dataesr/scientific-paragraphs-categorization huggingface dataset:
e. , increasing voiced segment detection specificity, at the expense of a higher probability of missing voiced segments, i. e. , reducing sensitivity. The investigated final features are the mean F 0 , jitter and F 0 standard deviation (stdF 0 ) estimated in each voiced segment. Camacho's SWIPE' algorithm was implemented in Android by porting the C code developed by Kyle Gorman and reported in  [28]. Such porting was made possible by the Java Native Interface  [29]. For this purpose, two libraries were linked to the application for fast Fourier transform  [30]  and managing sound files in wav format  [31].
A sampling frequency of 44. 1 kHz and a resolution of 32 bits were adopted. The smartphones used were two Samsung I9300 Galaxy S III. The audio data were acquired by the smartphones using a sampling frequency of 48 kHz and 32-bit resolution without any compression. We will refer to the high quality acquisitions as HQmic. The high quality and the smartphone recordings were obtained simultaneously. Two different positions of the smartphone with respect to the user were tested. Both positions allowed the user to read text or comment on the images displayed by the smartphone application while assuring a good recording quality. Specifically, two smartphones were used in each acquisition: one was kept on a table in front of the subject (SPmic table ), while another one was held by the subject (SPmic hand ).
Both the microphone and smartphone held by the subject were positioned in front of the subject, at 30 cm from her/his mouth, symmetrically with respect to the face. The smartphone on the table was kept in front of the subject at a 40-cm distance from the subject's mouth. The outcomes of an algorithm implemented in MATLAB  [32]  and running on a personal computer were used as the reference, i. e. , ground truth measures. This algorithm will be indicated as GTal (Ground Truth algorithm) and is formed by a two-stage procedure. In the first step, voiced segments are detected using both signal intensity and zero crossing rate as in  [18]. This approach was also tested in  [32]  and compared to the segmentation that can be obtained using an electroglottographic signal. In the second step, the estimates of F 0 are obtained using an algorithm based on SWIPE', whose good performance was shown in  [33].
Statistical Analysis For each of the above-described comparisons, both voiced segment detection performance and estimated feature reliability were evaluated. The voiced segmentation approach performance was assessed looking at segment length and defining sensitivity and specificity parameters using the segments obtained from high quality data as a reference. The evaluation of the estimated features was done both by looking at the feature distributions across segments and by correlating the measures estimated from voiced segments in the two conditions. Regarding the former evaluation, given the non-Gaussian distribution of the features, a two-sample Kolmogorov-Smirnov test was adopted, and a significance level equal to 0. 05 was adopted. The correlational analysis was performed taking into account only overlapping portions of the segments.
Patient Data A pilot study was performed on a patient suffering from bipolar disease to show the potentialities of such an application. The patient, a 36-year-old female, was able to lead an independent and active life in absence of any suicidal tendencies, or delusions, or hallucinations and did not suffer from any substance use disorders. The patient was recruited at the Department of Psychiatry and Mental Health of the Strasbourg University Hospital under the PSYCHE protocol and had a diagnosis of Type II bipolar disorder according the Diagnostic and Statistical Manual of Mental Disorders, 4th edition, text revision (DSM-IV-TR) criteria. Type II bipolar disorder is a condition in which there are depressive and hypomanic (but not manic) episodes. The patient signed an informed consent to participate in the study approved by the Ethical Committee of the Strasbourg University Hospital.
In fact, the identification of a voiced segment as an unvoiced one, i. e. , false negative event, was considered less dangerous with respect to the incorrect labeling of an unvoiced segment as voiced. Table  2  shows the results of the correlational analyses of the features estimated from high quality samples and smartphone data. Features were estimated using the GTal algorithm. The results obtained with the smartphones placed on the table and held by subjects are very similar. In both subjects, jitter estimated using data from a hand-held smartphone showed a weaker, although significant correlation with features estimated using high quality data. The comparison between the two smartphones, i. e. , SPmic hand and SPmic table , showed high correlation coefficients (see supplementary material).
Evaluation of the Algorithm The computational time of the algorithm running on the smartphones was approximately equal to 2 min for a speech recording length equal to 30 s. The impact of a pitch strength threshold used to detect voiced segments in ANDal was evaluated in terms of obtained specificity and sensitivity values. In Figure  2 , the obtained trends are reported. Statistical analysis of the feature distributions revealed that meanF 0 , as estimated by the two algorithms, was not significantly different when varying the pitch strength threshold. This happened in the analysis of Subject 1 data for the threshold values ranging from 0. 25 to 0. 45 and in the analysis of Subject 2 for all investigated thresholds. Significant differences were found in stdF 0 and Jitter in both subjects. The pitch strength thresholds adopted for subsequent analysis (i.
Discussion In this study, data were processed locally, on a smartphone, instead of a central server. Although a central server solution can provide a more complex analysis of the raw speech signal, the smartphone offers some advantages. Using a smartphone increases user trust because the speech data are stored locally. Moreover, data upload is reduced, occupying 75 kB instead of 6 MB for a 30-s recording. Local processing facilitates the use of the features to provide prompt feedback to the user for the realization of self-management applications without network accessibility. Regarding the processing time, the ever-increasing processing power of mobile phones will decrease the time lag between the end of the task and the availability of the results. In the current release of the application, the estimates of the F 0 values, the related times, as well as the strengths obtained by the algorithm were sent to a server, for a further processing step.
Open access is one of the important leverages for long-term data preservation for a HEP experiment. To guarantee the usability of data analysis tools beyond the experiment lifetime it is crucial that third party users from the scientific community have access to the data and associated software. The ALICE Collaboration has developed a layer of lightweight components built on top of virtualization technology to hide the complexity and details of the experiment-specific software. Users can perform basic analysis tasks within CernVM, a lightweight generic virtual machine, paired with an ALICE specific contextualization. Once the virtual machine is launched, a graphical user interface is automatically started without any additional configuration. This interface allows downloading the base ALICE analysis software and running a set of ALICE analysis modules.
Introduction The ALICE Collaboration adopts the policy of openness promoted publicly by CERN and laying the foundations of its legacy to the scientific community as well as to the general public. As stated by its data preservation policy, "the data harvested by the ALICE Experiment up to now and to be harvested in the future constitute the return of investment in human and financial resources by the international community". These data embed unique scientific information for the in-depth understanding of the profound nature and origin of matter. Documentation, long term preservation at various levels of abstraction, data access and software availability constitute the key elements of the ALICE data preservation strategy allowing future collaborators, the wider scientific community and the general public to analyse data for educational purpose and for eventual reassessment of the published results.
The implementation of the ALICE open access policy is currently in an early stage and only allows students and interested public to have limited access to data and analysis tools for educational purposes. The main objective was to initially test the deployment of simplified tools to give the user the feel of the real tools employed by the physicists for data analysis. An important requirement is the simplicity of use, requiring no prior knowledge of ALICE software installation procedures or specific frameworks. The use of virtualization technology that allows installing and running the software independently from the host architecture, achieved this. Applications and data are hosted by a portal used in common by all LHC experiments that collaborate with CERN IT and GS (General Infrastructure Services) divisions for its implementation, allowing for a coherent style and organization.
ALICE open access workflow The first step to allow external users to run specific ALICE analysis programs was to deploy a dedicated open access virtual machine having the ALICE software and environment installed. The intent was to automatically enter this environment after starting the virtual machine and to provide a graphical users interface (GUI) to guide the user step by step to perform a number of pre-set analyses. Once this was possible, we redesigned the GUI to allow fetching both the analysis modules and data on demand from a remote server. The last remaining step was to set up the open data portal as entry point for the installation and contextualization of the VM, providing all necessary instructions and documentation, uploading also the data and analysis modules to a public area.
Contextualizing with ALICE analysis environment To make the virtual machine as lightweight as possible, we started from a microCernVM bootable machine image, which contains just a kernel and runs a CernVM-FS client  [1]. We chose this approach because it minimises the amount of data to be transferred by the user and integrates in the standard VM installation prescribed by CernVM. CernVM allows running a bootstrapping procedure called contextualization, which can make the generic virtual machine behave in a custom way. The open access ALICE contextualization script creates the default user account alice with automatic logon and customises the user start-up script to enable a specific AliRoot [2] version, set the location of the CERN open data server and initialize a few environment variables.
This allows starting up automatically the analysis interface after booting the virtual machine. The usage of the contextualization procedure is explained in detail on the CernVM pages  [3] ; the ALICE open access context can be paired from the CernVM Market, as shown in figure1. software framework AliRoot are then enabled using copies stored on the CernVM-FS system, after which the GUI is automatically started. A terminal pointing to the user workspace is also opened, allowing restarting the GUI at any time. The GUI is the single entry point for the user and allows running one of the supported analysis modules. Each module has a very simple interface that presents some documentation on the use and functionality of the given analysis, as well as a set of widgets allowing changing analysis parameters and selecting the data to be processed.
An example of such interface is presented in figure  2. A set of outreach and educational analysis exercises are based on specifically selected ALICE data organised as ROOT files, and are widely used for the particle physics master classes. These exercises highlight some of the ALICE physics. As an example, one exercise is about the search for particles containing strange quarks, based on their V0 decays; the motivation is to give an insight to how strangeness enhancement, one of the first signatures for the Quark-Gluon Plasma (QGP), is observed. Another exercise examines charged particle tracks; the aim is to calculate the nuclear modification factor RAA by comparing particle yields in the case of lead-lead and proton-proton collisions; the fact that RAA is less than one indicates suppression of charged particles due to interactions of partons with the QGP.
At this stage we have not released modules performing analysis of ALICE primary datasets and reproducing published results. We have made however available the standard p T analysis tutorial presented to ALICE newcomers, which gives an introduction by examples to the use of the ALICE analysis framework and allows plotting inclusive transverse momentum and pseudorapidity distributions for reconstructed particles. Since the analysis is compiled dynamically as a ROOT macro, the users can run a "living" analysis by changing input parameters or adding new output histograms in a hands-on session.
Accessing ALICE primary datasets The primary datasets already available on the open data portal are intended for demonstration purposes only and are not statistically representative. From the p t analysis tutorial interface, the user can select and download one of the ALICE event summary data files made available from the portal. These data can be also separately downloaded from the portal. Following its data preservation policy ALICE aims to make publicly available 10% of the data after 5 years, and has currently approved the release of about 10 TBytes of 2010 p-p and Pb-Pb reconstructed datasets. These will become available from the open access portal by the summer 2015. At the moment this paper was written, the data were already copied on the final EOS  [5]  storage at CERN and have only to be indexed and made available in the open access portal.
Each dataset has associated metadata kept in a JSON schema, embedding the year, collision type, centre of mass energy and file-related information, as shown in Figure  4. Dataset": { "name": "LHC2010h_PbPb_ESD_138275", "description": "Pb-Pb ESD data sample at 3. 5 TeV", "path": "/eos/opendata/alice/2010/…", "files": 100, "file_details": [ { "file_path": "/eos/opendata/alice/…", "file_size": 200762886, "file_checksum": "41b466d6eb9b…", "file_timestamp": "2014-09-24 12:32:31" }, … In future, ALICE plans to release more compact datasets in a suitable analysis format, including filtered data for specific analysis. This will require extending the JSON schema to include new tags such as pointers to the filtering procedure or statistics information relevant for the sample.
Analysis software versioning The versioning of the analysis software made available for open access is using CernVM-FS in the same way as for the ALICE production software. Both analysis software and the object database (OADB) required by different analysis modules are regularly tagged, built and deployed within the ALICE distributed analysis infrastructure. On the public open access CernVM-FS enabled virtual machine any tag can be enabled on demand and become immediately available for one of the available analyses. This allows easy extension of the system in future for embedding new modules, even if requiring different versions of AliRoot.
ALICE information and tools on the open access portal The CERN open access portal [6] is meant as an entry point to the information, tools and data provided by all LHC experiments  [7]. It is developed by the CERN IT and GS groups in collaboration with researchers from the HEP community, and contains a growing range of data produced through the   [8]  for the digital library technology, CernVM as virtual software appliance allowing for portable and easy to configure user environment to develop and run locally LHC data analysis, and EOS as disk based low latency storage system allowing XROOT  [9]  based access to data. Among the four types of data categorized by the DPHEP study group (2009)  [10] , the portal focuses on the release of data from levels 2 and 3, namely simplified data formats for analysis in outreach and training exercises, and reconstructed data and simulations as well as the analysis level software to allow for full scientific analysis.
ALICE has released so far data from the two categories meant only for education purposes, sustained by a thorough documentation and usage instructions for the software. The available datasets contain minimum bias reconstructed events in the event summary data format (ESD), usable directly with scientific ALICE analysis tools. In the near future ALICE will release a larger set of such ESD datasets representing 5 to 10% of the data taken in 2010, together with datasets in a more compact analysis format (AOD) [2] and simulated samples anchored on the acquisition conditions for the released data. The software suite available so far on the portal contains educational tools and a demonstrator for the usage of the ALICE analysis framework, as described in the previous section. This corresponds to the education section of the open data portal, and will be further extended with more realistic analysis examples.
4% of the total approved data. Results from this dataset on the measurement of θ 23 and |∆m 2  32 | by ν µ disappearance  [14] , and of θ 13 and δ CP by ν e appearance have been published  [17]. It is reported in  [17]  that combining the T2K result with the world average value of θ 13 from reactor experiments leads to some values of δ CP being disfavored at 90% CL.
T2K and NOνA Combined Sensitivities Fig. 14:  The expected ∆χ 2 for the sin δ CP = 0 hypothesis, plotted as a function of POT. Plots assume true sin 2 2θ 13 = 0. 1, various true values of sin 2 θ 23 (as given in the plot legends), and δ CP and the MH as given in the figure captions. The solid curves include statistical errors only, while the dash-dotted (dashed) curves assume the 2012 systematic errors (the projected systematic errors). Note that the sensitivity heavily depends on the assumed conditions, and that the conditions applied for these figures correspond to the cases where the sensitivity for sin δ CP = 0 is maximal. parameters, δ CP , sin 2 θ 23 and the MH can be obtained by comparing the ν µ → ν e oscillation probability of the two experiments. To evaluate the benefit of combining the two experiments, we have developed a code based on GLoBES  [38, 39].
In the plot of predicted T2K rate versus the predicted NOνA rate (left) the blue (IH) and red (NH) upper bands are for neutrino-mode running while the red (NH) and blue (IH) bottom bands are for the antineutrino mode running. The predicted number of ν e + νe events produced in neutrino-mode running versus events produced in antineutrino mode running (right) are shown for T2K in red (NH) and blue (IH), and for NOνA in green (NH) and magenta (IH). Representative points at the edges of the δ CP and sin 2 θ 23 ranges are highlighted. Systematic and statistical uncertainties are not included. combining the results from T2K and NOνA quantitatively, we have conducted a T2K-NOνA combined sensitivity study. The GLoBES  [38, 39]  software package was used to fit oscillation parameters based on the reconstructed neutrino energy spectra of the two experiments.
The oscillation parameters, unless otherwise stated, are those shown in Table  3. The GLoBES three-flavor analysis package works very similarly to fitter used for the studies presented in Section 4. Several validation studies were done to ensure that the two methods produced the same results when given the same inputs. The T2K, NOνA, and combined sensitivities were generated using a modified version of GLoBES that allowed for use of inputs generated from Monte Carlo simulations of T2K neutrino interactions in the Super-Kamiokande detector. The inputs describing the NOνA experiment were developed in conjunction with NOνA collaborators, and validated against official NOνA sensitivity plots  [40, 41, 42]. We assume the same run plan as presented in NOνA's TDR: 1. 8 × 10 21 POT for ν and 1. 8 × 10 21 POT for ν modes, corresponding to 3 years of running in each mode.
In this paper, an open and generic storage simulator is proposed. It simulates with accuracy multi-tiered storage systems based on heterogeneous devices including HDDs, SSDs and the connecting buses. The target simulated system is constructed from the hardware configuration input, then sent to the simulator modules along with the trace file and the appropriate simulator functions are selected and executed. Each module of the simulator is executed by a thread, and communicates with the others via ZeroMQ, a message transmission API using sockets for the information transfer. The result is an accurate behavior of the simulated system submitted to a specific workload and represented by performance and reliability metrics. No restriction is put on the input hardware configuration which can handle different levels of details and makes this simulator generic.
etc. • diverse architecture configurations and design choice: multi-tiered architecture to provide the hierarchical scheme and at each tier, many organisations/volumes can coexist as JBOD (Just a Bunch Of Disks) and RAID (n+p) : a Redundant Arrays of Independent Disks with n disks of data and p disks of parity, • modularity and software evolution: all functions to run the devices according to their technologies. All functions of FTL (Flash Translation Layer) for the SSDs and defragmentation for HDDs as examples. In the rest of the paper, section 2 presents related works with their contributions and their limits, section 3 describes the simulator design and implementation in detail, it goes through all its modules and their functions and parameters. Section 4 presents the experimentation settings and covers both the workload and the hardware environments considered in the conducted tests.
RELATED WORKS A certain number of data storage system simulators exist in the literature, going from the HDD-based to the SSD-based systems. The pioneer in the area is DiskSim, initially developed by Ganger et al. [6]  and subject to many improvements via additional contributions, as the one by Bucy et al. [4]. It is dedicated to the HDD-based storage system simulation and reports only on the performance aspects. It is composed of modules representing the storage system main components: buses, controllers and disk drives. It accepts external I/O traces and integrates an internal trace generator to produce synthetic I/O traces -if needed-according to different probability distributions. The last release DiskSim 4. 0  [4]  includes the MEMS-based storage device but it is still has limitation on the shape of the storage system toplogy which limits the simulation possibilities using this tool.
Two extensions of DiskSim were proposed to handle the non-volatile devices (SSDs). The first one was proposed by Agrawal et al. [3]  and consists of adding a module in charge of the wear levelling and the block erasure. The second extension, called FlashSim  [12]  is an object oriented version which encapsulates three FTL (Flash Translation Layer) controllers implementing both the wear levelling and the garbage collection. FlashSim adds detailed performance evaluation capabilities up to the NAND channel level. However, this does not push the limitation on the storage system topology. Nevertheless, it is true that using these event or trace driven simulators, the internal behavior of the system can be observed and evaluated in its fine details with accuracy. More recently, simulators dedicated to SSDs were developed, motivated by the increasing interest and use of this technology.