dataesr/scientific-paragraphs-categorization huggingface dataset:
S. and China (Fig 1B; Dataset EV1). The maximum-likelihood phylogenetic tree based on 85,853 coregenome SNPs revealed that global isolates formed five major clades. The C-1, C-2, C-3 and C-5 consisted of ST638, ST475, ST2247 and ST543 isolates, respectively, while the C-4 comprised isolates of several STs, including ST1941 (n = 28), ST2375 (n = 1) and ST3986 (n = 1) (Fig 1A). Notably, 5 iNTS strains belong to ST543 and C-5.
Acknowledgements This study was supported by the  National Program on Key Research Project of China  ( 2019YFE0103900  and  2017YFC1600103 ) as well as the European Union's  Horizon 2020 Research and Innovation Programme  under Grant Agreement No. 861917-SAFFI ,  National Natural Science Foundation of China  ( 31872837 ,  32150410374 ),  Zhejiang Provincial Natural Science Foundation of China  ( LR19C180001 ),  Zhejiang Provincial Key R&D Program of China  ( 2022C02024 ,  2021C02008 ) and  Hainan Provincial Joint Project of Sanya Yazhou Bay Science and Technology City  ( 2021JJLH0083 ).
In most of these methods, a correlation filter is trained over a region of interest in the current frame which is then employed to track the target object in the subsequent frames by maximizing the filter response  [18] ,  [41] ,  [42]. More recently end-to-end deep learning-based trackers have also been proposed which have achieved excellent performance  [43] -  [45]. In many cases, classical object detectors such as Faster R-CNN have also been adapted for FIGURE 1. Many existing tracking methods including HCF  [9] , HDT  [10] , MCCF  [12] , and UPDT  [11]  are not able to effectively handle VOT in the presence of challenging scenarios. These sequences suffer from illumination variation, fast motion, motion blur, and occlusion challenges and selected from OTB100  [26]  and UAV123  [28]  datasets.
In the first case, Figure  2 (d)  shows the fused response map of the proposed CSF tracker using KCF as a baseline tracker on deep features (Figure  2  (c)) and on handcrafted features (Figure  2 (d) ). In the second case, Figure  2  (h) shows the fusion by the proposed CSF tracker over three existing SOTA correlation filters-based trackers including GFS-DCF  [41] , ASRCF  [42] , and RPCF  [48]. In both cases, the fused response map shows a higher signal peak and suppressed noisy peaks. The proposed tracker, which we name Common Subspace Fusion (CSF), is evaluated on seven tracking benchmark datasets and compared with the many SOTA trackers. Our experiments demonstrate a significant performance improvement in terms of both speed and accuracy. Specifically, our tracker demonstrates a 7. 0% improvement in terms of Expected Average Overlap (EAO) as compared to baseline GFS-DCF tracker  [41]  and an 3.
0% improvement as compared to PrDiMP tracker  [16]  on VOT2018 dataset  [25]. Further experiments on GOT-10k  [51] , OTB100  [26] , UAV123  [28]  and LaSoT  [30]  datasets have demonstrated significant improvement over existing SOTA trackers. The main contributions of the current work are as follows: • A novel common subspace fusion algorithm is proposed based on low-rank response map representation of various types of features and trackers. Using the individual low-rank representation response map, a common subspace-based representation is estimated which is constrained to be close to each individual representation on the Grassmannian manifold. • The proposed fusion scheme is employed on correlation filter-based trackers using different features resulting in significant performance improvement in all cases.
To address this problem, Bhat et al. proposed to learn the weights of the individual feature representation and demonstrated improved VOT performance as compared to the aforementioned fusion techniques  [11]. UPDT learned optimal fusion hyper-parameters on the OTB100 dataset  [26] , which were subsequently applied to other tracking datasets, albeit with no guarantee of the effectiveness of these learned parameters across different tracking challenges. It is observed that when weights are learned, higher priority is still given to deep features over shallow or handcrafted features. Decision-level fusion is exploited by the MCCF tracker in which a result is selected from multiple proposals based on the agreement of multiple feature combinations as well as temporal consistency  [12]. While it has been shown to improve performance in some scenarios, the significance of decision-level fusion strongly depends on the design of the baseline feature combinations.
IV. EXPERIMENTAL EVALUATIONS The performance of the proposed CSF algorithm is evaluated on seven tracking datasets including OTB100  [26] , UAV123  [28] , VOT2018 Short Term challenge (VOT2018-ST)  [25] , TC128  [27] , GOT-10K  [51] , VOT2018 Long Term challenge  [25] , and LaSoT  [30]. These datasets comprise a variety of tracking challenges including occlusion, background clutter, and scale variations to name a few  [28]. The description of each dataset is shown in Table  1. Our proposed algorithm is implemented on a PC with an Intel core i7 4GHz, Titan Xp GPU, and 64 GB RAM. The performance of the proposed CSF tracker is compared with 29 existing SOTA trackers including ASRCF  [42] , GFS-DCF  [41] , RPCF  [48] , ATOM  [65] , PrDiMP  [16] , UPDT  [11] , HDT  [10] , HCF  [9] , SRDCF  [66] , HCFTs  [67] , STRCF  [68] , CCOT  [69] , ECO  [70] , DeepMCCT  [12] , DGL  [71] , TADT  [72] , DeepSRDCF  [73] , GradNet  [74] , MCCT  [12] , BACF  [75] , TRACA  [76] , DeepRSST  [77] , MUSTER  [78] , LCT  [46] , UDT  [79] , CREST  [43] , DSLT  [80] , CFNET  [45]  and GCT  [81].
The tracking performance is evaluated using two popular measures known as precision and success rates  [26]  for OTB100, TC128, UAV123, and LaSoT datasets. The precision rate is defined as the percentage of frames with Euclidean distance between the predicted and ground truth target location less than 20 pixels threshold  [26]. The success rate is defined as the percentage of frames with overlap ratio b 1 ∩b 2 b 1 ∪b 2 > 0. 5  [26] , where b 1 and b 2 are the predicted and the ground truth bounding boxes, respectively. By varying the threshold from 0 to 1, the success plots are generated and the area under the curve is estimated. Moreover, following the protocols defined in VOT2017/VOT2018  [25] , we used three primary measures including Expected Average Overlap (EAO), Robustness (R), and Accuracy (A) to compare the performance of different trackers on VOT2018-ST dataset.
The EAO estimates the average overlap a tracker is expected to obtain on a large set of short-length sequences with the same visual properties as a given dataset. The robustness measures the number of times a tracker fails (loss the target) during tracking while accuracy is the average overlap between the ground truth and estimated bounding box during the successful tracking periods. The proposed CSF algorithm is tested in two different configurations: fusion using varying types of features for the same tracker (Config-1) and integrating multiple deep trackers (Config-2). In Config-1, we fuse the feature responses obtained by varying types of features while using the same tracker as a baseline. In Config-2 we fuse the responses of multiple trackers. Figures  2 (e )-(g) show the response maps of three SOTA CF based trackers.
The fusion map is smoothed and has a high signal to noise ratio. The objective is to complement the information captured by different trackers and to analyse the capability of our proposed CSF algorithm to fuse multiple deep trackers into a unified framework. The performance of the two configurations is evaluated using the VOT2018-ST challenge dataset in terms of Expected Average Overlap (EAO), Robustness (R), and Accuracy (A) using the protocols provided by the authors  [25]. Each of these experiments is discussed in detail in the following subsections.
A. FEATURE FUSION (CONFIG-1) Feature fusion is performed using the proposed CSF algorithm with three recent SOTA trackers as baselines: GFS-DCF, ASRCF, and RPCF. We use the same feature set for each tracker including HOG, Intensity Channel (IC), Color Names (CN), and deep features extracted from the 4th block of ResNet50. The performance comparison on the VOT2018 dataset is shown in Table  2 , demonstrating that the proposed CSF fusion algorithm improves the performance of each baseline tracker by a significant margin. In this experiment, the baseline trackers are used as the same as proposed by the original authors while the features set is proposed in GFS-DCF. The EAO measure has improved by up to 7. 0% for CSF-GFS-DCF while accuracy (A) is improved by 4. 0% and robustness by 3. 0%. In terms of robustness measure, RPCF is improved by 4.
B. TRACKER FUSION (CONFIG-2) In this experiment, the proposed CSF algorithm is used to fuse the output response maps of three existing SOTA deep trackers including ATOM, PrDiMP, and DSLT. Performance is evaluated on the VOT2018-ST dataset, with results shown in Table  3. The features and parameters suggested by the original authors are used in each case. We observe a significant EAO improvement of 3. 0% beyond PrDiMP tracker. In terms of accuracy, we observe an improvement of up to 3. 0% while in terms of robustness an improvement of up to 2. 0% is observed. This simple experiment demonstrates the effectiveness of the proposed CSF algorithm in fusing   complementary information from different deep trackers, resulting in a significant performance improvement.
C. COMPARISON OF CSF ALGORITHM WITH EXISTING FUSION SCHEMES The proposed CSF algorithm is also compared with four existing SOTA fusion-based trackers: UPDT, HCF, HDT, and MCCF. For a fair comparison among the compared methods, the classical KCF tracker  [31]  is used as a baseline and the same set of features including HOG, IC, CN, and deep features extracted from the 4th block of ResNet50 are used. Thus, this experiment only compares the strengths and weaknesses of different fusion schemes while keeping all other variables fixed. The experiments are repeated with and without scale estimation on three datasets: OTB100, TC128, and UAV123. The scale of the target object is estimated using the same coarse-to-fine search strategy on HOG features for all compared trackers as proposed in ASRCF tracker  [42].
D. ABLATION STUDY The proposed CSF algorithm has only one hyper-parameter to tune which is γ in Eq. (  5 ). To find a good value of γ , experiments are performed on OTB100 dataset using the protocols defined in  [11]   1  The authors divided OTB100 sequences into hard videos (OTB-H) as a validation set and easy videos (OTB-E) as a test set considering the performance of different trackers. The value of γ is varied from 0 to 1. 6 in intervals of 0. 4. In addition, a high value of γ = 50 is also tested. Figure  4  shows the performance comparison as a precision plot of results from OTB-E with varying values of hyperparameter γ. For γ = 0 in our objective function given by Eq. (  5 ), only the performance using the common low-rank representation component is evaluated while for γ = 50 the second term, which is the sum of projection distances between common subspace and individual subspaces, becomes more important.
E. QUALITATIVE RESULTS To evaluate the performance of the proposed CSF tracker, we present rigorous visual results on key frames of 13 challenging sequences selected from OTB100 dataset and five FIGURE 5. Precision and success plots using OPE for the proposed CSF algorithm against other fusion schemes. Top row shows the results on OTB100, middle row shows the results on TC128, and bottom row shows results on UAV123 datasets. The legends of the precision plots contain threshold scores at 20 pixels  [26]  while the legends of success plots contain area-under-the-curve scores for each compared tracker. Our proposed CSF algorithm has performed consistently better against these SOTA fusion schemes in all experiments. sequences from UAV123 dataset. Figure  6  presents the visual results of the proposed CSF tracker.
1) OTB100 DATASET The proposed CSF algorithm is evaluated on OTB100 dataset (average video length is 590 frames with 100 videos). The performance is evaluated using the success and precision over varying overlap thresholds. Some visual results of OTB100 dataset are presented in Figure  6. Figure  7  shows the precision and success plots of the proposed CSF tracker with other SOTA trackers including GFS-DCF, RPCF, ASRCF, MCCT, ECO, CCOT, HCFTs, STRCF, and SRDCF. It should be noted that the proposed CSF (ASRCF) tracker used the response maps estimated by ASRCF tracker on different features. In terms of precision plot, the proposed tracker has obtained 95. 1% precision score while the second best GFS-DCF tracker obtained 93. 2%. Compared to the baseline ASRCF tracker, the performance of the proposed tracker has improved by 2.
8%. In terms of success plot, the proposed tracker CSF tracker has obtained 72. 2% success rate while the second best is ECO tracker obtaining 70. 0% success rate. Compared to baseline ASRCF tracker, the performance FIGURE 6. Visual results of the proposed CSF algorithm and its comparison with existing SOTA trackers including ATOM PrDiMP  [16] , DSLT  [80] , GFS-DCF  [41] , ASRCF  [42] , and RPCF  [48]  on 12 challenging sequences selected from the OTB100  [26]  and 6 sequences from UAV123  [28]  datasets. Frame indexes and sequence names are shown for each video. Our proposed CSF algorithm has consistently performed better than the compared trackers. improvement is 3. 0%. It demonstrates the effectiveness of our proposed fusion algorithm. We have also evaluated the attribute-based performance on the OTB100 dataset.
In terms of Success Rate (SR), the proposed CSF tracker (baseline ASRCF) achieves the best results under 7 out of FIGURE 7. Precision and success plots using OPE of the proposed CSF tracker against other SOTA trackers on OTB100 dataset  [26]. The legend of precision plot contains threshold scores at 20 pixels, while the legend of success rate contains area-under-the-curve score for each tracker. 11 challenging tracking attributes including IV (72. 4%), SV (68. 1%), OCC (69. 3%), BC (72. 2%), DEF (67. 6%), OPR (69. 3%) and IPR (68. 2%). For sequences with MB, FM, OV and LR tracking challenges, the proposed tracker achieves the second best performance compared to other competing trackers. The improved performance of the FIGURE 8. Precision and success plots using OPE of the proposed CSF tracker against other SOTA trackers on UAV123 dataset  [28].
1 %). The improved performance in terms of both measures, mAO and mSR 0. 50 , is because of the fusion component introduced within the baseline tracker GFS-DCF. Precision and success plots using OPE of the proposed CSF tracker against other SOTA trackers on LASoT dataset  [30]. The legend of precision plot contains threshold scores at 20 pixels, while the legend of success rate contains area-under-the-curve score for each tracker.
G. EVALUATION ON LONG-TERM TRACKING DATASET We have also evaluated the performance of the proposed tracker on two long term visual object tracking datasets including LaSoT  [30]  and VOT2018-LT  [25]. In the below subsections, we describe the performance comparison of the proposed tracker on these datasets. 1) LaSoT DATASET  [30]  The test split of this dataset consists of 280 videos with an average length of 2448 frames  [30]. The results of the proposed CSF tracker compared with 15 SOTA ers including ECO, DSLT, BACF, HCFTs, CFNET, LCT, SRDCF, TRACA, STAPLE, STRCF, ASRCF, and GFS-DCF. The performance is reported in terms of PR and SR by using the protocols provided by the original authors  [30]. In terms of PR, CSF tracker has obtained 38. 20% which is 2. 6% better than the existing baseline tracker, GFS-DCF (35.
60%), and 4. 50% better than the ASRCF tracker as shown in Figure  9. In terms of SR, CSF tracker has achieved 3. 50% performance improvements compared to GFS-DCF and up to 5. 70% better accuracy than ASRCF. This experiment demonstrates the effectiveness of our proposed fusion algorithm on long term tracking challenges. 2) VOT2018-LT  [25]  The long term challenge of VOT2018 dataset consists of video sequences with an average resolution of 468×785 as shown in Table  1. proposed CSF tracker has also evaluated on this dataset in terms of F-score, Recall (Re), and Precision (Pr) measures as defined in VOT2018-LT evaluation kit In this dataset, ranking is achieved using maximum F-score attained by each tracker. Table  6  shows the performance comparison of the proposed CSF tracker with five SOTA trackers including CCOT, Deep-SRDCF, DeepSTRCF, UDT, and GFS-DCF.
Overall, it can be observed that our proposed CSF tracker has attained best F-score of 67. 80% and outperforms GFS-DCF, UDT, and CCOT trackers by 2. 60% and 5. 80% margin, respectively. The corresponding Re and Pr scores also demonstrated the improvements of the proposed tracker compared to second best tracking method. [51]. The performance is reported in terms of mAO (mean Average Overlap), mSR 0. 50 and mSR 0. 75 (Success Rate that measures the percentage of successfully tracked frames, where the overlap precision exceeds a threshold of 0. 50 and 0. 75). best and second best results are shown in red and blue respectively. Our proposed tracker has consistently performed better than the SOTA trackers. TABLE  6. Performance comparison of the proposed CSF tracker with current SOTA trackers on VOT2018-LT dataset  [25].
V. CONCLUSION In this work, an information fusion algorithm is proposed to encode the complementary information contained by various types of features and trackers to improve VOT performance. For this purpose, low-rank representations of response maps are computed which remove unwanted boundary effects and suppress noise. A common low-rank subspace representation is estimated such that it is close to each individual subspace on the Grassmann manifold in terms of projection distance. The common subspace representation acts as the fusion scheme which integrates information encoded by individual low-rank response maps. The CSF algorithm is generic and works well with various types of features, correlation filter-based trackers, and deep trackers. Evaluations are performed for feature fusion and tracker fusion on seven challenging tracking benchmark datasets and compared with several SOTA trackers.
8; K = 6. 910 5 M -1 ; H 0 = -8500 cal. mol -1 ; S 0 = -1. 9 cal. mol -1. K -1 ; G 0 = -7950 cal. mol -1 ). (right) Isothermal Calorimetric Titration data (reverse titration) of compound syn-2 recorded in the same conditions. TlOAc (c = 0. 10 mmol) was placed into the calorimeter cell and 28 successive aliquots (10 L) of syn-2 (c = 1. 02 mmol) were added at 3 min intervals (n = 1. 2; K = 3. 2510 5 M -1 ; H 0 = -8100 cal. mol -1 ; S 0 = -2. 0 cal. mol -1. K -1 ; G 0 = -7500 cal. mol -1 ). Although this procedure has a small impact on the measured binding constant, it remains questionable. Indeed, this small contribution may reflect additional interactions between the Tl + cation and syn-2. In such a case, it is recommended to perform the reverse titration, which requires to introduce the ligand (Tl + ) in the cell and the host molecule (syn-2) in the syringe.
Thus, this leads to the determination of two binding constants with different magnitude K 1 = 1. 110 5 M - 1 (G 0 = -6900 cal. mol -1 ) and K 2 = 1. 810 3 M -1 (G 0 = -4400 cal. mol -1 ) (Figure  6 ). In contrast, the data point fits well with a one-site model for the reverse titration and give a binding constant K = 6. 810 4 M -1. We can point out that the first binding constant is consistent with the data measured in LiOH (0. 1 M)/H 2 O for the forward and reverse experiments. K 1 = 1. 110 5 M -1 ; H 1 0 = - 6100 cal. mol -1 ; S 1 0 = 2. 6 cal. mol -1. K -1 ; G 1 0 = -6900 cal. mol -1 ; K 2 = 1. 810 3 M -1 ; H 2 0 = - 15200 cal. mol -1 ; S 2 0 = -36. 1 cal. mol -1. K -1 ; G 2 0 = -1100 cal. mol -1 ). (right) Isothermal Calorimetric Titration data (reverse titration) of compound syn-2 recorded in the same conditions.
Single-Crystal X-ray diffraction Single-crystals were selected and mounted on an Xcalibur Gemini kappa-geometry diffractometer equipped with an Atlas CCD. Data collections were achieved at 150 K using Copper X-ray radiation (l = 1. 54184 Å ) for anti-1, syn-2 and syn-3 and Molybdenum radiation (l = 0. 71073 Å ) for the second X-ray structure of syn-2. Intensities were collected by means of the CrysalisPro software. 18 Reflection indexing, unit-cell parameters refinement, Lorentz-polarization correction, peak integration and background determination were carried out with the CrysalisPro software. 18 An analytical absorption correction was applied using the modeled faces of the crystal. 19 The resulting set of hkl was used for structure solution and refinement. The structures were solved with the ShelXT structure solution program using the intrinsic phasing solution method and by using Olex2 [4] as the graphical interface.
Each microscope parameter (objective wheel, focus, fluorescence mode, image acquisition, …) can be remote-controlled by the irradiation control software, developed by CENBG. The culture dish is placed between the microscope and the beam exit window on a sample holder. This sample holder is mounted on a 3 axes high precision motorized stage (Newport™ M436 linear stages moved by Newport™ LTA-HS actuators). The stages movement range of 5 cm in X and Y directions (perpendicular to the beam axis) allows the exploration of the whole culture dish. The Z axis is used to bring the cell dish as close as possible to the exit window, i. e. approximately 250 µm due to the geometry of the system and the relative fragility of the window. The dish itself is described in  [36]. It has been designed to perform cell culture directly inside it, while minimizing optical distortions to optimize the targeting accuracy.
The count rate can then be visualized on the CRionScan webpage  [38]  and each measurement can be recorded by the irradiation software. Sets of single measurements can then be automatically performed, so that a reliable mean emission rate value can be obtained. Measurements are first carried out at the end of the tuning phase and, then, regularly to monitor the stability of the beam current throughout the experiment. A beam current of a few thousand ions per second is typically used for 4 MeV protons. An example of a set of measurements, for an opening time of 100 ms, is shown on Fig. 3 : the mean number of ions over 961 measurements is of 194 with a standard deviation of the Gaussian fit of σ = 15, close to the expected value (14) from a Poisson law.
Several radiation biology experiments and developments are planned using the MIRCOM microbeam. For example, the characterization of the relationship between energy deposition at the nanometric scale and the initial biological events, initiated in the framework of the BioQuaRT project  [60] , will continue, to provide additional reference data points to improve the success and reliability of Monte Carlo track structure simulation codes. The development of ultra-thin microfluidic chips will be carried out to allow the irradiation of additional biological samples such as circulating cell types, and small organisms (nematodes, fish embryos, etc. ). Following this, the biological consequences of mitochondrial irradiation will be studied, at the molecular, cellular and multicellular scale on cells and in the nematode Caenorhabditis elegans.
Acknowledgements The authors would like to thank  P. Barberet  ( CENBG, France ) for his help and precious advice all along the commissioning of the beamline,  H. Seznec  ( CENBG, France ) for his help in the sample preparation protocols,  P. Alfaurt ,  M. Roche , and  J. Outrequin  ( CENBG, France ) for their work in developing the microbeam line, and for their precious help in the setting-up and commissioning of the beamline.