robench-2024b
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48 items • Updated
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3:output the new weight vector 𝐰ksubscript𝐰𝑘{\bf w}_{k}bold_w start_POSTSUBSCRIPT italic_k end_POSTSUBSCRIPT in (41) | we next present the connection between C2-WORDsuperscriptC2-WORD\textrm{C}^{2}\textrm{-WORD}C start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT -WORD and | and have discussed the connection between C2-WORDsuperscriptC2-WORD\textrm{C}^{2}\textrm{-WORD}C start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT -WORD | (C2-WORDsuperscriptC2-WORD\textrm{C}^{2}\textrm{-WORD}C start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT -WORD). | IV Connection Between C2-WORDsuperscriptC2-WORD\textrm{C}^{2}\textrm{-WORD}C start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT -WORD and | D |
Here we also refer to CNN as a neural network consisting of alternating convolutional layers each one followed by a Rectified Linear Unit (ReLU) and a max pooling layer and a fully connected layer at the end while the term ‘layer’ denotes the number of convolutional layers. | The one layer module consists of one 1D convolutional layer (kernel sizes of 3333 with 8888 channels). | Although we choose the EEG epileptic seizure recognition dataset from University of California, Irvine (UCI) [13] for EEG classification, the implications of this study could be generalized in any kind of signal classification problem. | The UCI EEG epileptic seizure recognition dataset [13] consists of 500500500500 signals each one with 4097409740974097 samples (23.5 seconds). | For the purposes of this paper we use a variation of the database111https://archive.ics.uci.edu/ml/datasets/Epileptic+Seizure+Recognition in which the EEG signals are split into segments with 178178178178 samples each, resulting in a balanced dataset that consists of 11500115001150011500 EEG signals. | C |
We establish a multi-factor system model based on large-scale UAV networks in highly dynamic post-disaster scenarios. Considering the limitations in existing algorithms, we devise a novel algorithm which is capable of updating strategies simultaneously to fit the highly dynamic environments. The main contributions of t... | We formulate the UAV ad-hoc network game in large-scale post-disaster area as a multi-aggregator aggregative game [27], where we calibrate its definition in our UAV network model and put it as follows. | We propose a novel UAV ad-hoc network model with the aggregative game which is compatible with the large-scale highly dynamic environments, in which several influences are coupled together. In the aggregative game, the interference from other UAVs can be regarded as the integral influence, which makes the model more pr... | To investigate UAV networks, novel network models should jointly consider power control and altitude for practicability. Energy consumption, SNR and coverage size are key points to decide the performance of a UAV network [6]. Respectively, power control determines the signal to energy consumption and noise ratio (SNR) ... | We design a model which jointly considers multiple factors such as coverage and power control in multi-channel scenario. The model with more network influence factors ensures its reliability. | D |
Pascal VOC datasets: The PASCAL Visual Object Classes (VOC) Challenge (Everingham et al., 2010) was an annual challenge that ran from 2005 through 2012 and had annotations for several tasks such as classification, detection, and segmentation. The segmentation task was first introduced in the 2007 challenge and featured... | Table 2: A summary of papers for semantic segmentation of natural images applied to PASCAL VOC 2012 dataset. | Pascal VOC datasets: The PASCAL Visual Object Classes (VOC) Challenge (Everingham et al., 2010) was an annual challenge that ran from 2005 through 2012 and had annotations for several tasks such as classification, detection, and segmentation. The segmentation task was first introduced in the 2007 challenge and featured... | Cityscapes: The Cityscapes dataset (Cordts et al., 2016) contains annotated images of urban street scenes. The data was collected during daytime from 50 cities and exhibits variance in the season of the year and traffic conditions. Semantic, instance wise, and dense pixel-wise annotations are provided, with ‘fine’ anno... | PASCAL Context: The PASCAL Context dataset (Mottaghi et al., 2014) extended the PASCAL VOC 2010 Challenge dataset by providing pixel-wise annotations for the images, resulting in a much larger dataset with 19,740 annotated images and labels belonging to 540 categories. | D |
In this paper, we consider a dynamic mission-driven UAV network with UAV-to-UAV mmWave communications, wherein multiple transmitting UAVs (t-UAVs) simultaneously transmit to a receiving UAV (r-UAV). In such a scenario, we focus on inter-UAV communications in UAV networks, and the UAV-to-ground communications are not in... | When considering UAV communications with UPA or ULA, a UAV is typically modeled as a point in space without considering its size and shape. Actually, the size and shape can be utilized to support more powerful and effective antenna array. Inspired by this basic consideration, the conformal array (CA) [16] is introduced... | The first study on the beam tracking framework for CA-enabled UAV mmWave networks. We propose an overall beam tracking framework to exemplify the idea of the DRE-covered CCA integrated with UAVs, and reveal that CA can offer full-spatial coverage and facilitate beam tracking, thus enabling high-throughput inter-UAV dat... | The specialized codebook design of the DRE-covered CCA for multi-UAV mobile mmWave communications. Under the guidance of the proposed framework, a novel hierarchical codebook is designed to encompass both the subarray patterns and beam patterns. The newly proposed CA codebook can fully exploit the potentials of the DRE... | Therefore, the dynamic subarray localization and activation are very coupled and critical for the efficient utilization of the DRE-covered CA. Note that conventional ULA/UPA-oriented codebook designs mainly focus on the beam direction/width controlling via the random-like subarray activation/deactivation without specif... | B |
Though the above works have made a deep research on distributed stochastic optimization, the practical cases may be more complex. | In distributed statistical machine learning algorithms, the (sub)gradients of local loss functions cannot be obtained accurately, the graphs may change randomly and the communication links may be noisy. There are many excellent results on the distributed optimization with multiple uncertain factors ([11]-[15]). | In [12]-[14], the (sub)gradient measurement noises are martingale difference sequences and their second-order conditional moments depend on the states of the local optimizers. The random graph sequences in [12]-[15] are i.i.d. with connected and undirected mean graphs. In addition, additive communication noises are con... | previous time and the consensus error. However, this can not be obtained for the case with the linearly growing subgradients. Also, different from [15], the subgradients are not required to be bounded and the inequality (28) in [15] does not hold. | Besides, the network graphs may change randomly with spatial and temporal dependency (i.e. Both the weights of different edges in the network graphs at the same time instant and the network graphs at different time instants may be mutually dependent.) rather than i.i.d. graph sequences as in [12]-[15], | D |
More precisely, the force input applied to the system is u=Kv(Kpep+ev)𝑢subscript𝐾𝑣subscript𝐾𝑝subscript𝑒𝑝subscript𝑒𝑣u=K_{v}(K_{p}e_{p}+e_{v})italic_u = italic_K start_POSTSUBSCRIPT italic_v end_POSTSUBSCRIPT ( italic_K start_POSTSUBSCRIPT italic_p end_POSTSUBSCRIPT italic_e start_POSTSUBSCRIPT italic_p end_PO... | In the low level control of the plant, a cascade controller is employed for tracking the position and velocity reference trajectories | We model the system as two uncoupled axis with identical parameters. According to (1), the plant can be described by the transfer function G(s)𝐺𝑠G(s)italic_G ( italic_s ), from the force input to the the position of system, p𝑝pitalic_p, defined as | To bring the model close to the real system, we unify the terms required for the contour control formulation with the velocity and acceleration for each axis from the identified, discretized state-space model from (4). | One can easily obtain the transfer function from the reference trajectories to the actual position and velocity as | D |
It is worth noting that for both CPP and B-CPP, the choices b=2𝑏2b=2italic_b = 2 for quantization or k=5𝑘5k=5italic_k = 5 for Rand-k are more communication-efficient than b=4,6𝑏46b=4,6italic_b = 4 , 6 or k=10,20𝑘1020k=10,20italic_k = 10 , 20. | The compression and the communication are applied on the difference (𝒙i−𝒖i)subscript𝒙𝑖subscript𝒖𝑖(\bm{\mathit{x}}_{i}-\bm{\mathit{u}}_{i})( bold_italic_x start_POSTSUBSCRIPT italic_i end_POSTSUBSCRIPT - bold_italic_u start_POSTSUBSCRIPT italic_i end_POSTSUBSCRIPT ) and its compressed version, respectively. | This indicates that as the compression accuracy becomes smaller, its impact exhibits “marginal effects”. | To reduce the error from compression, some works [48, 49, 50] increase compression accuracy as the iteration grows to guarantee the convergence. However, they still need high communication costs to get highly accurate solutions. Techniques to remedy this increased communication costs include gradient difference compres... | When b=6𝑏6b=6italic_b = 6 or k=20𝑘20k=20italic_k = 20, the trajectories of CPP are very close to that of exact Push-Pull/𝒜ℬ𝒜ℬ\mathcal{A}\mathcal{B}caligraphic_A caligraphic_B, which indicates that when the compression errors are small, they are no longer the bottleneck of convergence. | B |
For the sequence-level tasks, which require only a prediction for an entire sequence, we follow \textciteemopia and choose the Bi-LSTM-Attn model from \textcitelin2017structured as our baseline, which was originally proposed for sentiment classification in NLP. | Being inspired by the Bi-LSTM-Attn model \parencitelin2017structured, we employ an attention-based weighting average mechanism to convert the sequence of 512 hidden vectors for an input sequence to one single vector before feeding it to the classifier layer, which comprises two dense layers. | Table 2: The testing classification accuracy (in %) of different combinations of MIDI token representations and models for four downstream tasks: three-class melody classification, velocity prediction, style classification and emotion classification. “CNN” represents the ResNet50 model used by \textcitelee20ismirLBD, w... | The model combines LSTM with a self-attention module for temporal aggregation. Specifically, it uses a Bi-LSTM layer to convert the input sequence of tokens into a sequence of embedding, which can be considered as feature representations of the tokens and then fuses these embeddings into one sequence-level embedding ac... | Instead of feeding the token embedding of each of them individually to the Transformer, we can combine the token embedding of either the four tokens for MIDI scores or six tokens for MIDI performances in a group by concatenation and let the Transformer model | C |
A. Balatsoukas-Stimming, M. B. Parizi, and A. Burg, “LLR-based successive cancellation list decoding of polar codes,” IEEE Trans. Signal Process., vol. 63, no. 19, pp. 5165–5179, Jun. 2015. | A. Graves, S. Fernández, F. Gomez, and J. Schmidhuber, “Connectionist temporal classification: Labelling unsegmented sequence data with recurrent neural networks,” in Proc. 23rd Int. Conf. Mach. Learning (ICML), Pittsburgh, USA, Jun. 2006, pp. 369–376. | D. Amodei, S. Ananthanarayanan, R. Anubhai, and etc., “Deep speech 2 : End-to-end speech recognition in english and mandarin,” in Proc. 33rd Int. Conf. Mach. Learning (ICML), New York, New York, USA, Jun. 2016, pp. 173–182. | D. Amodei, S. Ananthanarayanan, R. Anubhai, and etc., “Deep speech 2 : End-to-end speech recognition in english and mandarin,” in Proc. 33rd Int. Conf. Mach. Learning (ICML), New York, New York, USA, Jun. 2016, pp. 173–182. | A. Graves, S. Fernández, F. Gomez, and J. Schmidhuber, “Connectionist temporal classification: Labelling unsegmented sequence data with recurrent neural networks,” in Proc. 23rd Int. Conf. Mach. Learning (ICML), Pittsburgh, USA, Jun. 2006, pp. 369–376. | B |
The performance of all the models on the PCAM and IDC datasets is described in Table 3 and 4. All the indicators are measured on the test sets. Most of the model show a very good performance, with AUC scores around 0.90 or above. However, when we look at the details, there are clear differences. For instance the AUC of... | For each network architecture tested in this study, the same procedure is used: the model is trained on the training set for 15 epochs, with an evaluation on the validation set after each epoch. Depending on the accurracy value of the model, the weights are saved after each epoch to keep the best model, which is then e... | Precise staging by expert pathologists of breast cancer axillary nodes, a tissue commonly used for the detection of early signs of tumor spreading, is an essential task that will determine the patient’s treatment and his chances of recovery. However, it is a difficult task that was shown to be prone to misclassificatio... | Table 4: Performance of the models on the invasive ductal carcinoma (IDC) breast cancer test set. AUC: area under the ROC curve. | The performance of all the models on the PCAM and IDC datasets is described in Table 3 and 4. All the indicators are measured on the test sets. Most of the model show a very good performance, with AUC scores around 0.90 or above. However, when we look at the details, there are clear differences. For instance the AUC of... | C |
The uniform random expander is constructed by assigning each pixel a phase that is uniformly randomly chosen within [0,2π]02𝜋[0,2\pi][ 0 , 2 italic_π ]. To ensure at least 2π2𝜋2\pi2 italic_π phase is available for all wavelengths the [0,2π]02𝜋[0,2\pi][ 0 , 2 italic_π ] phase range is defined for 660 nmtimes660nm6... | In addition to field-of-view, we also investigate the eyebox that is produced with neural étendue expansion. 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] th... | The experimental findings on the display prototype verify that conventional non-étendue expanded holography can produce high-fidelity content but at the cost of a small FOV. Increasing the étendue via a binary random expander will increase the FOV but at the cost of low image fidelity, even at the design wavelength of ... | While our experimental prototype was built for a HOLOEYE-PLUTO which possesses a 1K-pixel resolution, corresponding to a 1 mm eyebox with 75.6∘superscript75.675.6^{\circ}75.6 start_POSTSUPERSCRIPT ∘ end_POSTSUPERSCRIPT horizontal and vertical FOV, the improvement in hologram fidelity persists across resolutions. Irresp... | The uniform random expander is constructed by assigning each pixel a phase that is uniformly randomly chosen within [0,2π]02𝜋[0,2\pi][ 0 , 2 italic_π ]. To ensure at least 2π2𝜋2\pi2 italic_π phase is available for all wavelengths the [0,2π]02𝜋[0,2\pi][ 0 , 2 italic_π ] phase range is defined for 660 nmtimes660nm6... | A |
GAN inversion framework that utilizes the powerful generative ability of StyleGAN-XL, which shows preferable quantitative and qualitative results in SISR. | Cycle Consistency: Cycle consistency assumes that there exist some underlying relationships between the source and target domains, and tries to make supervision at the domain level. To be precise, we want to capture some special characteristics of one image collection and figure out how to translate these characteristi... | et al., 2018) uses the test image and its downscaling versions with the data augmentation approaches to build the ”training dataset” and then applies the loss function to optimize the model. In addition, weakly-supervised learning also belongs to the unsupervised learning strategy. Among them, some researchers first le... | Although a series of models have been proposed for domain-specific applications, most of them directly transfer the SISR methods to these specific fields. This is the simplest and most feasible method, but it will also inhibit the model performance since they ignore the data structure characteristics of the domain-spec... | In SISR, the idea of cycle consistency has also been widely discussed. Given the LR images domain X𝑋Xitalic_X and the HR images domain Y𝑌Yitalic_Y, we not only learn the mapping from LR to HR but also the backward process. Researchers have shown that learning how to perform image degradation first without paired data... | A |
A second visualisation focusing on this specific region is displayed in Fig. 1(d). Ignoring for now whether or not the SHAP values are positive or negative, it exhibits a high degree of correlation to the fundamental frequency and harmonics in the spectrogram, indicating the focus of the classifier on these same compon... | This paper demonstrates how DeepSHAP can be applied to explain what influences the outputs produced by a spoofing detection model. The examples shown in the paper show how SHAP analysis can be used to highlight the attention applied by a given classifier at low-level spectro-temporal intervals. Nonetheless, the tool of... | In the remainder of this paper we describe our use of DeepSHAP to help explain the behaviour of spoofing detection systems. We show a number of illustrative examples for which the input utterances, all drawn from the ASVspoof 2019 LA database [13], are chosen specially to demonstrate the potential insights which can be... | Fig. 4 shows an example for which SHAP analysis reveals differences in classifier behaviour. The two plots show frequency-averaged SHAP values against time for the 2D-Res-TSSDNet classifier (middle) and the PC-DARTS classifier (bottom) and in both cases the support for the spoof class (blue) and bona fide class (red). ... | Plots of SHAP values such as those shown in Fig. 1(c) are not easily visualised without the use of dilation operations or some other such smoothing operations which distort the results. While they offer interesting insights, we need more easily visualised means with which to explore results. | D |
We summarize our algorithm to learn safe ROCBFs h(x)ℎ𝑥h(x)italic_h ( italic_x ) in Algorithm 1. We first construct the set of safe datapoints Zsafesubscript𝑍safeZ_{\text{safe}}italic_Z start_POSTSUBSCRIPT safe end_POSTSUBSCRIPT from the expert demonstrations Zdynsubscript𝑍dynZ_{\text{dyn}}italic_Z start_POSTSUBSCRI... | Finally, we discuss what behavior expert demonstrations in Zdynsubscript𝑍dynZ_{\mathrm{dyn}}italic_Z start_POSTSUBSCRIPT roman_dyn end_POSTSUBSCRIPT should exhibit. | Let the system in (1) and the set of safe expert demonstrations Zdynsubscript𝑍dynZ_{\mathrm{dyn}}italic_Z start_POSTSUBSCRIPT roman_dyn end_POSTSUBSCRIPT be given. Under Assumptions 1 and 2, learn a function h:ℝn→ℝ:ℎ→superscriptℝ𝑛ℝh:\mathbb{R}^{n}\to\mathbb{R}italic_h : blackboard_R start_POSTSUPERSCRIPT italic_n end... | 1:Input: Set of expert demonstrations Zdynsubscript𝑍dynZ_{\text{dyn}}italic_Z start_POSTSUBSCRIPT dyn end_POSTSUBSCRIPT, | For collecting safe expert demonstrations Zdynsubscript𝑍dynZ_{\text{dyn}}italic_Z start_POSTSUBSCRIPT dyn end_POSTSUBSCRIPT, we use an “expert” PID controller u(x)𝑢𝑥u(x)italic_u ( italic_x ) that uses full state knowledge of x𝑥xitalic_x. Throughout this section, we use the parameters α(r):=rassign𝛼𝑟𝑟\alpha(r):... | C |
As Ltsubscript𝐿𝑡L_{t}italic_L start_POSTSUBSCRIPT italic_t end_POSTSUBSCRIPT increases to reach Nt=8subscript𝑁𝑡8N_{t}=8italic_N start_POSTSUBSCRIPT italic_t end_POSTSUBSCRIPT = 8, the empirical histogram converge to chi-square distribution with 4 degrees of freedom. It is noteworthy that the channel gain, i.e., the... | where RHeffsubscript𝑅superscript𝐻effR_{H^{\rm eff}}italic_R start_POSTSUBSCRIPT italic_H start_POSTSUPERSCRIPT roman_eff end_POSTSUPERSCRIPT end_POSTSUBSCRIPT is the rank of the matrix Heffsuperscript𝐻effH^{\rm eff}italic_H start_POSTSUPERSCRIPT roman_eff end_POSTSUPERSCRIPT, and Plsubscript𝑃𝑙P_{l}italic_P start_P... | element-wise squared envelopes in Heffsuperscript𝐻effH^{\rm eff}italic_H start_POSTSUPERSCRIPT roman_eff end_POSTSUPERSCRIPT. The optimum polarization | the singular value of Heffsuperscript𝐻effH^{\rm eff}italic_H start_POSTSUPERSCRIPT roman_eff end_POSTSUPERSCRIPT; therefore | Without loss of generality, an element in Heffsuperscript𝐻effH^{\rm eff}italic_H start_POSTSUPERSCRIPT roman_eff end_POSTSUPERSCRIPT has the following description of its squared envelope. | D |
+\infty&\text{otherwise,}\end{cases}italic_f ( italic_x ) = { start_ROW start_CELL 1 end_CELL start_CELL if italic_x ∈ [ - 1 , 1 ] ∖ { 0 } , end_CELL end_ROW start_ROW start_CELL 0 end_CELL start_CELL if italic_x = 0 , end_CELL end_ROW start_ROW start_CELL + ∞ end_CELL start_CELL otherwise, end_CELL end_ROW | Unfortunately, this is not the case in the sparse and low rank examples. We observe that for fixed k,n𝑘𝑛k,nitalic_k , italic_n we have in both cases | Unfortunately, this construction is harder to generalize on an unbounded domain or in higher dimension. | It is an open question to generalize our framework for low-dimensional recovery in more general settings such as Banach spaces (e.g., for off-the-grid super-resolution). | a random kernel of fixed dimension. This measure for kernels of dimension ℓℓ\ellroman_ℓ and a descent cone K𝐾Kitalic_K is the following: | B |
It is also a public dataset including 909 X-ray images of hands. The setting of this dataset follows [25]. The first 609 images are used for training and the rest for testing. The image size varies among a small range, so all images are resized to 384×\times×384. | Figure 4: Visual Comparison of templates from our policy and random selection. Column “Template/Test 1/Test 2" refers to the templates and two test images. The row “Ours" and “Random" refers to the template selected by our method and random selection, respectively. | It is a widely-used public dataset for cephalometric landmark detection, containing 400 radiographs, and is provided in IEEE ISBI 2015 Challenge [14, 37]. There are 19 landmarks of anatomical significance labeled by 2 expert doctors in each radiograph. The averaged version of annotations by two doctors is set as the gr... | Few-shot medical landmark detection: Firstly, experiments are conducted on different numbers of templates for Cephalometric dataset. For Table 1, M𝑀Mitalic_M denotes the number of templates used in experiment. The columns “ours" refer to the results achieved by the proposed method, while “random" means to the average ... | This dataset is from [38] containing 10,000 faces with 7500 and 2500 in training and test sets, respectively. All images are collected from the WIDER FACE dataset [40] and manually labeled with 98 landmarks. The dataset contains different test subsets where the image appearances vary due to variations in pose, expressi... | D |
Fast automatic segmentation of hippocampal subfields and medial temporal lobe subregions in 3 tesla and 7 tesla t2-weighted mri. | Zeineldin, R.A., Karar, M.E., Elshaer, Z., Schmidhammer, M., Coburger, J., Wirtz, C.R., Burgert, O., Mathis-Ullrich, F., 2021. | Zeineldin, R.A., Karar, M.E., Elshaer, Z., Schmidhammer, M., Coburger, J., Wirtz, C.R., Burgert, O., Mathis-Ullrich, F., 2021. | Zeineldin, R.A., Karar, M.E., Elshaer, Z., Schmidhammer, M., Coburger, J., Wirtz, C.R., Burgert, O., Mathis-Ullrich, F., 2021. | Zeineldin, R.A., Karar, M.E., Elshaer, Z., Schmidhammer, M., Coburger, J., Wirtz, C.R., Burgert, O., Mathis-Ullrich, F., 2021. | A |
(X,ℬ,μ)𝑋ℬ𝜇(X,\mathcal{B},\mu)( italic_X , caligraphic_B , italic_μ ) be a measure space where X𝑋Xitalic_X is a set, | on the probability space (R1,ℬ,μ)superscript𝑅1ℬ𝜇(R^{1},\mathcal{B},\mu)( italic_R start_POSTSUPERSCRIPT 1 end_POSTSUPERSCRIPT , caligraphic_B , italic_μ ). Then the | (X,ℬ,μ)𝑋ℬ𝜇(X,\mathcal{B},\mu)( italic_X , caligraphic_B , italic_μ ) be a measure space where X𝑋Xitalic_X is a set, | ℬ=ℬ(X)ℬℬ𝑋\mathcal{B}=\mathcal{B}(X)caligraphic_B = caligraphic_B ( italic_X ) is the borel σ−limit-from𝜎\sigma-italic_σ -algebra on the set | X𝑋Xitalic_X and μ𝜇\muitalic_μ is a measure on the measurable space (X,ℬ)𝑋ℬ(X,\mathcal{B})( italic_X , caligraphic_B ). | C |
Now, we will present the theorem which prescribes the design requirements on the controller gains in order to guarantee both pISSf and ISSt for the PDE system (4)-(7). | Consider the system (4) with boundary conditions (8). Let us also consider the unsafe set for this system to be (12) and the metric measuring the distance from this unsafe set to be given by (13). If the controller gains are chosen such that the following inequalities are satisfied, | We also note here that if the gains satisfy the pISSf conditions given in (24), then (49) will be automatically satisfied and BT2<0𝐵subscript𝑇20BT_{2}<0italic_B italic_T start_POSTSUBSCRIPT 2 end_POSTSUBSCRIPT < 0. | then the system (4) satisfies the two conditions of Proposition 1, and is considered to be practical Input-to-State Safe (pISSf) with respect to the unsafe set 𝒰𝒰\mathscr{U}script_U. | Consider the system (4) with boundary conditions (8). If there exists controller gains that satisfy pISSf inequality conditions given in (24), then the system (4) is considered to be both pISSf and ISSt. | D |
In this setting, the PUs’ parameters are available—for determination of spectrum allocation. A PU’s parameters include its location, transmit power, and its PURs’ locations. | based on locations improved the performance of our models significantly compared to placing the SSs based on received powers. | For each SS, its parameters may include its location and aggregate received power from the PUs, and in general, may also include the mean and variance of the Gaussian distribution of the received power. | Allocation based on SSs parameters is implicitly based on real-time channel conditions, which is important for accurate and optimized spectrum allocation as the conditions affecting signal attenuation (e.g., air, rain, vehicular traffic) may change over time. | In such a crowdsourced sensing architecture, allocation decision is based on SS parameters, which includes each sensor’s location and received (aggregated) | D |
The following result states that, under Assumption 1, if the stepsize at each iteration is chosen by the doubling trick scheme, there is an upper bound for the static regret defined in (4). Moreover, the upper bound has the order of O(T)𝑂𝑇O(\sqrt{T})italic_O ( square-root start_ARG italic_T end_ARG ) for convex cost... | Suppose Assumptions 1 (i) and 2 hold. Furthermore, if the stepsize is chosen as αt=Pμtsubscript𝛼𝑡𝑃𝜇𝑡\alpha_{t}=\frac{P}{\mu t}italic_α start_POSTSUBSCRIPT italic_t end_POSTSUBSCRIPT = divide start_ARG italic_P end_ARG start_ARG italic_μ italic_t end_ARG. Then, the static regret (4) achieved by Algorithm 1 satis... | Suppose Assumption 1 holds. Furthermore, if the stepsize is chosen as αt=CTTsubscript𝛼𝑡subscript𝐶𝑇𝑇\alpha_{t}=\sqrt{\frac{C_{T}}{T}}italic_α start_POSTSUBSCRIPT italic_t end_POSTSUBSCRIPT = square-root start_ARG divide start_ARG italic_C start_POSTSUBSCRIPT italic_T end_POSTSUBSCRIPT end_ARG start_ARG italic_T end... | Suppose Assumption 1 holds. Furthermore, if the stepsize is chosen as αt=CTTsubscript𝛼𝑡subscript𝐶𝑇𝑇\alpha_{t}=\sqrt{\frac{C_{T}}{T}}italic_α start_POSTSUBSCRIPT italic_t end_POSTSUBSCRIPT = square-root start_ARG divide start_ARG italic_C start_POSTSUBSCRIPT italic_T end_POSTSUBSCRIPT end_ARG start_ARG italic_T end... | Suppose Assumption 1 holds. Furthermore, if the stepsize is chosen according to Definition 1. Then, the static regret (4) achieved by Algorithm 1 satisfies | D |
Deep learning models should not be considered as a replacement for clinical diagnosis by medical professionals. These models should be used as complementary tools to aid medical professionals in making more accurate diagnoses. It is also crucial to validate the accuracy and reliability of these models on diverse and re... | Finally, we conclude our paper with limitations in Section 5 and conclusions in Section 6, summarizing our key findings and contributions. We also discuss the implications of our work and highlight future directions for research in the field of thoracic disease prediction using deep learning techniques. | By analyzing these metrics, we gain insights into the model’s performance in terms of sensitivity, specificity, predictive values, discrimination power (ROC curve), and overall classification accuracy (F1 score). These evaluations help us understand the strengths and limitations of the model in accurately predicting di... | Deep learning models should not be considered as a replacement for clinical diagnosis by medical professionals. These models should be used as complementary tools to aid medical professionals in making more accurate diagnoses. It is also crucial to validate the accuracy and reliability of these models on diverse and re... | The deep learning model presented in this study has several limitations that should be acknowledged. These limitations include: | D |
The most straightforward use of this database is the classification of the emotions felt by a woman, using machine and deep learning algorithms with unimodal and multimodal approaches. | Familiarity with the emotion felt, the situation displayed in the clip, and the specific clip: annotated in three different questions. The two first consider a 9999-point Likert scale, whereas the last one considers a binary yes-no option. | 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). It means that the number of targeted emotions is smaller than the reported ones in th... | Moreover, the physiological signals were recorded during the entire experiment, so that synchronization can be made with the physiological and audio signals, leading to a multi-modal or fusion scheme. On this basis, a series of experiments carried out in mono- and multi-modal emotion recognition can be found in "Supple... | First, the physiological signals can be used together or separately to analyze their relationship with the annotated discrete or dimensional emotions. | D |
In retinal imaging, GANs have been used to create synthetic data. Li et al. [27] highlighted the importance of enhancing the quality of synthetic retinal images in their review, emphasizing that using synthetic images in training can improve performance and help mitigate overfitting. | In retinal imaging, GANs have been used to create synthetic data. Li et al. [27] highlighted the importance of enhancing the quality of synthetic retinal images in their review, emphasizing that using synthetic images in training can improve performance and help mitigate overfitting. | In the field of Optical Coherence Tomography (OCT) imaging, super-resolution GANs (like ESRGAN [24]) have demonstrated their value as a tool to enhance the quality of the image and improve AMD detection [25]. Das et al. [26] proposed a quick and reliable super-resolution approach concerning OCT imagery using GANs, achi... | Bellemo et al. [28] described the possible advantages and limitations towards synthetic retina image generation using GANs. The authors highlighted the potential clinical applications of GANs concerning early- and late-stage AMD classification. | We have employed a retinal image quality assessment model in preprocessing step. We have compared a number of synthetic medical image generation techniques and found StyleGAN2-ADA to be the most suitable using which we have developed a method to generate synthetic images. We have investigated the use of the synthetic i... | C |
We show how to guarantee the (uniform, in a set) ultimate boundedness property 6 of a discrete-time polytopic system when the ReLU approximation replaces a traditional stabilizing controller. Specifically, by focusing on the approximation error between NN-based and traditional controller-based state-to-input mappings, ... | We now characterize a stabilizing control law Φ(⋅)Φ⋅\Phi(\cdot)roman_Φ ( ⋅ ) from a geometrical perspective. While both of the vertex-based policies Φ(⋅)Φ⋅\Phi(\cdot)roman_Φ ( ⋅ ) defined in (3) or (4) are known to produce a controller with PWA structure, the structure underlying a selection-based controller Φ(⋅)Φ⋅\... | While the variable structure controller amounts to a continuous piecewise-affine (PWA) mapping by construction, we characterize the geometric properties of the selection-based controller. Specifically, for the resulting nonlinear multi-parametric program, we show that: | In fact, the continuous PWA structure of (3) comes by construction, since it is defined directly over a simplicial partition, while for (4) that structure can be proved by recognizing that the controller’s definition amounts to that of a strictly convex multi-parametric quadratic program (mp-QP), so that available resu... | We have considered the design of ReLU-based approximations of traditional controllers for polytopic systems, enabling implementation even on very fast embedded control systems. We have shown that our reliability certificate require one to construct and solve an MILP offline, whose associated optimal value characterizes... | B |
Note that the existence of such a sequence can be found via the breadth-first search [20] on the verifier with complexity O(|Xv|×|Eo|)𝑂subscript𝑋𝑣subscript𝐸𝑜O(|X_{v}|\times|E_{o}|)italic_O ( | italic_X start_POSTSUBSCRIPT italic_v end_POSTSUBSCRIPT | × | italic_E start_POSTSUBSCRIPT italic_o end_POSTSUBSCRIPT | )... | This paper deals with the problem of event concealment for concealing secret events in a system modeled as an NFA under partial observation. | Due to partial observation of the system, the event set E𝐸Eitalic_E can be partitioned into a set of observable events Eosubscript𝐸𝑜E_{o}italic_E start_POSTSUBSCRIPT italic_o end_POSTSUBSCRIPT and a set of unobservable events Euo=E−Eosubscript𝐸𝑢𝑜𝐸subscript𝐸𝑜E_{uo}=E-E_{o}italic_E start_POSTSUBSCRIPT italic_u ... | In this paper, we focus on the case when certain events of a system are deemed secret and study (i) the circumstances under which their occurrences get revealed to an external eavesdropper, and (ii) ways to conceal (as long as needed) the secret events using an obfuscation mechanism. | To answer these questions, the privacy of a system is considered in terms of concealing secret events and the concept of event concealment is proposed. | A |
Table 3, comparing Clear and PPIR(MPC), PPIR(FHE)-v1 and v2, showcases the metrics resulting from spline-based non-linear registration between grey matter density images without the application of gradient approximation. Additionally, the table includes results for the registration between whole-body PET images when th... | Point Cloud Data. In Supplementary Table A1 we present the registration metrics for PPIR(MPC) and PPIR(FHE)-v1. The registration shows that PPIR(MPC) achieves the best results compared to PPIR(FHE), which exhibits not only a longer computation time but also requires higher bandwidth, thanks to its non-iterative algorit... | Here, the limitations of PPIR(FHE)-v1 on the bandwidth size are even more evident than in the affine case, since the bandwidth increases according to the number of parameters. This result gives a non-negligible burden to the party1𝑝𝑎𝑟𝑡subscript𝑦1party_{1}italic_p italic_a italic_r italic_t italic_y start_POSTS... | Regarding the registration accuracy, we draw conclusions similar to those of the affine case, where PPIR(MPC) leads to minimum differences with respect to Clear, while PPIR(FHE)-v1 seems slightly superior. | Incorporating gradient approximation for handling whole-body PET data leads to similar conclusions as for the experiments on brain data. Qualitative results, reported in Supplementary Figure A6, show negligible differences between images transformed with Clear+GMS, PPIR(MPC)+GMS, and PPIR(FHE)-v1+GMS. | C |
})\in\mathcal{A}^{\ell+1}\times\mathcal{O}^{\ell+1}blackboard_Y start_POSTSUPERSCRIPT italic_θ , italic_π end_POSTSUPERSCRIPT start_POSTSUBSCRIPT italic_h end_POSTSUBSCRIPT ( italic_w start_POSTSUBSCRIPT italic_h - 1 end_POSTSUBSCRIPT , italic_a start_POSTSUBSCRIPT italic_h end_POSTSUBSCRIPT , italic_o start_POSTSUBSCR... | Based on the two density mappings defined in (3.6) and (3.7), respectively, we have the following identity for all h∈[H]ℎdelimited-[]𝐻h\in[H]italic_h ∈ [ italic_H ] and θ∈Θ𝜃Θ\theta\in\Thetaitalic_θ ∈ roman_Θ, | An Overview of Embedding Learning. We now summarize the learning procedure of the embedding. First, we estimate the density mappings defined in (3.6) and (3.7) under the true parameter θ∗superscript𝜃\theta^{*}italic_θ start_POSTSUPERSCRIPT ∗ end_POSTSUPERSCRIPT based on interaction history. Second, we estimate the Bel... | Under Assumptions 3.1 and 3.5, it holds for all the parameter θ∈Θ𝜃Θ\theta\in\Thetaitalic_θ ∈ roman_Θ that | where f∈L1(𝒜k×𝒪k+1)𝑓superscript𝐿1superscript𝒜𝑘superscript𝒪𝑘1f\in L^{1}(\mathcal{A}^{k}\times\mathcal{O}^{k+1})italic_f ∈ italic_L start_POSTSUPERSCRIPT 1 end_POSTSUPERSCRIPT ( caligraphic_A start_POSTSUPERSCRIPT italic_k end_POSTSUPERSCRIPT × caligraphic_O start_POSTSUPERSCRIPT italic_k + 1 end_POSTSUPERSCRIPT... | A |
V𝑉Vitalic_V, γ′superscript𝛾′\gamma^{\prime}italic_γ start_POSTSUPERSCRIPT ′ end_POSTSUPERSCRIPT, χ𝜒\chiitalic_χ, β𝛽\betaitalic_β and μ𝜇\muitalic_μ are constants. | \in\Xi_{3}\right|=\frac{1}{\cos(\beta)},| divide start_ARG ∂ italic_P end_ARG start_ARG ∂ italic_ξ end_ARG | italic_P ∈ roman_Σ start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPT ; italic_ξ ∈ roman_Ξ start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPT | = - italic_V start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT roman_cos ( italic_γ ) ; | divid... | We denote by ξ^(t)^𝜉𝑡\hat{\xi}(t)over^ start_ARG italic_ξ end_ARG ( italic_t ) the planed function for any state variable | the choice of x^^𝑥\hat{x}over^ start_ARG italic_x end_ARG, y^^𝑦\hat{y}over^ start_ARG italic_y end_ARG, z^^𝑧\hat{z}over^ start_ARG italic_z end_ARG and β^^𝛽\hat{\beta}over^ start_ARG italic_β end_ARG (or μ^^𝜇\hat{\mu}over^ start_ARG italic_μ end_ARG). We also denote by δξδ𝜉\updelta\xiroman_δ italic_ξ the differe... | red correspond to the planed trajectory ξ^^𝜉\hat{\xi}over^ start_ARG italic_ξ end_ARG, while curves in | B |
Model settings. The local observation data yi(k)subscript𝑦𝑖𝑘y_{i}(k)italic_y start_POSTSUBSCRIPT italic_i end_POSTSUBSCRIPT ( italic_k ) of node i𝑖iitalic_i is given by yi(k)=Hi(k)x0+vi(k)subscript𝑦𝑖𝑘subscript𝐻𝑖𝑘subscript𝑥0subscript𝑣𝑖𝑘y_{i}(k)=H_{i}(k)x_{0}+v_{i}(k)italic_y start_POSTSUBSCRIPT italic... | (000h¯s,t,k00),(00h¯s+2,t,k000000),(0h¯5,t,k0000h¯6,t,k00),s=1,2,t=1,2,formulae-sequencematrix000subscript¯ℎ𝑠𝑡𝑘00matrix00subscript¯ℎ𝑠2𝑡𝑘000000matrix0subscript¯ℎ5𝑡𝑘0000subscript¯ℎ6𝑡𝑘00𝑠12𝑡12\begin{pmatrix}0&0&0\\ | ]_{2\times 2}\},k\geq 0\}{ caligraphic_G ( italic_k ) = { caligraphic_V = { 1 , 2 } , caligraphic_A start_POSTSUBSCRIPT caligraphic_G ( italic_k ) end_POSTSUBSCRIPT = [ italic_w start_POSTSUBSCRIPT italic_i italic_j end_POSTSUBSCRIPT ( italic_k ) ] start_POSTSUBSCRIPT 2 × 2 end_POSTSUBSCRIPT } , italic_k ≥ 0 } with w12... | ΦZ(j,i)={Z(j)⋯Z(i),j≥iIn,j<i.,∏k=ijZ(k)=ΦZ(j,i).formulae-sequencesubscriptΦ𝑍𝑗𝑖cases𝑍𝑗⋯𝑍𝑖𝑗𝑖subscript𝐼𝑛𝑗𝑖superscriptsubscriptproduct𝑘𝑖𝑗𝑍𝑘subscriptΦ𝑍𝑗𝑖\Phi_{Z}(j,i)=\begin{cases}Z(j)\cdots Z(i),&j\geq i\\ | −λ(k)xi(k),k≥0,i∈𝒱.formulae-sequence𝜆𝑘subscript𝑥𝑖𝑘𝑘0𝑖𝒱\displaystyle~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}-\lambda(k)x_{i}(k),~{}% | A |
As motivation, we first argue that conventional notion of “graph gradients”, computed using Laplacian888A similar argument can be made using normalized Laplacian 𝐋nsubscript𝐋𝑛{\mathbf{L}}_{n}bold_L start_POSTSUBSCRIPT italic_n end_POSTSUBSCRIPT to compute gradients for a positive connected graph; | 𝐋≜𝐃−𝐖≜𝐋𝐃𝐖{\mathbf{L}}\triangleq{\mathbf{D}}-{\mathbf{W}}bold_L ≜ bold_D - bold_W, is ill-suited to define planar graph signals. | The most common graph smoothness prior to regularize an inherently ill-posed signal restoration problem is graph Laplacian regularizer (GLR) [3] 𝐱⊤𝐋𝐱superscript𝐱top𝐋𝐱{\mathbf{x}}^{\top}{\mathbf{L}}{\mathbf{x}}bold_x start_POSTSUPERSCRIPT ⊤ end_POSTSUPERSCRIPT bold_Lx, where 𝐋𝐋{\mathbf{L}}bold_L is a graph Lapl... | \mathbf{I}}-{\mathbf{D}}^{-1/2}{\mathbf{W}}{\mathbf{D}}^{-1/2}bold_L start_POSTSUBSCRIPT italic_n end_POSTSUBSCRIPT ≜ bold_D start_POSTSUPERSCRIPT - 1 / 2 end_POSTSUPERSCRIPT bold_LD start_POSTSUPERSCRIPT - 1 / 2 end_POSTSUPERSCRIPT = bold_I - bold_D start_POSTSUPERSCRIPT - 1 / 2 end_POSTSUPERSCRIPT bold_WD start_POSTS... | A combinatorial graph Laplacian matrix 𝐋𝐋{\mathbf{L}}bold_L is defined as 𝐋≜𝐃−𝐖≜𝐋𝐃𝐖{\mathbf{L}}\triangleq{\mathbf{D}}-{\mathbf{W}}bold_L ≜ bold_D - bold_W. | A |
In contrast, recent works employ advanced platforms such as MRiLab [7] and Brainweb [13], which rely on biophysical models that use complex non-linearities to estimate MR images in different parameters. MRiLab is an MR image simulator equipped with the generalized multi-pool exchange model for accurate MRI simulations. | In contrast, recent works employ advanced platforms such as MRiLab [7] and Brainweb [13], which rely on biophysical models that use complex non-linearities to estimate MR images in different parameters. MRiLab is an MR image simulator equipped with the generalized multi-pool exchange model for accurate MRI simulations. | For our training, we require the MRI scans in two different parameter settings of {TE, TR}. 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 diffe... | These works also utilize the multi-pool modeling capabilities of MRiLab to simulate the effects of fat-water interference in macromolecular-rich tissues and validate them in a physical phantom. Brainweb is a Simulated Brain Database generated using an MRI simulator, developed at the McConnell Brain Imaging Centre. This... | In our work, we propose a coarse-to-fine fully convolutional network for MR image re-parameterization mainly for Repetition Time (TR) and Echo Time (TE) parameters. As the model is coarse-to-fine, we use image features extracted from an image reconstruction auto-encoder as input instead of directly using the raw image.... | C |
The previous literature showed that the summation method with DFE has the advantage of a higher OOK data rate [10, 12, 25]. However, at data rates when the pulses are countable and the output pulses do not overlap, the thresholding method for single photon counting is still necessary, especially in scenarios where the ... | Moreover, since the current commercially available SiPMs have a higher PDE in the visible blue-green spectrum, for example, in UWOC, VLC and Li-Fi applications, it is expected that a lower optical power is required to achieve the same BER at a longer wavelength. However, these SiPM are not yet suitable for near-infrare... | In this work, the detector is a commercially available 1mm21𝑚superscript𝑚21~{}mm^{2}1 italic_m italic_m start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT C-Series SiPM from On Semiconductor [30]. The technical parameters for the SiPM are listed in Table I. | Considering the bandwidth limitation of the PMOD connector on the FPGA evaluation board, we focused on the SiPM standard output to present the SiPM’s dynamic range and Poisson limited BER performance, as well as the bandwidth limitation on the SiPM readout circuit. We have experimentally verified that the Poisson limit... | In this paper, we have demonstrated a novel real-time SiPM-based receiver with a low bit rate and high sensitivity, which has the potential for low transmitter power consumption. The work provides the evaluations of the analog chain of the receiver to show the potential for lower power consumption. The numerical simula... | B |
If the autonomy is restricted to the operation around the asteroid, that is when the transition from the ground to the autonomous operation takes place. In this case, the spacecraft would rely on the ground up to the moment when the asteroid is found as a point source in its optical cameras. After that, a hybrid approa... | We have purposely selected those specific asteroids to underscore the fact that our proposed guidance, navigation, and control (GN&C𝐺𝑁𝐶GN\&Citalic_G italic_N & italic_C) approach is not reliant on the size or shape of the asteroid. The mission profile can be customized based on the specific objectives of the missio... | By “far-approach”, we consider the period of the mission when the spacecraft changes from heliocentric to relative navigation about the small-body, which is the same as phase 1 of the Hayabusa 2 campaign [49]. That phase ends with the spacecraft at an arbitrarily far distance from the small body, when a preliminary ass... | The more or less constant dynamics in the far-approach phase make it easy to use simple guidance laws, such as an LQG or a ZEM/ZEV that considers the state’s uncertainty, for approaching the body. As it will become apparent soon, with our future assumptions, there is no need for an exact orbit determination at this poi... | A mission could have different profiles depending on the mission’s goals and the availability of prior knowledge about the asteroid’s environment and properties. We consider that after the preliminary environment assessment at the end of the far-approach phase, the spacecraft could opt between different profiles, depen... | C |
The study of the motion of aerial vehicles is a complex subject that has been investigated since the early appearance of the first airplanes. There is a large body of literature on the aerodynamic aspects of these vehicles and their modeling. In this section, we will discuss the quadrotor aerial robot, which is a basic... | The study of the motion of aerial vehicles is a complex subject that has been investigated since the early appearance of the first airplanes. There is a large body of literature on the aerodynamic aspects of these vehicles and their modeling. In this section, we will discuss the quadrotor aerial robot, which is a basic... | In [131], the communications-related term is the outage probability of the communication link. In [119], the optimization target is the number of users served by a UAV operating as an aerial BS. In [132], the communications-related term is the coverage radius of a Low-altitude aerial platform (LAP) acting as a BS for ... | As we have explained above, the oversimplification of MR models can have serious consequences, thus the importance of selecting an adequate model complexity. In order to help researchers with no (or little) robotics background, the rest of this section provides a general description of mathematical models describing th... | Multirotor aerial robots (also called rotary-wing aerial robots) are one of the most popular types of aerial robots nowadays. One of the most common type of these UAVs is the quadrotor, which is the subject of this section. | D |
Theorem 20 requires the existence of storage functions that satisfy several properties. Therefore, in practice, the conditions in | The IQC based Theorem 28 makes use of a quadratic form involving incrementally bounded multipliers, while the supply rate in the | In both Examples 6 and 7, even though the systems may be described by dissipativity with respect to some static supply rates, the advantage of using dynamic supply rates lies in offering great flexibility in system characterisation as well as reducing conservatism in feedback stability analysis, similarly to the benefi... | Theorem 28 may be easier to verify. In particular, the use of dynamic multipliers is both natural and well known in the theory of | In contrast to Assumption 12, the lower and upper bounds in Assumption 14 depend on both x𝑥xitalic_x and z𝑧zitalic_z. A byproduct of this assumption is that the stability with respect to both x=0𝑥0x=0italic_x = 0 and z=0𝑧0z=0italic_z = 0 in (17) and (18) may be established, even though we are only concerned with th... | C |
Because the compensator diverges at ∂\partial∂ χ~~𝜒\tilde{\chi}over~ start_ARG italic_χ end_ARG, it may have the potential to cage the solution x𝑥xitalic_x in χ~~𝜒\tilde{\chi}over~ start_ARG italic_χ end_ARG with probability one. The answer will be given in a later section. | 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. Therefore, in the CBF approach, the derivation of a safety-critical con... | For a stochastic system, a subset of the state space is generally hard to be (almost sure) invariance because the diffusion coefficient is required to be zero at the boundary of the subset111The detail is discussed in[18], which aims to make the state of a stochastic system converge to the origin with probability one a... | The above discussion also implies that if a ZCBF is defined for a stochastic system and ensures “safety with probability one,” the good robust property of the ZCBF probably gets no appearance. The reason is that the related state-feedback law generally diverges at the boundary of the safe set. Hence, the previous work ... | In the context of a CBF, the control objective is to make a specific subset, which is said to be a safe set, on the state space invariance forward in time (namely, forward invariance [2]). There are various types of CBFs, the most commonly used currently are a reciprocal control barrier function (RCBF) [2, 4, 5] and a... | B |
In practice, it is also possible that the wind farm (GFL converter) and the GFM converter are connected to one common 35 kV bus, as shown in Fig. 6. The equivalent inductor of the transformer is 0.08pu0.08pu0.08~{}{\rm pu}0.08 roman_pu. Hence, the typical value of Zlocalsubscript𝑍localZ_{\rm local}italic_Z start_POST... | In this case, the electrical distance between the GFL converter and the GFM converter becomes smaller, and one may need fewer GFM converters to enhance the equivalent power grid strength, as illustrated in the next example. | Combining the power grid strength quantified by gSCR in this section and the analysis of the voltage source behaviors of GFM converters in Section II, it is once again emphasized that it is necessary to install GFM converters to provide effective voltage source behaviors and thus enhance the power grid strength, which ... | 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 ref... | 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 p... | A |
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. The multi-scale kernel can implicitly encode features from coarse to fine, which allows the model to mimic ... | 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. The multi-scale kernel can implicitly encode features from coarse to fine, which allows the model to mimic ... | This paper proposes a multi-scale attention network (MAN) for super-resolution under multiple complexities. MAN adopts transformer-style blocks for better modeling representation. To effectively and flexibly establish long-range correlations among various regions, we develop multi-scale large kernel attention (MLKA) th... | The attention mechanism can force networks to focus on crucial information and ignore irrelevant ones. Previous SR models adopt a series of attention mechanisms, including channel attention (CA) and self-attention (SA), to obtain more informative features. However, these methods fail to simultaneously uptake local info... | We propose multi-scale large kernel attention (MLKA) for obtaining long-range dependencies at various granularity levels by combining large kernel with gate and multi-scale mechanisms, which significantly increases model representation capability. | D |
For example, a UAV might experience stronger wind during its flight than it anticipated, or the off-shore landing pad of a rocket might have drifted away from its original position. | The agent can use these parameter-conditioned reachable sets online to activate the safety function corresponding to the current environment and system factors, leading to a real-time adaptation of safety assurances. | Thus, during the run time, the system can sample the environmental factors and other parameters and activate the corresponding safety function via a simple DNN query, leading to a real-time adaptation of safety assurances. | In such situations, it is hard to provide safety guarantees prior to the system deployment; instead, the system needs to perform an efficient evaluation and adaptation of safety assurances online in light of the environment and system evolution. | Various simulation studies are presented to demonstrate the utility of the proposed method in maintaining safety despite the system and environment evolution. | C |
\boldsymbol{S}_{\alpha\alpha})^{-1}italic_N ( italic_θ , italic_φ ) = italic_N start_POSTSUBSCRIPT italic_F end_POSTSUBSCRIPT start_POSTSUPERSCRIPT ∞ end_POSTSUPERSCRIPT italic_N start_POSTSUBSCRIPT 0 end_POSTSUBSCRIPT ⋅ roman_max ( bold_italic_s start_POSTSUBSCRIPT italic_α italic_β end_POSTSUBSCRIPT ( italic_θ , ital... | (𝑺L−1−𝑺αα)−1=∑k=0∞(𝑺L𝑺αα)k𝑺L=𝑺L+𝑺L𝑺αα𝑺L+…,superscriptsuperscriptsubscript𝑺𝐿1subscript𝑺𝛼𝛼1superscriptsubscript𝑘0superscriptsubscript𝑺𝐿subscript𝑺𝛼𝛼𝑘subscript𝑺𝐿subscript𝑺𝐿subscript𝑺𝐿subscript𝑺𝛼𝛼subscript𝑺𝐿…\displaystyle(\boldsymbol{S}_{L}^{-1}-\boldsymbol{S}_{\alpha\alpha})^{-1}\!=\!... | 𝑩(𝑺L−1−𝑺αα)−𝖧𝒔αβ(θ,φ)∗.𝑩superscriptsuperscriptsubscript𝑺𝐿1subscript𝑺𝛼𝛼𝖧subscript𝒔𝛼𝛽superscript𝜃𝜑\displaystyle~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{% | 𝑺L−H(𝑺L−1−𝑺αα)−Hα𝑯UE−RISH(α𝑯UE−RIS(𝑺L−1−𝑺αα)−1\displaystyle\boldsymbol{S}_{L}^{-\rm H}(\boldsymbol{S}_{L}^{-1}-\boldsymbol{S% | 𝑩(𝑺L−1−𝑺αα)−𝖧𝒔αβ(θ,φ)∗−Aλ2cosθ,0),\displaystyle~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}\boldsymbol{B}(% | D |
Haptic communication has been incorporated by industries to perform grasping and manipulation, where the robot transmits the haptic data to the manipulator. The shape and weight of the objects to be held are measured using cutaneous feedback derived from the fingertip contact pressure and kinesthetic feedback of finger... | Haptic communication has been incorporated by industries to perform grasping and manipulation, where the robot transmits the haptic data to the manipulator. The shape and weight of the objects to be held are measured using cutaneous feedback derived from the fingertip contact pressure and kinesthetic feedback of finger... | Due to the difficulty in supporting massive haptic data with stringent latency requirements, JND can be identified as important goal-oriented semantic information to ignore the haptic signal that cannot be perceived by the manipulator. Two effectiveness-aware performance metrics including SNR and SSIM have been verifie... | Difference (JND) is identified as valuable semantic information to filter the haptic signal that cannot be perceived by the human, where Weber’s law serves as an important semantic information extraction criterion. | In the Augmented Reality (AR) display task, the central server transmits the rendered 3D model of a specific virtual object to the user. It is noted that the virtual object identification and its pose information related to the real world is the key to achieving alignment between virtual and physical objects. Therefore... | B |
Distributed energy resources (DERs) are being rapidly deployed in distribution systems. Fluctuations in DER power outputs and varying load demands can potentially cause violations of voltage limits, i.e., voltages outside the bounds imposed in the ANSI C84.1 standard. These violations can cause equipment malfunctions, ... | In this paper, we consider a sensor placement problem which seeks to locate the minimum number of sensors and determine corresponding sensor alarm thresholds in order to reliably identify all possible violations of voltage magnitude limits in a distribution system. We formulate this sensor placement problem as a bileve... | 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 violat... | To mitigate the impacts of violations, distribution system operators (DSOs) must identify when power injection fluctuations lead to voltages exceeding their limits. To do so, sensors are placed within the distribution system to measure and communicate the voltage magnitudes at their locations. Due to the cost of sensor... | In contrast to previous work, this problem does not attempt to ensure full observability of the distribution system. Rather, we seek to locate (a potentially smaller number of) sensors that can nevertheless identify all voltage limit violations for any power injections within a specified range of power injection variab... | C |
Table X lists the MDE results of the comparison 2D speaker localization systems with the linear arrays on the real-world data. From the table, we see that, although the CNN-based methods were only trained on the simulated data, they generalize well on the real-world data, and consistently outperform the conventional me... | From the table, we see that the performance of all DOA algorithms in the multi-speaker localization scenarios drops compared to that in the single-speaker scenarios. However, the CNN-based methods still outperform conventional methods. CNN-ULD performs the best in the CNN-based methods, while CNN-LBT outperforms CNN-PI... | Table X lists the MDE results of the comparison 2D speaker localization systems with the linear arrays on the real-world data. From the table, we see that, although the CNN-based methods were only trained on the simulated data, they generalize well on the real-world data, and consistently outperform the conventional me... | TABLE X: MDE (in meters) of the comparison 2D speaker localization methods on the real-world data. Note that in the single-source scenario, CNN-LBT degrades into CNN-Mask. | Due to the strong interference from ghost speakers, the MDE produced from conventional methods seem to be too large, which indicates that they perform random guess to the speaker positions. Therefore, our focus is on the CNN-based methods. (i) For the single-source localization, the MDE is controlled to a sufficiently ... | D |
If a more refined modeling of p(x)𝑝𝑥p(x)italic_p ( italic_x ) is necessary, we can increase the output resolution N𝑁Nitalic_N of the IPU. | The primary drawback of this approach is that neither of the two optimization goals is achieved optimally. | A pragmatical method to achieve the two goals is to find a suitable compromise, akin to the approach taken by sparse coding methods [63]. | This is referred to as the node-wise loss function, in contrast to the original sample-wise loss function. | This approach to balancing the two objectives in our discrete IPU model is referred to as even coding. | D |
(b) The actual image that the CNN sees at “A” (yellow star in Fig. 3(a)). The CNN confuses the runway marking as the centreline. (c) Modified image with an artificial patch over the runway marking. | The observed images along the aircraft trajectory (Fig. 5(c),(d)) expose that at night time the CNN is indeed unable to properly see the centreline due to illumination issues guiding the aircraft off the runway (blue trajectory from location A to B in Fig. 5(b)). However, such errors are avoided in the morning (red tra... | We simulate the aircraft trajectory from a state in the BRT (marked with the yellow star in Fig. 1(a)) and query the images observed by the aircraft along the trajectory. | Figure 3: (a) Top-view of the runway in the morning. The trajectory followed by the aircraft under the CNN policy (red line) takes it off the runway. The successful trajectory (in green) takes the aircraft from “A” to “C”, on adding the patch over the runway marking during ablation. The trajectory (in cyan) from “A” to... | (a) The morning (red shaded) and night (blue shaded) BRTs overlaid for pysubscript𝑝𝑦p_{y}italic_p start_POSTSUBSCRIPT italic_y end_POSTSUBSCRIPT = 190m. The state, shown with a yellow star, is only included in the night BRT. (b) Top view of the runway. In the morning, the CNN policy accomplishes the taxiing task by t... | B |
An upward pointing arrow leaving node (t,u)𝑡𝑢(t,u)( italic_t , italic_u ) represents y(t,u)𝑦𝑡𝑢y(t,u)italic_y ( italic_t , italic_u ), the probability of outputting an actual label; and a rightward pointing arrow represents Ø(t,u)italic-Ø𝑡𝑢\O(t,u)italic_Ø ( italic_t , italic_u ), the probability of outputting a... | introduces big blank symbols. Those big blank symbols could be thought of as blank symbols with explicitly defined durations – once emitted, the big blank advances the t𝑡titalic_t by more than one, e.g. two or three. | Note that when outputting an actual label, u𝑢uitalic_u would be incremented by one; and when a blank is emitted, t𝑡titalic_t is incremented by one. | With the multi-blank models, when a big blank with duration m𝑚mitalic_m is emitted, the decoding loop increments t𝑡titalic_t by exactly m𝑚mitalic_m. | In standard decoding algorithms for RNN-Ts, the emission of a blank symbol advances input by one frame. | B |
The data structure and the detailed configurations of acoustic scene manipulation in the SceneFake dataset are illustrated in Figure 4. | The statistics of the SceneFake dataset are shown in Table 3, where #Speakers, #SE, #Scenes, #Real, #Fake, and #Total denote the number of speakers, speech enhancement methods, acoustic scene types, real utterances, fake utterances, and all utterances in the SceneFake dataset. | Table 7: The results of the fake utterances using different speech enhancement models in terms of PESQ on our SceneFake dataset. “Avg.” denotes the average PESQ of the fake utterances using all speech enhancement models on the corresponding sets. “BeforeSE” denotes the results of the original utterances of fakes ones, ... | Table 6 illustrates the statistic distribution of the noisy LA dataset, where #Speakers, #Genuine, #Spoofed, and #Total denote the number of speakers, genuine utterances, spoofed utterances, and all utterances in noisy LA dataset of ASVspoof 2019. | The description of 10 acoustic scenes in the DCASE 2022 Challenge is reported in the Table 1. The statistics of the LA dataset of ASVspoof 2019 are listed in Table 2, where #Speakers, #Genuine, #Spoofed, and #Total denote the number of speakers, genuine utterances, spoofed utterances, and all utterances in the three se... | A |
+βt,t−1*ut−1+⋯+βt,t−q*ut−q.subscriptsuperscript𝛽𝑡𝑡1subscript𝑢𝑡1⋯subscriptsuperscript𝛽𝑡𝑡𝑞subscript𝑢𝑡𝑞\displaystyle+\beta^{*}_{t,t-1}u_{t-1}+\cdots+\beta^{*}_{t,t-q}u_{t-q}.+ italic_β start_POSTSUPERSCRIPT * end_POSTSUPERSCRIPT start_POSTSUBSCRIPT italic_t , italic_t - 1 end_POSTSUBSCRIPT italic_u start_POS... | We note here that the LS-solution (30) and the “fundamental solution” (7) need not be the same; nonetheless, both can predict the initial + forced response of the linear system (2) after time t𝑡titalic_t s.t. mt≥n𝑚𝑡𝑛mt\geq nitalic_m italic_t ≥ italic_n, under the observability assumption 3.1. | An immediate consequence of the above result is that the LS-ARMA model (30) also predicts the response of the linear system (2). | This paper describes a new system realization technique for the system identification of linear time-invariant as well as time-varying systems. The system identification method proceeds by modeling the current output of the system using an ARMA model comprising of the finite past outputs and inputs. A theory based on l... | The results show that the information-state model can predict the responses accurately. The TV-OKID approach also can predict the response well in the oscillator experiment when the experiments have zero initial conditions, but it suffers from inaccuracy if the experiments have non-zero initial conditions as seen in Fi... | B |
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