Miracle-dz/UniFAR-FTUs huggingface dataset:
THE AGWA – KINEROS 2 SUITE OF MODELING TOOLS IN THE CONTEXT OF WATERSHED SERVICES VALUATION

KINEROS originated in the 1970’s as a distributed e vent-based rainfall-runoff erosion model. A unique feature at that time was its interactive cou pling of a finite difference approximation of the kinematic overland flow equations to the Smith-Parl ange infiltration model. Development and improvement of KINEROS has continued for a variety of projects and purposes. As a result, a suite of KINEROS-based modeling tools has been deve loped that can be executed from a single shell. The tools range from the event-based KINERO S2 flash-flood forecasting tool to the continuous KINEROS-OPUS biogeochemistry tool. The KINEROS2 flash flood forecasting tool is being tested with the National Weather Serv ic . It assimilates the NWS Digital Hybrid Reflectivity (DHR) radar product in near-real time and can simultaneously run ensembles using multiple radar-reflectivity relationships. In addi tion to simulation of runoff and sediment transport, KINEROS-OPUS can simulate management, pl ant growth, nutrient cycling (nitrogen, phosphorus and carbon), water quality and chemical runoff. Like any detailed, distributed watershed modeling software, the KINEROS suite of t o ls often requires considerable effort to delineate watersheds, discretize them into modeling elements and parameterize these elements. This need motivated the development of the Automate d Geospatial Watershed Assessment (AGWA) tool. This ArcGIS based tool uses commonly available, national, GIS data layers to fully parameterize, execute, and visualize results from both the SWAT and KINEROS models. By employing these two models AGWA can conduct hydr ologic modeling and watershed assessments at multiple temporal and spatial scales . A variety of new capabilities have been added to AGWA to configure KINEROS inputs to simula te a number of land-management practices or changes (fire, urbanization, BMPs) as well as incorporating decision-management tools for rangelands. An overview of these tools w ill be provided pointing to other more detailed presentations and computer demonstrations of these tools being made at the conference. In the larger context these tools are components of a wate rshed management framework, which embodies decision tools, scenario development and b oth market and non-market valuation of watershed services (e.g. provisioning of clean wate r, natural flood attenuation, etc.). 1 All authors have contributed to this paper and/or the development of the KINEROS2-AGWA in the past
Clasificación ordinal de los grados de afectación de la enfermedad de Parkinson empleando imágenes de transportadores presinápticos de dopamina

Resumen—La enfermedad de Parkinson se caracteriza por un descenso de la densidad de transportadores presinápticos de dopamina en los núcleos de la base. El método habitual de clasificación está basado en la observación y el análisis cualitativo de las imágenes obtenidas tras la administración de I-ioflupano al paciente que se va a diagnosticar. De esta forma, las técnicas recientes de neuroimagen, como la imagen dopaminérgica utilizando tomografı́a computarizada por emisión de fotón único (SPECT-CT) con I-ioflupano (DaTSCAN ®), han demostrado detectar la enfermedad, incluso en etapas tempranas. Sin embargo, los comités internacionales recomiendan un análisis cuantitativo, asociado a la construcción de modelos de apoyo que complementen el diagnóstico visual. El objetivo del presente estudio es establecer un sistema de apoyo a la decisión, mediante la clasificación ordinal de las imágenes asociadas a los diferentes grados de afectación de la enfermedad mediante técnicas de aprendizaje automático. La base de datos utilizada está formada por 316 estudios realizados a pacientes entre septiembre de 2015 y mayo de 2018, distribuidos en tres grupos: 191 no padecen la enfermedad de Parkinson, 55 la padecen con un nivel de afectación leve y 70 con un nivel de afectación grave. Tras la administración intravenosa de 5 mCi (185 MBq), se realizó una SPECT-CT, preprocesando y normalizando espacialmente las imágenes. Como clasificador ordinal utilizamos un método de regresión logı́stica, que nos permite obtener las caracterı́sticas (vóxeles de la imagen) más informativas para la tarea de clasificación. El mejor modelo alcanzó un error absoluto medio máximo (MMAE) de 0,4857, tras la aplicación de un diseño experimental de tipo 5-fold. El análisis de los vóxeles más informativos, de acuerdo con el modelo obtenido, destaca regiones del cerebro que no son consideradas habitualmente por los especialistas para el
Physiology and Pharmacology Spectrophotometric Retinal Oximetry in Pigs

Citation: Traustason S, Kiilgaard JF, Karlsson RA, Hardarson SH, Stefansson E, la Cour M. Spectrophotometric retinal oximetry in pigs. Invest Ophthalmol Vis Sci. 2013;54:2746–2751. DOI:10.1167/ iovs.12-11284 PURPOSE. To assess the validity of spectrophotometric retinal oximetry by comparison to blood gas analysis and intravitreal measurements of partial pressure of oxygen (pO2). METHODS. Female domestic pigs were used for all experiments (n 1⁄4 8). Oxygen fraction in inspired air was changed using a mixture of room air, pure oxygen, and pure nitrogen, ranging from 5% to 100% oxygen. Femoral arterial blood gas analysis and retinal oximetry were performed at each level of inspiratory oxygen fraction. Retinal oximetry was performed using a commercial instrument, the Oxymap Retinal Oximeter T1. The device simultaneously acquires images at two wavelengths (570 nm and 600 nm), and specialized software automatically detects retinal blood vessels. In three pigs, invasive pO2 measurements were performed after the initial noninvasive measurements.
Consumer Preferences change when it comes to green Marketing

A lot of concerns have been expressed by manufacturers and customers about the negative impact of products on the environment. It is now becoming a regular feature of Consumer Perception, who fear the adverse impact of damage caused to the environment by different production activities undertaken to produce various goods and services. Consumers and manufacturers, alike have suddenly directed all their attention towards producing and consuming environment friendly products that are presumed to be “green” or environment friendly. The products classified as environment friendly have various features like energy-efficient, use of electrical appliances, use of organic fertilisers foods, lead free paints, recyclable paper, and phosphate free detergents and so on. Indian marketers are also realizing the importance of the Green Marketing Concept. Although a lot of research on green marketing has been conducted across the globe; little academic research on consumer perceptions and preferences has been carried out in India per se. This paper provides a brief review of environmental issues and identifies the green values of the consumers, their level of awareness about environmental issues, green products and practices. The research paper highlights the consumers’ perception and preferences towards green marketing practices and products with the help of a structured questionnaire. A study was conducted on 106 respondents. High level of awareness about green marketing practices and products was found among the consumers. Green values were also found to be high among the respondents. Research has given good insights for marketers of the green products and suggests the need of designing the marketing communication campaigns promoting green products due to high green value among the consumers. Results of regression analysis reveals the view that overall green values, awareness about green products and practices and the perception regarding seriousness of marketing companies towards green marketing had positive significant impact on consumer persuasion to buy and prefer green products over conventional products, which are often labelled as harmful to the consumers and environment. This Research provides good insights for marketers of the green products and suggests the need of designing the marketing communication campaigns so as to promote green products by creating the consumer awareness levels so that the consumers attribute high value to green products.
Attention-based Dialog Embedding for Dialog Breakdown Detection

Despite the recent advent of dialog systems, there are still many challenges unresolved and the system often generates responses causing a breakdown in the interaction between a user and the system. The dialog breakdown significantly damages user experience, and thus detecting such failure is significant. We propose a model to detect dialog breakdown using a recurrent neural network and attention layer to embed a previous dialog context. This model determines the probability of breakdown using two representation vectors, an extracted dialog context vector and the sentence vector of the target sentence. We submitted this model to Dialog Breakdown Detection Challenge 3 of Dialog System Technology Challenge 6, and the results showed that it significantly outperforms most of the other submitted models in estimating breakdown probability.
EVALUATION OF A LOW-POWER HADOOP CLUSTER BASED ON THE ZYNQ ARM-FPGA SOC

Distributed computing is important to many web and scientiﬁc applications. The quest for lower power dissipation and energy consumption in distributed applications has led to the implementation of ARM processor based distributed systems. The advent of integrated accelerators such as Field Programmable Gate Arrays (FPGA) promises to provide further reductions in power, but it is yet unclear to the scientiﬁc community how FPGAs should be utilized to maximize their beneﬁt to the distributed sys-tem. To answer this research question, in this paper we evaluate a ARM-FPGA distributed system which utilizes the Xilinx Zynq SoC as processor and Apache Hadoop as a programming environment and task scheduler. We evaluate the system on industry-standard benchmarks: MRBench, TestDF-SIO, Wordcount. Compared to a similar system with x86 processors, the ARM-based system dissipates 5 times less power. Additionally, we observe that all applications are IO bound on the ARM system, which indicates that the most eﬃcient use for FPGAs in such a system is for oﬄoading IO compression algorithms.
Partnership for Advanced Computing in Europe NUMA-CIC : Issues and Challenges for Scaling Scientific Applications on a Large Scale ccNuma Prototype

This whitepaper studies the various aspects and challenges of performance scaling on large scale shared memory systems. Our experiments are performed on a large ccNUMA machine that consists of 72 IBM 3755 nodes connected with NumaConnect and provides shared memory over a total of 1728 cores, a number that is far beyond conventional server platforms. As benchmarks, three data-intensive and memory-bound applications with different communication patterns are selected, namely Jacobi, CSR SpM×V and Floyd-Warshall. Our results illustrate the need for numa-aware design and implementation of shared-memory parallel algorithms in order to achieve scaling to high core counts. At the same time, we observed that, depending on its communication pattern, an application could benefit more from explicit communication using message passing.
Cornea Expression and Role of Nucleotide-Binding Oligomerization Domain 2 ( NOD 2 ) in the Ocular Surface of Murine Dry Eye

Ying Li, Rujun Jin, Lan Li, Hyeon Jeong Yoon, Joo-Hee Choi, Jong-Hwan Park, Zuguo Liu, Wei Li, Zhengri Li, and Kyung Chul Yoon Department of Ophthalmology, Chonnam National University Medical School and Hospital, Gwangju, Korea College of Veterinary Medicine, Chonnam National University, Gwangju, Republic of Korea Xiamen University affiliated Xiamen Eye Center, Xiamen, China Department of Ophthalmology, Affiliated Hospital of Yanbian University, China
Reduced‐order dynamic model for droop‐controlled inverter/converter‐based low‐voltage hybrid AC/DC microgrids – Part 2: DC sub‐microgrid and power exchange

This study focuses on reduced-order dynamical modelling of droop controlled converter-based DC sub-microgrid (MG) in a hybrid AC/DC MG. In hybrid MGs, electrical power is exchanged between the AC and DC sub-MGs by a bidirectional AC/DC converter. The authors aim to develop a comprehensive reduced-order dynamical model for the DC side in this part, incorporating standard classes of electrical loads including constant current, constant power, and constant resistance loads. Furthermore, dynamical behaviour of the power exchange between the AC and DC sub-MGs is modelled, considering that the bidirectional power converter controller aims to equalise the load ratios of AC and DC sub-MGs in order to facilitate overall decentralised control over the hybrid MG. Analytical derivations of steady-state values of main variables are given and the overall dynamical and algebraic equations are determined. In order to validate the developed model, a hybrid MG is implemented in PSCAD. Then, the proposed model for the case study is implemented in Matlab/Simulink and the results are compared with the PSCAD outputs. The comparative results show the validity of the developed reduced-order comprehensive model. The reduced-order models are preferred in designing observers such as model-based fault detection and diagnosis observers.
A New WKNN Localization Approach

Location aware applications are constantly under development. By using an Indoor Location System (ILS), a user can localize himself, plan an indoor route and destination, or receive useful information and services in malls, airports, shopping centers, etc. To this purpose, various signal strength or timing based localization methods exist, each one having their own advantages and disadvantages. Localization based on the Received Signal Strength (RSS) methods, typically require only off-theshelf equipment to operate. Moreover, they can utilize the existing infrastructure. This paper presents a new method on how to use the captured RSS values for localization purposes. The proposed method analyses a captured signal over a short distance and stores it into the fingerprint database for later comparison, rather than using an average value obtained from static measurements. Performance of proposed method is compared with a typical RSS-based localization method. Real-world measurements are used in order to validate our approach.
Near-Infrared Face Database

This paper introduces the establishment of PolyU Near-Infrared Face Database (PolyU4 NIRFD) for face recognition. The PolyU-NIRFD contains images from 350 subjects, each 5 contributing about 100 samples with variations of pose, expression, focus, scale and time, etc. In total, 6 35,000 samples were collected in the database. The PolyU-NIRFD provides a platform for 7 researchers to develop and evaluate various near-infrared face recognition techniques under large 8 scale, controlled and uncontrolled conditions. Finally, we provide three protocols to evaluate the 9 baseline face recognition methods, including Gabor based Eigenface, Fisherface and LBP (local 10 binary pattern) on the PolyU-NIRFD database. 11 12 Index Terms — Near-infrared face recognition, face database, feature extraction 13 14 Corresponding author: Lei Zhang (cslzhang@comp.polyu.edu.hk). Tel: 852-27767355. Fax: 852-27740842. Lei Zhang and David Zhang are with the Biometrics Research Center, Dept. of Computing, The Hong Kong Polytechnic University, Hong Kong. Baochang Zhang is with the School of Automation Science and Electronic Engineering, Beihang University, Beijing, China. Linlin Shen is with the Dept. of Electronic Engineering, Shenzhen University, Shenzhen, China. Baochang ZHANG, Lei ZHANG, David ZHANG, Linlin SHEN, Zhenhua GUO PolyU Near-Infrared Face Database
Implementation of Low-Power High-Performance VLSI Architecture for High Throughput Considerations based on CAVLC Decoding

This paper proposes a novel algorithm and its very large scale integration design for context-based adaptive variable length code (CAVLC) decoding. In order to improve throughput of CAVLC decoder, we propose two new methods, which are multiple level decoding (MLD) and nonzero skipping for run−before decoding (NZS). By performing parallel operations on the level decoder, MLD can decode two levels in one cycle at most situations, and NZS can produce several values of run−before in the same cycle. These two methods have the advantages of low complexity and regularity. The proposed architecture needs 141 cycles/macroblock. Moreover, the proposed CAVLC decoder can run at 33.5MHz to meet the real time requirement for 1920×1088 resolution. The power consumption for the 1920×1088 resolution is about 1.83mW. The operation frequency can be reduced about 29.1% to 71.5% compared with other architectures. With an aid on a lower operation frequency, it is suitable for many low power applications. The synthesis result shows that the gate count is 13 175 gates, and the maximum frequency can archive 160 MHz.
Political connections and operational performance of non-financial firms : New evidence from a Central European economy

This study analyses the impact of political connect ions on the operational performance of non-financial firms in a context of a post-transiti on European economy. Using a hand collected data set covering the 2001-2011 period, w e found that political connections lowered in Poland profitability measured on the level of in come from sales. The negative impact was the strongest when a firm possessed multiple connec tio s, had politically connected members of the supervisory board and employed ex-politician s with central government experience. Therefore, our findings did not support claims base d on the resource dependence theory that political connections improve the operational perfo rmance of firms. The reason for the Polish market’s specificity is most likely related to the instability of the political scene. In a more general sense, our study documents that political, nstitutional and economic environments play an important role in shaping the relationship between political connections and the performance of firms. JEL: G30; H80
Can a building read your mind? Results from a small trial in facial action unit detection

In the last few decades, the energy consumption of individual buildings has been steadily improving. As a result, research efforts are shifting towards acquiring a deeper understanding of occupant comfort, health, and well-being in the built environment. However, existing techniques used to measure and predict the comfort of occupants have seen little change since Fanger. New research attempts are hence focusing on methods to gather more data, more frequently, and less intrusively. A little explored source of data is the one gathered from real-time videos of occupants, the so-called facial action units (FAU), which is the focus of this paper. These are the facial movements and positions that constitute the basic elements of emotions. Using software developed in the realm of affective computing, seven building occupants were monitored for a period of 2 weeks, whilst also completing surveys that gathered information about the office environment, and their work and personal life. Results found that participants that were happy with their office space showed significantly higher average values of the Cheek Raiser (AU06) and Lid Tightener (AU07) facial action units. These findings show the potential of using FAUs to assist in the control and design of buildings in a human-centric manner.
CLASSIFICATION OF VOIP AND NON-VOIP TRAFFIC USING MACHINE LEARNING APPROACHES

Enhancing network services and security can be achieved by performing network traffic classification identifying applications, which is one of the primary components of network operations and management. The traditional transport-layer and port-based classification approaches have some limitations in achieving accurate identification. In this paper, a real test bed is used to collect first-hand traffic dataset from five different VoIP and Non-VoIP applications that are used by majority of Internet community, namely Skype, YouTube, Yahoo Messenger, GTalk and PayPal. The collected data encompasses new features that have never been used before. In addition, a classification step is performed using off-the-shelf machine learning techniques, specifically Random Forest J48, meta.AdaBoost (J48) and MultiLayer Perceptron to classify the traffic. Our experimental results show that using the new features can dramatically improve the true positive ratio by up to 98% and this is significant outcome towards providing accurate traffic classification.
Using "just in time" online feedback to improve first year undergraduate nursing students' essay writing performance

Undergraduate nursing students often find essay writing challenging, and feel underprepared, yet the impact of using online feedback to support essay writing has been underexplored. First-year nursing students from a regional university were involved in a project that encouraged them to access an online tutoring service, as part of their development of an essay task. Significant differences were found in students’ final essay marks for those who accessed the online writing support. Students who accessed online writing support were also more likely to be deep, rather than surface learners. The findings indicate that the provision of prompt or ‘just in time’ feedback, using an online feedback mechanism, can greatly enhance students’ essay writing performance.
Grizzly Bears and Forestry : Increased Mortality Leading to Lower Abundance in Heavily-roaded Landscapes

Extended Abstract: Both human-caused mortality and food are recognized to be the primary determinants of the distribution and abundance of grizzly bears (Ursus arctos); however, before our study, there was no information on the ecology of grizzly bears in the arctic watershed of central British Columbia (B.C.). From 1998 to 2002, we monitored habitat use and survival of 59 radio-collared grizzly bears (n = 37 females, n = 22 males) in an area surrounding the Parsnip River in central-eastern B.C. The 18,100-km study area encompassed a heavily timber-harvested plateau and an adjacent relatively pristine mountainous region. Thirty of the radio-collared bears were from the plateau (n = 19 females, n = 11 males); 29 (n = 18 females, n = 11 males) were from the mountainous region. We asked whether grizzly bears were less abundant in a heavily human-modified landscape (plateau) than in a relatively pristine landscape (mountains), and if so, why? A DNA population estimate concluded that mountain bears lived at a density of 49 bears per 1000 km, while plateau bears lived at a density of 12 bears per 1000 km (Mowat et al., in press). This was a > 4-fold density difference between the two adjacent landscapes. Capture data indicated that adult plateau bears were heavier than adult mountain bears (spring captures: x = 127 kg vs. 85 kg females, x = 266 kg vs. 126 kg males; fall captures: x = 171 kg vs. 121 kg females, no male captures), and were in better condition (57% of plateau bears were in good to excellent condition at capture as opposed to 24% of mountain bears). Adult female mountain bears also spent an average of 36 days longer in their dens than adult female plateau bears (Ciarniello et al., in review). This translates to approximately one month less foraging opportunity for female bears that lived in the mountains (foraging season: x = 201 days plateau, x = 165 days mountains). We were able to determine the age of first reproduction for three female plateau bears: one bred at four years of age, and two bred at five years of age ( x = 4.3 years). We also monitored