Miracle-dz/UniFAR-FTUs huggingface dataset:
Sexuality Research in Iran: A Focus on Methodological and Ethical Considerations

Background: Research on sensitive topics, such as sexuality, could raise technical, methodological, ethical, political, and legal challenges. The aim of this paper was to draw the methodological challenges which the authors confronted during sexuality research with young population in the Iranian culture. Methods: This study was an exploratory mixed method one conducted in 2013–14. We interviewed 63 young women aged 18–34 yr in qualitative phase and 265 young women in quantitative phase in (university and non-university) dormitories and in an Adolescent Friendly Center. Data were collected using focus group discussions and individual interviews in the qualitative phase. We employed conventional content analysis to analyze the data. To enhance the rigor of the data, multiple data collection methods, maximum variation sampling, and peer checks were applied. Results: Five main themes emerged from the data: interaction with opposite sex, sexual risk, sexual protective, sex education, and sexual vulnerability. Challenges while conducting sex research have been discussed. These challenges included assumption of promiscuity, language of silence and privacy concerns, and sex segregation policy. Conclusion: We described the strategies applied in our study and the rationales for each strategy. Strategies applied in the present study can be employed in contexts with the similar methodological and moral concerns.
Nonparametric Time Series Model Selection

Nonparametric procedures are an interesting alternative to classical time series analysis. The nonparametric technique follows the principle ofìetting the data speak for themselves,' and provides guidance in choosing a para-metric models. In this paper local polynomial estimators are given for vector conditional heteroskedastic autore-gressive nonlinear (CHARN) model in which both the conditional mean and the conditional variance (volatility) matrix are unknown functions of the past. We examine the rates of convergence of these estimators and their asymptotic normality. These are applied to estimation of volatility matrices of foreign exchange rates. As the usual nonparametric models often have less than satisfactory performance when dealing with more than one lag, we also give the joint estimation of the additive mean and the multiplicative volatility, which fully exploits the additive/multiplicative structure. We then discuss the usefulness of this approach in selecting the correct lags without assumptions on the forms of the structures. 1 Nonparametric Vector Autore-gression There are two very important aspects of model selection: the selection of form of the function, and the selection of signiicant variables. We concentrate on the rst aspect in Sections 1 to 3, which also provide the necessary tools for investigating the second aspect in Section 4. Multivariate time series occur in the modelling of dynamics over time and help explaining interdependence Acknowledgements: We would like to thank Alexander Tsybakov, Rolf Tschernig, Helmut L utkepohl and Christian Hafner for helpful discussions.
Liquid density analysis of sucrose and alcoholic beverages using polyimide guided Love-mode acoustic wave sensors

A liquid density sensor using Love-mode acoustic waves has been developed which is suitable for use in the food and drinks industries. The sensor has an open flat surface allowing immersion into a sample and simple cleaning. A polyimide waveguide layer allows cheap and simple fabrication combined with a robust chemically resistant surface. The low shear modulus of polyimide allows thin guiding layers giving a high sensitivity. A dual structure with a smooth reference device exhibiting viscous coupling with the wave, and a patterned sense area to trap the liquid causing mass loading, allows discrimination of the liquid density from the square root of the density–viscosity product (ρη)0.5. Frequency shift and insertion loss change were proportional to (ρη)0.5 with a non-linear response due to the non-Newtonian nature of viscous liquids at high frequencies. Measurements were made with sucrose solutions up to 50% and different alcoholic drinks. A maximum sensitivity of 0.13 µg cm−3 Hz−1 was achieved, with a linear frequency response to density. This is the highest liquid density sensitivity obtained for acoustic mode sensors to the best of our knowledge.
Transitions in cognitive development: prospects and limitations of a neural dynamic approach

In this chapter we discuss old-fashioned cognitive development from the point of view of two modern approaches, connectionism, and non-linear dynamical system theory. Old-fashioned cognitive development refers to the development of typical human cognitive capabilities, such as reasoning, planning, and abstract thinking. These abilities are distinctively human, in contrast to abilities usually studied in connectionism and the dynamic system approach, which generally focus on perception, pattern recognition, association, motor action, and simple memory. Such abilities are of interest, of course, but do not constitute the greatest challenge for researchers in cognitive development. Yet, connectionists and dynamic system modelers do claim that their models are relevant to higher cognitive functioning. This is apparent in their rejection of other approaches, such as the Piagetian and the information processing accounts of development. The main assertion in both the connectionist and the dynamic system approaches is that higher cognitive functioning is largely based on non-symbolic, graded, and dynamic properties, of which these same approaches provide the best account.
Using machine learning on sensor data

Developing hardware, algorithms and protocols, as well as collecting data in sensor networks are all important challenges in building good systems. We describe a vertical system integration of a sensor node and a toolkit of machine learning algorithms. Based on a dataset that combines sensor data with additional introduced data we predict the number of persons in a closed space. We analyze the dataset and evaluate the performance of two types of machine learning algorithms on this dataset: classification and regression.
Characterizing Network Complexity and Learning Efficiency by the Ratio of Weight Interdependence to Sensitivity

We extend previous research on parameter dynamics of digital filters to examine weight sensitivity and interdependence in feedforward networks. Weight sensitivity refers to the effect of small weight perturbations on the network's output, and weight interdependence refers to the degree of co-linearity between weights. A combined measure of the weight space (τ), defined as the ratio of weight interdependence to sensitivity, is explored in networks with hidden-unit activation functions of different complexity in the contexts of learning (1) a non-linearly separable bivariate normal classification task, (2) the XOR problem, (3) sigmoidal functions, and (4) sine functions. Simulations show that networks with more complex activation functions give rise to a smaller τ and more rapid learning, suggesting that weight sensitivity and interdependence together are indicative of network complexity and are predictive of learning efficiency.
Data Categories for a Normalized Reference Annotation Scheme.

This paper compiles a core set of data categories to be used in combination with the Reference Annotation Framework. It shows also that the underlying objective − ensuring internal and external coherence − is a difficult issue, since reference annotation articulates various levels of linguistic description which all contribute to the identification or qualification of markables and referential links.
NAD+ Utilization in Pasteurellaceae: Simplification of a Complex Pathway

Bacteria of the family Pasteurellaceae are well known to exist in close relationship with mammalian hosts, mostly as constituents of commensal bacterial flora. However, at the same time, some species also have the ability to cause serious diseases. For example, Mannheimia haemolytica and Haemophilus
An Autoconfigurated Hybrid Honeypot for Improving Security in Computer Systems

— Providing computer system security is one of the important areas of consideration in Information Technology. There is a rapid advancement in this area because no one exactly wants his system to be attacked by an intruder and the data to be compromised. An experienced attacker may get to know the weaknesses of the system and may obtain the sensible data. So its necessary to give protection against intrusion. So, we make use of honeypots that have some autoconfiguration feature based on the collected parameters.
An area and configuration-bit optimized CLB architecture and timing-driven packing for FPGAs

This paper proposes a function-generation based area-aware configurable logic block (CLB) architecture and an associated packing technique, for SRAM-based FPGAs. The new CLB architecture provides the same logic functionality, but occupies 38% less area, consumes 38.31% less power and requires 50% less configuration-bits per CLB when compared to the standard 4-LUT CLB architecture. The proposed packing technique is timing-driven and is shown to produce designs with almost same routing cost and performance overhead as that produced by the T-VPack algorithm on standard benchmark circuits.
Finding antipodal point grasps on irregularly shaped objects

The authors consider two-finger antipodal point grasping of arbitrarily shaped 2D and 3D objects. An object function which maps a finger contact space to the object surface is introduced. Conditions are developed to identify the feasible grasping region F in the finger contact space. A grasping energy function E is introduced which is proportional to the distance between two grasping points. The antipodal points correspond to critical points of E at F. Optimization and/or continuation techniques are used to find these critical points. In particular, global optimization techniques are applied to find the maximal grasp. Modeling techniques for representing 2D and 3D objects using B-spline curves and spherical product surfaces are described. <<ETX>>
An Epidemiological Investigation of Male-Female Differences in Drinking and Drinking-Related Problems between US-Born and Foreign-Born Latino and Asian Americans

Background. It has been widely documented that males were more likely to drinking alcohol and have alcohol use disorders (AUD). The degrees of the male-female differences in drinking and AUD have varied across countries. The reasons behind these variations have not been fully understood. The current study compared the estimated male-female differences across US-born and foreign-born Latino and Asian Americans with respect to alcohol drinking behavior and AUD. Method. Data come from the National Latino and Asian American Study (NLAAS), a national household survey of adults with Latinos and Asian decent in the United States. Male-female differences were estimated for drinking behavior and AUD among drinkers for US-born and foreign-born individuals, respectively. Zero-inflated Poisson regressions were utilized to estimate male-female differences in the number of AUD clinical features once it occurs. Results. Larger male-female differences were found for foreign-born individuals as compared to US-born individuals, especially the occurrence of AUD among drinkers. Once AUD clinical feature occurs, there was no male-female difference for foreign-born individuals, while there was a males excess in the number of clinical features for US-born individuals. Conclusion. Results from this study supports the importance of sociocultural influence in drinking and AUD. Implications for prevention and intervention programs were discussed.
Off-policy TD( l) with a true online equivalence

Van Seijen and Sutton (2014) recently proposed a new version of the linear TD(λ) learning algorithm that is exactly equivalent to an online forward view and that empirically performed better than its classical counterpart in both prediction and control problems. However, their algorithm is restricted to on-policy learning. In the more general case of off-policy learning, in which the policy whose outcome is predicted and the policy used to generate data may be different, their algorithm cannot be applied. One reason for this is that the algorithm bootstraps and thus is subject to instability problems when function approximation is used. A second reason true online TD(λ) cannot be used for off-policy learning is that the off-policy case requires sophisticated importance sampling in its eligibility traces. To address these limitations, we generalize their equivalence result and use this generalization to construct the first online algorithm to be exactly equivalent to an off-policy forward view. We show this algorithm, named true online GTD(λ), empirically outperforms GTD(λ) (Maei, 2011) which was derived from the same objective as our forward view but lacks the exact online equivalence. In the general theorem that allows us to derive this new algorithm, we encounter a new general eligibility-trace update.
Simulating Boolean circuits by finite splicing

As a computational model to be simulated in a DNA computing context, Boolean circuits are especially interesting because of their parallelism. Simulations in concrete biochemical computing settings have been given by Ogihara and Ray (1996) and Amos and Dunne (1997). In this paper, we show how to simulate Boolean circuits by finite splicing systems, an abstract model of enzymatic recombination. We argue that using an abstract model of DNA computation as a basis leads to simulations of greater clarity and generality. In our construction, the running time of the simulating system is proportional to the depth, and the use of material is proportional to the size of the Boolean circuit simulated. However, the rules of the simulating splicing system depend on the size of the Boolean circuit, but not on the connectives used.
Identification of a Novel Enterovirus E Isolates HY12 from Cattle with Severe Respiratory and Enteric Diseases

In this study, a virus strain designated as HY12 was isolated from cattle with a disease of high morbidity and mortality in Jilin province. Biological and physiochemical properties showed that HY12 isolates is cytopathic with an extremely high infectivity. HY12 is resistant to treatment of organic solvent and acid, and unstable at 60°C for 1 h. Electron microscopy observation revealed the virus is an approximately 22–28 nm in diameter. The complete genome sequence of HY12 consists of 7416 nucleotides, with a typical picornavirus genome organization including a 5′-untranslated region (UTR), a large single ORF encoding a polyprotein of 2176 amino acids, and a 3′-UTR. Phylogenetic analysis clustered HY12 isolates to a new serotype/genotype within the clade of enterovirus E (formerly BEV-A). Alignment analysis revealed a unique insertion of 2 amino acid residues (NF) at the C-terminal of VP1 protein between aa 825 and 826, and several rare mutations in VP1 and VP4 of HY12 isolates in relation to known bovine enterovirus (BEV) strains. This is the first report of an enterovirus E in China, which is potentially associated with an outbreak in cattle with severe respiratory and enteric diseases.
Sparse Representation Based Projections

In dimensionality reduction most methods aim at preserving one or a few properties of the original space in the resulting embedding. As our results show, preserving the sparse representation of the signals from the original space in the (lower) dimensional projected space is beneficial for several benchmarks (faces, traffic signs, and handwritten digits). The intuition behind is that taking a sparse representation for the different samples as point of departure highlights the important correlations among the samples that one then wants to exploit to arrive at the final, effective low-dimensional embedding. We explicitly adapt the LPP and LLE techniques to work with the sparse representation criterion and compare to the original methods on the referenced databases, and this for both unsupervised and supervised cases. The improved results corroborate the usefulness of the proposed sparse representation based linear and non-linear projections.
A Fistful of Red-Pills: How to Automatically Generate Procedures to Detect CPU Emulators

Malware includes several protections to complicate their analysis: the longer it takes to analyze a new malware sample, the longer the sample survives and the larger number of systems it compromises. Nowadays, new malware samples are analyzed dynamically using virtual environments (e.g., emulators, virtual machines, or debuggers). Therefore, malware incorporate a variety of tests to detect whether they are executed through such environments and obfuscate their behavior if they suspect their execution is being monitored. Several simple tests, we indistinctly call red-pills, have already been proposed in literature to detect whether the execution of a program is performed in a real or in a virtual environment. In this paper we propose an automatic and systematic technique to generate red-pills, specific for detecting if a program is executed through a CPU emulator. Using this technique we generated thousands of new red-pills, involving hundreds of different opcodes, for two publicly available emulators, which are widely used for analyzing malware.
Optimization-Based Algorithms for Tensor Decompositions: Canonical Polyadic Decomposition, Decomposition in Rank-(Lr, Lr, 1) Terms, and a New Generalization

The canonical polyadic and rank-$(L_r,L_r,1)$ block term decomposition (CPD and BTD, respectively) are two closely related tensor decompositions. The CPD and, recently, BTD are important tools in psychometrics, chemometrics, neuroscience, and signal processing. We present a decomposition that generalizes these two and develop algorithms for its computation. Among these algorithms are alternating least squares schemes, several general unconstrained optimization techniques, and matrix-free nonlinear least squares methods. In the latter we exploit the structure of the Jacobian's Gramian to reduce computational and memory cost. Combined with an effective preconditioner, numerical experiments confirm that these methods are among the most efficient and robust currently available for computing the CPD, rank-$(L_r,L_r,1)$ BTD, and their generalized decomposition.
Towards a terramechanics for bio-inspired locomotion in granular environments

Granular media (GM) present locomotor challenges for terrestrial and extraterrestrial devices because they can flow and solidify in response to localized intrusion of wheels, limbs, and bodies. While the development of airplanes and submarines is aided by understanding of hydrodynamics, fundamental theory does not yet exist to describe the complex interactions of locomotors with GM. In this paper, we use experimental, computational, and theoretical approaches to develop a terramechanics for bio-inspired locomotion in granular environments. We use a fluidized bed to prepare GM with a desired global packing fraction, and use empirical force measurements and the Discrete Element Method (DEM) to elucidate interaction mechanics during locomotion-relevant intrusions in GM such as vertical penetration and horizontal drag. We develop a resistive force theory (RFT) to account for more complex intrusions. We use these force models to understand the locomotor performance of two bio-inspired robots moving on and within GM.
Comparative Genomic Analysis of Human Fungal Pathogens Causing Paracoccidioidomycosis

Paracoccidioides is a fungal pathogen and the cause of paracoccidioidomycosis, a health-threatening human systemic mycosis endemic to Latin America. Infection by Paracoccidioides, a dimorphic fungus in the order Onygenales, is coupled with a thermally regulated transition from a soil-dwelling filamentous form to a yeast-like pathogenic form. To better understand the genetic basis of growth and pathogenicity in Paracoccidioides, we sequenced the genomes of two strains of Paracoccidioides brasiliensis (Pb03 and Pb18) and one strain of Paracoccidioides lutzii (Pb01). These genomes range in size from 29.1 Mb to 32.9 Mb and encode 7,610 to 8,130 genes. To enable genetic studies, we mapped 94% of the P. brasiliensis Pb18 assembly onto five chromosomes. We characterized gene family content across Onygenales and related fungi, and within Paracoccidioides we found expansions of the fungal-specific kinase family FunK1. Additionally, the Onygenales have lost many genes involved in carbohydrate metabolism and fewer genes involved in protein metabolism, resulting in a higher ratio of proteases to carbohydrate active enzymes in the Onygenales than their relatives. To determine if gene content correlated with growth on different substrates, we screened the non-pathogenic onygenale Uncinocarpus reesii, which has orthologs for 91% of Paracoccidioides metabolic genes, for growth on 190 carbon sources. U. reesii showed growth on a limited range of carbohydrates, primarily basic plant sugars and cell wall components; this suggests that Onygenales, including dimorphic fungi, can degrade cellulosic plant material in the soil. In addition, U. reesii grew on gelatin and a wide range of dipeptides and amino acids, indicating a preference for proteinaceous growth substrates over carbohydrates, which may enable these fungi to also degrade animal biomass. These capabilities for degrading plant and animal substrates suggest a duality in lifestyle that could enable pathogenic species of Onygenales to transfer from soil to animal hosts.
A Data Layout Transformation (DLT) accelerator: Architectural support for data movement optimization in accelerated-centric heterogeneous systems

Technology scaling and growing use of accelerators make optimization of data movement of increasing importance in all computing systems. Further, growing diversity in memory structures makes embedding such optimization in software non-portable. We propose a novel architectural solution called Data Layout Transformation (DLT) associated with a simple set of instructions that enable software to describe the required data movement compactly, and free the implementation to optimize the movement based on the knowledge of the memory hierarchy and system structure. The DLT architecture ideas can be applicable to both general-purpose and accelerator-based heterogeneous systems. Experiment results first show that the proposed DLT architecture can make use of the full bandwidth (>97%) of a wide range of memory systems (DDR3 and HMC) while its implementation cost is relatively low, occupying only 0.24 mm2 and consuming 75mW at 1GHz in 32nm CMOS technology. Our evaluation of using the DLT accelerator in accelerated-based heterogeneous system across DDR3 and HMC memory shows that the DLT can enhance system performance in range of 4.6x-99x (DDR3), 4.4x-115x (HMC) which turns out 2.8x-48x (DDR3), 1.4x-39x (HMC) improvement for energy efficiency.
FHMESH: A Flexible Heterogeneous Mesh Networking Platform

Cyber-Physical Systems require the integration of various heterogeneous networks. To evaluate proposed algorithms in network research, real-world test beds are an indispensable complement to simulations. In this paper, we present a flexible heterogeneous mesh networking platform (FHMESH) which interconnects various heterogeneous networks, i.e. wireless sensor network, wireless mesh network, FM radio network and the Internet. FHMESH builds agile and universal gateways using software defined radio technology, and the gateways form mesh backbone to interconnect those heterogeneous networks. Then, we present the platform architecture and the specific implementation details for FHMESH, and carry out some preliminary experiments. Even though the testing results of our system are not so perfect, we believe that the integration of various heterogeneous networks makes FHMESH work as a prototype for heterogeneous network research, and the flexible and universal gateway can facilitate the cross-layer research.