at large-scale.
A Multi-View Clustering-Based Method for Individual and Group Cortical Parcellations with Resting-State fMRI

Cortical parcellation provides an important tool for revealing the organization of cerebral cortex. Despite the increasing number of attempts to developing parcellation algorithms using resting-state fMRI, generating reliable, functionally coherent brain parcels at both subject-level and group-level remains challenging due to the difficulty in balancing individual variability and group consistency without prior information. To overcome this challenge, we proposed to treat each subject as a view of population data and use multi-view clustering approach to learn individual and group parcellations. Specifically, it integrates spectral embedding and tensor learning into a unified optimization framework to optimize individual embedding matrices (for individual parcellation) and group consensus matrix (for group parcellation) jointly. In this process, an optimal balance between subject-specific and group-consensus parcellation can be achieved in an adaptive manner. Experiments on a test-retest dataset from Human Connectome Project showed that our method outperformed the existing state-of-the-art algorithms.
FeST: Learning Spatio-Temporal Priors for Fetal Brain MRI Segmentation

Segmentation of anatomic brain structures on fetal magnetic resonance imaging is key in detecting and diagnosing congenital disorders. We propose FeST: a Fetal brain segmentation method, which includes information on the gestational age through Spatio-Temporal priors. We include gestational age in three different priors. We used it as input in our model through a sinusoidal encoding, and in the loss function through KL divergence and a size-prior for modeling the volumetric growth of the brain during development, and that anatomical structures of the brain grow at different rates [1]. We evaluate FeST in the FeTA dataset achieving a Dice similarity coefficient of 0.917, a Volume similarity of 0.974, and a 95th percentile Hausdorff distance of 10.96.
G2LL: Global-To-Local Self-Supervised Learning for Label-Efficient Transformer-Based Skin Lesion Segmentation in Dermoscopy Images

Skin lesion segmentation in dermoscopy images is highly relevant for lesion assessment and subsequent analysis. Recently, automatic transformer-based skin lesion segmentation models have achieved high segmentation accuracy owing to their long-range modeling capability. However, limited labeled data for training the lesion segmentation models results in sub-optimal learning results. In this paper, we propose a Global-to-Local self-supervised Learning (G2LL) method for transformer-based skin lesion segmentation models to alleviate the problem of insufficient annotated data. Firstly, a structure-wise masking strategy for Masked Image Modeling (MIM) is proposed to force the model to learn the reconstruction of masked structures by exploring the semantic local contexts. Instead of masking patches randomly in the whole view, it computes super-pixels to divide the images into several structured regions. Then, it masks the fixed number of patches in each region, thus it allows the exploration of the structural knowledge and solves the shape variance in the meanwhile. Secondly, a self-distilling architecture is deployed to enhance global context learning where the masked images are sent to a student network and the relative unmasked images are fed to a teacher network for knowledge distillation. In this context, extensive experiments on both the ISIC-2017 and the ISIC-2019 datasets containing a total of 28081 images show that the proposed approach is superior to state-of-the-art self-supervised learning methods.
Mining ESG Semantic Features for success prediction in Green-Oriented Crowdfunding Campaigns

ESG disclosures are attracting attention. Narrative disclosure allows companies to overcome information asymmetries by presenting more information. This study uses ESG key metrics provided in the textual data of sustainable crowdfunding projects to construct predictive models for the success of the sustainable crowdfunding project. This study uses exposed ESG-related textual data to predict crowdfunding project success. It makes a valuable contribution to the research on the deployment of ESG performance.
Iterative Data Refinement for Self-Supervised Learning MR Image Reconstruction

Magnetic Resonance Imaging (MRI) is an important technique in the clinic. Fast MRI based on k-space undersampling and high-quality image reconstruction has been widely utilized, and many deep learning-based methods have been developed in recent years. Although promising results have been achieved, most existing methods require fully-sampled reference data. Unfortunately, fully-sampled MRI data are difficult to obtain in real-world applications. To address this issue, we propose a data refinement framework for self-supervised MR image reconstruction. Specifically, we first analyze the reason of the performance gap between self-supervised and supervised methods and identify that the bias in the training datasets is one major factor. Then, we design an effective self-supervised data refinement learning framework to reduce this data bias. With the data refinement, an enhanced self-supervised MR image reconstruction method is developed to prompt fast MR imaging. We evaluate our method on an in-vivo MRI dataset. Experimental results show that without utilizing any fully sampled MRI data, our self-supervised learning framework possesses strong capabilities in capturing image details and structures at high acceleration factors.
Cross-Modal Contrastive Learning for Robust Representation of the Extracellular Matrix in Static and Dynamic Full-Field OCT Images

The full-field optical coherence tomography imaging system can produce two complementary modalities: static (FFOCT) and dynamic (DCI). FFOCT captures the highly scattering tissue fibers, while DCI leverages an endogenous cell contrast derived from moving scatterers. We learn a common embedding in a cross-modal latent space which generates a robust representation of the extracellular matrix in DCI, where it is subject to several imaging inconsistencies, but is well rendered in FFOCT. In this regard we train a siamese convolutional neural network which learns the cosine distance between feature vectors of pairs of corresponding images. We show that a classification inspired loss implicitly learns a hard margin metric, we also propose two acceptance measures to validate the method: the identity error and the symmetry error, both accounting for errors < 1%.
Lightweight Metasurface Absorber Customization with a Conditional Generative Adversarial Network

Deep learning algorithm known as conditional generative adversarial network (cGAN), which enables the generation of novel graphical patterns. Here we propose a deep learning method based on cGAN for rapid customization of lightweight metasurface absorbers. As a result, the proposed method offers design flexibility and diversity, facilitating the rapid creation of compliant metasurface designs. Moreover, this approach can be readily applied to the design of other on-demand designs.
SegCrop: Segmentation-based Dynamic Cropping of Endoscopic Videos to Address Label Leakage in Surgical Tool Detection

In recent times, surgical data science has emerged as an important research discipline in interventional healthcare. There are many potential applications for analysing endoscopic surgical videos using machine learning (ML) techniques such as surgical tool classification, action recognition, and tissue segmentation. However, the efficacy of ML algorithms to learn robust features drastically deteriorates when models are trained on noise-affected data [1]. Appropriate data preprocessing for endoscopic videos is thus crucial to ensure robust ML training. To this end, we demonstrate the presence of label leakage when surgical tool classification is performed naively and present SegCrop, a dynamic U-Net model with an integrated attention mechanism to dynamically crop the arbitrary field of view (FoV) in endoscopic surgical videos to remove spurious label-related information from the data. In addition, we leverage explainability techniques to demonstrate how the presence of spurious correlations influences the model’s learning capability.
PATRIoTA: A Similarity-based IoT Malware Detection Method Robust Against Adversarial Samples

Detecting malware targeting IoT devices has became an important challenge with the recent emergence of IoT botnets. Gateways at the edge between the Internet and IoT devices deployed in the field are particularly well-positioned for the task of malware detection, as malware typically spreads over the Internet and resource-constrained field devices may not have the means to protect themselves. Hence, we believe that, among other things, edge intelligence should also include effective and efficient IoT malware detection. A recently proposed similarity-based IoT malware detection method, called SIMBIoTA, would be suitable in this context, but its robustness against adversarial malware samples has been shown to be rather weak. In this paper, we propose PATRIoTA, a similarity-based IoT malware detection method inspired by SIMBIoTA, but being significantly more robust than SIMBIoTA is. We describe the operation of PATRIoTA, and compare its malware detection performance and robustness against adversarial samples to that of SIMBIoTA. We show that PATRIoTA outperforms SIMBIoTA with respect to both measures.
Wideband Dual-Polarized ME Dipoles-Fed Quasi- Spherical Lens Antenna for Millimeter-Wave Multibeam Applications

This paper presents a broadband modified spherical lens antenna with multiple radiation patterns for low-cost millimeter-wave (mm-Wave) communications. A dual polarization magnetoelectric dipole (DPME) antenna with a microstrip feeding structure is first designed. Then, a multiport feed structure consisting of six proposed feed elements is developed and works as the primary feed of the lens. With this arrangement, a set of the deviated beams are obtained with the space and polarization diversities. As a demonstration, a prototype of the proposed ME dipole-fed quasi-spherical lens antenna operated within the frequency range of 27-37 GHz is fabricated. The measured overlapped impedance bandwidth is wider than 30%. And the measured peak gains are about 18 dBi at different beams.
Data Compression and Decompression of Handwritten Digital-Ink Using Sparse Gaussian Process

In this paper, we address the problem of compressing and decompressing handwritten digital-ink data, comprising N points, obtained from electronic devices such as pen tablets, etc. We propose a novel method for data compression and decompression using sparse Gaussian process regression, which allows us to compress the digital-ink data into a set of $M(\ll N)$ pseudo inputs. Furthermore, we demonstrate that adjusting the hyperparameters of the kernel function during the decompression process enables the conversion of digital-ink style to a cursive form, providing enhanced flexibility and customization in the resulting digital-ink. Additionally, we show that the blurring of brush strokes can be represented by generating and superimposing characters from a Gaussian process, based on the mean and variance obtained. The performance of our proposed method is demonstrated through a series of experimental studies.