Miracle-dz/UniFAR-FTUs huggingface dataset:
Grouping-By-ID: Guarding Against Adversarial Domain Shifts

When training a deep neural network for supervised image classification, one can broadly distinguish between two types of latent features of images that will drive the classification of class Y. Following the notation of Gong et al. (2016), we can divide features broadly into the classes of (i) “core” or “conditionally invariant” features X^ci whose distribution P(X^ci | Y) does not change substantially across domains and (ii) “style” or “orthogonal” features X^orth whose distribution P(X^orth | Y) can change substantially across domains. These latter orthogonal features would generally include features such as position, rotation, image quality or brightness but also more complex ones like hair color or posture for images of persons. We try to guard against future adversarial domain shifts by ideally just using the “conditionally invariant” features for classification. In contrast to previous work, we assume that the domain itself is not observed and hence a latent variable. We can hence not directly see the distributional change of features across different domains. We do assume, however, that we can sometimes observe a so-called identifier or ID variable. We might know, for example, that two images show the same person, with ID referring to the identity of the person. In data augmentation, we generate several images from the same original image, with ID referring to the relevant original image. The method requires only a small fraction of images to have an ID variable. We provide a causal framework for the problem by adding the ID variable to the model of Gong et al. (2016). However, we are interested in settings where we cannot observe the domain directly and we treat domain as a latent variable. If two or more samples share the same class and identifier, (Y, ID)=(y,i), then we treat those samples as counterfactuals under different style interventions on the orthogonal or style features. Using this grouping-by-ID approach, we regularize the network to provide near constant output across samples that share the same ID by penalizing with an appropriate graph Laplacian. This is shown to substantially improve performance in settings where domains change in terms of image quality, brightness, color changes, and more complex changes such as changes in movement and posture. We show links to questions of interpretability, fairness and transfer learning.
Thyroid Detection and Classification Using DNN Based on Hybrid Meta-Heuristic and LSTM Technique

In the field of medical research, prediction, as well as diagnosis of thyroid disease, is a major cause that is a challenging onset axiom. In metabolism regulation, thyroid hormone secretions play a significant role. Two frequent thyroid diseases are hypothyroidism and hyperthyroidism that release the hormones like the thyroid, which regulate the body’s metabolism rate. For analytics, the approach of data cleansing is utilized to analyze enough primitive data, which demonstrates the patients’ risk. Deep Neural Networks (DNN) is the most vital as well as efficient technology, which predict the disorder of thyroid. To avoid the errors of human, the evaluation of manual process consumes expertise domain as well as time. To detect disease, a novel Long Short-Term Memory based Convolution Neural Network (LSTM-CNN) is utilized with occurrence area Vgg-19. For selecting the feature, the approach of bias field correction is integrated with the hybrid optimization technique i.e., Black Widow Optimization as well as Mayfly Optimization Approach (HBWO-MOA), also for classifying the disease the LSTM as well as Vgg-19 of Deep Learning (DL) is presented. From DDTI dataset image of ultrasound, the disease of thyroid prediction as well as classification is efficiency. This analysis shown that the proposed technology is accurate than the convolutional methodology. When compared to existing prediction techniques i.e., AlexNet-LSTM, ResNet-LSTM, Vgg16-LSTM, the proposed approach of Vgg-19-LSTM’s precision, sensitivity, accuracy, recalls as well as F1_score is effective.
Programming with Purity Reflection: Peaceful Coexistence of Effects, Laziness, and Parallelism

We present purity reflection, a programming language feature that enables higher-order functions to inspect the purity of their function arguments and to vary their behavior based on this information. The upshot is that operations on data structures can selectively use lazy and/or parallel evaluation while ensuring that side effects are never lost or re-ordered. The technique builds on a recent Hindley-Milner style type and effect system based on Boolean unification which supports both effect polymorphism and complete type inference. We illustrate that avoiding the so-called ’poisoning problem’ is crucial to support purity reflection. We propose several new data structures that use purity reflection to switch between eager and lazy, sequential and parallel evaluation. We propose a DelayList, which is maximally lazy but switches to eager evaluation for impure operations. We also propose a DelayMap which is maximally lazy in its values, but also exploits eager and parallel evaluation. We implement purity reflection as an extension of the Flix programming language. We present a new effect-aware form of monomorphization that eliminates purity reflection at compile-time. And finally, we evaluate the cost of this new monomorphization on compilation time and on code size, and determine that it is minimal.
Exploring the Role of Enhancer-Mediated Transcriptional Regulation in Precision Biology

The emergence of precision biology has been driven by the development of advanced technologies and techniques in high-resolution biological research systems. Enhancer-mediated transcriptional regulation, a complex network of gene expression and regulation in eukaryotes, has attracted significant attention as a promising avenue for investigating the underlying mechanisms of biological processes and diseases. To address biological problems with precision, large amounts of data, functional information, and research on the mechanisms of action of biological molecules is required to address biological problems with precision. Enhancers, including typical enhancers and super enhancers, play a crucial role in gene expression and regulation within this network. The identification and targeting of disease-associated enhancers hold the potential to advance precision medicine. In this review, we present the concepts, progress, importance, and challenges in precision biology, transcription regulation, and enhancers. Furthermore, we propose a model of transcriptional regulation for multi-enhancers and provide examples of their mechanisms in mammalian cells, thereby enhancing our understanding of how enhancers achieve precise regulation of gene expression in life processes. Precision biology holds promise in providing new tools and platforms for discovering insights into gene expression and disease occurrence, ultimately benefiting individuals and society as a whole.
Printed Nanolaser on Silicon

We propose and demonstrate a direct integration of a wavelength-scale III–V nanolaser onto a silicon-on-insulator (SOI) waveguide. By employing high-precision microtransfer printing techniques, with an optimally designed photonic crystal nanolaser structure, we experimentally achieved a coupling efficiency of 83% between the InGaAsP nanobeam laser and the SOI waveguide. Our III–V nanobeam laser is designed as an asymmetric one-dimensional photonic crystal cavity, which allows unidirectional coupling to the combined III–V nanobeam waveguide with high efficiency. Through the compact vertical coupler in the region where the III–V and SOI waveguides overlap at the optimal length of 3.2 μm, 88% of the light from the printed III–V nanolaser can theoretically be coupled to a vertically integrated SOI waveguide.
Localisation using LiDAR and Camera

Localisation of the vehicle is one of the most important part of autonomous driving and can be achieved in many different ways. Most autonomous vehicles today are not able to navigate in complex environments, for instance snow covered roads or heavy rain. This thesis proposes a method for localising a vehicle using LiDAR and Camera fused together with INS and GNSS. The method is intended to work in the complex environments mentioned earlier. The system consists of a vehicle equipped with a 3D scanning LiDAR, a forward looking camera, a combined INS/GNSS unit and a 3D map over the area of interest. The LiDAR and camera are used together to measure distances to points of interest in the 3D map which are then used to triangulate the position of the vehicle. The position from the triangulation is then fused with INS and GNSS in an Ensemble Kalman Filter along with a motion model of the vehicle to estimate the position of the vehicle. It is concluded that the system is promising with a mean translational error of about 40 centimeters which is a good result considering that DGPS has about the same accuracy.
Studying the outskirts of reverberation mapped AGNs

Abstract About 100 AGNs have their black hole mass measured directly using the reverberation mapping technique over the past few decades. By now we have high enough numbers to explore unique subsamples within these objects and to study phenomena across variety of AGNs. I will review recent reverberation mapping studies which focus on high-redshift high-luminosity AGNs and on AGNs with super-Eddington accreting massive black holes. These studies enable to investigate the BLR size, mass, and luminosity relations in different subsamples of AGNs and to check whether there are differences in these relations in different types of AGNs. In particular I will discuss the following questions: Is the BLR size - luminosity relation the same over the whole AGNs luminosity range? Are there different relations for different types of AGNs? What are these studies teaching us about theory of accretion into black holes in AGNs?
A2BAR Antagonism Decreases the Glomerular Expression and Secretion of Chemoattractants for Monocytes and the Pro-Fibrotic M2 Macrophages Polarization during Diabetic Nephropathy

Some chemoattractants and leukocytes such as M1 and M2 macrophages are known to be involved in the development of glomerulosclerosis during diabetic nephropathy (DN). In the course of diabetes, an altered and defective cellular metabolism leads to the increase in adenosine levels, and thus to changes in the polarity (M1/M2) of macrophages. MRS1754, a selective antagonist of the A2B adenosine receptor (A2BAR), attenuated glomerulosclerosis and decreased macrophage-myofibroblast transition in DN rats. Therefore, we aimed to investigate the effect of MRS1754 on the glomerular expression/secretion of chemoattractants, the intraglomerular infiltration of leukocytes, and macrophage polarity in DN rats. Kidneys/glomeruli of non-diabetic, DN, and MRS1754-treated DN rats were processed for transcriptomic analysis, immunohistopathology, ELISA, and in vitro macrophage migration assays. The transcriptomic analysis identified an upregulation of transcripts and pathways related to the immune system in the glomeruli of DN rats, which was attenuated using MRS1754. The antagonism of the A2BAR decreased glomerular expression/secretion of chemoattractants (CCL2, CCL3, CCL6, and CCL21), the infiltration of macrophages, and their polarization to M2 in DN rats. The in vitro macrophages migration induced by conditioned-medium of DN glomeruli was significantly decreased using neutralizing antibodies against CCL2, CCL3, and CCL21. We concluded that the pharmacological blockade of the A2BAR decreases the transcriptional expression of genes/pathways related to the immune response, protein expression/secretion of chemoattractants, as well as the infiltration of macrophages and their polarization toward the M2 phenotype in the glomeruli of DN rats, suggesting a new mechanism implicated in the antifibrotic effect of MRS1754.
A Facile Two-Step Hydrothermal Synthesis of Co(OH)2@NiCo2O4 Nanosheet Nanocomposites for Supercapacitor Electrodes

The composites of NiCo2O4 with unique structures were substantially investigated as promising electrodes. In this study, the unique structured nanosheets anchored on nickel foam (Ni foam) were prepared under the hydrothermal technique of NiCo2O4 and subsequent preparation of Co(OH)2. The Co(OH)2@NiCo2O4 nanosheet composite has demonstrated higher specific capacitances owing to its excellent specific surface region, enhanced rate properties, and outstanding electrical conductivities. Moreover, the electrochemical properties were analyzed in a three-electrode configuration to study the sample material. The as-designed Co(OH)2@NiCo2O4 nanosheet achieves higher specific capacitances of 1308 F·g−1 at 0.5 A·g−1 and notable long cycles with 92.83% capacity retention over 6000 cycles. The Co(OH)2@NiCo2O4 nanosheet electrode exhibits a long life span and high capacitances compared with the NiCo2O4 and Co(OH)2 electrodes, respectively. These outstanding electrochemical properties are mainly because of their porous construction and the synergistic effects between NiCo2O4 and Co(OH)2. Such unique Co(OH)2@NiCo2O4 nanosheets not only display promising applications in renewable storage but also reiterate to scientists of the unlimited potential of high-performance materials.
Agreement between Diaphragm Ultrasound and Pulmonary Function Testing in the Evaluation of Diaphragm Function in Patients with ALS

Background: In ALS patients, diaphragmatic dysfunction is usually assessed by pulmonary function testing (PFTs) that requires patient cooperation. Evidence suggest that diaphragm ultrasound (DU) can be utilized, as an alternative to PFTs, to detect reduced diaphragmatic motility in ALS patients. This study aimed to verify the agreement between the results obtained by DU with those obtained by PFTs in both standing-up and supine positions.Methods: Twenty nine spinal ALS and thirteen healthy controls were studied in standing up and lying 30° supine position. All subjects performed PFTs and DU, to assess of diaphragmatic excursion, delta-thickness between end inspiration and end expiration (ΔT), and the thickening fraction (TF). Results: The standing position, ΔT correlates with VC (r=0.58p=0.001) and FVC (r=0.61 p<0.001); in lying 30° supine position, ΔT correlates with VC(r=0.59 p=0.001) and FVC (r=0.62 p<0.001). In standing up position, TF correlates with VC (r=0.55 p=0.003) and FVC (r=0.53 p=0.005); in lying 30° supine position, TF correlates with VC (r=0.55 p=0.003) and FVC (r=0.51 p=0.007). The following correlations were also observed: the standing position, diaphragmatic excursion correlated with VC(r=0.55 p=0.007) and FVC (r =0.65 p<0.001). In lying 30° supine position, diaphragmatic excursion correlated with VC(r=0.59 p=0.003); and FVC(r=0.63 p=0.001). Conclusion: DU adequately assesses the diaphragmatic impairment in ALS patients. Thickening and excursion appear as complementary indices in the evaluation of diaphragm motility.
A Vector Potential Function-Based Collision Avoidance Control for Differential-Steered Robots

A collision avoidance control that tracks a vector potential field-based velocity plan of a differential-steered robot is designed. Vector potential function (VPF) is a type of potential function used for motion planning. The plan resulted by the VPF is the desired velocity vector of the robot on all points in collision-free space. The problem to address in this paper is velocity tracking control in the environment of a circular obstacle. A controller is designed to track the VPF-based velocity plan. A concept of collision cone will be used to evaluate the ability of the controller to avoid collision between the robot and the obstacle. The stability of the controller is verified by using the Lyapunov stability analysis. Simulations of the controllerâ€™s performance are presented.
Assessing Environmental Sensitivity to Desertification in Heet-Haditha Region-Based on Method Multiple Criteria Decision Analyses

The study area suffers from significant climatic changes and high salinity, in addition to urban sprawl, which causes desertification. This study aims to analyze the indicators of desertification by means of multi-criteria decision analysis methods and to identify the most effected on the land's sensitivity to desertification. Nine main indicators have been used. The results show that the evaporation index, wind speed, relative humidity, and temperature are among the most sensitive indicators of the desertification phenomenon in the Heet- Haditha region, Iraq.
Vehicle Parking Navigation Based on Edge Computing With Diffusion Model and Information Potential Field

Based on sensor networks within a dynamic and real-time environment, a novel parking-lot navigation method is proposed based on diffusion equation and Poisson equation to achieve convenient and efficient navigation process with edge computing mind to aid information query and navigation. From the perspective of theoretical proof, is presented parallel method mainly for ordinary differential equations (ODEs) by partitioning the time domain. In this article, our new method is combined by parallelization for linear heat equations. The model problem is decoupled into several sub-problems in space-time sub-domains. We prove the super-linear convergence when the time interval is bounded. Numerical experiments testify our theoretical analysis. Simultaneously, the proposed method holds the lower constraint condition and some improper navigation routes can be updated. Mathematical analysis and simulations show that the method is accurately and efficiently enabled to solve typical sensor network configuration information navigation problem.
Modelling and solving dynamic entry pedestrian flow assignment problem

In this paper, a dynamic entry pedestrian flow assignment problem is investigated. The problem concerns a scenario, i.e. a number of pedestrians enter a public place with multiple entrances and internal obstacles, and then get to their destinations in the public place. The problem aims at determining the access entrance, moving route, and destination position choice of pedestrians so as to minimize the total entry time of all pedestrians. A mesoscopic model is developed to formulate the dynamic entry process of pedestrians in the scenario and a heuristic algorithm is proposed to solve the dynamic entry pedestrian flow assignment problem. Moreover, a set of numerical investigations are provided to demonstrate the applications of the model and the effectiveness of the algorithm, especially to a case with a larger number of entry pedestrians.
Teaching How to Calibrate a Process Using Experimental Design and Analysis: The Ballistat

As part of the University of Newcastle's Total Quality Management (TQM) course, students study Experimental Design (ED) and Statistical Process Control (SPC) within the framework of the scientific approach to process improvement. A sufficient balance of theory and application is required to keep Business and Management students, most with a largely non-quantitative background, interested and aware of the need and method to correctly implement ED and SPC in industry. Tools to facilitate a basic understanding of the importance of the 3Rs, namely, Randomization, Replication, and Blocking, as well as highlighting the potential for mistakes or inefficient calibration techniques are essential in the learning process. This paper describes the use of a particular tool, called the “Ballistat,” to illustrate TQM concepts, which enables students to obtain the hands-on experience needed to control processes in industry.
A model-data integrated iterative learning controller for flexible tracking with application to a piezo nanopositioner

In precise motion systems, feedforward controller is a key component for significant performance enhancement. However, traditional iterative learning control (ILC) works efficiently under strictly repetitive reference input, and the performance of model-based feedforward controllers is limited by the non-minimum phase zeros and modeling uncertainties during executing tasks. In this paper, a model-data integrated ILC is proposed for flexible tracking, where the stable part of the identified model is utilized to compose the model-based part, and the modeling error and gain mismatch are compensated by the data-driven approach via constructing a parameterized finite impulse response filter. In order to diminish the effect of noise, an instrumental variable method is adopted in the cost criterion. The proposed controller has an analytic solution and retains stability during iterations, which is verified on a piezo nanopositioner. Comparative experimental results indicate that the proposed controller can realize flexible tracking in comparison with norm optimal ILC, and achieve the best performance compared with zero-phase-error tracking controller and polynomial basis functions feedforward controller.
Nanostructured Antimicrobials for Quality and Safety Improvement in Dairy Products

In the food sector, one of the most important economic activities is the dairy industry, which has been facing many challenges in order to meet the increasing demand by consumers for natural and minimally processed products with high quality. In this sense, the application of innovative and emerging technologies can be an interesting alternative, for example, the use of nanotechnology in packaging and as delivery systems. This technology has the potential to improve the quality and safety of dairy products, representing an interesting approach for delivering food preservatives and improving the mechanical, barrier and functional properties of packaging. Several applications and promising results of nanostructures for dairy product preservation can be found throughout this review, including the use of metallic and polymeric nanoparticles, lipid-based nanostructures, nanofibers, nanofilms and nanocoatings. In addition, some relevant examples of the direct application of nanostructured natural antimicrobials in milk and cheese are presented and discussed, as well as the use of milk agar as a model for a preliminary test. Despite their high cost and the difficulties for scale-up, interesting results of these technologies in dairy foods and packaging materials have promoted a growing interest of the dairy industry.
Assessing the validity of the zero-velocity update method for sprinting speeds

The zero-velocity update (ZUPT) method has become a popular approach to estimate foot kinematics from foot worn inertial measurement units (IMUs) during walking and running. However, the accuracy of the ZUPT method for stride parameters at sprinting speeds remains unknown, specifically when using sensors with characteristics well suited for sprinting (i.e., high accelerometer and gyroscope ranges and sampling rates). Seventeen participants performed 70-meter track sprints while wearing a Blue Trident IMeasureU IMU. Two cameras, at 20 and 70 meters from the start, were used to validate the ZUPT method on a stride-by-stride and on a cumulative distance basis. In particular, the validity of the ZUPT method was assessed for: (1) estimating a single stride length attained at the end of a 70m sprint (i.e., stride at 70m); (2) estimating cumulative distance from ∼20 to ∼70 m; and (3) estimating total distance traveled for a 70-meter track sprint. Individual stride length errors at the 70-meter mark were within - 6% to 3%, with a bias of -0.27%. Cumulative distance errors were within -4 to 2%, with biases ranging from -0.85 to -1.22%. The results of this study demonstrate the ZUPT method provides accurate estimates of stride length and cumulative distance traveled for sprinting speeds.