dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Source The dataset used in this paper comes from the UCI database. The "Simulated Falls and Daily Living Activities Data Set"  [31]  included in the database in June 2018 was chosen for our experiment. The dataset was completed by 17 volunteers, including ten men and seven women. Each volunteer must complete 36 behaviors, including 20 falling behaviors and 16 daily behaviors, each repeated five times. In this way, there are 3060 sampled data for each part. The original signal is a total of 21-dimensional signals such as threedimensional acceleration, three-dimensional angular velocity, three-dimensional velocity, three-dimensional geomagnetism, pressure, and more output from the sensor. The sensor's sampling frequency is 25 Hz, and the average sampling time for each action is 15 s. It has been shown that breasts can achieve better recognition accuracy  [32].
Data Division There are 20 fall behaviors in the original data, of which we selected 12 behaviors. In the unused fall data, the four actions numbered 905, 906, 914, and 916 are those the tester would quickly or slowly recover after a fall, which shows that this type of fall does not cause the user to lose control of their body, so it is beyond the scope of our research. The remaining four behaviors numbered 917, 918, 919, and 920 did not indicate the fall's direction and posture, so these four datasets were discarded. In addition, this paper's focus is to recognize the posture when falling, and the 16 daily behaviors were classified into one category, and no further subdivision was required. Finally, the selected 12 fall behaviors and 16 daily behaviors were classified into seven different behaviors, which were ADLs, front-lying, front-knees, back-lying, back-sitting, fall to the left, fall to the right.
ACKNOWLEDGMENTS A. K. and D. P. G. thank  Yang Liu ,  Félix Bussières  and  Erika Andersson  for helpful conversations. M. B. , A. C. and E. D. acknowledge financial support from the  French National Research Agency (ANR)  project quBIC. A. K. and D. P. G. acknowledge financial support from the  UK Quantum Communications Hub  grants no. EP/M013472/1  and  EP/T001011/1. A. K. is partially supported by  Perimeter Institute for Theoretical Physics. Research at Perimeter Institute  is supported by the  Government of Canada through Industry Canada  and by the  Province of Ontario  through the  Ministry of Research and Innovation.
The effect of this data truncation can be seen in Figure  4 , where the power spectral density of the noise floor, as obtained by shorting together the differential inputs of the ADC, is reported. As it can be seen, the noise floor is essentially the same with both output resolutions. Though in principle word truncation is of course not a lossless operation (because it can reduce or cancel insignificant details of the signal), in this context we deem it to be a property of the ADC output format, as it is customary to do with ADCs having different output resolutions; especially the sigma-delta type, where the number of output bits is often just that of the internal digital filters, and do not directly match their effective analog resolution. With this truncation the estimated per-sample entropy reduces by 4 bits on average, from about 15 bits and 10 bits (original/differentiated signal respectively) to about 11 bits and 6 bits, as expected.
To do so, those compressors use huge block sizes, which are impractical to implement in an embedded system and incompatible with real-time streaming constraints, plus some require disproportionate amounts of memory for encoding, which are simply not available on the sensor nodes. For instance, from the official documentation, at its default "best" setting bzip2 uses 900 kB long blocks, and 7600 kB of memory for compression. LZMA is even more resource-hungry, using 96,256 kB of memory for compression at its default "moderate" setting. The other three standard compressors (gz, lz4, zstd) use fewer resources and can operate with smaller (but still large for real-time applications) block sizes, but do not offer a satisfactory performance on the EMG signals. Indeed, a simple byte-oriented variable length differential encoding (VLDE) of the data could reach similar performance.
Of course, it does not make sense to use VLDE for the non-differentiated signal. Its performance has also been reported in Table  1  for sake of completeness, but, obviously, it turned out to be largely ineffective as data is not centered around 0. On differentiated data (Table  2 ), instead, it beat most general-purpose compressors. The simple VLDE scheme devised to efficiently send the differentiated EMG signal. Either 8, 16, or 24 bit words are used to store the difference, depending on how many bits are needed to code it in straight binary. "s" is the sign bit, and the fixed red bits allow the discovery of the used word length. 20 significant bits plus sign is exactly the maximum needed to store differences between 20 bit signal samples, so longer words cannot ever be needed. On the other hand, a compression algorithm born for lossless audio compression behaved much better on the EMG signals as well.
FLAC Encoder Implementation on the Sensor Node Firmware Implementing the FLAC encoder on the sensor node for real-time streaming required some adjustments to the reference implementation, and a completely redesigned buffering strategy. Indeed, although FLAC compresses each channel independently (unless in joint-stereo mode, which is only meaningful for audio and has thus been disabled), the reference implementation takes in interleaved buffers as they are commonly produced by sound cards. These intermediate buffers are not useful in an embedded application and have therefore been removed, and the de-interleaving of the data coming from the ADC was performed simultaneously to data scaling and directly into the buffers used for compression. Moreover, since the reference implementation needs to be portable across a large variety of systems, and on some of them also implements code optimization using singleinstruction multiple-data (SIMD) instructions, which may require very large memory alignments, the FLAC encoder waits to fill the buffers with some look-ahead samples from the next block before starting compression, to ensure no uninitialized data is ever read by SIMD instructions.
This unfortunately leads to an increase in latency, up to another full data block in the worst case scenario, which we deemed incompatible with the present need of real-time streaming. After all, the 32 bit CPU used in the sensor node never requires more than 32 bit word alignments, and all the math in the FLAC library is done in 32 bit, and there are no SIMD instructions large enough on the microcontroller to possibly leverage them. Due to these considerations, the buffering scheme of the reference library was rewritten from scratch. Data acquired by the ADC is quickly moved without any processing into a temporary queue from within the interrupt service routine itself, waiting for the main thread to be free. When the main thread is free, the data into the queue is properly scaled and transferred into the compression buffers, one for each EMG channel, which operate as ping-pong buffers, whereas one is being filled while the compressor operates on the other.
The photorelay driver contains pulse-shaping circuitry to drive the relays with minimum turn-on and turn-off times, allowing makebefore-break operation while switching from one sensing resistor to another. The DAC-controlled LDO allows selection of output voltage from 1 V to 5 V in 1 mV increments. The whole system is software controlled by a Raspberry Pi (not shown) that drives the digital signals that control the photorelays and the multiplexers through some GPIO pins, plus acquire the data through its I2C bus. Current measurement can be performed at up to a 3 kHz sampling frequency, and sampling times are accurately timestamped at microsecond resolution against absolute time as recorded by a clock servoed to a local time server, so that current measurements can be accurately related to BLE events as recorded from the receiving PC, as shown in  [10].
Results To evaluate the performance of different compression algorithms and formats, some of these have been implemented in the firmware of the EMG sensor node so as to be able to experimentally determine their effective power consumption. To obtain reproducible results, while all the sensor subsystems had been left operational, data from the ADC was discarded and replaced on-the-fly with a pre-stored sequence, which gets looped. This also allowed us to verify that, once decompressed, the received data was actually identical to data before compression, so as to validate our firmware code. The pre-stored sequence is 256 samples long, longer than the compression block size employed, so as to avoid repeatedly compressing always the same sequence. It was selected to contain a muscle contraction, since it was experimentally determined that those are the signal sections which are less compressible, besides being the ones of greatest interest.
The 20 bit RAW format had not been implemented since packing data in a non-byte-aligned structure would require more complex code than VLDE, but without the compression benefits, and so it is quite useless. As it can easily be seen, and could also be expected, FLAC at its lowest compression setting, level 0, is the clear winner in terms of energy consumption, if the latency can be tolerated. At 24 bit, it halves both the data rate and the energy consumption, and behaves even better at 20 bit. VLDE also performs remarkably well, almost on a par with FLAC at level 1 for power consumption, because it of course has a negligible computational overhead and the energy savings are basically all due to the reduced transmission requirements. It can hence be considered a very appealing choice when low latencies are needed.
CPU Usage Analysis To further investigate the performance of some of the compression algorithms, a hightime resolution analysis of the current has also been performed, so that individual contributions to the overall power could be better highlighted. Figure  10  shows traces of the node power supply current as obtained with our programmable source meter. From top to bottom, they refer to uncompressed RAW data streaming, to VLDE encoded 20 bit data, and to FLAC compressed 20 bit data, at compression levels 0 (lowest), 1, and 7 (highest). As can be seen, RAW and VLDE transmit packets at every connection event (we used a 7. 5 ms connection interval), though of course VLDE transmits less packets per event. VLDE code is trivial and negligible with respect to all the other code the microcontroller runs e.
Training We trained our model with the Youtube VIS dataset. For each training iteration, we used two images randomly sampled from the same video sequence. We denote by I t and I t+τ the two sampled images. The ground-truth sets are denoted as {y t i } N i=1 and {y t+τ i } N i=1 , respectively. Both sets are padded with ∅ as no object. To be clear, the y t i and y t+τ i represent the same instance i at frame t and frame t + τ. We pass the I t to the network with the learnt embeddings as queries. This step is exactly the same as the DETR single image routine and we obtain the prediction set { ŷt i } N i=1. We extract the outputs from the transformer decoder and use them as the ISQs for the process of frame t + τ. Thus, we obtain the prediction set { ŷt+τ i } N i=1. Similarly, the ŷt i and ŷt+τ i represent predictions in the same query slot i at frame t and frame t + τ.
Dataset In order to compare with other methods, we used the Youtube-VIS 2019 version  [10]  dataset to evaluate our method. The dataset has 2238 training videos, 302 validation videos and 343 test videos. The dataset has a category label set including 40 common objects such as people, animals and vehicles. Each video sequence contains at most 36 frames. Each frame is a temporal downsampling of the original sequence with an interval of five frames, which leads to a greater variation between consecutive frames. This can actually simulate the scenarios with high speed motion. Such scenarios usually require the algorithm to be more robust. As the test set evaluation server is closed, we followed most previous works  [3, 10, 11, 13, 14]  and evaluated our method on the validation set. The evaluation metrics are Average Precision (AP) calculated based on multiple intersection-over-union (IoU) thresholds and Average Recall (AR) defined as the maximum recall given some fixed number of segmented instances per video.
Implementation Details We used ResNet-50 as our backbone network, unless otherwise noted. We used the same hyperparameters as DETR to build the losses. We set the number of ISQs N to 10, which is enough for the dataset. The model was trained with the AdamW optimizer  [27]. We set the initial transformer's learning rate to 10 -4 , the backbone's to 10 -5 and the weight decay to 10 -4. The backbone network and transformers were initialized with DETR pretrained weights on MS COCO  [28]. We trained the networks for 50 epochs and the learning rate drops by a factor of 10 at the 30th epoch. The model was trained on two Titan Xp GPUs of 12G RAM for about 20 h, with a batch size of 14. All frames were resized to 640 × 360. Random horizontal flip was used as data augmentation.
DETR Baseline We compare our methods with the DETR baseline. On the one hand, we try to adapt DETR for VIS tasks with post-processing. As mentioned above, the Youtube VIS dataset is a temporal down sampling version of the original videos. This largely increases the instance displacement between consecutive frames. Simply applying an IoU association will actually break an instance track into several pieces. Each piece of track will be considered as a different instance in the evaluation. Such duplicates of tracks hurts the AP of VIS. On the other hand, we directly use the query slot index of DETR as an instance index to generate VIS results. To our surprise, this can actually conduct 32 points in AP. However, the object queries of DETR show spatial and object size distribution. As shown in Figure  2 , once the instance passes the boundary of a query slot, it leads to a switch ID error.
It not only makes joint decisions but also protects individual rights and interests. The blockchain itself is a large-scale massive database. All the data and information recorded on the chain are open and transparent. Any node can use the Internet to query information on the blockchain platform and no third-party organization can modify or cancel existing information recorded on the blockchain. The use of blockchains can be divided into three categories. Applications for the transfer of assets (monetary use, but not only: securities, votes, shares, bonds. ), applications as a registry to ensure better traceability of products and assets, and smart contracts. Smart contracts are stand-alone programs that automatically execute the terms and conditions of a contract, without requiring human intervention once started.
INFRASTRUCTURE LAYER Generally speaking the IoT architecture is defined with three parts: the network of connected objects, one or more applications (such as data cleansing, selection of interesting information, etc. ) and the information exchanged model with public data and data that must remain confidential. Two main types of architecture are defined: i) a centralized architecture  (Alli and Alam, 2020)  using the cloud and associated with big data analysis or ii) a decentralized architecture with the help of blockchains  (Singh et al. , 2020). As far as IoTs are concerned, the most developed architecture at present remains the centralized one, which is widely used for domestic IoTs but also by firms  (Alli and Alam, 2020). Our complete architecture is composed by three subsystems: a multi-agent module, the blockchain network (Hyperledger Fabric), and the IoT network.
CONCLUSIONS In this work, we propose an enterprise platform dedicated to logistics services and activities. This platform allows to integrate or to cancel different customers and providers, but also different kind of connected objects and applications. To do this, an Internet of Things network is managed by a blockchain to secure exchanges of possible competitive enterprises. Indeed, the blockchain network will provide us with the security, flexibility and speed of response necessary in the management of IoTs. Furthermore, to assure not only traceability and security of data but the proposed blockchain network allows smart contracting and applications for the transfer of assets by a close plug in with the IoT. Nevertheless, controlling and optimizing the use of services can be more complex to implement because it is defined via a heterogeneous set of components and a possible phenomenon of competition at the extremities of the network.
Overall accuracy was calculated as the sum of the correct predictions for both hand positions divided by the total number of predictions. To map multisensory facilitation more locally, we compared Bonferroni-corrected MG values for each position a g a i n s t z e r o a n d p e r f o r m e d a H a n d ( c l o s e v s. distant)*Position (V-P1 to V-P10) within-subject ANOVA. To compare the shape of these multisensory facilitation maps, we first tested which function better fit the spatial pattern and, second, we cross-correlated them to test their shapes for isomorphism. MG values were fitted to sigmoidal and normal curves, limited to two parameters. Table  1  reports formulas for curve fitting (Curve Fitting toolbox) with MATLAB (version R2016b, MathWorks, Natick, MA, USA). Similar to previous work  (Canzoneri et al.
Acknowledgements This work was supported by the  Labex/Idex  ( ANR-11-LABX-0042 ) and by grants from the  James S. McDonnell Foundation , the  ANR-16-CE28-0015-01  and  ANR-10-IBHU-0003  to A. F. E. B. was supported by the  European Union 's  Horizon 2020 research and innovation program  ( Marie Curie Actions)  under grant agreement  MSCA-IF-2016-746154 ; a grant from  MIUR (Departments of Excellence   DM 11/05/2017 n. 262 ) to the  Department of General Psychology, University of Padova. C. B. was supported by a grant from the  Swedish Research Council  ( 2015-01717 ) and  ANR-JC  ( ANR-16-CE28-0008-01 ). We thank  S. Alouche ,  J. L. Borach ,  S. Chinel ,  A. Fargeot , and  S. Terrones  for administrative and informatics support, and  F. Volland  for customizing the setup.
Motivation: One avenue to address the paucity of clinically testable targets is to reinvestigate the druggable genome by tackling complicated types of targets such as Protein-Protein Interactions (PPIs). Given the challenge to target those interfaces with small chemical compounds, it has become clear that learning from successful examples of PPI modulation is a powerful strategy. Freely accessible databases of PPI modulators that provide the community with tractable chemical and pharmacological data, as well as powerful tools to query them, are therefore essential to stimulate new drug discovery projects on PPI targets. Results: Here, we present the new version iPPI-DB, our manually curated database of PPI modulators. In this completely redesigned version of the database, we introduce a new web interface relying on crowdsourcing for the maintenance of the database.
, 2015; Sperandio et al. , 2010). Over the last decade, the emergence of the 'big data' era has promoted a great interest in exploiting publicly available chemical information for drug discovery  (Kim, 2016)  through major initiatives including PubChem  (Kim et al. , 2019)  and ChEMBL  (Gaulton et al. , 2017). Both databases offer an impressive number of online tools and web services to query and analyze both chemical and pharmacological data on all types of therapeutic targets. In the PPI community, two databases paved the way in the late 2000's. The first one is the Timbal database  (Higueruelo et al. , 2013)  which automatically extracts relevant data from ChEMBL following the identification of PPI targets. The second is the 2P2I database  (Basse et al. , 2016)  which is manually curated and exclusively focused on experimental structures of PPI modulators derived from the Protein DataBank (PDB)  (Berman, 2000).
We first reported iPPI-DB in 2013  (Labbeet al. , 2013) , and improvements were made on a subsequent version by adding more PPI modulators and more PPI targets, as well as a chemical similarity mode for querying the database  (Labbeet al. , 2016). Here, we present a new generation of this database. It now includes a graphical interface that facilitates the entry of new data. This improvement enables the provision of a community contribution mode, whereby experts external to our group can enter new data based on any published relevant work. After a review by the core curators of the database, these contributions are integrated into the database and become available in the web interface (see Fig. 1 ). Moreover, the architecture of this application has been entirely redesigned. The database and web application now support complex queries over thousands of compounds, easily accessible from an interactive exploration interface.
A paradigm shift in our model of data curation The pharmacological results that are valuable for specialized databases like iPPI-DB are retrieved from peer-reviewed publications including scientific articles and patents. This type of information has been recently defined as DARCLP relationships in a very interesting review  (Southan, 2020). This definition illustrates the relationships that bioscientists reading papers or patents have to establish in order for the data to become tractable: a document (D) in which bioactivities (A) have to be associated with quantitative results (R), chemical compounds (C), explicit location references (L) for the compounds within the document (e. g. 'compound 10b') and a protein target (P), here a PPI target. A rapid survey of iPPI-DB-related published studies available on PubMed illustrates that there are now more than 2000 publications a year that are of potential interest for iPPI-DB (Fig.
2 ). This represents a large amount of data published annually for which DARCLP relationships need to become tractable. In this context, our previous model of database curation that relied essentially on manual intervention is no longer sustainable. Two complementary solutions can be adopted to ensure the sustainable maintenance of databases such as iPPI-DB: a fully automated process relying on automated chemical and biomedical entity recognition from text (e. g. via Natural Language Processing and artificial intelligence)  (Southan, 2020) ; or a community curation based on a cohort of experts. Artificial intelligence efforts have clearly made great progresses toward the fully automated extraction of explicit DARCLP relationships from documents. Nonetheless, those procedures are not yet exhaustive, and still leave unaddressed data.
Among the difficulties which contribute to this persistent gap, one can identify the access to paywalled literature, or the complexity of the automation when interpreting affinity tables that rely on R-groups to describe chemical series and structure-activity-relationships. Moreover, as opposed to the users of generic public chemistry databases like PubChem or ChEMBL, users of specialized databases like iPPI-DB, are not only interested in associating a protein target with compound activity, they need tractable pharmacological data about compounds that are capable of modulating an interaction between several protein partners. Therefore, this implies more complex DARCLP relationships that are hardly reachable by fully automated processes. These persistent limitations stand in sharp contrast to community curation approaches whereby biocurators have the ability to discern such relationships from a paper in minutes  (Southan, 2020).