dataesr/scientific-paragraphs-categorization huggingface dataset:
We note that SOM and NG are originally designed for unsupervised learning and an adaptation to a supervised scenario is needed for comparability with the approaches which are in focus here. TOpology-Preserving knowledge InCrementer ( )  [71]  is a very recent such work which adapts NG to class incremental learning for visual datasets with focus on few-shot learning, with a method named , but experiments are also run for the standard scenario. First, introduces an NG network to learn feature space topologies for knowledge representation. The network grows to learn new classes while also dealing with changes in the feature space due to deep model update. This is achieved using a Min-Max loss that pushes new classes that share the same label to a new NG node, while pulling the new nodes of different labels away from each others.
However, a notable difference arises from the fact that new data are progressively incorporated. Learning without Forgetting ( )  [36]  is a pioneering work that does not require a memory of past classes. It leverages knowledge distillation  [27]  to minimize the discrepancy between representations of past classes from the previous and current IL states. first performs a warm-up step that freezes the past parameters and trains only the new ones and then jointly trains all network parameters until convergence. Incremental Classifier and Representation Learning ( )  [56]  is a popular IL method that combines the use of and of a memory for past class exemplars storage. Classification is performed with a nearest-mean-ofexemplars method instead of the raw scores predicted by the network. This external classifier is deployed to reduce the prediction bias in favor of new classes, which occurs due to data imbalance between past and new classes.
The memory is thus adjusted incrementally to match the data distribution in an effective way, leading mnemonic exemplars to yield separation between classes. In embedding systems, Semantic Drift Compensation ( )  [80]  was proposed to estimate the semantic drift of past knowledge while learning new knowledge to compensate for it, to further improve performance. The drift is computed at the class-meanembedding level, which means that this approach is based on NCM classifier that does not need exemplars storage, since the past class-mean embeddings are estimated using new data only. In  [68] , authors propose an approach that calibrates activation maps of the CNN in order to accommodate new knowledge. Calibration is done using spatial and channel-wise calibration modules, and only the calibration parameters are trained at each new incremental state.
Problem formalization We propose a formalization of class incremental learning which builds on those introduced in  [7, 8, 14, 54]. Given an initial non-incremental state S 0 , a model M 0 is trained from scratch on a dataset D 0 = {( 0 , 0 ); = 1, 2,. , 0 }. 0 and 0 are respectively the set of images and labels for the ℎ class in S 0 , 0 = 0 is the number of classes in the first non-incremental state. We note the total number of states, including the initial state and -1 incremental states. A new batch of new classes is streamed in each incremental state S and the objective is to learn a model M which recognizes = 0 + 1 +. + classes. This model is trained using the previous state model M -1 on a dataset D = {( , 0 ); = 1, 2,. , } ∪ K. Note that all data of the new classes are available with only a bounded exemplar subset K of data from the -1 = 0 + 1 +.
Datasets Four datasets designed for object, face, and landmark recognition are used here. The choice of significantly different tasks is important to study the adaptability and robustness of the tested methods. The main dataset statistics are provided in Table  3. • ILSVRC  [60]  is a subset of 1000 classes used in the challenges. It is constituted of leaves of the hierarchy which most often depicts specific visual concepts. • VGGFACE2  [13]  is designed for face recognition. We selected 1000 classes having the largest number of associated images. Face cropping is done with MTCNN  [83]  before further processing. • Google Landmarks  [46]  (LANDMARKS below) is built for landmark recognition, and we selected 1000 classes having the largest number of associated images. • CIFAR100  [34]  is designed for object recognition and includes 100 basic level classes  [57].
Implementation details A ResNet-18 architecture  [26]  with an SGD optimizer is used as a backbone for all the methods. [23], -  [24] ,  [75]  and  [28]  are run using the optimal parameters of the public implementations provided in the original papers. [56] is run using the code from  [28]  since it provides better performance than the original implementation. [36] is run using the code from  [56] , The SVMs in  [6]  are implemented using LinearSVC solver from Scikit-Learn toolbox  [51]. The SVMs were optimized using classical grid search as described in the original paper. and its derivatives are based on the same fine-tuning backbone and are implemented in Pytorch  [50]. Training images are processed using randomly resized 224 × 224 crops, horizontal flipping, and are normalized afterward.
Given the difference in scale and the number of images between CIFAR100 and the other datasets, we found that a different parametrization was needed for this dataset. Note that the parameters' values presented below are largely inspired by the original ones given in  [26]. For CIFAR100, the first non-incremental state and are run for 300 epochs with ℎ = 128, = 0. 9 and ℎ = 0. 0005. The is set to 0. 1 and is divided by 10 when the error plateaus for 60 consecutive epochs. The incremental states of and are trained for 70 epochs with ℎ = 128, = 0. 9 and ℎ = 0. 0005. The learning rate is set to = 0. 1∕ at the beginning of each incremental state S and is divided by 10 when the error plateaus for 15 consecutive epochs. For ILSVRC, VGGFACE2 and LANDMARKS, the first non-incremental state and are run for 120 epochs with ℎ = 256, = 0.
9 and ℎ = 0. 0001. The is set to 0. 1 and is divided by 10 when the error plateaus for 10 consecutive epochs. The incremental states of and are trained for 35 epochs with ℎ = 256, = 0. 9 and ℎ = 0. 0001. The learning rate is set to = 0. 1∕ at the beginning of each incremental state S and is divided by 10 when the error plateaus for 5 consecutive epochs. Dataset details and the code of all tested approaches and their adaptations are publicly available to facilitate reproducibility. 1.
Evaluation measures The main evaluation measure used here is the popular top-5 accuracy  [60]. Following  [14] , accuracy is averaged only for incremental states (i. e. , excluding the initial, nonincremental state), which is not of interest from an IL perspective. The sizes of the past memory K and the number of states are varied to evaluate the robustness of algorithms. Since a relatively large number of configurations is tested, it is convenient also to use a global measure. We use the incremental learning gap measure ( )  [8] , which computes the average performance gap between a classical learning and each IL configuration for each algorithm. is defined as: = 1 × ∑ =1 - - ( 11 ) where: is the number of tested configurations; is the top-5 score for each configuration; is the upperbound accuracy of the dataset ( accuracy); is the maximum theoretical value obtainable for the measure ( = 100 here).
5% and = 50. In this configuration, performance drops more significantly than that of competing methods. The loss is most striking for CIFAR100, the smallest of all datasets tested, where a performance of only 19. 6% is obtained compared to 50. 5% for |K| = 0. 5% and = 20. For fixed-representation methods, larger values of decrease performance because the size of the first non-incremental state becomes smaller. Consequently, the fixed representation obtained from this state has lower generalization power and is less transferable to later states. None of the methods is best in all configurations tested. On aggregate, the best results are obtained with ℎ , with a = -3. 62 points loss compared to , the classical learning upper-bound. The other methods with strong performance were all proposed recently: [8] ( = -3.
However, it is interesting to note that and have a low dependency on memory size, and their performance becomes competitive for |K| = 0. 5%. In this latter setting, fine-tuning based methods suffer more from catastrophic forgetting since memory becomes insufficient for an efficient replay of past classes. Globally, has a better behavior compared to , especially for large scale datasets, and this confirms that the optimization of an external classifier is easier than that of the classification layer of a deep model. performs better than and for large datasets and has a better global score ( = -6. 02 VS. = -7. 62 and = -6. 92 respectively). However, its performance drops significantly compared to when the number of states is increased from = 10 to = 20 and = 50. For.
The   [9]. This approach outperforms the other methods with a large margin. Overall, the best results are obtained with the fixedrepresentation methods because their dependence on past exemplars is much lower compared to fine-tuning based methods. In order, the best global score is obtained by , -, and. As we mentioned, is easier to optimize compared to and has a comparable accuracy variation with -for most tested configurations. However, provides the best performance when no memory is allowed. The performance of fixed-representation methods drops when the number of incremental states increases because the initial state includes a lower number of classes. This is notably the case for CIFAR100, the smallest dataset tested, where the fixed-representations have lower performance compared to all variants of for all tested values.
However, , , -and have consequently better performance for ILSVRC, VGGFACE2, and LANDMARKS where their initial representations are trained with at least 20 classes. The analysis of individual datasets shows that variants have a strong performance for CIFAR100, the smallest one among the four tested. scales worse than , which is better for the three larger datasets. The performance inversion is probably explained by the handling of inter-class separation in. This indicates that knowledge distillation alone does not scale well because when the number of past classes increases, the confusions between them hamper the performance of the method. Further analysis of this point is provided in Subsection 7. 4.
Role of knowledge distillation In  [26] , authors hypothesize that distillation is useful when the teacher model is trained with a large and balanced dataset. This is not the case in IL due to the fact that the dataset progressively includes knowledge about more classes and that there is an imbalance between past and new classes. In spite of this observation, knowledge distillation is commonly used to tackle catastrophic forgetting  [14, 25, 29, 32, 48, 54, 85]. Its use in IL with memory was encouraged by the experimental results presented in the influential paper  [54]. There, the original comparison between and was not fair since the first method is implemented without memory, while the second exploits a memory of the past. The results reported in Table  4  for vanilla and methods built on top of it challenge the assumption that distillation loss L is necessary in IL with memory.
These experiments show that is globally better since the score is over 2 points smaller than that of. is more effective for ILSVRC and VGGFACE2 datasets only for a small number of incremental states ( = 10) and the smallest memory (|K| = {1%, 0. 5%}). is a version of without distillation. The results from Table  4  show that the use of the NEM classification layer further improves performance compared to vanilla fine-tuning. When no memory is allowed for past classes, the experiments reported in Table  6  confirm those presented in  [56]. There, is clearly better than vanilla , and the usefulness of distillation is confirmed. Even without memory, the reuse of the weights of past classes from their initial states in is better than the sole use of knowledge distillation in  [36]. Only a more sophisticated scheme which combines distillation and an inter-class separation component in  [28]  outperforms , but stays way below + that does not need distillation.
Additional experiment We present a supplementary experiment which compares the performance of a very recent Neural Gas (NG) based approach to that of other methods. TOpology-Preserving knowledge InCrementer  [71]  relies on the NG network to preserve the feature space topology using a Hebbian learning  [42]. Two variants of are tested: (1) TOPIC-AL -uses an Anchor Loss to stabilize the NG network in order to preserve past knowledge and (2) TOPIC-AL-MML -uses an Anchor Loss and also a Min-Max Loss to control the network growth while adapting it to new knowledge. They are compared to , ℎ , , , , and. Note that we use a single dataset because the authors of  [71]  did not provide their complete code and, while we tried to reproduce their results independently, that was not possible. Following  [71] , we use IMAGENET100  [60]    do not belong to the first state.
If enough memory is available, and the number of incremental states is low, distillation based approaches become competitive. The choice of the method will thus depend on the computation and storage capacities but also on the expected characteristics of the data stream, which needs to be processed. When no memory is allowed, fixed-representation methods are globally much more competitive than fine-tuning ones while also being simpler and faster to deploy. They are particularly advantageous for large datasets, where distillation-based methods fail to scale-up. This finding is surprising insofar fixed-representation methods exploit a classical transfer learning scheme  [33, 53]. They do not update models across incremental states and were considered less apt for usage in IL without memory  [56].
The evaluation is done with four different datasets dedicated to distinct visual tasks. This setting can be reused and enriched to ensure a robust testing of class incremental algorithms. We will release the detailed implementations of all presented methods to facilitate reproducibility. The comparison presented here shows that recently proposed approaches reduce the performance gap between nonincremental and incremental learning processes. The analysis of existing algorithms proposed here highlights a series of open problems which could be investigated in the future. First, handling class IL as an imbalanced learning problem provides very interesting results with  [75]  or without  [8]  the use of a distillation component. Here, we introduced a competitive method where classification bias in favor of new classes is reduced by using prior class probabilities  [12].
It would be interesting to investigate more sophisticated bias reduction schemes to improve performance further. Second, a more in-depth investigation of why distillation fails to work for large scale datasets is needed. The empirical findings reported here should be complemented with a more theoretical analysis to improve its usefulness. Already, the addition of inter-class separation from  [28]  is promising. More powerful distillation formulations, such as the relational knowledge distillation  [49] , also hold promise. Third, the results obtained with herding based selection of exemplars are better compared to a random selection for all methods tested. Further work in this direction could followup on  [37]  and investigate in more depth which exemplar distribution is optimal for replay.
, a subset of 100 classes extracted from the ILSVRC dataset, where each class contains 500 training images and 100 test images. To start with a good data representation, the first model M 0 is trained on 0 = 60 initial classes. The remaining 40 classes are divided in 8 incremental states containing each = 5 new classes. The past classes' memory is set to |K >0 | = 400 + 4 × ( -0 ), where 400 images are divided equally between the first state classes, and 4 images per class are used for the classes that IL Method ℎ TOPIC-AL TOPIC-AL-MML Random 23. 0 32.
Setting We conducted a single-center study in the ED of the French Rouen University Hospital. This large ED (110,000 visits per year) is divided into one triage area, one critical care area, and six areas for non-critical care (medical or surgical), with two ambulatory pathways. VAS/NRS triage protocols have been implemented since 2015. VAS/NRS assessments are systematically performed in the triage area of our ED and compulsorily recorded in the electronic health records (M-UrQual software v. 7) of our hospital's information system (HEO software 8. 2; v 8. 2; Maincare Solutions, France). If the pain assessment is not recorded, the remaining medical records cannot be completed by the triage nurse. VAS/NRS assessment is compulsory even when patients do not spontaneously express pain. IV morphine titration is a daily practice recommended as an automatic analgesic modality (local guidelines), isolated or combined with other methods and/or drugs for each patient with severe pain identified at triage and admitted in the care areas, regardless of the time of day.
Moreover, in a one-step interview, the decisionmaking process leading to morphine protocol avoidance in the simulated condition was evaluated via self-assessment. Related reasons in the event of IV morphine avoidance in the simulated condition were recorded. At the end of the investigation, all data were collated and anonymized for analysis. Furthermore, we performed a subgroup analysis involving patients with a decision for protocol avoidance in both real and simulated prescription conditions. Overlapping reasons leading to the use of IV morphine-saving strategies were identified.
Data Collection and Evaluation Criteria Questionnaires included items related to situations considered as well-known factors of oligoanalgesia. Factors linked to morphine avoidance (already identified or presumably involved, according to current literature) were divided according to classical domains: patient characteristics, physician characteristics, and organizational components. Moreover, we met ED nurses several times to collect information to complete the study questionnaire with supplementary items. Four mixed panels of grade, age, gender, and responsibility level were constituted to ensure representativeness of the nurse population. The questionnaire comprised a 60-item grid to evaluate the real intention to perform a morphine protocol (Figure  2 ). The first step included 37 items from 6 patterns that explored various factors leading to a morphine-saving strategy; for example, one pattern was dedicated to the need for a re-assessment of pain, with the aim of confirming an initial high score at triage.
Subgroup Analysis We considered the 79 patients in the real and simulated prescription conditions who did not receive IV morphine. In 51. 9% of patients (41/79), we found one overlapping reason for IV morphine avoidance between physicians in both conditions. Detailed data are presented in Supplementary Materials Table  S1. The item "prioritization of another class of analgesic" was the most frequent same reason (20 times); in the other cases, the overlap was linked to pain-related patterns. Discordant decisions appeared more frequent in patients with traumatological presentations, but diverse reasons were provided. The titles of the patterns, provided in the interview grid, are in bold type. The thematic clusters that emerged from the interviews are in italics. NRS: Numeric Rating Scores, ED: Emergency Department.
Limitations The main limitation of this study was its single-center setting, with a weekday design, which might have introduced selection bias. The results might have been different in a non-teaching hospital that lacked pain experts. Nevertheless, our medical and paramedical staff is large and representative of ED caregivers. This study was conducted in a sample of a typical population eligible for IV morphine protocols in daily routine. With respect to our methodological choices, other limitations are worthy of discussion. First, our bedside approach focused on anonymized reasons for protocol avoidance, and the individual caregiver data were not exhaustive. Second, there was no quantitative evaluation of individual physician workload, but the global activity indexes of our ED show standard occupancy.
Acknowledgments We thank  David Hilditch  for discussions on the initial boundary value problem, and  Laurent Freidel ,  Marc Geiller ,  Roberto Oliveri ,  Daniele Pranzetti ,  Carlo Rovelli  and  Wolfgang Wieland  for discussions on surface charges. We are grateful to an anonymous referee for explaining to us how the boundary Legendre transform is necessary in order to obtain the correct match between covariant and canonical results with non-orthogonal corners. We thank  Michael Anderson  and  Zhongshan An  for useful feedback on our draft.
Cas12 Sensing Reactions Fresh Cas12 reactions were assembled by mixing reaction buffer (40 mM Tris-HCl, 60 mM NaCl, 6 mM MgCl2, pH 7. 3), 45 nM AsCas12a V3 (IDT), 45 nM crRNA (IDT), 1 µM ssDNA Fluorophore-Quencher (FQ) Probe (IDT), and lyoprotectant. Prior to addition of FQ Probe and lyoprotectant, the mixture was equilibrated at 37 °C for 15 minutes. Upon addition of the analyte, the reaction was incubated at 37 °C and fluorescence was measured (490 nm Excitation, 525 nm Emission) using a Synergy H1 microplate reader (BioTek, USA) and Gen5 v. 2. 09 (BioTek) software. Lyophilized Cas12 reactions were assembled as described above and then flash frozen with liquid nitrogen in PCR strip tubes (Thermo Scientific, AB2000) with a small hole melted in the cap of each tube. The reactions were lyophilized using a VirTis BenchTop Pro lyophilizer (SP Scientific) at ≤100 mtorr and -80 °C overnight or until fully freeze-dried.
Acknowledgement We would like to acknowledge MiniPCR for their contribution of materials and reagents. We would also like to thank  Sarah Bordenstein  and the Wolbachia Project for helpful conversation and for N. vitripennis specimens. We would also like to acknowledge the  Centennial High School DNA Club  for providing collaborative biology research opportunities for high school students. The authors would like to acknowledge members of the  Jewett Lab  for helpful discussions. M. C. J. acknowledges support from the  Department of Energy  Grants  DE-SC0018249 ,  DE-NA0003525 , and  8J-30009-0029A. the  DOE Joint Genome Institute ETOP program , the  Office of Energy Efficiency and Renewable Energy  Grant  DE-EE0008343 , the  David and Lucile Packard Foundation , the  Camille Dreyfus Teacher-Scholar Program , the  Defense Threat Reduction Agency  Grants  HDTRA1-15-10052/P00001  and  HDTRA-12-01-0004 , the  Army Research Office  Grants  W911NF-20-1-0195 ,  W911NF-18-1-0200 , and  W911NF-16-1-0372 , the  National Science Foundation  Grants  1936789  and  1844336 , and the  Air Force   Office for Research Grant   FA2386-21-1-4078 , Army Contracting Command Contract  W52P1J-21-9-3023.
Hospitalized patients receiving hematopoietic cell transplants provide a unique opportunity to study how the human gut microbiome changes in response to perturbations, and how the resulting changes in the microbiome feedback on its living host. We previously compiled a largescale longitudinal dataset of stool microbiome compositions from these patients and associated metadata 1. In that dataset the microbiome analysis was limited to the taxonomic composition of the bacterial population obtained from 16S rRNA gene sequencing. Here, we augment those data with shotgun metagenomic sequences from a nested subset of 395 stool samples. We provide accession numbers that link each sample to the paired-end sequencing files deposited in a public repository, which can be directly accessed by the online services of PATRIC 2 to be analyzed without the users having to download or transfer the files.