dataesr/scientific-paragraphs-categorization huggingface dataset:
Weighted gene co-expression networks analysis WGCNA was used to identify gene co-expression networks associated with clinicopathological factors of asthma. For example, the GSE43696 dataset contains all clinical information of asthma severity in the Gene Expression Omnibus database. In total, 108 samples identified the severity of asthma and 30,723 genes were included. As module identification required intensive computation, the top 5,000 genes with highest expression variance and closely connected were selected to construct the weighted gene co-expression network. Then, a correlation matrix was constructed using calculated pairwise Pearson Correlations among all genes. To achieve a scale-free network, β = 8 was used as the proper soft-thresholding power to convert the pairwise correlation into an adjacency matrix of connection strengths (connection strength = |correlation| β ).
To identify gene modules, a dissimilarity matrix with via a dynamic tree-cutting algorithm was used based on the topological overlap measure. All gene modules were allocated with appropriate colors. The gene modules with similar expression profiles were also merged. these gene modules using the Gostats package in R  (Falcon & Gentleman, 2007). The hypergeometric test was used to estimate the GO term association, while the P value was adjusted by the Benjamini-Hochberg method. Gene modules were named according to the most significant GO enrichment.
Development of a random forest model and feature selection A tenfold cross validation (CV) technique was used to build and verify the 108 samples. The entire dataset was randomly divided into 10 subsets, with approximately 10% test data. In each round of CV, 9 subsets were used to train the model and to predict the outcome of tested subset. This process was performed 10 times until each subset was fully tested. The statistical indicators, such as out of the bag (OOB) estimates of error rate between the CV predictions and the observed values, were used to evaluate the prediction accuracy of the model. Then, recursive feature elimination based on random forest analysis was used to select the feature genes associated with asthma severity  (Nguyen & Ohn, 2006). Recursive feature elimination random forest algorithm is a built-in feature selector, which follows the backward elimination method.
Construction of weight gene co-expression network The WGCNA was performed to identify the gene co-expression networks associated with the clinicopathological factors for asthma. The asthma dataset, namely GSE43696, was adopted from the GEO database  (Voraphani et al. , 2014). It worth noting that soft threshold is a key parameter for WGCNA to measure gene relationship. Adjusting soft threshold can convert simulated gene network into justified biological network. In this regard, when soft thresholding is adjusted to value 8, the simulated gene network has the optimal correlation to the real biological network (Fig. 1 ). After this soft threshold of 8 was implemented, 18 significant gene modules were thus detected (Fig. 2 ). The relationships between gene modules are shown in Fig. 3. The results indicated that some gene modules strongly correlated with each other, such as red and black, midnight blue and tan, tan and dark green, as well as midnight blue and purple.
Gene ontology and pathway enrichment analysis of gene modules associated with asthma severity The biological functions of the gene modules associated with asthma severity were explored by GO term enrichment analysis. For these module genes, the significant enriched terms in the GO and pathway databases were the followings: ''regulation of hormone secretion'', ''actin filament organization'', ''negative regulation of immune response'', ''regulation of blood coagulation'', ''G-protein-coupled receptor signaling pathway'', ''epithelial-tomesenchymal transition'', and ''lipid homeostasis'' (Table  1 ). Thus, the enriched terms in the annotation systems were related to different pathological mechanisms.
Calculation of module-trait correlations in asthma severity For each module, correlations between gene expression and asthma severity were calculated. Multiple gene modules were found to be associated with asthma severity after WGCNA, each named after their representative color: black, red, tan, dark red, dark green, light yellow, and   1 ). The significance of module-trait relationship is shown in Fig. 4. The result showed that different pathological mechanisms had varying degrees of change from mild to severe asthma. Some biological functions decreased, including hormone release, airway remodeling, and activation of the G-protein-coupled receptor. Other biological functions increased, including blood coagulation and angiogenesis, transition from epithelial to mesenchymal, and negative regulation of immune response.
Combination of feature genes selection and WGCNA The feature genes, which were screened out based on the random forest analysis, were endowed with clinical significance using WGCNA. Then, the feature genes were clustered according to the specific pathological process (Fig. 10 ). Most genes were classified in a few pathogenese. Some feature genes were classified in the hormone secretion regulation, including SEMA3E, PER2, WNK4 and SYT13; some in the airway remodeling, including CPXM1, TLL1 and NAT8B; some in the blood coagulation and angiogenesis, including DNAJC1, COMTD1, and SLC9B1; and some in the negative regulation of immune response, including KCNK6, COPZ2, and SOD2. Overall, 22 of the 37 genes were not classified in the WGCNA gene module or significant gene modules associated with asthma severity.
These feature genes can accurately distinguish different severity of asthma (Fig. 9 ), playing an essential role in asthma. In previous analysis of this asthma dataset (GSE43696), thyroid peroxidase (TPO) plays an important role in asthma  (Voraphani et al. , 2014). TPO and its metabolome drives nitrative stress in severe asthma. Similarly, TPO is attributed to the feature gene set after the screening of feature genes in our study. These gene sets can effectively distinguish severe asthma patients from the control. However according to the classification, feature gene contribution shows that TPO is low-ranked in the feature gene set. Thus, asthma, a complex disease, is more likely to be the result of multi-gene interactions. Due to the multiple functions of genes, it is challenging to locate the exact asthma mechanism  (Cao et al.
A photovoltaic-thermal (PVT) collector is a solar-based micro-cogeneration system which generates simultaneously heat and power for buildings. The novelty of this paper is to conduct energy and exergy analysis on PVT collector performance under two different European climate conditions. The performance of the PVT collector is compared to a PV panel. Finally, the PVT design is optimized in terms of thermal and electrical exergy efficiencies. The optimized PVT designs are compared to the PV panel performance as well. The main focus is to find out if the PVT is still competitive with the PV panel electrical output , after maximizing its thermal exergy efficiency. The PVT collector is modelled into Matlab/Simulink to evaluate its performance under varying weather conditions. The PV panel is modelled with the CARNOT toolbox library.
The optimization is conducted using Matlab gamultiobj-function based on Non-Dominated Sorting Genetic Algorithm-II (NSGA-II). The results indicated 7. 7% higher annual energy production in Strasbourg. However, the exergy analysis revealed a better quality of thermal energy in Tampere with 72. 9% higher thermal exergy production. The electrical output of the PVT is higher than from the PV during the summer months. The thermal exergy-driven PVT design is still competitive compared to the PV panel electrical output.
The operation of the PVT collector is highly dynamic and influenced on the time-varying weather conditions. Due to this steady-state analysis of the PVT performance is not sufficient but a dynamic model has to be implemented to evaluate the performance of the collector. In the literature, a range of theoretical PVT models has been introduced and validated based on the experimental data and literature  [7] [8] [9]. Da Silva and Fernandes  [10]  conducted the dynamic simulations of the PVT collector combined with thermal storage. They implemented a dynamic model of the PVT system into the modular environment of Simulink in Matlab. The model was validated based on the literature and showed good agreement. The PVT collector performance is highly depending on weather. Because of that, it is important to study the PVT performance in different locations with different climate conditions.
The further contribution of this paper is to study the multi-objective optimization method of the PVT collector design with the genetic algorithm to optimize electrical and thermal exergy efficiencies and to understand how different PVT variables behave in the optimized design configurations. The Pareto optimal set of design solutions is derived The contribution is extended to compare selected optimized PVT design configurations with the PV panel performance and to find out if the PVT is still competitive with the PV panel electrical output , after maximizing its thermal exergy efficiency. First, the PVT collector and considered climate conditions are described. Next, the mathematical model of the PVT collector is pr esented. The covering equations of the model are implemented in Matlab/Simulink, which is a programming environment for numerical computation and data analysis.
The similar data for Strasbourg is obtained from the Meteonorm Database. The following Figures present samples of the hourly climate data of Tampere and Strasbourg. Figure  2  shows the solar irradiation, ambient temperature and wind speed, respectively, during a summer week in Tampere and Strasbourg. In Figure  2a  can be seen that during the summer period the solar irradiation in Tampere can reach the same maximum values around 800 W/m 2 than in Strasbourg. Figure  2b  presents the ambient temperature and it can be seen again that in Tampere as high temperatures as in Strasbourg can be reached. Figure  2c  shows that the wind speed is more stable in Tampere and stays the most of the time between 2. 5 m/s to 5 m/s. In Strasbourg the wind speed is really low the most of the week getting the values below 2.
Tf is the mean temperature of the fluid. The mass flow rate of the fluid in the channels is (kg/s) and Tf,in and Tf,out are the inlet and outlet temperatures of the fluid, respectively. The full derivation of the governing equations is presented by Chow  [8]. The Equations  (2, 9, 16, 22, 26, 27)  are implemented into Matlab/Simulink to assess the energy performance of the PVT collector. The following thermal, electrical and overall efficiencies can be calculated:.
Multi-objective optimization using gamultiobj-function In this study, the Matlab function called gamultiobj of the Global Optimization Toolbox is used to run the multi-objective optimization of the PVT collector design. The gamultiobj-function creates a set of optimal solutions in the space of decision variables on the Pareto front. The function uses a controlled, elitist genetic algorithm, which is a variant of NSGA-II  [29]. Generally, the multi-objective optimization problem of two or more conflicting objectives is described mathematically as follows  [15] : Minimized/Maximized fm(x), m = 1,2,…,M; Subject to gj(x) ≥ 0, j = 1,2,…,J; hk(x) = 0, k = 1,2,…,K; xi (L) ≤ x ≤ xi (U) , i = 1,2,…,n; In this formulation, the objective functions are minimized or maximized subject to certain constraints.
Multi-objective optimization The simulation-based multi-objective optimization process was conducted with the dynamic PVT model in Matlab/Simulink. The main objective was to find different optimal design solutions for the PVT collector depending on its operational purpose, which can be defined by the user. Additionally, the objective was to study the impact of the decision variables, such as water mass flow rate and inlet temperature, on the comparison between the PV panel and PVT collector electrical output and PV cell operating temperature. First, the selected decision variables were varied and the initial population and score matrixes for the genetic algorithm were generated by the simulation model. These matrices were given for the gamultiobj-function, which started the iterative process by evaluating the fitness function with the simulation model and making selection, crossover and mutation of the population.
Conclusion The objective and novelty of this study were to conduct energy and exergy analysis of the PVT performance under two different European climate conditions in Tam pere, Finland and Strasbourg, France. The dynamic model of the water -cooled PVT collector was implemented into Matlab/Simulink to assess the collector performance under varying weather conditions. Additionally, the objective was to conduct a numerical comparison between the PVT collector and PV panel in terms of the PV cell operating temperature, efficiency and electrical output. In the first comparison, the packing factor of the PVT collector was varying, and its influence on the operating temperature and electrical output was investigated. The main findings of the energy and exergy analyses were:.
Methodology The analysis presented in this paper required an indepth understanding of Apple Continuity protocols for which most specifications are not public. Hence, we had to rely on additional sources of information namely BLE traffic traces, outputs of Apple debugging tools and disassembled binaries. Although limited in details and quantity, there exist some official documents about Apple Continuity protocols and associated services. Such documents include the iOS security guide  [19]  and the HomeKit Accessory Protocol (HAP) specifications  [20]. As Apple Continuity messages are embedded in BLE advertisement packets, capture and generation of those packets were at the core of our study. Capture and generation of advertisement packets were respectively done using a sniffer based on the bluepy 3  python library and a bash script based on hcitool  4.
Those tools, available on the Apple Developer 5  website, provide information on the content of received BLE frames, as well as on steps in the corresponding protocol. Finally, we had recourse to disassembling to gain additional knowledge on Apple Continuity protocols. We used the Hopper 6  software to disassemble several macOS binaries such as PacketLogger, PacketDecoder, CoreSpeech, HomeKit Accessory Simulator and sharingd. Based on the disassembled codes, we were able to identify the precise format of continuity messages, and the signification of some codes (device model, activity level, action type, etc. ). We were also able to analyze the implementation of some functionalities such as the perceptual hash of Siri voice commands (see Section 9). activity. For instance, advertisement packets generated by an iPhone will always include a Nearby Info message, and will occasionally include a Handoff message when a compatible application is running.
Content protection The Apple Continuity messages are never encrypted as a whole and their content is thus exposed in clear as part of advertisement packets. However, Proximity Pairing and Handoff messages include an encrypted payload (through AES-128 and 256 in ECB mode). Other elements of data are not transmitted in clear but are hashed using SHA-256 then truncated. This is the case of Wi-Fi SSIDs, e-mail addresses and phone numbers found in AirDrop  [19, p. 45]  and some Nearby Action messages (see  Section 8). Finally, Handoff, Nearby Action and Nearby Info messages are authenticated through an Auth Tag that is computed using AES-256 in GCM mode  [19, p. 42].
Magic Switch The Magic Switch protocol is undocumented but appears to be related to the watchOS operating system. As observed by  [1] , only Watches send those messages. Magic Switch messages are transmitted when the Watch has lost the Bluetooth connection to its paired iPhone and when its screen is on. Those messages (see Fig. 8 ) include a 2-byte Data field that can expose the user to tracking (see Section 4) followed by a Confidence on Wrist field that appears to be an indicator that the watch is worn (see Table  9  in Appendix A).
The devices were installed in the office of one of the authors and the HomeKit messages transmitted over BLE were recorded by a sniffing device. The GSN was extracted from the captured BLE frames and translated into a temporal sequence of state changes for each device. The corresponding traces are shown in Fig. 17 , in parallel with the time of presence and activity of the occupant. It is possible to leverage the GSN to get insight on the activity in the office. First, any movement in the field of the motion sensor will induce a change of GSN, thus revealing activity and presence in the room. Similarly, turning on or off the lightbulb will be reflected by a change of GSN. Note that, manually turning on/off the light using a physical switch also triggers a GSN change. As shown on Fig. 17 , GSN of the motion sensor is a good indicator of presence in the office, while the data provided by the lightbulb reveal the boundaries of the office work activity.
A posteriori confirmation In the case where the guesswork returns several identifiers, the attacker will need additional information to narrow down the one used by the iCloud account. One possible approach to perform this confirmation is to exploit the device status information leaked by Nearby Info messages through an event correlation attack (see Section 6. 1). The attacker can send messages to each identifier while monitoring for a device that goes from idle (code 0x03) to active (code 0x07). The attacker can also rely on visual observations: the target will pick up the phone or read the message.
Phone numbers Phone numbers follow a general format that corresponds to 8. 11 × 10 14 possible values  [37]. This space can be significantly reduced if the attacker has additional information on the target such as the region in which the phone number is registered. This set can be further reduced by selecting a subdivision of the geographical regions or number ranges corresponding to specific operators. In particular, the attacker could focus on phone numbers of mobile operators as iCloud are usually configured with a mobile phone number. A resourceful attacker can also have access to a database of all registered phone numbers in a region. In order to evaluate a potential attack, we considered several sets that an attacker could use: 1) all possible values following the number format of a region, 2) all possible values following the mobile number format of a region and 3) all registered mobile/landline numbers in the region.
E-mail addresses E-mail addresses can be up to 307 characters 10 long  [36]  corresponding to 2 1666 different values. Thus, it is not realistic to envision an exhaustive search over all the possible values, and the attacker will have to rely on dictionary of practical size for the attack. To build such a dictionary, an attacker could either obtain a list of existing e-mails or try to generate e-mail addresses by assembling names and domains. For the sake of simplicity, we will focus on the first case (list of existing e-mails), and point the interested readers to existing works on synthesis of e-mail addresses and identifiers  [36]. We considered several lists that could be used by an attacker: 1) all existing e-mail addresses worldwide, 2) addresses found in leaked database  [38] , 3) addresses of Gmail users, 4) addresses of university department and 5) addresses of PETS 2017 participants.
Observations on Siri's perceptual hash The perceptual hash included in "Hey Siri" messages is 16-bit long, so twice as short as the output of the standard PRH algorithm. By analyzing the CoreSpeech binary, we identified the function in charge of computing the perceptual hash. This function, called pHash(. ), takes as input an audio signal and returns a 16-bit integer. We were unable to fully reverse engineer the function, however we were able to identify several characteristics: it uses a FFT, it computes energy in the frequency domain and bits of the hashes are computed based on the differences between consecutive elements of a buffer. All those features suggest that this code implements a variant of the PRH algorithm. We analyzed the stability of the hashes among different users using an experiment involving two male users.
Our contribution aims at developing a system able to detect specific objects, track them and measure their depth, for our smart wheelchair. For that objective, we use a combination of a deep-learning algorithm for object detection and various techniques for the other tasks. For object detection, we developed a specific dataset, and validated the complete approach through the ESIGELEC Autonomous Navigation Laboratory (ANL). Our objective is not only to develop three key steps in wheelchair environment perception, namely: detection, localization and tracking, but also to implement them on the wheelchair. For that matter, we intend on taking into account the embedded system constraints such as real time and CPU/GPU computing resources, and memory space, in order to increase the wheelchair autonomy in terms of semi-autonomous navigation.
The model is trained on stereo images but infers disparity maps from monocular images. Instead of using ground-truth depth data, the model exploits the fact that depth information can be trivially computed from a calibrated stereo camera setup using disparity images. In  [13] , a Multi-Scale Local Planar Guidance model is presented for depth estimation. In the same category, DenseDepth  [14]  has an encoder part which is initialized with meaningful weights and based on a pretrained DenseNet  [15] , and then trained with NYU Depth v2  [16] , Unreal-1K  [14]  and KITTI datasets. The approach gives a depth map that capture object boundaries more faithfully, thanks to a specific loss function penalizing high-frequency distortion. In this work, we chose to rely directly on a depth sensor instead of using the depth estimation algorithm (that we have used in a previous work  [17] ), in order to alleviate the computational cost.
Tracking Methods For the benchmarking of object-tracking algorithms, a library of source-code, an annotated dataset and all the tracking results are available online  [18]. Here, we focus on realtime algorithms, as it is a strong constraint for our application. In  [19] , single-target tracking is performed at 100 Frames Per Second (FPS) on a GPU by means of Deep Regression Networks (DRN), after training with a set of labeled videos and images. A search region from the current frame and a target from the previous frame are given as input to a 2-branch CNN with a simple architecture. Another algorithm called SORT (for Simple Online Realtime Tracking)  [20]  is also aimed at running in realtime, with a FPS of 260 on a single-core CPU. It is based on Faster R-CNN and uses Kalman filter for prediction of target positions in future frames.
Datasets For object detection and tracking, we need one or more datasets for the learning of the CNNs. We are primarily interested by datasets that contain classes directly related to our application (i. e. , doors and door handles). Datasets that do not include these objects can nonetheless be useful to pre-train the first layers of a CNN, as those layers learn features low level image features. Various datasets are freely available for object detection. ImageNet [28] includes 14,000,000 object instances from 1000 classes  [29]. In Open Images  [30] , a community dataset with over 9,000,000 images, objects are labeled with ROI. It consists of 7881 different classes  [31]  very varied and constantly expanding. COCO  [32]  includes 1,500,000 images for which the objects from 80 classes are labeled and categorized.