dataesr/scientific-paragraphs-categorization huggingface dataset:
Additionally, spatial information is modeled using HMM bringing in neighbor location relationships. A study  [21]  for place recognition in lifelogs images found that the best matching technique is to use bidirectional matching which nevertheless adds computational complexity. This problem is resolved by using robust and rapid to extract SURF features which are then hierarchically clustered using the -means algorithm in a vocabulary tree. The vocabulary tree allows the rapid descriptor comparison of query image descriptors to those of the database and where the tree leaf note descriptor votes for the database image. The success of matching for place recognition depends greatly on the database which should contain a large amount of annotated images. In many applications, this is a rather strong assumption about the environment.
In the absence of prior knowledge brought by completely annotated image database covering the environment, topological place recognition discretizes otherwise continuous space. A typical approach following this idea is presented in  [22]. Authors propose the gradient orientation histograms of the edge map as image feature with a property that visually similar scenes are described by a similar histogram. The Learning Vector Quantization method is then used to retain only the most characteristic descriptors for each topological location. An unsupervised approach for robot place recognition indoors is adapted in  [23]. The method partitions the space into convex subspaces representing room concepts by using approximated graph-cut algorithm with the possibility for user to inject can group and cannot group constraints.
The modeling involves sensor and motion models, which have to be trained offline with sufficiently large annotated database. Indeed, the model has to learn the model of allowed transitions between places, which require the annotated data to represent all possible transitions to be found in the test data. An important group of methods performing simultaneous place recognition and mapping (SLAM) is widely used in robotics  [31, 32]. The main idea in these methods is to simultaneously build and update a map in an unknown environment and track in real time the current position of the camera. In our work, the construction of such map is not necessary and may prove to be very challenging since the environment can be very complex and constantly changing.
[60]  is a wrapper algorithm that learns two discriminant classifiers in a joint manner. The method trains iteratively two classifiers such that in each iteration the highest confidence estimates on unlabeled data are fed into the training set of another classifier. Classically two views on the data or two single feature splits of a dataset are used. The main idea is that the solution or hypothesis space is significantly reduced if both trained classifiers agree on the data and reduce the risk of overfitting since each classifier also fits the initial labeled training set. More theoretical background and analysis of the method is given in Section 3. 3. 2.
The Co-Training Algorithm in the (1) Compute visual features x  = (x (1)  , x (2)   ) for every image   in the dataset (2) Initialize  1 = {(x (1)   ,   )}  (1) ,  (2) using the sets  1 ,  2 respectively; (b) Classify the patterns in the sets Ũ1 and Ũ2 using the classifiers  (1) and  (2) respectively; (i) Compute scores  (1)  test and confidences  (1) on the set Ũ1 (ii) Compute scores  (2)  test and confidences  (2) on the set Ũ2 (c) Add the  top confidence estimations 𝐿 1 ⊂ Ũ1 , 𝐿 2 ⊂ Ũ2 (i) 𝐿 1 := 𝐿 1 ∪ 𝐿 1 (ii) 𝐿 1 := 𝐿 1 ∪ 𝐿 1 (d) R (𝑓) = 1 𝑙 𝑙 ∑ 𝑖=1 ℓ (x 𝑖 , 𝑦 𝑖 , 𝑓 (x 𝑖 )) (17) which measures how well the classifier fits the training data. It is well known that minimizing only training error, the resulting classifier is very likely to overfit. In practice regularized risk (RRM), minimization is performed instead: 𝑓 RRM = arg min 𝑓∈F R (𝑓) + 𝜆Ω (𝑓) , (18) where Ω() is a nonnegative functional or regularizer that returns a large value or penalty for very complicated functions (typically the functions that fit perfectly to the data).
The parameter  > 0 controls the balance between a fit to the training data and the complexity of the classifier. By selecting a proper regularization parameter, overfitting can be avoided and better generalization capability on the novel data can be achieved. A good example is the SVM classifier. The corresponding regularizer Ω SVM () = (1/2)‖w‖ 2 selects the function that maximizes the margin. The Co-Training in the RRM. In semisupervised learning we can select a regularizer such that it is sufficiently smooth on unlabeled data as well. Keeping all previous discussion in mind, indeed a function that fits the training data and is respecting unlabeled data will probably perform better on future data. In the case of the Co-Training algorithm, we are looking for two functions  (1) ,  (2) ∈ F that minimize the regularized risk and agree on the unlabeled data at the same time.
Experimental Evaluation In this section we evaluate the performance of the methods presented in the previous section on two datasets. Experimental evaluation is organized in two parts: (a) on the public database IDOL2 in Section 4. 1 and (2) on our inhouse database IMMED in Section 4. 2, respectively. The former database is relatively simple and is expected to be annotated automatically with small error rate, whereas the latter database is recorded in a challenging environment and is a subject of study in the IMMED project. For each database, two experiment setups are created: (a) randomly sampled training images across all corpus and (b) more realistic video-versus-video setup. First experiment allows for the gradual increase of supervision which gives insights of place recognition performance for algorithms under study.
The second setup is more realistic and is aimed to validate every place recognition algorithm. On the IDOL2 database, we extensively assess the place recognition performance for each independent part of the proposed system. For instance, we validate the utility of multiple features, effect of temporal smoothing, unlabeled data, and different confidence measures. The IMMED database is used for validation purposes on which we evaluate all methods and summarize their performances. Datasets. The IDOL2 database is a publicly available corpus of video sequences designed to assess place recognition systems of mobile robots in indoor environment. The IMMED database represents a collection of video sequences recorded using a camera positioned on the shoulder of volunteers and capturing their activities during observation sessions in their home environment.
These sequences represent visual lifelogs, for which indexing by activities is required. This database presents a real challenge for imagebased place recognition algorithms, due to the high variability of the visual content and the unconstrained environment. The results and discussion related to these two datasets are presented in Sections 4. 1 and 4. 2, respectively. Visual Features. In this experimental section, we will use three types of visual features that have been used successfully in image recognition tasks: Bag of Visual Words (BOVWs)  [25] , Composed Receptive Field Histograms (CRFHs)  [26] , and Spatial Pyramid Histograms (SPHs)  [27]. In this work we used 1111 dimensional BOVW histograms which was shown to be sufficient for our application and feasible from the computation point of view.
Description of the Experimental Setup. The considered database consists of 12 video sequences recorded with the "minnie" robot (98 cm above ground) using a Canon VC-C4 camera at a frame rate of 5 fps. The effective resolution of the extracted images is 309 × 240 pixels. All video sequences were recorded in the same premises and depict 5 distinct rooms-"One-Person Office, " "Two-Person Office, " "Corridor, " "Kitchen, " and "Printer Area". Sample images depicting these 5 topological locations are shown in Figure  4. The annotation was performed using two annotation setups: random and video versus video. In both setup three image sets were considered: labeled training, validation set, and an unlabeled set. The unlabeled set is used as the test set for performance evaluation. The performance is evaluated using the accuracy metric, which is defined as the number of correctly classified test images divided by the total number of test images.
Random Sampling Setup. In the first setup, the database is divided into three sets by random sampling: training, validation, and testing. The percentage of training data with respect to the full corpus defines the supervision level. We consider 8 supervision levels ranging from 1% to 50%. The remaining images are split randomly in two halves and used, respectively, for validation and testing purposes. In order to account for the effects of random sampling, 10-fold sampling is made at each supervision level and the final result returned as the average accuracy measure. It is expected that global place recognition performance raises from mediocre performance at low supervision to its maximum at high supervision level. Video-versus-Video Setup. In the second setup, videos sequences are processed in pairs.
Utility of Multiple Features. We study the contribution of multiple features for the task of image-based place recognition on the IDOL2 database. We will present a complete summary of performances for baseline single feature methods compared to early and late fusion methods. These experiments were carried out using the random labeling setup only. The DAS Method. The DAS method leverages two visual feature classifier outputs and provides a weighted score sum in the output on which class decision can be made. In Figure  5 , the performance of DAS using SPH Level 3 and CRFH feature embeddings is shown as a function of fusion parameter  at different supervision levels. Interesting dynamics can be noticed for intermediary fusion values that suggest for feature complementarity. The fusion parameter  can be safely set to an intermediary value such as 0.
5 and the final performance would exceed that of every single feature classifier alone at all supervision levels. The SVMDAS Method. In Figure  6 , the effect of the supervision level on the performances of classification is shown for single feature and multiple features approaches. It is clear that all methods perform better if more labeled data is supplied, which is an expected behavior. We can notice differences in the performances on the 3 single feature approaches, with SPH providing the best performances. Both SVMDAS (late fusion approach) and SimpleMKL (early fusion approach) operate fusion over the 3 single features considered. They outperform the single feature baseline methods. There is practically no difference between the two fusion methods on this dataset. Selection of the Late Fusion Method.
Utility of Unlabeled Data Motivation. The Co-Training algorithm belongs to a semisupervised learning algorithm grouped. Our goal is to assess its capacity to leverage unlabeled data in practice. First, we compare standard single feature SVM to a semisupervised SVM using graph smoothness assumption. Second, we study the proposed CO-DAS method. Third, we are interested to observe the evolution of performance if multiple Co-Training iterations are performed. Finally, we present a complete set of experiments on the IDOL2 database comparing single feature and multifeature baselines compared to the proposed semisupervised CO-DAS and CO-TA-DAS methods. Our primary interest is to show how a standard supervised SVM classifier compares to a state-of-the-art semisupervised Laplacian SVM classifier. Performance of both classifiers is shown in Figure  8.
The results show that semisupervised counterpart performs better if a sufficiently large initial labeled set of training patterns is given. The low performance at low supervision compared to standard supervised classifier can be explained by an improper parameter setting. Practical application of this method is limited since the total kernel matrix should be computed and stored in the memory of a computer, which scales as ( 2 ) with number of patterns. Computational time scales as ( 3 ), which is clearly prohibitive for medium and large sized datasets. Co-Training with One Iteration. The CO-DAS method proposed in this work avoids these issues and scales to much larger datasets due to the use of a linear kernel SVM. In Figure  9 , performance of the CO-DAS method is shown where we used only one Co-Training iteration.
Random Setup: Comparison of Global Performance Motivation. Random labeling setup represents the conditions with training patterns being scattered across the database. Randomly labeled images may simulate situation when some small portions of video are annotated in a frame by frame manner. In its extreme, few labeled images from every class may be labeled manually. In this context, we are interested in the performance of the single feature methods, early and late fusion methods, and the proposed semi-supervised CO-DAS and CO-TA-DAS methods. In order to simulate various supervision levels, the amount of labeled samples varies from a low (1%) to relatively high (50%) proportion of the database. The results depicting these two setups are presented in Figures  11(a ) and 11(b), respectively. The early fusion is performed using the MKL by attributing equal weights for both visual features.
Video versus Video: Comparison of Global Performance Motivation. Global performance of the methods may be overly optimistic if annotation is performed only in a random labeling setup. In practical applications a small bootstrap video or a short portion of a video can be annotated instead. We study in a more realistic setup the case with one video being used as training and the place recognition method evaluated on a different video. Confidence Measure. On this dataset, a good illustration concerning the amount of high confidence is showed in Figure  12. It is clear that only a portion of the test set data can be useful for classifier re-training. This is governed by two major factors-quality of the data and robustness of the confidence measure. For this dataset, the best portion of high confidence estimates is around 20-50% depending on the method.
Results on IMMED. Compared to the IDOL2 database, the IMMED database poses novel challenges. The difficulties arise from increased visual variability changing from location to location, class imbalance due to room visit irregularities, poor lighting conditions, missing or low quality training data, and the large amount of data to be processed. exceeds 10 hours. All recordings were performed using a portable GoPro video camera at a frame rate of 30 frames per second with the frame of the resolution of 1280 × 960 pixels. For practical reasons, we downsampled the frame rate to 5 frames per second. Sample images depicting the 6 topological locations are depicted in Figure  14. Most locations are represented with one short bootstrap sequence depicting briefly the available topological locations for which manual annotation is provided.
Conclusion In this work we have addressed the challenging problem of indoor place recognition from wearable video recordings. Our proposition was designed by combining several approaches, in order to deal with issues such as low supervision and large visual variability encountered in videos from a mobile camera. Their usefulness and complementarity were verified initially on a public video sequence database IDOL2 then applied to the more complex and larger scale corpus of videos collected for the IMMED project which contains realworld video lifelogs depicting actual activities of patients at home. The study revealed several elements that were useful for successful recognition in such video corpuses. First, the usage of multiple visual features was shown to improve the discrimination power in this context.
Second, the temporal continuity of a video is a strong additional cue, which improved the overall quality of indexing process in most cases. Third, real-world video recordings are rarely annotated manually to an extent where most visual variability present within a location is captured. Usage of semi-supervised learning algorithms exploiting labeled as well as unlabeled data helped to address this problem. The proposed system integrates all acquired knowledge in a framework which is computationally tractable, yet takes into account the various sources of information. We have addressed the fusion of multiple heterogeneous sources of information for place recognition from complex videos and demonstrated its utility on the challenging IMMED dataset recorded in real-world conditions. The main focus of this work was to leverage the unlabeled data thanks to a semi-supervised strategy.
M 3 DA-RF Based Tile-level Annotation Algorithm of Satellite Images The flowchart of the M 3 DA-RF based tile-level annotation algorithm is illustrated in Figure  4 , and the pseudocode of this algorithm is shown in Algorithm 1. Here, the visual words are obtained through several steps, which are tile partition, feature extraction, vector quantization, and K-means clustering, in that order. Tiles and visual words represent documents and words respectively in the topic models. The set of visual words (bag-of-word representation) is then used to represent an image regardless of their spatial arrangement similar to how documents can be represented as an unordered set of words in text analysis. The image of BOW representation is handled in two ways simultaneously: by using the visual word histogram to train a SVM classifier, we get the scene class label distribution (the so-called soft label probability) of each tile; by training the MedLDA, we can also obtain the soft label distribution of each tile.
Previous intraspecific comparisons between paralogous megasatellite-containing genes showed that megasatellite motifs are under purifying selection and that this selection is stronger in C. glabrata than in S. cerevisiae  (16). It was proposed that megasatellites propagate by three different mechanisms: (i) duplication of a megasatellite-containing gene, (ii) gene conversion between homologous sequences, and (iii) "jumping" of one or several motifs from one megasatellite-containing gene to another gene  (16). However, besides the intraspecific analyses carried out in S. cerevisiae and C. glabrata, very few studies in yeast or other fungal species are available to extensively characterize and compare megasatellite distribution. In order to do so, we developed a methodology, based on the Tandem Repeat Finder (TRF) program  (17, 18) , to analyze 21 Dikarya genomes covering a large phylogenetic spectrum (mostly Ascomycota).
Megasatellite detection. A database containing 142,121 annotated genes from 21 fungal genomes was built (Table  1 ), and the Tandem Repeat Finder (TRF) program  (17)  was used to extract all motifs from this set of genes, using the following parameters: match weight ϭ 2, mismatch penalty ϭ 7, insertion/deletion penalty ϭ 7, match probability ϭ 80, insertion/deletion probability ϭ 10, minimum alignment score to report ϭ 50, and maximum period size to report ϭ 2,000. For each genome, the fol-FIG 1 Distribution of megasatellites in the 21 genomes studied. Left, tree topology  (68, 69). Branch lengths are arbitrary. Motif families are represented by a color code. Motifs drawn on the tree indicate their proposed time of appearance during evolution, under a parsimony hypothesis. Right, protein clusters containing two or more proteins are represented by vertical columns.
0. Each nonunique peptide was assigned to a cluster denoted Mp. q (for motif clusters), with p the number of peptides contained and q an arbitrary index number  (38). Peptides included in each of the determined clusters were aligned using the ClustalW program  (39) , and conserved blocks were determined using the Gblock program  (40). Comparison and clustering of all proteins against themselves. All proteins, from which the tandemly repeated peptides were removed, were compared to each other, using blastp  (35). A blastp similarity score was considered significant when the corresponding E value was equal to or lower than 10 Ϫ9 , as previously described  (41). Nonunique proteins were clustered using the mcl  (37)  program with the same options as indicated above for the megasatellites and assigned to a cluster denoted Pp.
q (with p being the number of proteins contained and q an arbitrary index number). Motif consensus and structure. When three or more megasatellites were found in a given family (ALS, FLO, SHITT, SFFIT, WD, etc. ), motif consensus were determined by alignment of all the motifs using the Jalview program  (42). All motifs sequences are given in Table  S1  in the supplemental material. Subsequently, each of the motifs (or motif consensus) was analyzed using the metaserver MeDor  (43)  in order to determine whether any part of the motif was predicted to be disordered. The eight motifs that were predicted probably not to be disordered (see Results) were compared to the Protein Data Bank (PDB)  (44)  in order to determine if their structures were already known. In addition, megasatellite-containing proteins and peptidic motifs were also compared to the Conserved Domain Database (CDD) (45) version 3.
Structure prediction for megasatellite peptidic motifs. Primary sequences of peptidic motifs encoded by megasatellites are generally not conserved, despite the existence of families as described above. This, however, does not exclude the possibility that common secondary structures exist. To address this question, several secondary structure and disorder predictors were used on each motif (see Materials and Methods). Out of 97 different peptidic motifs analyzed, 88 show an extensive level of disorder (50 to 100% of the motif) and no obvious secondary structure (data not shown). The eight remaining motifs, showing lower levels of disorder, were compared to the Protein Data Bank (PDB), and seven of them (all threonine-poor motifs) were found to correspond to known secondary structures. Motifs M8 (in PODANSg6698 and AN8019), M10 (in AN3543, AN8085 and AO090166000058), M21 (in PODANSg8665), and M56 (in AO090102000421) all correspond to ankyrin motifs, a common repeat in eukaryotic proteins, but also found in bacteria and archaea (PDB ID 2L6B).
It is interesting to note that primary sequence similarity with the ANK motif described above in other megasatellites (Fig. 2 ) was not detected, but only the three-dimensional (3D) structure predicted that these four motifs should share the tertiary structure of ankyrin repeats. M69 (in ZYRO0G06028g) has a match in PDB with the structure of a carbohydrate epimerase from Pseudomonas aeruginosa (PDB ID 2IXI), hence linking this motif to carbohydrate metabolism  (48). Finally, M49 (in AO090009000369) corresponds to a putative L-allo-threonine aldolase from Listeria monocytogenes (PDB ID 3PJ0). In addition, each megasatellite peptidic motif was compared to the CDD  (45). Only eight significant hits were detected, corresponding to ankyrin repeats, WD repeats (twice), TPR repeats, the cohesin-HEAT domain (associated with chromosome cohesion and condensation), DUF3659 (a 70-amino-acid domain of unknown function found in bacteria and eukaryotes), a putative 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase domain, and a deoxyhypusine synthase domain essential for translation initiation in eukaryotes.