bluuebunny/crossref_metadata_2025_split huggingface dataset:
Generative Adversarial Networks for fast simulation
Authors: Carminati Federico , Khattak Gulrukh , Loncar Vladimir , Nguyen Thong Q , Pierini Maurizio , Brito Da Rocha Ricardo , Samaras-Tsakiris Konstantinos , Vallecorsa Sofia , Vlimant Jean-Roch 
Published: July 2020

Abstract
Deep Learning techniques are being studied for different applications by the HEP community: in this talk, we discuss the case of detector simulation. The need for simulated events, expected in the future for LHC experiments and their High Luminosity upgrades, is increasing dramatically and requires new fast simulation solutions. Here we present updated results on the development of 3DGAN, one of the first examples using three-dimensional convolutional Generative Adversarial Networks to simulate high granularity electromagnetic calorimeters. In particular, we report on two main aspects: results on the simulation of a more general, realistic physics use case and on data parallel strategies to distribute the training process across multiple nodes on public cloud resources.
Boosting Performance of Data-intensive Analysis Workflows with Distributed Coordinated Caching
Authors: Heidecker C , von Cube R F , Giffels M , Quast G , Sauter M , Schnepf M J 
Published: July 2020

Abstract
Data-intensive end-user analyses in high energy physics require high data throughput to reach short turnaround cycles. This leads to enormous challenges for storage and network infrastructure, especially when facing the tremendously increasing amount of data to be processed during High-Luminosity LHC runs. Including opportunistic resources with volatile storage systems into the traditional HEP computing facilities makes this situation more complex.
Bringing data close to the computing units is a promising approach to solve throughput limitations and improve the overall performance. We focus on coordinated distributed caching by coordinating workows to the most suitable hosts in terms of cached files. This allows optimizing overall processing efficiency of data-intensive workows and efficiently use limited cache volume by reducing replication of data on distributed caches.
We developed a NaviX coordination service at KIT that realizes coordinated distributed caching using XRootD cache proxy server infrastructure and HTCondor batch system. In this paper, we present the experience gained in operating coordinated distributed caches on cloud and HPC resources. Furthermore, we show benchmarks of a dedicated high throughput cluster, the Throughput-Optimized Analysis-System (TOpAS), which is based on the above-mentioned concept.
A hybrid deep learning approach to vertexing
Authors: Fang Rui , Schreiner Henry F , Sokoloff Michael D , Weisser Constantin , Williams Mike 
Published: July 2020

Abstract
During the LHC Run 3, the instantaneous luminosity received by LHCb will be increased going from 1.1 to 5.6 expected visible Primary Vertices (PVs) per event. To face this challenge, the LHCb detector will be upgraded and will adopt a purely software trigger. This has fueled increased interest in alternative highly-parallel and GPU friendly algorithms for tracking and reconstruction. We will present a novel prototype algorithm for vertexing in the LHCb upgrade conditions. We use a custom kernel to transform the sparse 3D space of hits and tracks into a dense 1D dataset, and then apply Deep Learning techniques to find PV locations. By training networks on our kernels using several Convolutional Neural Network layers, we have achieved better than 90% efficiency with no more than 0.2 False Positives (FPs) per event. Beyond its physics performance, this algorithm also provides a rich collection of possibilities for visualization and study of 1D convolutional networks. We will discuss the design, performance, and future potential areas of improvement and study, such as possible ways to recover the full 3D vertex information.
Particle Generative Adversarial Networks for full-event simulation at the LHC and their application to pileup description
Authors: Arjona Martínez Jesus , Nguyen Thong Q , Pierini Maurizio , Spiropulu Maria , Vlimant Jean-Roch 
Published: July 2020

Abstract
We investigate how a Generative Adversarial Network could be used to generate a list of particle four-momenta from LHC proton collisions, allowing one to define a generative model that could abstract from the irregularities of typical detector geometries. As an example of application, we show how such an architecture could be used as a generator of LHC parasitic collisions (pileup). We present two approaches to generate the events: unconditional generator and generator conditioned on missing transverse energy. We assess generation performances in a realistic LHC data-analysis environment, with a pileup mitigation algorithm applied.
Tag-based Analysis at the BESIII Experiment
Authors: Deng Z Y , Zou J H , Sun S S , Liu B J , Wang L , Shi J Y , Xiong X A , Zhang S F 
Published: July 2020

Abstract
As more and more data is accumulated from BESIII detector, the reduction of the CPU time of data processing procedures is significant for the experiment to get physics results efficiently with limited hardware resources. In this study, we designed a tag-based analysis method in BESIII offline software system. Typical physics analysis results show the tag-based analysis with reformed DST file can reduce the jobs running time to about 1/10, with high CPU efficiency and low read throughput, without inducing additional disk space occupation.
Digitization of Cylindrical GEM Detector
Authors: Zhao Jingyi , Miao Nannan , Wu Linghui , Wang Liangliang , Sun Tong , Huang Zhen , Mitchell Ryan , Li Weiguo , Liu Huaimin , Lou Xinchou , Sun Shengsen , Yuan Ye , Zhang Yao 
Published: July 2020

Abstract
The BESIII inner drift chamber is suffering from aging effect. Cylindrical Gas Electron Multiplier Inner Tracker (CGEM-IT) is considered as one of the upgrade candidates. A simulation study with Garfield++ program has been performed to understand the drift behaviour and to build a full digitization model. Parameters related to Lorentz angle, diffusion effect, drift time, multiplication and induction are obtained from the Garfield++ simulation. A preliminary digitization model is implemented in the BESIII Offline Software System (BOSS) based on those parameters.
Meta-Learning for Artificial Neural Network Hyper-Parameter Optimization for CERN CMS Offline Data Certification
Authors: Stankevicius Mantas , Marcinkevicius Virginijus , Rapsevicius Valdas 
Published: July 2020

Abstract
The Compact Muon Solenoid (CMS) is one of the general-purpose detectors at the CERN Large Hadron Collider (LHC) which collects enormous amounts of physics data. Before the final physics analysis can proceed, data has to be checked for quality (certified) by passing a number of automatic (like physics objects reconstruction, histogram preparation) and manual (checking, comparison and decision making) steps. Last manual step of decision making is very important, error-prone and demands a lot of manpower. Decision making (certification) is currently under active research in computer science for automation by applying recent advancements from computer science, specifically, machine learning (ML).
Ultimately, CMS data certification is a binary classification task where various ML techniques are being investigated for applicability. Just like in any other ML task the hyperparameter tuning is a difficult problem, there is no golden rule and each use case is different. This study explored meta-learning applicability, it is a hyper-parameters finding technique where algorithm learns hyper-parameters from previous training experiments. An Evolutionary genetic algorithm has been used to tune hyper-parameters of a neural network, like number of hidden layers, number of neurons per layer, activation functions, dropouts, training batch size and optimizer. Initially, the genetic algorithm takes manually specified set of hyper-parameters and then evolves towards the near-optimal solution. Genetic stochastic operators, crossover and mutation, were applied to avoid local optimal solutions.
This study shows that by carefully seeding the initial solution the optimal is likely to be found. The proposed solution has improved AUC score of neural network used for CERN CMS data certification. Similar algorithm can be applied for other machine learning models for hyper-parameter optimization.
Scaling the training of particle classification on simulated MicroBooNE events to multiple GPUs
Authors: Hagen A , Church E , Strube J , Bhattacharya K , Amatya V 
Published: July 2020

Abstract
Measurements in Liquid Argon Time Projection Chamber (LArTPC) neutrino detectors, such as the MicroBooNE detector at Fermilab [1], feature large, high fidelity event images. Deep learning techniques have been extremely successful in classification tasks of photographs, but their application to LArTPC event images is challenging, due to the large size of the events. Events in these detectors are typically two orders of magnitude larger than images found in classical challenges, like recognition of handwritten digits contained in the MNIST database or object recognition in the ImageNet database. Ideally, training would occur on many instances of the entire event data, instead of many instances of cropped regions of interest from the event data. However, such efforts lead to extremely long training cycles, which slow down the exploration of new network architectures and hyperparameter scans to improve the classification performance. We present studies of scaling a LArTPC classification problem on multiple architectures, spanning multiple nodes. The studies are carried out on simulated events in the MicroBooNE detector. We emphasize that it is beyond the scope of this study to optimize networks or extract the physics from any results here. Institutional computing at Pacific Northwest National Laboratory and the SummitDev machine at Oak Ridge National Laboratory’s Leadership Computing Facility have been used. To our knowledge, this is the first use of state-of-the-art Convolutional Neural Networks for particle physics and their attendant compute techniques onto the DOE Leadership Class Facilities. We expect benefits to accrue particularly to the Deep Underground Neutrino Experiment (DUNE) LArTPC program, the flagship US High Energy Physics (HEP) program for the coming decades.
Deep learning for Directional Dark Matter search
Authors: Golovatiuk Artem , De Lellis Giovanni , Ustyuzhanin Andrey 
Published: July 2020

Abstract
We provide an algorithm for detection of possible dark matter particle interactions recorded within NEWSdm detector. The NEWSdm (Nuclear Emulsions for WIMP Search directional measure) is an underground Direct detection Dark Matter search experiment. The usage of recent developments in the nuclear emulsions allows probing new regions in the WIMP parameter space. The directional approach, which is the key feature of the NEWSdm experiment, gives the unique chance of overcoming the neutrino floor. Deep Neural Networks were used for separation between potential DM signal and various classes of background. In this paper, we present the usage of deep 3D Convolutional Neural Networks to take into account the physical peculiarities of the datasets and report the achievement of the required 104 background rejection power.
Analyzing the streamflow-sediment links of three major river basins in India using multifractal theory
Authors: Adarsh S , Watorek Marcin 
Published: July 2020

Abstract
This study investigated the cross-correlation between daily streamflow and total suspended sediment (TSS) data of 65 gauging stations located in three major river basins in a multifractal perspective. The novel Multifractal Cross Correlation Analysis (MFCCA) method of cross correlation studies is used to analyze the streamflow-sediment links of Krishna, Godavari and Mahanadi basins. The results showed that for the records of a particular station, the joint persistence of streamflow and TSS is approximately the mean of the persistence of individual series. The streamflow displayed higher persistence than TSS in 60 % of the stations while in majority of stations of Godavari basin the trend was opposite. The annual cross correlation is higher than overall cross correlation in majority of stations but at these time scales strength of their association differs with river basin.
Assessment of meteorological droughts over Hoshangabad district, India
Authors: Swain Sabyasachi , Kumar Mishra Surendra , Pandey Ashish 
Published: July 2020

Abstract
In this study, the meteorological drought characteristics (severity, frequency, and persistence) over the Hoshangabad district, Madhya Pradesh, India are analyzed. The percent departure from mean (PDM) is employed to describe the drought characteristics, considering the monthly rainfall data for the duration of 62 years (1951-2012). Rainfall during monsoon season contributes over 95% of the annual rainfall and thus, only monsoon season is considered for identifying the drought years. The entire duration of 62 years was divided into two epochs of 31 years i.e. 1951-1981 and 1982-2012. The results revealed that the rainfall over the district possesses remarkable inter-annual variability. The district is prone to droughts with a frequency of once in four years. More importantly, the comparative assessment of two epochs indicates an increase in frequency, severity, and persistence of droughts in the latter epoch. The frequency of droughts has tripled in 1982-2012 as compared to 1951-1981. Since Hoshangabad is a monsoon-dominated district with high agricultural importance, proper management strategies need to be devised to minimize the harmful consequences of droughts.
Nonlinear finite element modelling of concrete columns confined with textile reinforced mortar
Authors: John Shaise K , Nadir Yashida , Girija K 
Published: July 2020

Abstract
Textile reinforced mortar (TRM) is an innovative material developed to overcome the limitations of fibre reinforced polymer (FRP). It consists of high strength textile fibre mesh embedded in cementitious binder which is organic in nature and compatible with concrete substrate. This paper presents the development of a finite element model for confinement of concrete columns using TRM. Concrete damaged plasticity model was used to represent the behaviour of TRM. The model accounts for nonlinear material behaviour in concrete core and mortar. The model was validated against experimental result from literature. Maximum divergence between experimental and numerical results was 5%. Following the validation, the developed model was used to carry out parametric studies on circular and square column confinement. The thickness of mortar layer, cross section shape of the core and corner radius of square column were considered in the study to gain a profound knowledge of the confinement and to study its influence on the behaviour of confined concrete column.
A review on the deterioration of cement-based materials in ammonium salt solutions
Authors: Babu Sheba , Ramaswamy K P , Nazeer M 
Published: July 2020

Abstract
It is essential to understand the durability aspects as the cementitious systems exposed to agricultural zones, fertilizer plants, waste waters, mining and industrial applications can be subjected to chemical attack by ammonium based compounds. This paper reviews the different mechanisms of deterioration of cement composites on exposure to different ammonium compounds namely ammonium sulphate, nitrate and chloride. The magnitude and mechanism of degradation follows different trends depending on the type of anion associated with the ammonium salts. The principle process involved in the deterioration of cementitious systems in the presence of ammonium compounds is the chemical reaction of portlandite with the anion of the ammonium salts. Generally the interaction of ammonium salts with cement based materials results in decalcification, leaching and/or expansion accompanied by strength loss. The presence of Supplementary Cementitious Materials (SCMs) along with cement reduces the amount of portlandite which may be attributed to the dilution of cement component or increased pozzolanic activity, resulting in the production of additional or secondary C-S-H gel. It is observed that the incorporation of SCMs is found to have low penetrability, thus capable of controlling the degradation process upon exposure to ammonium based salt solutions.
A critical review on the durability of geopolymer composites in acidic environment
Authors: Ajay Athira , Ramaswamy K P , Thomas Anu V 
Published: July 2020

Abstract
Deterioration of concrete in acidic environments result in premature degradation in terms of microstructural alteration of phases leading to mass changes, weakening of mechanical properties, increase in porosity due to calcium leaching etc. Industries are found to dispose acidic effluents directly into the environment without proper treatments. Again, these acids can be organic as well as inorganic acids. Organic acids unlike inorganic acids are found to be weak acids due to their partly dissociative nature. The mechanism of acid attack varies based on the acid type and the characteristics of the calcium salt that are formed. Conventional concrete made with Ordinary Portland Cement (OPC) are not resistant to acids. Also, as we strive towards sustainable development, alkali activated or geopolymer concrete has started to gain attention as it is found to have better mechanical properties and durability comparing to conventional concrete. This paper reviews the damage mechanisms of sulphuric acid, citric acid, nitric acid and acetic acid on the alkali activated binders.
A critical review on the effect of organic acids on cement-based materials
Authors: Mariam Ninan Chinnu , Ajay Athira , Ramaswamy K P , Thomas Anu V , Bertron Alexandra 
Published: July 2020

Abstract
Ingression of acids into concrete structure arrests its long-term durability by the progressive degradation of the highly alkaline cementitious materials. Acid attack results in the decalcification of hydration products leading to higher inherent porosity and both micro-macro cracks, providing a path for the passage of aggressive ions into the interior of concrete. Amplification of agriculture and food industries such as the breeding, dairy or sugar industries produce large quantities of effluents that are the major sources of organic acids. These effluents have complex acidic compositions leading to serious degradation of structures made for their production, collection, storage and treatment. New binder systems that are capable of proposing extensive long term durability in aggressive acidic environment and also being a sustainable option need to be evolved. From earlier studies, application of various binder systems containing materials such as fly ash, ground granulated blast furnace slag, silica fume, bagasse ash etc. has an influence on the hydration products, micro-structural and macro-structural changes in cement-based materials when subjected to aggressive acidic media, thus enhancing the performance and durability of concrete structures. This paper outlines the basic mechanism of organic acid attack and highlights the influence of various binder systems on the alteration of degradation kinetics when exposed to various organic acids.
Preparation of magnetic composite materials and Its Application in Heavy metal wastewater Treatment
Authors: Zeng Qian , Lei Zengjiang , Yang Jinhui , Ma Hongda , Luo Yi 
Published: July 2020

Abstract
The heavy discharge of industrial wastewater containing heavy metal ions has caused serious pollution to water bodies and soils. Due to the heavy toxicity and biodegradability of heavy metals, the polluted water bodies have serious consequences for humans, animals and plants. The surface of magnetic materials is easy to be functionally modified and has good magnetic sensitivity. However, some single magnetic materials have problems such as easy aggregation and difficult to disperse and separate, which limits their application. And by combining a single magnetic material with other materials, colleagues who can play their advantages can avoid the defects. The magnetic composite material changes the movement of heavy metal ions in sewage to achieve the effect of adsorption under the action of a magnetic field. It has the advantages of wide source of materials, simple preparation method, efficient adsorption, and repeated use. With a wide range of applications, the research and development of magnetic composite materials has also received widespread attention. This article gives a comprehensive overview of the structural properties of different magnetic composites and their adsorption effects on heavy metal ions, in order to provide research directions and new ideas for magnetic composites on heavy metal ion adsorption.
Study on Equivalent Transformation of Structural Design Parameters Based on Overseas Codes
Authors: Ren Hongmei , Peng Gongsheng 
Published: July 2020

Abstract
With the development of the Belt and Road Initiative and economic globalization, we are going deep into the world. China’s overseas engineering projects are increasing day by day, which puts forward higher requirements for structural designers. While skillfully using domestic codes and standards, designers must also understand the design codes of overseas project locations, and master the mutual transformation of basic design parameters at home and abroad. Taking a project in Pakistan as an example, this paper expounds in detail how to convert the basic design parameters of Pakistan into the design parameters corresponding to the domestic specifications, with a view to providing reference for colleagues to carry out overseas project design.
Research on Load Checking of In-service Pressure Vessel Nozzle
Authors: Pu Zhe , Ren Bin , Tang Xiaoying , Wang Jifeng 
Published: July 2020

Abstract
Pressure vessel is an important part of industrial production. Pressure vessels usually have many nozzles, which are connected to other devices through pipeline. Due to the large thermal stress caused by the thermal expansion of the pipeline, the load on the nozzle of the connected vessel will be increased. When the load on the nozzle is too large, it may cause local deformation of the vessel, or even cause cracking at the root of the nozzle. Increasing the flexibility of the pipeline can reduce the load on the nozzle. Taking the flare system of the factory as an assessment case, this paper introduced a new load checking method. The software was used to simulate the load on the liquid separation tank connected with the flare gas pipeline. The calculated load of the liquid separation tank nozzle met the technical conditions of the manufacturer.
Precise Identification of Water Flooded Degree and Oil-water properties of Reservoir Based on Logging Interpretation
Authors: Wang Zhihui 
Published: July 2020

Abstract
The crude oil reserves are very rich in the peripheral tight oil-water layer. However, due to the lack of effective production technical means, reserves utilization is relatively low. Imbibition flooding is one of the critical technical measures for the efficient development of (weak) hydrophilic tight oil-water layer. To obtain high efficiency production technology for weak hydrophilic tight oil-water layer, we investigated the imbibition flooding effect of negative/non-ionic surfactant “HLX”, “SYH (petroleum sulfonate) +Na2CO3”, cationic surfactant “YANG” and non-ionic surfactant “DCY” on weak hydrophilic core with permeability of about 2.0 × 10−3 μm2. The study results suggest that the imbibition recovery rate and imbibition rate are affected not only by the interfacial tension of the oil-water layer but also by the rock pore wettability. The physical simulation experimental results indicate that the “YANG” and “DCY” imbibition can change the wettability of rock pore surface and reduce the interfacial tension. Hence, the imbibition flooding effect is relatively good, and the recovery rate is significantly increased.