dataesr/scientific-paragraphs-categorization huggingface dataset:
They used a combination of a Siamese-CNN and a Long-Short-Term-Memory (LSTM) model to compute a similarity score, used for vehicle re-identification. Instead of training a CNN to classify vehicles, Liu et al. [8]  suggested to directly learn a distance metric using a triplet loss function to fine-tune a pre-trained CNN. They also provided another large dataset containing a high number of vehicles, called vehicleID. Liu et al. [9]  introduced a CNN architecture that jointly learns LRs of the global appearance and of local regions of the car. Attribute features (colors, model) are additionally used to jointly train their deep model. Finally, they concatenated global LR, local LR and attribute features. They concluded that the more information is combined, the higher the re-identification performance is.
The literature on vehicle detection and tracking is very rich, and numerous methods are today available to perform automatic vehicle detection and tracking in a given camera  [19]. Therefore, assuming that the video segment selected by the user has to be processed by such algorithms, the query vehicle could be represented by a track, which would provide more information for the re-identification. So far, the use of a query containing more than one image has not been fully addressed in vehicle re-identification. We address this issue by considering the track-to-track process (T2TP). In this paper, we propose to i) evaluate the impact of the metric choice in re-identification and ii) extend the vehicle re-identification to T2TP and assess the performances in comparison with I2TP. To this extent, the main experiments in this paper are made using the VeRi dataset.
Indeed, unlike other large-scale dataset, VeRi contains image-based tracks of vehicles, allowing performances comparison between I2TP and T2TP, as well as comparison of performances with state-of-the-art methods. Note that since I2IP is not based on the same ranking support than I2TP and T2TP, I2IP is not considered in these experiments. In addition, we further investigated the impact of the metric in other I2IP-based vehicle retrieval tasks using three other large-scale datasets of the literature, namely the VehicleID  [8] , CompCars  [20]  and BoxCars116k  [21]. Let us underline that this paper focuses on visual information-only re-identification processes: no extra or contextual information is used in the studied processes. It is worth noting that the goal of this article is not to provide another re-identification system, but rather to evaluate the impact of the metric choice in the re-identification performance, and the potential benefits of T2TP on state-of-the-art methods.
Notations and problem statement Let consider C = {C 1 , C 2 ,. , C nc }, the set of n c cameras of a CCTV system, and V = {V 1 , V 2 ,. , V nv }, the set of n v vehicles captured by the cameras in C. Each vehicle of V is uniquely identified. We denote T = {T 1 , T 2 ,. , T nt } the set of n t tracks captured by the cameras of C, and stored in a database. A track T k , captured by one camera of C, is associated with one of the vehicles of V denoted V k. Since a vehicle can be recorded by multiple cameras, two tracks T i and T l (with l = i) can be associated with the same vehicle, such that V i = V l. A track T i = {I i,1 , I i,2 ,. , I i,Ni } is a set composed of N i images, all representing the same vehicle V i. Each image I i,j of T i is cropped within the frame of its corresponding video segment from where it has been recorded.
Experiments We compared I2TP and T2TP performances and the impact of the metric by running experiments on the large-scale benchmark dataset VeRi  [4]. We further evaluated the impact of the metric on other I2IT-based vehicle retrieval tasks VehicleID  [8] , CompCars  [20]  and BoxCars116k  [21]. First of all, since the VeRi dataset is the only dataset containing several tracks for the same vehicle, we used it to evaluate the impact of the distance metric in I2TP and T2TP, as well as a performance comparison between them. We conducted our experiments on the VeRi dataset as follows. First, we used the training set of the VeRi dataset on five well-known CNN architectures to specialize them in the vehicle recognition task. We then used these fine-tuned CNNs to extract LR on every image of the testing set.
Second, we evaluated I2TP and T2TP with respects to distance metrics defined in Section 3 and Section 4. Second of all, we extended the evaluation of the impact of the distance metric on other vehicle-based retrieval tasks using the datasets VehicleID, CompCars and BoxCars116k. These experiments were conducted using the DenseNet201 CNN architecture, which showed to be the best CNN architecture found during VeRI experiments. With the VehicleID dataset we conducted two kinds of experiments, vehicle re-identification and vehicle retrieval tasks as originally proposed by the authors in  [8]. To evaluate the metric comparison to other LR-based retrieval tasks, we compared the impact of the metric on vehicle type recognition task as in  [21] , using the datasets BoxCars116k and CompCars. With the dataset BoxCars116k, since it contains the unique identifier of vehicles, we also conducted experiments of vehicle retrieval as for VehicleID experiments.
The VeRi dataset The VeRi dataset is composed of 49357 images of 776 vehicles recorded by 20 cameras in a real-world traffic surveillance system. Every vehicle of the dataset has been recorded by several of the 20 cameras of the system, constituting a totality of 6822 tracks of vehicles (each track is composed of a mean number of 6 images, varying from 3 to 14 images). The VeRi dataset is divided into two sets, a training set, composed of 37778 images representing 576 vehicles (5145 tracks), and a testing set, composed of 11579 images representing 200 vehicles (1677 tracks). The training set is used to fine-tune the CNN for the task of vehicle recognition as explained in section 5. 1. 3. Evaluation of I2TP is performed through 1677 query images preselected in each track of the testing set.
CNN architectures and LR extraction To extract LR, we used the second-to-last layer of popular CNN architectures, namely ResNet18  [30] , VGG16  [31] , AlexNet  [32] , InceptionV3  [33]  and DenseNet201  [34]  pre-trained on the dataset ImageNet  [35]. These architectures, widely analyzed  [36, 37]  and easily accessible  [38] , have been chosen as a basis to evaluate the impact of the metrics and to compare I2TP and T2TP. In order to comply with the inputs dimension of these CNNs, every image of the VeRi dataset was resized to 224×224. The different dimensions of the second to the last layer of ResNet18, VGG16, AlexNet, InceptionV3 and DenseNet201 are respectively 512, 4096, 4096, 2048 and 1920.
Evaluation protocol To evaluate the vehicle ranking, we use the Cumulative Matching Characteristic (CMC) curve which is widely used in object re-identification  [4, 5]. We reported the two measures rank1 and rank5 of the CMC curves, corresponding respectively to the precision at ranks 1 and 5. Regarding the dataset VeRi, since there are several tracks that correspond to the query, we also computed the mean average precision (mAP) which is classically used in vehicle re-identification evaluation. mAP takes recall and precision into account to evaluate the overall vehicle re-identification. Given a query q and a resulting ranked set Tq , the average precision (AP) is computed as AP (q) = 1 N gt Nt k=1 δ(T q,k ) k i=1 δ(T q,i ) k , (9) where δ(T q,i ) is a function equal to 1 if the track T q,i represents the vehicle V q , or 0 otherwise.
Experiments on VehicleID, BoxCars116k and CompCars datasets: impact of the distance metric in other vehicle retrieval tasks For the experimentation on VehicleID, BoxCars116k and CompCars, we used the DenseNet201 architecture. As in Section 5. 1. 2, we extracted LR from the second-to-last layer of the DenseNet201, constructing a LR of size 1920 for every image of the dataset. For each dataset experiment, we fine-tuned the CNN for classification using their respective training set (described below), following the procedure described in Section 5. 1. 3. Since these datasets do not contain several tracks for each vehicle, experiments are using I2IP for the ranking. Thus, in these experiments, M ED and M CD respectively correspond to Euclidean distance and cosine distance. Furthermore, due to vector normalization applied for the resolution of sparse coding (Section 5.
Experiment on the VehicleID dataset The For vehicle re-identification, in each subset (Small, Medium and Large), an image of each vehicle is randomly selected as a gallery image and the other images are used as query images, resulting in 5693, 11777, and 17377 query images. In this experiment, since only one gallery image correspond to the query image, only rank1 and rank5 are reported. Regarding vehicle retrieval, for a given vehicle containing N t images, max(6, N t -1) are selected as gallery images and the rest as query images as in  [8]. For evaluation, rank1, rank5 and mAP measures are reported.
Experiment on the BoxCars116k dataset The BoxCars116k dataset is composed of 116286 images of 27496 unique vehicles of 693 different vehicle models (brand, model, submodel, model year) collected from 137 different CCTV cameras with various angle viewpoints. BoxCars116k has been originally designed for fine-grained vehicle classification and vehicle type recognition  [21]. For this purpose, authors constructed a subset, named "hard", containing 107 fine-grained vehicle classes (precise type of vehicle, including the model year) with uniquely identified vehicle divided into a training set of 11653 tracks (51961 images) and a testing set of 11125 tracks (39149 images). We used this subset for vehicle type recognition and vehicle re-identifications tasks. For vehicle type recognition, we used the training set to fine-tuned the CNN considering the 107 classes of fine-grained vehicle models.
Experiment on the CompCars dataset The CompCars dataset is composed of 214345 images of 1687 vehicles collected from the web and urban surveillance cameras. For our experiment, we used the Part-I and the "surveillance data" subsets of CompCars defined by the authors in  [20]. Part-I subset contains 30955 images of 431 vehicle models; the training set and testing set contain respectively 16016 and 14939 images of the same 431 vehicle models. The subset "surveillance data" is composed of 44481 images of 281 car models captured in the front view; the training set and testing set contains respectively 31709 and 13894 images of the same 281 car models. For both subsets, we used the training set to fine-tune the CNN for fine-grained vehicle classification task considering the vehicle models as classes (431 classes for PART-I and 281 classes for the "surveillance data").
Implementations details CNN architecture construction and training have been implemented using the Pytorch framework in Python  [38]. Regarding the RSCR, we solved equations (  5 ) and (  7 ) by using the lasso-LARS algorithm (Lasso model with a regularization term L 1 , fitted with Least Angle Regression)  [40] , with α = 1. We computed the kernel distance KRBF with γ = 1 f , f being the LR dimension of the considered CNN. Distance metric computations were implemented using the package scikit-learn in Python. Source codes for LR extraction (Section 2. 2), distance metric computations (Sections 3 and 4) and vehicle ranking (Section 2. 3) are available at https://github. com/GeoTrouvetout/Vehicle_ReID.
Results on the BoxCars116k dataset Table  3  reports the performances obtained on the BoxCars116k dataset regarding the vehicle re-identification and vehicle type recognition tasks. For the vehicle retrieval, M CD systematically outperforms M ED, with mAP (+1. 95%), rank1 (+1. 78%) and rank5 (+1. 89%). For vehicle type recognition task, M ED and M CD reach high performances ([96. 26% -99. 07%]), and M CD performed slightly better than M ED (mAP +0. 96%, rank1 +0. 94% and rank5 +0. 94%).
Results on the CompCars dataset Table  3  shows the performances on the vehicle type recognition task obtained with the two subsets "PART-I" and "surveillance data" of CompCars. Regarding the "PART-I" subset, M CD outperformed M ED with +2. 79% for rank1 and +0. 93% for rank5. Reaching high performances with the "surveillance data" subset ([97. 84% -98. 92%]), M CD performed slightly better than M ED (+0. 35% for rank1 and equally for rank5).
Discussion and perspectives From a general point of view, we can observe high variability of performance between CNNs. As expected, such results confirm the impact of the CNN architectures on the re-identification performance. This demonstrates the relevance of previous works focusing on the definition of specific CNN architectures and on the learning of efficient LR. Besides, considering a given CNN architecture to produce LR, our results also show high variability of performance depending on the distance metric, showing that the choice of the metric for the matching process has a major impact on re-identification performance. Globally, experiments on the VeRi dataset show a clear gain of performance from M ED to M CD (mAP gain ranging from +2. 02% to +5. 79%). More precisely, we can observe big difference of performance between M ED and M CD/RSCR, especially when associated with AlexNet and VGG16.
Alternatively, the aggregation function mean50 has the advantage of aggregating the distances between query and gallery track images, while truncating irrelevant images contained in the query track. Such aggregation function is thus supposed to be less dependent on the existence of similar images between tracks of   [6]  12. 20 33. 91 53. 69 VGG  [6]  12. 76 44. 10 62. 63 GoogleLeNet  [6]  17. 89 52. 32 72. 17 FACT  [6]  18. 75 52. 21 72. 88 nuFACT  [6]  48. 47 76. 76 91. 42 RAM (baseline: only LR)  [9]  55. 0 84. 8 93. 1 RAM  [9]  61. 5 88. 60 94. 00 QD_DLF  [10]  61 Further experiments including more diversity in tracks of vehicles are thus needed. For instance, the PKU-VD  [45]  and ToCaDa  [46]  datasets provide tracks of vehicles containing different points of view (e. g. a track containing images of the vehicle in front and side-view).
We tested metrics based on the minimal Euclidean distance (M ED), the minimal cosine distance (M CD), and the residual of the sparse coding reconstruction (RSCR). We applied these metrics using features extracted from five different CNN architectures (namely ResNet18, AlexNet, VGG16, InceptionV3 and DenseNet201). We used the VeRi dataset to fine-tune these CNNs and to evaluate the results in I2TP. Results show a major impact of the metric on the re-identification performance. In overall, independently of the CNN used, M CD metric outperforms M ED (mAP: [+2. 02% -+5. 79%]). This result is of great importance since the literature mainly uses Euclidean-based distance (or a variant) during the re-identification process. Keeping the CNN providing the best performances (DenseNet201), we further evaluated the impact of the metric in other I2IP-based vehicle retrieval tasks using three other datasets (VehicleID, CompCars and BoxCars116k).
-IMPROVING VEHICLE RE-IDENTIFICATION USING CNN LATENT SPACES: METRICS COMPARISON AND TRACK-TO-TRACK EXTENSION a testing set, composed of 108221 images representing 13164 vehicles. To evaluate the effect of the scale of the dataset on retrieval performances, 3 subsets are extracted from the testing set: the Small subset composed of 800 vehicles (6493 image), the Medium composed of 1600 vehicles (13377 images) and the Large subset composed of 2400 vehicles (19777 images). For both vehicle re-identification and vehicle retrieval experiments, the training set is used to fine-tune the CNN for the task of vehicle classification (with 13164 classes).
-IMPROVING VEHICLE RE-IDENTIFICATION USING CNN LATENT SPACES: METRICS COMPARISON AND TRACK-TO-TRACK EXTENSION a testing set, composed of 108221 images representing 13164 vehicles. To evaluate the effect of the scale of the dataset on retrieval performances, 3 subsets are extracted from the testing set: the Small subset composed of 800 vehicles (6493 image), the Medium composed of 1600 vehicles (13377 images) and the Large subset composed of 2400 vehicles (19777 images). For both vehicle re-identification and vehicle retrieval experiments, the training set is used to fine-tune the CNN for the task of vehicle classification (with 13164 classes). VehicleID dataset contains 221763 images of 26267 vehicles (each vehicle is represented by 8. 42 images in average). It is divided into two sets, a training set, composed of 113346 images representing 13164 vehicles, and POSTPRINT.
78 88. 55 55. 63 84. 38 94. 93 60. 07 88. 43 96. 9 52. 04 82. 29 93. 8 minM CD 38. 4 71. 79 87. 95 52. 83 82. 65 94. 57 58. 08 84. 2 95. 53 62. 31 87. 06 96. 78 55. 48 83. 06 94. 69 meanM CD 36. 13 70. 24 87. 66 52. 1 81. 45 93. 8 58. 24 83. 72 95. 11 62. 53 86. 64 96. 6 54. 56 81. 75 93. 56 medM CD 35. 86 70. 18 87. 95 51. 63 81. 04 94. 04 57. 89 83. 3 95. 35 62. 08 86. 82 96. 72 54. 37 82. 41 93. 98 mean50M CD 37. 93 72. 09 87. 78 53. 26 82. 89 94. 28 59. 17 84. 38 95. 95 63. 2 87. 36 97. 02 55. 94 83. 84 94. 69 med50M CD 37. 8 71. 91 88. 01 52. 91 82. 47 94. 22 58. 92 84. 2 95. 59 62. 82 87. 06 97. 08 55. 71 83. 3 95. 95 Note: 6. 1. 3 Results on other dataset Track-to-track re-identification performances (mAP, rank1 and rank5) depending on the metrics and the CNN architecture used. Best performances are highlighted in bold (RSCR, kernel distances, MED-and MCD-based metrics separately).
6. 1. 4ResultsontheVehicleIDdatasetTable3reports the performances obtained on the VehicleID dataset regarding the vehicle re-identification and vehicle retrieval tasks. Regarding the vehicle re-identification task, for all the three subsets (small, medium, large), M CD systematically outperforms M ED for rank1 ([+0. 88% -+1. 71%]) and rank5 ([+0. 75% -+1. 35%]). POSTPRINT -IMPROVING VEHICLE RE-IDENTIFICATION USING CNN LATENT SPACES: METRICS COMPARISON AND TRACK-TO-TRACK EXTENSION.
Values are in percentages. The higher, the better. *method using image-to-image process for the ranking the same vehicle. Nevertheless, since the VeRi dataset mainly contains tracks with similar images, such effects are hard to evaluate. 83 88. 50 94. 46 I2TP+Densenet201+M CD 58. 60 84. 14 95. 41 T2TP+Densenet201+mean50M CD 63. 2 87. 36 97. 02 GS-TRE [14]* 59. 47 96. 24 98. 97 SSL[11]* 61. 07 88. 57 93. 56 SSL+re-ranking [11]* 69. 90 89. 69 95. 41 MRM [15]* 68. 55 91. 77 95. 82 Note:.
Acknowledgments The authors would like to thank  Anne Buisson  on behalf of the patients' Association François Aupetit for her assistance in writing the patient information sheets and publicizing the study on the association's website. We thank  Bradley MacIntyre  and  Pr Otley  for allowing us to use the IMPACT-III questionnaire and providing us with the France-French translation and scoring method. We also thank  Pr Jean-Jacques Parienti  for his advice on the study method and statistical analysis. Finally, we thank  Cécile Valentin  for her help in collecting the data,  Fabien Chaillot  for providing regulatory support and the  Caen University Hospital  for research grant funding ( APRIM 2019 ).
✩ Funding source: The study was funded by a hospital research grant (grant number:  APRIM 2018 CHU Caen 17,160 ) from  Caen University Hospital. ✩✩ Author contributions: All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by  Léa Tran ,  Camille Grandjean  and  Claire Dupont-Lucas. The first draft of the manuscript was.
The TOF MS data were collected in the m/z 50-1500 range with a scan time of 0. 2 s. The MS/MS spectra of the extracts were collected in the 50-1000 range by a collision energy ramp from 10 to 30 eV. Leucine-enkephalin (m/z 556. 2771 for positive mode) was used at a frequency of 10 s. Acquired MS data included m/z, retention time, and ion intensity, and all data were extracted with UNIFI software (Waters). Collection, normalization, and alignment of the MS datasets analyzed by UPLC-QTOF/MS were obtained using UNIFI software (Waters, Milford, MA, USA). The metabolites were identified using the Human Metabolome Database (HMDB) (www. hmdb. ca, accessed on 10 March 2021), Metabolite and Chemical Entity (METLIN) database (metlin. scripps. edu, accessed on 10 March 2021), chemspider (www. chemspider.
Measurement of Intracellular ROS Level The cells were irradiated with 8 mJ/cm 2 UVB, and then the cells were washed once with 1 mL PBS, and a culture solution containing AYC-CS-E (0. 125 to 64 µL/mL) was added. After 24 h, cells were harvested and stained with 5 µM 2 ,7 -dichlorofluorescein diacetate (DCFH-DA; Sigma-Aldrich) at 37 • C for 30 min and washed twice with PBS. The intracellular ROS expression levels (DCFH-DA-positive cells) of each cell were analyzed with a Life Launch Attune Nxt Flow Cytometer (ThermoFisher Scientific, Waltham, MA, USA) using FlowJo software (Version 10; Tree Star, Inc. , Ashland, OR, USA).
Analysis of the Secondary-Structure Content and Segments of NCYM Using VUVCD and SELCON 3 The contents of a-helices, b-strands, turns, and unordered structures of proteins were estimated from the corresponding VUVCD spectra using the SELCON3 program and a database of VUVCD spectra and secondary-structure contents for 31 reference proteins  (13) (14) (15) 19). The number of a-helix and bstrand segments was calculated from the distorted a-helix and distorted b-strand contents, respectively  (19). The root-meansquare deviation (d) and the Pearson correlation coefficient (r) between the X-ray and VUVCD estimates of the secondarystructure contents of the reference proteins were 0. 058 and 0. 85, respectively  (13, 15).
Analysis of the Positions of the Secondary Structures of NCYM Using VUVCD and Neural-Network Method The positions of a-helix and b-strand segments in the aminoacid sequence were predicted using a neural-network (NN) method based on the secondary-structure contents and the number of segments obtained in the VUVCD analysis (VUVCD-NN method). The computational protocol is described in detail elsewhere  (14). Briefly, we utilized an NN algorithm  (20)  that predicts the position of secondary structures using the evolutionary sequence information based on the positionspecific scoring matrices generated using the PSI-BLAST tool. A training dataset of 607 proteins used in the NN algorithm was obtained from the X-ray structures in the PDB and the weights and biases of 20 amino acids for a-helices and b-strands were calculated from the secondary structures and amino-acid sequences of these 607 proteins.
VUVCD Analyses Revealed the Secondary Structure of the NCYM Protein Significant differences in the spectra were observed in the wavelength range of 190-240 nm between the tagged proteins and tag only solution (Figure  1C ). These differences arise from the spectra of NCYM. The secondary structure contents and segments of the isolated SUMO tag were analyzed using the VUVCD spectra and SELCON3 program  (15, 19). The isolated SUMO tag (129 residues) contained 14. 2% a-helices and 29. 2% b-strands in the hydrogenated state and 13. 9% a-helices and 28. 7% b-strands in the perdeuterated state (Table  1 ). The atomic structure (PDB ID: 3PGE) of tag fragment (80 residues) from Xray crystallography showed that this fragment contains 14% helices and 32% sheets. The lengths of the tags in the X-ray and VUVCD methods differed but the secondary structure contents estimated by VUVCD agreed well with those of the crystal structure.