nils-herrmann/clean_scirepeval huggingface dataset:
Photo-inspired model-driven 3D object modeling

We introduce an algorithm for 3D object modeling where the user draws creative inspiration from an object captured in a single photograph. Our method leverages the rich source of photographs for creative 3D modeling. However, with only a photo as a guide, creating a 3D model from scratch is a daunting task. We support the modeling process by utilizing an available set of 3D candidate models. Specifically, the user creates a digital 3D model as a geometric variation from a 3D candidate. Our modeling technique consists of two major steps. The first step is a user-guided image-space object segmentation to reveal the structure of the photographed object. The core step is the second one, in which a 3D candidate is automatically deformed to fit the photographed target under the guidance of silhouette correspondence. The set of candidate models have been pre-analyzed to possess useful high-level structural information, which is heavily utilized in both steps to compensate for the ill-posedness of the analysis and modeling problems based only on content in a single image. Equally important, the structural information is preserved by the geometric variation so that the final product is coherent with its inherited structural information readily usable for subsequent model refinement or processing.  DL PDF WEB VIDEO
Hypergraph Models and Algorithms for Data-Pattern-Based Clustering

In traditional approaches for clustering market basket type data, relations among transactions are modeled according to the items occurring in these transactions. However, an individual item might induce different relations in different contexts. Since such contexts might be captured by interesting patterns in the overall data, we represent each transaction as a set of patterns through modifying the conventional pattern semantics. By clustering the patterns in the dataset, we infer a clustering of the transactions represented this way. For this, we propose a novel hypergraph model to represent the relations among the patterns. Instead of a local measure that depends only on common items among patterns, we propose a global measure that is based on the cooccurences of these patterns in the overall data. The success of existing hypergraph partitioning based algorithms in other domains depends on sparsity of the hypergraph and explicit objective metrics. For this, we propose a two-phase clustering approach for the above hypergraph, which is expected to be dense. In the first phase, the vertices of the hypergraph are merged in a multilevel algorithm to obtain large number of high quality clusters. Here, we propose new quality metrics for merging decisions in hypergraph clustering specifically for this domain. In order to enable the use of existing metrics in the second phase, we introduce a vertex-to-cluster affinity concept to devise a method for constructing a sparse hypergraph based on the obtained clustering. The experiments we have performed show the effectiveness of the proposed framework.
Agile and resilient approaches to supply chain management: influence on performance and competitiveness

Supply chain management must adopt different and more innovative strategies that support a better response to customer needs in an uncertain environment. Supply chains must be more agile and be more capable of coping with disturbances, meaning that supply chains must be more resilient. The simultaneous deployment of agile and resilient approaches will enhance supply chain performance and competitiveness. Accordingly, the main objective of this paper is to propose a conceptual framework for the analysis of relationships between agile and resilient approaches, supply chain competitiveness and performance. Operational and economic performance measures are proposed to facilitate the monitoring of the influence of these practices on supply chain performance. The influence of the proposed agile and resilient practices on supply chain competitiveness is also examined in terms of time to market, product quality and customer service.
Influence of HV inductive voltage transformers core design to the ferroresonance occurrence probability

The occurrence of ferroresonance oscillations of high voltage inductive (electromagnetic) voltage transformers (VT) has been recorded and reported in a number of papers and reports. Because of its non-linear characteristic, inductive voltage transformer has the possibility of causing ferroresonance with capacitances present in the transmission network, if initiated by a transient occurrence such as switching operation or fault. One of the solutions for ferroresonance mitigation is introducing an air gap into voltage transformer core magnetic path, thus linearizing its magnetizing characteristic and decreasing the possibility of a ferroresonance occurrence. This paper presents results of numerical ATP-EMTP simulation of typical ferroresonance situation involving inductive voltage transformers in high voltage networks with circuit breaker opening operation after which the voltage transformer remains energized through the circuit breaker grading capacitance. Main variable in calculating to the ferroresonance occurrence probability was the magnetizing characteristic change caused by the introduction of an air gap to the VT core, and separate diagrams are presented for VTs with different air gap length, including the paramount gapped transformer design – open core voltage transformers.
An Ultra-Wideband 80 GHz FMCW Radar System Using a SiGe Bipolar Transceiver Chip Stabilized by a Fractional-N PLL Synthesizer

A radar system with an ultra-wide FMCW ramp bandwidth of 25.6 GHz (≈32%) around a center frequency of 80 GHz is presented. The system is based on a monostatic fully integrated SiGe transceiver chip, which is stabilized using conventional fractional-N PLL chips at a reference frequency of 100 MHz. The achieved in-loop phase noise is ≈ -88 dBc/Hz (10 kHz offset frequency) for the center frequency and below ≈-80 dBc/Hz in the wide frequency band of 25.6 GHz for all offset frequencies >;1 kHz. The ultra-wide PLL-stabilization was achieved using a reverse frequency position mixer in the PLL (offset-PLL) resulting in a compensation of the variation of the oscillators tuning sensitivity with the variation of the N-divider in the PLL. The output power of the transceiver chip, as well as of the mm-wave module (containing a waveguide transition), is sufficiently flat versus the output frequency (variation <;3 dB). In radar measurements using the full bandwidth an ultra-high spatial resolution of 7.12 mm was achieved. The standard deviation between repeated measurements of the same target is 0.36 μm.
Path loss modeling in durian orchard for wireless network at 5.8 GHz

The path loss measurements and modeling in durian orchard for wireless network at 5.8 GHz are presented. The path loss model is important to predict and design the wireless communication system in the durian orchard. The transmitter is set up near the bole of a durian tree and received the signal strength by spectrum analyzer. The communication channel is connected between the communication nodes. From the measurement results, it is observed that the path loss along the distance of 128 meters from the transmitter is less than −50 dB. The dual-slope modeling is employed to represent the path loss propagation in the durian orchard at 5.8 GHz.
Adaptive Image Segmentation Using a Genetic Algorithm

1 Asst Professor IMS Engg College Ghaziabad, Uttar Pradesh, India 2 Professor , Institute of Management Technology, Ghaziabad, Uttar Pradesh, India _________________________________________________________________________________________ Abstract: In this paper we present an Adaptive approach for image segmentation using genetic algorithm, a natural evolutionary approach for optimisation problems. In addition, method of implementing genetic algorithm has also been reviewed with a summary of research work on Adaptive approach for image segmentation techniques. We have proposed an efficient color image segmentation using Adaptive approach along with genetic algorithm. The advantage of the proposed method lies in its utilisation of prior knowledge of the RGB image to segment the image efficiently. __________________________________________________________________________________________
A comparative pharmacological study on the effect of Tagara (Valeriana wallichii) AND Jatamansi (Nardostachys jatamansi) in the management of anidra w.s.r to primary insomnia

The present study was undertaken to evaluate Tagara and Jatamansi for certain CNS activities to ascertain the basis for their use as a sedative and hypnotics. The drugs were evaluated for hypnotic and sedative, anti anxiety, anti depressant and anti psychotic activities using standard experimental protocol. The data generated suggests that the test drugs Tagara and Jatamansi have complex CNS activity profile which is not easy to categorize under general CNS activity profile. Both possess significant anti-anxiety activity without significant sedative-hypnotic activity. This increases their utility for the patients suffering from anxiety due to sleep disturbances.
Compass: A scalable simulator for an architecture for cognitive computing

Inspired by the function, power, and volume of the organic brain, we are developing TrueNorth, a novel modular, non-von Neumann, ultra-low power, compact architecture. TrueNorth consists of a scalable network of neurosynaptic cores, with each core containing neurons, dendrites, synapses, and axons. To set sail for TrueNorth, we developed Compass, a multi-threaded, massively parallel functional simulator and a parallel compiler that maps a network of long-distance pathways in the macaque monkey brain to TrueNorth. We demonstrate near-perfect weak scaling on a 16 rack IBM® Blue Gene®/Q (262144 CPUs, 256 TB memory), achieving an unprecedented scale of 256 million neurosynaptic cores containing 65 billion neurons and 16 trillion synapses running only 388X slower than real time with an average spiking rate of 8.1 Hz. By using emerging PGAS communication primitives, we also demonstrate 2X better real-time performance over MPI primitives on a 4 rack Blue Gene/P (16384 CPUs, 16 TB memory).
An approach to validity indices for clustering techniques in Big Data

Clustering analysis is one of the most used Machine Learning techniques to discover groups among data objects. Some clustering methods require the number of clusters into which the data is going to be partitioned. There exist several cluster validity indices that help us to approximate the optimal number of clusters of the dataset. However, such indices are not suitable to deal with Big Data due to its size limitation and runtime costs. This paper presents two clustering validity indices that handle large amount of data in low computational time. Our indices are based on redefinitions of traditional indices by simplifying the intra-cluster distance calculation. Two types of tests have been carried out over 28 synthetic datasets to analyze the performance of the proposed indices. First, we test the indices with small and medium size datasets to verify that our indices have a similar effectiveness to the traditional ones. Subsequently, tests on datasets of up to 11 million records and 20 features have been executed to check their efficiency. The results show that both indices can handle Big Data in a very low computational time with an effectiveness similar to the traditional indices using Apache Spark framework.
A Tetrahedral Microphone Processor for Ambisonic Recording Fons ADRIAENSEN

This paper introduces a Linux audio application that provides an integrated solution for making full 3-D Ambisonics recordings by using a tetrahedral microphone. Apart from the basic A to B format conversion it performs a number of auxiliary functions such as LF filtering, metering and monitoring, turning it into a complete Ambisonics recording processor. It also allows for calibration of an individual microphone unit based on measured impulse responses. A new JACK backend required to make use of a particular four-channel audio interface optimised for Ambisonic recording is also introduced.
Lipid imaging within the normal rat kidney using silver nanoparticles by matrix-assisted laser desorption/ionization mass spectrometry

The well-characterized cellular and structural components of the kidney show distinct regional compositions and distribution of lipids. In order to more fully analyze the renal lipidome we developed a matrix-assisted laser desorption/ionization mass spectrometry approach for imaging that may be used to pinpoint sites of changes from normal in pathological conditions. This was accomplished by implanting sagittal cryostat rat kidney sections with a stable, quantifiable and reproducible uniform layer of silver using a magnetron sputtering source to form silver nanoparticles. Thirty-eight lipid species including seven ceramides, eight diacylglycerols, 22 triacylglycerols, and cholesterol were detected and imaged in positive ion mode. Thirty-six lipid species consisting of seven sphingomyelins, 10 phosphatidylethanolamines, one phosphatidylglycerol, seven phosphatidylinositols, and 11 sulfatides were imaged in negative ion mode for a total of seventy-four high-resolution lipidome maps of the normal kidney. Thus, our approach is a powerful tool not only for studying structural changes in animal models of disease, but also for diagnosing and tracking stages of disease in human kidney tissue biopsies.
Privacy-preserving deep learning

Deep learning based on artificial neural networks is a very popular approach to modeling, classifying, and recognizing complex data such as images, speech, and text. The unprecedented accuracy of deep learning methods has turned them into the foundation of new AI-based services on the Internet. Commercial companies that collect user data on a large scale have been the main beneficiaries of this trend since the success of deep learning techniques is directly proportional to the amount of data available for training. Massive data collection required for deep learning presents obvious privacy issues. Users' personal, highly sensitive data such as photos and voice recordings is kept indefinitely by the companies that collect it. Users can neither delete it, nor restrict the purposes for which it is used. Furthermore, centrally kept data is subject to legal subpoenas and extra-judicial surveillance. Many data owners--for example, medical institutions that may want to apply deep learning methods to clinical records--are prevented by privacy and confidentiality concerns from sharing the data and thus benefitting from large-scale deep learning.
 In this paper, we design, implement, and evaluate a practical system that enables multiple parties to jointly learn an accurate neural-network model for a given objective without sharing their input datasets. We exploit the fact that the optimization algorithms used in modern deep learning, namely, those based on stochastic gradient descent, can be parallelized and executed asynchronously. Our system lets participants train independently on their own datasets and selectively share small subsets of their models' key parameters during training. This offers an attractive point in the utility/privacy tradeoff space: participants preserve the privacy of their respective data while still benefitting from other participants' models and thus boosting their learning accuracy beyond what is achievable solely on their own inputs. We demonstrate the accuracy of our privacy-preserving deep learning on benchmark datasets.
Moulds and yeasts in fruit salads and fruit juices.

Thirty-eight fruit salad samples including cantaloupe, citrus fruits, honeydew, pineapple, cut strawberries and mixed fruit salads, and 65 pasteurized fruit juice samples (apple, carrot, grapefruit, grape and orange juices, apple cider, and soy milk) were purchased from local supermarkets in the Washington, DC area and tested for fungal contamination. The majority of fruit salad samples (97%) were contaminated with yeasts at levels ranging from <2.0 to 9.72 log10 of colony forming units per gram (cfu/g). Frequently encountered yeasts were Pichia spp., Candida pulcherrima, C. lambica, C. sake, Rhodotorula spp., and Debaryomyces polymorphus. Low numbers of Penicillium spp. were found in pineapple salads, whereas Cladosporium spp. were present in mixed fruit and cut strawberry salads. Twenty-two per cent of the fruit juice samples tested showed fungal contamination. Yeasts were the predominant contaminants ranging from <1.0 to 6.83 log10 cfu/ml. Yeasts commonly found in fruit juices were C. lambica, C. sake, and Rhodotorula rubra. Geotrichum spp. and low numbers of Penicillium and Fusarium spp. (1.70 and 1.60 log10 cfu/ml, respectively) were present in grapefruit juice.
A Novel Carrier Phase Recovery Method for Turbo-coded QPSK System

This paper proposes a novel phase recovery method for Turbo-coded QPSK systems. The phase estimation is combined with the turbo decoding instead of applying separate synchronisation prior to the Turbo decoder. It is achieved through Maximum Likelihood Estimation (MLE) which employs the extrinsic information obtained from the turbo decoder. Look-up tables are pre-computed to reduce the computation burden. QPSK systems over AWGN channel with unknown phase error are investigated. The phase error can be completely removed up to ° 37 . The result proves that this method greatly outperforms the traditional technique (M-law). A further improvement is also proposed using four such schemes 2 π apart, which can remove most phase errors completely.
Allograph errors and impaired access to graphic motor codes in a case of unilateral agraphia of the dominant left hand.

This paper describes the case of a unilateral agraphic patient (GG) who makes letter substitutions only when writing letters and words with his dominant left hand. Accuracy is significantly greater when he is writing with his right hand and when he is asked to spell words orally. GG also makes case errors when writing letters, and will sometimes write words in mixed case. However, these allograph errors occur regardless of which hand he is using to write. In terms of cognitive models of peripheral dysgraphia (e.g., Ellis, 1988), it appears that he has an allograph level impairment that affects writing with both hands, and a separate problem in accessing graphic motor patterns that disrupts writing with the left hand only. In previous studies of left-handed patients with unilateral agraphia (Zesiger & Mayer, 1992; Zesiger, Pegna, & Rilliet, 1994), it has been suggested that allographic knowledge used for writing with both hands is stored exclusively in the left hemisphere, but that graphic motor patterns are represented separately in each hemisphere. The pattern of performance demonstrated by GG strongly supports such a conclusion.
MANET simulation studies: the incredibles

Simulation is the research tool of choice for a majority of the mobile ad hoc network (MANET) community. However, while the use of simulation has increased, the credibility of the simulation results has decreased. To determine the state of MANET simulation studies, we surveyed the 2000-2005 proceedings of the ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc). From our survey, we found significant shortfalls. We present the results of our survey in this paper. We then summarize common simulation study pitfalls found in our survey. Finally, we discuss the tools available that aid the development of rigorous simulation studies. We offer these results to the community with the hope of improving the credibility of MANET simulation-based studies.
Detecting and classifying lesions in mammograms with Deep Learning

In the last two decades, Computer Aided Detection (CAD) systems were developed to help radiologists analyse screening mammograms, however benefits of current CAD technologies appear to be contradictory, therefore they should be improved to be ultimately considered useful. Since 2012, deep convolutional neural networks (CNN) have been a tremendous success in image recognition, reaching human performance. These methods have greatly surpassed the traditional approaches, which are similar to currently used CAD solutions. Deep CNN-s have the potential to revolutionize medical image analysis. We propose a CAD system based on one of the most successful object detection frameworks, Faster R-CNN. The system detects and classifies malignant or benign lesions on a mammogram without any human intervention. The proposed method sets the state of the art classification performance on the public INbreast database, AUC = 0.95. The approach described here has achieved 2nd place in the Digital Mammography DREAM Challenge with AUC = 0.85. When used as a detector, the system reaches high sensitivity with very few false positive marks per image on the INbreast dataset. Source code, the trained model and an OsiriX plugin are published online at https://github.com/riblidezso/frcnn_cad.
A paradigm shift in the surgical reconstruction of extra-articular distal humeral fractures: single-column plating.

The study aimed (1) to examine if there are equivalent results in terms of union, alignment and elbow functionally comparing single- to dual-column plating of AO/OTA 13A2 and A3 distal humeral fractures and (2) if there are more implant-related complications in patients managed with bicolumnar plating compared to single-column plate fixation.



This was a multi-centred retrospective comparative study.



The study was conducted at two academic level 1 trauma centres.



A total of 105 patients were identified to have surgical management of extra-articular distal humeral fractures Arbeitsgemeinschaft für Osteosynthesefragen/Orthopaedic Trauma Association (AO/OTA) 13A2 and AO/OTA 13A3).



Patients were treated with traditional dual-column plating or a single-column posterolateral small-fragment pre-contoured locking plate used as a neutralisation device with at least five screws in the short distal segment.



The patients' elbow functionality was assessed in terms of range of motion, union and alignment. In addition, the rate of complications between the groups including radial nerve palsy, implant-related complications (painful prominence and/or ulnar nerve neuritis) and elbow stiffness were compared.



Patients treated with single-column plating had similar union rates and alignment. However, single-column plating resulted in a significantly better range of motion with less complications.



The current study suggests that exposure/instrumentation of only the lateral column is a reliable and preferred technique. This technique allows for comparable union rates and alignment with increased elbow functionality and decreased number of complications.
Feature modelling and traceability for concern-driven software development with TouchCORE

This demonstration paper presents TouchCORE, a multi-touch enabled software design modelling tool aimed at developing scalable and reusable software design models following the concerndriven software development paradigm. After a quick review of concern-orientation, this paper primarily focusses on the new features that were added to TouchCORE since the last demonstration at Modularity 2014 (were the tool was still called TouchRAM). TouchCORE now provides full support for concern-orientation. This includes support for feature model editing and different modes for feature model and impact model visualization and assessment to best assist the concern designers as well as the concern users. To help the modeller understand the interactions between concerns, TouchCORE now also collects tracing information when concerns are reused and stores that information with the woven models. This makes it possible to visualize from which concern(s) a model element in the woven model has originated.