dataesr/scientific-paragraphs-categorization huggingface dataset:
S2 ). This use case demonstrates that deepG can successfully run on small sequences such as 150 nt, which enables direct alignment-free applications. While the model was trained here on fragments larger than sequencing reads, the context size of the model was set to 150 nt to allow for the inference process of FASTQ les. However, models might be trained directly from FASTQ les holding the read information. This use case showcases the ability of deepG to e ciently process small sequence data and apply it to real-world metagenomic screening scenarios. When comparing the performance of model-based contamination detection on a synthetic evaluation dataset, where 75% of the reads originate from E. coli and the remaining 25% of the reads originate from a human genome, the deepG model shows similar accuracy compared to the alignment-based read removal tools BMTagger 6 with 97.
58% vs. 97. 98% accuracy (non-signi cant differences on ten datasets according to a paired Wilcoxon test). However, compared to alignment-based methods, the deepG model generalizes better to other eukaryotes: When replacing the human contaminant with mouse reads, our methods successfully predict 98. 13% of the reads correctly, compared to 75. 25% of BMTagger (Fig. At the locus level, we trained a model that is capable of predicting CRISPR arrays, which are variablelength features present in non-coding parts of sequences. The CRISPR array identi cation problem is of interest, since it cannot be captured with models leveraging the pro le of a multiple sequence alignment using pro le Hidden Markov Models (pHMM)  7, 8  , and there are no clear conserved motifs, but there are conserved higher-order structures  9.
8. To demonstrate the application of deepG at the genome level, we predicted bacterial morphology from the genomic sequence. Here, we used labels provided by the BacDive database  12  on sporulation, which is of particular interest due to their potential epidemiological danger. With the library, we trained a model that predicts the ability to sporulate based on subsequences of length of 1M nt. We applied the model on full genomes (test data) unseen during training, with an average inference time of less than 7 seconds per genome. Since the aforementioned context size of 1M nt might be smaller than the size of the desired genome to be predicted, deepG runs multiple predictions over the genomic sequence (every 100,000 nt) and aggregate these to the nal prediction. On this task, deepG had a balanced accuracy of 97.
1% on the test set and incorrectly classi ed 15 genomes out of 512, while Traitar, a tool that infers sporulation and other phenotype properties using presence/absence information of gene families  13  , falsely classi ed 29 genomes (94. 3% balanced accuracy). The improved performance of deepG compared to established approaches like Traitar demonstrates the application of deepG on predicting bacterial sporulation from genomic sequence, due to deepG's comparably high accuracy and short prediction time. Our model was trained for around 10 days on a single data center GPU and trained on a different dataset than Traitar, which could also be responsible for the increase in accuracy. For demonstrating the application of deepG to full metagenomes, we trained a supervised model on the Chinese cohort from the colorectal cancer (CRC) study (128 metagenomes)  14.
The input data are the full metagenome samples of the study, with individuals grouped into CRC and healthy subjects. The AUC ROC of the resulting deepG model is 0. 82 -similar to the AUC ROC score reported by the authors of the original study, with 0. 81 and 0. 87 AUC ROC based on different analysis strategies. Therefore, deepG achieved a similar performance without the need for any alignment, taxonomic annotations, or functional annotations. Additionally, deepG is not limited to coding regions or identi cation of functional groups. Furthermore, deepG's ability to train on full metagenomes allows for a more comprehensive data analysis compared to methods relying on annotations or coding regions. To facilitate model training in scenarios where the sequence length is long, where one input sample might be a whole metagenomic sample, deepG can be run using a "set training"  15  regime, where no order is encoded within subsamples (contigs).
While most aforementioned applications describe the construction of a supervised binary classi er, where a model is trained from scratch with the goal to discriminate between two classes, deepG can also run in a multi-label setting -e. g. , for the prediction of multiple classes at a taxonomic rank or when metagenomes are grouped into more than two sets. Moreover, since deepG models are implemented with the Keras functional API, it supports custom and more advanced models -e. g. , when the user has access to additional information, such as clinical metadata, that can be used as input for the model. Besides supervised training, deepG also supports unsupervised training with support for additional training modes, such as Contrastive Predictive Coding  16  and Self-GenomeNet#. In this setting, a model is trained on unlabeled data and can later serve as a fundament for supervised tasks to increase performance and speed up model convergence, as this enables the e cient use of unlabeled datasets by e ciently capturing representations.
deepG also supports Tensorboard, a tool that allows users to track training runs and generate custom metrics, such as balanced accuracy (Fig. S3 ). While further tools for implementing DL models for genome sequence data are available (as reviewed in Alharbi et al. 17  , the python packages Janggu  18  and Selene 19 also apply DL models on genomic input), these tools are designed with a focus on applying DL on human genome data. deepG further implements data augmentation strategies to handle scenarios that arise when DL is applied to microbial data collections. Furthermore, we demonstrated deepG to be effective for bacterial and viral and mixed sequence origins of different species, from 150 nt read-level to (meta)genomes using set learning. To support the full taxonomic range and different sequence lengths, deepG comes with a range of data augmentation methods and training schemes that are required for such input types (Fig.
1C ) and makes DL training possible for non-human datasets. Another key feature of deepG is its support for both supervised and unsupervised models. This provides researchers the capability to use pre-trained models, e. g. , to improve supervised tasks to speed up training time and accuracy. It could also be used for clustering based on the neural representation which could be used as an alternative to classical clustering methods which may provide more accurate and robust results compared to traditional methods  20. The deepG software resource will enable many researchers to create and apply customized learning models when the classi cation problem is very speci c and other software tools are outdated or unavailable, or where classical machine learning tools reach their limitations, e. g.
Research on mechanisms of organic matter degradation, bacterial activities, phytoplankton dynamics, and other processes has led to the development of numerous sophisticated water quality models since one of the first in 1925, based on first order kinetics for organic matter degradation. The community-centered aquatic biogeochemistry model RIVE was initially developed in 1994 and has since been integrated into several software programs such as Seneque-Riverstrahler, pyNuts-Riverstrahler, PROSE/PROSE-PA and Barman. After 30 years of research, the use of different programming languages including Qbasic, Visual Basic, Fortran, ANSI C and Python, as well as parallel evolution and the addition of new formalisms, raise questions about their comparability. This paper presents a unified version of the RIVE model for the water column, including formalisms for bacterial communities (heterotrophic and nitrifying), primary producers, zooplankton, nutrients, inorganic carbon, and dissolved oxygen cycles.
The unified RIVE model is open source and implemented in Python 3 to create pyRIVE 1. 0, and in ANSI C to create C-RIVE 0. 32. The organic matter degradation module is validated by simulating batch experiments. The comparability of the pyRIVE 1. 0 and C-RIVE 0. 32 softwares is verified by modeling a river stretch case study, which considers the full biogeochemical cycles (microorganisms, nutrients, carbon, and oxygen) in the water column, as well as the effects of light and water temperature. The results show that the simulated concentrations of all state variables, including microorganisms and chemical species, are very similar for pyRIVE 1. 0 and C-RIVE 0. 32. This open-source project highly encourages contributions from the aquatic biogeochemistry community to further advance the project and achieve common objectives.
, 2017). Based on above implementations, different versions of the RIVE model code has simulated successfully a large variety of aquatic systems (lake, reservoirs, river systems) across the world with parameter values determined through laboratory experiments or calibrated with observation data  (Garnier et al. , 1992a; Servais and Garnier, 1993; Garnier and Billen, 1994; Billen et al. , 1994; Garnier et al. , 1995). These applications (Tab. A1) were carried out for different networks and scales as well as various degrees of anthropogenic impacts in a wide climatic gradient using either Riverstrahler (possibly with its Seneque or pyNuts environments) or PROSE/PROSE-PA, such as the Seine River (France)  (Billen et al. , 1994; Garnier et al. , 1995; Even et al. , 1998 Even et al. , , 2004 Even et al.
, 2009 (Thieu et al. , , 2010)) , the Loire River (France)  (Garnier et al. , 2018a) , the Lot River (France)  (Garnier et al. , 2018b)  and the Orgeval watershed (France)  (Flipo et al. , 2004 (Flipo et al. , , 2007;; Garnier et al. , 2014). Moreover, the RIVE model has been applied to the stagnant systems (sand-pit lake, reservoirs) also  (Garnier and Billen, 1994; Garnier et al. , 2000; Yan et al. , 2022a). After 30 years of research, the parallel evolutions of these codes, the numerical adaptations inherent in programming languages (Qbasic, Visual Basic, Fortran, Python and ANSI C) and the addition of new formalisms, raise the question of their comparability and the identification of a unified version of RIVE model. A project aiming at unifying these RIVE implementations to bring together all recent developments, especially the ones achieved with Python 3 and ANSI C programming languages, was undertaken to strengthen the collaboration of the research teams involved in the development of the model.
Inorganic carbon An inorganic carbon module is implemented in unified RIVE v1. 0. The carbonate system is described by a set of equations (named the CO 2 module) based on a previous representation provided by  Gypens et al. (2004)  and adapted for freshwater environments  (Marescaux et al. , 2020). In this module, four state variables are defined: dissolved inorganic carbon (DIC), total alkalinity (TA), acidity (pH) and aqueous carbon dioxide (CO 2 (aq)).
Digital implementation with )), respectively. A Jupyter Notebook is used for pedagogical exercises with pyRIVE 1. 0, while C-RIVE 0. 32 needs to be compiled with gcc under a Linux or a MAC OS operating system. In addition, the user interface of C-RIVE 0. 32 uses its own parser based on flex and bison, which allows the software to read ASCII files. In practice, the number of living species is predefined in pyRIVE 1. 0 while we have the ability to define as many species as desired in C-RIVE 0. 32 (Tab. 1). For instance, three communities of phytoplankton (DIA: Diatoms; GRA: Green algae; CYA: Cyanobacteria), two populations of heterotrophic bacteria distinct by their growth rate and size (small one SHB and large one LHB;  Garnier et al. (1992a) ) and two zooplankton communities (ZOR: Rotifer and ZOC: MicroCrustaceans;  Billen et al.
Simulation settings and evaluation strategy The concentrations of all water quality variables of inflow are defined as their initial concentrations in the stretch and remain constant during the simulation (Tab. 2). Since this paper focuses on the conceptualization of the unified RIVE v1. 0 in water column, no exchange between benthic layer and water column are considered. The time step of the simulation is 6 min and a simulation period of 365 days is considered. To compare the results of the two digital implementations of unified RIVE v1. 0, daily concentrations at 00:00 are plotted. Three statistical criteria (PBIAS: Percent Bias (%); MAD: Mean Absolute Difference; MaAD: Maximum Absolute Difference) are calculated to evaluate the similarity of the two set of results. The closer the criteria are to 0, the more similar are the concentrations simulated by the two softwares (pyRIVE 1.
0 and C-RIVE 0. 32). P BIAS = 100 i=N i=1 (C i -P y i ) i=N i=1 (59) M AD = i=N i=1 |C i -P y i | N (60) M aAD = max(|C i -P y i |) (61) Where C i represent the concentrations simulated by C-RIVE 0. 32 (in ANSI C) and P y i those simulated by pyRIVE 1. 0 (in Python 3). N is the number of values. DIA -0. 1 1. 07 •10 -3 3. 63 •10 -3 GRA -0. 1 4. 98 •10 -4 1. 87 •10 -3 CYA -1. 2 7. 26 •10 -3 3. 15 •10 -2 ZOR -0. 2 2. 26 •10 -4 2. 89 •10 -3 ZOC 0 5. 25 •10 -9 1. 69 •10 -7 SMS -0. 6 6. 65 •10 -4 4. 31 •10 -3 DOM1 0. 1 6. 54 •10 -5 3. 61 •10 -4 DOM2 -0. 1 3. 94 •10 -4 1. 34 •10 -3 DOM3 0 4. 06 •10 -4 2. 06 •10 -3 OXY 0 6. 78 •10 -3 3. 07 •10 -2 POM1 -0. 3 4. 68 •10 -4 2. 27 •10 -3 POM2 -0. 3 7. 80 •10 -4 3. 97 •10 -3 POM3 -0. 2 4. 06 •10 -4 2. 06 •10 -3 NH4 0 4. 66 •10 -5 5. 28 •10 -4 NO2 0 2. 13 •10 -5 3.
Model limitations and future developments of unified RIVE 550 Currently, the unified RIVE v1. 0 presented in this paper describes only the biogeochemical processes in water column. Comparison of benthic processes and simulations have not been investigated yet. Previous studies showed that sediment plays an important role on the metabolism of river  (Vilmin et al. , 2016)  and lakes  (Yan et al. , 2022b). A unified sediment module should be further elaborated, based on existing modules  (Even et al. , 2004; Flipo et al. , 2004; Thouvenot et al. , 2007; Billen et al. , 2015; Vilmin et al. , 2015b)  and implemented into unified RIVE. This sediment module will have to take into account not only the dissolved exchanges between the water column and the sediment but also the resuspension of particulates.
Code availability. The C-RIVE 0. 32 implements the unified RIVE v1. 0 in ANSI C. It is available under Eclipse Public License 2. 0 in the following Zenodo repository: https://doi. org/10. 5281/zenodo. 7849609; Wang et al. (2023b). pyRIVE 1. 0 implements the unified RIVE v1. 0 in Python 3 and is available under the Eclipse Public License 2. 0 in InDoRES repository: https://doi. org/10. 48579/PRO/Z9ACP1; Thieu et al. (2023).
V. SYSTEM HaptiComm comprises the user interface (receiver's side), a custom-made electronics driver, and software translating text or voice to tactile output (interpreter's side). Table  I  shows a detailed cost analysis of the HaptiComm. The overall system weighs 2. 5 kg and can be carried in a 20 × 25 × 30 cm 3 box. The following section highlights the main features of the system. A. User Interface 1) Hand rest: This hand rest has been empirically modelled and was designed to tolerate minor hand readjustments for comfort purposes during prolonged use. Most critically, is the  ability for the users to quickly release their hand from the device if needed, see Fig. 3a.
3) Minimization of Mechanical Crosstalk: The actuators generate mechanical noise when activated due to frictional contacts in the guidance mechanisms. This noise was within the tactile perceptual range and could pollute the intended signal. If not dampened, mechanical noise propagated in the rigid parts of the HaptiComm and affected the clarity of the messages sent by the device. To reduce mechanical crosstalk, the device was organized into two distinct sub-assemblies connected through elastomeric viscoelastic elements, see Fig. 3  and 3e. The first sub-assembly comprised the actuators, and the second served as the handrest. The noise was recorded while displaying the letters A and H to evaluate the effectiveness. The vibrations recorded on the hand rest were lower than 2% of the intensity recorded on the actuator sub-assembly (data recorded with a 3-axis accelerometer, ADXL335b).
C. Drive Electronics and Software A custom-made electronics drive was developed to control each actuator with a maximum signal of 4. 5 V full swing from a 5 V power rail, see Fig. 3f. A separate DAC channel drove each power amplifier by steps of 2 mV. The DAC (AD5383) featured 32 outputs and was driven at 30 MHz via an SPI bus connected to a microcomputer (Raspberry Pi 3), ensuring a refresh rate of at least 2 kHz per channel. The microcontroller ran software that received inputs from a keyboard and converted them to DAC commands. The software used a configuration file (audio waveform stored in wav. files), which made it possible to easily design new tactile symbols and link them to specific letters. The software, written in C++, was double threaded, separating the high priority, 30 MHz DAC driver from the keyboard-to-DAC-commands conversion.
ACKNOWLEDGEMENTS We thank  MES AP09058050  for their support, the  SMART project of Sorbonne Université , and the  School of Engineering and Digital Sciences, Nazarbayev University. We are thankful to  Sarah McIntyre ,  Florian Richer ,  William Frier ,  Thomas Daunizeau ,  Cyril Bounakoff ,  Mia Cirrincione , and  Nurlan Kabdyshev  for their help during this project.
The seasonal evolution of snow cover has significant impacts on the hydrological cycle and microclimate in mountainous regions. However, snow processes also play a crucial role in triggering alpine mass movements and flooding, posing risks to people and infrastructure. To mitigate these risks, many countries use operational forecast systems for snow distribution and melt. This paper presents the Swiss Operational Snow-hydrological (OSHD) model system, developed to provide daily analysis and forecasts on snow cover dynamics throughout Switzerland. The OSHD system is a sophisticated snow hydrological model designed specifically for the high-alpine terrain of the Swiss Alps. It leverages exceptional station data and high-resolution meteorological forcing data, as well as various reanalysis products to combine snow modeling with advanced data assimilation and meteorological downscaling methods.
The system offers models of varying complexity, each tailored to specific modeling strategies and applications. For snowmelt runoff forecasting, monitoring snow water resources, and research-grade purposes, the OSHD system employs physics-based modeling chains. For snow climatological assessments, a conceptual model chain is available. We are pleased to present two comprehensive datasets from the conceptual and physics-based models that cover the entirety of Switzerland. The first dataset comprises a snow water equivalent climatology spanning 1998-2022, with a spatial resolution of 1 km. The second dataset includes snow distribution and snow melt data spanning 2016-2022 at a high spatial resolution of 250 m. To meet the needs of a multipurpose snow hydrological model framework, the OSHD system employs various strategies for process representation and sub-grid parameterizations at the snowcanopy-atmosphere interface, particularly in complex terrain.
The operational snow-hydrological model framework The Swiss Operational Snow-Hydrological Service (OSHD) was originally established to meet the demand for Swiss-wide daily forecasts on snow distribution and snow melt, which are essential for national flood forecasting in Switzerland. In addition, the OSHD has been at the forefront of model development, and its framework and resulting datasets have been used for various research applications. Overall, the OSHD has become an invaluable resource for flood forecasting in Switzerland and has contributed significantly to scientific knowledge in the field of snow hydrology. The development of a snow hydrological model system in Switzerland was made possible by several key factors, including the high-alpine terrain of the Swiss Alps, the availability of exceptional station data, especially the high density of highquality snow stations at mid to high elevations, and access to high-resolution meteorological input data from the Swiss regional weather model and various reanalysis products provided by the Federal Office of Meteorology and Climatology MeteoSwiss.
Based on these prerequisites a OSHD modular model framework was developed that combines snow modeling with data assimilation techniques and downscaling methods. This section will provide a comprehensive overview of the OSHD model framework, including its individual aspects. We will begin by introducing the physics-based OSHD model chain (Section 2. 1), which represents the benchmark for this publication and has undergone several years of development to reach its latest stage. Next, we will provide detailed information on the representation and parameterizations for snowpack processes (Section 2. 1. 1), snow cover fraction (Section 2. 1. 2), and forest snow processes (Section 2. 1. 3). Additionally, we will describe the climatological model chain (Section 2. 2) in detail to provide a comprehensive understanding of the OSHD model framework.
The physics-based OSHD model chain for seasonal snow cover modeling The physics-based FSM2oshd model (https://github. com/oshdslf/FSM2oshd) solves the complete mass and energy balance of the snowpack for open and forested areas  (Mazzotti et al. , 2020a; Mazzotti et al. , 2020b)  at an hourly temporal resolution (Figure  1, FSM2oshd ). FSM2oshd was originally based on the FSM2 snow cover model (Flexible snow model, https://github. com/RichardEssery/FSM2), with adapted snowpack process parameterizations and new model components for snow cover fraction  (Helbig et al. , 2021a)  and for forest processes  (Mazzotti et al. , 2021; Mazzotti et al. , 2023; Webster et al. , 2023). In its operational setting the physics based model chain is run at a horizontal resolution of 250 m, but is also applied at higher resolutions (up to 25 m) for research purposes.
The current physics-based OSHD model chain was initiated by  Magnusson et al. (2015)  who built a Swiss snow model framework suitable for predicting daily SWE and snowmelt using the first multi-model framework of physically based energy-balance models, JULES Investigation model (JIM), from  Essery et al. (2013). The spatialized version of JIM was set up to run with downscaled General OSHD model framework including data assimilation (orange boxes) for solid precipitation (P_solid), snow depth (HS) and air temperature (TA) and meteorological input from regional weather and nowcasting models such as relative humidity (RH), short-and longwave radiation (SWR, LWR), precipitation and wind. Input sources are shown for the physics-based multi-layer snow model (FSM2oshd, FSM2-HN) combined with Bias-Detecting Ensemble (BDE) and the conceptual models based on temperature index models (TICL, ETI) and the Ensemble Kalman filter (EnKF).
To allow for an optimal transition between hindcast and forecast data, precipitation is obtained from the nowcasting product INCA (Integrated Nowcasting through Comprehensive Analysis)  (Meteoswiss, 2020)  to drive the FSM2oshd-HN model. All other meteorological variables are forced by the COSMO analysis or forecast models. Within the operational model framework, the HN model is updated whenever new forecast and analysis data from COSMO becomes available. The physics-based OSHD model chain has a wide range of applications, including operational use as well as in various research projects. These projects aim to study high-resolution snow melt processes in alpine terrain  (Griessinger et al. , 2019)  and forested areas  (Mazzotti et al. , 2023) , and to project future snow water resources and rain-on-snow events (e.