dataesr/scientific-paragraphs-categorization huggingface dataset:
The crop type distribution is highly imbalanced with the meadow class representing the highest frequency, nearly half of the total samples. The code for the different data preprocessing steps is publicly available at https://github. com/ellaampy/CropTypeMapping (accessed on 16 February 2021). Figure  2  displays the reference crop map for the whole studied area, and Table  1  gives the actual number of parcels per class and per district. In the experiments (presented in Section 4. 2), the train-validation-test split is made at district level. Crop instances from Châteaulin and Quimper are combined to form the training set, whereas Morlaix crops form the validation set. The Brest district is used to report the final performance for the different baselines and fusion scenarios.
Satellite Data The Copernicus mission is the European Earth observation program with a constellation of optical and radar satellites called Sentinel, providing free, accurate, continuous, global coverage for a better understanding and sustainable management of the environment. Sentinel-1 (radar) and Sentinel-2 (optical) image time series are acquired between October 2018 and December 2019 to obtain enough information to capture the phenology of different crops. Data from the end of 2018 are retained to provide additional information on soil structural changes during tilling in preparation for sowing  [13]. We consider a multi-seasonal rather than a multi-annual time series. Figure  3  displays the acquisition dates for Sentinel-1 and Sentinel-2 time series. Each mission is equipped with twin polar-orbiting satellites at 180°apart, enabling acquisitions at about 5 and 6 days repeat cycles for Sentinel-2 and Sentinel-1, respectively.
Sentinel-2 provides multi-spectral images, whereas Sentinel-1 provides data from a dual-polarized C-band SAR enabling imaging through clouds. Both data are collected from Google earth engine (GEE), a cloud-based geospatial analysis platform hosting a rich catalog of publicly available data  [63]  including Sentinel, Landsat, and MODIS. Figure  4  shows NDVI extracted from Sentinel-2 images (left) and Sentinel-1 VV backscatter (right) in a time composite, highlighting different vegetation dynamics (which corresponds to winter and summer crops) observed through an optical and radar lens. Changes prominent between January and April, May and August, and September and December are shown as red, green, and blue, respectively. We describe in the following paragraphs the different preprocessing steps applied for each type of data.
Sentinel-1 data have longer time series but only two (polarimetric) features, whereas Sentinel-2 data present shorter time series but a higher number of (spectral) features. This results in a similar number of trainable parameters for the two baseline models and slightly different training times (about 1 h for Sentinel-2 and 2 h for Sentinel-1). The early fusion also results in a similar number of trainable parameters such as the single sensor baselines, which is expected as only the amount of features slightly increases. The TAE and late fusion strategies double the number of trainable parameters as two PSE-TAE streams are used to process independently the two modalities. Finally, the PSE fusion implies the highest number of trainable parameters. This is due to the increase in the input embedding size for the TAE module that would require double the size of the weight matrix involved in the self-attention computations.
Experimental Setting We adapt and extend the original GitHub implementation of the PSE-TAE  [43]  to (i) accommodate multi-sensor inputs and (ii) design the different fusion strategies. Our code is made publicly available at https://github. com/ellaampy/CropTypeMapping (accessed on 16 February 2021). We use a similar configuration to the original PSE-TAE work. Networks are trained using the Adam optimizer with a learning rate of 0. 001 for 100 epochs, which is, as in the original paper and in our experiments, enough for all models to achieve convergence. The loss function used is the focal loss (γ = 2 as suggested in  [71] ), which was proposed to address the class imbalance issue. As in  [43] , the number of randomly sampled pixels per parcel in the PSE module is set to 64, and the dropout rate (used in TAE) is set to 0.
As more data are ingested, i. e. , increasing the number of Sentinel-2 acquisitions, there is a general increase observed in F-score. Up until 100% sampling of optical data, most of the fusion strategy outperforms Sentinel-2. The TAE fusion, which uses all Sentinel-1 data, outperforms early fusion when the number of Sentinel-2 observations available is low. If a higher number of Sentinel-2 observations is available, early and TAE fusion strategies perform similarly. We also observe that the use of only five Sentinel-2 observations already allows a significant increase (about 0. 036) in the F-Score compared to the use of only Sentinel-1 time series data.
Incremental Learning The goal of this experiment is to study the benefit of fusion for mapping crops before having a full year of data. For this purpose, we rerun our experiments by quarterly increments in data from October 2018. Figure  7  displays the overall F-Score averaged over five runs for single sensors and the best fusion strategy (early fusion). We observe similar incremental patterns in F-score across all experiments as the season progresses. Sentinel-1 time series yields the lower performance, which is expected as optical data are a better estimator of phenological activity that is crucial to distinguish crops. Moreover, the early fusion dominates from March 2019 and onward. The performance tends to saturate after September 2019, which corresponds to the harvesting period of the summer crops.
These are corollary consequences of increasing (resp. decreasing) the probability of an early upper (resp. , lower) bound hit. This is because when an increasing proportion of stochastic paths eventually hit a bound very early, this squeezes the distribution of hitting times just above null hitting times. Note that the outcome ratios are not equal to 1/2 when x 0 0. This is because the default drift rate v is positive, and therefore favors FIGURE 1 | Impact of initial bias x 0. In all panels, the color code indicates the decision outcomes (green: "up" decisions, red: "down" decisions). The black dotted line indicates the default parameter value (for ease of comparison with other figures below). Upper-left panel: mean hitting times (y-axis) as a function of initial bias (x-axis). Upper-right panel: hitting times' variance (y-axis) as a function of initial bias (x-axis).
7 to generate data under the DDM. In other terms, Eq. 7 is only useful when analyzing empirical RT data. Figure  5  below exemplifies the accuracy of DDM's selfconsistency equation, using a Monte-Carlo simulation of 200 trials under the vanilla DDM. One can see that the DDM's self-consistency equation is valid, i. e. , simulated response times almost always equate their FIGURE 5 | Self-consistency equation. Monte-Carlo simulation of 200 trials of a DDM, with arbitrary parameters (in this example, the drift rate is positive). In all panels, the color code indicates the decision outcomes, which depends upon the sign of the drift rate (green: correct decisions, red: incorrect decisions). Upper-left panel: simulated trajectories of the decision variable (y-axis) as a function of time (x-axis).
APPLICATION TO A VALUE-BASED DECISION MAKING EXPERIMENT To demonstrate the above overcomplete likelihood approach, we apply it to data acquired in the context of a value-based decision making experiment  (Lopez-Persem et al. , 2016). This experiment was designed to understand how option values are compared when making a choice. In particular, it tested whether agents may have prior preferences that create default policies and shape the neural comparison process. Prior to the choice session, participants (n 24) rated the likeability of 432 items belonging to three different domains (food, music, magazines). Each domain included four categories of 36 items. At that time, participants were unaware of these categories. During the choice session, subjects performed series of choices between two items, knowing that one choice in each domain would be randomly selected at the end of the experiment and that they would stay in the lab for another 15 min to enjoy their reward (listening to the selected music, eating the selected food and reading the selected magazine).
Data and code availability d All quantified data are available in main, supplemental figures, and supplementary tables. All original physiological, imaging, histological data are archived at Harvard Medical School Fishell lab Server. d The custom MATLAB scripts train the neural network and demonstrate how to perform the slice to atlas matching. Data to train the neural network is included in the repository. Code for the automatic detection program is available on Github: https://github. com/yannicko-neuro/Pouchelon_etal_2021 and archived at Zenodo: https://doi. org/10. 5281/zenodo. 5573200. d Any additional information required to reanalyze the data reported in this work paper is available from the lead contact upon request.
Resource Imaging and data analysis Each brain section containing labeled cells was acquired as a tiled image on a motorized Zeiss Axio Imager A1. Brains with no helper expression were used as controls for pseudotyping and specificity of the EnvA-N2cRV (data not shown). Starter cells (colocalization of GFP + helpers and tdTomato + N2cRV) were manually quantified on Adobe Photoshop software. Representative brains with less than 10 starter cells were discarded. The highest concentration of starter cells was in deeper layers for all brains (L5-6. Extended Data Figure  1A ). RV + retrogradely labeled cells were registered for each region of the Allen Reference Brain atlas for adult brain and of the ''Atlas of Developing Mouse Brain at P6'' from George Paxinos 2006. For the automatic detection, we aligned the histological sections to the 3D Allen Mouse Brain Atlas both in adult and development  (Wang et al.
, 2020). We first manually determined the corresponding coronal sections of the atlas to the histological sections. The sections were then matched using the Advanced Normalization Tools software (ANTs)  (Avants et al. , 2014). We used three consecutive steps with decreasing rigidity (rigid, affine, syn). At each of these steps, a down-sampling scheme  (8,6,4,2,1x)  with Gaussian smoothing  (8,6,4,2,1x)  was employed until we reached the convergence criterion using mutual information as our metric. To visualize the accuracy, we drew borders according to the Allen Mouse Brain Atlas on the matched histological section. To automatize the cell counting we first manually segmented 350 fields of view per channel of 78 3 78 pixels, containing at least once cell to generate a ground truth. We then fed these into a neural network using dilated convolutions (dilation factor 1,2,3,4), data augmentation (shear 10% and scaling 15%) and dropout layers to reduce overfitting.
All heatmaps were generated using the 'ComplexHeatmap' package in R. ANOVA and t test analysis were performed using Prism (GraphPad). Statistical significance was tested with non-paired, two-sided t test, with a 95% confidence interval. The data distribution was assumed to be normal based on previous studies  (A ¨hrlund-Richter et al. , 2019) , but this was not formally tested. To test for cell type or areal-specificity, values in bar graph were first tested with oneway ANOVA tests followed by the post hoc Tukey multiple comparison test. To test developmental changes within each condition, a Student's t test was applied. All values in bar graphs are expressed as mean ± sem. Circles in bar graphs denote individual animals. In vitro electrophysiology P10-11 mice for development and P40-P45 mice for adult time points were used for electrophysiology experiments.
9 numerical aperture). To visualize SST versus PV cINs, MIGHTEX LED attached to the upright microscope was used. During recording slices were continuously perfused with oxygenated acsf (95%O 2 / 5% CO 2 ) at a flow rate of 1ml/min at room temperature. For whole cell patch clamp recording, 5-7 MU pipettes were pulled from thick-walled borosilicate glass capillaries on a P-1000 Flaming Micropipette Puller (Sutter Instrument). The pipettes were filled with internal solution of the following composition: 130 K-Gluconate, 10 KCl, 10 HEPES, 0. 2 EGTA, 4 MgATP, 0. 3 NaGTP, 5 Phosphocreatine and 0. 4% biocytin (pH = 7. 3). Recordings were performed using a Multiclamp 700B amplifier and digitized using Digidata 1440A using a sampling rate of 20KHz. Series resistance was continuously monitored during the recording and cells were discarded if the series resistance was > 40MOhms or if it changed by > 20% during the recording.
ACKNOWLEDGMENTS We thank  Nusrath Yusuf  and  Marian Fernandez-Otero  for their support all along the project and  Justin McMahon  for the plasmid submission to Addgene. This work was supported by an  EMBO Long-Term  fellowship, early and advanced Swiss Foundation postdoctoral fellowships, a Hearst foundation grant (to G. P. ), and grants from the  National Institutes of Health (NIH) ,  MH071679 ,  NS08297 ,  NS074972 ,  MH095147 , as well as support from the  Simons Foundation (SFARI)  (to GF).
Security engineers and practitioners may use various privacy protection principles, technologies, or Privacy by Design (PbD) strategies. PbD involves various technological and organizational components, implementing privacy as well as data protection principles. Hoepman  [2]  proposed eight privacy design strategies, i. e. , Minimize, Hide, Separate, Aggregate, Inform, Control, Enforce, Demonstrate. Privacy protection techniques, better known as Privacy-Enhancing Technologies (PETs), can implement most of these privacy strategies. PETs are usually based on the principles of data minimization, anonymization, pseudonymization, and data protection that allow users to protect their Personally Identifiable Information (PII). The European Union Agency for Network and Information Security (ENISA) defines PETs as the broader range of technologies that are designed for supporting privacy and data protection.
In the recent ENISA report  [3] , a fundamental inventory of the existing approaches and privacy design strategies were provided. The report distinguishes the privacy enabling techniques such as authentication, attribute-based credentials, secure private communications, communications anonymity/pseudonymity, privacy in databases, storage privacy, privacy-preserving computations, transparency enhancing techniques, and intervenability enhancing techniques. Privacy protection is already an important part of many regulations and international standards. In 2011, the ISO organization released the ISO/IEC 29100:2011 Privacy Framework Standard 1 which aimed at protecting PII based on 11 distinct principles, from data collection, data usage, data storage to data destruction. Furthermore, Europe's general data protection regulation (GDPR) replaced the Data Protection Directive 95/46/EC in 2018  [4].
A. PRIVACY CONCERNS IN IoT/II There are plenty of privacy and security issues in current IoT/II systems since they typically rely on mobile connectivity and resource-constrained devices. In a recent article featured in Forbes magazine, Rotem et al. showed how an IoT management platform run by an Asian company Orvibo was easily accessible over an HTTP connection. 2  Through a simple Internet Protocol connection to the database, they gained access to over 3 billion records, including a slew of private information such as usernames, account codes for reset, payment information, and user passwords. This breach of Orvibo highlights the different types of data accessible once a system is compromised in an unsecured IoT/II system in the reality. Next, we describe some application domains in IoT/IIs and provide some example privacy issues to motivate the following-up discussions.
Internet of Vehicles: Autonomous vehicle technology is a hot area of research and will become quite common in the years to come. The Internet of Vehicles (IoV) is an ongoing service connecting a large set of sensors, controllers, and devices attached to vehicles or vehicle infrastructure to ease autonomous control. It is quite a challenge to design effective privacy mechanisms that, in turn, can make sure a collection of IoV Big Data is both trusted and not tampered with. For example, there is a massive risk involved with injecting a malicious or fraudulent message into IoV by malicious vehicles. This process can endanger the entire traffic system(s). Moreover, once compromised, an entire network may endanger the lives of any persons involved in the network. In a specific use case, smart vehicle parking services could also encounter several privacy issues, e.
B. PRIVACY THREATS CATEGORIZATION In the IoT era, privacy can be affected by personal information collection, processing, sharing, and invasion/leakage  [52]. Information collection, processing, and sharing activities are fundamental in running these cooperative IoT/II systems. Personal information is collected, which may include: 1) user identity in general 2) geolocation in transportation 3) health conditions in healthcare 4) lifestyle habits inferred from intelligent surveillance, smart energy, and home Service providers process the provided and disseminated data to query required functions and use cloud servers to provide personalized or group/crowd-sourced services. As data in IoT/II systems become abundant for its use in intelligent applications (i. e. , assisted or autonomous driving  [53] , healthcare services in Smart Cities  [54] , Smart Homes), the implication of privacy invasion/leakage is increasingly becoming a major concern.
2) SEARCHABLE ENCRYPTION Searchable Encryption (SE) is used to perform keyword search operations over encrypted data. SE enables the users or data owners to delegate search capabilities using some keywords over encrypted data to an untrusted service provider without disclosing any sensitive information about the searched keywords and the actual plaintext data  [133]. In SE, the data owner generates an index of keywords associated with a data file and encrypts the data file and the index before storing it into storage servers, which are maintained by a service provider. Whenever a user wants to retrieve the ciphertexts, the user generates a search query in the form of a trapdoor using a key and the desired keywords and sends that trapdoor to the service provider. Afterward, the service provider performs a search operation over the encrypted indexes using the trapdoor and returns the data files associated with the indexes if the keyword associated with the trapdoor matches with the keywords associated with the indexes.
5) CODE-BASED CRYPTOGRAPHY IN IoT/II Strenzke and Falko  [219]  implemented the McEliece scheme (100-bits security level) on a microcontroller. Nevertheless, the key generation algorithm could not be implemented on the microprocessor for exceeding the card's RAM size. Heyse et al. [220]  dealt with QC-MDPC McEliece implementations on embedded devices (8-bit AVR microcontroller). They present a compact implementation on the microcontroller using only 4800 and 9600 bits for the public and secret key (80-bits security level). Recently, the paper  [221]  presents the implementation of code-based BIKE on a Cortex-M4 microcontroller. The implementation employs reduced data representation and adequate decoding algorithms to achieve 6 million cycles for key generation, 7 million cycles for encapsulation, and 89 million cycles for decapsulation for BIKE-1.
A. PRACTICAL DEPLOYMENT OF PETs This section identifies the current state, technology readiness, and the presentation of existing significant pilots, products, and projects. The CORDIS search engine is used for the detection of significant research projects in the EU. Table  8  maps the PETs in current or past research projects. Some PETs are directly implemented as privacy-preserving products and pilots. For example, onion routing is already widely used by privacy-preserving communication applications such as ToR  [118] , Tribler 9 and Tox. 10 Attribute-Based Credential schemes are implemented in Idemix  [263] , U-Prove  [264] , and IRMA 11 systems. Further, ring signatures are practically deployed in several cryptocurrencies, e. g. , Monero  [91] , Cryptonote. 12 On the other hand, some PETs serve mainly for experimental purposes as software libraries, e.
Role-Based Encryption (RBE) is a promising cryptographic encryption primitive. The main idea of RBE is to integrate the properties of the RBAC model and the public-key encryption method. The first concept of RBE was proposed in  [288]  by Zhou et al. for securing cloud data. Afterward, a few schemes  [289] -  [291]  have been designed for the cloud to achieve various functionalities and increase efficiency. RBE uses RBAC access policies to encrypt data, and any user, who possesses qualified roles, can access the data after decryption using their decryption key. One of the crucial features of RBE is the inheritance property, where one role can inherit access rights of the other roles. It is a suitable encryption technique for an environment where the access rights are organized in a hierarchical form.
Recently, Alabdulatif et al. [293]  introduced a novel privacy-preserving distributed big data analytics framework for cloud-based applications using fully homomorphic encryption proposed by Brakerski et al. [249]. They improve encrypted analysis tasks by splitting large datasets into small subsets and processed them in a distributed manner. One of the promising applications of HE is in machine learning, especially in deep learning. Research has been conducted so far and addressed neural network operations  [294] ,  [295] , pre-trained neural networks  [296] -  [298] , and high parallelizable machine learning operations  [299] ,  [300]. However, some challenges are yet to be addressed, such as performance-boosting by efficiently switching to GPUs, doing the full training over encrypted data, and making the processing highly parallelizable.
g. carrageenan (GH16), agar (GH16) and alginate (PL6, PL7 and PL17). The many predicted CAZymes suggests that this marine bacterium can degrade several complex seaweed carbohydrates. Seven putative sulfatases, containing the sulfatase domain signature IPR000917, were also identified in the genome (Supplementary Table  4 ). Five of these proteins, named PsSUL1 to 4 and PsFucS1 contained the conserved sulfatase domain (CXXXRXXXXXG). BLASTp comparisons using the SulfAtlas database (v1. 2; http:// abims. sb-rosco ff. fr/ sulfa tlas)  17  , grouped these putative sulfatases into subfamilies S1_4 (PsSUL2) and S1_13 (PsFucS1, PsSUL1, PsSUL3-4; Supplementary Table  4 ). While the subfamily S1_4 contains several characterized sulfatases (EC 3. 1. 6. 1), the specificity of S1_13 sulfatases is unknown.
Multiple alignment of protein sequences (Supplementary Fig. 4 ) revealed that PsSUL2 is slightly related to ARS (identity 30%) (previously AtsA; accession number: P51691) from Pseudomonas aeruginosa PAO1, which is active on small sulfated compounds, including pNCS  38, 39  , while PsSUL1, 3, 4 and PsFucS1 seem more related to the agar sulfatase Ary423 from F. pacifica (identity 51, 47, 46 and 29% respectively; Accession Number: AKL72071. 1)  23. Functional characterization of the PsFucS1 sulfatase. The Pseudoalteromonas sp. MB47 sulfatases PsSUL1-4, PsFucS1 and the previously characterized sulfatases ARS and Ary423, used as benchmark sulfatases, were expressed in Escherichia coli and purified (Supplementary Fig. 5 ; Supplementary Table  5 ). ARS and PsFucS1 showed sulfatase activity, while the other sulfatases did not (Supplementary Fig.