Miracle-dz/UniFAR-FTUs huggingface dataset:
New PDE models for imaging problems and applications

Variational methods and Partial Differential Equations (PDEs) have been extensively employed for the mathematical formulation of a myriad of problems describing physical phenomena such as heat propagation, thermodynamic transformations and many more. In imaging, PDEs following variational principles are often considered. In their general form these models combine a regularisation and a data fitting term, balancing one against the other appropriately. Total variation (TV) regularisation is often used due to its edgepreserving and smoothing properties. In this thesis, we focus on the design of TV-based models for several different applications. We start considering PDE models encoding higher-order derivatives to overcome wellknown TV reconstruction drawbacks. Due to their high differential order and nonlinear nature, the computation of the numerical solution of these equations is often challenging. In this thesis, we propose directional splitting techniques and use Newton-type methods that despite these numerical hurdles render reliable and efficient computational schemes. Next, we discuss the problem of choosing the appropriate data fitting term in the case when multiple noise statistics in the data are present due, for instance, to different acquisition and transmission problems. We propose a novel variational model which encodes appropriately and consistently the different noise distributions in this case. Balancing the effect of the regularisation against the data fitting is also crucial. For this sake, we consider a learning approach which estimates the optimal ratio between the two by using training sets of examples via bilevel optimisation. Numerically, we use a combination of SemiSmooth (SSN) and quasi-Newton methods to solve the problem efficiently. Finally, we consider TV-based models in the framework of graphs for image segmentation problems. Here, spectral properties combined with matrix completion techniques are needed to overcome the computational limitations due to the large amount of image data. Further, a semi-supervised technique for the measurement of the segmented region by means of the Hough transform is proposed.
Pushing the Limits: The Performance of Maximum Likelihood and Bayesian Estimation With Small and Unbalanced Samples in a Latent Growth Model

Longitudinal developmental research is often focused on patterns of change or growth across different (sub)groups of individuals. Particular to some research contexts, developmental inquiries may involve one or more (sub)groups that are small in nature and therefore difficult to properly capture through statistical analysis. The current study explores the lower-bound limits of subsample sizes in a multiple group latent growth modeling by means of a simulation study. We particularly focus on how the maximum likelihood (ML) and Bayesian estimation approaches differ when (sub)sample sizes are small. The results show that Bayesian estimation resolves computational issues that occur with ML estimation and that the addition of prior information can be the key to detect a difference between groups when sample and effect sizes are expected to be limited. The acquisition of prior information with respect to the smaller group is especially influential in this context.
Smooth State Feedback Control of a New Nonholonomic Manipulator Coping With Singularities

This paper presents a smooth state feedback control algorithm for set point stabilization of a three-link planar nonholonomic manipulator (NHM). Compared to known control algorithms for such a manipulator, the one presented here is able to control the NHM in its singular positions. In the control algorithm synthesis, we consider the kinematic model of the NHM. Our control algorithm solution is based on an extension of models state variables. Smooth point stabilization is achieved by implementing the transverse function approach. The control algorithm synthesis is performed with utilization of a Lie group representation. Because the Lie algebra associated with the NHM is not nilpotent, we apply a homogeneous nilpotent approximation. Analytical results have been reinforced by simulations and experiments, which were executed using a newly built three-link planar NHM. To the best of our knowledge, this manipulator is the third of its kind in the world, but due to its distinct kinematic structure, it is a unique mechanism.
Contextual Bandits and Imitation Learning via Preference-Based Active Queries

We consider the problem of contextual bandits and imitation learning, where the learner lacks direct knowledge of the executed action’s reward. Instead, the learner can actively request the expert at each round to compare two actions and receive noisy preference feedback. The learner’s objec-tive is two-fold: to minimize regret associated with the executed actions, while simultaneously, minimizing the number of comparison queries made to the expert. In this paper, we assume that the learner has access to a function class that can represent the expert’s preference model under appropriate link functions and present an algorithm that leverages an online regression oracle with respect to this function class. For the contextual ban-dit setting, our algorithm achieves a regret bound that combines the best of both worlds, scaling as O (min {√ T, d/ ∆ } ) , where T represents the number of interactions, d represents the eluder dimen-sion of the function class, and ∆ represents the minimum preference of the optimal action over any suboptimal action under all contexts. Our al-gorithm does not require the knowledge of ∆ , and the obtained regret bound is comparable to what can be achieved in the standard contextual bandits setting where the learner observes reward signals at each round. Additionally, our algorithm makes only O (min { T, d 2 / ∆ 2 } ) queries to the expert. We then extend our algorithm to the imitation learning setting, where the agent engages with an unknown environment in episodes of length H , and provide similar guarantees regarding regret and query complexity. Interestingly, with preference-based feedback, our imitation learning algorithm can learn a policy outperforming
Dynamic Ontology Matching Challenge

The labour market is currently heavily struggling with friction in demand and supply. Skills-based approaches deliver promising results. These approaches ask for a common and up-to-date skills language to achieve their full potential. Skills ontologies such as ESCO and O*NET exist, but tend to get outdated soon, as the labour market changes quickly and intensive manual expert-based labour is needed for updating the ontologies. The existence of multiple skills ontologies allows for applicability in different contexts (such as different countries), but requires mappings between them to be able to relate, reuse and transfer knowledge. In this challenge, we invite participants to implement novel ideas on how to keep mappings between ontologies up-to-date in a dynamic context. We will provide different versions of the ESCO ontology and the mapping to O*NET as the ontology and data. It is necessary to use a hybrid method in this challenge, and it is allowed to use some human annotation in the challenge.
Investigation of conceptus stimulated gene expression in buffalo peripheral blood mononuclear cells as potential diagnostic markers of early pregnancy

Abstract Exploration of novel strategies for early pregnancy diagnosis is pivotal in enhancing the reproductive potential and monetary gains from dairy herds. In buffalo, the trophectoderm cells of the elongating conceptus secrete interferon-tau that stimulates the transcription of various genes in peripheral blood mononuclear cells (PBMC) during the peri-implantation period. We explored the differential expression of classical (ISG15) and novel (LGALS3BP and CD9) early pregnancy markers in PBMC of buffaloes during various stages of pregnancy. Natural heat was detected in buffaloes by assessing the vaginal fluid, and artificial insemination (AI) was done. Whole blood was collected from the jugular vein in EDTA-containing vacutainers for PBMC isolation before AI (0-day) and 20, 25 and 40 d post-AI. On day 40, transrectal ultrasonography examination was performed to confirm pregnancy. The inseminated non-pregnant animals served as control. Total RNA was extracted using the TRIzol method. The temporal abundance of ISG15, LGALS3BP and CD9 genes in PBMC was compared between pregnant and non-pregnant groups (n = 9 per group) using real time-qPCR. Results showed transcripts of ISG15 and LGALS3BP were more abundant at 20 d in the pregnant group compared to the 0 d and 20 d values of the non-pregnant group. However, due to variability in expression, threshold (Ct) cycle of RT-qPCR alone could not distinguish pregnant and non-pregnant animals. In conclusion, ISG15 and LGALS3BP transcripts abundance in PBMCs are potential candidate biomarkers for early prediction of buffalo pregnancy 20-days post-AI, but further work is required to allow the development of a reliable new methodology.
The Complexity of Network Design for s-t Eff ective Resistance

We consider a new problem of designing a network with small s-t effective resistance. In this problem, we are given an undirected graph G = (V,E) where each edge e has a cost ce and a resistance re, two designated vertices s, t ∈ V , and a cost budget k. Our goal is to choose a subgraph to minimize the s-t effective resistance, subject to the constraint that the total cost in the subgraph is at most k. This problem has applications in electrical network design and is an interpolation between the shortest path problem and the minimum cost flow problem. We present algorithmic and hardness results for this problem. On the hardness side, we show that the problem is NP-hard by reducing the 3-dimensional matching problem to our problem. On the algorithmic side, we use dynamic programming to obtain a fully polynomial time approximation scheme when the input graph is a series-parallel graph. Finally, we propose a greedy algorithm for general graphs in which we add a path at each iteration and we conjecture that the algorithm is a 3.95-approximation algorithm for the problem.
Posterior convergence rates for high‐dimensional precision matrix estimation using G‐Wishart priors

We study the posterior convergence behaviour of a precision matrix corresponding to a Gaussian graphical model in the high‐dimensional set‐up under sparsity assumptions. Recent works include studying posterior convergence rates of precision matrices assuming an approximate banding structure, and extension of such result to arbitrary decomposable graphical models using a transformation to Cholesky factor of the precision matrices. In this paper, we study the same for the wider class of arbitrary decomposable graphical models under similar sparsity assumptions using a G‐Wishart prior, but without the complications of using a Cholesky factor, and arrive at identical posterior convergence rates. Copyright © 2017 John Wiley & Sons, Ltd.
Bias Temperature Instability of Normally‐Off GaN MIS‐FET with Low‐Pressure Chemical Vapor Deposition SiNx Gate Dielectric

In this work, characterizations are conducted to investigate the threshold voltage (VTH) stability of the normally‐off GaN metal–insulator–semiconductor (MIS‐) field‐effect transistor (FET) with fully recessed gate structure and highly reliable low‐pressure chemical vapor deposition SiNx gate dielectric. We conducted bias‐temperature instability (BTI) tests under both positive and negative gate bias. We demonstrated the highly stable VTH of the high‐performance MIS‐FETs with small BTI, which benefits from the effective interfacial protection layer. More specifically, combining the BTI tests and drain current 1/f noise analysis, we present extensive investigation of the physical origins of BTI. According to the experimental evidence and analysis, we ascribe the VTH instability to the trapping/detrapping of the pre‐existing trap states located at the SiNx/GaN interface and/or in the gate dielectric.
Variational methods in economics

Advanced Textbooks in Economics, Volume 1: Variational Methods in Economics focuses on the application of variational methods in economics, including autonomous system, dynamic programming, and phase spaces and diagrams. The manuscript first elaborates on growth models in economics and calculus of variations. Discussions focus on connection with dynamic programming, variable end points-free boundaries, transversality at infinity, sensitivity analysis-end point changes, Weierstrass and Legendre necessary conditions, and phase diagrams and phase spaces. The text then ponders on the constraints of classical theory, including unbounded intervals of integration, free boundary conditions, comparison functions, normality, and the problem of Bolza. The publication explains two-sector models of optimal economic growth, optimal control theory, and connections with the classical theory. Topics include capital good immobile between industries, constrained state variables, linear control problems, conversion of a control problem into a problem of Lagrange, and the conversion of a nonautonomous system into an autonomous system. The book is a valuable source of information for economists and researchers interested in the variational methods in economics.
Reynolds-Number Dependence of the Feedback Control of Turbulent Channel Flow

Active control of turbulent wall flows is an emerging discipl ine, which is gaining more and more attention from the aerospace community; active control techniques look in fac t promising in many technological areas such as drag reduction, separation delay, noise suppression and mixing enhancement. In particular, encouraging results have been recently obta ined by using modern linear control theory in the design of active distributed feedback controllers, aimed at reduc ing skin friction drag in turbulent channel flow. It has been shown that a substantial reduction of the skin friction drag can be achieved with a limited control effort, by using wall blowing and suction at the channel walls with zero net mass flux. However, these results have been obtained for a limited value of the Reynolds number, as low as Re= 1450, where Re is based on the bulk velocity and the channel half-width. To date, the Reynolds-number dependence of the effectivene ss of these controllers has not been directly addressed. Aim of the present paper is thus to address the performance of optimal controllers at higher Reynolds numbers; to this purpose, results of direct numerical simulations perf ormed at higher values of Re are reported and discussed. In particular, skin friction drag as well as turbulent kinet ic energy reduction are quantified and an indication of a possible scaling law for the initial response to the control is reported.
A Content Analysis of Postgraduate Theses Examining the Effectiveness of Writing Interventions in Türkiye

This study aimed to examine postgraduate theses in Türkiye in which the effectiveness of writing interventions was investigated in a multidimensional way. In this context, 157 theses designed with action research or experimental/quasi-experimental methods were subjected to content analysis. Descriptive characteristics of the theses such as type, year, educational/grade level and research method were analyzed using the manifest approach, whereas the sections suitable for interpretation and synthesis such as recommendations were analyzed using the latent approach. Both approaches were used to analyze writing interventions, writing outcomes, keywords, and the implementation processes in the theses. The data were analyzed with MAXQDA 2022 software. According to the study results, lower secondary school was the level of education in which the most applications were made, and the pre-test/post-test control group design was the most preferred in theses. Creative writing, web-based technology, text type/structure-based instruction and the process writing approach were the writing interventions, the effects of which were most frequently researched. The elements of writing with the most reported findings were internal structure/content, external structure/form, and the stages of writing. The study included results that would enable teachers, researchers, and educational administrators to make theoretical and pedagogical inferences about writing instruction.
Critical current and flux dynamics in Ag-doped FeSe superconductor

The measurements of DC magnetization as a function of the temperature M(T), magnetic field M(H), and time M(t) have been performed in order to compare the superconducting and pinning properties of an undoped FeSe0.94 sample and a silver doped FeSe0.94 + 6 wt% Ag sample. The M(T) curves indicate an improvement of the superconducting critical temperature and a reduction of the non-superconducting phase Fe7Se8 due to the silver doping. This is confirmed by the field and temperature dependent critical current density Jc(H,T) extracted from the superconducting hysteresis loops at different temperatures within the Bean critical state model. Moreover, the combined analysis of the Jc(T) and of the pinning force Fp(H/Hirr) indicate that the pinning mechanisms in both samples can be described in the framework of the collective pinning theory. The U*(T, J) curves show a pinning crossover from an elastic creep regime of intermediate size flux bundles, for low temperatures, to a plastic creep regime at higher temperatures for both the samples. Finally, the vortex hopping attempt time has been evaluated for both samples and the results are comparable with the values reported in the literature for high Tc materials.
Interlayer tunnel field-effect transistor (ITFET): physics, fabrication and applications

The scaling challenges of complementary metal oxide semiconductors (CMOS) are increasing with the pace of scaling showing marked signs of slowing down. This slowing has brought about a widespread search for an alternative beyond-CMOS device concept. While the charge tunneling phenomenon has been known for almost a century, and tunneling based transistors have been studied in the past few decades, its possibilities are being re-examined with the emergence of a new class of two-dimensional (2D) materials. By stacking varying 2D materials together, with two electrode layers sandwiching a tunnel dielectric layer, it could be possible to make vertical tunnel transistors without the limitations that have plagued such devices implemented within other material systems. When the two electrode layers are of the same material, under certain conditions, one can achieve resonant tunneling between the two layers, manifesting as negative differential resistance (NDR) in the interlayer current–voltage characteristics. We call this type of device an interlayer tunnel FET (ITFET). We review the basic operation principles of this device, experimental and theoretical studies, and benchmark simulation results for several digital logic gates based on a compact model that we developed. The results are placed in the context of work going on in other groups.
of A-phases

— A statistical analysis of the separability of EEG A-phases, with respect to basal activity, is presented in this study. A-phases are short central events that build up the Cyclic Alternating Pattern (CAP) during sleep. The CAP is a brain phenomenon which is thought to be related to the construction, destruction and instability of sleep stages dynamics. From the EEG signals, segments obtained around the onset and offset of the A-phases were used to evaluate the separability between A-phases and basal sleep stage oscillations. In addition, a classifier was trained to separate the different A-phase types (A1, A2 and A3). Temporal, energy and complexity measures were used as descriptors for the classifier. The results show a percentage of separation between onset and preceding basal oscillations higher than 85 % for all A-phases types. For Offset separation from following baseline, the accuracy is higher than 80 % but specificity is around 75%. Concerning to A-phase type separation, A1-phase and A3-phase are well separated with accuracy higher than 80, while A1 and A2-phases show a separation lower than 50%. These results encourage the design of automatic classifiers for Onset detection and for separating among A-phases type A1 and A3. On the other hand, the A-phase Offsets present a smooth transition towards the basal sleep stage oscillations, and A2-phases are very similar to A1-phases, suggesting that a high uncertainty may exist during CAP annotation.
Image watermarking, steganography, and morphological processing

With the fast development of computer technology, research in the fields of multimedia security, image processing, and robot vision have recently become popular. Image watermarking, steganographic system, morphological processing and shortest path planning are important subjects among them. In this dissertation, the fundamental techniques are reviewed first followed by the presentation of novel algorithms and theorems for these three subjects. The research on multimedia security consists of two parts, image watermarking and steganographic system. In image watermarking, several algorithms are developed to achieve different goals as shown below. In order to embed more watermarks and to minimize distortion of watermarked images, a novel watermarking technique using combinational spatial and frequency domains is presented. In order to correct rounding errors, a novel technique based on the genetic algorithm (GA) is developed. By separating medical images into Region of Interest (ROI) and non-ROI parts, higher compression rates can be achieved where the ROI is compressed by lossless compression and the non-ROI by lossy compression. The GA-based watermarking technique can also be considered as a fundamental platform for other fragile watermarking techniques. In order to simplify the selection and integrate different watermarking techniques, a novel adjusted-purpose digital watermarking is developed. In order to enlarge the capacity of robust watermarking, a novel robust high-capacity watermarking is developed. In steganographic system, a novel steganographic algorithm is developed by using GA to break the inspection of steganalytic system. In morphological processing, the GA-based techniques are developed to decompose arbitrary shapes of big binary structuring elements and arbitrary values of big grayscale structuring elements into small ones. The decomposition is suited for a parallel-pipelined architecture. The techniques can speed up the morphological processing and allow full freedom for users to design any type and any size of binary and grayscale structuring elements. In applications such as shortest path planning, a novel method is first presented to obtaining Euclidean distance transformation (EDT) in just two scans of image. The shortest path can be extracted based on distance maps by tracking minimum values. In order to record the motion path, a new chain-code representation is developed to allow forward and backward movements. By placing the smooth turning-angle constraint, it is possible to mimic realistic motions of cars. By using dynamically rotational morphology, it is not only guarantee collision-free in the shortest path, but also reduce time complexity dramatically. As soon as the distance map of a destination and collision-free codes have been established off-line, shortest paths of cars given any starting location toward the destination can be promptly obtained on-line.