Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Database for Writer Demographics Attributes Detection Based on Off-Line Persian and English Handwriting

This paper describes a database of multi-script (Persian and English) for typical and new aspects and challenges of offline handwriting automatic analysis field. This database can be used for typical aspects such as different levels of segmentation and recognition and writer identification in text-dependent and text-independent modes. Also, new aspects can be used such as writer identification and gender, age and handedness detection based on script-dependent and script-independent. Two pages of forms in three regions were designed and collected from 200 native Persian writers of different age, handednesses, genders and education level. To the best of our knowledge, so far no attempt has been conducted on providing Persian database for writer identification and gender, age and handedness detection based on script-dependent and script-independent in multi-script environment.
The Antiparasitic Drug, Potassium Antimony Tartrate, Inhibits Tumor Angiogenesis and Tumor Growth in Nonsmall-Cell Lung Cancer

Repurposing existing drugs not only accelerates drug discovery but rapidly advances clinical therapeutic strategies. In this article, we identified potassium antimonyl tartrate (PAT), an antiparasitic drug, as a novel agent to block angiogenesis by screening US Food and Drug Administration–approved chemical drugs. By comparing the cytotoxicity of PAT in various nonsmall-cell lung cancer (NSCLC) cells with that observed in primary cultured human umbilical vein endothelial cells (HUVECs), we found that HUVECs were much more sensitive to the PAT treatment. In in vivo tumor xenograft mouse models established either by PAT-resistant A549 cells or by patient primary tumors, PAT significantly decreased the tumor volume and tumor weight of NSCLC xenografts at dosage of 40 mg/kg (i.p., daily) and, more importantly, augmented the antitumor efficacy of cisplatin chemotherapy. Remarkable loss of vascularization in the treated xenografts indicated the in vivo antiangiogenesis property of PAT, which was well correlated with its tumor growth inhibition in NSCLC cells. Furthermore, in the in vitro angiogenic assays, PAT exhibited dose-dependent inhibition of HUVEC proliferation, migration, and tube formation in response to different stimuli. Consistently, PAT also abolished the vascular endothelial cell growth factor–induced angiogenesis in the Matrigel plugs assay. Mechanistically, we found that PAT inhibited the activities of several receptor tyrosine kinases and specifically blocked the activation of downstream Src and focal adhesion kinases in HUVECs. Taken together, our results characterized the novel antiangiogenic and antitumor function of PAT in NSCLC cells. Further study of PAT in anticancer clinical trials may be warranted.
Note - Commonality Strategies: Value Drivers and Equivalence with Flexible Capacity and Inventory Substitution

Commonality strategies assemble different products from at least one common component and one other product-specific component. The distinguishing feature of commonality, i.e., the presence of dedicated components to be assembled with a common component, is shown to be mathematically inconsequential in the sense that the unified commonality problem for two products can be reduced to an equivalent substitution flexibility problem without those dedicated components. This significant simplification provides the first general, closed-form condition for commonality adoption and identifies its value drivers. Commonality is optimal even for perfectly correlated demands if products have sufficiently different margins. This introduces the "revenue-maximization option" of commonality as a second benefit that is independent of the traditional risk-pooling benefit. "Pure commonality" strategies are never optimal unless complexity costs are introduced. Dual sourcing, externalities, and operational hedging features of commonality are discussed.
Asymmetric directional diffusion based image filtering and enhancement

This paper introduces a new method for directional diffusion based image filtering and enhancement. The main novelty of our approach consists in considering asymmetric orientation information for steering a diffusion process along the main directions of the constituent structures of an image. The diffusion process is obtained by generalizing an existing approach defined on a symmetric framework. The increased efficiency of the method for the preservation and enhancement of junctions and corners, coupled with good noise filtering capabilities, is illustrated through an experimental setup involving both synthetic and real images.
A functional reformulation of UnCAL graph-transformations: or, graph transformation as graph reduction

This paper proposes FUnCAL, a functional redesign of the graph transformation language UnCAL. A large amount of graph-structured data are widely used, including biological database, XML with IDREFs, WWW, and UML diagrams in software engineering. UnCAL is a language designed for graph transformations, i.e., extracting a subpart of a graph data and converting it to a suitable form, as what XQuery does for XMLs. A distinguished feature of UnCAL is its semantics that respects bisimulation on graphs; this enables us to reason about UnCAL graph transformations as recursive functions, which is useful for reasoning as well as optimization. However, there is still a gap to apply the program-manipulation techniques studied in the programming language literature directly to UnCAL programs, due to some special features in UnCAL, especially markers. In this paper, following the observation that markers can be emulated by tuples and λ-abstractions, we transform UnCAL programs to a restricted class of usual (thus, marker-free) functional ones. By this translation, we can reason, analyze or optimize UnCAL programs as usual functional programs. Moreover, we introduce a type system for showing that a small modification to the usual lazy semantics is enough to run well-typed functional programs as finite-graph transformations in a terminating way.
Discriminative data selection for lightly supervised training of acoustic model using closed caption texts

We present a novel data selection method for lightly supervised training of acoustic model, which exploits a large amount of data with closed caption texts but not faithful transcripts. In the proposed scheme, a sequence of the closed caption text and that of the ASR hypothesis by the baseline system are aligned. Then, a set of dedicated classifiers is designed and trained to select the correct one among them or reject both. It is demonstrated that the classifiers can effectively filter the usable data for acoustic model training without tuning any threshold parameters. A significant improvement in the ASR accuracy is achieved from the baseline system and also in comparison with the conventional method of lightly supervised training based on simple matching and confidence measure scores.
The route less taken: pulmonary models of enteric Gram-negative infection.

Many pathogens are capable of causing a fulminant infection in pulmonary tissues of mammals. Animal models have provided an extensive understanding of the genetic and molecular mechanisms of bacterial pathogenesis as well as host immune response in the lungs. Many clinically relevant Gram-negative bacteria are host-restricted. Thus, the powerful, informative tools of mouse models are not available for study with these organisms. However, over the past 30 years, enterprising work has demonstrated the utility of pulmonary infection with enteric pathogens. Such infection models have increased our understanding host-pathogen interactions in these organisms. Here, we provide a review and comparison of lung models of infection with enteric, Gram-negative bacteria relative to naturally occurring lung pathogens.
Image Denoising Based on Wavelet Thresholding with Continuity and Self-Adaptability

This article proposes a new approach of thresholding aiming at achieving the best trade-offs between filtering the noises and retaining the image finenesses. A 3-order polynomial is utilized to interpolate on the hard-thresholds, enabling the continuity and differentiability for the new thresholding function. On the basis of Birge-Massart rules, the thresholds are quantized according to the actual condition of coefficients distribution at each decomposition layer, resulting with the thresholds possessing good local self-adaptability. The proposed thresholding is put to the test and the results suggest that the denoising effects are up to technical expectation and superior to that of other given conventional thresholding algorithms in terms of both PSNR and visual judgment
What matters in education: a decomposition of educational outcomes with multiple measures

ABSTRACT Significant variations in educational outcomes across both the spatial and socioeconomic spectra in Australia have been widely debated by policymakers in recent years. This paper examines these variations and decomposes educational outcomes into 3 major input factors: availability of school resources, socioeconomic background, and a latent factor that links to the specificities of the local education system such as efficiencies. The proposed method respects the multi-dimensional nature of educational outcomes by estimating structural parameters of an extended education production function with multiple outcome measures. The results indicate that all factors contribute to the variations. Nevertheless, socioeconomic factors dominate non-school-based measures such as the tertiary education enrolment rate. The study also reveals significant differences in education system efficiencies across areas, suggesting compounding factors are often responsible for poor educational performance.
The Use of Indigenous Cultural Practices by the Ashantis for the Conservation of Forests in Ghana

Indigenous cultural practices play a significant role in the conservation of forests. Most of the forests within traditional communities in the Ashanti region of Ghana were preserved centuries ago through traditional beliefs and practices. Yet, less attention has been given to them in modern forest management. In most communities, these traditional practices are gradually “dying out. ” This work identified cultural practices that have been used to successfully conserve forests by four communities purposively selected from the Ashanti region of Ghana (i.e., Semanhyiakrom, Akegyesu, Kubease, and Jachie), their benefits, and the perceived reasons for their neglect in the management of public forests. Qualitative data were collected through semistructured interviews using stratified random sampling technique to select respondents. The study found that beliefs, taboos, myths, proverbs, and songs were vital traditional systems used by the Ashantis to effectively conserve their forests. The Ashantis believe that the neglect of cultural practices in the management of public forests has resulted in increasing rate of deforestation, destruction of water bodies, and disasters inflicted by the gods such as prolonged drought and loss of soil fertility. To avoid forest degradation, it is important for forest managers, decision makers, and governments to recognize various cultural practices and traditional beliefs as very useful tools and integrate them into current national and international forestry plans and programs.
Experiences and Lessons from Implementing a Wireless Sensor Network MAC Protocol in the Castalia Simulator

We describe our experience from the implementation of the T-MAC protocol for wireless sensor networks in the open-source Castalia simulator. Notwithstanding the popularity of the protocol in the research literature in recent years, we find several practical issues that are not addressed in the original protocol description, which lead to a degree of freedom in the protocol design and implementation and have an impact on its resulting performance. These issues include the ability of the underlying physical layer and hardware to efficiently detect the activation events in the protocol, and necessary changes to the collision resolution and clock synchronization procedures in the presence of varying sleep patterns. Our results highlight the need for rigorous detail in protocol descriptions in the research literature and provide important insights into some of the common pitfalls.
The A-rich RNA sequences of HIV-1 pol are important for the synthesis of viral cDNA

The bias of A-rich codons in HIV-1 pol is thought to be a record of hypermutations in viral genomes that lack biological functions. Bioinformatic analysis predicted that A-rich sequences are generally associated with minimal local RNA structures. Using codon modifications to reduce the amount of A-rich sequences within HIV-1 genomes, we have reduced the flexibility of RNA sequences in pol to analyze the functional significance of these A-rich ‘structurally poor’ RNA elements in HIV-1 pol. Our data showed that codon modification of HIV-1 sequences led to a suppression of virus infectivity by 5–100-fold, and this defect does not correlate with, viral entry, viral protein expression levels, viral protein profiles or virion packaging of genomic RNA. Codon modification of HIV-1 pol correlated with an enhanced dimer stability of the viral RNA genome, which was associated with a reduction of viral cDNA synthesis both during HIV-1 infection and in a cell free reverse transcription assay. Our data provided direct evidence that the HIV-1 A-rich pol sequence is not merely an evolutionary artifact of enzyme-induced hypermutations, and that HIV-1 has adapted to rely on A-rich RNA sequences to support the synthesis of viral cDNA during reverse transcription, highlighting the utility of using ‘structurally poor’ RNA domains in regulating biological process.
Stability and capacity of peer-to-peer assisted video-on-demand applications

We propose a mathematical framework for the optimal design of a video on-demand (VoD) application under regime. Peers join the network following a Poissonian process, download progressively one or possibly many concurrent video contents, and abort the system when they wish. The system is supported by static servers managed by the operator, called super-peers, and the altruism of peers, that upload resources and might stay online even after completing downloading. Our goal is to minimize the expected download time perceived by end-users under regime. We propose a general fluid model, and show that it is stable, reaching its regime. Via the Little's law, we find closed expressions for the expected waiting time under regime in terms of the popularity of different contents, file sharing efficiency and other network parameters. We state theoretically that this system outperforms traditional Content Delivery Networks (CDN). The operator can decide only the number of video replicas stored in each super-peer, a fact that has a direct impact on the mean waiting times. Hence, a combinatorial optimization problem is introduced, whose nature is similar to the multi-knapsack problem (i.e. the items are video contents, and the knapsacks are the super-peers storage). A greedy randomized resolution is here designed, and a comparison between traditional content distribution systems promotes the deployment of peer-to-peer video on-demand services. Finally, real-life scenarios are studied based on traces taken from YouTube. The results confirm that the peer-to-peer model can outperform five times the throughput of traditional CDN under flash-crowded scenarios.
RCHIG: An Effective Clustering Algorithm with Ranking

In this paper, we address the problem of generating clusters for a specified type of objects, as well as ranking information for all types of objects based on these clusters in a heterogeneous information graph. A novel clustering framework called RCHIG is proposed that directly generates clusters integrated with ranking. Based on initial K clusters, ranking is applied separately, which serves as a good measure for each cluster. Then, we use a mixture model to decompose each object into a K-dimensional vector, where each dimension is a component coefficient with respect to a cluster, which is measured by rank distribution. Objects then are reassigned to the nearest cluster under the new measure space to improve clustering. As a result, quality of clustering and ranking are mutually enhanced, which means that the clusters are getting more accurate and the ranking is getting more meaningful. Such a progressive refinement process iterates until little change can be made. Our experiment results show that RCHIG can generate more accurate clusters and in a more efficient way than the state-of-the-art link-based clustering methods. Moreover, the clustering results with ranks can provide more informative views of data compared with traditional clustering.
Spectroscopic analysis of two peculiar emission-line stars: RJHA 49 and SS73 21

Aims. We investigate the spectra and the evolutionary stages of two peculiar emission-line stars: RJHA 49 and SS73 21. Methods. We used low and high resolution optical data. Line identifications and measurements were performed for several features in their spectra. Results. For each object, we derived the extinction and the excitation temperature from a set of [Fe ii] lines and the electron density from [N ii] lines. For RJHA 49, no detailed spectroscopic study has been done so far. Our low resolution spectrum have confirmed the main characteristics found in previous works on SS73 21. On the other hand, from our high resolution data, we find that the H α line presents a double peak, in contrast with the suggestion in the literature that it should reveal a P-Cygni profile. Surprisingly, we found a few He i transitions resembling P-Cygni profiles (e.g. He i $\lambda 5876$), directly suggesting that mass loss is active in SS73 21. We also discuss the nature of these two objects based on data obtained. Although the evolutionary status of SS73 21 seems well established from previous studies (a proto-planetary nebula), the situation for RJHA 49 is not very clear mainly due to its unknown distance. However, from the strength of [N ii] $\lambda 5754$ relative to [O i] $\lambda 6300$, the possibility of RJHA 49 being a LBV object is reduced, and a B[e]-supergiant or a proto-planetary nebula status is more plausible.
Spatial Organization and Correlations of Cell Nuclei in Brain Tumors

Accepting the hypothesis that cancers are self-organizing, opportunistic systems, it is crucial to understand the collective behavior of cancer cells in their tumorous heterogeneous environment. In the present paper, we ask the following basic question: Is this self-organization of tumor evolution reflected in the manner in which malignant cells are spatially distributed in their heterogeneous environment? We employ a variety of nontrivial statistical microstructural descriptors that arise in the theory of heterogeneous media to characterize the spatial distributions of the nuclei of both benign brain white matter cells and brain glioma cells as obtained from histological images. These descriptors, which include the pair correlation function, structure factor and various nearest neighbor functions, quantify how pairs of cell nuclei are correlated in space in various ways. We map the centroids of the cell nuclei into point distributions to show that while commonly used local spatial statistics (e.g., cell areas and number of neighboring cells) cannot clearly distinguish spatial correlations in distributions of normal and abnormal cell nuclei, their salient structural features are captured very well by the aforementioned microstructural descriptors. We show that the tumorous cells pack more densely than normal cells and exhibit stronger effective repulsions between any pair of cells. Moreover, we demonstrate that brain gliomas are organized in a collective way rather than randomly on intermediate and large length scales. The existence of nontrivial spatial correlations between the abnormal cells strongly supports the view that cancer is not an unorganized collection of malignant cells but rather a complex emergent integrated system.
CBCARS: Content boosted context-aware recommendations using tensor factorization

Matrix Factorization (MF) is widely used by researchers in Collaborative Filtering (CF) technique to generate recommendations. In literature it is used for predicting the missing ratings by approximating the two dimensional rating matrix. These predictions do not analyze items (item content) and users (user context). Integrating item content and user context increases the quality of recommendation. Incorporating item content and user context in recommendations calls for a modified version of MF technique as it can no more be represented using a two dimensional matrix. This paper proposes a hybrid RS known as Content Boosted Context-Aware Recommender System (CBCARS), by incorporating item content and user context using the principle of Tensor Factorization. A tensor which is a generalization of matrix in numerous dimensions is used to accommodate item content and user contextual information. Recommendations are generated using Tensor Factorization (TF) which factorizes the sparse user-item-context tensor to fill in the missing values. The performance of CBCARS is evaluated using Mean Average Error (MAE) and Root Mean Square Error (RMSE) with experiments conducted on restaurant dataset. Results show that CBCARS outperforms the three approaches of traditional MF, content-boosted and context-aware MF by significantly decreasing both RMSE and MAE.
Analytical gradients for MP2, double hybrid functionals, and TD‐DFT with polarizable embedding described by fluctuating charges

A polarizable quantum mechanics (QM)/ molecular mechanics (MM) approach recently developed for Hartree–Fock (HF) and Kohn–Sham (KS) methods has been extended to energies and analytical gradients for MP2, double hybrid functionals, and TD‐DFT models, thus allowing the computation of equilibrium structures for excited electronic states together with more accurate results for ground electronic states. After a detailed presentation of the theoretical background and of some implementation details, a number of test cases are analyzed to show that the polarizable embedding model based on fluctuating charges (FQ) is remarkably more accurate than the corresponding electronic embedding based on a fixed charge (FX) description. In particular, a set of electronegativities and hardnesses has been optimized for interactions between QM and FQ regions together with new repulsion–dispersion parameters. After validation of both the numerical implementation and of the new parameters, absorption electronic spectra have been computed for representative model systems including vibronic effects. The results show remarkable agreement with full QM computations and significant improvement with respect to the corresponding FX results. The last part of the article provides some hints about computation of solvatochromic effects on absorption spectra in aqueous solution as a function of the number of FQ water molecules and on the use of FX external shells to improve the convergence of the results. © 2015 The Authors. Journal of Computational Chemistry Published by Wiley Periodicals, Inc.