dataesr/scientific-paragraphs-categorization huggingface dataset:
Furthermore, this configuration also allows ECG data traffic to have a much higher probability of accessing the medium in contrast to other traffic types. The proposed CW min value for temperature is the largest, resulting in the lowest priority to the access category. The aim here is to squeeze the CW sizes for high priority and release for low priority traffic. The sensor node connected to the body performs medium sensing when it is ready to transmit. The transmission is initiated upon sensing the medium as available. If an acknowledgement of this transmission is received, the data transmission is deemed successful. Sensor data of all access categories are sent to the mobile Wi-Fi access point, where it can then be forwarded to the ehealthcare medical facility even from remote locations.
The 5G gateway is connected to the 5G base station via router and Ethernet links, with 20ms as mean delay. All nodes consist of protocols such as Internet Protocol (IP) and User Datagram Protocol (UDP), along with Ethernet in the core network and wireless protocols in the access and backhaul domains. The protocols in the backhaul domain also include Radio Link Control (RLC), Medium Access Control (MAC) and Packet Data Convergence Protocol (PDCP). The information related to sensor nodes' mobility and channels is kept in the form of a database at the node labeled 'global_node_list,' which is accessible from all the nodes in the simulation environment. All the node-related parameters are initialized in the 'global_node_list' at the start of each simulation run. The simulation environment and protocol stack for the sensor node are depicted in Figures  4  and 5 , respectively.
If an error bar is missing, this would imply that the confidence interval is almost zero in that specific case. The results in Figure  6  show that, with the PQ scheme, superior end-to-end delay results with proper QoS differentiation could be achieved when compared to DQ and NQ schemes. The PQ scheme provides the low end-to-end delay to ECG devices and high-temperature devices. Moreover, a high ECG throughput in the PQ scheme is achieved in comparison to the ECG throughput performance of other schemes, as depicted in Figure  7. a high ECG throughput in the PQ scheme is achieved in comparison to the ECG throughput performance of other schemes, as depicted in Figure  7. The results for ECG end-to-end delay show that, in the case of the PQ scheme, ECG data traffic is treated with high priority, hence small end-to-end delays, as compared to DQ and NQ schemes, are achieved.
Temperature devices accomplish end-to-end delay results in such a way that, in the case of the PQ scheme, high end-to-end delays are noticed as compared to DQ and, eventually, NQ schemes. In Figure  7 , it is seen that the PQ scheme achieves better throughput as compared to DQ and NQ schemes. The evident reason for this performance is the high priority scheduling that the PQ scheme renders to ECG nodes. Consequently, the transfer of large volumes of ECG data to the network with improved spectral efficiency can be accomplished, and the overall system performance in terms of cell throughput can be enhanced. In contrast, the mean temperature throughput stays unchanged for all three schemes in all sub-scenarios because of the higher ECG priority. The results for ECG end-to-end delay show that, in the case of the PQ scheme, ECG data traffic is treated with high priority, hence small end-to-end delays, as compared to DQ and NQ schemes, are achieved.
The justification for these higher delays is the small ECG frame inter-transmission interval that produces a highly loaded traffic environment. A high load results in delays in temperature traffic and, with the PQ scheme, sufficient resources could not be provided all the time for every temperature sensor. The sensors under superior channel conditions would get frequent chances to be served consistently. Increasing the number of ECG sensors causes the mean PQ based throughput for ECG data traffic to increase until the number reaches four sensors, where the channel reaches maximum capacity and throughput would not improve further even if the nodes for ECG are increased. The justification for such a behavior is that the rise in number of ECG nodes would result in the intensification of ECG traffic, but only until the highest achievable level of throughput is reached.
The mean PQ throughput for sensor nodes with temperature data traffic remains unchanged for all the schemes even if the ECG nodes increase in number. This behavior is due to the fact that the number of temperature nodes in all sub-scenarios would not change. Therefore, the traffic from temperature nodes towards the access point remains persistent in the three sub-scenarios. Increasing the number of ECG sensors causes the mean PQ based throughput for ECG data traffic to increase until the number reaches four sensors, where the channel reaches maximum capacity and throughput would not improve further even if the nodes for ECG are increased. The justification for such a behavior is that the rise in number of ECG nodes would result in the intensification of ECG traffic, but only until the highest achievable level of throughput is reached.
The results reveal that the performance of the PQ scheme is better than the other schemes when it comes to QoS differentiation. The proposed scheme provides low end-to-end packet delay results for ECG nodes, high end-to-end delay for BP nodes and even higher delays for temperature traffic nodes. Moreover, high throughput is noticed in the case of ECG sensors, low in the case of BP nodes and lowest in the case of temperature nodes. Electronics 2020, 9, x FOR PEER REVIEW 14 of 21 and even higher delays for temperature traffic nodes. Moreover, high throughput is noticed in the case of ECG sensors, low in the case of BP nodes and lowest in the case of temperature nodes. The mean end-to-end packet delay graphs for ECG nodes of NQ, DQ and PQ scheduling schemes show that, in the PQ scheme, ECG data on the access point with the highest priority among all the schemes and end-to-end packet delays are small as compared to the DQ scheduler, as the endto-end packet delays are high.
The reason for this behavior is that the PQ scheduler offers a better scheduling opportunity for ECG nodes. The mean end-to-end packet delay graphs for ECG nodes of NQ, DQ and PQ scheduling schemes show that, in the PQ scheme, ECG data on the access point with the highest priority among all the schemes and end-to-end packet delays are small as compared to the DQ scheduler, as the end-to-end packet delays are high. Moreover, the PQ scheme results for the end-to-end delay of ECG and BP traffic are not influenced significantly by increasing the number of temperature nodes. The mean BP end-to-end delay results of NQ, DQ and PQ schemes show that the PQ end-to-end packet delay for BP is higher as compared to DQ and NQ. Hence, ECG data traffic is treated on a priority basis. Because of the larger interarrival time between temperature frames, the PQ scheme would not prolong the ECG, BP and temperature packet end-to-end delays.
Moreover, improved ECG throughput is realized in comparison to ECG throughput achieved by other schemes, as illustrated in Figure  13. It is shown in these results that, in the case of the PQ scheme, ECG data traffic is transmitted with high-level priority, thus these nodes achieve low end-to-end delays when compared to DQ and NQ schemes, where end-to-end delays are larger. In the case of the PQ scheduler, the BP devices have greater end-to-end delays in comparison to the DQ scheme. The reason for this relatively poorer performance is that the scheme focuses on providing better services to ECG devices, while BP data traffic is considered more tolerant of delays. Delays of up to several minutes are acceptable in case of BP traffic. In case of temperature data traffic delays, the PQ scheme performs poorly as well, with higher delays noticed as compared to NQ and DQ schemes.
In the case of the PQ scheduler, the BP devices have greater end-to-end delays in comparison to the DQ scheme. The reason for this relatively poorer performance is that the scheme focuses on providing better services to ECG devices, while BP data traffic is considered more tolerant of delays. Delays of up to several minutes are acceptable in case of BP traffic. In case of temperature data traffic delays, the PQ scheme performs poorly as well, with higher delays noticed as compared to NQ and DQ schemes. However, even temperature delays of the order of minutes are considered to be acceptable and temperature data traffic is not as delay-sensitive as ECG. In throughput results, the PQ scheme accomplishes a high ECG throughput when compared to NQ and DQ schemes, as PQ provides higher transmission rates of data.
Our contribution is thus threefold: 1. Develop a convolutional denosing autoencoder architecture for the detection of curved lines within a reflection (TD) image. 2. Implement a computationally efficient method that combines deep learning pre-processing with a probabilistic algorithm applied over a track-before-detect approach. 3. Create a statistically large-enough database containing clutter and reflections of acoustic patterns, which we freely share with the community for reproducibility and further research. Our results show that even in low Signal to Clutter Ratios (SCR), where the reflection pattern from the target is weak, our method yields a favorable trade-off between precision and recall, which exceeds the performance of fully probabilistic approaches (i. e. , the Viterbi algorithm) at a much lower computational complexity, and also allows for a more accurate fine-grained tracking of the target path.
Further, the results show that our approach easily scales to scenarios featuring multiple targets. The paper is organized as follows-in Section 2, we discuss the state of the art in probabilistic tracking and ML-based detection. Our system's model and objectives are outlined in Section 3, along with a description of the sea experiments that allowed to create the large-scale dataset of real measurements used for training and testing our system. Our CDA-TBD methodology is explained in Section 4, and system performance is analyzed in Section 5. Conclusions are drawn in Section 6. Preliminary results, which did not include the TBD method and only explored CDA performance on simulated data, have been recently presented as a conference paper  [19].
Data Description Our deep-learning-based solution requires the availability of a large database of TD matrices, annotated with the corresponding ground truth information about the locations of the target reflections (if any). Producing annotated samples in underwater scenarios is challenging. One option to circumvent this issue could be to train the deep network with a simulative model, and perform data augmentation to generate a large set of annotated images (e. g. , References  [48] [49] [50] ). However, we argue that this approach would fail for the considered task mainly because of two factors: (1) The TD matrix is a representation of a time-varying impulse response of the underwater acoustic reverberation channel. This channel is hard to model, especially due to the highly non-linear reflection pattern within the target's body, but also even for a simple clutter reflection from the non-homogeneous sea surface.
(2) The creation of the TD matrix based on the normalization in Equation (  1 ) is a non-linear operation that is hard to simulate. For example, in the proximity of a strong reflection the normalization factor would decrease the matched filter result, leading to a shadowing effect that highly depends on the SCR. In light of this, we opted for the creation of a database based on a sea campaign of measurements. To obtain our database we performed more than 50 sea experiments. Each experiment included a single transceiver deployed from a buoy or a small vessel. As shown in Figure  2 , data was obtained from two system configurations: a standalone Subnero M25M acoustic modem, which analyzed the data on the fly emitting signals at frequency range 20 kHz-30 kHz, and a remotely operated EvoLogics LF acoustic modem (S2C-R) that emitted 10 ms duration chip signals at the range of 7 kHz-17 kHz.
To verify the reliability of our tagging system, during the experiments we also included targets with verified ground truth information, such as divers dragging buoys with GPS receivers or sharks and tuna fish released after capture for tagging purposes. Further, among others, we identified opportunistic targets such as a dolphin, mackerel, and parrot fish. Our dataset is made freely available through the Open Science Framework (https://osf. io/h79mt/). Overall, our experiments yielded roughly 1000 different target-based TD matrices, and more than 20,000 clutter-based TD matrices. To balance the database and improve the generalization ability of the CDA, we implemented a data-mixing approach. More specifically, referring to the block diagram in Figure  3 , we augmented our database to create a larger number of 10,000 target-based TD metrics by slicing buffers of normalized matched filter outputs around identified target's reflection, and inserting those over clutter-based TD matrices according to a desired SCR value.
Using this methodology, we could also generate different types of reflection lines to reflect various target's dynamics. This was performed by a smoothed drunken step of an auto-regression model of the TD matrix column number where the target is inserted. These locations where then served as the ground truth information for the training and testing of the CDA. The result is a balanced database of 20,000 clutter-and target-based TD matrices: an example of such a formed target-based TD matrix is shown in Figure  3.
In the following, we show that our CDA-TBD approach clearly outperforms both benchmark solutions. However, we should note that CDA-TBD holds the disadvantage of setting threshold T L by for example, training, whereas, in the CDA-Max approach, both P act and ρ can be set by some knowledge about the motion and shape of the expected target. This observation emphasizes the need for a sufficiently large dataset, such as the one we share. Results are first qualitatively shown in terms of representative examples of TD matrices and their denoised version, over which we highlight the target path detected by our CDA-TBD approach. Samples might contain either a single target or multiple targets, and refer to different sea experiments. We then present quantitative measures referring to average errors and ROC curves computed over the entire dataset of more than 20,000 clutter-based TD matrices, separately grouped according to SCR level.
Conclusions In this paper, we presented an innovative CDA-TBD approach for the efficient detection of multiple mobile submerged targets by active acoustics. Our method takes as input a time-distance (TD) matrix, which concatenates reflections from a train of emitted signals. Motivated by the curved-like pattern created by the target along the time domain, the TD matrix is then filtered through a convolutional denoising autoencoder (CDA) in order to highlight potential patterns in the images. The CDA is trained by an augmented database collected during 50 designated sea experiments, performed under a variety of sea environments. The denoised image is further processed by a probabilistic track-before-detect (TBD) approach to choose paths that fits user-defined expectations about the targets' maximum size and velocity.
This is performed through dynamic programming such that, rather than exploring single reflections, all reflections are considered, thereby allowing detection and tracking even at very low signal-to-clutter ratios. Notably, combining dynamic programming with deep learning allows to cut down computational complexity, which makes the proposed approach a perfect candidate for low-power marine monitoring devices. Moreover, results over the collected dataset of sea experiments show a favourable detection-false alarm trade-off and far better tracking performance over two benchmark schemes. In order to promote further developments, we freely share our dataset with the community. As a promising research directions, in future work we will explore how detection performance might be improved by training the CDA in a completely unsupervised way, for example by implementing an anomaly detection scheme where a change in the structure of the clutter could be interpreted as the presence of a potential target.
Objectives: Alpha-mannosidosis is a rare autosomal recessive lysosomal storage disorder resulting from deficient lysosomal alpha-mannosidase activity. Clinical manifestations include progressive balance disorders, immune deficiency, skeletal abnormalities and cognitive deficits beginning in early childhood. Enzyme replacement therapy with recombinant human alpha-mannosidase (velmanase alfa) is scheduled for clinical development in the US beginning in 2020 and has been approved in the EU for treatment of non-neurological manifestations in cases of mild to moderate disease. This study assessed effects of velmanase alfa on fine and gross motor proficiency in children and adults. Methods: Integrated Bruininks-Oseretsky (BOT-2) test of Motor Proficiency data from velmanase alfa clinical trials was stratified by age for 14 adults and 19 children treated for up to 4 years.
Phase 1/2 and 3 clinical trials of velmanase alfa have demonstrated sustained decrease in serum oligosaccharides and improvements in physical endurance. [14, 15]. Long-term integrated analysis of clinical trial safety and efficacy data have shown that improvements in biochemical and functional measures were sustained for up to 4 years, with better functional improvements related to initiation of treatment at an early age  [16]. Post-hoc analysis using a global treatment response endpoint incorporating pharmacodynamic, functional, and quality of life measures showed that 87% of individuals receiving velmanase alfa were treatment responders compared to 30% receiving placebo  [17]. The Bruininks-Oseretsky test of Motor Proficiency -2nd Edition (BOT-2) is used to assess improvements in functional capacity in children and young adults with differentiated measures of gross and fine motor proficiency.
The BOT-2 is a standardized, normative assessment (including age-and gender-specific norms) for individuals 4-21 years of age and can be extended to use beyond 21 years without normative data. Although availability of BOT-2 assessments varies, it was administered during clinical development of velmanase alfa to assess proficiency in fine motor control, manual coordination, running speed and agility. Integrated analysis of patients enrolled in velmanase alfa trials showed that BOT-2 total point scores increased from baseline to last observation, and the percent change from baseline was statistically significant (p =. 035) [16]. In the previous publication, however, results were not stratified by age, and only total point scores for the study population were presented. In order to better determine changes in fine and gross motor proficiency over time, raw BOT-2 subtest data can be normalized by age and gender and converted to age equivalent scores for the entire population, and scale scores for pediatric patients.
Methods Data on BOT-2 and other functional outcomes were obtained from an integrated analysis of long-term safety and efficacy of velmanase alfa in 33 pediatric and adult patients with alpha-mannosidosis (rhLAMAN-10; NCT 02478840). All patients or caregivers provided informed consent. Individual patient data from previous phase 1, 2 or 3 velmanase alfa trials and extension studies were integrated into a single database for analyses. Data were included for patients receiving weekly intravenous infusions of 1 mg/kg velmanase alfa through either followon extension studies or compassionate use programs. Fig. 1  illustrates patient distribution and the contributing studies. Integrated analyses were evaluated in the overall population, in the adult cohort (≥18 years of age) and in the pediatric cohort (< 18 years of age).
Indeed, it is only when we take into account all disease and virus drivers, including Please use Adobe Acrobat Reader to read this book chapter for free. Just open this same document with Adobe Reader. If you do not have it, you can download it here. You can freely access the chapter at the Web Viewer here. biotic and abiotic factors of the natural and human environments, that some mechanisms of the Ebolavirus disease emergence, such as spread and circulation, can be ultimately unveiled. For that, we have collected all information available, often estimated, from the time and place of the virus emergence long before the emerging event was identified as it and the epidemic phase was brought to public attention. Moreover, when available we also collect all data on potential natural and accidental hosts, weather and environment chorology, among other multiple factors potentially involved.
Data Availability Statement Computing and data storage resources, including the Cheyenne supercomputer (doi:10. 5065/D6RX99HX), were provided by the Computational and Information Systems Laboratory (CISL) at NCAR. We thank Bodeker Scientific, funded by the DSNSC, NASA, and KNMI for providing the total-column ozone databases. Processed total-column ozone data sets and scripts needed to produce the figures and numerical results presented here are available online (at https://zenodo. org/record/3923155). Original data used in this study are all publicly available at https://acd-ext. gsfc. nasa. gov/Data_services/merged/data/additional_files/toms_ sbuv. v8. mod_v3. 78-05. za. rev2. txt, https://www. bodekerscientific. com/data/total-column-ozone, https:// www. temis. nl/protocols/O3global. html, and the CMIP6 portals (https://esgf-node.
Acknowledgments We thank  CERN  for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of  ANPCyT , Argentina; YerPhI, Armenia;  ARC, Australia ;  BMWFW  and  FWF , Austria; ANAS, Azerbaĳan; SSTC, Belarus;  CNPq  and  FAPESP, Brazil ;  NSERC ,  NRC  and  CFI ,  Canada ; CERN; CONICYT, Chile; CAS,  MOST  and  NSFC, China ;  COLCIENCIAS, Colombia; MSMT CR, MPO CR  and  VSC CR, Czech Republic ;  DNRF  and  DNSRC, Denmark ;  IN2P3-CNRS  and  CEA-DRF/IRFU , France; SRNSFG, Georgia; BMBF,  HGF  and  MPG , Germany; GSRT, Greece; RGC and  Hong Kong SAR, China ;  ISF and Benoziyo Center, Israel ;  INFN, Italy ;  MEXT  and  JSPS, Japan ; CNRST, Morocco; NWO, Netherlands; RCN, Norway;  MNiSW  and  NCN , Poland; FCT, Portugal; MNE/IFA, Romania;  MES of Russia  and  NRC KI , Russia Federation; JINR; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZŠ, Slovenia; DST/NRF, South Africa; MINECO, Spain;  SRC and Wallenberg Foundation, Sweden ;  SERI ,  SNSF  and  Cantons of Bern and Geneva, Switzerland; MOST, Taiwan ;  TAEK, Turkey; STFC, United Kingdom ;  DOE  and  NSF ,  United States of America.
Finally, the neutralising character of the detected antibodies depends on the viral antigens against which the detected antibodies are directed  [47] [48] [49] [50] [51] [52]. Consequently, the only place of serology in the diagnostic strategy to date is in addition to a chest CT-scan and a new PCR sample if the first PCR in a symptomatic patient is negative and the symptoms have been evolving for at least 7 to 10 days. New data may change its place in the diagnostic algorithm in the future, especially if it allows the formal detection of patients who are genuinely cured and protected against re-infection, so that surgery can be performed without risk for the patient and staff. For emergency surgery (Fig. 2 ) By definition non-deferrable, the surgery has to take place. However, PCR sampling should be performed in symptomatic or mildly symptomatic patients who have had close contact with a COVID-19 patient within the last 15 days, or who themselves have risk factors for severe forms of COVID-19 or are operated from surgery with postoperative respiratory risk.