Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving and Unfolding Statistical Models of Nonstationary Signals

Improving and Unfolding Statistical Models of Nonstationary Signals Scott Thomas Wisdom Co-Chairs of the Supervisory Committee: Professor Les E. Atlas Electrical Engineering Affiliate Associate Professor James Pitton Electrical Engineering Improving the modeling and processing of nonstationary signals remains an important yet challenging problem. In the past, the most effective approach for processing these signals has been statistical modeling. Statistical models can effectively encode domain knowledge and lead to principled algorithms for the fundamental tasks of enhancement, detection, and classification. However, the performance of statistical models can be limited because they inherently make assumptions about the distribution of the data. Deep neural networks, in contrast, have recently outperformed state-of-the-art statistical models of nonstationary signals. Deep neural networks are completely data-driven, and learn to set their parameters by training on large datasets that are assumed to match the distribution of the data. This dissertation follows two approaches for improving modeling and processing of nonstationary signals. The first approach examines conventional model assumptions and suggests improvements that lead to improved performance for processing nonstationary signals. Specifically, noncircular distributions of the complex-valued short-time Fourier transform are shown to improve detection of realistic nonstationary signals. Then the parameterization of a recently-proposed recurrent neural network for processing nonstationary signals is reexamined. By using an optimization method that preserves the capacity of the recurrence matrix, superior performance is achieved on a battery of benchmarks that test the ability of recurrent neural networks to process nonstationary signals. The second approach uses the recently-proposed framework of deep unfolding, which provides a principled means of transforming statistical model inference algorithms into deep networks. This dissertation expands the deep unfolding framework specifically for nonstationary signals. Using this framework, a model-based explanation is provided for state-ofthe-art recurrent neural architectures, including gated recurrent unit and unitary recurrent neural networks. Additionally, deep unfolding results in deep network architectures that arise in principled ways from statistical model assumptions. This statistical model foundation provides initializations for the unfolded networks, which lead to better generalization, faster training, and competitive or superior performance on a variety of tasks, including singleand multichannel acoustic source separation and classification of acoustic signals.
Surface temperature a critical parameter to control peanut quality during far infrared pretreatment process

Far infrared (FIR) radiation was used for surface decontamination of peanuts at varying exposure temperatures between 150-300°C. Depending on the holding time, FIR heating resulted in complete inactivation of total mold and yeast population. 5 log reduction in total mold and yeast population was observed when surface temperature of the peanuts reached 70°C. Besides the microbial reduction and changes on quality characteristics, the effect of infrared heating on physical quality was also investigated during FIR heating process. As a result, the surface temperature was considerable in relation to both microbiological and physical quality characteristics and the measurement of the surface temperature can be suggested as an effective and rapid method to control majority of quality parameters of peanut samples during the FIR heating treatment which could also find commercial use as an effective postharvest process for nuts considering the final microbiological and physical quality.
Chapter A Thalidomide in Mann ’ s Pharmacovigilance

Thalidomide was marketed in 1957 as a very safe sedative drug. But the clinical safety documentation was very poor and more than 10 000 malformed children were born before withdrawal from the market in 1961. The lessons from the thalidomide story have had very strong impact on drug development, the drug regulatory process, and on the science and application of pharmacovigilance. Today, thalidomide is back on the market for treatment of specific life-threatening conditions and with a specific follow-up system to avoid malformations. The aim of this chapter is to tell the thalidomide story in a scientific perspective based on chemical and biomedical aspects of the molecule and on the pharmacology and use. This journey is documented in several hundred publications. Therefore, reviews are normally used instead of original references, except for the most paramount and recent publications. HISTORICAL PERSPECTIVE
Wormhole: The Hidden Virus Propagation Power of the Search Engine in Social Networks

Today search engines are tightly coupled with social networks, and present users with a double-edged sword: they are able to acquire information interesting to users but are also capable of spreading viruses introduced by hackers. It is challenging to characterize how a search engine spreads viruses, since the search engine serves as a virtual virus pool and creates propagation paths over the underlying network structure. In this paper, we quantitatively analyze virus propagation effects and the stability of the virus propagation process in the presence of a search engine. First, although social networks have a community structure that impedes virus propagation, we find that a search engine generates a propagation wormhole. Second, we propose an epidemic feedback model and quantitatively analyze propagation effects based on a model employing four metrics: infection density, the propagation wormhole effect, the epidemic threshold, and the basic reproduction number. Third, we verify our analyses on four real-world data sets and two simulated data sets. Moreover, we prove that the proposed model has the property of partial stability. Evaluation results show that, compared the cases without a search engine, virus propagation with the search engine has a higher infection density, shorter network diameter, greater propagation velocity, lower epidemic threshold, and larger basic reproduction number.
LASER DIODE INTENSITY NOISE INDUCED BY MODE HOPPING

Despite their advantages, laser diodes exhibit some limitations: multi-mode operation, mode hops and broad linewidth. Mode hopping is directly correlated to the noise in the total optical intensity and it can be easily revealed using a PIN photodiode. There are combinations of laser case temperature and injection current that lead to mode hopping and others for which the laser is stable. Recording such noise characteristics of laser diode has implications for controlling and circumventing mode hopping effects.
TAGUCHI MODELING WITH THE INTERACTION TEST FOR HIGHER DRIVE CURRENT IN WSI X / TIO 2 CHANNEL VERTICAL DOUBLE GATE NMOS DEVICE

This paper presents a study in which Taguchi method has been utilized to increase the drive current (ION) in the WSix/TiO2 Vertical Double Gate NMOS Device. The performance of the device is evaluated in term of its highest drive current (ION) value. Initially, the Vertical Double Gate NMOS device is virtually fabricated and characterized by using both ATHENA and ATLAS modules of SILVACO TCAD tools. The L12 orthogonal array, main effects, signal-to noise ratio (SNR) and analysis of variance (ANOVA) are utilized in order to analyze the effect of process parameter variations on the drive current (ION). The interaction between the process parameters are also investigated by using L8 orthogonal array of Taguchi method. Based on the results of the analysis, source/drain (S/D) implant energy has been identified as the most dominant process parameter in which it has contributed 90% of factor effects on SNR. The highest possible value of drive current (ION) after the optimization and the interaction test is 2859.7 μA/μm.
Threatened by Stereotype : An investigation of the effect of stereotype threat on female and minority student ’ s STEM learning in the context of a computing intervention

To contribute to the growing field of STEM education, we examined the presence of stereotype threat for female and African American students during a large-scale computing intervention. Namely, this paper examines whether gender and race have an effect on students’ self-perceived technology efficacy, technology anxiety, STEM attitude, and college expectations. In total, 1,085 student survey responses were analyzed in order to better understand the effects of stereotype threat on females’ and minorities’ STEM learning. The results suggest that gender was a consistent determinant of STEM attitude and technology efficacy. Furthermore, race was found to be a predictor of technology anxiety. In regards to college expectations, gender and race were not predictors. Instead, STEM attitude and self-efficacy were found to be positively associated with students’ expectations to attend college.
Vitamin D and Systemic Effects of Vitamin D Deficiency

INTRODUCTION Vitamin D is a vital vitamin, which has antiproliferative, pro-differentiative, proapoptotic, and immunomodulatory functions. It is produced in the skin after exposure to sunshine. Recently, many diseases have been associated with vitamin D deficiency in the literature. In this review, we aimed to demonstrate the metabolism, functions and effects of vitamin D, increased risks of chronic diseases that are likely to be seen in vitamin D deficiency and the importance of vitamin D replacement therapy. Vitamin D sources Vitamin D has two basic forms: Vitamin D2 (ergocalciferol), ergosterol exists in plants as an Vitamin D and Systemic Effects of Vitamin D Deficiency D Vitamini ve D Vitamini Eksikliğinin Sistemik Etkileri Address correspondence to: Mehmet Emin Cem Yildirim, Necmettin Erbakan Üniversitesi, Meram Tıp Fakültesi Hastanesi, Plastik Rekonstrüktif ve Estetik Cerrahi Anabil im Dalı, Konya, Turkey e-mail: dr.cem_yildirim@hotmail.com Cite this article as: Ince B, Yildirim MEC, Ismayilzade M, Dadaci M. Vitamin D and Systemic Effects of Vitamin D Deficiency. Selcuk Med J 2018;34(2): 84-89 . Disclosure: None of the authors has a financial interest in any of the products, devices, or drugs mentioned in this article. The research was not sponsored by an outside organization. All authors have agreed to allow full access to the primary data and to allow the journal to review the data
Simultaneous quantification of Cd , Cu , Pb and Zn in Thai fermented food by DPASV with a microwave digestion

A differential pulse anodic stripping voltammetry (DPASV) has been developed for the determination of Cd, Pb, Cu and Zn in the local traditional fermented foods of northeastern Thailand. An optimized reagent mixture for microwave digestion composed of 6 mL of HNO3, 2 mL of H2O2 and 2 mL of HF used in microwave digestion. Detection limits of 1.0, 2.0, 0.5 and 0.5 μg/L for Cd, Cu, Pb and Zn, respectively, were obtained. Percentage recoveries obtained of each metal to the sample solution were in the range of 80.6–112.5. It was found that the content of Cd, Cu, Pb and Zn extracted from the samples were in the range < 1.0, 10.36-12.45, 18.50-20.75 and 309.65-335.80 μg/L, respectively. The method was successfully applied to determine Cd, Pb, Cu and Zn in Thai traditional fermented food. The results were in agreement with those of graphite furnace atomic absorption spectrometry (GFAAS).
Recent progress of lysophosphatidylcholine acyltransferases in metabolic disease and cancer

With the rapid development of the economy, the updating of different diseases, especially metabolic diseases and cancers, is increasingly rapid and the difficulty of curing diseases is becoming greater. Lysophosphatidylcholine acyltransferase (LPCAT) has been shown to be more likely to participate in the development of related diseases. In the present review, we mainly focused on progress of the role of LPCAT in metabolic diseases and cancers and wish to provide new points to prevent and treat metabolic diseases and cancers. Related research of LPCAT in metabolic diseases, inflammatory diseases, and cancers have been reviewed. LPCAT 1-4 have different roles in metabolic diseases, such as diabetes, hyperlipidemia, hyperglycemia, and nonalcoholic steatohepatitis. Furthermore, LPCAT1 has been an independent predictor of early tumor recurrence and represents a novel prognostic biomarker for hepatocellular carcinoma, gastric cancer, breast cancer, oral squamous cell carcinoma, prostate cancer, colorectal cancer, and clear cell renal cell carcinoma. LPCAT2 is recognized as a novel aggressive prostate cancer susceptibility gene, and LPCAT4 participates in the elevated expression of PC (16:0/16:1) in colorectal cancer. LPCATs will provide a foundation for potential novel therapeutic approaches and highlight the important role of phospholipid metabolism in the prevention and treatment of metabolic diseases and cancers.
HEAT TRANSFER ENHANCEMENT IN LAMINAR FLOW OVER FLAT PLATE USING SMALL PULSATING JET

W. Rakpakdee W. Chaiworapuek* Department of Mechanical Engineering, Faculty of Engineering, Kasetsart University, 50 Ngamwongwan Road, Ladyao, Chatuchak, Bangkok, Thailand ABSTRACT: A technique of heat transfer enhancement over a heated plate using a water injection to disturb a laminar boundary layer is presented in this paper. The water jet was injected from the heated surface to the mainstream flow, having turbulence intensity of 1% through a 1 mm diameter hole. After the injection, a turbulent spot playing an important role on the heat transfer enhancement was generated artificially and investigated experimentally using thermochromic liquid crystals coated on the hot wall. In this study, contours of temperature and heat transfer coefficient were presented when the Reynolds number in the mainstream flow was varied as 117,000, 158,000, and 199,000 under the constant heat flux condition. The results show that this technique can increase heat transfer capability between the hot plate and freestream flow. The results from this research were found to be an important information that will be able to increase the efficiency of heat exchanger in the future.
Performance Analysis of Routing Protocols in Mobile Ad-hoc Networks Using NS 2

Mobile Ad-hoc Networks (MANETs) are considered as a new emerging technology of infrastructure-less mobile wireless communication systems. Routing in MANETs is considered a challenging task due to the unpredictable changes in the network topology, resulting from the random and frequent movement of the nodes and due to the absence of any centralized control. Many routing protocols for MANETs have been developed and reported in the literature but they still lack in performance. With this motivation, these five routing protocols viz. AODV (Ad-hoc On Demand Routing protocol), AOMDV (Ad-hoc On-demand Multipath Distance Vector, DSR (Dynamic source routing), DSDV (Destination Sequence Distance Vector )and PAAODV (Power Aware Ad-hoc On Demand Distance Vector) are selected for their performance evaluation by considering the Packet Delivery Ratio (PDR), average end-to-end delay, residual energy and throughput with respect to the number of mobile nodes varies from 10 to 50.The simulation are performed using NS2 simulator to study inter-layer interactions and performance of the protocols are analyzed. KeywordsAOMDV, DSDV, DSR, MANET, PAAODV etc. __________________________________________________*****_________________________________________________
Contributions of Continuous Max-Flow Theory to Medical Image Processing

Discrete graph cuts and continuous max-flow theory have created a paradigm shift in many areas of medical image processing. As previous methods limited themselves to analytically solvable optimization problems or guaranteed only local optimizability to increasingly complex and non-convex functionals, current methods based now rely on describing an optimization problem in a series of general yet simple functionals with a global, but non-analytic, solution algorithms. This has been increasingly spurred on by the availability of these general-purpose algorithms in an open-source context. Thus, graph-cuts and max-flow have changed every aspect of medical image processing from reconstruction to enhancement to segmentation and registration. To wax philosophical, continuous max-flow theory in particular has the potential to bring a high degree of mathematical elegance to the field, bridging the conceptual gap between the discrete and continuous domains in which we describe different imaging problems, properties and processes. In Chapter 1, we use the notion of infinitely dense and infinitely densely connected graphs to transfer between the discrete and continuous domains, which has a certain sense of mathematical pedantry to it, but the resulting variational energy equations have a sense of elegance and charm. As any application of the principle of duality, the variational equations have an enigmatic side that can only be decoded with time and patience. The goal of this thesis is to show the contributions of max-flow theory through image enhancement and segmentation, increasing incorporation of topological considerations and increasing the role played by user knowledge and interactivity. These methods will be rigorously grounded in calculus of variations, guaranteeing fuzzy optimality and providing multiple solution approaches to addressing each individual problem.
The Influence of Marketing Mix and Perceived Risk Factors on Online Purchase Intentions

In the technology evolution era, many changes on different level have been noticed (i.e., business, strategies, markets, lifestyle, etc.). These changes, further, increase the challenges faced by the companies. Therefore, companies and businessmen have no option only to adopt and adapt with evolution in the market, especially, some of the changes have greatly improve the lifestyle of people by reducing the redundant works and making processes more efficient. With the introduction of internet, information has become accessible and more functions have been added daily to improve the lives of the people in the world. Nowadays the usage of internet is rapidly growing either for social purposes, searching information, complete an assignment, shopping and many more other purposes (Freedom on the net, 2015). Undoubtedly, introducing smart phones enhances and facilitates using internet among people over world (Freedom on the net, 2015). Hence, the number of internet users are increasing with every passing years and it is still increasing (Bernama, 2014; Freedom on the net, 2015). Using internet is growing to extend that people handle and manage many aspects of their life online. Likewise purchasing is one of the life‟s aspects that started to transform to be online(Zendehdel, Paim, Osman, & Wright, 2015). Due to the several advantages and services which are offered by online purchasing, it becomes more attractive for both consumers and vendors (Lee, Ariff, Zakuan, Sulaiman, & Saman, 2016).
Context-Aware Self-Healing for Small Cell Networks

These can be an invaluable source of information for the management of the network, in a way that we have denominated as context-aware SON, which is the approach proposed in this thesis. To develop this concept, the thesis follows a top-down approach. Firstly, the characteristics of the cellular deployments are assessed, especially for indoor small cell networks. In those scenarios, the need for context-aware SON is evaluated and considered indispensable. Secondly, a new cellular architecture is defined to integrate both context information and SON mechanisms in the management plane of the mobile network. Thus, the specifics of making context an integral part of cellular OAM/SON are defined. Also, the real-world implementation of the architecture is proposed. Thirdly, from the established general SON architecture, a logical self-healing framework is defined to support the context-aware healing mechanisms to be developed. Fourthly, different self-healing algorithms are defined depending on the failures to be managed and the conditions of the considered scenario. The mechanisms are based on probabilistic analysis, making use of both context and network data for detection and diagnosis of cellular issues. The conditions for the implementation of these methods are assessed. Their applicability is evaluated by means of simulators and testbed trials. The results show important improvements in performance and capabilities in comparison to previous methods, demonstrating the relevance of the proposed approach.
ALGORITHMS FOR THE AMALGAMATION OF TOPOGRAPHIC DATA

One of the biggest challenge faced by National Mapping Agencies is to produce and maintain a single geographic database from which it would be possible to derive a broad range of products on demand. While such databases are now a reality, the tools to derive products from them automatically are not yet available. One of the main problem to derive products from a single very detailed database is to master generalisation techniques. Automating generalisation has been the focus of lots of research in the last three decades. Early research focused on designing standalone algorithms and was then extended to address the modelling of the knowledge necessary to successfully automate the whole process. This in itself led to studies on classification of generalisation operations, measures and spatial analysis, ending up in designs for systems able to combine algorithms to achieve a specific generalisation. The result of research like the AGENT project (ESPRIT/LTR/24939), shows that we can be optimistic about the prospect of building an automatic system to generalise a map, given a set of specifications. What we still cant do is allow the user to modify these specifications and expect the system to perform adequate generalisation. The most the system can take into account is slight changes in the threshold values, but as soon as a change of representation style is required it can not cope. To go further, we need to improve our knowledge of the different generalisation operations. Each operation usually has many possible variations. We need to identify what they are, and when they are used. This will allow us to create better algorithms and incorporate in the map specifications the right information needed for the system to achieve the appropriate generalisation. After discussing the role of amalgamation during the generalisation process, this paper presents a classification of the different types of amalgamation, based on the observation of different series of paper maps available at the Ordnance Survey. Then it proposes a set of amalgamation algorithms that would be useful for generalising topographic data to produce medium scales graphic products.