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The learning rate of the extant algorithm is also not desirable [13]. Recently, a new fast algorithm called binary log-linear learning algorithm (BLLA) has been proposed by [14]. <|MaskedSetence|> It means that UAVs are not permitted to update strategies at the same time. <|MaskedSetence|> If the algorithm can learn synchronously, more than one UAV can update strategies based on the current game state in one iteration. <|MaskedSetence|> To sum up, synchronous update algorithms which can learn from previous experiences are desirable, but only a little research investigated on it. .

**A**: Besides, to determine which UAV to update strategy, the coordinating process will occupy plenty of channel capacities and require more time between two iterations [15]. **B**: Thus, the algorithm can be more efficient. **C**: However, in this algorithm, only one UAV is allowed to change strategy in one iteration based on current game state, and then another UAV changes strategy in the next iteration based on the new game state.

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<|MaskedSetence|> Incorporating domain/prior knowledge (such as coding the location of different organs explicitly in a deep model) is more sensible in the medical datasets. As shown in Taghanaki et al. <|MaskedSetence|> <|MaskedSetence|> However, the cross-entropy loss returns a reasonable score for the same cases. Besides using integrated cross-entropy based loss functions, future work can be exploring a single loss function that follows the behavior of the cross-entropy and at the same time, offers more features such capturing contour distance. This can be achieved by revisiting the current distance and overlap based loss functions. Another future path can be exploring auto loss function (or regularization term) search similar to the neural architecture search mentioned above. Similarly, gradient based optimizations based on Sobolev (Adams and Fournier, 2003) gradients (Czarnecki et al., 2017), such as the works of Goceri (2019b, 2020) are an interesting research direction. .

**A**: (2019e), when only a distance-based or overlap-based loss function is used in a network, and the final layer applies sigmoid function, the risk of gradient vanishing increases. **B**: Although overlap based loss function are used in case of a class imbalance (small foregrounds), in Figure 13, we show how using (only) overlap based loss functions as the main term can be problematic for smooth optimization where they highly penalize a model under/over-segmenting a small foreground. **C**: In medical image segmentation works, researchers have converged toward using classical cross-entropy loss functions along with a second distance or overlap based functions.

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<|MaskedSetence|> <|MaskedSetence|> First, the algorithm used to predict MSI in the next T𝑇Titalic_T t-slots after MSI exchanging is introduced. Due to the movement inertia, the MSI between adjacent slots is correlated with each other. Hence, the historical MSI can be used to predict the future MSI. According to the GP-based MSI prediction algorithm, the predicted position and attitude are estimated by the mean of the predictive distribution of the outputs (the future MSI) on the specific test dataset. <|MaskedSetence|>

**A**: The predictive distribution of the output (the future MSI) is given by . **B**: The tracking error of beam angles has a negative influence on the beam gain obtained by CCA. **C**: The proposed tracking error bounding algorithm uses the position/attitiude prediction error of the GP-based MSI prediction to obtain the beam angle tracking error, wherein the geometry relationship between UAVs and the Monte-Carlo method is utilized.

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III. The co-existence of random graphs, subgradient measurement noises, additive and multiplicative communication noises are considered. <|MaskedSetence|> What’s more, multiplicative noises relying on the relative states between adjacent local optimizers make states, graphs and noises coupled together. It becomes more complex to estimate the mean square upper bound of the local optimizers’ states (Lemma 3.1). <|MaskedSetence|> <|MaskedSetence|> Finally, we get an estimate of the mean square increasing rate of the local optimizers’ states in terms of the step sizes of the algorithm (Lemma 3.2). .

**A**: Compared with the case with only a single random factor, the coupling terms of different random factors inevitably affect the mean square difference between optimizers’ states and any given vector. **B**: We firstly employ the property of conditional independence to deal with the coupling term of different random factors. **C**: Then, we prove that the mean square upper bound of the coupling term between states, network graphs and noises depends on the second-order moment of the difference between optimizers’ states and the given vector.

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<|MaskedSetence|> We account for model mismatch by automated tuning of both the MPC-related parameters and the low level cascade controller gains, to achieve precise contour tracking with micrometer tracking accuracy. <|MaskedSetence|> In our approach the tracking error is coupled with the progression along the path through the cost function. The automated tuning of the parameters is performed using a cost that accounts for the global performance over the whole trajectory. Additional constraints in the Bayesian optimization algorithm allow for balancing traversal time, accuracy, and minimization of oscillations, according to the specific crucial requirements of the application. <|MaskedSetence|>

**A**: In this work, we use the model predictive contouring control (MPCC) which is an MPC-based contouring approach to generate optimized tracking references. **B**: We demonstrate enhanced performance in simulation for a 2-axis gantry, for geometries of different nature. . **C**: The MPC-planner is based on a combination of the identified system model with the contouring terms.

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Figure 6: Confusion tables (in %) for style classification on the test split of Pianist8. Each row shows the percentage of sequences of a class predicted as another class. Notation—“C”: R. Clayderman (pop), “Y”: Yiruma (pop), “H”: H. <|MaskedSetence|> Einaudi (contemporary), “J”: H. <|MaskedSetence|> <|MaskedSetence|>

**A**: Joe (contemporary), “S”: R. **B**: Sakamoto (contemporary), “M”: Bethel Music (religious) and “W”: Hillsong Worship (religious). . **C**: Hancock (jazz), “E”: L.

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<|MaskedSetence|> <|MaskedSetence|> Based on JSCC, an image transmission system, integrating channel output feedback, can improve image reconstruction[15]. Similar to text transmission, IoT applications for image transmission have been carried out. <|MaskedSetence|> A deep joint source-channel coding architecture, name DeepJSCC, has been investigated in[17] to process image with low computation complexity. .

**A**: Recently, there are also investigations on semantic communications for other transmission contents, such as image and speech. **B**: A DL-enabled semantic communication system for image transmission, named JSCC, has been developed in[14]. **C**: Particularly, a joint image transmission-recognition system has been developed in[16] to achieve high recognition accuracy.

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<|MaskedSetence|> <|MaskedSetence|> The performance is improved in the two models with an AUC of 0.95 for the Inception V3 model and an AUC of 0.96 for the VGG19 model. These performances are comparable to current state–of–the–art models for computational pathology analysis. It is within the top 5 best algorithms of the CAMELYON16 challenge [12] and is within the top 10 best models for the PCAM dataset (https://tinyurl.com/3rhk6ph6). <|MaskedSetence|> Indeed, histology images are typically symmetric under rotation, meaning that each orientation is equally as likely to appear. Rotation–equivariance removes the necessity lo learn this type of transformation from the data, thus allowing more discriminative features to be learned and also reducing the number of parameters of the model. Conclusion .

**A**: The current best PCAM models have an AUC around 0.97 and implement rotation equivariant strategies [26, 27, 28]. **B**: Fine tuning of the hyperparameters was done for the Inception V3 and the VGG19 models on the PCAM dataset. **C**: Two hyperparameters (Adam learning rate and batch size) were fine-tuned using the Keras Tuner with the hyperband algorithm.

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<|MaskedSetence|> By initializing the learning process with a uniform random expander we bias the optimized solution towards expanders that distribute energy throughout the eyebox, in contrast to a quadratic phase profiles[28] that concentrate the energy at fixed points. Thus, the viewer’s eye pupil can freely move within the eyebox and observe the wide field-of-view hologram at any location. We incorporate pupil-aware optimization[37] to preserve the perceived hologram quality at different eye pupil locations. <|MaskedSetence|> <|MaskedSetence|> We note that existing methods on étendue expanded holography has focused on monochromatic 3D holograms[7, 28, 29]. Photon sieves[21] only achieves 3D color holography for sparse points. See Supplementary Note 4 for a discussion of these findings. .

**A**: See Supplementary Note 5 for findings. Finally, we also investigate 3D étendue expanded holograms. **B**: In addition to field-of-view, we also investigate the eyebox that is produced with neural étendue expansion. **C**: We find that neural étendue expansion also enables higher fidelity étendue expanded 3D color holograms.

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<|MaskedSetence|> <|MaskedSetence|> In SISR, as the LR image and HR image share most of the same information, it is easy to explicitly model the residual image between LR and HR images. Residual learning enables deeper networks and remits the problem of gradient vanishing and degradation. With the help of residual learning, Kim et al. (Kim et al., 2016a) proposed a very deep super-resolution network, also known as VDSR. <|MaskedSetence|>

**A**: proposed a residual learning framework, where a residual mapping is desired instead of fitting the whole underlying mapping (Fig. 5). **B**: In ResNet (He et al., 2016), He et al. **C**: For the convenience of network design, the residual block (He.

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<|MaskedSetence|> Here we see which spectral bins lend the most support on average to either bona fide or spoofed classes. In addition to other less substantial differences, there is predominantly greater support for the bona fide class at  0.5 kHz but substantially greater support for the spoofed class at  0.6 kHz. We observed many instances of such differences suggesting that some spoofing attacks leave artefacts in specific spectral intervals while they are largely effective in replicating the characteristics of bona fide speech in others. <|MaskedSetence|> <|MaskedSetence|>

**A**: Fig. 3 shows time-averaged SHAP values against frequency for the spoofed ‘LA_E_2634822’ utterance and the 2D-Res-TSSDNet model, for frequencies up to 4 kHz. **B**: Similar observations have been reported previously [24, 7]. **C**: Such characteristics may help not just to distinguish between bona fide and spoofed speech, but also to identity a particular spoofing attack algorithm or its nature, e.g., whether it is a synthetic speech, converted voice or replay attack. .

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Learning CBFs: An open problem is how valid CBFs can be constructed. <|MaskedSetence|> For certain types of mechanical systems under input constraints, analytic CBFs can be constructed [30]. The construction of polynomial barrier functions towards certifying safety for polynomial systems by using sum-of-squares (SOS) programming was proposed in [31]. Finding CBFs poses additional challenges in terms of the control input resulting in bilinear SOS programming as presented in [32, 33] and summarized in [34]. The work in [35] considers the construction of higher order CBFs and their composition by, similarly to [32, 33], alternating-descent heuristics to solve the arising bilinear SOS program. Such SOS-based approaches, however, are known to be limited in scalability and do not use potentially available expert demonstrations. A promising research direction is to learn CBFs from data. The authors in [36] construct CBFs from safe and unsafe data using support vector machines, while authors in [37] learn a set of linear CBFs for clustered datasets. The authors in [38] proposed learning limited duration CBFs and the work in [39] learns signed distance fields that define a CBF. <|MaskedSetence|> The authors in [41] learn parameters associated with the constraints of a CBF to improve feasibility. These works present empirical validations, but no formal correctness guarantees are provided. The authors in [42, 43, 44, 45] propose counter-example guided approaches to learn Lyapunov and barrier functions for known closed-loop systems, while Lyapunov functions for unknown systems are learned in [46]. In [47, 48, 49] control barrier functions are learned and post-hoc verified, e.g., using Lipschitz arguments and satisfiability modulo theory, while [50] uses a counter-example guided approach. As opposed to these works, we make use of safe expert demonstrations. <|MaskedSetence|> In our previous work [53], we proposed to learn CBFs for known nonlinear systems from expert demonstrations. We provided the first conditions that ensure correctness of the learned CBF using Lipschitz continuity and covering number arguments. In [54] and [55], we extended this framework to partially unknown hybrid systems. In this paper, we focus on state estimation and provide sophisticated simulations of our method in CARLA. .

**A**: In [40], a neural network controller is trained episodically to imitate an already given CBF. **B**: Indeed, the lack of systematic methods to construct valid CBFs is a main bottleneck. **C**: Expert trajectories are utilized in [51] to learn a contraction metric along with a tracking controller, while motion primitives are learned from expert demonstrations in [52].

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Various other aspects of polarization in MIMO systems have been investigated as well. <|MaskedSetence|> [16] showed that space-time block coding (STBC) with single polarization outperforms STBC with dual polarization in Rayleigh and Ricean fading channels. <|MaskedSetence|> <|MaskedSetence|> Various channel sounding campaigns and channel models provide insights into the characteristics of wireless channel polarization [26, 21, 22, 20, 27, 28, 23, 29, 30]. .

**A**: It is noteworthy that the extent of benefit from dual-polarized antennas depends on the associated schemes to exploit the characteristics of polarized wireless channel [15, 16, 17, 1, 6]. **B**: A MIMO system with dual-polarized antenna elements can have lower spatial diversity but higher spatial multiplexing gain than a conventional MIMO system with single-polarized antennas, particularly, in Ricean fading channels with high K𝐾Kitalic_K-factor [17]. **C**: Ref.

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Many works intent to find a convex proxy to a non-convex objective function. In [7], adding a Lagrangian term to the regularization of a constrained non-convex minimization permits to build an equivalent minimization problem that is convex locally. Another possibility is to try to perform a regularization by infimal regularization [8] for lower semicontinuous objective functionals. <|MaskedSetence|> propose a high dimensional lifting of the Lagrangian formulation of (2) where the data-fit functional is non-convex. <|MaskedSetence|> <|MaskedSetence|> Finally, convex closure of submodular functions also permits to cast sparsity inducing objective functions (where the regularizer is a submodular function of the support) into convex problems [5]. Note that if one aims to find a non-convex, but continuous, regularization, several works of interest may be cited, such as the use of ℓpsuperscriptℓ𝑝\ell^{p}roman_ℓ start_POSTSUPERSCRIPT italic_p end_POSTSUPERSCRIPT minimization [21], SCAD [19], or CEL0 [33]. Nevertheless, in this paper, we focus on convex functions. .

**A**: The drawback of this method is the computational cost that makes it impractical for high-dimensional problems. **B**: In the context of non-convex polynomial optimization, Lasserre’s hierarchies [26] are used to recast the original problem in a hierarchy of convex semi-definite positive problems which provide global convergence results. **C**: In [29], in a function space setting, Pock et al.

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<|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> Meanwhile, some researchers attempt to drain all potential of limited labeled data. With the power of self-training and self-supervised learning [1, 4, 24, 39, 43, 47, 51], it is possible to develop a robust, few-shot model even with several labeled samples. For example, Yao et al. [42] introduce a self-supervised proxy task that matches multi-layers features from images with different augmentations in the training stage, and use a single image as the template, whose patches centered at landmarks are matched with target images to make predictions. .

**A**: To alleviate this problem, many researchers [3, 17, 19, 34] utilize labeled data together with unlabeled data in a semi-supervised style to boost performance. **B**: A classic method is mean teacher [17, 34], which aggregates multiple predictions of unlabeled data by a teacher model pre-trained from labeled data. **C**: The aggregated results work as more reliable pseudo labels for unlabeled data in rest part of the method. Another group of researchers, aiming to achieve a high performance at a low labeling cost, propose a strategy to select instances for annotation incrementally [15, 31, 35, 45]. The basic idea is to first train a model with few labeled data, and then use the model to select instances from unlabeled data iteratively, which are annotated by specialists for the next round of training.

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Team GradICON* GradICON’s training protocol and hyperparameters (Tian et al., 2022) were adopted. Its ability to generalize was assessed by investigating its performance without explicitly modeling image differences due to tumor resection with two significant changes in the original approach. Initially, the number of input channels of the first convolutional layer was increased to match the number of modalities. <|MaskedSetence|> The image similarity was computed by defining the local normalized cross correlation (LNCC) as an average over the LNCCs for each modality (channel). The second modification consisted of a new training strategy to alleviate overfitting caused by the small available training dataset. The network was pre-trained following the original training process in (Tian et al., 2022) with inter-patient pairs from the train set. <|MaskedSetence|> <|MaskedSetence|> In the pre-train phase, random pairs of pre-operative and follow-up images were picked as training pairs. In the fine-tune phase, the paired images provided by the challenge were used..

**A**: The input images were normalized to [0, 1] per modality. **B**: This adjustment enabled the utilization of visual cues across different modalities. **C**: Subsequently, the entire network (Stage1 and Stage2) was fine-tuned using intra-patient pairs from the train set.

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the periodic orbits of the angle map by analyzing the fixed points of ϕq(.)\phi^{q}(.)italic_ϕ start_POSTSUPERSCRIPT italic_q end_POSTSUPERSCRIPT ( . <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> ) map. Note also that, the asymptotic average inter-event.

**A**: ) map by analyzing stability and region of convergence of fixed points of ϕq(.)\phi^{q}(.)italic_ϕ start_POSTSUPERSCRIPT italic_q end_POSTSUPERSCRIPT ( . **B**: Further, we can analyze stability and region of convergence of periodic orbits of ϕ(.)\phi(.)italic_ϕ ( . **C**: ) map.

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Control of PDE systems has been widely explored over the years [15, 16, 17, 18]. Similar to ODEs, notions of ISSt for PDE systems have garnered a lot of attention recently (see survey paper [19]). <|MaskedSetence|> Notions of practical ISSt for PDEs have been explored in [28]. In contrast to ISSt, ISSf has remained mostly unexplored in the context of PDEs. In [29], safety verification using barrier functionals for homogeneous distributed parameter systems has been considered. In this work, numerical strategies based on semi-definite programming has been used for the construction of barrier functionals. However, control performance under disturbances has not been considered in this work. Given the importance of maintaining system safety under disturbances, it is critical to consider control system design for PDE systems under these disturbances. In [30], safe control of Stefan system under disturbances is considered. In the framework proposed in [30], an operator is allowed to manipulate the control input as long as safety constraints are satisfied; however, the safety control overrides the operator control signal realizing a feedback control ultimately guaranteeing safety. The feedback law for safety control is designed utilizing backstepping, quadratic programming, and a control barrier function. <|MaskedSetence|> Specifically, we design a control law that employs feedback from the boundaries and an in-domain point, by utilizing a practical ISSf (pISSf) barrier functional characterization (inspired by the notion presented in [4]). Subsequently, utilizing ISSt Lyapunov functional characterization, we prove that such designed safety control is also an input-to-state stabilizing control under certain additional conditions. <|MaskedSetence|>

**A**: In this way, we ultimately propose a feedback control law that satisfies the conditions of both ISSt and pISSf. . **B**: In our current work, we attempt an alternate approach to achieve safety control of a class of linear parabolic PDEs under disturbances. **C**: For example, PDE ISSt have been explored for reaction-diffusion systems [20], hyperbolic systems [21], [22], parabolic systems [23], parabolic PDE systems with boundary disturbances [24], [25], systems with distributed time-delays [26], and diffusion equation with time-varying distributed coefficients [27].

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<|MaskedSetence|> Recall that a sample in PU-Setting is comprised of a sample of PUs’ parameters (location and power) and the optimal power allocated to the SU. In SS-Setting, a training sample is comprised of spectrum sensors’ received power readings. <|MaskedSetence|> <|MaskedSetence|> Then, we compute the area under the PSD curve over the 1 MHz channel of interest (see below), and finally, convert the computed area to an appropriate unit. Determining Labels (Optimal Power Allocated to SU). We essentially do a binary search to estimate the optimal power that can be allocated to SU. To determine whether PU to PUR transmission is incurring any harmful interference from SU, we have PU continuously streaming ASCII messages over the 1 MHz bandwidth channel centered at frequency 915.8 MHz, and check if the messages are successfully received at the PUR. This end-to-end communication system is implemented using GNU Radio. .

**A**: The location of entities is available by using a GPS dongle connected to the laptops as described below, and the sensor’s received power is computed as follows. **B**: Collecting Training Samples. **C**: First, we compute an FFT on the I/Q samples collected within a time window to get a power spectral density (PSD) plot.

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<|MaskedSetence|> <|MaskedSetence|> On the other hand, the use of an irregular order is then considered by researchers to accelerate convergence. Particularly, it is shown in [31] that randomization leads to faster convergence in terms of expectation. <|MaskedSetence|> The Gauss-Southwell method leads to faster convergence at the cost of extra computations and evaluations of gradients during the selection of coordinates which can be an issue in large-scale problems [25]. .

**A**: Obviously, this is not guaranteed for each instance of the algorithm. **B**: The cyclic selection of coordinates is normally assumed to ensure convergence of the algorithm. **C**: A substantial review of variants of coordinate descent algorithms can be found in [4, Section 6.5.1].

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Most existing studies on disease diagnosis using chest X-rays primarily focus on detecting a single pathology, such as pneumonia or COVID-19 (Bar et al. (2015); Cicero et al. (2017); Rajpurkar et al. (2017); Dasanayaka and Dissanayake (2021); Hussain et al. (2023)). However, an X-ray image can exhibit multiple pathological conditions simultaneously. <|MaskedSetence|> Single-label classifications may produce false negatives when patients have multiple diseases, as they focus solely on the primary condition. Multi-label classification can help reduce false negatives by identifying secondary or co-occurring diseases. Multi-label classification can also be valuable in epidemiological studies and public health research. It can provide insights into the prevalence and co-occurrence of diseases in specific populations, aiding in resource allocation and healthcare planning. <|MaskedSetence|> <|MaskedSetence|> The detection of these 14 different pathology conditions, including ‘Atelectasis’, ‘Cardiomegaly’, ‘Consolidation’, ‘Edema’, ‘Emphysema’, ‘Effusion’, ‘Fibrosis’, ‘Hernia’, ‘Infiltration’, ‘Mass’, ‘Nodule’, ‘Pneumothorax’, ‘Pleural Thickening’, and ‘Pneumonia’, presents a multi-label classification problem. The input to the DenseNet architecture is a chest X-ray image; the output is a label that provides the probability of each pathology being present in the X-ray. The code for our approach is available on Github111https://github.com/dipkamal/chestxrayclassifier. .

**A**: Additionally, we utilize the GRADCAM explanation method to localize specific areas within the chest radiograph to visualize the regions to which the model paid attention to make disease predictions, enhancing our understanding of the model’s predictions. **B**: Detecting multiple pathologies can provide a comprehensive view of the patient’s health from a single image. **C**: In this research, we employ a 121-layer DenseNet architecture to perform diagnostic predictions for 14 distinct pathological conditions in chest X-rays.

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<|MaskedSetence|> The agreement between the target class and the self-reported discrete emotion annotations by the volunteers in this experiment is shown in the matrix in Figure 2, where it is observed as the ratio of times a targeted emotion is identified and felt as such by the volunteers. <|MaskedSetence|> <|MaskedSetence|> Analyzing this figure it can be found that the non-included emotions (attraction, contempt, hope and tedium) are very scarcely selected with the exception of the 17%percent1717\%17 % of times a stimulus expected to represent anger is taken as contempt. It is also observed that sadness, calm, joy and fear are the emotions best identified, being the agreement in the fear emotion especially relevant for the use case. Tenderness and disgust are also quite well portrayed by the stimuli while anger is often taken as disgust or contempt, and amusement as joy or disgust. .

**A**: Emotional elicitation and labeling is a complex task, and sometimes the expected (or targeted) emotions are not the ones the volunteers experienced (or reported). **B**: Thus, a value of 1.00 means a perfect agreement between the targeted emotion and the emotion felt, and 0.00 means no agreement. As introduced before, only 8888 of the 12121212 emotions initially selected were included in WEMAC (see the Stimuli Section), although the 12121212 emotions were considered for the discrete emotion labeling (see the Measures Section). **C**: It means that the number of targeted emotions is smaller than the reported ones in this matrix.

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<|MaskedSetence|> With an aging population, such numbers are expected to rise to 288288288288 million by 2040 [35]. <|MaskedSetence|> Figure 1 depicts an example of a retina fundus affected by AMD. This work has introduced an alternative approach for generating synthetic images for training deep networks and tested it for AMD identification, which consists in using a retinal image quality assessment model [37] and the StyleGAN2-ADA [38]. Retina images, positive and negative to AMD, from multiple databases having a range of image qualities and lesions were used. <|MaskedSetence|> Different percentages of synthetic data were employed in the augmentation. .

**A**: Age-related macular degeneration (AMD) is a major cause of vision impairment and has affected approximately 200200200200 million people worldwide in 2020 [34]. **B**: AMD is a progressive disorder of the macular region that causes central vision loss and is one of the most common causes of irreversible vision impairment in people over 50505050 years-old [36]. **C**: Ten different GAN architectures were compared to generate synthetic eye-fundus images and the quality was assessed using the Fréchet Inception Distance (FID), two independent clinical experts who were label blinded and deep-learning classification.

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Among the available approaches, the concept of control invariant set is one of the most exploited historically, since it ensures the existence of some feedback law able to steer the closed-loop trajectories of the uncertain system within a prescribed state set 25, 6, 8, 37. <|MaskedSetence|> <|MaskedSetence|> We will also refer to these policies as traditional stabilizing controllers for linear uncertain systems. Once fixed feasible control inputs at the vertices of the invariant set have been computed, a variable structure controller either takes a convex combination of those values by exploiting the vertex reconstruction of any state belonging to such a set, or coincides with a purely linear gain stemming from a triangulation, i.e., a simplicial partition 16, of the underlying set. These methods therefore require one to solve a linear program (LP) online or to generate a lookup table to identify the region in which the current state resides. If the simplicial partition-based implementation is considered, then one has also to account for the complexity of the resulting invariant set, which is typically high 6, 8, 49, 10, 2, 9. These methods can therefore require significant memory to store the vectors and/or matrices describing every simplicial partition and associated linear control gain. <|MaskedSetence|> By requiring the online resolution of a nonlinear optimization problem, parametric in the current measured state, this method directly enforces a certain degree of contraction possessed by the CLF at every control step. While solving a numerical optimization problem online provides flexibility and performance guarantees, the real-time computational efforts required complicate its application in polytopic linear systems characterized by high sampling rates..

**A**: With a specific focus on discrete-time polytopic systems, an admissible control policy that actually makes a polyhedral CLF a suitable Lyapunov candidate for the closed-loop system is typically synthesized in two ways: through a variable structure 25, 46, 47, or a (minimal) selection-based controller 3. **B**: This is traditionally achieved by associating a control Lyapunov function (CLF) with the invariant set design, which for polytopic systems has been proven to be universal, namely the stabilization of the linear uncertain system and the existence of a polyhedral CLF can be used interchangeably 7. **C**: As a common drawback affecting both the implementations, however, fixing the input values at the vertices may result in poor control performance for the stabilization task. A more sophisticated control method coincides with the selection-based policy.

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All the experiments are executed on a machine with an Intel(R) Core(TM) i7-7800X CPU @ (3.50GHz x 12) using 132GB of RAM. <|MaskedSetence|> 2: Qualitative results for affine registration with MI over 3D medical images using ADNI dataset [33]. <|MaskedSetence|> In the first column of each row, the moving image obtained using PET modality is shown, while in the second column, the fixed image obtained using MRI modality is displayed. <|MaskedSetence|> The different protocols are highlighted by red and green frames, respectively. .

**A**: For each registration configuration, the optimization is repeated 10 times to account for the random generation of MPC shares and FHE encryption keys. Fig. **B**: The third column shows the checkerboard alignment result using Clear, while the fourth column shows the result using PPIR(MPC). **C**: The images are presented in a 3×4343\times 43 × 4 grid, with the first row representing the axial axis, the second row the coronal axis, and the third row the sagittal axis.

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Related Work. Our work follows the previous studies of POMDPs. In general, solving a POMDP is intractable from both the computational and the statistical perspectives (Papadimitriou and Tsitsiklis, 1987; Vlassis et al., 2012; Azizzadenesheli et al., 2016; Guo et al., 2016; Jin et al., 2020a). Given such computational and statistical barriers, previous works attempt to identify tractable POMDPs. In particular, Azizzadenesheli et al. (2016); Guo et al. (2016); Jin et al. (2020a); Liu et al. (2022) consider the tabular POMDPs with (left) invertible emission matrices. Efroni et al. (2022) considers the POMDPs where the state is fully determined by the most recent observations of a fixed length. Cayci et al. (2022) analyze POMDPs where a finite internal state can approximately determine the state. In contrast, we analyze POMDPs with the low-rank transition and allow the state and observation spaces to be arbitrarily large. Meanwhile, our analysis hinges on the future and past sufficiency assumptions, which only require that the density of the state is identified by that of the future and past observations, respectively. In recent work, Cai et al. <|MaskedSetence|> Nevertheless, Cai et al. (2022) assumes that the feature representation of state-action pairs is known, thus relieving the agent from feature learning. In contrast, we aim to recover the efficient state-action representation for planning. In terms of the necessity of exploration, Azizzadenesheli et al. (2016); Guo et al. (2016) analyze POMDPs where an arbitrary policy can conduct efficient exploration. Similarly, Cayci et al. (2022) consider POMDPs with a finite concentrability coefficient (Munos, 2003; Chen and Jiang, 2019), where the visitation density of an arbitrary policy is close to that of the optimal policy. <|MaskedSetence|> (2020a); Efroni et al. (2022); Cai et al. <|MaskedSetence|> In our work, we follow Jin et al. (2020a); Efroni et al. (2022); Cai et al. (2022) and design strategic exploration to attain sample efficiency in solving the POMDPs. To learn a sufficient embedding for control, we utilize the low-rank transition of POMDPs. Our idea is motivated by the previous analysis of low-rank MDPs (Cai et al., 2020; Jin et al., 2020b; Ayoub et al., 2020; Agarwal et al., 2020; Modi et al., 2021; Uehara et al., 2021). In particular, the state transition of a low-rank MDP aligns with that in our low-rank POMDP model. Nevertheless, we remark that such states are observable in a low-rank MDP but are unobservable in POMDPs with the low-rank transition. Such unobservability makes solving a low-rank POMDP much more challenging than solving a low-rank MDP..

**A**: In contrast, Jin et al. **B**: (2022) consider POMDPs where strategic exploration is necessary. **C**: (2022) also utilizes the low-rank property in the transition.

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Chen et al. <|MaskedSetence|> <|MaskedSetence|> [36] investigated the decentralized online linear regression problem, where the graph is assumed to be strongly connected and balanced. They gave the regret upper bounds. Wang et al. [38] investigated a consensus plus innovation based decentralized linear regression algorithm over random networks with random regression matrices. <|MaskedSetence|>

**A**: [35] proposed a saturated innovation update algorithm for the decentralized estimation under sensor attacks, where the interagent communication is noiseless. **B**: They proved that if the regression matrices and communication graphs satisfy the stochastic spatio-temporal persistence of excitation condition, properly choosing the algorithm gains guarantees the convergence of the estimations of all nodes to the unknown true parameter. Some scholars have also considered both measurement and communication noises among nodes, e.g. [28]-[29] and [31].. **C**: They proved that if the communication graph is undirected and fixed, the nodes are locally observable, and the number of attacked nodes is less than half of the total, then all nodes’ estimations converge to the unknown true parameter with a polynomial rate. Yuan et al.

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<|MaskedSetence|> One serves as input to the model, and the other as the ground truth corresponding to the desired parameter setting to compute the loss. We use MRiLab [7] which is an MRI Simulator to generate these synthetic brain scans in different parameter settings of {TE, TR}. <|MaskedSetence|> The TR values were chosen uniformly at random in the range 1.2 s to 10s. The TE values ranged from 20 ms to 1s non-uniformly. The distribution was such that lower TE values were selected with higher probability. <|MaskedSetence|> The T1 and T2 relaxation times used by MRiLab were matrices of size 108×90×901089090108\times 90\times 90108 × 90 × 90 with values in the range 0s to 4.5s for T1 and 0s to 2.2s for T2. For each pair of {TE, TR}, we generated 24 different 2D axial MR slices of a 3D brain volume, so in total we obtained 4800 MR slices. We used 1500 samples of these slices for training, while the rest were kept for testing. The generated scans were rescaled to a 256×256256256256\times 256256 × 256 matrix. .

**A**: This was done because the scans were more sensitive toward changes in lower values of TE. **B**: We generated these brain MRI scans for 200 random pairs of {TE, TR}. **C**: For our training, we require the MRI scans in two different parameter settings of {TE, TR}.

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<|MaskedSetence|> The reduced light intensity enables the SiPM to operate in its linear response range and enhance the measurement accuracy. Table III shows that the transmitter power consumption of commercially available BLE modules is higher than that of an LED-based transmitter. <|MaskedSetence|> <|MaskedSetence|> Hence, the total power consumption needed for LED transmitters is around 695 μ𝜇\muitalic_μW, significantly less than the power consumption of BLE modules, which ranges from several to tens of milliwatts. However, because the LED operates near its forward voltage, the effective current consumption of the LED might be less than 100 times the attenuated value by the ND20 filter. Also, it is essential to note that large LEDs may not achieve micro-watt level power consumption. Conversely, smaller LED dies can achieve lower power consumption, allowing for power usage at the micro-watt level[50, 51]..

**A**: In the experimental setup illustrated in Fig. 2 and Fig. 6, considering the LED operating near its forward voltage and the receiver requiring only low light intensity, an ND20 Filter is used to attenuate the light intensity of the LED. **B**: Considering the 100 times intensity attenuation due to the ND20 filter, an appropriately sized LED’s current consumption is approximately 100 μ𝜇\muitalic_μA. **C**: For example, with the MCU running at 1 MHz, the MCU power usage is at 495 μ𝜇\muitalic_μW.

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Figure 3f illustrates the control commands during the 10-day operation in close-proximity to Eros. <|MaskedSetence|> The Δ⁢VΔ𝑉\Delta Vroman_Δ italic_V budget is 76.77 m/s, with the Monte Carlo-Lambert guidance accounting for most of it. <|MaskedSetence|> To provide context, NEAR-Shoemaker spent 50.38 m/s in the one year after the rendezvous burn (TCM-17), from TCM-18 to TCM-23 202020https://near.jhuapl.edu/NewMissionDesign/prpevent422.html. The 25.69 m/s Δ⁢VΔ𝑉\Delta Vroman_Δ italic_V in orbital insertion and maintenance is noteworthy. <|MaskedSetence|>

**A**: Extended periods of idle thrusters are observed during the orbital phase of the mission. **B**: Compare this to NEAR-Shoemaker’s 31.57 m/s for orbital insertion (OIM) and orbital operations (OCM-1 to OCM-25) 212121https://near.jhuapl.edu/NewMissionDesign/prpevent422.html. . **C**: Despite operating with significant uncertainties about the spacecraft’s state and environment, the 51.08 m/s spent by the Monte Carlo-Lambert guidance before the orbital insertion burn is reasonable.

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Selection 3

<|MaskedSetence|> <|MaskedSetence|> We will present an overview of the different types of models, their implications, and their limitations. This does not constitute an exhaustive list of all the mathematical models associated with these MRs, but rather an introductory presentation for common models used in trajectory planning. Readers familiar with this research area can skip to section III where we discuss the modelling of communication systems and the wireless channel. We must mention that selecting the appropriate complexity of the models is not always an easy task and sometimes does not have a clear, or even a unique correct answer. This is highly dependent on the particular and specific conditions of the problem to be solved. <|MaskedSetence|> Indeed, overly complex models might solve some issues due to oversimplification, but may also cause other problems, such as a lack of tractability and the high computational burden of the solutions. .

**A**: In this tutorial, we aim to raise awareness about the problems related to oversimplification, while underlining that the solution to the latter is not an overcomplexification. **B**: In order to help researchers with no (or little) robotics background, the rest of this section provides a general description of mathematical models describing the motion and energy consumption for three popular MRs: ground wheeled robots, rotary-wing(s) aerial robots, and fixed-wing aerial robots. **C**: As we have explained above, the oversimplification of MR models can have serious consequences, thus the importance of selecting an adequate model complexity.

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In this paper, a general notion of dissipativity with dynamic supply rates was introduced for nonlinear systems, extending the notion of classical dissipativity. Lyapunov and asymptotic stability analyses were performed for feedback interconnections of two dissipative systems satisfying dissipativity with respect to dynamic supply rates. <|MaskedSetence|> Satisfaction of the dissipation inequalities is aided by the dynamics of possibly distinct auxiliary systems. <|MaskedSetence|> A noteworthy specialisation of the results is a simple coupling test to verify whether the feedback interconnection of two nonlinear systems, each satisfying independent (Ψ,Π,Υ,Ω)ΨΠΥΩ(\Psi,\Pi,\Upsilon,\Omega)( roman_Ψ , roman_Π , roman_Υ , roman_Ω )-dissipation inequalities, is asymptotically stable. This coupling test is simple to compute if the supply rate operators are chosen to be LTI. <|MaskedSetence|>

**A**: In these results, both dynamical systems are characterised by compatible dissipation inequalities with respect to “coupled” dynamic supply rates. **B**: The results were shown to recover several knowns results in the literature. **C**: Moreover, a meaningful comparison with the integral quadratic constraint based input-output approach to feedback stability was.

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Selection 3

The aforementioned literature [1, 2, 3, 4, 5, 6] focuses on systems without stochastic disturbances. Because a stochastic disturbance often affects a real system, a safe set is desirable to maintain invariance even when influenced by the disturbance. Recently, various types of CBF-based stochastic safety-critical control have been proposed in [9, 10, 11, 7, 8, 12, 13, 14, 15]. Jagtap et al. [7] conducts a systematic and detailed study, and then it is developed into a data-driven framework by Salamati and Zamani [8]. Prajna et al. <|MaskedSetence|> [10]. Wisniewski and Bujorianu [11] also discuss in detail safety in an infinite time-horizon named p𝑝pitalic_p-stability. Bai et al. [14] analyzes a probability for a trajectory to reach a target set, which is a subset of a safe set. Nejati et al. [15] develop a compositional approach for constructing CBFs for stochastic hybrid systems, which forms an excellent theory in terms of applications because they use numerical methods such as the sum-of-squares optimization program under the free design of safe sets. On the other hand, the CBF approach is closely related to a control Lyapunov function (CLF), which immediately provides a stabilizing control law from the CLF, as in Sontag [16] for deterministic systems and Florchinger [17] for stochastic systems. <|MaskedSetence|> For this discussion, the problem setting in which the safe set is coupled with the CBF is appropriate, as in Ames et al. <|MaskedSetence|> The stochastic version of the Ames’s et al.’s result is recently discussed by Clark [12]; he insists that his RCBF and ZCBF guarantee the safety of a set with probability one. At the same time, Wang et al. [13] analyze the probability of a time when the sample path leaves a safe set under conditions similar to Clark’s ZCBF. Wang et al. also claim that a state-feedback law achieving safety with probability one often diverges toward the boundary of the safe set; the inference is also obtained from the fact that the conditions for the existence of an invariance set in a stochastic system are strict and influenced by the properties of the diffusion coefficients [18]. This argument is in the line of stochastic viability by Aubin and Prato [20]. For CBFs, Tamba et al. [19] provides sufficient conditions for safety with probability one, which require difficult conditions for the diffusion coefficients. Therefore, we need to reconsider a sufficient condition of safety with probability one, and we also need to rethink the problem setup to compute the safety probability obtained by a bounded control law..

**A**: [9] provides a safety verification procedure, and then it is developed to control design procedure by Santoyo et al. **B**: [2]. **C**: Therefore, in the CBF approach, the derivation of a safety-critical control law immediately from the CBF is also important.

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Intuitively, since GFM converters behave like voltage sources, installing a GFM converter near a GFL converter should improve the local power grid strength of the GFL converter and thus improve its small signal stability margin (as GFL converters may become unstable in weak grids). This intuition was confirmed in our previous work [9], where we investigated the impact of GFM converters on the small signal stability of power systems integrated with GFL converters. <|MaskedSetence|> However, the approach [9] can only be used to determine the optimal locations to replace GFL converters with GFM converters, but it still remains unclear how to configure newly installed GFM converters in the grid and more importantly, how to decide their capacities (or equivalently, how many GFM converters we will need) to ensure the system’s small signal stability. <|MaskedSetence|> Such an approach might not apply to other GFM methods once they have weaker voltage source behaviors than VSMs in [9], as it remains unclear how to quantify the voltage source behaviors of different GFM methods and analyze their interaction with GFL converters. Moreover, one important question is: since GFL converters can perform constant AC voltage magnitude control, do they also have effective voltage source behaviors to enhance the power grid strength? To be specific, one can introduce the terminal voltage magnitude as a feedback signal to generate the reactive current reference and regulate the voltage magnitude to a reference value [3, 4]. In this case, though the terminal voltage magnitude is well regulated, it remains unclear if the GFL converters can be considered as effective voltage sources to enhance the power grid (voltage) strength. <|MaskedSetence|>

**A**: We believe that it is essential to answer the above question before studying how many GFM converters we will need to enhance the power grid strength, as one may simply resort to modifying GFL converters to enable voltage source behaviors if they can be used to enhance the power grid strength. . **B**: Furthermore, the analysis in  [9] only considers one type of GFM control (i.e., VSM) and directly approximates a VSM as an ideal voltage source (without deriving the equivalent impedance as will be done in this paper). **C**: We demonstrated that replacing GFL converters with GFM converters is equivalent to enhancing the power grid strength, characterized by the so-called generalized short-circuit ratio (gSCR).

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<|MaskedSetence|> The multi-scale kernel can implicitly encode features from coarse to fine, which allows the model to mimic both CNNs and transformers. Moreover, to avoid potential block artifacts aroused by dilation, we adopt the gate mechanism to recalibrate the generated attention maps adaptively. <|MaskedSetence|> Although transformer-style MAB can deliver higher performance, the MLP feed-forward module is too heavy for large images. <|MaskedSetence|> Arming with the simple yet striking MLKA and GSAU, the MABs are stacked to build the multi-scale attention network (MAN) for the SR task. In Fig. 1, we present the superior performance of our MAN. To summarize, our contributions are as follows: • .

**A**: Inspired by recent work [5, 47], we propose a simplified gated spatial attention unit (GSAU) by applying spatial attention and gate mechanism to reduce calculations and include spatial information. **B**: To maximize the benefits of MLKA, we place it on the MetaFormer [53]-style (Norm-TokenMixer-Norm-MLP) structure rather than RCAN-style (Conv-Act-Conv-TokenMixer) to construct a multi-attention block (MAB). **C**: Motivated by these issues, we propose multi-scale large kernel attention (MLKA) that combines classical multi-scale mechanism and emerging LKA to build various-range correlations with relatively few computations.

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In practice, real-time reconfigurability in the range of milliseconds might be still difficult to achieve as it requires stringent timing requirements for the control channel. Alternatively, beam-hopping techniques that are popular in satellite communications [34] can be considered. Beam-hopping consists of serving sequentially users spots in turn according to a predetermined schedule. <|MaskedSetence|> Section IV-A). Therefore, the reconfiguration needs to be done only occasionally with long cycle times and the requirements on the control channel are significantly relaxed. <|MaskedSetence|> <|MaskedSetence|> Therefore, the RIS node is designed to support a medium number of wide initial access wide beams or, alternatively, a permanent directive link is dedicated between the access point and the RIS node. While the control overhead is reduced, synchronous operation (for instance via GPS) between the RIS nodes and the donor nodes is still required. A notable advantage of the redirective RIS system is the simultaneous beam hopping of multiple beams at full aperture gain, particularly when the RIS node is shared among several donor sites (e.g. Fig 2) as explained in the next subsection. .

**A**: The periodic beam hopping time plan can be determined and updated based on the varying traffic demand and the RIS scattering pattern can be optimized based on long-term statistical channel information [35] which also reduces the training overhead (c.f. **B**: To allow for initial access, all potential beam directions are sequentially illuminated and scanned (beam sweeping) during multiple synchronization signal blocks (SSB). **C**: This results in substantial initial access latency and a long beam-hopping period.

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<|MaskedSetence|> The effectiveness-aware performance metrics of the SFL and HSFL are learning accuracy and training latency. However, SFL and HSFL assume that the model is split at the same cut layer and the server-side model is trained in a synchronous mode. <|MaskedSetence|> Thus, how to select an optimal split point and deal with the asynchronization of SL remain important challenges to solve. <|MaskedSetence|> Thus, how to merge these smashed data in the server-side model should be considered. .

**A**: Splitting at the same cut layer leads to asynchronization of device-side model training and smashed data transmission. **B**: To adapt the SL to time-critical tasks, splitfed learning (SFL) [12] and hybrid split and federated learning (HSFL) [13] are proposed. **C**: Also, different split points can result in different smashed data.

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To address challenges associated with power flow nonlinearities, we employ a linear approximation of the power flow equations that is adaptive (i.e., tailored to a specific system and a range of load variability) and conservative (i.e., intend to over- or under-estimate a quantity of interest to avoid constraint violations). <|MaskedSetence|> <|MaskedSetence|> They linearly relate the voltage magnitudes at a particular bus to the power injections at all PQ buses. <|MaskedSetence|> Additionally, in the context of long-term planning, the CLAs can be readily computed with knowledge of expected DER locations and their potential power injection ranges. The accuracy and conservativeness of our proposed method is based on the information of the location of DERs and their power injections variability. As inputs, our method uses the net load profiles including the size of PVs when computing the CLAs. In practice, this data can be obtained by leveraging the extensive existing research on load modeling and monitoring to identify the locations and capabilities of behind-the-meter devices (refer to, e.g., Grijalva2021 ; Schirmer2023 ). An example of an overestimating CLA of the voltage magnitude at bus i𝑖iitalic_i is the linear expression .

**A**: These linear approximations are called conservative linear approximations (CLAs) and were first proposed in BUASON2022 . **B**: These linear approximations can also effectively incorporate the characteristics of more complex components (e.g., tap-changing transformers, smart inverters, etc.), only requiring the ability to apply a power flow solver to the system. **C**: As a sample-based approach, the CLAs are computed using the solution to a constrained regression problem across all samples within the range of power injection variability.

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II-A2 CNN-Mask Inspired by [33], we designed a mask layer for both single and multiple speakers, implemented with Bi-directional Long Short-Term Memory (BiLSTM). Table II outlines the CNN architecture incorporating the mask layer. <|MaskedSetence|> <|MaskedSetence|> They are designed to learn B𝐵Bitalic_B ratio masks. <|MaskedSetence|>

**A**: We replaced the original second dense layer in CNN-MLC with B𝐵Bitalic_B parallel BiLSTM layers, which results in the CNN-Mask backbone network. **B**: The B𝐵Bitalic_B BiLSTM layers take the sigmoid function as the activations. **C**: See the following for the details..

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Selection 4

Misunderstanding certain obstacles as traversable. Our BRAT slices indicate that the robot is able to traverse through hallways reasonably well; however, sometimes, it fails. Figure 10: (a) Notice the highlighted area in the top-right location of the BRAT for the robot heading of −π/2𝜋2-\pi/2- italic_π / 2 radians. Even though the robot faces down (wrt the top view), it cannot escape from the recessed region. (b) On simulating the robot from one of the highlighted states, we saw that the CNN predicts a waypoint into the wall to its right and crashes the robot. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|>

**A**: (c) Another situation was observed where the robot crashed into a glass door due to the low height of the wooden pane around it. **B**: We show the specific wall and its corresponding location on the top view with the magenta arrow. **C**: We show the glass door and its corresponding location on the top view with the magenta arrow..

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Selection 2

Recently, a few attempts have been made to develop datasets mainly for fake audio detection systems. Reimao et al. [23] design a dataset for synthetic speech detection. The fake utterances are generated by the open-sourced tools only using the latest speech synthesis technology. Frank et al. [24] develop a fake dataset named WaveFake, which contains fake utterances generated by the latest speech synthesis models. <|MaskedSetence|> <|MaskedSetence|> The ADD 2022 challenge was motivated to fill the gap [13]. The ADD 2022 consists of various datasets including fully fake utterances containing various noises, partially fake utterances, and adversarial examples. <|MaskedSetence|> Most recently, Zang et al [25] present a dataset named SingFake containing deepfake song clips. The above-mentioned datasets have played a key role in accelerating the development of anti-spoofing and audio deepfake detection. However, the fake utterances in these datasets mainly involve changing timbre, prosody, linguistic content or channel noise of the original audio. They do not consider the manipulation of the acoustic scene of the original audio with a forged one..

**A**: ASVspoof 2021 [12] includes audio deepfake attacks, replay, speech synthesis and voice conversion spoofing methods. **B**: However, the ADD 2023 [14] focuses on surpassing the constraints of binary discrimination, and actually localizing the manipulated intervals in a partially fake speech as well as pinpointing the source responsible for generating any fake audio. **C**: However, these datasets have not covered many real-life challenging situations.

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First, we show the importance of having a system identification technique that is immune to non-zero initial conditions. <|MaskedSetence|> <|MaskedSetence|> Next, we discuss the performance comparison of TV-OKID and the Information-state approach. For the oscillator, two experiments were performed - one with zero-initial conditions and another with non-zero initial conditions, and the results are shown in Fig. 5. The results for the nonlinear systems are shown in Fig 6. <|MaskedSetence|> .

**A**: We show in Fig. **B**: 4 that the non-zero initial conditions, in general, don’t decay to zero in finite time. **C**: The error shown in the figures is the 1-norm of the mean error between the true response and the predicted response from 100 independent simulations, across all the output channels.

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