nils-herrmann/clean_scirepeval huggingface dataset:
Simultaneous Multiparametric PET/MRI with Silicon Photomultiplier PET and Ultra High Field MRI for Small Animal Imaging
Venue: The Journal of Nuclear Medicine

Visualization of biologic processes at molecular and cellular levels has revolutionized the understanding and treatment of human diseases. However, no single biomedical imaging modality provides complete information, resulting in the emergence of multimodal approaches. Combining state of the art PET and MRI technologies without loss of system performance and overall image quality can provide opportunities for new scientific and clinical innovations. Here, we present a multiparametric PET/MR imager based on a small animal dedicated, high performance, silicon photomultiplier (SiPM) PET system and a 7 T MR scanner.  A SiPM based PET insert that has the peak sensitivity of 3.4% and center volumetric resolution of 1.92/0.53 mm3 (filtered backprojection/ordered subset expectation maximization) was developed. The SiPM PET insert was placed between the mouse body transceiver coil and gradient coil of a 7 T small animal MRI scanner for simultaneous PET/MRI. Mutual interference between the MRI and SiPM PET systems was evaluated using various MR pulse sequences. A cylindric corn oil phantom was scanned to assess the effects of the SiPM PET on the MR image acquisition. To assess the influence of MRI on the PET imaging functions, several PET performance indicators including scintillation pulse shape, flood image quality, energy spectrum, counting rate, and phantom image quality were evaluated with and without the application of MR pulse sequences. Simultaneous mouse PET/MRI studies were also performed to demonstrate the potential and usefulness of the multiparametric PET/MRI in preclinical applications.  Excellent performance and stability of the PET system were demonstrated, and the PET/MRI combination did not result in significant image quality degradation of either modality. Finally, simultaneous PET/MRI studies in mice demonstrated the feasibility of the developed system for evaluating the biochemical and cellular changes in a brain tumor model and facilitating the development of new multimodal imaging probes.  We developed a multiparametric imager with high physical performance and good system stability and demonstrated its feasibility for small animal experiments, suggesting its usefulness for investigating in vivo molecular interactions of metabolites, and cross validation studies of both PET and MRI.
Hybrid segmentation of vessels and automated flow measures in in vivo ultrasound imaging
Venue: 2016 IEEE International Ultrasonics Symposium (IUS)

Vector Flow Imaging (VFI) has received an increasing attention in the scientific field of ultrasound, as it enables angle independent visualization of blood flow. VFI can be used in volume flow estimation, but a vessel segmentation is needed to make it fully automatic. A novel vessel segmentation procedure is crucial for wall to wall visualization, automation of adjustments, and quantification of flow in state of the art ultrasound scanners. We propose and discuss a method for accurate vessel segmentation that fuses VFI data and B mode for robustly detecting and delineating vessels. The proposed method implements automated VFI flow measures such as peak systolic velocity (PSV) and volume flow. An evaluation of the performance of the segmentation algorithm relative to expert manual segmentation of 60 frames randomly chosen from 6 ultrasound sequences (10 frame randomly chosen from each sequence) is also presented. Dice coefficient denoting the similarity between segmentations is used for the evaluation. The coefficient ranges between 0 and 1, where 1 indicates perfect agreement and 0 indicates no agreement. The Dice coefficient was 0.91 indicating to a very agreement between automated and manual expert segmentations. The flowrig results also demonstrated that the PSVs measured from VFI had a mean relative error of 14.5% in comparison with the actual PSVs. The error for the PSVs measured from spectral Doppler was 29.5% indicating that VFI is 15% more precise than spectral Doppler in PSV measurement.
Semantic Mapping on Underwater Environment Using Sonar Data
Venue: 2016 XIII Latin American Robotics Symposium and IV Brazilian Robotics Symposium (LARS/SBR)

The use of robots in underwater exploration is increasing in the last years. The automation of the monitoring, inspection and underwater maintenance tasks requires the understanding of the environment. One of the key issues of these systems is to recognize in the objects in the scene. This paper proposes a method to provide a semantic mapping using acoustic images acquired by forward looking sonar (FLS) The method represents the environment using Gaussian probability density functions. Furthermore, we efficiently segment and classify the structures in the scene. Finally, we create a semantic map of the scene. We evaluate the method in a real dataset acquired by an underwater vehicle performing autonomous navigation and mapping tasks in a harbor area.
Multibeam 3D Underwater SLAM with Probabilistic Registration
Venue: Sensors

This paper describes a pose based underwater 3D Simultaneous Localization and Mapping (SLAM) using a multibeam echosounder to produce high consistency underwater maps. The proposed algorithm compounds swath profiles of the seafloor with dead reckoning localization to build surface patches (i.e. point clouds) An Iterative Closest Point (ICP) with a probabilistic implementation is then used to register the point clouds, taking into account their uncertainties. The registration process is divided in two steps: (1) point to point association for coarse registration and (2) point to plane association for fine registration. The point clouds of the surfaces to be registered are sub sampled in order to decrease both the computation time and also the potential of falling into local minima during the registration. In addition, a heuristic is used to decrease the complexity of the association step of the ICP from O(n2) to O(n) The performance of the SLAM framework is tested using two real world datasets: First, a 2.5D bathymetric dataset obtained with the usual down looking multibeam sonar configuration, and second, a full 3D underwater dataset acquired with a multibeam sonar mounted on a pan and tilt unit.
Unequal Thailand: Aspects of Income, Wealth and Power ed. by Pasuk Phongpaichit and Chris Baker (review)

Unequal Thailand is the newest volume of research to be compiled by the husband and wife team of Baker and Pasuk, the latter a professor of economics at Chulalongkorn University and the former a public intellectual of some note who has written widely on contemporary Thai history and politics. The two have edited an excellent and informative book that focuses on many of the issues that underlie Thailand's current political crises. The editors have brought together painstakingly executed and well written papers that examine different aspects of inequality of income, wealth, power and knowledge. Taken together, these papers provide a reader with a very clear understanding of what might be done to move Thailand out of it current depressing morass of political disenfranchisement and societal mistrust. Any scholar of the region or the country will find at least several of these chapters of interest, whether her or his own focus is economic, political or sociological. Most of the contributors to the volume are relatively young scholars working in Thailand. This may account for the wealth of knowledge about how the country works as well as the excellent quality of the data used in the majority of the chapters. This generally high quality of data and analysis is impressive, in that many of the authors rely on information that I, for one, was unaware was available in Thailand. The authors often allude to the fact that the data on which they are relying come from sources which have not previously been exploited or which in some cases have been specifically created for the purposes of analysing inequality in the country. One chapter, for example, is the first paper ever to explore the concentration of land ownership on a national scale by using new data from the country's Socio Economic Survey and recently computerized titled landholding information from the Land Department of Thailand. Duangmanee Laovakul, an assistant professor of Economics at
Fear of Self and Unacceptable Thoughts in Obsessive Compulsive Disorder.
Venue: Clinical psychology psychotherapy

Cognitive behavioural models have linked unacceptable or repugnant thoughts in obsessive compulsive disorder (OCD) with vulnerable self themes and fear of self concerns. To investigate this notion, Aardema and coworkers recently developed and validated the Fear of Self Questionnaire (FSQ) in non clinical samples, finding it had strong internal inconsistency, and good divergent and convergent validity, including strong relationships to obsessional symptoms and with other processes implicated in cognitive models of OCD (e.g. obsessive beliefs and inferential confusion) The current article describes two studies that aim to replicate and extend these findings in clinical OCD and non clinical samples. Study 1 investigated the psychometric properties of an Italian translation of the FSQ in a non clinical sample (n 405) Results of confirmatory factor analysis supported the unidimensionality of the scale; the FSQ also showed very good internal consistency and temporal stability. Study 2 investigated the role of fear of self in OCD symptoms, and unacceptable thoughts and repugnant obsessions in particular, using a clinical OCD sample (n 76) As expected, fear of self was a unique, major predictor of unacceptable thoughts independent of negative mood states and obsessive beliefs. Moreover, even when considered with obsessive beliefs, anxiety and depression, the feared self was the only unique predictor of obsessionality, providing support for the notion that self themes could explain why some intrusions convert into obsessions, whereas others do not. Implications for current cognitive behavioural models are discussed.
Irrigation water requirements for seed corn and coffee under potential climate change scenarios

The impact of potential future climate change scenarios on the irrigation water requirements (IRRs) of two major agricultural crops (coffee and seed corn) in Hawai'i was studied using the Irrigation Management System (IManSys) model. In addition to IRRs calculations, IManSys calculates runoff, deep percolation, canopy interception, and effective rainfall based on plant growth parameters, site specific soil hydrological properties, irrigation system efficiency, and long term daily weather data. Irrigation water requirements of two crops were simulated using historical climate data and different levels of atmospheric CO2 (330, 550, 710 and 970 ppm) temperature +1.1 and +6.4 degC) and precipitation 5, 10 and 20% chosen based on the Intergovernmental Panel on Climate Change (IPCC) AR4 projections under reference, B1, A1B1 and A1F1 emission scenarios. IRRs decreased as CO2 emission increased. The average percentage decrease in IRRs for seed corn is higher than that of coffee. However, runoff, rain canopy interception, and deep percolation below the root zone increased as precipitation increased. Canopy interception and drainage increased with increased CO2 emission. Evapotranspiration responded positively to air temperature rise, and as a result, IRRs increased as well. Further studies using crop models will predict crop yield responses to these different irrigation scenarios.
Differential Scanning Calorimetry and Infrared Spectroscopy Combined with Chemometric Analysis to the Determination of Coffee Adulteration by Corn

Roasted and ground coffee is targeted by fraudulent addiction of products. In this way the determination of contaminants in coffee has economic and nutritional importance. In this study, the coffee adulteration by corn were detected using DSC (differential scanning calorimetry) and FTIR (Fourier transform infrared spectroscopy) coupled to PCA (principal component analysis) and PLS (partial least squares) models. Three different levels of roasted and ground Coffea arabica L. were used to prepare mixtures with roasted and ground corn. The level of adulteration used was between 0.5 to 40% (m/m) It was observed that both DSC and FTIR coupled with PCA are able to discriminate adulterated from unadulterated samples of coffee by corn at levels below 1% PLS models were built with DSC and FTIR data reaching good correlation between the values of estimated and reference concentrations, with RMSECV (root mean square error of cross validation) lower than 3.5% for DSC data and 2.7% for FTIR data.
Effect of drying on flavour quality of Indian spearmint Mentha spicata L.

The essential oils obtained from Indian spearmint Mentha spicata L. subjected to different drying procedures were analysed employing gas chromatography and gas chromatography mass spectrometry techniques. Forty three flavour components were identified, of which eucarvone, cubenol and a cadinol are reported for the first time. The essential oil had a higher percentage of limonene (26.82% compared to Italian and American oils. Carvone to limonene ratios were 2.2:1.0 and 2.3:1.0 in the oil from fresh and shade dried spearmint, respectively. Blanching leaves prior to drying yielded products which were unattractive with respect to colour and appearance and were also bland and odourless due to loss of volatile oil during drying. Storing fresh leaves for 12 h after spraying with water increased the volatile oil content by about 6% Shade drying leaves resulted in a product with a good green colour and minimum loss of volatile oil compared to other drying methods.
User popularity based packet scheduling for congestion control in ad hoc social networks
Venue: J. Comput. Syst. Sci.

Traditional ad hoc network packet scheduling schemes cannot fulfill the requirements of proximity based ad hoc social networks (ASNETs) and they do not behave properly in congested environments. To address this issue, we propose a user popularity based packet scheduling scheme for congestion control in ASNETs called Pop aware. The proposed algorithm exploits social popularity of sender nodes to prioritize all incoming flows. Pop aware also provides fairness of service received by each flow. We evaluate the performance of Pop aware through a series of simulations. In comparison with some existing scheduling algorithms, Pop aware performs better in terms of control overhead, total overhead, average throughput, packet loss rate, packet delivery rate and average delay. User popularity based packet scheduling is explored for ad hoc social networks.The scheme can avoid the dropping of the most popular node's data.It helps effectively control congestion and fully utilize available bandwidth.It can maintain fairness among flows and nodes.Simulations have been conducted for performance evaluation and comparison.
Organic inorganic hybrid CH3NH3PbI3 perovskite materials as channels in thin film field effect transistors

Organic inorganic hybrid perovskite materials promise both the superior carrier mobility of inorganic semiconductors and the processability of organic materials, which make organic inorganic hybrid perovskite materials good substitutes in all the applications put forth for organic materials and extend their application to higher speed devices than is presently possible with either a Si or organic semiconductors. Recent reports have shown high carrier mobility and long electron hole diffusion lengths of organic inorganic hybrid perovskite materials. We have demonstrated a thin film field effect transistor with an organic inorganic hybrid CH3NH3PbI3 material as the semiconducting channel based on these advantages via a low temperature vapor assisted solution process. The obvious electrical field effect is obtained in organic inorganic hybrid CH3NH3PbI3 perovskite TFTs with a field effect mobility of 396.2 cm2 V 1 s 1, current modulation greater than 104, sub threshold current of 0.4035 V per decade and threshold voltage of 3.501 V.
Organic inorganic hybrid semiconductor thin films deposited using molecular atomic layer deposition (MALD)

Molecular atomic layer deposition (MALD) is employed to fabricate hydroquinone (HQ)/diethyl zinc (DEZ) organic inorganic hybrid semiconductor thin films with accurate thickness control, sharp interfaces, and low deposition temperature. Self limiting growth is observed for both HQ and DEZ precursors. The growth rate remains constant at approximately 2.8 A per cycle at 150 degC. The hybrid materials exhibit n type semiconducting behavior with a field effect mobility of approximately 5.7 cm2 V 1 s 1 and an on/off ratio of over 103 following post annealing at 200 degC in nitrogen. The resulting films are characterized using ellipsometry, Fourier transform infrared spectroscopy (FTIR) transmission electron microscopy (TEM) UV Vis spectroscopy, transistor behavior, and Hall effect measurements. Density functional theory (DFT) and many body perturbation theory within the GW approximation are also performed to assist the explanation and understanding of the experimental results. This research offers n channel materials as valuable candidates for efficient organic CMOS devices.
Frontal to profile face verification in the wild
Venue: 2016 IEEE Winter Conference on Applications of Computer Vision (WACV)

We have collected a new face data set that will facilitate research in the problem of frontal to profile face verification `in the wild' The aim of this data set is to isolate the factor of pose variation in terms of extreme poses like profile, where many features are occluded, along with other `in the wild' variations. We call this data set the Celebrities in Frontal Profile (CFP) data set. We find that human performance on Frontal Profile verification in this data set is only slightly worse (94.57% accuracy) than that on Frontal Frontal verification (96.24% accuracy) However we evaluated many state of the art algorithms, including Fisher Vector, Sub SML and a Deep learning algorithm. We observe that all of them degrade more than 10% from Frontal Frontal to Frontal Profile verification. The Deep learning implementation, which performs comparable to humans on Frontal Frontal, performs significantly worse (84.91% accuracy) on Frontal Profile. This suggests that there is a gap between human performance and automatic face recognition methods for large pose variation in unconstrained images.
Approach for Automated Planning Using 5D BIM

Early planning decisions are usually dependent on time demanding, manually produced cost estimations and schedules. There is a trend in the construction industry to use 5D BIM to speed up these processes and automated approaches can be used to further improve effectiveness. This research aims to investigate how to create an automated 5D BIM planning process when using industrialized building systems. We propose an approach that combines a BIM manual with predefined databases based on the building system and its properties. A case study at one of Scandinavia's largest construction and property development companies was conducted where the presented approach was tested. The findings show that planning using 5D BIM is possible to partially automate through our approach but that there are challenges for a fully automated process in the standardization required and assuring that the quality of data in each step is adequate.
Improving Children's Speech Recognition Through Out of Domain Data Augmentation
Venue: INTERSPEECH

Children's speech poses challenges to speech recognition due to strong age dependent anatomical variations and a lack of large, publicly available corpora. In this paper we explore data augmentation for children's speech recognition using stochastic feature mapping (SFM) to transform out of domain adult data for both GMM based and DNN based acoustic models. We performed experiments on the English PF STAR corpus, augmenting using WSJCAM0 and ABI. Our experimental results indicate that a DNN acoustic model for childrens speech can make use of adult data, and that out of domain SFM is more accurate than in domain SFM.
Mismatched training data enhancement for automatic recognition of children's speech using DNN HMM
Venue: 2016 10th International Symposium on Chinese Spoken Language Processing (ISCSLP)

The increasing profusion of commercial automatic speech recognition technology applications has been driven by big data techniques, using high quality labelled speech datasets. Children's speech has greater time and frequency domain variability than typical adult speech, lacks good large scale training data, and presents difficulties relating to capture quality. Each of these factors reduces the performance of systems that automatically recognise children's speech. In this paper, children's speech recognition is investigated using a hybrid acoustic modelling approach based on deep neural networks and Gaussian mixture models with hidden Markov model back ends. We explore the incorporation of mismatched training data to achieve a better acoustic model and improve performance in the face of limited training data, as well as training data augmentation using noise. We also explore two arrangements for vocal tract length normalisation and a gender based data selection technique suitable for training a children's speech recogniser.