dataesr/scientific-paragraphs-categorization huggingface dataset:
Comparison to other ground-based (sub-)millimetre data This particular region of the Galactic plane is well covered in many recent surveys of (sub-)millimetre continuum emission. The most similar ground-based surveys to our NIKA2 observations are the 1. 1 mm BGPS at 33 arcsec resolution  (Aguirre et al. 2011; Ginsburg et al. 2013 ) and, at 870 µm and with 19. 2 arcsec resolution, AT-LASGAL  (Schuller et al. 2009). While the data from these surveys are very well matched to GASTON in terms of wavelength, they were designed to cover far wider regions of the GP (covering 192 and 360 deg 2 , respectively), and so their sensitivities are lower than those of the data presented here. We have identified a total of 1467 GASTON clumps (dendrogram leaves), compared to 346 ATLASGAL compact sources  (Urquhart et al.
Although this positional matching based on centroid coordinates will be less effective for sources that are not centrally concentrated, ∼95 per cent of GASTON clumps have angular radii of less than three beam radii (i. e. 39 arcsec), and so it is clear that the majority of GASTON clumps are new identifications. In Fig. 4 , we compare the GASTON 1. 15 mm imaging in a region centred on ℓ = 24. • 465,  = -0. • 205 to observations from ATLAS-GAL and the BGPS, and 8 µm emission from the GLIMPSE. The greater angular resolution of the GASTON GP data compared to BGPS and ATLASGAL clearly allows new substructures to be resolved within previously-detected emission regions, in addition to the detection of new faint compact sources. By comparison to the Spitzer/GLIMPSE 8 µm image, it can also be seen that the 1.
ACKNOWLEDGEMENTS We wish to thank the anonymous referee for their helpful comments and suggestions that have improved the quality of this paper. AJR would like to thank his newborn son, Joseph, whose tardy arrival assisted with the completion of this work. AJR and NP would like to thank the  STFC  for financial support under the consolidated grant number  ST/N000706/1. We would like to thank the  IRAM  staff for their support during the campaigns. The NIKA dilution cryostat has been designed and built at the  Institut Néel. In particular, we acknowledge the crucial contribution of the Cryogenics Group, and in particular  Gregory Garde ,  Henri Rodenas ,  Jean Paul Leggeri ,  Philippe Camus. This work has been partially funded by the  Foundation Nanoscience Grenoble  and the  LabEx FOCUS   ANR-11-LABX-0013.
This work is supported by the  French National Research Agency  under the contracts "MKIDS", " NIKA " and  ANR-15-CE31-0017  and in the framework of the " Investissements d'avenir" program  ( ANR-15-IDEX-02 ). This work has benefited from the support of the  European Research Council Advanced Grant ORISTARS  under the European Union's  Seventh Framework Programme  (Grant Agreement no. 291294 ). F. R. acknowledges financial supports provided by  NASA  through  SAO Award Number   SV2-82023  issued by the  Chandra X-Ray Observatory Center , which is operated by the  Smithsonian Astrophysical Observatory  for and on behalf of  NASA  under contract  NAS8-03060. A. B. acknowledges the support of the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Skłodowska-Curie Grant  agreement No.
Finally, the identity is determined by calculating the similarities among the characteristic vectors adopting a distance measurement of the K-nearest neighbors (K-NN) classifier. Extensive experiments on several subsets of the unconstrained Alex and Robert (AR) and Labeled Faces in the Wild (LFW) databases show that the MB-C-BSIF achieves superior and competitive results in unconstrained situations when compared to current state-of-the-art methods, especially when dealing with changes in facial expression, lighting, and occlusion. The average classification accuracies are 96. 17% and 99% for the AR database with two specific protocols (i. e. , Protocols I and II, respectively), and 38. 01% for the challenging LFW database. These performances are clearly superior to those obtained by state-of-the-art methods.
They can be subdivided into four fundamental approaches depending on the method used for feature extraction and classification: holistic, local, hybrid, and deep learning approaches  [11]. The deep learning class  [12] , which applies consecutive layers of information processing arranged hierarchically for representation, learning, and classification, has dramatically increased state-of-the-art performance, especially with unconstrained large-scale databases, and encouraged real-world applications  [13, 14]. Most current methods in the literature use several facial images (samples) per person in the training set. Nevertheless, in real-world systems (e. g. , in fugitive tracking, identity cards, immigration management, or passports), only SSFR systems are used (due to the limited storage and privacy policy), which employ a single sample per person in the training stage (generally neutral images acquired in controlled conditions), i.
e. , just one example of the person to be recognized is recorded in the database and accessible for the recognition task  [15]. Since there are insufficient data (i. e. , we do not have several samples per person) to perform supervised learning, many well-known algorithms may not work particularly well. For instance, Deep Neural Networks (DNNs)  [13]  can be used in powerful face recognition techniques. Nonetheless, they necessitate a considerable volume of training data to work well. Vapnik and Chervonenkis  [16]  showed that vast training data must ensure learning systems' generalization in their statistical learning theorem. In addition, the use of three-dimensional (3D) imaging instead of two-dimensional representation (2D) has made it possible to cover several issues related to image acquisition conditions, in particular pose, lighting and make-up variations.
The blocks and the data between them are thus essential for SSFR tasks. Our idea was to segment the image into equal non-overlapping blocks and calculate the BSIF operator's histograms related to the different blocks. The histogram 2 represents the fusion of the regular histograms calculated for the different blocks, as shown in Figure  5. In the face recognition literature, some works have concentrated solely on analyzing the luminance details of facial images (i. e. , grayscale). This paper suggests a different and exciting technique that exploits color texture information and shows that analysis of chrominance can be beneficial to SSFR systems. To prove this idea, we can separate the RGB facial image into three channels (i. e. , red, green, and blue) and then compute the MB-BSIF separately for each channel.
K-Nearest Neighbors (K-NN) Classifier During the classification process, each tested facial image is compared with those saved in the dataset. To assign the corresponding label (i. e. , identity) to the tested image, we used the K-NN classifier associated with a distance metric. In scenarios of general usage, K-NNs show excellent flexibility and usability in substantial applications. Technically speaking, for a presented training set  ,  │  1,2, … ,  , where  ∈  denotes the  person's feature vector,  denotes this person's label,  is the dimension of the characteristic vector, and  represents the number of persons. For a test person  ∈  that is expected to be classified, the K-NN is used to determine a training person  * resembling to  based on the distance rate and then attribute the label of  * to.
Database Description The Alex and Robert (AR) face database  [51]  includes more than 4000 colored facial photographs of 126 individuals (56 females and 70 males); each individual has 26 different images with a frontal face taken with several facial expressions, lighting conditions, and occlusions. These photographs were acquired at an interval of two-weeks and their analysis was in two sessions (shots 1 and 2). Each session comprised 13 facial photographs per subject. A subset of facial photographs of 100 distinct individuals (50 males and 50 females) was selected in the subsequent experiments. Figure  6.
This feature extraction method is called MB-C-BSIF, as described in Section 3. 2. The AR database images are already in RGB and so do not need a transformation of the first color-space. However, the images must be converted from RGB to HSV and RGB to YCbCr for the other color-spaces. Tables  10 11 12 display and compare the recognition accuracy of the MB-C-BSIF using several colorspaces with subsets of facial expression variations, occlusion by sunglasses, and occlusion by a scarf, respectively. The best results are in bold. From the resulting outputs, we can see that: -The results are almost identical for subsets of facial expression variation with all checked color-spaces. In fact, with the HSV color-space, a slight improvement is reported, although slight degradations are observed with both RGB and YCbCr colorspaces.
- The MB-BSIF is applied to all RGB image components, i. e. , color texture information is exploited. To summarize this first experiment, the performance of the proposed approach was evaluated using the AR database. In this experiment, the issues studied were changes in facial expression, lighting and occlusion by sunglasses and headscarf, which are the most common cases in real-world applications. As presented in Tables  13  and 14 , our system obtained very good results (i. e. , 96. 17% with Protocol I and 99% with Protocol II) that surpass all the approaches compared (including the handcrafted and deep-learning-based approaches), i. e. , that the approach we propose is appropriate and effective the presence of the problems mentioned above.
Database Description The Labeled Faces in the Wild (LFW) database  [52]  comprises more than 13,000 photos collected from the World Wide Web of 5749 diverse subjects in challenging situations, of which 1680 subjects possess two or more shots per individual. Our tests employed the LFW-a, a variant of the standard LFW where the facial images are aligned with a commercial normalization tool. It can be observed that the intra-class differences in this database are very high compared to the well-known constrained databases and face normalization has been carried out. The size of each image is 250 × 250 pixels and uses the jpeg extension. LFW is a very challenging database: it aims to investigate the unconstrained issues of face recognition, such as changes in lighting, age, clothing, focus, facial expression, color saturation, posture, race, hairstyle, background, camera quality, gender, ethnicity, and other factors, as presented in Figure  7.
Experimental Protocol This study followed the experimental protocol presented in  [30, [32] [33] [34]. From the LFW-a database, we selected only those subjects possessing more than 10 images to obtain a subset containing the facial images of 158 individuals. We cropped each image to a size of 120 × 120 pixels and then resized it to 80 × 80 pixels. We considered the first 50 subjects' facial photographs to create the training set and the test set. We randomly selected one shot from each subject for the training set, while the remaining images were employed in the test set. This process was repeated for five permutations and the average result for each was taken into consideration.
Limitations of SSFR Systems In this section, the SSFR systems, and particularly the method we propose, will be voluntarily tested in a situation that is not adapted to their application: they are applicable in the case where only one sample is available and, very often, this sample is captured in very poor conditions. We are particularly interested in cases where hundreds of samples are available, as in the LFW database, or when the training stage is based on millions of samples. In such a situation, deep learning approaches must be obviously chosen. Therefore, the objective of this section is to assess the limitations of our approach. We can observe that the traditional approaches did not achieve particularly good identification accuracies. This is primarily because the photographs in the LFW database have been taken in unregulated conditions, which generates facial images with rich intraclass differences and increases face recognition complexity.
In another experiment, we implemented and tested the successful DeepFace algorithm  [12] , whose weights were trained on millions of images from the ImageNet database that are close to real-life situations. As presented in Table  15 , the DeepFace algorithm shows significant superiority to the compared methods. This success is down to the profound and specific training of the weights in addition to the significant number of images employed in its operation. In a recent work by Zeng et al. [72] , the authors combined traditional (handcrafted) and deep learning (TDL) characteristics to overcome the limitation of each class. They reached an identification accuracy of near 74%, which is something of a quantum leap in this challenging topic. In the comparative study presented in  [73] , we can see that current face recognition systems employing several examples in the training set achieve very high accuracy with the LFW database, especially with deep-learning-based methods.
However, SSFR systems suffer considerably when using the challenging LFW database and further research is required to improve their reliability. In the situation where the learning stage is based on millions of images, the proposed SSFR technique cannot be used. In such a situation, References  [12]  and  [72] , which use deep learning techniques with data augmentation  [12]  or deep learning features combined with handcrafted features  [72] , allow one to obtain better accuracy. Finally, the proposed SSFR method is reserved for the case where only one sample per person is available, which is the most common case in the real world through remote surveillance or unmanned aerial vehicles' shots. In these applications, faces are most often captured under harsh conditions, such as changing lighting, posture, or if the person is wearing accessories such as glasses, masks, or disguises.
In these cases, the method proposed here is by far the most accurate. Finally, it would be interesting to explore and test some proven approaches that have shown good performance in solving real-world problems, in order to evaluate their performance using the same protocol and database, such as multi-scale principal component analysis (MSPCA)  [74] , signal decomposition methods  [75, 76] , generative adversarial neural networks (GAN)  [77] , and centroid-displacementbased-K-NN  [78].
Conclusion and Perspectives In this paper, we have presented an original method for Single-Sample Face Recognition (SSFR) based on the Multi-Block Color-binarized Statistical Image Features (MB-C-BSIF) descriptor. It allows for the extraction of features for classification by the K-nearest neighbors (K-NN) method. The proposed method exploits various kinds of information, including local, regional, global, and color texture information. In our experiments, the MB-C-BSIF has been evaluated on several subsets of images from the unconstrained AR and LFW databases. Experiments conducted on the AR database have shown that our method significantly improves the performance of SSFR classification when dealing with several variations of facial recognition. The proposed feature extraction strategy achieves a high accuracy, with an average value of 96.
17% and 99% for the AR database with Protocols I and II, respectively. These significant results validate the effectiveness of the proposed method compared to state-of-the-art methods. The potential applications of the method are oriented towards a computer-aided technology that can be used for real-time identification. In the future, we aim to explore the effectiveness of combining both deep learning and traditional methods in addressing the SSFR issue. Hybrid features combine handcrafted features with deep characteristics to collect richer information than those obtained by a single feature extraction method, thus improving the level of recognition. Besides, we plan to develop a deep learning method based on semantic information, such as age, gender, and ethnicity, to solve the problem of SSFR, which is an area that deserves further study.
g. , loading dynamic libraries (let alone run jited code like java or. NET bytecodes)  [8]. This leaves us with cross-ISA dynamic binary translation (DBT) as solution. QEMU  [9]  might not be the fastest DBT engine today  [10] , but it has unparalleled stability, support for many targets and hosts CPUs, is open-sourced, and has a large and very active community supported by the industry. It has become the reference for many users and developers, which makes it the de facto standard for cross-ISA emulation. In particular, the support for the RISC-V ISA was quickly added, and so did the support for parallel execution of multicore RISC-V target systems on multicore hosts. QEMU vanilla does not provide the ability to pin emulated processors on † Institute of Engineering Univ. Grenoble Alpes 1https://github.
II. Background and related work When it comes to running parallel workloads on a multi or manycore platform, having a simulator able to take benefit from the parallel nature of the host is a must. Even parallelized, many simulators, usually in their quest for evaluating performance accurately, incur too many synchronizations to be scalable. This means that processor simulation should progress until an actual synchronization is needed by the application, to ensure maximum lookahead while guaranteeing correctness. Work on QEMU parallelization was initiated by  [12]  and  [13]. The approach they propose is conceptually intuitive: it consists of emulating the target CPU's (also called vCPU) atomic instructions using the host CPU's atomic instructions. Said so, this seems relatively straightforward, however slight semantic differences between the target and the host memory models lead to generating complex code snippets that are also difficult to ensure correct  [14].
But this is only the tip of the iceberg, and many complicated technical details have to be solved, among which parallel target code generation and caching, choice of the next emulated processor to execute, interprocessor communication, etc. These efforts where not upstreamed in QEMU, and rapidly became unusable given the pace at which its code evolves. The idea was taken over by  [15]  and  [16]  that ended up in a solution accepted by the community. It was given the name mttcg, for multi-thread tiny code generator. The primary host was x86-64, and the first target was, 2Hardware threads. without surprise either, the ARM ISA. Since then, the approach has been ported to a handful of hosts and targets, enhanced and generalized  [17] , and fully integrated into QEMU. A great care has been taken to minimize locking  [18] , and whenever possible, shared structures are accessed using atomic operations.
To avoid useless re-translations, a single code generation buffer is shared by all vCPUs, however code generation itself takes place in a local buffer to avoid locking the translation cache. These papers generally focus, and rightly so, on the specific points that they aim at improving, but without providing detailed simulation results. Among those, the most relevant one is  [17] , first because it reflects the current implementation of QEMU, and second because the authors used the PARSEC benchmarks to experiment. They use Aarch64 as target, and report results for user emulation, not for full system emulation, the target of this work. The RISC-V ISA defines 9 atomic operations and the loadreserved and store-conditional instructions, for 32-bit and 64-bit. In addition, two bits can be used to accommodate release consistency in these 11 instructions.
III. Pinning in QEMU Pinning might improve simulation speed, as by assigning a vCPU to a CPU we prevent migrations and thus cache trashing. To devise a pinning strategy, we must study how QEMU handles parallelism and how the host machine provides parallelism: • QEMU allows to specify the number of vCPUs and a target non-uniform memory access (NUMA) architecture. However, although the NUMA description allows the target operating system to gain insight on the topology, using typically hwloc  [19] , it does not influence performance-wise the execution. QEMU mttcg creates as many software threads as vCPUs. QEMU also create threads for emulated I/Os handling peripherals, and for a few other bookkeeping tasks, • All server hosts today are multicore, NUMA, and generally also support simultaneous multithreading  [20]  (SMT), two harts being the norm on x86-64 architectures.
IV. Methodology Our goal is to measure the scalability of the emulator with presumably scalable non-synthetic workloads. To that aim, we use the PARSEC benchmark with the "simlarge" inputs for all programs, using Linux implementation of the POSIX thread library as support to parallelism. We are aware that scalability issues in the PARSEC programs have been pointed out  [21]. However, for lack of a better benchmark, we will use it. The programs are cross-compiled on the host. Some of them are not meant to be cross-compiled, so they fail and we ignore them, some others fail at run-time, due to either initialization or later memory allocation problems, we also skip them. This is why our plots contain a subset of the benchmark, namely blackscholes, bodytrack, cholesky, ferret, fft, fluidanimate, freqmine, lu_cb, lu_ncb, ocean_cp, radix, swaptions, water_nsquared and water_spatial.
Some of these programs have additional restrictions. Five of them cannot be executed if the number of threads is not a power of 2: fft, fluidanimate, ocean_cp, radix and swaptions. Furthermore, swaptions cannot be executed when the number of threads is higher than the swaptions parameter value. We also cross-compiled Linux 5. 9. 6 and OpenSBI 0. 8, configured with up to 128 processors, as run-time for the guest. For each program we measure the wall clock time for the full execution, T full , and the execution of the region-of-interest (ROI), T roi , as defined by the PARSEC benchmark. The sequential nature of the initializations and of some others parts might have a non negligible influence on the actual scalability of the programs, as Amdahl's law teaches us  [22]. Figure  2. (a) presents a high level view of the typical structure of the programs included in the PARSEC benchmark: 1) sequential allocation and initialization of data structures, parsing of files, etc, 2) activation of the ROI timer, 3) start the compute threads.
In addition, we noted that the Linux timer rate heavily impacts the boot time of the vCPUs. As in QEMU the timer is synchronized with the host real-time clock, the slower the boot the higher the number of timer interrupts. So we opted for a value of 100 Hz, classical for server workloads. Furthermore, we force the frequency of the physical cores to be 1862 MHz to get comparable results, and we ensure only the base Linux kernel, an ssh connection and QEMU are running on the machines while doing the experimentations. Finally, we measure 10 executions of each program for a given set of parameters and give as "wall-clock time" the mean.
E. QEMU performance for a large number of vCPUs A significant difference found between the x86 and emulated performance is the steep increase in execution time above 32 threads. In order to investigate this phenomenon, we've monitored with the Linux Perf tool  [24]  the number of cpu cycles spent for a single execution of lu_ncb's ROI section, on a recompiled QEMU with the optimizations disabled. This benchmark was chosen for the high ratio between the execution time with 128 vCPUs versus 16, and because it runs long enough to isolate the ROI section. The result showed that the vast majority of the time (approximately 70% of the profiled cycles) was spent in the Linux futex syscall, used by QEMU's global mutex. The code generator's interrupt handling function requires a lock on this mutex, causing resource starvation on setups with a large number of vCPUs.