Miracle-dz/UniFAR-FTUs huggingface dataset:
PERFORMANCE COMPARISON OF DENOISING METHODS FOR HISTORICAL DOCUMENTS

Image denoising plays an important role in image processing. It is also part of the pre-processing technique in a binarization complete procedure that consists of pre-processing, thresholding, and post-processing. Our previous research has confirmed that the Discrete Cosine Transform (DCT)-based filtering as the new pre-processing process improved the performance of binarization output in terms of recall and precision. This research compares three classical denoising methods; Gaussian, mean, and median filtering with the DCT-based filtering. The noisy ancient document images are filtered using those classical filtering methods. The outputs of this process are used as the input for Otsu, Niblack, Sauvola and NICK binarization methods. Then the resulted binary images of the three classical methods are compared with those of DCT-based filtering. The performance of all denoising algorithms is evaluated by calculating recall and precision of the resulted binary images. The result of this research is that the DCT based filtering resulted in the highest recall and precision as compared to the other methods.
Exogenous Calcium Reinforces Photosynthetic Pigment Content and Osmolyte, Enzymatic, and Non-Enzymatic Antioxidants Abundance and Alleviates Salt Stress in Bread Wheat

One of the main environmental stresses that hinder crop development as well as yield is salt stress, while the use of signal molecules such as calcium (Ca) has a substantial impact on reducing the detrimental effects of salt on different crop types. Therefore, a factorial pot experiment in a completely randomized design was conducted to examine the beneficial role of Ca (0, 2.5, and 5 mM) in promoting the physiological, biochemical, and growth traits of the wheat plant under three salt conditions viz. 0, 30, and 60 mM NaCl. Foliar application of Ca increased the growth of salt-stressed wheat plants through increasing photosynthetic pigments, IAA, proline, and total soluble sugars contents and improving antioxidant enzymes in addition to non-enzymatic antioxidants glutathione, phenol and flavonoids, β-carotene, and lycopene contents, thus causing decreases in the over-accumulation of free radicals (ROS). The application of Ca increased the activity of antioxidant enzymes in wheat plants such as superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT), which scavenge reactive oxygen species (ROS) and relieved salt stress. An additional salt tolerance mechanism by Ca increases the non-antioxidant activity of plants by accumulating osmolytes such as free amino acids, proline, and total soluble sugar, which maintain the osmotic adjustment of plants under salinity stress. Exogenous Ca application is a successful method for increasing wheat plants’ ability to withstand salt stress, and it has a considerable impact on the growth of wheat under salt stress.
A Framework for Evaluating the Planning and Development of a New Community

Abstract: The planning and development of new communities is a challenging and expensive venture. This article describes the framework and methods employed to evaluate the use of socially sensitive planning in the development of Tumbler Ridge, a new coal-mining community in northeastern British Columbia. The authors describe how a set of social principles and design elements were used to develop an overall framework and detailed strategy for the evaluation. Central themes that emerged from this experience included the importance of documenting the development processes and context of the community; the utility of a multiple-method, multiple-perspective approach; the appropriateness of both quantitative and qualitative information; and the necessity of grounding such an evaluation in the community through discussion, negotiation, responsiveness, and timely provision of results.
OBSERVABLE IMPLICATIONS OF MODELS WITH

endogenous variables. This note offers a simple theoretical framework that can be used to organize the debate about the question of whether a given model with multiple equilibria is identified, and about the question of how much predictive content such a model retains. The note shows that the set of distributions on observable outcomes that are consistent with a given structure can be quite large. As a result it is likely, but certainly not necessary, that the model is hard to identify, and that it loses much of its predictive content. Of course, much of the work on models with multiple equilibria has implicitly faced the issues that this note describes; the aim here is simply to force more explicit consideration of these issues and thereby to induce a more systematic evaluation of the restrictive identifying assumptions
Exact ensemble dynamics for spike-timing-dependent plasticity

Exact Ensemble Dynamics for Spike-Timing-Dependent Plasticity Frank Alan Adrian Master of Science Division of Biomedical Computer Science within The Department of Science & Engineering and the Oregon Health & Science University School of Medicine October 2008 Thesis Advisor: Dr. Todd K. Leen Under the influence of input stimuli, synaptic strength changes based on the relative timing of preand postsynaptic events. This mechanism is called SpikeTiming-Dependent Plasticity (STDP) and is recognized as a basis of neural plasticity in biological systems. Changes in synaptic strength (or weight) are described mathematically using a stochastic learning rule that generates a Markov process over the weights. This process determines a master equation whose solution provides the time evolution of the probability density function for the weights. The master equation has an expansion in a perturbation-like series (the Kramers-Moyal expansion), which when truncated after the second term gives a Fokker-Planck equation. Solving this equation provides an approximation to the probability density. Van Rossum et al. [1] use this approach to analytically predict the equilibrium distribution of synaptic weights governed by anti-symmetric spike-timingdependent learning rules of the type observed by Bi and Poo [2]. However, the use of the Fokker-Planck equation is ill-advised and does not always lead to an accurate approximation, as was shown by Heskes and Kappen [3, 4] in the context of machine learning. We show that if for all k ≤ K, the kth jump moment is a polynomial of order less than or equal to k in the weights, the Kramers-Moyal expansion pro-
Diet as a Source of Acrolein: Molecular Basis of Aldehyde Biological Activity in Diabetes and Digestive System Diseases

Acrolein, a highly reactive α,β-unsaturated aldehyde, is a compound involved in the pathogenesis of many diseases, including neurodegenerative diseases, cardiovascular and respiratory diseases, diabetes mellitus, and the development of cancers of various origins. In addition to environmental pollution (e.g., from car exhaust fumes) and tobacco smoke, a serious source of acrolein is our daily diet and improper thermal processing of animal and vegetable fats, carbohydrates, and amino acids. Dietary intake is one of the main routes of human exposure to acrolein, which is a major public health concern. This review focuses on the molecular mechanisms of acrolein activity in the context of its involvement in the pathogenesis of diseases related to the digestive system, including diabetes, alcoholic liver disease, and intestinal cancer.
Delusion of Pregnancy: A Case Report and Literature Review

Delusion of pregnancy is defined as a persistent belief that one is pregnant despite concrete evidence to the contrary. Despite being reported as an isolated event, delusion of pregnancy has been reported in many patients with underlying psychiatric conditions such as schizophrenia, bipolar disorder, psychotic depression, and other physiological disorders of mental function. This case study reports a case of a 44-year-old, drug-naïve female with delusion of pregnancy affected by paranoid schizophrenia. Pharmacological treatments are frequently insufficient in controlling this condition and are often supplemented with adjunctive psychotherapy.
A Novel Unbiased Deep Learning Approach (DL-Net) in Feature Space for Converting Gray to Color Image

Gray to Color conversion causes difficulties because of the nature of its intrinsic multi-modality. Despite recent significant advancements in this domain by numerous learning-based approaches, there still have two drawbacks: 1) implausible color assignment and 2) contextual ambiguity. Recently deep learning models are being used for colorization as they outperform others. In a training image, desaturated color components are greater than saturated color components due to the larger background areas (clouds, pavement, dirt, walls, etc.) compared to the focused objects. This imbalanced feature representation biases the learning model in favor of major features. However, small regions with specific colors are the region of interest. To solve this problem, we proposed the Deep Localization Network (DL-Net) by modifying the mean squared error backpropagation algorithm. We compute chromatic component-based Local Losses (LLs) which are the primary component of the proposed DL-Net. The LL employs priority on rare semantic components of the original image features. It works to improve diverse-range dependency modeling in an effort to reduce contextual ambiguity and color leakage that promotes the production of more plausible coloring. With a number of current methodologies, we contrast our proposed approach. The experimental findings demonstrate that our proposed method produces good colorization of images and outperforms other methods in terms of SSIM, MSE, and PSNR quality criteria.
Prehomogeneous Super Vector Spaces

In this thesis, we develop the theory of prehomogeneous super vector spaces. In the case that $G$ is a complex connected Lie supergroup, acting linearly on a super vector space $V$ and if $G$ has an open orbit in $V$ then this orbit, as an open sub supermanifold, is unique, connected and dense. This allows us to define prehomogeneous super vector spaces. We introduce the notion of relative superinvariants and show that the relative superinvariants are already determined by the supercharacters which are invariant under the isotropy supergroup. Furthermore, we construct two examples of prehomogeneous super vector spaces. The supercharacters of the general linear supergroup will be classified and thereby all relative superinvariants for the space of supersymmetric matrices. We introduce the local zeta superfunction for the prehomogeneous super vector space of supersymmetric matrices and prove that these are entire functions. Moreover, for a compactly supported superfunction $\Phi_c$ on a connected component $V_{ij}$ of the orbit the local zeta superfunction \[ F_{ij}(s,\Phi_{c})=\frac{1}{\gamma(s)}\int_{V_{ij}} |D(X)|\ |\mathrm{Ber}(X)|^s\ \Phi_{c}(X),\\ \] is a regularization of the integral on the right-hand side to the greater space of Schwartz superfunctions. These are defined on the associated $cs$-vector space. Unlike the regularization methods of Marcel Riesz and Hadamard, where homogeneous distributions lose their homogeneity, this regularization method, already used by Gelfand and Sato, maintains the algebraic property of relative superinvariant. Moreover, we demonstrate that the Fourier supertransform is, up to a supercharacter, an equivariant map for the induced action on the super vector space of Schwartz superfunctions. We also prove that the Fourier supertransform of the dual local zeta superfunctions transforms with the same supercharacter as the local zeta superfunction, shifted in the complex parameter.
Understanding the response in Pugionium cornutum (L.) Gaertn. seedling leaves under drought stress using transcriptome and proteome integrated analysis

Background Drought is one of the crucial constraints limiting horticultural plant’s production and development around the world. Pugionium cornutum is an annual or biennial xerophyte with strong environmental adaptability and drought resistance; however, the mechanisms with respect to response to drought stress remain largely unclear. Methods After seedling emergence, the gravimetric method was used to control soil relative water content (SRWC). Drought stress was applied to the six-leaf stage P. cornutum seedlings. The soil water content of different drought stress levels (L) was controlled by gravimetric method as follows: control (L1): 70–75% SRWC; moderate drought level (L2): 40–45% SRWC; severe drought level (L3): 30–35% SRWC, and the water was added to different drought stress levels at about 18:00 p.m. every day. The experiment ended when the leaves of P. cornutum showed severe wilting (10-leaf stage). Samples were harvested and stored at −80 °C for physiological determination, and transcriptomic and proteomic sequencing. Results Compared with L1, the leaves of P. cornutum seedlings were increasingly wilted after drought treatment; the SRWC of the drought-stress leaves decreased notably while the leaf water potential was rose; the proline, malondialdehyde (MDA) content increased with the continuous drought treatment but peroxidase (POD) activity decreased. Besides, 3,027 differential genes (DGs) and 196 differential proteins (DPs), along with 1,943 DGs and 489 DPs were identified in L2-L1 and L3-L1, respectively. The transcriptome and proteome integrated analysis manifested that only 30 and 70 were commonly regulated both in L2-L1 and L3-L1, respectively. Of which, 24 and 61 DGs or DPs showed the same trend including sHSPs, APX2, GSTU4, CML42, and POD, etc. However, most of DGs or DPs were regulated only at the transcriptome or proteome level mainly including genes encoding signal pathway (PYR1, PYLs, SnRK2J, PLC2, CDPK9/16/29, CML9, MAPKs), transcription factors (WRKYs, DREB2A, NAC055, NAC072, MYB and, HB7) and ion channel transporters (ALMT4, NHX1, NHX2 and TPK2). These genes or proteins were involved in multiple signaling pathways and some important metabolism processes, which offers valuable information on drought-responsive genes and proteins for further study in P. cornutum.
Conspectus of Techniques to Monitor and Control Diabetic Foot Ulcers

Diabetic foot ulcers (DFUs) are severe impediments among diabetic people; whose glycemic levels are not regulated efficiently. A person with diabetes remains a chief therapeutic task for the management of foot ulcers throughout the world. They are usually caused by peripheral neuropathy, uncontrolled glycemic levels, vascular anomalies, and immunosuppression. Severe instances may entail amputations of the foot. For the purpose of preventing foot ulcerations and amputations, early recognition of high-risk feet and enlightening diabetic patients are crucial. An enhanced diagnosis is guaranteed through accurate evaluation and management. Revascularization methodologies, wound debridement, infection treatment, and ulcer offloading are practiced regularly in the management of the disease. The tending to the wound and types of dressing used are customized based on the severity of the wound. The very motive of this review study is to peruse diabetic foot ulcers, commencing with the assessment of the foot for susceptible regions, foot ulcer management, and progressing to different treatment methods.
A New Monitoring Method for the Dynamic Stray Current Intrusion of Subways

Metro dynamic stray current invades urban power grid, which leads to DC bias hazard of transformer. In view of the shortcomings of the current dynamic stray current intrusion monitoring technology, Firstly, the fluxgate technology scheme of large-size flexible stray current sensor has been proposed. According to the principle of self-excited oscillation fluxgate circuit, an open-loop high-precision dynamic stray current sensor with a radius of 1m has been developed, and it has passed the tests without AC current interference and 50A AC current interference. Compared with the current Hall sensor and four-point stray current sensor, the new stray current sensor improves the signal conditioning circuit, which can effectively improve the sampling resolution of stray current to 10Hz and meet the requirements of dynamic tracking of stray current. Finally, the effectiveness of this method has been proved by different tests and application cases, and the next improvement direction has been pointed out, which provides new technical support for stray current monitoring.
Some contributions to Markov decision processes

In a nutshell, this thesis studies discrete-time Markov decision processes (MDPs) on Borel Spaces, with possibly unbounded costs, and both expected (discounted) total cost and long-run expected average cost criteria. In Chapter 2, we systematically investigate a constrained absorbing MDP with expected total cost criterion and possibly unbounded (from both above and below) cost functions. We apply the convex analytic approach to derive the optimality and duality results, along with the existence of an optimal finite mixing policy. We also provide mild conditions under which a general constrained MDP model with state-action-dependent discount factors can be equivalently transformed into an absorbing MDP model. Chapter 3 treats a more constrained absorbing MDP, as compared with that in Chapter 2. The dynamic programming approach is applied to a reformulated unconstrained MDP model and the optimality results are obtained. In addition, the correspondence between policies in the original model and the reformulated one is illustrated. In Chapter 4, we attempt to extend the dynamic programming approach for standard MDPs with expected total cost criterion to the case, where the (iterated) coherent risk measure of the cost is taken as the performance measure to be minimized. The cost function under our consideration is allowed to be unbounded from the below, and possibly arbitrarily unbounded from the above. Under a fairly weak version of continuity-compactness conditions, we derive the optimality results for both the finite and infinite horizon cases, and establish value iteration as well as policy iteration algorithms. The standard MDP and the iterated conditional value-at-risk of the cost function are illustrated as two examples. Chapter 5 and 6 tackle MDPs with long-run expected average cost criterion. In Chapter 5, we consider a constrained MDP with possibly unbounded (from both above and below) cost functions. Under Lyapunov-like conditions, we show the sufficiency of stable policies to the concerned constrained problem. Furthermore, we introduce the corresponding space of performance vectors and manage to characterize each of its extreme points with a deterministic stationary policy. Finally, the existence of an optimal finite mixing policy is justified. Chapter 6 concerns an unconstrained MDP with the cost functions unbounded from the below and possibly arbitrarily unbounded from the above. We provide a detailed discussion on the issue of sufficient policies in the denumerable case, establish the average cost optimality inequality (ACOI) and show the existence of an optimal deterministic stationary policy. In Chapter 7, an inventory-production system is taken as an example of real-world applications to illustrate the main results in Chapter 2 and 5.
Phytochemical Analysis and Genotoxicological Evaluation of Prickly Pear Peel Extracts

This study investigated the beneficial properties of prickly pear peel (PPP) extracts from Opuntia ficus-indica (L.) Mill. Extracts were obtained via the Soxhlet extraction method using methanol (P1), ethanol (P2) and ethanol-water (P3) as extraction solvents. Their total phenolic and flavonoid content (TPC and TFC, respectively) and their antioxidant activity (AA) were determined. The PPP extracts were characterized in detail using mass spectrometry techniques. Their cyto-genotoxic effect and antigenotoxic potential against mitomycin C were evaluated via the cytokinesis block micronucleus (CBMN) assay on human lymphocytes. Enhanced TPC, TFC and AA values were recorded for all the extracts. Moreover, P1 and P2 were cytotoxic only at the highest concentrations, whereas P3 was found to be cytotoxic in all cases. No significant micronucleus induction was observed in the tested extracts. The PPP extracts contain bioactive compounds such as flavonoids, carboxylic acids, alkaloids, fatty acids and minerals (mainly K, Si, Mg, Ca, P and Zn). The results showed that all three extracts exerted high antigenotoxic activity. Our findings confirm the beneficial and genoprotective properties of PPP extracts and further studies on the bioactive compounds of Opuntia ficus-indica (L.) Mill. are recommended, as it constitutes a promising plant in pharmaceutical applications.
Inverting an Introductory Statistics Classroom

Abstract The inverted classroom allows more in-class time for inquiry-based learning and for working through more advanced problem-solving activities than does the traditional lecture class. The skills acquired in this learning environment offer benefits far beyond the statistics classroom. This paper discusses four ways that can make the inverted classroom successful in an Introductory Statistics class: how to motivate students to prepare for each class, how to move the teaching of technology out of the classroom, how to balance student classwork, and how to create a mindset for learning.
Sparse Quadratic Approximation for Graph Learning

Learning graphs represented by <inline-formula><tex-math notation="LaTeX">$M$</tex-math><alternatives><mml:math><mml:mi>M</mml:mi></mml:math><inline-graphic xlink:href="schenk-ieq1-3263969.gif"/></alternatives></inline-formula>-matrices via an <inline-formula><tex-math notation="LaTeX">$\ell _{1}$</tex-math><alternatives><mml:math><mml:msub><mml:mi>ℓ</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:math><inline-graphic xlink:href="schenk-ieq2-3263969.gif"/></alternatives></inline-formula>-regularized Gaussian maximum-likelihood method is a popular approach, but also one that poses computational challenges for large scale datasets. Recently proposed methods cast this problem as a constrained optimization variant of precision matrix estimation. In this paper, we build on a state-of-the-art sparse precision matrix estimation method and introduce two algorithms that learn <inline-formula><tex-math notation="LaTeX">$M$</tex-math><alternatives><mml:math><mml:mi>M</mml:mi></mml:math><inline-graphic xlink:href="schenk-ieq3-3263969.gif"/></alternatives></inline-formula>-matrices, that can be subsequently used for the estimation of graph Laplacian matrices. In the first one, we propose an unconstrained method that follows a post processing approach in order to learn an <inline-formula><tex-math notation="LaTeX">$M$</tex-math><alternatives><mml:math><mml:mi>M</mml:mi></mml:math><inline-graphic xlink:href="schenk-ieq4-3263969.gif"/></alternatives></inline-formula>-matrix, and in the second one, we implement a constrained approach based on sequential quadratic programming. We also demonstrate the effectiveness, accuracy, and performance of both algorithms. Our numerical examples and comparative results with modern open-source packages reveal that the proposed methods can accelerate the learning of graphs by up to 3 orders of magnitude, while accurately retrieving the latent graphical structure of the data. Furthermore, we conduct large scale case studies for the clustering of COVID-19 daily cases and the classification of image datasets to highlight the applicability in real-world scenarios.