nils-herrmann/clean_scirepeval huggingface dataset:
Brain Default Mode Network Changes after Renal Transplantation: A Diffusion Tensor Imaging and Resting State Functional MR Imaging Study.
Venue: Radiology

To investigate structural and functional alterations of the default mode network (DMN) in the brain after renal transplantation in patients with end stage renal disease by using diffusion tensor imaging and resting state functional MR imaging.  This prospective study was approved by the local medical research ethics committee, and written informed consent was obtained. Twenty one patients with end stage renal disease (15 men, six women; mean age standard deviation, 32 years 9.5) who were scheduled to undergo renal transplantation and 21 healthy control subjects (15 men, six women; mean age, 31 years 6.5) were included. Diffusion tensor imaging and resting state functional MR imaging were performed in all subjects. Patients were imaged both before and 1 month after renal transplantation. Structural (mean diffusivity, fractional anisotropy, path length, and number of tracts derived from diffusion tensor imaging tractography) and functional (temporal correlation coefficient derived from resting state functional MR imaging) connectivity of the DMN were quantitatively compared with two sample t tests or paired t tests. Intergroup correlation analysis was performed to compare structural or functional indexes and results of neuropsychological or blood biochemistry tests.  Mean diffusivity was decreased in the fiber bundles connecting the posterior cingulate cortex and the precuneus to the bilateral inferior parietal lobules in patients after renal transplantation compared with that in patients before transplantation (P .05) Temporal correlation coefficients for patients after renal transplantation nearly reached the levels of those for control subjects (all, P .05) The change in mean diffusivity of the fiber bundles connecting the posterior cingulate cortex and the precuneus to the right inferior parietal lobule positively correlated with the change in hematocrit levels (r 0.522, P .015) the change in temporal correlation coefficients between the posterior cingulate cortex or precuneus and left or right inferior parietal lobules correlated with changes in number connection test type A scores (r 0.549, P .010) and digit symbol test scores (r 0.533, P .013) CONCLUSION Functional connectivity changes in the DMN, which were associated with improved hematocrit levels and cognitive function, may recover earlier than structural connectivity changes do 1 month after renal transplantation.
All The Catalytic Active Sites of MoS2 for Hydrogen Evolution.
Venue: Journal of the American Chemical Society

MoS2 presents a promising low cost catalyst for the hydrogen evolution reaction (HER) but the understanding about its active sites has remained limited. Here we present an unambiguous study of the catalytic activities of all possible reaction sites of MoS2, including edge sites, sulfur vacancies, and grain boundaries. We demonstrate that, in addition to the well known catalytically active edge sites, sulfur vacancies provide another major active site for the HER, while the catalytic activity of grain boundaries is much weaker. The intrinsic turnover frequencies (Tafel slopes) of the edge sites, sulfur vacancies, and grain boundaries are estimated to be 7.5 s 1 (65 75 mV/dec) 3.2 s 1 (65 85 mV/dec) and 0.1 s 1 (120 160 mV/dec) respectively. We also demonstrate that the catalytic activity of sulfur vacancies strongly depends on the density of the vacancies and the local crystalline structure in proximity to the vacancies. Unlike edge sites, whose catalytic activity linearly depends on the length, sulfur vacancies show optimal catalytic activities when the vacancy density is in the range of 7 10% and the number of sulfur vacancies in high crystalline quality MoS2 is higher than that in low crystalline quality MoS2, which may be related with the proximity of different local crystalline structures to the vacancies.
Exploring Dynamic Risk Prediction for Dialysis Patients
Venue: AMIA

Despite substantial advances in the treatment of end stage renal disease, mortality of hemodialysis patients remains high. Several models exist that predict mortality for this population and identify patients at risk. However, they mostly focus on patients at a particular stage of dialysis treatment, such as start of dialysis, and only use the most recent patient data. Generalization of such models for predictions in later periods can be challenging since disease characteristics change over time and the evolution of biomarkers is not adequately incorporated. In this research, we explore dynamic methods which allow updates of initial predictions when patients progress in time and new data is observed. We compare a Dynamic Bayesian Network (DBN) to regularized logistic regression models and a Cox model with landmarking. Our preliminary results indicate that the DBN achieves satisfactory performance for short term prediction horizons, but needs further refinement and parameter tuning for longer horizons.
Design of a MRAS based estimator for the speed and rotor time constant of the induction motor using sliding mode
Venue: 2016 International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM)

The paper presents a method to estimate the fluxes, speed and rotor time constant of the induction motor (IM) drive. The estimation process is based on the model of the motor in the stationary reference frame. Estimation is done using successive observers that also involve the Model Reference Adaptive System (MRAS) principle. The fluxes are obtained first using a Voltage Model Observer they are used to calculate the rotor flux angle of the drive. With known fluxes, the paper attempts to design a sliding mode observer for simultaneous estimation of the speed, load torque and rotor time constant. The mathematics of the observer is presented; it is shown that, due to singularity in the required gain design, an asymptotically stable observer cannot be obtained. Then, the paper presents an observer for the rotor time constant this takes the speed signal as an input. The properties of this observer are investigated and it is shown how to make it less sensitive to the potential inaccuracy in the speed input. The method can be used in a sensorless IM control implementation where the fluxes and speed are required and where knowledge of the rotor time constant improves the control performance. The theoretical claims are supported by the simulation results shown.
Fabrication of Yttrium Doped Barium Zirconate for High Performance Protonic Ceramic Membranes

Barium zirconate has emerged as the leading candidate material for fabricating dense ce ramic membranes for hydrogen separation. B sites in the ABO3 perovskite are acceptordoped with a +3 cation most commonly yttrium charge compensated by the formation of oxygen ion vacancies in the lattice. A minor fraction of B sites can be filled with cerium to give BaZr0.9 xCexY0.1O3 d, x 0.2. Upon hydration at elevated temperatures, weaklybound protons are formed in the lattice. This produces a cubic perovskite ceramic proton conductor useful in diverse applications, such as protonic ceramic fuel cells, electrolysers, and catalytic membrane reactors operating at temperatures between 600 and 800 degC. A necessary requirement for fabricating thin ceramic membranes for proton diffusion is to maximize grain size while eliminating percolating porosity. However, high density, large grained barium zirconate is a very difficult material to prepare by traditional pow der sintering methods. This chapter describes a new methodology for making protonic ceramic membranes with large grains and virtually no residual porosity. This discovery has the potential to have a profound impact on energy conversion efficiency of the vari ous membrane devices envisioned for the coming hydrogen energy economy.
Building End To End Dialogue Systems Using Generative Hierarchical Neural Network Models
Venue: AAAI

We investigate the task of building open domain, conversational dialogue systems based on large dialogue corpora using generative models. Generative models produce system responses that are autonomously generated word by word, opening up the possibility for realistic, flexible interactions. In support of this goal, we extend the recently proposed hierarchical recurrent encoder decoder neural network to the dialogue domain, and demonstrate that this model is competitive with state of the art neural language models and back off n gram models. We investigate the limitations of this and similar approaches, and show how its performance can be improved by bootstrapping the learning from a larger question answer pair corpus and from pretrained word embeddings.
End to End Learning for Self Driving Cars
Venue: ArXiv

We trained a convolutional neural network (CNN) to map raw pixels from a single front facing camera directly to steering commands. This end to end approach proved surprisingly powerful. With minimum training data from humans the system learns to drive in traffic on local roads with or without lane markings and on highways. It also operates in areas with unclear visual guidance such as in parking lots and on unpaved roads. The system automatically learns internal representations of the necessary processing steps such as detecting useful road features with only the human steering angle as the training signal. We never explicitly trained it to detect, for example, the outline of roads. Compared to explicit decomposition of the problem, such as lane marking detection, path planning, and control, our end to end system optimizes all processing steps simultaneously. We argue that this will eventually lead to better performance and smaller systems. Better performance will result because the internal components self optimize to maximize overall system performance, instead of optimizing human selected intermediate criteria, e.g. lane detection. Such criteria understandably are selected for ease of human interpretation which doesn't automatically guarantee maximum system performance. Smaller networks are possible because the system learns to solve the problem with the minimal number of processing steps. We used an NVIDIA DevBox and Torch 7 for training and an NVIDIA DRIVE(TM) PX self driving car computer also running Torch 7 for determining where to drive. The system operates at 30 frames per second (FPS)
Vehicle Detection from 3D Lidar Using Fully Convolutional Network
Venue: Robotics: Science and Systems

Convolutional network techniques have recently achieved great success in vision based detection tasks. This paper introduces the recent development of our research on transplanting the fully convolutional network technique to the detection tasks on 3D range scan data. Specifically, the scenario is set as the vehicle detection task from the range data of Velodyne 64E lidar. We proposes to present the data in a 2D point map and use a single 2D end to end fully convolutional network to predict the objectness confidence and the bounding boxes simultaneously. By carefully design the bounding box encoding, it is able to predict full 3D bounding boxes even using a 2D convolutional network. Experiments on the KITTI dataset shows the state of the art performance of the proposed method.
Fast Vehicle Detection in Satellite Images Using Fully Convolutional Network
Venue: IVS

Detecting small targets like vehicles in high resolution satellite images is a significant but challenging task. In the past decade, some detection frameworks have been proposed to solve this problem. However, like the traditional ways of object detection in natural images those methods all consist of multiple separated stages. Region proposals are first produced, then, fed into the feature extractor and classified finally. Multi stage detection schemes are designed complicated and time consuming. In this paper, we propose a unified single stage vehicle detection framework using fully convolutional network (FCN) to simultaneously predict vehicle bounding boxes and class probabilities from an arbitrary sized satellite image. We elaborate our FCN architecture which replaces the fully connected layers in traditional CNNs with convolutional layers and design vehicle object oriented training methodology with reference boxes (anchors) The whole model can be trained end to end by minimizing a multi task loss function. Comparison experiment results on a common dataset demonstrate that our FCN model which has much fewer parameters can achieve a faster detection with lower false alarm rates compared to the traditional methods.
A "Quick Dirty" Strategic Audit.

In teaching Strategic Management, it is imperative that students first learn how to audit the firm before they begin analysis, planning and implementation. Unfortunately this is a step often overlooked. Without a complete and up to date audit, any analysis conducted would have questionable validity and reliability. This report focuses on an instructional tool that faculty can utilize to guide students through a strategic audit of a company. The objective in auditing the organization is to describe the firm in terms of its current position in the industry, its strategy, structure, and performance. This paper describes the Company Profile Sheet and explains how it can be an invaluable tool for the students to understand the company under review and the strategic management concepts. Dorothy E. Brawley A "QUICK DIRTY" STRATEGIC AUDIT 16 Journal of Learning in Higher Education 17 Fall 2016 (Volume 12 Issue 2) TERMINOLOGY Strategic Management is usually taken at the end of the program of study in business the capstone experience. Although students taking this course have been exposed to the basic concepts of business in their core courses (Management, Accounting, Finance, Marketing, Economics, Information Systems) their ability to apply those concepts in an integrative way into a company study is limited. Because of the dynamic and complex nature of the field of strategy there are many concepts and variables students must consider in conducting a strategic audit of a firm. Exposure to such concepts as company demographicssector, trading, company type, industry classification, distribution areas tend to be abstract concepts, not grounded in application. Strategic concepts including corporate/ business/functional level strategies, structural forms, and process strategies tend to be new variables to students, covered superficially in earlier courses but forgotten. Performance indicators including an ability to really look at balance sheets, income statements and ratios, analyzed and memorized for tests in earlier courses, have long been put aside as unimportant. Additionally, students tend to have been exposed to text cases, where data is available in a concise, problem specific context. Reality is not written like a textbook case. Asking students to learn to pull current data from real company documents like the annual reports and 10k may be a new experience for them. OPERATIONAL DEFINITIONS AND METRICS It is one thing to be exposed to the concepts it is another to understand the metrics required to operationalize those concepts. Most Strategic Management texts give theoretical definitions of terminology. My objective in developing the template with definitional instructions was to give the students a guide with operational metrics for each of the variables under audit.
CFD Modeling with Experimental Validation of the Internal Hydrodynamics in a Pilot Scale Slurry Bubble Column Reactor

A multiphase Eulerian, three dimensional (3 D) computational fluid dynamics (CFD) model was built to investigate the local hydrodynamics of a pilot scale (0.29 m ID, 3 m height) Slurry Bubble Column Reactor (SBCR) The model was first validated against the gas holdup radial profiles in an air water glass beads system obtained in a 0.254 m ID and 2.5 m height column under ambient conditions at various superficial gas velocities by Yu and Kim (Bubble characteristics in the radial direction of three phase fluidized beds.  2069 2072, 1988) The model was next validated against the gas holdup radial profile data for N2 Drakeol glass beads system obtained in a 0.44 m ID and 2.44 m height reactor, including internals, operating under ambient conditions at various superficial gas velocities by Chen et al. (Fluid dynamic parameters in bubble columns with internals. Chemical Engineering Science 54, 2187 2197, 1999) The model was also validated against experimental data obtained in our lab for N2 Fischer Tropsch (F T) reactor wax Fe catalyst system obtained in a pilot scale, Slurry Bubble column Reactor, SBCR (0.29 m ID, 3 m height) under pressures and temperatures up to 25.9 bar and 490 K, respectively. These three validations led to the selection of the turbulence and interphase drag coefficient models, and the optimization of the solution method, mesh size and structure and the step size. Moreover, the inclusion of RNG k e turbulence model coupled with the Wen Yu (Mechanics of Fluidization.  1966) Schiller Naumann (A drag coefficient correlation. ) drag correlations, and the mass transfer coefficients were found to provide the most accurate predictions of the experimental data. The CFD model was then used to investigate local gas holdup, liquid recirculation, local turbulence intensities, bubble diameters, and solids distribution throughout our pilot scale SBCR, operating under typical F T process conditions. The model predictions showed strong liquid recirculation and backmixing near the walls of the reactor, and the solid phase velocity vectors closely followed those of the liquid phase. A relatively high liquid turbulence intensities were observed in the vicinity of the sparger upon startup, however, after reaching a steady state, the liquid turbulence intensities became more evenly distributed throughout the reactor. The liquid turbulence intensities were slightly higher near the center of the reactor, and closely resembled the velocity vectors. Also, the Sauter mean bubble diameters increased, whereas the solids distribution decreased with reactor height above the gas distributor.
A novel FPGA based system for real time calculation of the Spectral Kurtosis: A prospective application to harmonic detection

Autonomous measurement units are indispensable for the development of the smart grid in order to get faster processing responses, stability, and to provide the user with information regarding power quality (losses, harmonics, outages, steady state and transient events) In this paper, the development of a novel FPGA based real time analyzer is presented. The signal processing nucleus is based on higher order statistics, introducing the Spectral Kurtosis as an online assessment. Indeed, this fourth order spectrum enhances non Gaussian behavior in very unfavorable noise conditions. In the Power Quality assessment frame, the outline of this spectrum can be used to reveal the presence of underlying electrical perturbations. In this paper it is presented an autonomous analyzer that uses the Spectral Kurtosis to detect the presence of low level harmonics. These harmonics are difficult to detect using conventional techniques and can generate problems in power distribution when they are associated to a resonant response in the powered load. The proposed system has been described in synthesizable VHDL and performs an indirect estimator of a poly spectrum slide, based on the FFT. This synthesizable description has been implemented using a low cost FPGA, enabling a continuous monitoring of the power signal (without dead times) with a high oversampling rate. The proposed solution has been conceived to be introduced in smart grids with high penetration of Distributed Energy Resources (DER)
Fusing Similarity Models with Markov Chains for Sparse Sequential Recommendation
Venue: 2016 IEEE 16th International Conference on Data Mining (ICDM)

Predicting personalized sequential behavior is a key task for recommender systems. In order to predict user actions such as the next product to purchase, movie to watch, or place to visit, it is essential to take into account both long term user preferences and sequential patterns (i.e. short term dynamics) Matrix Factorization and Markov Chain methods have emerged as two separate but powerful paradigms for modeling the two respectively. Combining these ideas has led to unified methods that accommodate long and short term dynamics simultaneously by modeling pairwise user item and item item interactions. In spite of the success of such methods for tackling dense data, they are challenged by sparsity issues, which are prevalent in real world datasets. In recent years, similarity based methods have been proposed for (sequentially unaware) item recommendation with promising results on sparse datasets. In this paper, we propose to fuse such methods with Markov Chains to make personalized sequential recommendations. We evaluate our method, Fossil, on a variety of large, real world datasets. We show quantitatively that Fossil outperforms alternative algorithms, especially on sparse datasets, and qualitatively that it captures personalized dynamics and is able to make meaningful recommendations.
Fusing Similarity Models with Markov Chains for Sparse Sequential Recommendation

Predicting personalized sequential behavior is a key task for recommender systems. In order to predict user actions such as the next product to purchase, movie to watch, or place to visit, it is essential to take into account both long term user preferences and sequential patterns (i.e. short term dynamics) Matrix Factorization and Markov Chain methods have emerged as two separate but powerful paradigms for modeling the two respectively. Combining these ideas has led to unified methods that accommodate long and short term dynamics simultaneously by modeling pairwise user item and item item interactions. In spite of the success of such methods for tackling dense data, they are challenged by sparsity issues, which are prevalent in real world datasets. In recent years, similarity based methods have been proposed for (sequentially unaware) item recommendation with promising results on sparse datasets. In this paper, we propose to fuse such methods with Markov Chains to make personalized sequential recommendations. We evaluate our method, Fossil, on a variety of large, real world datasets. We show quantitatively that Fossil outperforms alternative algorithms, especially on sparse datasets, and qualitatively that it captures personalized dynamics and is able to make meaningful recommendations.
Mutual Influence Potential Networks: Enabling Information Sharing in Loosely Coupled Extended Duration Teamwork
Venue: IJCAI

Complex collaborative activities such as treating patients, co authoring documents and developing software are often characterized by teamwork that is loosely coupled and extends in time. To remain coordinated and avoid conflicts, team members need to identify dependencies between their activities which though loosely coupled may interact and share information appropriately. The loose coupling of tasks increases the difficulty of identifying dependencies, with the result that team members often lack important information or are overwhelmed by irrelevant information. This paper formalizes a new multi agent systems problem, Information Sharing in Loosely Coupled Extended Duration Teamwork (ISLET) It defines a new representation, Mutual Influence Potential Networks (MIP Nets) and an algorithm, MIP DOI, that uses this representation to determine the information that is most relevant to each team member. Importantly, because the extended duration of the teamwork precludes team members from developing complete plans in advance, the MIP Nets approach, unlike prior work on information sharing, does not rely on a priori knowledge of a team's possible plans. Instead, it models collaboration patterns and dependencies among people and their activities based on team member interactions. Empirical evaluations show that this approach is able to learn collaboration patterns and identify relevant information to share with team members.