dataesr/scientific-paragraphs-categorization huggingface dataset:
We also observed that DLWSS and OMP are robust to changes in channel conditions, which is expected since channel conditions' impact on the spectrum sensing is fairly limited. For the HSS spectrum (Fig. 8(b) ), the DLWSS significantly outperforms the OMP algorithm with an average performance improvement of around 24%. In practical deployments, spectrum occupancy changes dynamically with time and hence, knowledge of spectrum sparsity is not available. In Fig. 9 , we analyze the performance of the DLWSS and OMP algorithms when spectrum sparsity is not known. It is observed that the proposed DLWSS significantly outperforms the OMP algorithm at all range of SNRs with an average improvement of around 36. 4% and 31. 8% for for ESS and HSS dataset, respectively. In  Fig 10,  we compare the impact of sparsity on the performance of DLWSS and OMP algorithms when spectrum sparsity is unknown.
We consider two SNRs: 0 dB and 10 dB. We trained the DLWSS model using the dataset comprising an equal number of samples from each sparsity level. It can be observed that the DLWSS offers superior performance and significantly outperforms OMP as the sparsity level increases. Thus, results in Fig. 8 , Fig. 9  and Fig. 10  confirms the superiority of the DLWSS over the OMP based approach.
A. Hardware Software Co-Design (HSCD) A heterogeneous SoC such as Zynq SoC from Xilinx contains ARM Cores as the processing system (SW) and FPGA as the programmable logic (HW). An integral aspect of developing an efficient mapping of the DLWSS on the ZSoC is to design an optimal HSCD strategy to facilitate functionally accurate architecture for the desired latency and resource constraints. Specifically, we need to decide how to partition the algorithm between HW and SW and minimize the data communication overhead between them. The HSCD is important since sequential operations, scheduling tasks, and GUI are preferred on SW. At the same time, FPGA can efficiently handle the task, which can be accelerated via parallel processing. However, in certain situations, serial tasks are preferred on HW, while parallel tasks are preferred on SW to avoid data communication overhead between SW and HW.
For instance, Table  IV  shows the analysis to determine the optimal integer width for hardware realization of CV and FC layers of the DLWSS, where we infer the model on samples of the dataset to estimate the ranges of intermediate activations and the model weights. It can be observed that the minimum value of I is 9 and 2 bits for activation and weights, respectively. The value of W depends on the number of bits for accurate fractional number representation to get the desired functional performance, and we select them via heuristic experiments. We have designed and implemented DLWSS architectures of various WLs on ZSoC and analyzed their performance to identify appropriate WL for fractional number representation. In Table  V , we compare 10 different DLWSS architectures with fixed integer WL of 9 for activation and 2 for weights.
C. Impact of Memory Tiling Approach As discussed in Section IV, memory tiling is essential to enable an efficient implementation of the memory-intensive convolution layer operations on HW. In this section, we discuss the impact of tiling size on the performance of the DLWSS architecture realized on the SW and HW. In Fig. 12 , we compare the acceleration factor, i. e. the ratio of execution time on SW and HW, for different tiling sizes on SW and HW realizations. It can be observed that the acceleration factor  increases with the increase in tiling size of the HW architecture for a given tiling size of SW realization. Such behavior can be attributed to the following: 1) Higher tile size on HW means fewer accesses to external DDR memory since more data is buffered in on-chip block RAM. 2) Higher tile size allows more opportunities for intratile parallelization i.
e. dot products within a tile can be computed in parallel on HW. Interestingly, an increase in the tiling size of the SW realization results in degradation in the execution time of the DLWSS on SW. For instance, acceleration factor is higher for SW tiling size of < 20, 16, 20, 20 > compared to SW tiling size of < 2, 2, 2, 2 >. This behavior is exactly reverse compared to that of HW, and one possible reason is data caching in SW realization. Smaller tile size allows tiles to be cached in the local data cache memory of ARM cores, thereby limiting the number of accesses to external DDR memory. Since cache size is significantly smaller than on-chip BRAM on HW, larger tile size results in frequent cache flush requirements, resulting in execution time degradation. Though the tiling size does not impact functional accuracy, a larger tiling size leads to higher resource utilization, as shown in Table  VI.
Methodology To analyse the results of the elicitation study, we used the CSV data to replay the gestures in Unity after the study and code them. A first coder proceeded to create an initial gesture classification that was refined with another coder. Once all gestures coded, we measured the agreement rate (  ) for each referent. It was calculated using Equation 1 proposed by Tsandilas  [44]. Having a closer look at Tsandilas  [44]  notation,  is the total number of unique signs (i. e. gestures) produced,   is the number of occurrences of a sign for referent   , and   is the total number of signs suggested for referent. The overall agreement rate AR is the average of all. 𝐴𝑅 𝑖 = 𝑞 ∑︁ 𝑘=1 𝑛 𝑖𝑘 (𝑛 𝑖𝑘 -1) 𝑛 𝑖 (𝑛 𝑖 -1) (1).
Implementation We implemented a prototype integrating our gesture set and the optimisation approach using MRTK for Hololens 2 and Unity. 6. 3. 1 Gesture. We implemented the gestures with the MRTK core services input system, to detect hands and to get data from pointers. Once the environment is registered by the MRTK spatial awareness system, we used the integrated raycast pointer to perform interaction. For example, a surface container is created by pinching and dragging the raycast pointer on the environment mesh. When the system detects a drag gesture, the system saves the pointer position into a list. The system provides the user with visual feedback of the trace of the raycast, viewed through the AR display. When releasing the pinch, the system iterates over the list of previously created points and generates a container mesh.
Optimisation. Once the region with a specific parameter is created, we optimise our virtual content placement using the MRTK SolverHandler. Every virtual object has a Solver script attached to it. To attach the virtual content to a given region, we adapted the MRTK solver SurfaceMagnetism to our needs. By default, the SurfaceMagnetism allows virtual objects to be attached to surfaces when looking or pointing at them, as well as to the custom surfaces. Since we wanted to specify the region where to attach our virtual objects, we added weights to each type of region and changed them dynamically depending on the type of constraint applied to the region. The system then searches for the highest region weight and uses it to attach virtual objects. Another default behaviour of SurfaceMagnetism we wanted to change is virtual object attachment  to the center of the custom Transform.
ACKNOWLEDGMENTS This work was supported by the  ANR JCJC   PERFIN  grant ( ANR-18-CE33-0009 ) on pervasive freeform interfaces [39,40] and by the  ANR   PRC Interplay  ( ANR-21-CE33-0022 ). For the purpose of open access, the author has applied a CC-BY public copyright licence to any Author Accepted Manuscript (AAM) version arising from this submission.
Recent deep-learning techniques have made it possible to manipulate facial expressions in digital photographs or videos, however, these techniques still lack fine and personalized ways to control their creation. Moreover, current technologies are highly dependent on large labeled databases, which limits the range and complexity of expressions that can be modeled. Thus, these technologies cannot deal with non-basic emotions. In this paper, we propose a novel interdisciplinary approach combining the Generative Adversarial Network (GAN) with a technique inspired by cognitive sciences, psychophysical reverse correlation. Reverse correlation is a data-driven method able to extract an observer's 'mental representation' of what a given facial expression should look like. Our approach can generate 1) personalized facial expression prototypes, 2) of basic emotions, and non-basic emotions that are not available in existing databases, and 3) without the need for expertise.
e. , happiness, anger, sadness, surprise, fear, and disgust  [13]. Non-basic emotion labels, such as self-confidence, are not explicitly available in existing databases. In addition to the lack of large labeled data, creating such a database with various emotion labels comes with many concerns: 1) time-consuming for the annotation and 2) requiring expertise (e. g. , certified FACS coders  [13] ) for some labeling tasks.
As a result, the entire facial expressions (global attributes) are copied from the source video to the target video. The other attributes that are not related to the facial expression such as gender, age, face shape, face structure (global attributes), and hairstyle, beard, accessories (local attributes) are totally the same as the target video. In this task, the Face2Face model  [31]  and the dual-generator-based approach  [32]  use 3D landmarks to encode head pose, face shape, and facial expressions, and approaches such as ReenactGAN  [33]  and FReeNet  [34]  use 2D facial landmarks. Differing from face reenactment that directly copies facial expressions, the following approaches aim at editing the overall expressions. StarGAN  [4]  proposed a unified model that transfers real faces from one of the six basic emotions to another.
G2-GAN  [25]  employed the overall facial geometry as controllable parameters to synthesize the six basic emotions. By modeling the motion of facial landmarks as curves encoded as points on a hypersphere, Otberdout et al. [26]  proposed an approach that generated the six basic emotions from a given neutral face. Although the purpose of these mentioned approaches is generating realistic facial expressions, manipulating global attributes for FEM can not fulfill the aforementioned requirements (see Subsection Requirements). Flexibility: For a given emotion, the prototypes of this emotion can be limited by the database. For instance, if all the happy faces in the database have lip corners raised and mouths opened, the model cannot August 18, 2023 4/21 generate a happy face with lip corners raised but with mouth closed.
Exhaustiveness: Due to the limitation of the database that only a few emotion labels are available, these FEM models  [4, 25, 26]  that directly controlled global attributes (such as emotion labels and overall facial landmarks) can only deal with limited emotion categories such as the six basic emotions of Ekman. However, the reality is that there are already more than 4000 emotional labels  [12]. Local attribute manipulation. Otberdout et al  [29]  proposed an approach both modeling the temporal dynamics of expressions and deforming the neutral mesh to obtain the expressive counterpart. This approach achieved dynamically generating different local facial movements mostly around the mouth (e. g. , bare teeth, high smile, lips up, mouth down, mouth extreme, mouth open). Zaied et al  [27, 28]  proposed geometric and geometric-machine learning methods mainly to personalize smiles.
For more information, see S1 Appendix in supporting information. In this paper, we chose GANimation  [3]  as a tool to synthesize facial expressions by controlling local attributes (i. e. , AUs). Compared to editing global attributes such as emotions of the face, editing AUs (i. e. , local attributes) can achieve fine-grained control and thus have the potential to personalize facial expressions that meet different needs of different observers. This has the potential to fulfill the requirement of Flexibility. Even though GANimation  [3]  can generate various facial expressions by combining different AUs, it lacks the ability to determine which AUs should be activated to generate the desired facial expressions that are not explicitly available in the existing database. This limitation arises from the need for expert knowledge to identify the appropriate AUs for generating the desired facial expression.
Experiment setting Implementation: GANimation model. We choose GANimation  [3]  as the tool to generate facial expressions by editing local attributes, namely action units (AUs)  [13]. We use the code of GANimation released by its authors. All settings are unchanged. The input image and the output image are 148px × 148px. To crop, align, and resize the face, we employ OpenFace  [43]. Implementation: mental representation computation. As aforementioned, we determine a dominant AU for one emotion as the action unit that dominantly drives the observer's perception. For the complementary AUs, we need to determine which AUs combined with the dominant AU have a significant effect on driving the observer's perception. Therefore, we need to set a relatively high threshold T q to eliminate most AUs with less significant proportions P (j|Ω {d,j * } ).
7yo) adults of three cultural groups: Brazil (1), China (2), and France (1), respectively denoted by observers #1 to #4. Only one observer had experience in affective computing, and nobody is a certified coder in Facial Action Coding System  [13]  or a psychologist. Each observer signed informed consent, and the experimental data were anonymous. Experimental protocol: perceptual experiment. The perceptual experiment aims to illustrate that our approach can personalize the facial expressions of a given emotion, even though this emotion is not available in existing deep-learning databases. Note that the purpose of the perceptual experiment is not to give extensive results or to discuss facial expression prototypes. We chose three basic emotions (happiness, sadness, and anger) that existed in deep-learning databases and one non-basic emotion (self-confidence) that is not explicitly available in existing deep-learning databases.
Personalized prototypes We reconstruct the personalized prototypes of each observer by activating the dominant and complementary AUs. Fig  4  shows the personalized prototype of each observer, as well as state-of-the-art prototypes from the literature  [13, 18]  (denoted by "Ek" and "Yu"). The corresponding activated AUs are listed at the bottom of the faces. For the personalized prototypes, we highlight the dominant AU in square brackets; the others are the complementary AUs. Note that there is not any published database of confidence, and there is no state-of-the-art prototype for confidence. According to the results in  Fig 4,  we observe that the personalized prototypes are compatible with state-of-art prototypes. All 12 prototypes of basic emotions generally convey expressions similar to that of Ekman  [13]  and Yu  [18].
All dominant AUs: AU12 for happiness, AU4 or AU15 for sadness, and AU4 or AU9 for anger, can be found in state-of-the-art prototypes. The prototypes are personalized. In each perceptual experiment task, although observers were asked the same questions, all observers acquired subtly different mental August 18, 2023 representations and, especially, different complementary AUs. With the exception of observer #1-sad and Ekman  [13] , all synthesized facial expressions also differed from the state-of-the-art prototypes by at least one AU. For instance, observer #2-happy is the same as Yu  [18] , plus the addition of AU20 (lip stretcher), resulting in a wider smile. Our manipulations can be extended to the emotions that are not available in existing databases, such as confidence. We had no comparison prototypes for confidence.
Procedure In detail, each one performed 4 ranking tasks. Each task corresponds to one of the four emotions. In each ranking task, all prototypes (6 listed prototypes for happy, sad, and angry, 4 listed prototypes for confident as in  Fig 4)  were presented anonymously and shuffled. Participants were asked to rank these faces from the happiest / saddest / angriest / most confident to the least. Everyone was informed that all data collected were totally anonymous. For the "observers" group. In order to investigate if the prototypes are really personalized, we calculate the probability that each observer ranks his/her own personalized prototypes in each position (i. e. , from the 1st place to the 6th place). For the "non-observers" group. Since "non-observers" are not involved in personalizing these prototypes, the measurement applicable to the "observers" group is not suitable for the "non-observers" group.
As mentioned in Convergence efficiency, the procedure can be greatly reduced, to about 270 trials, without losing accuracy. While this amount of 'training' data is several orders of magnitude smaller than what would be needed to e. g. train a GAN from a dataset of annotated examples for each emotion, the time burden on observers (about 20 minutes) can still be high in certain application contexts. An automatic optimization process can be considered to further speed up the process. Finally, a limitation common to our approach and relative reverse correlation work is that all the stimuli are unimodal. Reverse-correlation multimodal prototypes (i. e. , both how a face should look and how it should sound) have the potential to enrich affective computing studies such as  [47]  in the future.
A point of departure (PoD) was estimated using OECD guidance values for a benchmark response. The resulting benchmark dose estimate was translated back to human concentration values using a similar mixture approach (SMACH)  [3]. In the first step, the subset of the cohort with sufficiently similar exposures to the reference mixture (MIX N) was established. In the second step, a Similar Mixture Risk Index (SMRI) was estimated based on exposure concentrations relative to the PoD, where values exceeding 1 indicate exposures of concern. The objective of this paper was to perform a SMACH analysis and identify risk in US populations based on the derived PoD from MIX N as described in Caporale et al. [3]. To do that, biomonitoring data were extracted from the US National Health and Nutrition Examination Survey (NHANES) database and translated into plasma concentrations using gestation physiologically based toxicokinetic (PBTK) modeling.
Study Participants Participants included subjects from NHANES cycles G-I (2011-2016) with human biomonitoring (HBM) values for the eight compounds, including phthalates (urinary): Mono-ethyl phthalate (MEP), Mono-n-butyl phthalate (MBP), Mono-benzyl phthalate (MBzP), Monoisononyl phthalate (MINP), and bisphenol A, (BPA); as well as perfluorinated compounds (serum): Perfluorohexane sulfonic acid (PFHxS), Perfluorononanoic acid (PFNA), and Perfluorooctane sulfonic acid (PFOS). Demographic values included age, sex, pregnancy survey question, and NHANES respondent identifiers (i. e. , survey sampling weights and sequence number). Survey sampling weights were included as a measure to evaluate how representative the collected dataset is to the population. For further analysis, female participants between 15 and 46 years of age were selected and represent women of reproductive age.
From In Vitro to Cohort The reference mixture described in Caporale et al. [3]  for use in SMACH consists of 8 EDCs with human-relevant mixing proportions given in Table  1. Derivation of mixing proportions and description of the tested in vitro assay have been described previously  [3]. In brief, analysis of biomonitoring data collected during gestation from a Swedish pregnancy cohort, the SELMA study, highlighted 8 EDCs associated with language delay based on weights from a Weighted Quantile Sum (WQS) regression analysis (e. g. ,  [3] ). Biomonitoring data were derived from measurements in urine or plasma at the 10th week of gestation (GW10). Caporale et al. [3]  proposed a method to determine the maternal plasma concentrations for all 8 EDCs, and the derived proportions were defined as MIX N (Table  1 ).
MIX N was assessed using the XETA assay  [6]  in a dose response (0. 1-1000X, with 1X concentrations presented in Table  1 ). The derived PoD of MIX N from the XETA assay was the basis to perform a SMACH analysis in the SELMA cohort. For those women with exposures sufficiently similar to MIX N, a SMRI was calculated and exposures of concern were determined based on values exceeding 1. Input data for SMACH analysis are plasma concentrations in µM. For PFHxS, PFNA, PFOS, NHANES measurements were performed in serum and expressed in µg/L. For MEP, MBP, MBzP, MINP, and BPA, NHANES measurements were performed in urine (ng/mL) and toxicokinetic modeling was performed to estimate plasma concentrations. To do that, the solve_pbtk function of httk was used  [7, 8]. The httk is an R software package for high-throughput toxicokinetics that integrates (a) calculation of chemical parameters to describe partition across modeled tissues based on pKa, logKow, molecular weight (MW) and fraction unbound, (b) experimentally derived hepatic clearance rates for more than 9600 chemicals, and (c) physiological parametrization for toxicokinetic simulations  [7].
Testing for Sufficient Similarity Following Marshall et al. [14] , we tested for sufficient similarity to the experimentally evaluated reference mixture (Table  1 ) using an equivalence testing approach. In short, we evaluated the similarity between an individual's plasma concentrations to the reference mixture using the Euclidean distance between the benchmark dose (BMD) of the two mixtures. The BMD, and lower one-sided confidence limit BMDL, for the reference mixture is based on experimental results; in the data poor case described by Marshall et al. [14]  (Equation (  5 )), we estimated the BMD for the ith subject's mixture by adjusting the total dose of the mixture to that of the reference mixture (i. e. , Tr ). The estimated distance measure and its variance estimate are then given by di = Tr Using equivalence testing methodology, we tested for sufficient similarity using the principle of confidence interval inclusion, i.
Results NHANES data were extracted from the CDC website from the 2011-16 cycles. There were n = 5735 participants with concentrations from the eight EDCs in MIX N. Using sampling weights, these participants represent nearly 242 million non-institutionalized people in the United States (Table  2 ). The concentrations are expected to be somewhat similar between the estimates for the full population and women of reproductive age (Table  2 ). Additionally, included in Table  2  are published biomonitoring guidance values, retrieved by the HB2GV Dashboard (https://biomonitoring. shinyapps. io/guidance/ (accessed on 25 January 2023)). As seen in Table  2 , the BE values generally exceed the 95th percentiles from NHANES, with the notable exception of PFOS. In contrast to comparing human exposures to BE values one chemical at a time, we used a similar mixture approach.
A similar mixture approach (SMACH) incorporates experimental evidence of adversities from a human-relevant mixture and compares its estimated PoD to biomonitoring concentrations. Epidemiological observations from one cohort are anchored with experimentally derived evidence. The derived PoD is relevant to plasma concentrations; therefore, by using toxicokinetics, it is possible for cross-study comparisons where geographical and temporal variables of the study are not to be considered as confounding factors. For the present work, toxicokinetic modeling was performed with the httk package developed by the US Environmental Protection Agency to translate urinary concentrations from study participants to estimate plasma concentrations so that all compounds are expressed in the same units as the mixture.