dataesr/scientific-paragraphs-categorization huggingface dataset:
By contrast, it significantly deteriorated the predictions for the human KCNH2 by ∆ρ = -0. 14 (Additional file 1: Figure  S6A , red outlier). The limited influence of metagenomics can also be observed when using JackHMMer as the search algorithm, as attested by the similar performance obtained for ProteinGym-MSA and ProteinNet (Table  1 ). Both protocols rely on JackHMMer as the search algorithm, but while ProteinGym-MSA considers only annotated sequences from UniRef100, ProteinNet searches against the UniParc archive, grouping several databases of annotated sequences, and the IMG environmental database. This expanding search results in alignments containing 3 times more sequences on average. However, we identified only a few human proteins, namely P53, BRCA1, SUMO1, and YAP1, as well as IF1 and CCDB from E.
For the remaining 30%, the gain brought by Pfam does not exceed ∆ρ max = 0. 077. Along this line, the yeast protein GAL4 gives an illustration of the importance of the extent of the sequence context (Figure  2C  and Additional file 1: Figure  S10 ). While the ProteinGym-MSA protocol could retrieve 16,159 sequences by querying the full-length query, the ProteinNet protocol retrieved only 249 sequences by querying a very small portion of the protein (6% that is 55 residues out of 881, PDB code: 1HBW). As a consequence, ProteinNet yielded a mutational landscape of a much poorer quality compared to ProteinGym, with a Spearman rank correlation of 0. 217 versus 0. 497 computed over the same residue range. Expanding on our assessment against the ProteinGym benchmark, we scaled the application of GEMME using ColabFold alignments to the entire human proteome.
Conclusion Multiple sequence alignments are critical to many protein-related questions. For instance, the last edition of the Critical Assessment of Structure Prediction (CASP, round 15) showed that MSA-based methods still significantly outperform protein language models in predicting protein 3D structures  (Rigden et al. , 2023; Elofsson, 2023). In this report, we assessed the influence of the search algorithm and the database choice for generating MSAs on the quality of in silico protein mutational landscapes. We ensured a clear readout of the input alignments using an unsupervised predictor relying on a few biologically meaningful parameters. The MMseqs2-based strategy implemented in ColabFold showed a good balance between prediction accuracy and computational time. It yields the best overall performance on a set of 87 DMS spanning a wide variety of proteins and covers protein regions lacking structural data or domain annotations.
In addition, we observed that extending the sequence search space to environmental datasets only marginally improves the accuracy of the predictions. Finally, we found that it is beneficial to make predictions with the knowledge of the full sequence context, rather than focusing on individual domains and concatenating the predictions afterwards. This result emphasises the importance of long-range inter-residue dependencies and suggests that deep learning methods are strongly limited by the maximal input sequence length, and thus context, they can handle. By establishing that fast MSA generation by MMseqs2 suffices, this study demonstrates the feasibility of MSA-based computational scans of entire proteomes at a very large scale. Combining ColabFold with GEMME, it takes only a few days to generate the complete single-mutational landscape of the human proteome on the supercomputer "MeSU" of Sorbonne University (64 CPUs from Intel Xeon E5-4650L processors, 910GB shared RAM memory).
We made our human proteome-scale predictions available to the community. Moreover, our findings imply ways to save resources for other MSA-based methods. database compiling several environmental sequence sets (Additional file 1: Table  S1 ). It maximises diversity while limiting the number of sequences through an expand-and-filter strategy. Specifically, it iteratively identifies representative hits, expand them with their cluster members, and filters the latter before adding them to the MSA. We used the same sequence queries as those defined in ProteinGym-MSA. For all but 5 proteins, the query corresponds to the full-length UniProt sequence. For each query, we generated two MSAs by searching against UniRef30 and ColabFold environmental database, respectively, and we then concatenated them.
In this work, we took the alignments provided with the ProteinGym benchmark  (Notin et al. , 2022). The ProteinNet protocol (AlQuraishi, 2019) also performs 5 iterations of JackHMMER, but it extends the sequence database to the whole UniProt Archive (Uniparc)  (Consortium et al. , 2018)  complemented with metagenomic sequences from IMG  (Nordberg et al. , 2014)  (Additional file 1: Table  S1 ). Another difference from ProteinGym-MSA is that the queries correspond to sequences extracted from experimentally determined protein structures available in the PDB  (Berman et al. , 2002). The MSAs are readily available and organised in a series of data sets, each one encompassing all proteins structurally characterised prior to different editions of the Critical Assessment of protein Structure Prediction (CASP)  (Kryshtafovych et al.
, 2021). We chose the most complete set, namely ProteinNet12. It covers all proteins whose structure was deposited in the PDB before 2016, the year of CASP round XII  (Moult et al. , 2018). For each protein from the ProteinGym benchmark, we retrieved the corresponding PDB codes from the Uniprot website (https://www. uniprot. org) and picked up the structure with the highest coverage among those represented in ProteinNet12 (Additional file 2: Table  S2 ). We could treat 42 proteins, out of 72 in total. For the remaining ones, the positions covered by the available MSAs were out of the range of mutated positions. The Pfam database  (Mistry et al. , 2021)  is a resource of manually curated protein domain families. Each family, sometimes referred to as a Pfam-A entry, is associated with a profile HMM built using a small number of representative sequences, and several MSAs.
We chose to work with the full UniProt alignment, obtained by searching the family-specific profile-HMM against UniProtKB (Additional file 1: Table  S1 ). The proteins sharing the same domain composition will have exactly the same MSAs. To avoid such redundancy, we focused on the non-redundant set of 59 proteins from ProteinGym. For each protein, we first retrieved its Pfam domain composition and downloaded the corresponding MSAs from the Pfam website (https://pfam. xfam. org, release 34. 0). We could retrieve at least one (and up to 5) MSA overlapping with the range of mutated positions for 39 proteins (Additional file 2: Table  S2 ). Each detected Pfam domain appears only once in the set.
We determine the evolutionary distance associated to the substitution of a into b at position i as the minimal conservation-weighted Hamming distance between the query wild-type sequence and any sequence from the input alignment displaying b at position i. Then, to be able to compare substitutions occurring at different positions, GEMME weights the predicted effects with the conservation levels. In the original GEMME publication  (Laine et al. , 2019) , we gave two sets of sequences as input to GEMME. We used the ProteinGym-MSA protocol to generate an input alignment and we compiled an additional set of input sequences using PSI-BLAST  (Altschul et al. , 1997)  against the NCBI's non-redundant (NR) database  (O'Leary et al. , 2015)  (Figure  1 ). GEMME used the later to estimate the conservation levels, and the former to computed amino acid frequencies and evolutionary distances.
Since then, we observed that the additional set of sequences had a limited impact on the performance (average ∆ρ = 0. 012 on the dataset reported  (Hopf et al. , 2017) ). Hence, in more recent studies  (Tsuboyama et al. , 2023; Mohseni Behbahani et al. , 2023) , we solely relied on an input alignment generated with the ProteinGym-MSA protocol. In the present work, for all calculations, we asked GEMME to exploit only a single input MSA generated by one of the four tested protocols and resources (see Additional file 1: Supplementary Methods for computational details).
Application to the human proteome We retrieved 20 586 protein identifiers and their amino acid sequences from the Swiss-Prot reviewed human proteome available in UniProt (uni, 2023), as of August 2023. We generated MSAs with the ColabFold protocol against UniRef30 v2302 and ColabFold Environmental Database v202108. We systematically regenerated the MSAs containing less than 200 sequences without the filter step. We modified the sequences that contained undefined residues ('X' or 'U' symbol) in the following way. When the undefined residue was located at the beginning of the sequence, the corresponding column in the alignment was always filled with gaps, and thus we removed that column. Otherwise, we replaced the undefined residue(s) by the most frequent amino acid appearing at the corresponding position(s) in the MSA.
We ran GEMME through the Docker image available at: https://hub. docker. com/r/elodielaine/gemme with default parameters. A subset of 102 sequences were too short (≤20 residues) to be considered as proteins and were thus not treated. Another subset of 145 proteins displayed MSAs too shallow for GEMME to estimate conservation levels. In total, GEMME generated mutational landscapes for 25 339 proteins.
DMS benchmark set We downloaded the ProteinGym substitution benchmark  (Notin et al. , 2022)  from the following repository: https://github. com/OATML-Markslab/Tranception. It contains measurements from 87 DMS collected for 72 proteins of various sizes (between 72 and 3,423 residue long), functions (e. g. kinases, ion channels, gprotein coupled receptors, polymerases, transcription factors, tumor suppressors), and origins (Additional file 1: Figure  S1A-C ). The DMS cover a wide range of functional properties, including thermostability, ligand binding, aggregation, viral replication, and drug resistance. Up to four experiments are reported for each protein (Additional file 1: Figure  S1D ). Although the benchmark mostly focuses on single point mutations, it also reports multiple amino-acid variant measurements for 11 proteins (Additional file 2: Table  S2 ).
4a. The standard approach of the conventional free-model reinforcement learning was developed using the Python framework (https://pypi. org/project/ pyqlearning/). As shown in Fig. 4f , the bio-inspired solution overcomes the state-of-the-art free-learning algorithm in terms of accuracy. The better results are due to the more plastic and resilient algorithm to find the solution, which leads to a faster convergence to the optimum result. Fig. 4  | Recall property in dynamics environments and power efficiency of the system. a Experimental results for 9 successive trials of a maze which changes topological configuration every 3 trials. The system explores the environment to find the reward and it recalls the first solution once the previous configuration is proposed. b The time to get the solution improves from trial to trial along with the optimization of the policy.
Mars rover navigation In the last few years, there have been several works which have started to apply reinforcement learning algorithms to terrain images of Mars, to test the artificial networks based on learning by reinforcement in harsh natural landscapes  58, 59. In particular, the terrain images of Mars were taken from HiRISE, which collects an entire dataset of highresolution Martian images  60. We performed the Mars Rover Navigation test step-by-step. The first regarded the definition of the environment from readapted HiRISE pictures: we elaborated the images, and we individuated slopes and descents to build a proper description of the environment in the SoC; secondly, we defined the experimental setup; thirdly, we performed the experimental measurements. Finally, we used the experimental measurements for performing Monte Carlo simulations and assessing performance studies.
Discussion Deep learning techniques using standard Von Neumann processors enable accurate autonomous navigation but require great power consumption and long time for making training algorithms effective, Fig. 4f, g (https: //pypi. org/project/pyqlearning/). In particular, the environmental information is often sparse, noisy and delayed, while training procedures are supervised and require direct association between inputs and targets during the backpropagation. Hence, complex models of convolutional neural networks are needed to numerically find the best combination of parameters for the deep reinforcement computation 62 , (https://pypi. org/project/pyqlearning/). Thus, the standard approaches to reinforcement learning enable freepolicy learning by reinforcement, but this is paid in terms of lower accuracy with respect to the same environmental configuration, Fig.
Contrarily, the bio-inspired learning procedure relies on training-free in-situ hardware computation. This approach improves a lot the time efficiency, Fig. 4b , and the energy consumption, Fig. 4c , while keeping high the accuracy, Fig. 4f. Furthermore, the STDP does not require dedicated methods for stochastic optimization, and it assures an optimum behaviour also when the configuration of the space to explore is not constant  36. Related to this context, in the supplementary appendix "Comparison of the resilient properties between bio-inspired and deep learning approaches", we report a theoretical study over the adaptation capabilities of the neuromorphic solution with respect to the standard Python-based approach (https://pypi. org/project/pyqlearning/). The Markov decision process, Q-learning, TD(λ) and deep learning are not the only topics to which the scientific community refers to for modelling and designing reinforcement learning algorithms.
In order to characterize the devices and study the main features in terms of resistive window and multilevel capability, a "write and verify" algorithm was used, constituted by a series of alternative application of write and read pulses. To provide both set and reset switching activities, the polarity of the cells was switched accordingly to the transition to obtain: during set, a positive polarity was applied to the top electrode of the cell; during reset a positive polarity was applied to the bottom electrode of the cell. Conversely, the device was also tested by applying positive pulses at the top electrode during set and negative pulses during reset. During reset the maximum gate voltage is applied to the gate of the transistor to get the lowest possible ohmic resistance. The data were collected and studied using Matlab or the libraries "Matplotlib", "Pandas", "Seaborn" in Python environment.
Simulation setup In order to design the system, a high-level environmental simulation was implemented in Matlab. The simulation was initially carried out with an ideal definition of the weights for then inserting the values coming from the characterization of the devices. The algorithm has three main sections, and it deals with a simplified behavioural description: 1. The first part deals with the creation of the maze, the definition of the constants (such as the initial position, the rate of maze modulation in time, the maximum number of trials and epochs) and the initialization of the variables (such as the current calculation, the current integration, and the cardinal points). 2. The second part regards the loading of the experimental data. The agent is connected to the cardinal points by SiO x RRAM synapses that are potentiated (set) or depressed (reset) by Hebbian learning.
Introduction Omnidirectional optical cameras can effectively capture their environment in a single shot thanks to their ultra-wide field of view (FoV). As a result, many robotic applications are interested in using such a type of image that can provide rich information about the scene, especially helpful for obstacle avoidance. Various recent works have shown the great potential of omnidirectional images, such as  [1, 2]  for simultaneous visual localization and mapping (SLAM) and, more recently, ref. [3]  on deep reinforcement learning (DRL). These solutions have shown better performances than their counterparts based on conventional images with a limited FoV. Recent learning-based methods have greatly advanced the research for various vision and robotic tasks. This can be mainly contributed to the fast development of a GPU but more importantly to a large number of labeled datasets.
Nevertheless, most existing datasets are with perspective images, with few datasets collected by omnidirectional sensors. Indeed, building an accurate and complete dataset is labor intensive and time consuming. In addition, omnidirectional sensors capable of extracting the ground truth are rare, complex to calibrate, and often subject to reconstruction errors. There are several recent attempts to build benchmark spherical datasets, such as Matterport3D  [4]  and Standford-2D3D  [5]. However, these works were built virtually and with indoor scenes. Even though we can train networks on these datasets, extending the application to real cases or outdoor scenes is not trivial. Hence, developing a novel method to adapt from the networks pretrained on perspective images is highly demanded for omnidirectional applications.
• Adaptation-based methods. Several works add distortion awareness over the features from the latent space by using a specific mathematical formulation, such as the fast Fourier transform  [10, 11]  or polyhedra  [12]. Despite the elegance of these solutions, the adapted network needs to be trained from scratch with specific training datasets. In addition, the adaptation methods are very demanding in terms of the computational cost. Therefore, it is difficult to implement such methods on edge devices for real-time robotic applications. To address the abovementioned dilemmas, in this paper, we propose to directly replace standard convolution operations with distortion-aware convolutions. Therefore, we can benefit from all the development on perspective images to boost the performance on various tasks with omnidirectional images.
We compare our adapted network with its baseline version on complex and unstructured outdoor datasets. We also present a new equirectangular photorealistic forest dataset with ground-truth semantic segmentation and depth. Finally, we test our solution on real outdoor images taken with an omnidirectional camera. In all cases, the adapted networks outperform their non-spherical counterparts. The structure of this paper is as follows. First, Section 2 presents the proposed spherical adaptation using distortion-aware convolutions. Then, a brief overview of the three visual modalities is proposed in Section 3, along with the presentation of the selected networks for the spherical adaptation. Finally, Section 4 provides the comparison results between the adapted models and their baselines on virtual and real outdoor equirectangular images.
Implementation in a Perspective Network The distortion-aware convolution strategy does not require additional parameter learning. As a result, it avoids using large and complex spherical datasets for training. Figure  2  presents a schema of the general implementation process. The overall architecture and weights of the network are derived from a model trained in a supervised manner using perspective images and ground-truth modalities. We directly reuse the code and pretrained weights provided by the models' authors. This highlights the simplicity of our solution integration into previously published work and ensures good performance fidelity to the original publication. We replace the standard convolution layers with new layers handling the equirectangular distortions. In practice, the convolution operations are modified to add fixed offsets to each coordinate of the kernel points.
Semantic Segmentation Semantic segmentation is an essential task in robotic vision. It provides a dense understanding of the different locations and object categories present in the image with pixel-level accuracy. This offers abundant cues for upper-level navigation operations. Furthermore, thanks to omnidirectional cameras, a moving agent can obtain a holistic and precise understanding of its surroundings. To estimate the semantic segmentation in outdoor images, we choose the solution published by the MIT Scene Parsing team  [19]. They propose a classical encoder-decoder architecture trained on the ADE20K dataset. This dataset contains 20,000 mixed indoor and outdoor scenes with 150 semantic classes. The chosen architecture uses the ResNet50 dilated version as the encoder and PPMdeepsup as the decoder.
Monocular Depth Monocular depth estimation is an important computer vision task that aims to predict dense depth maps based on a single image. Thanks to its robustness to scene changes, this is the most commonly used visual modality for obstacle avoidance in navigation. We select the MIDAS  [22]  network to test our solution. This supervised model presents a classical encoder-decoder architecture, mainly built to be embedded in resourceconstrained devices such as drones, resulting in one of the lightest depth estimation networks. In addition, this network is highly versatile thanks to its training on multiple indoor and outdoor perspective datasets. We specifically choose the midas_v21_small pretrained version of the network.
Testing Datasets Outdoor scenes are generally more challenging for networks than indoor scenes, mainly due to the diversity of lighting and the limited amount of outdoor images in the training datasets. However, the available outdoor omnidirectional datasets are very limited and do not include multiple visual modalities ground truths. In addition, forest images are often not used in perspective training datasets which further tests the robustness of the tested models. Therefore, forest scenes are an ideal environment to challenge the networks presented above. For semantic segmentation and monocular depth estimation, we build a photorealistic forest environment RWFOREST. Unfortunately, the ground-truth extraction of the spherical optical flow is not yet available in this environment. As a result, we use two other datasets to test this visual modality: OmniFlowNet  [14]  and Flow360  [23].
OmniFlowNet and Flow360 Datasets The OmniFlowNet dataset, published in  [14] , features three different scenes called CartoonTree, Forest, and LowPolyModel. These sets are generated using Blender  [26] , with free 3D models available online. This dataset gathers 1200 equirectangular RGB images with an associated ground-truth optical flow. The Flow360 dataset, published by  [23] , proposes several urban driving scenes during different times of the day or weather. This dataset provides the ground-truth omnidirectional optical flow associated with RGB image sequences. Figure  4  shows a brief overview of the OmniFlowNet and Flow360 datasets. Flow360 OmniFlowNet RGB Optical Flow.
Quantitative Comparison on Virtual Outdoor Datasets To facilitate the reading of the results, we have grouped in Table  2  the comparison of the different visual modalities. To do so, we selected an error metric specific to each modality: we use the complement of the Mean Intersection Over Union (1 -MIoU) for the semantic segmentation, the End-Point Error for the optical flow, and the Relative Absolute Error for the monocular depth. We also offer additional comparison metrics in Appendix A. In addition, the definitions of all the metrics used are provided in Appendix B. The metric comparison reveals that adapted networks with distortion-aware convolutions always perform better than their counterparts from the baseline perspective. By observing this persistent improvement for all the modalities considered, we conclude that our proposed adaptation approach has excellent generalization capabilities.