dataesr/scientific-paragraphs-categorization huggingface dataset:
The first category uses data generated by the user to model and recognize the activity, and it is based on machine learning and data mining techniques. The second category uses rule-based tests, prior knowledge of the domain, and logical reasoning by an expert. KDMs are based on the observation that most activities and routines of work and daily life take place in a relatively circumscribed place and time. References  [17, 18]  proposed context and activity ontologies for explicit domain modeling. The limits of the proposed technique include the requirement of a complete knowledge of the domain to elaborate an activity model, and its weakness in handling uncertainty and adaptability to new settings. Bayesian network (BN) is a method to predict the activities of occupants based both on dataset and on expert knowledge.
The DDM approaches for human activity recognition require labeled data on which the learning algorithm is trained. After the training step, the algorithm is then able to classify the unknown data. The DDA's strength is its probabilistic modeling capacity. These models are able to handle noisy, uncertain, and incomplete sensor data. They can also capture domain heuristics, e. g. , some activities are more likely than others. They do not require a predefined domain knowledge. However, they require much data, and producing quality training datasets is expensive to meet mandatory criteria: data of a significant size, in a usable form, the need for tools and manpower (qualified and rigorous) to label data, and control steps to avoid bias and errors in the dataset. In the literature, there is an important number of learning techniques used for recognizing human activities, such as support vector machines (SVM)  [20] , hidden Markov models (HMM)  [21, 22] , naive Bayes  [23] , dynamic Bayes nets  [21] , and nearest neighbor  [24].
However, this work is limited to binary sensor use and there is a need to test the efficiency of this method on other types of sensors. Reference  [29]  proposed an HAR framework using a CNN architecture bootstrapped by a location-based stigmergy encoding technique for the emergent representation of daily activities. In  [30, 31] , a CNN-LSTM architecture is investigated. It is a temporally and spatially deep architecture that ameliorates the performance of prediction and diminishes the complexity of the model. With these techniques, the features are automatically learned and a high level representation can be extracted in the deep layer. In this work, we extend the works mentioned above and employ a CNN2D deep learning architecture to accomplish the objective of real-time HAR. Most models of deep learning use only pre-segmented datasets.
Real-Time Human Activity Recognition Framework This section describes the real-time HAR framework that integrates three steps: (1) Real-time dynamic segmentation method that determines the beginning and the end of each activity segment; (2) encoding; and (3) classification using a neural network architecture (CNN2D). These steps are preceded by a data pre-processing step. The whole process comprises two phases (offline and real-time phases). In the offline phase, the data are cleaned (i. e. , delete duplicated data, correct the order of sensor activation traces which are not correctly temporarily ordered in the dataset) and sampled. The dynamic segmentation and the encoding step are the real-time phases, and the objectives of these steps are to establish the corresponding sliding window when a new sensor event occurs, and to encode this segment, respectively.
These steps are followed by a multi-class recognition model, which classifies the ongoing activity. For each incoming event, the correlation between this event and the last event is calculated. If they are strongly correlated then they belong to the same segment, otherwise the last event is the start of a new segment. Once the beginning and end of each segment are determined, the segment is transformed to a representative trajectory of activity. This trajectory is calculated using a DWN which returns a matrix that describes the spatial-temporal traces of the occupant and that will be considered as input of a classifier which classifies the activity. The architecture of the proposed framework is shown in Figure  1. Firstly, raw data from sensors are cleaned and sampled and then split using a real-time dynamic windowing (Section 3.
Encoding In the previous section, we explained that the extraction of features is an important step for a human activity recognition system. From these features, a classifier attempts to identify patterns in order to associate the correct activity label. Therefore, human activity recognition can be reduced to a pattern recognition and classification problem. However, thanks to deep learning, in addition to patterns, these models are able to automatically extract features and finally perform the task of classification. Deep learning allows to consider models able to extract the features from raw data and to perform the classification task at the same time. CNN models demonstrate potential in automatic feature extraction, pattern recognition, and classification. The CNN1D is used in many research works for real-time HAR and there is a lot of feedback on its classification capacity.
• Anomaly detection and cleaning-after a detailed analysis of the existing public datasets, we noticed that they can contain anomalies. In fact, they may contain duplicated data, full or partial days. Some rows in the file contains character errors, e. g. , "Oc" instead of "ON" value. Also, sensor activation traces in the dataset are not correctly ordered temporarily, i. e. , within the time series, events with a later timestamp may be recorded before events with an earlier timestamp. In the dataset, activities are labeled with a "begin" or "end" keyword to set the beginning and the end of activity. However, activities can be nested (i. e. , begin1 begin2 end2 end1). Also, it appears that they are interleaved (i. e. , begin1 begin2 end1 end2). According to these observations, it is therefore necessary to take into account these particular cases when pre-segmenting the dataset into sequences of activities.
2. In the case of the Aruba dataset, only two activities (Meal Preparation and Wash Dishes) can overlap because they are realized in the same zone, which is the kitchen. The different tests carried out in this step do the following: 1. Calculate the representative matrix of each segment of activity for different values of parameter ρ using the concept of DWN. M 1 and M 2 represent, respectively, two matrices of two segments of activities "Meal Preparation" and "Wash Dishes" for ρ = 0. 02. The periods of these segments, respectively, are the following: • Period 1: 4th November 2010 from 08: 11: 00 to 08: 27: 02 for meal preparation activity. • Period 2: 4th November 2010 from 10: 03: 21 to 10: 04: 25 for wash dishes activity. M 1 =                  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2.
3. Choose the value of ρ that maximizes the distance between two matrices. This value is calculated using Equation (3): ρ = ArgMax ρ i (d(M1, M2)) (3) Table  3  summarizes the Euclidean distance between the representative matrix for meal preparation activity and the representative matrix of wash dishes activity for different values of ρ. To conclude, the optimal value of ρ is 0. 02. This value is adopted to calculate the DWN for all activities in the dataset. The third step of the considered methodology is the classification using a classifier. We use the CNN2D architecture to learn the activity classification model because it is adequate to the encoded segments of activities. A period of data from the year 2010 is used. Of this data, 70% is used for training the algorithm and 30% is used for testing.
2. Method 2: Sensor event based windowing. This method is also investigated in  [34]. In this method, each event contributes equally in the vector of features. The resulting accuracy of classification using this method is equal to 0. 775 3. Method 3: Sensor window-time window. This method is also investigated in  [34]. This method used a time-based weighting factor to calculate the contributions of each event regarding the feature vector. In this technique, there are an equal number of events in each window. The resulting accuracy of classification using this method is 0. 78. 4. Method 4: Sensor window-mutual information (SWMI). This method is also investigated in  [34]. It assumes that the size of windows is constant and each window contains an equal number of sensor events. The extraction of features is based on the concept of mutual information, which is defined as the probability of the fact that these two sensor events appear sequentially in the entire dataset.
5. Method 5: Incremental SVM algorithms. This method is investigated in  [37]. Two new methods for feature extraction that can be used for incremental learning HAR task are proposed. • Modified dependency sensor (MDS) feature extraction method. The dependency between two sensors depends on the triggering order of sensors in the dataset. It is computed by calculating their frequency of occurrence within an interval of n sensor events along the entire data sequence (instead of tracking, consecutive occurrences only when a specific activity runs). • Last-state (LS) sensor-based method. Considering the fact that segments of activities can contain sensors with active and inactive status (ON and OFF), sensors with high sensitivity can be triggered even if the occupant is not in the zone of these sensors.
The second step is to design an encoder architecture for CNN for HAR in smart home. The encoder architecture adopts the DWN that draws the human trajectory tracks. The technique is inspired by the algorithms of ants. The DWN is calculated under constraint of non overlapping activities. The resulting output of the DWN is a matrix that serves in future works as input for the CNN. The third step is classification using a CNN2D classifier architecture. The methodology was validated on Aruba dataset. The performance of the proposed approach is demonstrated to be better than that of existing methods in comparative experiments with an accuracy of 81. 6%. Consequently, the effectiveness of the proposed online activity recognition method integrating the dynamic segmentation and emergent modeling is proven.
In addition, experiments were conducted to measure the computation time of our proposed framework (segmentation, encoding, and classification with a CNN2D architecture) and the computation time using Liciotti-based LSTM classification algorithm  [38]  under the same conditions (i. e. , same dataset, experiment on the same benchmark with the same characteristics). In the first case, the calculation time is equal to 23. 48 s and it is of the order of 1 min for the second case. Therefore, the proposed framework is more efficient in terms of computation time, with a performance of 39. 13%. This makes us think that the implementation of an embedded system can be as efficient in terms of computation time. Future works will address the following: • Testing the efficiency of this method on different hardware architectures considering the cost of each solution.
Protein repeats at the DNA level Repeats at the DNA level are very common. A recent study puts the fraction of the repetitive human genome at 53. 9% of the genome sequence  (Hoyt et al. , 2022). Initially thought of as "junk", repetitive DNA regions have been recognized as important sites for innovation in genomes  (Makałowski, 2000) , carrying out various important structural and functional roles  (Shapiro and von Sternberg, 2005). Repetitive DNA can be further distinguished into tandem and interspersed repeats, with many sub-classes captured in the manually curated Dfam database  (Storer et al. , 2021). Protein repeats are not necessarily translated from repeats at the DNA level. The degeneracy of the genetic code implies that even the most simple repeats, homorepeats (stretches of consecutive repetitions of a single amino acid  (Jorda and Kajava, 2010) , also known as polyX  (Mier et al.
, 2017) ), are not always codified by pure codon repeats. However, in most cases, the DNA replication slippage mechanism, causative of the generation of homorepeats, produces DNA repeats that simply translate to homorepeat sequences  (Jorda and Kajava, 2010). Examples of this dichotomy are found in two adjacent homorepeats in human protein MEF2A (UniProtKB acc. :Q02078): polyQ (positions 420-430) is translated from DNA repeat [CAG]11, while polyP (positions 431-435) is translated from [CCG]2[CCA]2[CCG]1 (GenBank acc. : X68505. 1). Synonymous point mutations within DNA repeats translating to homorepeats have been described to be evolutionarily selected in some cases to hamper the DNA slippage mechanism and to maintain a controlled non-toxic length  (Rolfsmeier and Lahue, 2000; Mier and Andrade-Navarro, 2018).
Sequence complexity At the amino acid level, regions with tandem repeats can be formed by sequences of very different complexity. Sequence complexity is defined as the divergence of the residues forming a sequence or region from the expected background in a complete dataset in terms of usage and periodicity. There is a full range of examples from low to high complexity, from homorepeats to globular proteins. The shorter the repeats the lower the complexity of the resulting sequence. Repeats of length one exemplify this perfectly. Homorepeats (polyX) represent the sequence with the lowest possible complexity. Repeats of short lengths bias the sequence as well because the repetition of the pattern only allows for a few amino acids. Only when the repeat length is large enough to accommodate a rich composition of amino acids can repeats eventually be formed by complex sequences.
Repeats with high complexity assemble to form entire domains, such as the beta-barrel structure in which protein CymA (UniProtKB:Q48391) folds into (PDB code: 4D51, van den  Berg et al. , 2015) , or the betapropeller formed by WD repeats in the bovine protein GNB1 (Uni-ProtKB acc. :P62871), (PDB code: 1A0R;  Loew et al. , 1998). In these two examples, each of the repeats is long enough (about 40 amino acids), so they can have a rich amino acid composition and gain beta structure.
Analysis of the human proteome in Swiss-Prot reveals that structured and unstructured repeat proteins are different in terms of repeat unit length and the number of repetitions. Unstructured repeat proteins, detected by overlapping the repeat annotation by Swiss-Prot and the disorder prediction from MobiDB-lite  (Necci et al. , 2020) , mostly feature units of very short length and are repeated several times. The length of disordered repeat units varies considerably, ranging from less than ten amino acids to more than 60. STRiPs instead, identified in the human Swiss-Prot proteome from the repeat structures database RepeatsDB  (Paladin et al. , 2021) , usually have longer (between 20 and 60 residues long) and the number of repetitions corresponding to the different length does not vary as much as for disordered repeats.
The protein consists of one long extracellular region rich in Thr and Ser, which is heavily glycosylated, a single transmembrane domain and an intracellular domain. The extracellular region contains 16 SEA (Pfam ID: PF01390) domains linked by low complexity/disordered regions. Another protein with repeated low complexity regions is the Huntingtin (HD_HUMAN) protein  (Martin et al. , 2014). This protein has one 21 residue long polyQ region at the N-termini, and two shorter polyP regions. In addition, it contains more than 30 HEAT tandem repeats. The structure of this protein has been solved in complex with HAP1  (Guo et al. , 2018). A repeat protein where the lines between structure and disorder are blurry is nebulin  (Björklund et al. , 2010; Yuen and Ottenheijm, 2020). Nebulin has a very interesting repeat structure, where seven domains, often covering four exons, are repeated in tandem  (Björklund et al.
Insertions The modularity and symmetry of tandem repeat structures can be sometimes interrupted by non-repetitive segments. These segments are commonly referred to as "insertions", and can be found either inside or in-between units. Structurally, insertions serve no purpose in maintaining the stability of the repeat fold, but instead, they might provide the ability to perform specific functions, such as binding. For example, some alpha integrins have a von Willebrand factor type A domain inserted in their propeller regions, which contains a metal-iondependent adhesion site (MIDAS)  (Takada et al. , 2007). Another example of functional insertions inside a repeat unit is the insertion of a helix or a bigger domain in beta-barrels, which regulate their opening and closing, like in the case of Retinol-binding protein 2 (P50120, PDB code: 4ZGU)  (Chen et al.
, 1998)  or the bacterial porin oprP (P05695, PDB code: 2O4V) (Fig. 2 ). Smaller insertions, like those composed of longer loops, can also be functional. For example, it has been proposed that the longer loops present in the beta-helix protein Pertactin of Bordetella pertussis can help in immune evasion by epitope masking  (Hijnen et al. , 2007).
Repeat perfection Proteins that are able to fold and have the same amino acid sequence usually adopt the same 3D structure. Similarly, if a protein has an array of perfect tandem repeats in its sequence, i. e. every repeat has exactly the same sequence, each repetitive unit usually has the same 3D structure (examples, PDB code: 1NA0 designed TPR structure and PDB code: 3UGM TAL effector with HEAT-like repeats and PDB code: 4YCW, P. anserina TPR containing protein  (Marold et al. , 2015) ). Repeat perfection can be estimated by a parameter called P sim  (Jorda et al. , 2010) , in which P sim = 1 represents total perfection, and values of 0. 7 ≤ P sim < 1 represent nearly perfect repeats. However, protein tandem repeats are frequently not perfect, containing a number of mutations (substitutions, insertions, deletions) accumulated during evolution.
In reality, naturally occuring perfect tandem repeats seem to be very rare, with only a very low percentage of them having a P sim = 1  (Jorda et al. , 2010) , and most of which adopt disordered conformations. Most of the time, the decrease of the amino acid sequence similarity between repeat units leads to the increase of the difference in their 3D structure, but the conservation of specific amino acids highlights their importance for the maintenance of the repeat fold. (Example, PDB code: 1DAB, pertactin). However, an interesting case is the Pumilio family (PFAM Identifier: PF0806), also known as PUF repeats, composed of proteins that regulate translation and mRNA stability of eukaryotic organisms including mammals, flies, worms, slime mold, and yeast  (Zamore et al. , 1997). This is a good example of structure perfection and sequence degeneration.
The seven families where no reliable structure is predicted include two families with short motifs C_tripleX (PF02363) and Lipoprotein_15 (PF03640), (Baker model), two membrane-associated families SVM_repeat (PF13753), Ish1 (PF10281), two short families, Nebulin (PF00880), INT_rpt (PF14882) and one longer the WD repeat and coiled-coil-containing protein family (PF15390). Models for all these seven families are available from Pfam for single repeat units. As mentioned above many repeat proteins contain disorder, if these proteins have a structure in some physiologically relevant environments and if such structures can be predicted remains to be seen. Likely the latest progress by DeepMind will make these early attempts to model these repeat proteins outdated, once they are applied to repeat proteins en masse.
, 2015; Hallinan et al. , 2021; Brunette et al. , 2015). The improvements in structure prediction methods have the potential to greatly facilitate the design of tandem repeat proteins with desired properties. This could lead to more efficient design processes and the development of novel proteins for various applications in biotechnology, medicine, and research. In recent work, proteins with repeating units were designed to bind peptides with repeating sequences, and geometric hashing was used to identify compatible protein backbones and peptide-docking arrangements  (Wu et al. , 2023). Furthermore, the progress made in natural language processing has enabled the development of protein language models, with ProGPT2 being one example  (Ferruz et al. , 2022). These models have the capability to produce sequences that exhibit distant relationships to natural protein sequences and possess structures that resemble those found within the known structural space, even including non-idealized complex structures.
Improve repeat annotation As it has been mentioned throughout this work, repeat proteins present multiple dichotomies. These dichotomies represent a challenge when it comes to repeat annotation. RepeatsDB  (Paladin et al. , 2021)  is a structured tandem repeat proteins database that serves as an important source of both manually and automatically annotated experimental protein structures for the scientific community. The database provides the user with information about start and end of regions, units and insertions, as well as classification. Currently, computational methods to automatically detect STRPs lack accuracy, and their long execution times defy their application on big structural databases. Consequently, manual curation of repeat protein structures is a time consuming task, which requires human inspection of every structure, even those belonging to the same protein.
Acknowledgements The authors are grateful to  Lisanna Paladin  for initial help in the manuscript, and  Jiachen Lu  for helping in the manual curation of RepeatsDB. This work was supported by  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  823886  ( H2020 MSCA-RISE  " REFRACT ") and based upon work from  COST Action ML4NGP ,  CA21160 , supported by  COST (European Cooperation in Science and Technology).
Acknowledgments This work was supported by the  French Environment and Energy Management Agency (ADEME)  and the  University of French Polynesia. The authors would like to thank the  Pacific Beachcomber group  and especially its  CEO Richard Bailey , its manager  Bruno Chevallereau  and  Clément Capel  for allowing us to exploit data from The Brando SWAC installation. Its instrumentation was funded as a part of the  COPSWAC  project by the  ADEME  and the Polynesian government which we thank as well. The authors acknowledge the financial support given by  Region Reunion and European Regional Development Fund (FEDER)  under the  POE-FEDER 2014-2020  (n •  2020-2497-0027544 ) grant for the mobility of  Dr. Olivier MARC  in French Polynesia.
"Medications to treat obesity" or "antiobesity medications" is extremely different from "weight loss medications" In a simple Google search by June 2023, the term "weight loss drugs" leads to 2,200,000 results and "weight loss medications", to an extra 630,000 results. On the other hand, a search for "antiobesity medications", "antiobesity drugs", or "drugs (or medications) to treat obesity", leads to only 428,000 results, or 14% of the first search. "Obesity medications (or drugs)" leads to 170,000 extra results, but the term can be misleading. Of course, differences in interpretation exist between Why language matters when we talk about anti-obesity medications Arch Endocrinol Metab, 2023, v. 67(4), 1-8, e230174. terms in different languages, but this search is a good example of the most common terms used on a public database.