dataesr/scientific-paragraphs-categorization huggingface dataset:
1. 3). Provision of this assembly gold standard allowed us to decouple the performance analyses of binning and profiling tools from assembly performance. Developers could submit their binning and profiling results until 07/18/2015. Overall, 215 submissions representing 25 different programs were obtained for the three challenge datasets and samples, from initially 19 external teams and CAMI developers, with 16 teams consenting to publish (Supplementary Table  1 ). The genome data used to generate the simulated datasets was kept confidential until the end of the challenge and then released  9. The CAMI challenge and toy datasets including the gold standard are available for download and in the CAMI benchmarking platform, where further predictions can be submitted and a range of metrics calculated for benchmarking (https:// data.
cami-challenge. org/participate). Average purity (x-axis) and completeness (y-axis) and their standard errors (bars) for genomes reconstructed by genome binners; for genomes of unique strains with equal to or less than 95% ANI to others (a) and common strains with more than 90% ANI to each other (b). For each program and complexity dataset, the submission with the largest sum of purity and completeness is shown (Supplementary Tables 1, 10, 12). In each case, small bins adding up to 1% of the data set size were removed. (c) Number of genomes recovered with varying completeness and contamination (1-purity, Supplementary Table  S17 ). (d) The Adjusted Rand Index (ARI, x-axis) in relation to fraction of the sample assigned (in basepairs) by the genome binners (y-axis). The ARI was calculated excluding unassigned sequences, thus reflects the assignment accuracy for the portion of the data assigned.
(b) Best scoring profilers using different performance metrics summed over all samples and taxonomic ranks to the genus level. A lower score indicates that a method was more frequently ranked highly for a particular metric. The maximum (worst) score for the Unifrac metric is 38 = (18 + 11 + 9) profiling submissions for the low, medium and high complexity datasets respectively), while the maximum score is 190 for all other metrics (= 5 taxonomic ranks * (18 + 11 + 9) profiling submissions for the low, medium and high complexity datasets respectively).
Acknowledgments We thank  C. Della Beffa ,  J. Alneberg ,  D. Huson , and  P. Grupp  for their inputs and the  Isaac Newton Institute for Mathematical Sciences  for its hospitality during the programme MTG, which was supported by  EPSRC  Grant Number  EP/K032208/1. The sequencing work conducted by the  U. S. Department of Energy Joint Genome Institute , a  DOE Office of Science User Facility , is supported under Contract No. DE-AC02-05CH11231. R. G. O. acknowledges support by the " Cluster of Excellence on Plant Sciences" program  funded by the  "Deutsche Forschungsgemeinschaft ". P. D. B was supported by the  National Science Foundation  under Grant No. DBI-1458689. This work used the  Extreme Science and Engineering Discovery Environment (XSEDE) , which is supported by  National Science Foundation  grant number  OCI-1053575.
XManDroid  [3] , a security framework, extends the monitoring mechanism of Android to detect side channel attacks such as Soundcomber. However, it cannot detect subset of side channels such as timing channel, processor frequency and free space on filesystem. TaintDroid  [9] , an extension of the Android mobile phone platform, uses dynamic taint analysis to detect direct buffer attack. The dynamic analysis approach  [9] ,  [19] ,  [11]  defined in smartphones cannot detect software side channel attacks presented in  [2] ,  [15]. In this paper, we modify the Android OS to detect software side channel attacks that try to bypass detection mechanisms based on dynamic taint analysis. We propagate taint in timing, memory cache and GPU channels and in meta data (file and clipboard length) using taint propagation rules.
Dynamic Taint Analysis The dynamic taint analysis technique is used for tracking information flows in operating systems. The principle of this mechanism is to tag some of the data in a program with a taint mark, then propagate the taint to other objects depending on this data when the program is executed. It is used primarily for vulnerability detection, protection of sensitive data, and more recently, for binary malware analysis. To detect vulnerabilities, the sensitive data must be monitored to ensure that they are sent through interfaces to the outside world. Many dynamic taint analysis tools are based on bytecode instrumentation to analyze sensitive data  [16] ,  [6]. TaintDroid implemented similar concepts to prevent leakage of private data in Android system. We present the TaintDroid system in more details in the following section.
TaintDroid TaintDroid improves the Android mobile phone OS to control the manipulation of users personal data in realtime by third-party applications. It analyzes application behavior to determine when privacy sensitive information is leaked. TaintDroid considers that information acquired through low-bandwidth sensors (location and accelerometer), high-bandwidth information source (microphone and camera), information databases (address books and SMS messages) and device identifiers(the phone number, SIM card identifiers (IMSI, ICC-ID), and device identifier (IMEI)) are privacy sensitive information that should be tainted. So, it uses dynamic taint analysis to track propagation of tainted data at different levels: instruction level, message-level between applications and file-level. TaintDroid defines taint sinks to detect vulnerabilities.
Timing Attack Detection The V M T hread sleep(constu4 * args, JV alue * pResult) function in Dalvik virtual machine native code suspends the execution of the current thread until the value of a tainted variable has elapsed. Then, the attacker reads the system clock after the waiting period. We test the argument of V M T hread sleep() to implement the T iming Context T aint. We modify the currentT imeM illis(constu4 * args, JV alue * pResult) function in the Dalvik virtual machine native code to propagate taint in the system clock if the T iming Context T aint is activated. Therefore, the difference in time readings before and after a waiting period that indicates the value of sensitive data is tainted.
Meta Data Attacks Detection TaintDroid implements taint source placement where privacy sensitive information types are acquired (low-bandwidth sensors, e. g location and accelerometer; high-bandwidth sensors, e. g. , microphone and camera; information Databases, e. g address books and SMS messages; Device Identifiers, e. g SIM card identifiers (IMSI, ICC-ID), and device identifier (IMEI)). In each taint source placement, we implement the M eta Data Context T aint wich is activated if private data is acquired. To detect the meta data class of attacks, we associate taint to the return value of the length() method at libcore level in File and String classes if the M eta Data Context T aint is activated.
Discussion We have proposed in a previous work  [10]  an enhancement of the TaintDroid approach that propagates taint along control dependencies to track implicit flows in smartphones. We have shown that our previous approach can resist to code obfuscation attacks based on control dependencies in the Android system. In addition, we have successfully detected side channel attacks exploiting control flows by combining static and dynamic analyses. However, we did not propagate taint in side channels. Consequently, we could not detect these class of attacks when they do not use control flows which generates false negatives. An attacker can obfuscate the application code by exploiting side channels to evade detection of leakage of private data in the Android system. The approach proposed in this paper propagates taint in a specific side channels.
Software Side channels Attacks Memento  [13]  is a side-channel attack based on tracking changes in the browser's memory footprint to infer which pages the victim is browsing. Soundcomber  [18]  analyzes audio side channel in a user's phone conversations to infer sensitive data. TouchLogger  [4]  exploits different vibrations when typing on different locations on the touch screen to extract sequences of entered text on smartphones. AC-Cessory  [17]  uses the accelerometer to get the data entered by user. Chen et al. [5] exploit a shared-memory side channel to stealthily inject into the foreground a phishing activity and steal sensitive information. Kim et al. [15]  utilized screen bitmap memory attack proposed by Sarwar et al. [2]  to propose a collection system that retrieves IMEI and IMSI information through screenshot images.
Conclusion The dynamic taint analysis approaches implemented in the Android system can be bypassed by exploiting side channel attacks. We have improved the Taint-Droid approach to propagate taint in side channels. We have analyzed 100 free Android applications to evaluate the effectiveness of our approach. We successfully detected sensitive information leakage caused by side channels. We found that 35% of analyzed applications leaked private data through side channels. We showed that our approach generates significant false positives (9%). Our approach creates a 9% overhead with respect to the TaintDroid system. Future work will be to improve our approach for detecting other side channel attacks inferring private data. Also, we will demonstrate the completeness of the taint propagation rules.
Here, we focus on sleep efficiency assessed in the late evening nap (on average from 9 p. m. to 10:20 p. m. ) of the first day during NP. Calculation of spectral power in the EEG delta (0. 7-4 Hz) range during visually scored NREM sleep in baseline and recovery nights was performed by the ASEEGA package for the analyses of electrophysiological sleep recordings (ASEEGA, Version 3. 35. 11, Physip, France  52  , please see supplemental material for further information on spectral power in the EEG delta (0. 7-4 Hz) range during automatically scored NREM sleep). This algorithm was used to benefit from automatic artefact rejection, but also in order to verify that a potential bias in visually scored sleep stages did not affect the results (see supplemental results). NREM sleep was considered as sum of sleep stages 2, 3 and 4.
EEG power of the central bipolar derivation (Cz-Pz) as well as on the monopolar central frontal derivation (Fz) was calculated using a fast Fourier transform with a Hanning window for consecutive 30-sec artefact-free epochs. For analyses, an ANOVA for repeated measures was computed using SPSS (IBM SPSS Statistics for Windows, Version 22. 0. Armonk, NY, USA), with the factors condition (NP vs. SD) and night (baseline vs. recovery). Overall, data of ten nights were lost due to technical problems (four in the NP baseline, three in the SD baseline, two in the NP recovery, and one in the SD recovery night). Melatonin assays. Salivary melatonin levels were analysed by a direct double-antibody radioimmunoassay (validated by gas chromatography-mass spectroscopy with analytical least detectable dose of 0.
65 pm/ml; Bühlmann Laboratories, Schönenbuch, Switzerland). A bimodal skewed baseline cosine function  53  was fitted to raw values as described in ref. 54 using MATLAB (MathWorks, Natick, MA). The evening onset of melatonin secretion was defined as the time when the melatonin level crossed 50% of the maximum at the rising limb of the curve  55. The latter served as marker of circadian phase position  56. Acquisition and analyses of fMRI data. Functional MRI images were assessed with a 3 Tesla MR Scanner (MAGNETOM Verio, Siemens Healthcare) using a standard twelve-channel head coil. Multislice T2*-weighted fMR images were acquired with a gradient echo-planar sequence applying axial slice orientation (32 slices; voxel size: 3 × 3 × 3 mm³ with 17% interslice gap; matrix size 76 × 76 × 32; repetition time = 2200 ms; echo time = 32 ms; flip angle = 82°).
For anatomical reference, structural T1-weighted images were obtained with a magnetization-prepared rapid gradient echo (MPRAGE) sequence (repetition time = 2000 ms, echo time = 3. 37 ms, flip angle = 8°, field of view = 25. 6 cm, matrix size = 25. 6 × 25. 6 × 17. 6 cm 3 , voxel size = 1 × 1 × 1 mm 3 ). 176 contiguous axial slices covering the entire brain were assessed in sagittal direction. Due to technical problems, 3 out of 93 datasets were lost (all assessed during SD at the second evening). For analyses, we used SPM8 (http:// www. fil. ion. ucl. ac. uk) implemented in MATLAB 12. Images were realigned with iterative rigid body transformations. Following normalization to the Montreal Neurological Institute (MNI) EPI template (third-degree spline interpolation; voxel size 2 × 2 × 2 mm 3 ), scans were spatially smoothed (full width at half maximum 8 mm).
fMRI scanning sessions were scheduled in the late evening (green arrows), when wakefulness was strongly promoted by the circadian system. Participants were scanned once in the NP and two times in the sleep deprivation protocol (after 13 hours of wakefulness [i. e. , under normal sleep pressure, referred to as normal waking (NW)] and 37 hours of wakefulness [i. e. , under high sleep pressure, referred to as SD]). (b) Box plots illustrate median (solid line) and mean (dotted line) performance in the n-back across the scanning sessions, i. e. , after different times awake before data acquisition. Mean performance was particularly impaired after 37 hours of continuous wakefulness (SD), mirroring the negative impact of high sleep pressure on working memory performance. Individual data are shown as dots.
Acknowledgements We thank  Anna Wirz-Justice  and  David Meyer  for their valuable comments on the manuscript,  Claudia Renz ,  Giovanni Balestrieri ,  Marie-France Dattler ,  Susanne Timus ,  Catherine Malgat ,  Stefanie Lisibach ,  Marc van Oostrum ,  Diana Hinninger ,  Janine Weibel ,  Sarah Chellappa  and  Amandine Valomon  for their help in data acquisition;  Julia Krebs  for medical screenings; and the volunteers for participating.
Briefly, from a training set of diseased patients versus normal controls, the most discriminant voxels must be pre-selected to avoid the so-called curse of dimensionality. Many approaches can be performed for that, first base on neuroscientific knowledge or in a second approach feature selection that rely on univariate statistical testing such as t-test or ANOVA. On the reduced feature voxel dataset, a SVM is trained and coefficients are identified and then applied to another set of patients and controls to determine the diagnostic performance. Then, the aim was to generate and evaluate a SVM procedure to classify patients between healthy or MMF 18 F-FDG brain profiles.
Healthy subjects Healthy controls from the NCT00484523 clinical trial matched for age were included. They were free from neurological/psychiatric disease and cognitive complaints, had a normal brain MRI, and underwent a PET/CT scan prior to a standardized neurological examination including a Mini Mental State Examination to check the lack of cognitive impairment. Due to a limited number of healthy patients in our institution, the healthy patients included in the current study came from another centre (Marseille). Using SPM analysis, no significant difference in FDG uptake was found between 18 F-FDG PET brain images of these patients and images of 5 healthy subjects of our institution.
Statistical parametric mapping analysis All 18 F-FDG-PET brain image volumes were spatially normalized onto the Montreal Neurological Institute template (McGill University, Montreal, Canada). Dimensions of the resulting voxels were 2x2x2 mm 3. Images were smoothed using a Gaussian filter (FWHM 8 mm). Spatial preprocessing and statistical analysis were performed using the statistical parametric mapping (SMP12) software implemented in Matlab version R2015a (Mathworks Inc. , Sherborn, MA). The population of our previous report  [8]  (100 MMF patients, 44 healthy subjects) was used to train the SVM classifier. Thirty-nine additional subjects (19 MMF patients and 20 healthy subjects) served as a testing population. As shown in Fig 1, using analysis of covariance (ANCOVA) in SPM12 with adjustment for age, a t-test mask was generated from the comparison between cerebral 18 F-FDG PET images of MMF patients and healthy subjects.
Support vector machine classifier On the reduced feature set when considering only the significant voxels provided with SPM, a SVM classifier was trained on the training population set with a linear kernel. SVM, in the multidimensional space of the N features, searched the best hyperplane with the maximum margin, which means that the separation of the two classes is maximized by the choice of the hyperplane. The resulting coefficients (SVM discriminative weights) were represented as a glass brain in Fig 1. They represent the weights that, when dot multiplicated with brain PET values, allow to differentiate between pathologic or healthy. All these computations were performed using python 2. 7 with nilearn and scikit-learn  [18]  packages. To assess the performance of the SVM classifier, values of sensitivity (Se), specificity (Sp), positive predictive value (PPV), negative predictive value (NPV) and accuracy (Acc) were calculated on the testing set of 39 patients.
Population characteristics 18 F-FDG PET brain images of 119 patients with MMF and 64 healthy subjects were retrospectively analyzed. The whole-population was divided into two groups; a training set with 100 MMF and 44 healthy subjects (mean age: 46 ± 14 y) recently published  [8] , and a new testing set with 19 additional MMF patients and 20 additional healthy subjects (mean age: 52 ± 15 y). Population characteristics for the training and testing groups are given in Table  1. All relevant data are provided in S1 Table.
This work was supported by grants from  Region Ile-de-France  and patient's association E3M through " Partenariats institutions-citoyens pour la recherche et l'innovation" (PICRI programs 2010  and  2014 ), and from the  Association Francaise contre les Myopathies though Translamuscle program. This work has been carried out thanks to the support of the A Ã  MIDEX  project (n˚ANR-11-the t-test mask generated from the training set, correctly classified MMF patients of the testing set with following Se, Sp, PPV, NPV and Acc:  89% ,  85% ,  85% ,  89% , and  87%. IDEX-0001-02 ) funded by the  « Investissements d'Avenir » French Government program , managed by the  French National Research Agency (ANR).
In this paper, we introduce the so-called DEEP-SEE framework that jointly exploits computer vision algorithms and deep convolutional neural networks (CNNs) to detect, track and recognize in real time objects encountered during navigation in the outdoor environment. A first feature concerns an object detection technique designed to localize both static and dynamic objects without any a priori knowledge about their position, type or shape. The methodological core of the proposed approach relies on a novel object tracking method based on two convolutional neural networks trained offline. The key principle consists of alternating between tracking using motion information and predicting the object location in time based on visual similarity. The validation of the tracking technique is performed on standard benchmark VOT datasets, and shows that the proposed approach returns state-of-the-art results while minimizing the computational complexity.
In Section 3, we introduce the proposed architecture, denoted by DEEP-SEE, and we describe the main steps involved: obstacle detection, tracking and recognition. Section 4 presents the experimental evaluation of the tracking system performed on standard benchmarks (VOT2016). In Section 5, we describe the DEEP-SEE navigational assistant, with the proposed hardware/software architecture, main features and performance evaluation. We show that it is possible to obtain high accuracy and recall rates with a computationally efficient approach that notably achieves real-time performances on wearable devices. Finally, Section 6 concludes the paper and opens novel directions for further work and developments.
TricTRACK uses a regression model with incremental learning to track arbitrary shape objects. In  [5] , the KCF2014 tracker is introduced. An analytic model trained on a datasets of thousands of translated and scaled patches is here used in order to cope with natural image changes. The system is designed to analyze the video stream in the Fourier domain in order to reduce both the storage and the computational resources. The sequentially training of convolutional networks (SCT4) for object tracking is introduced in  [6]. The SCT4 considers the online CNN training as an ensemble and each channel of the output feature map as an individual base learner. Then, each network is trained using a different loss criterion in order to reduce correlation and avoid overfitting. At the end, in order to make a decision all base learners are sequentially sampled into the ensemble.
Due to the global object representations, the system is less likely to return false alarms or missed detections when applied to novel/unexpected video instances. To fit the requirements of the considered VI-related application, we have extended YOLO with additional categories, specific to assistive devices for visually impaired users. The system was trained on the standard ImageNet dataset from which we retained as relevant the following object classes: vehicles; bicycles, motorcycles and pedestrians. In addition, we have constructed a set of categories relevant from the perspective of a VI people, including garbage cans, fences, pylons, edge of pavements and descending stairs. These elements have been included in a single, global category called static obstacles. The class of static obstacles can be extended with additional elements (e.
g. , bushes, tress, branches, etc. ), depending on the user needs. However, the DEEP-SEE system cannot identify descending stairs or holes in the ground, which are difficult to distinguish when using monoscopic vision. To this purpose, the utilization of depth cameras would be more appropriate, but this issue will not be addressed in the paper. The CNN has been trained by using the same training dataset as the one presented in  [16] , which includes a total number of 4500 images. To train the network we employed the parameters recommended by authors in  [1]  that returned the top-1 performances in the evaluation tests. Thus, we used the stochastic gradient descent with a starting learning rate of 0. 1 and polynomial rate decay with a power of 4. YOLO presents the following advantages: (1) the processing time is reduced, the system being able to operate at more than 100 fps when running on an Nvidia 1050Ti GPU; (2) it takes as input the entire image during both training and testing phases and so it encodes contextual information about classes and their appearance; and (3) it has the highest score of correctly detected objects and the lowest number of false positives when compared to state of the art methods  [1].
Initial Object Position Estimation: CNN Trained with Motion Patterns The tracking system takes as input, in a given reference frame, the object's bounding box predicted by the detection and recognition module (cf. Section 3. 1). Then, the objective is to estimate the object's position in the successive frame. To obtain an initial estimation of the object's position, we have adopted the GOTURN  [15]  network architecture that exploits a regression technique to train offline a CNN with generic relationships between the objects appearances and their associated motion patterns. We performed the offline training of the network on a collection of 312 videos selected from the ALOV 300+ benchmark  [17]. Let us underline that none of the videos used for our experimental evaluation (cf. Section 4) have been used in the training process.