Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of Substrate IntegratedWaveguide Power Divider and Parameter optimization using Neural Network

Motivated by the key technology Substrate Integrated Waveguide (SIW) and an effective approach of Artificial Intelligence (AI), this paper aims at the design of an SIW based power divider for low return loss using Artificial Neural Network (ANN). A 15GHz SIW based power divider is presented, and parameters are optimized using error back-propagation in ANN. Results achieved shows how one can take help of ANN to make the design more efficient and faster by reducing the number of simulation, As we know that simulation time is a major issue in SIW components. In this paper, presented SIW based power divider is designed using T-junction configuration possess return loss of -31.72 dB and output power equality achieved is 3.39 dB and 3.40 dB at port 2 and 3 respectively at the designed frequency.
LIMITED INFORMATION LIKELIHOOD INFERENCE IN STOCHASTIC VOLATILITY JUMP-DIFFUSION MODELS

We develop a novel method for estimation and filtering of continuous-time models with stochastic volatility and jumps using so-called Approximate Bayesian Computation which build likelihoods based on limited information. The proposed estimators are computationally attractive relative to standard likelihood-based estimators since they rely on low-dimensional auxiliary statistics and so avoid computation of high-dimensional integrals. We also develop a simple filtering algorithm that allows one to track the latent volatility process in real time without any heavy computational burden. Despite their computational simplicity, we find that estimators and filters perform well in practice and leads to precise estimates of model parameters and latent variables. We show how the methods can incorporate intra-daily information to improve on the estimation and filtering. In particular, the availability of realized volatility measures help us in learning about parameters and latent states. The method is employed in the estimation of a flexible stochastic volatility model for the dynamics of the Stoxx50 equity index. We find evidence of the presence of jumps and in favor of a structural break in parameters. During the recent financial crisis, volatility has a higher mean and variance, and is less persistant than in the before crisis period. Jumps occur slightly less frequently, and are more likely to be negative when they do occur.
MODELING OF SILICON CHARACTERISTICS IN THE SEMICONDUCTOR-METAL PHASE TRANSITION REGION

In the temperature range of the semiconductor-metal phase transition by means of molecular dynamic modeling (MDM), the equilibrium properties of crystalline and molten silicon are determined. The pressure dependences of the specific heat of melting and the equilibrium melting temperature, the temperature dependences of the density, the linear size of the sample, the coefficient of linear expansion, enthalpy, and heat capacity are determined. The obtained dependences of the properties of silicon are approximated by polynomials of low degrees. The results of the comparison of the obtained characteristics of silicon with experimental data show an acceptable qualitative and quantitative agreement. Numerical and graphical information on the obtained properties and results of comparison with experimental data are presented.
Supplementary Material : Attacking Optical Flow

The universal patch is shown in Figure 3. Table 1 shows the performance of optical flow methods when the adversarial patch has zero motion w.r. t. the camera. In comparison to the moving Black-box attacks considered in the main paper, we observe similar effects on all networks and baselines with the adversarial patch. While encoder-decoder networks are strongly affected by the attacks, spatial pyramid networks and classical methods are more robust. In Figures 4 10 we show some additional qualitative results for the Black-box attack with patches moving according to the scene as described in the main paper. These examples demonstrate the feasibility of such attackes in the real world. In Figure 5, for instance, the patch is attached to and moves with a traffic sign, while Figures 6, 9 illustrate cases when a patch is printed on a wall and a car.
Search for Higgs boson production in trilepton and like-charge electron-muon final states with the D0 detector

We present a search for Higgs bosons in multilepton final states in pp-bar collisions at sqrt(s)=1.96 TeV recorded with the D0 detector at the Fermilab Tevatron Collider, using the full Run II data set with integrated luminosities of up to 9.7 fb-1. The multilepton states considered are two electron plus muon, electron with two muons, muon with two hadronic tau leptons, and like-charge electron-muon pairs. These channels directly probe the HVV (V=W,Z) coupling of the Higgs boson in production and decay. The muon with two hadronic tau lepton channel is also sensitive to H to tau lepton pair decays. Upper limits at the 95% C.L on the rate of standard model Higgs boson production are derived in the mass range 100<= M_H<= 200 GeV. The expected and observed limits are a factor of 6.3 and 8.4 above the predicted standard model cross section at M_H=125 GeV. We also interpret the data in a fermiophobic Higgs boson model.
State-Space Predictive Control of a Lyophilization P lant : a

This paper describes a design methodology for model predictive controller based on Hammerstein State-Space model. The model nonlinearity is approximated by using a simple TakagiSugeno inference, while the linear part is flexibly introduced. The antecedent part of the fuzzy rules represents a general State-Space system and its parameters are scheduled at each sampling period by minimization of an instant error measurement function. As optimization procedure it is used an implementation of Hildreth Quadratic Programming algorithm. The effectiveness of the presented approach is demonstrated by experiments for control of a lyophilization plant.
Optimal first-passage time in gene regulatory networks

The inherent probabilistic nature of the biochemical reactions, and low copy number of species can lead to stochasticity in gene expression across identical cells. As a result, after induction of gene expression, the time at which a specific protein count is reached is stochastic as well. Therefore events taking place at a critical protein level will see stochasticity in their timing. First–passage time (FPT), the time at which a stochastic process hits a critical threshold, provides a framework to model such events. Here, we investigate stochasticity in FPT. Particularly, we consider events for which controlling stochasticity is advantageous. As a possible regulatory mechanism, we also investigate effect of auto–regulation, where the transcription rate of gene depends on protein count, on stochasticity of FPT. Specifically, we investigate for an optimal auto-regulation which minimizes stochasticity in FPT, given fixed mean FPT and threshold. For this purpose, we model the gene expression at a single cell level. We find analytic formulas for statistical moments of the FPT in terms of model parameters. Moreover, we examine the gene expression model with auto–regulation. Interestingly, our results show that the stochasticity in FPT, for a fixed mean, is minimized when the transcription rate is independent of protein count. Further, we discuss the results in context of lysis time of an E. coli cell infected by a λ phage virus. An optimal lysis time provides evolutionary advantage to the λ phage, suggesting a possible regulation to minimize its stochasticity. Our results indicate that there is no auto–regulation of the protein responsible for lysis. Moreover, congruent to experimental evidences, our analysis predicts that the expression of the lysis protein should have a small burst size.
Credit Risk Models

We want to dispute in this article the idea that structural models are used just as intensity models, that is, as a "probabilistic black box" designed to be calibrated on market data such as the credit spreads of CDS. We begin this paper by o ering a quick introduction to the CDS market, and then present a selected review of some existing models (intensity and structural) to compute the mark-to-market of a CDS. Calibration algorithms are also discussed. We then present a non representative sample of recent models from quantitative corporate that aim to provide a more realistic explanation of the default process.
Utah Stakeholders' Attitudes toward Selected Cougar and Black Bear Management Practices

We examined Utahns' attitudes (n=901) toward use of recreational hunting to manage black bears (Ursus americanus) and cougars (Puma concolor), use of hounds to hunt hese species, and the practice of bear baiting. Independent variables included urban versus rural residence, gender, educational attainment, age, duration of in-state residence, and stakeholder group classification. Most Utahns disapproved of the cougar and black bear management practices examined. Differences inresponses were associated with sociodemographic characteristics and with participation i wildlife-related recreation. The following groups were less opposed to the selected practices than their counterparts: rural residents, men, those with lower levels of education, longtime residents, younger espondents, and hunters. Survey analyses can help wildlife managers identify areas of controversy where public involvement and educational efforts might be prescribed.
Unsteady flow of a Newtonian Fluid through a cylindrical collapsible tube Research Article

In this research, unsteady flow of a Newtonian fluid flowing through a cylindrical collapsible tube has been investigated. The Governing equations are the equation of continuity, momentum and the tube law relation. This study has formulated a mathematical model of a Newtonian fluid in collapsible tube to simulate physiological flows such as flow of blood and urine within the human body system. The study has considered a two-dimensional model. A non-linear equation has been obtained from the governing equations. Finite difference approximation techniques have been employed to solve numerically this non-linear equation since they cannot be solved analytically. A computer program code has been used to obtain results which has been presented inform of graphs. The effects of varying different flow parameters on various flow variables has been determined numerically. It was noted that a change in flow parameters leads to either increase or decrease on flow variables. This study is very important because of its widespread, especially in the medical field where it will help the clinician to predict and prevent diseases such as cardiovascular diseases (heart stroke) or lung disease (asthma). MSC: 74Bxx • 92Cxx
Insect Cyborgs: Bio-mimetic Feature Generators Improve ML Accuracy on Limited Data

We seek to auto-generate stronger input features for ML methods faced with limited training data. Biological neural nets (BNNs) excel at fast learning, implying that they extract highly informative features. In particular, the insect olfactory network learns new odors very rapidly, by means of three key elements: A competitive inhibition layer; randomized, sparse connectivity into a high-dimensional sparse plastic layer; and Hebbian updates of synaptic weights. In this work we deploy MothNet, a computational model of the moth olfactory network, as an automatic feature generator. Attached as a front-end pre-processor, MothNet’s readout neurons provide new features, derived from the original features, for use by standard ML classifiers. These “insect cyborgs” (part BNN and part ML method) have significantly better performance than baseline ML methods alone on vectorized MNIST and Omniglot data sets, reducing test set error averages 20% to 55%. The MothNet feature generator also substantially out-performs other feature generating methods including PCA, PLS, and NNs. These results highlight the potential value of BNN-inspired feature generators in the ML context.
Informational Shocks, O -Label Prescribing and the E ects of Physician Detailing

The relationship between pharmaceutical detailing and o -label prescribing has been the subject of considerable regulatory scrutiny, with more than $12 billion in regulatory settlements for o label promotion since 2004. Using a physician panel combining detailing data with patient chart information, I study how detailing causes physicians to prescribe for on-label versus o label uses for the branded anti-psychotic drug, Seroquel. Between 2001 and 2006, Seroquel received two informational shocks in the form of good news about its side e ect pro le relative to other treatments. These shocks were each immediately followed by large increases in detailing to primary care physicians. I exploit the gap between the incidence of the shock and the rst detailing visit to separate the direct e ect of the information, such as reading the journal articles, from the incremental e ect of the detailing. While detailing was indeed targeted at many physicians who never prescribed on-label, I nd the e ect of detailing on o -label prescriptions is small in both absolute and relative terms. Detailing on net tilts the prescribing distribution toward on-label.
The Impact of Country Personality Dimensions on Country Attractiveness and Word-of-Mouth: A Cambodian Tourism Case

Conceptualizing a country as a brand, which requires a marketing promotion, can lead to the idea of relating personality to a country. The current study thus focuses on the dimensions of country personality traits on country attractiveness and word-of-mouth in terms of tourism. A survey was conducted with a sample of Thai undergraduate students and Cambodia as the country context. Data were analyzed by a multiple regression in order to explore the relationship among country personality traits, country attractiveness and word-of-mouth. Results reveal that only the competence and sophistication country personality dimensions significantly impact country attractiveness and word-of-mouth. Furthermore, country attractiveness also partially mediates the influence of the sophistication dimension and fully mediates the influence of the competence dimension on word-of-mouth. These findings provide stakeholders in tourism business, both in public and private sectors, a better understanding of country 1 Faculty of Management Science, Surindra Rajabhat University, Surin, Thailand 2 Faculty of Business Administration and Accountancy, Khon Kaen University, Khon Kaen, Thailand * Corresponding author E-mail: sirintip_tri@hotmail.com MFU Connexion, 6(2) || page 110 personality so that they can create a marketing campaign for a country by positioning an appropriate country personality.
BCOSN: A Blockchain-Based Decentralized Online Social Network

Online social networks (OSNs) are becoming more and more prevalent in people’s life, but they face the problem of privacy leakage due to the centralized data management mechanism. The emergence of distributed OSNs (DOSNs) can solve this privacy issue, yet they bring inefficiencies in providing the main functionalities, such as access control and data availability. In this article, in view of the above-mentioned challenges encountered in OSNs and DOSNs, we exploit the emerging blockchain technique to design a new DOSN framework that integrates the advantages of both traditional centralized OSNs and DOSNs. By combining smart contracts, we use the blockchain as a trusted server to provide central control services. Meanwhile, we separate the storage services so that users have complete control over their data. In the experiment, we use real-world data sets to verify the effectiveness of the proposed framework.
Diagnostic value of a microRNA signature panel in exosomes for patients with hepatocellular carcinoma.

BACKGROUND Hepatocellular carcinoma (HCC) is the fifth most frequently diagnosed cancer worldwide and the second most frequent cause of cancer death. The aim of this study is to investigate the early diagnostic value of a panel of peripheral blood exosomal micro-RNAs (miRNAs) in patients with HCC compared with patients with Hepatitis B virus (HBV) and hepatocirrhosis. PATIENTS AND METHODS Blood samples from 72 patients with HCC, 72 patients with hepatocirrhosis and 72 patients with HBV were obtained at Beijing Friendship Hospital, Capital Medical University. The miRNA expression levels were detected by real-time polymerase chain reaction (RT-PCR). Areas under curve (AUCs) were used to compare diagnostic values of plasmic and exosomal miRNAs. RESULTS We screened plasmic and exosomal solutions of 3 HCC, 3 cirrhosis and 3 HBV patients to perform miRNA microarray analysis. Three distinctly differential microRNAs including miRNA-26a, miRNA-29c, and miRNA-21 were selected to perform further evaluation. First, we found that the expressions of miRNA-26a, miRNA-29c, and miRNA-21 were significantly lower in patients with HCC compared with cirrhotic and HBV group in both exosomes and plasma. Second, we found miRNA-26a, miRNA-29c, and miRNA-21 were significantly down-regulated in HCC tumor tissues compared with normal tissues. Thirdly, we found miRNAs in exosomes had better diagnostic value for patients with HCC compared with plasmic miRNAs among different groups. CONCLUSIONS In conclusion, we found that the expression of miRNA-26a, miRNA-29c, and miRNA-21 were significantly lower in patients with HCC, and we confirmed miRNA-26a, miRNA-29c, and miRNA-21 could be identified as independent diagnostic biomarkers for patients with HCC.
Visual Designers Perspective for use of Information Visualization in Electronic Health Records

Visual Designers are one of the core functional stakeholders involved in designing of Information Visualization (IV) tools to facilitate the understanding of Electronic Health Records (EHR). Interface design, icons selection, color adjustment to complex data representation in numerical to graphic metaphoric formats are main areas of Visual Designers efforts to make an IV application more viable for its use by other stakeholders such as Doctors and Database Administrators (DBA). Current IV applications lack the simplicity and completeness in interface understanding point of view due to complex designs and incomplete patient information representation. This is resulting lesser interactional input of Visual Designers in the development of IV applications for EHR. Complex and non-aligned IV designs formulate a difficult representation of EHR that cause difficult understanding, complexity of information flow and poor user interaction. Less knowledge about IV design, poor functional interpretation of visualization and incomplete representation lead to weaker and non-implementation phase of IV in EHR. The key features with Visual designers are understanding user requirements, VAS limitations knowledge, and future improvement perspective from design point of view. This paper presents the outcome of a survey based questionnaire study conducted on Visual designers working in public hospitals and temporal data formats. This research provides an outline of Visual designer’s knowledge factors identification affecting overall performance of IV applications.
Analysis of Network Operators’ Revenue with a Dynamic Pricing Model Based on User Behaviour in NGWN Using JCAC

Next Generation of Wireless Networks are expected to be heterogeneous, whereby multiple Radio Access Technologies will work together in a coordinated fashion. These networks will be required to accommodate different types of users with different QoS requirements. Effective Joint Call Admission Control (JCAC) algorithms are a prerequisite in NGWN to ensure that users are offered optimal QoS. Studies have therefore been undertaken where JCAC algorithms for NGWN have been proposed. Correspondingly, pricing has been used with JCAC to obtain optimal QoS. In these studies, the revenue obtained by network operators has not been analysed. This paper proposes a dynamic pricing model for NGWN. The price of services will determine how users behave as they access the network. Users are considered to be price sensitive whereby user demand increases when the price is lowed and vice versa. This phenomenon is used to increase utility during periods of low load and reduce congestion during periods of overload. The revenue obtained by network operators in the NGWN is hence determined over a 24-hour period. To bench mark the dynamic pricing model, comparison is made with flat pricing incorporated in the NGWN. The dynamic pricing model is found to offer higher revenue, better user utility and improved system performance.
A Case for Quantifying Statistical Robustness of Specialized Probabilistic AI Accelerators

Statistical machine learning often uses probabilistic algorithms, such as Markov Chain Monte Carlo (MCMC), to solve a wide range of problems. Many accelerators are proposed using specialized hardware to address sampling inefficiency, the critical performance bottleneck of probabilistic algorithms. These accelerators usually improve the hardware efficiency by using some approximation techniques, such as reducing bit representation, truncating small values to zero, or simplifying the Random Number Generator (RNG). Understanding the influence of these approximations on result quality is crucial to meeting the quality requirements of real applications. Although a common approach is to compare the end-point result quality using community-standard benchmarks and metrics, we claim a probabilistic architecture should provide some measure (or guarantee) of statistical robustness. This work takes a first step towards quantifying the statistical robustness of specialized hardware MCMC accelerators by proposing three pillars of statistical robustness: sampling quality, convergence diagnostic, and goodness of fit. Each pillar has at least one quantitative metric without the need to know the ground truth data. We apply this method to analyze the statistical robustness of an MCMC accelerator proposed by previous work, with some modifications, as a case study. The method also applies to other probabilistic accelerators and can be used in design space exploration.
Metastasis-on-a-chip mimicking the progression of kidney cancer in the liver for predicting treatment efficacy

Metastasis is one of the most important factors that lead to poor prognosis in cancer patients, and effective suppression of the growth of primary cancer cells in a metastatic site is paramount in averting cancer progression. However, there is a lack of biomimetic three-dimensional (3D) in vitro models that can closely mimic the continuous growth of metastatic cancer cells in an organ-specific extracellular microenvironment (ECM) for assessing effective therapeutic strategies. Methods: In this metastatic tumor progression model, kidney cancer cells (Caki-1) and hepatocytes (i.e., HepLL cells) were co-cultured at an increasing ratio from 1:9 to 9:1 in a decellularized liver matrix (DLM)/gelatin methacryloyl (GelMA)-based biomimetic liver microtissue in a microfluidic device. Results: Via this model, we successfully demonstrated a linear anti-cancer relationship between the concentration of anti-cancer drug 5-Fluorouracil (5-FU) and the percentage of Caki-1 cells in the co-culture system (R2 = 0.89). Furthermore, the Poly(lactide-co-glycolide) (PLGA)-poly(ethylene glycol) (PEG)-based delivery system showed superior efficacy to free 5-FU in killing Caki-1 cells. Conclusions: In this study, we present a novel 3D metastasis-on-a-chip model mimicking the progression of kidney cancer cells metastasized to the liver for predicting treatment efficacy. Taken together, our study proved that the tumor progression model based on metastasis-on-a-chip with organ-specific ECM would provide a valuable tool for rapidly assessing treatment regimens and developing new chemotherapeutic agents.