Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison between creatinine-based equations among diabetic nephropathy patients Article Information

Background: Diabetes is a leading cause of chronic kidney disease (CKD). Using some creatinine-based equations is the most widely distributed method in evaluating the kidney function. it can give an inaccurate estimated result, especially in individuals with typical renal function Modification of Diet in Renal Disease (MDRD) formula, Cockcroft-Gault (CG) equation, and the newly published formula of Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation are widely used during estimation the kidney function practice.
Online Optimization and Personalization of Teaching Sequences

In this work we are focused on the tutoring model, that is, how to choose the activities that provide a better learning experience based on the estimation of the student competence levels and progression, and some knowledge about the cognitive and student model. We can imagine a student wanting to acquire many different skills, e.g. adding, subtracting and multiplying numbers. A teacher can help by proposing activities such as: multiple choice questions, abstract operations to compute with a pencil, games where items need to be counted through manipulation, videos, or others. The challenge is to decide what is the optimal sequence of activities that maximizes the average competence level over all skills.
VM Common Runtime for Applications ( CRA ) Scan Analytics Streaming Pipelines Iterative ML ... HDFS User Applications Micro-services

Today, a modern data center hosts a wide variety of applications comprising batch, interactive, machine learning, and streaming applications. A largemajority of these applications can be abstracted as a “distributed dataflow” graph. Even though this commonality exists and can be exploited in the applications’ design and implementation, each application is typically written from scratch, resulting in significant inefficiencies in developer productivity. In this paper, we factor out the commonalities for many types of big data applications, into a generic dataflow layer called Common Runtime for Applications (CRA). In parallel, another trend, with containerization technologies, has taken a serious hold on cloud-scale data centers, with direct implications on the design, implementation, and deployment of next-generation of data-center application. Container engines (e.g., Docker and CoreOS) and cloud-scale container orchestrators (e.g., Kubernetes and Docker Swarm) are two important technologies that enable this trend. Container orchestrators have made deployment a lot easy, and they solve many infrastructure level problems, e.g., service discovery, auto-restart, and replication. For best in class performance, there is a need tomarry the next generation applications with containerization technologies. To that end, CRA leverages and builds upon containerization and resource orchestration capabilities of Kubernetes/Docker, and makes it easy to build a wide range of cloud-edge applications on top. To the best of our knowledge, we are the first to present a cloud native runtime for building data center applications. To show the practicality of our approach, we built a distributed analytics engine on top of CRA, namely Quill. We show through in-depth microand macro-benchmark results, that CRA provides significant performance improvement over an unoptimized implementation on modern cloud platforms. CRA is available as open source, and can be downloaded at https://github.com/Microsoft/CRA.
Financial Volatility and The Leverage Effect

In today’s financial markets, volatility is a fundamental concept in regards of the risk assessment of assets and instruments. Financial volatility is commonly used to measure the quantitative aspects of risk and is given a significant amount of attention in past literature and research. The leverage effect refers to the well-established negative relationship between return and future volatility. The relation is usually explained by the increased leverage ratio that arises from a drop in the share price for a firm. A lower price means lower value of the equity and while the debt remains unchanged, the leverage ratio will rise. The leverage ratio affect how risky the equity is from an investor’s perspective, hence affects the volatility of the stock. This paper aims to analyse whether the theory is applicable on the Swedish stock exchange and takes both individual stocks and the OMXS30-index into account. Further theories related to the model is acknowledged in order to enhance the analysis of the findings. The study is performed by a regression model where volatility, estimated through an EGARCH model, represents the dependent variable. Lagged return, together with a number of control variables, constitutes the explanatory variables. The findings claims that the leverage effect is present for individual stocks but can be rejected on the index level. Additionally, significant improvement was noticed when a dynamic approach was added to the model. The conclusions drawn is that the Swedish stock exchange facilitates the leverage effect for individual firms but it is off-set by other theories such as risk-return trade-off and volatility clustering for the index. Key7words' Volatility,! Leverage! effect,! Risk,! Return,! Clustering,! Modigliani! and! Miller,! EGARCH,! Capital! structure,! Leverage! ratio!
Prototyping Design and Learning in Outdoor Mobile Robots operating in unstructured outdoor environments

Natural outdoor environments present major navigation challenges to autonomous mobile robots as they involve large amounts of complex sensory data with uncertainties that are inherent to the sensing systems and the unstructured highly dynamic environments involved. The traditional type-1 Fuzzy Logic Controller (FLC) using crisp type-1 fuzzy sets cannot directly model and handle all such uncertainties. A Type-2 Fuzzy Logic Controller (FLC) using type-2 fuzzy sets can model and minimise the effect of uncertainties to produce a better performance. In this paper we present a novel control architecture for autonomous mobile robots based on a hierarchy of Type-2 FLCs to implement the basic navigation behaviours and the coordination between robot control behaviours. We have implemented this architecture in different types of mobile robots navigating in indoor and outdoor unstructured and challenging environments. The type-2 based control system dealt with the linguistic and numerical uncertainties associated with the changing environment and robot conditions and resulted in very good performance that out-performed the type-1 based control system whilst achieving a significant rule reduction compared to the type-1 system.
Effect of walnut shell powder on the mechanical properties of case-hardened steel

In recent times, most previously-considered agricultural wastes are now gaining importance in the enhancement of some engineering product. One of such cases is in case hardening of steel surface. These works focused on the use of walnut shell powder to case harden steel. Pretreated and as-received powder was used in the experiment at 850oC and varied soaking times of 45, 60 and 70 minutes designated as A, B and C respectively. Uniaxial tensile test method was adopted to characterize the tensile properties and the fracture toughness, while metallurgical microscope was used to view and analyze the microstructure of the samples. From the result, Sample A which was case hardened in pretreated walnut shell powder at 850oC and soaked for 45 minutes was observed to have the best combined improved properties with a uniquely reduced and more compacted grain size at the edge of the microstructure.
Client Server TCP SYN TCP SYN + ACK Client Server TCP SYN TCP RST Open Port Closed Port Client Server ? Client Server ICMP

In order to protect ICT systems against remote attacks and exploitation, insight into which systems are targeted is necessary as soon as possible. Given the lack of advance information, current network-based attack detection and mitigation techniques, such as virus scanners or intrusion prevention systems, are typically aimed at countering the delivery and exploitation. This paper presents a novel approach capable of detecting threats while they scan a local network for potential targets and even before an intrusion attack has been made. This allows the defender to single out scan traffic and selectively deny access to an attacker performing reconnaissance while maintaining the availability to other users. We implement a proofof-concept on commodity graphics cards, and demonstrate fast prediction of scanner behavior on a /16 network telescope.
‘ Unmasking the Face : A Guide to Recognizing Emotions from Facial Clues

The aim of the article is to present results of the research that was performed with 57 secondand third-year Persian students of English Translation. The purpose of the research was to examine how conscious manipulation of facial expressions aids acquisition of foreign vowels by learners, regardless of their native language and the culture they have been brought up in. Taking advantage of achievements derived from such disciplines as psychology of emotions and phonetics depicted as a physical process, an attempt is made to find a tool that improves teaching/learning of foreign vowels, that is to say an effort is put in search of a useful method to make the phonetic process faster and more accurate. Teachers of English are encouraged to put the method, which is described in detail in the paper, into practice with their own mother languages and to share opinions about the method with colleagues. Similarly, it is believed that it can be applied to courses of other languages than just English. Teachers of those languages are encouraged to try to use it, too.
Simulation , modelling and optimization of different chilled ammonia-based process configurations for CO 2 capture applied to cement plants

Introduction Cement manufacturing accounts for approximately 6-7% of CO2 emissions produced by anthropogenic activity worldwide. About 60% of these CO2 emissions are intrinsic to the production of clinker, the main constituent of cement, as the calcination of limestone to produce CaO, one of the reactants required to produce clinker, leads to the formation of CO2. As a consequence, CO2 capture and storage (CCS) is essential to decrease the carbon footprint of this industrial process effectively. Among the different options, CO2 removal from the flue gas generated in the pre-calciner and the rotary kiln is of particular interest as it makes use of the extensive existing experience in post-combustion capture technologies applied to power plants while leaving the cement process unchanged. Within this framework, the Chilled Ammonia Process (CAP) technology is a promising candidate, since it shows a competitive energetic performance with respect to conventional amines, and because aqueous NH3 solutions offer significant advantages concerning global availability, environmental footprint, costs and chemical stability. A simplified flow scheme of the standard CAP is shown in Figure 1.
Compressed Sensing Photoacoustic Imaging based on Correlation Criterion

For different tissues, the sparstiy of photoacoustic images changes greatly. In compressed sensing photoacoustic imaging, the required measurement increases as the image sparsity decreases to acquire a certain image quality. In this paper, a compressed sensing photoacoustic imaging method based on correlation criterion is proposed. The photoacoustic imaging scheme based on arc-compressed sensing and two-view observation is built firstly. Then the correlation coefficient between two-view observation images is defined and computed. As the measurement increases, the correlation coefficient rises. When the correlation coefficient reaches to a certain value, the reconstructed artifacts become slight enough and the measurement process finishes. The proposed method is testified by the subsequent simulation. Two tissue phantoms with different sparsity are built in the simulation. The corresponding photoacoustic signals are detected and reconstructed respectively. Simulation results show that the optimal measurement can be determined by the proposed correlation criterion.
Improving Spark Application Throughput Via Memory Aware Task Co-location: A Mixture of Experts Approach

Data analytic applications built upon big data processing frameworks such as Apache Spark are an important class of applications. Many of these applications are not latencysensitive and thus can run as batch jobs in data centers. By running multiple applications on a computing host, task co-location can significantly improve the server utilization and system throughput. However, effective task co-location is a non-trivial task, as it requires an understanding of the computing resource requirement of the co-running applications, in order to determine what tasks, and how many of them, can be co-located. State-of-the-art co-location schemes either require the user to supply the resource demands which are often far beyond what is needed; or use a one-size-fits-all function to estimate the requirement, which, unfortunately, is unlikely to capture the diverse behaviors of applications. In this paper, we present a mixture-of-experts approach to model the memory behavior of Spark applications. We achieve this by learning, off-line, a range of specialized memory models on a range of typical applications; we then determine at runtime which of the memory models, or experts, best describes the memory behavior of the target application. We show that by accurately estimating the resource level that is needed, a co-location scheme can effectively determine how many applications can be co-located on the same host to improve the system throughput, by taking into consideration the memory and CPU requirements of co-running application tasks. Our technique is applied to a set of representative data analytic applications built upon the Apache Spark framework. We evaluated our approach for system throughput and average normalized turnaround time on a multi-core cluster. Our approach achieves over 86.4% of the performance delivered using an ideal memory predictor. We obtain, on average, 8.75x improvement on system throughput and a 51% reduction on turnaround time over executing application tasks in isolation, which translates to a 1.31x and 1.75x improvement over a state-of-the-art co-location scheme for system throughput and turnaround time respectively. CCS Concepts • Computing methodologies → Distributed algorithms;
0 Ju l 2 01 9 Tails of Triangular Flows

Triangular maps are a construct in probability theory that allows the transformation of any source density to any target density. We consider flow based models that learn these triangular transformations which we call triangular flows and study the properties of these triangular flows with the goal of capturing heavy tailed target distributions. In one dimension, we prove that the density quantile functions of the source and target density can characterize properties of the increasing push-forward transformation and show that no Lipschitz continuous increasing map can transform a light-tailed source to a heavy-tailed target density. We further precisely relate the asymptotic behavior of these density quantile functions with the existence of certain function moments of distributions. These results allow us to give a precise asymptotic rate at which an increasing transformation must grow to capture the tail properties of a target given the source distribution. In the multivariate case, we show that any increasing triangular map transforming a light-tailed source density to a heavy-tailed target density must have all eigenvalues of the Jacobian to be unbounded. Our analysis suggests the importance of source distribution in capturing heavy-tailed distributions and we discuss the implications for flow based models.
- 0801 Jet and Droplet Impingement on Superhydrophobic Surfaces

Jet and Droplet Impingement on Superhydrophobic Surfaces Jonathan Glenn Stoddard Department of Mechanical Engineering, BYU Master of Science The effect of superhydrophobicity on liquid water impingement on a flat horizontal surface was explored. The surfaces combined a hydrophobic surface chemistry with a patterned microstucture in order to produce high contact angles with water. Three sets of experiments were performed, one for jet impingement and two for droplet impingement, which advance previous work in characterizing the interaction of water and superhydrophobic surfaces. Jet impingement experiments were performed to characterize a transitional regime between an unsubmerged and a completely submerged superhydrophobic surface by varying an imposed downstream depth. For low downstream depths, the surface remained unsubmerged and displayed only break up of the thin film, while at high downstream depths, the surface was completely submerged and only a hydraulic jump occurred. Within the transition, the surface was partially submerged and both thin film breakup and a hydraulic jump were observed. Experiments were performed for three Reynolds numbers, Re, ranging from 1.9×104 to 2.2×104 (based on the volume flow rate). For all Re, the transition was characterized by a reduction in the hydraulic jump radius as downstream depth increased. Also, as Re increased, the downstream depths over which the transition occurred was greater. When a droplet impinges on a surface covered with a liquid film, a thin liquid wall, or crown, forms and propagates outward. Here a comparison of this crown dynamic was made for smooth hydrophilic surfaces and superhydrophobic (SH) surfaces patterned with post or rib microfeatures. Due to the high contact angle of the SH surfaces, a relatively thick film (h ≈ 5 mm) of water was required to maintain a film. This resulted in negligible differences between the surfaces utilized. Droplet train impingement on the same post and rib SH surfaces was also investigated. When each individual droplet impinged on the surface, a crown formed which spread out radially until reaching a semi-stable or regularly oscillating breakup diameter. At this point, the water would either build up or breakup into droplets or filaments and then continue radially outward. In some cases the crown would break up, causing splashing. A comparison to previous experiments on hydrophilic surfaces shows a distinct difference in splashing at low frequency. The breakup diameter was measured over a Weber number range of 72-2800. The data was collapsed as a function of a combination of the Reynolds number (Re), Capillary number (Ca), and Strouhal number (St), resulting in Re0.7CaSt. The rib SH surface displayed an elongated breakup due to the anisotropic surface features. The breakup diameter for the droplet train was compared to the breakup diameter which has been shown to occur with a jet impinging on a SH surface.
Implementation of Secure Mobile Agent for Ad-Hoc Netwoks

Security is an important issue for the widespread deployment of applications based on software agent technology. It is generally agreed that without the proper countermeasures in place, use of agentbased applications will be severely impeded. However, not all applications require the same set of countermeasures, nor can they depend entirely on the agent system to provide them. Instead, countermeasures are applied commensurate with the anticipated threat profile and intended security objectives for the application. Such countermeasures can be integrated directly into an agent system, or incorporated into the design of an agent to supplement the capabilities of an underlying agent system. Key-Words: Mobile Agent Security, Secure Ad-Hoc Networks, Secure M-Commerce
Emerging knowledge distribution means and their characterization

Despite the amount of attention that processes and structures for knowledge management has received within research during the last decade, little attention has been directed towards the actual means used for the distribution of knowledge by individuals. Here is presented results from a study of emerging electronically mediated distribution means followed with a characterization and analysis. For the characterization and analysis a framework for interpretation of the different distribution means was created based on the constructs of organizational learning and the levels of knowledge system interpretation. The results show that knowledge distribution is not an isolated event. It takes place in larger context, such as organizational learning, since it touches upon other activities or phenomena such as knowledge acquisition, knowledge interpretation, and organizational memory.
Compiling Neural Networks into Tractable Boolean Circuits

We show how to exploit tools and methods from the knowledge compilation literature in order to explain the behavior and verify the properties of neural networks. In particular, we show how to compile neural networks into tractable Boolean circuits to facilitate their efficient explanation and verification. We first show how to reduce a neural network over binary inputs and step activation functions into a Boolean circuit. We then compile this Boolean circuit into a tractable one (a core problem in the domain of knowledge compilation). Once we obtain such a tractable Boolean circuit, the explanation and verification of the original neural network can be done efficiently, as we illustrate through a case study.
A space-time coded OFDM with dual Viterbi decoder

In this paper, a space-time coded orthogonal frequency division multiplexing (STC-OFDM) scheme with dual Viterbi decoder is proposed and analyzed. In this scheme, two independent half-rate OFDM symbols are generated after convolutional coding. A dual Viterbi decoder is used for the independent decoding of the recovered sequence, and their path metrics are compared. Accordingly, the recovered binary data is a combination of the sequences which have larger path metrics. The proposed STC-OFDM scheme has better performance than the conventional one for all signal-to-noise ratios.
Chapter 2 Pairwise Interchange Argument and Priority Rules

In this chapter, we consider a group of methods that are applicable to problems of optimizing functions over a set of all permutations. The correctness of the basicmethods is typically proved by the pairwise interchange argument, and the basic methods can be further extended to become applicable to solve more general problems. In this chapter and throughout the book, for job j = π(r) that occupies the r th position in the sequence π = (π(1),π(2), . ,π(n)) of jobs of set N = {1, 2, . , n}, the completion time in a schedule S that processes the jobs on a single machine in accordance with permutation π is denoted either by C j (S) or by Cπ(r). The corresponding value can be written as
Efficient Multi-temporal and In-season Crop Mapping with Landsat Analysis Ready Data via Long Short-term Memory Networks

Globe crop analysis from plentiful satellite images yields state-of-the-art results about estimating climate change impacts on agriculture with modern machine learning technology. Generating accurate and timely crop mapping across years remains a scientific challenge since existing non-temporal classifiers are hardly capable of capturing complicated temporal links from multitemporal remote sensing data and adapting to interannual variability. We developed an LSTMbased model trained by previous years to distinguish corn and soybean for the current year. The results showed that LSTM outperformed random forest baseline in both in-season and end-of-theseason crop type classification. The improved performance is a result of the cumulative effect of remote sensing information that has been learned by LSTM model structure. The work provides a valuable opportunity for estimating the impact of climate change on crop yield and early warning of extreme weather events in the future.