dataesr/scientific-paragraphs-categorization huggingface dataset:
Deep learning models are also included in some smartphone applications in this domain, e. g. , to help identify fish catch and inform about regulations in the catching area  [19, 20]. Yang et al. [1]  review deep learning approaches in the field of aquaculture. Among the advantages of deep learning with respect to expert systems with manually-engineered features, the authors emphasize the capability to automatically extract relevant image features. Moreover, they identify three future trends, namely (i) detailed fish classification, including fish diseases and misbehaviors, (ii) dataset availability, and (iii) advanced models combining deep learning with handcrafted features, and accounting for spatio-temporal sequences. Classical machine learning algorithms are employed in  [21]  to detect fish.
In order to overcome the limitations of stereo visual systems, the authors propose a two-phase processing scheme. First, they reduce the dimensionality of the dataset via, e. g. , principal component analysis (PCA), which results in a more distilled correspondence between pixel-level characteristics and fish shapes. After identifying the shape of a fish, the scheme typically proceeds by segmenting the head of the fish and by recognizing the remaining body objects with pixel or sub-pixel accuracy. Other efforts rely even stronger on convolutional neural processing. For example, the authors of  [22]  propose Fast R-CNN (for Regions with CNN), a CNN-based algorithm for fish identification in images. The algorithm proceeds by first generating candidate image segments, that are later combined into larger regions using a greedy algorithm.
These segments are finally processed to propose final candidate image regions that likely contain fish specimens. The authors report a mean average precision (mAP) of 81. 4% using a dataset of 24k fish images from twelve species. An end-to-end convolutional network is one of the two methods proposed in  [23]  to differentiate fish. The method is compared against a scheme built around the Histogram of Oriented Gradients (HoG) technique that extracts object features and inputs them to a classifier, such as a support vector machine (SVM). The automatic recognition of fish and underwater fauna in general helps underwater research in several ways. For instance, the Fish4Knowledge project  [24]  focuses on preprocessing underwater camera images, in order to extract information of value to biologists and oceanographers.
This helps reduce the otherwise very sizeable data that always-on underwater cameras would produce. Automatic image recognition in Fish4Knowledge is one way to automatically interpret images and store only significant data to be queried later, e. g. , to infer the behavior of a given fish species in the environment being monitored. The relatively more recent Fishial. ai project  [25]  also targets the automatic fish detection and classification using deep neural networks, with the ultimate objective to assist environmental conservation efforts. At its current stage, the project is calling for contribu-tions from anyone who can upload tagged fish images, in any environment, both under and above the water. Earlier works such as  [26]  employ denoising schemes and the mean shift algorithm to clean an image and split it into subregions.
Summary of Our Main Ideas For the reasons explained in Section 1. 1 and given the state of the art described in Section 1. 2, we focus our effort on the engineering and evaluation of small deep learning models, that preferably incorporate fewer than 20,000 parameters, or up to three orders of magnitude less than state-of-the-art deep learning models for image classification. This significantly reduces the complexity of the models, but at the same time it entails a number of challenges. First, the representation capacity of small models is limited. Second, any dataset collection issues resulting in a low-image quality or unbalanced dataset may compound and lead to poor classification performance. Therefore, different kinds of small models need to be tested, in order to single out the best neural architecture that still offers a workable accuracy while keeping complexity at bay.
Challenges Related to Datasets A considerable challenge, in our work, is to produce relevant datasets containing images of Symbiosis's target pelagic fish species. While many fish image and video repositories exist, most scenes are captured outside the water, and portray additional subjects (e. g. , a person fishing) or non-natural backgrounds (e. g. , the floor of a sailing ship or fishing boat). We chose to avoid such scenes, as they do not conform to the operational environment of the Symbiosis platform. Hence, we decided to use only observation photos and videos, showing the fish specimens swimming free underwater (see Figure  2 ). Unfortunately, there exists a limited number of such images and videos, since the target specimens usually live in comparatively deep waters. Multiple fish images can be extracted from each video: the drawback is that these images will typically show a few specimens, possibly grouped together, and repeated across several subsequent frames from the same video (see Figure  3 ).
Our Contributions and Main Results The main contributions of this paper are: • the engineering and evaluation of small neural network models that can be implemented with constrained embedded platforms; • a training method that works with small and unbalanced datasets, often taken from similar images extracted from videos; • a method that assists the decisions of the classifier through object tracking. With reference to Figure  1 , image processing in the Symbiosis platform has the objective to (i) automatically detect fish in a sequence of video frames and segment them, and (ii) to classify the fish segments and predict the species of the specimens therein. For both problems, we employ convolutional neural networks. The former problem, image detection and segmentation, takes place through a method independently developed, which uses a specifically trained instance of the neural network RetinaNET  [27, 28].
The final dense layer D y is either D 2 or D 6 , for binary and multi-class classification, respectively. We remark that we used a pre-trained version of VGG16, where we only re-trained the final layer of the network to match our dataset. All architectures discussed in the present paper are listed, together with their number of trainable parameters, in Table  1. The proposed architectures have between 10 and 20,000 parameters, with one exception of roughly 50,000 parameters. In all cases these numbers are considerably smaller than the 1. 6 million parameters of Chollet, the 3 to 8. 7 million parameters of MobileNetV3, the over 15 million parameters of VGG16, and the almost 70 million of RBB. In particular, we observe that the RBB network is three orders of magnitude larger than the networks we propose, and hence will not be considered as a practical option.
Image Dataset Creation Neural networks require a large set of training examples. This is especially true for the deep architectures employed for image processing and recognition. In the absence of a sufficiently rich dataset, the many trainable parameters (or weights) of the network converge to values that overfit the limited number of training examples provided, so that the model becomes unable to generalize to different, previously unseen images  [35]. Moreover, for classification problems, the training set should representing the different classes uniformly, so that the neural network does not "specialize" on a specific class while remaining unable to recognize the others. In Symbiosis, one of the most challenging tasks was precisely to compile a reasonably large and curated training dataset.
• Mediterranean horse mackerel (Trachurus mediterraneus). • Swordfish (Xiphias gladius). The Biology team of the Symbiosis project helped compile a first dataset containing raw data from multitude of private and public data sources. This included underwater videos and photographs of the above six species. Each item was manually revised and labeled as one of the six species, and those images and videos that included several different species were discarded in this phase to help dataset labeling. After extracting frames from the videos, we formed a first collection of training samples by segmenting the extracted frames and images through the algorithm in  [28]. This produced about 1. 5 million image segments, each containing a region-of-interest (ROI) with a potential fish. As the algorithm was still being optimized at the time, we proceeded by inspecting all images manually to discard those that did not contain a fish, or those where the size or quality of the fish image was too low, leaning itself to misinterpretation even by a human.
We employed the Symbiosis dataset for both classification tasks of interest in this work: binary classification (meaning that the algorithm should distinguish between our species of interest, grouped into two classes), and multi-species classification (or distinguishing among our six species of interest). For this purpose, we split the dataset into train, validation and test subsets, containing a distribution of (60%, 20%, 20%) of the dataset related to each species. It is worth noting that we kept segments from the same videos together in the same set during this process. This data organization further helps limit overfitting by avoiding that very similar images from the same video spread from the training set to the validation and test sets. The size of the Symbiosis dataset for each of the six Symbiosis species appears in Table  2.
After these pairs, comes a sequence of dense layers (denoted D x , x ∈ {8, 16, 32}), and a final classification layer (D y , y ∈ {2, 6}, see Figure  4 ). We remark that we leverage the pre-trained version of VGG16, and rather only re-train the final layer of the architecture using our particular dataset. When images are segmented from subsequent video frames, tracking becomes an option to help the classification task. Our tracking component exploits SORT, an opensource tracking algorithm that performs well in the presence of typical fish mobility  [36] [37] [38]. We did not build a third deep learning model for this purpose, in order to keep the computational complexity low, and thus facilitate the deployment of the algorithm on constrained embedded systems. The tracker receives image segment coordinates from every subsequent frame, that correspond to two opposite corners of each ROI.
Classification Results with and without Tracking To evaluate our neural network architectures, we have produced two versions of the Symbiosis dataset: in the first version, we group the six species of interest into two classes, one including all instances of Seriola dumerili and Coryphaena hippurus, and another class grouping the remaining species; in the second version, we keep the classes separate, such that each class represents a different species. We then employ the first dataset for binary classification, and the second for multi-class classification, respectively. We remark that the above binary classification experiment is slightly different from the focus of Symbiosis, which would require us to differentiate between the species of interest for Symbiosis and all other species. However, performing the above experiment allows us to (i) rely on a sufficiently balanced dataset; (ii) train and test our networks using data taken in a uniform environment; (iii) employ images validated by experts; and (iv) evaluate the impact of tracking on the quality of the classification.
Later on, in Section 3. 4, we will instead comment on the results of a proper binary classification experiment, that distinguishes Symbiosis species from other species depicted in independently collected photo datasets. The setup for our experiments is identical in both cases: segmented images are first sent to a classifier, in order to establish the fish species. A group of segmented images, along with their class, are combined via tracking, in order to determine if the segments represent the same object. In the presence of a positive tracking output, that ties multiple images to the same subject, we place classification results into a vector, and use one of three methods to finally identify the fish species: by taking the mean of all values (denoted as Mean), by taking the maximum value (Max) or by using a majority voting scheme (Voting).
Like for binary classification, tracking significantly improves accuracy. In order to compare these results with state-of-the-art mobile networks, we have run the multiclass classification with the MobileNetV3 networks  [32]. Interestingly, even with the large number of trainable parameters of these networks, the average accuracy obtained is roughly 25% (24. 56% with MobileNetV3 Large and 25. 01% with MobileNetV3 Small). This is much lower than the accuracy obtained with the other networks. It is important to point out that the VGG16 has been pre-trained, and can thus take advantage of previously-learned parameters. Furthermore, relatively small datasets can prevent large architectures from achieving their peak representational capacity, due to overfitting (potentially explaining the relatively poor performance of architectures such as Chollet).
• Symbiosis-NCC dataset: This dataset contains a subset of the segments in the Symbiosis dataset, in which images are guaranteed to have Normalized Cross-Correlation (NCC) below 50%. The NCC filter applied to the original Symbiosis dataset removes segments of the same fish extracted from the same video that are very similar. The number of images of each species in the Symbiosis-NCC dataset are shown in Table  5. The total number of images is 9262, all of our 6 species of interest.
• QUT+NOA dataset: 4372 images of QUT 2 (see also Figure  6 ) and 749 images of NOAA 3 , totaling 5121 images. These are collections of fish images, which do not include videos. They include mostly reef fishes, and do not include images of our 6 species of interest. With these datasets we have carried out 3 experiments, two of binary classification and one of multi-class classification.
• Multi-class-Mix: In the multi-class classification experiment the Symbiosis-NCC dataset is randomly split in exactly (80%, 10%, 10%) proportions, as shown in Table  7. Again, images form the same video can fall in different sets. Table  8  shows the accuracy of the 3 experiments. All experiments have large accuracy. As can be seen, the Binary-Mix experiment has slightly higher accuracy than that of Binary-No mix, which seems to indicate that training and testing with images from the same video indeed helps. This is also confirmed by the fact that accuracy of the experiment Multi-class-Mix is much higher than the one observed in the previous section, Table  4. One important remark that must be made here is that, while training and testing with images extracted from the same video may indeed be helpful to increase the accuracy of the models, care must be taken that the frames representing different examples are sufficiently different to prevent simple memorization.
In this work we applied a filter that provided an indication of the level of similarity between two frames, and discarded the ones that were not sufficiently distinguishable. Furthermore there is a risk that the model may learn other background objects that may appear in two images, and use them in the classification process. This problem is partially mitigated by the similarity filter and partially by the object detection and cropping performed prior to classification, a process that removes most of the surrounding background. It is worth mentioning that the accuracy observed with VGG and Chollet is not significantly higher than the one observed with the small architectures that are proposed in this paper. Yet, all experiments show significantly larger accuracy than those in Table  3. We believe that this is is due to the different setting of the QUT+NOA dataset, as well as to its limited size (about 1/2 of our dataset).
Again, the segments in different images did not correspond to the same artifact, so tracking did not help in the classification. The VGG binary classification applied by the deployed system to all the segments classified manually in both deployments decided that all of them belong to the six species of interest. By processing these segments offline after the deployment, we obtained the same result when performing the binary classification with the small custom architectures proposed here. The multi-class classification process has been carried out offline to the segments obtained in both deployments. Every architecture has been trained with the images of the Symbiosis dataset presented in Table  2 , but the training set used is the union of the training and test sets of Table  2 , while the validation set is the same.
Conclusions Underwater fish recognition via autonomous probes that rely on constrained embedded computers is a challenging tasks, due to limitations on computational power, memory availability, and image quality, among others. Considering the above constraints, in this work we engineered convolutional neural network models to yield architectures of lim-ited complexity and much fewer trainable parameters than complex object recognition architectures such as VGG16 or MobileNet. We showed that our solutions produce results comparable to such much larger and broadly used architectures, especially when aided by a computationally inexpensive object tracking algorithm that combines multiple classified images to yield a more accurate output. Our experience in this work suggests that the most challenging task is the acquisition and preparation of a dataset.
In general, the number of relevant images available for the classes of interest is limited, especially considering that the images should be captured under water. We compensated for the lack of images by extracting relevant segments from video frames, and mitigated the higher dataset similarity that results by placing nearly identical images only in one subset among training, validation, and test. The imbalance of the training data also posed a significant challenge. As seen in Table  2 , the number of images collected for the different species of interest in our work is very different. We argue that this is one possible reason causing a lower classification accuracy in our models: for binary classification, where the six classes have been split into two categories by maintaining a similar number of images per class, some of our architectures yield comparable results with respect to VGG16, which has two orders of magnitude more parameters.
Accuracy of the binary classification task with and without the tracking component. The Mean, Max and Voting columns refer to the type of decision scheme used when the tracking mechanism is applied. The dataset is divided into train, validation, and test subsets as shown in Table2, but with the species aggregated into two classes: (1) Seriola dumerili and Coryphaena hippurus, and (2) Thunnus alalunga, Xiphias gladius, Scomber scombrus and Trachurus mediterraneus. The results shown are the mean of 3 realizations.
Accuracy of the additional experiments: Binary-No mix is a binary classification experiment using the dataset QUT+NOA versus the Symbiosis-NCC dataset of Table5, with the split of Table6. Binary-Mix is a binary classification experiment using the dataset QUT+NOA versus the Symbiosis-NCC dataset of Table5, with a (80%, 10%, 10%) split. Multi-class-Mix is a multiclass classification experiment using the Symbiosis-NCC dataset of Table5, with the (80%, 10%, 10%) split of Table7.
Funding: This work has received support from the  European Union 's  Horizon 2020 Research and Innovation Programme  under grant agreement no. 773753  (SYMBIOSIS), and from the  Italian Ministry for University and Research (MIUR)  under the initiative " Departments of Excellence " (Law  232/2016 ). We also gratefully acknowledge the support of  NVIDIA Corporation  for the donation of the Jetson TX-2 board used to develop the real-time version of our algorithm.
DIPG-23. Artificial intelligence for detecting ACVR1 mutations in patients with DIPG using MRI and clinical data Fahad Khalid, Thibault Escobar, Jessica Goya-Outi, Vincent Frouin, Nathalie Boddaert, Jacques Grill, Frederique Frouin To cite this version: munoglobulin and mucin domain protein family. It is expressed on multiple immune cell types, including T cells, NK cells, myeloid populations, and microglia, regulating adaptive and innate immunity. In silico assessment of TIM-3 expression in DIPG datasets showed a robust expression of this gene. Single-cell sequencing analyses of DIPG biopsies uncover TIM-3 expression, especially in microglia. In vivo efficacy studies showed that treatment with AbTIM-3 significantly increased overall survival in two DIPG immunocompetent orthotopic models, led to long-term survivors (50%), and showed immune memory.
METHODS: This retrospective monocentric study includes 65 patients with known ACVR1 status. Patients were scanned at the diagnosis time with at least one of the four structural MRI modalities (pre-and post-contrast T1, T2, FLAIR) and basic clinical information (age and sex) was collected. Radiomic features were extracted within the tumor region from each modality. For each modality, a recursive feature elimination method was used to select the most relevant features. Inside a leave-one-out framework, up to five logistic regression models were built: one per MRI modality and one for the clinical information. The final prediction for each patient was computed as the mean of the probabilities of ACVR1 mutation for the up to 5 different models. Assigning a different weight to clinical data according to age, (more or less than 10 years old) was also tested.
RESEARCH METHODS In this study, the author uses the content analysis method as a tool to see the sustainability report at PT Unilever Indonesia Tbk. This study focuses on the sustainability report at PT Unilever Indonesia Tbk for the 2019-2021 period, focusing on the sustainability report indicators according to GRI. The type of data in this study uses secondary data. In this study, the authors used observational data sources and a literature study. Activities in data analysis are data reduction, data display, and conclusion drawing/verification.
Methods Overview The data used for analysis contained digital interactions of 14,643 students in three universities located in the North American, South-East Asian, and the Pacific regions. To describe the similarity of learner interactions in online course forums, the study took a network-based approach. We constructed networks for three separate student cohorts, based on the enrolment year. If student A posted a message into a discussion thread, and student B posted a message into the same discussion, both within a particular time frame, then these two students would be linked with a tie and treated as a pair of linked nodes in the interaction networks. To address RQ1, these interaction networks were examined descriptively using two different approaches. First, we analyzed the similarity of performance within each pair of interacting students.
Therefore, we could explain if social ties underpinning some of the interactions examined in RQ1 were formed through the process of social selection or of social influence (RQ2). A choice of 3 time periods for repeated interaction networks was due to empirical examinationnetworks with ties sustained in fewer than 2 time periods did not have enough overlap in students between them, required to fit a Siena model. Interaction data from 3 and 4 years was split into time periods for temporal modelling (time periods specified per university); but also, were analyzed as one entire networkwith unique number of students in such a network reported above. U1 contained only 3 years of data because most students were finished with their studies by then.