context stringlengths 100 5.69k | A stringlengths 100 3.76k | B stringlengths 100 3.61k | C stringlengths 100 5.61k | D stringlengths 100 3.87k | label stringclasses 4
values |
|---|---|---|---|---|---|
C2-WORDsuperscriptC2-WORD\textrm{C}^{2}\textrm{-WORD}C start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT -WORD outperforms | A2RCsuperscriptA2RC{\textrm{A}}^{2}{\textrm{RC}}A start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT RC and WORD in the sense of WNG. | selection of A2RCsuperscriptA2RC{\textrm{A}}^{2}{\textrm{RC}}A start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT RC is optimal in the sense | the existing A2RCsuperscriptA2RC{\textrm{A}}^{2}{\textrm{RC}}A start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT RC | A2RCsuperscriptA2RC{\textrm{A}}^{2}{\textrm{RC}}A start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT RC (in the sense of WNG). | D |
The two layer CNN S2I achieved worse even compared with the 1D variants, indicating that increase of the S2I depth is not beneficial. | 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. | The two layer CNN S2I achieved worse even compared with the 1D variants, indicating that increase of the S2I depth is not beneficial. | The spectrogram S2I results are in contrary with the expectation that the interpretable time-frequency representation would help in finding good features for classification. | The names of the classes are depicted at the right along with the predictions for this example signal. | C |
UAVs have several power levels and altitude levels. In the midst of extreme environments, UAVs cannot change its voltage dramatically but merely change to the adjacent power level [12]. Similarly, the altitude changing also has a limitation that only adjacent altitude level conversion is permitted in each move. We deno... | In post-disaster scenarios, a great many of UAVs are required to support users [4]. Therefore, we propose aggregative game theory into such scenarios and permit UAV to learn in the constrained strategy sets. Because the aggregative game can integrate the impact of all other UAVs on one UAV, it reduces the complexity of... | Fig. 12 presents the sketch diagram of a UAV’s utility with power altering. The altitudes of UAVs are fixed. When other UAVs’ power profiles are altering, the interference increases and the curve moves down. The high interference will reduce the utility of the UAV. Fig. 12 also shows that utility decreases and increase... | When UAVs need communications, and the signal to noise rate (SNR) mainly determines the quality of service. UAVs’ power and inherent noise are interferences for each other. Since there are hundreds of UAVs in the system, each UAV is unable to sense all the other UAVs’ power explicitly, but only sense and measure aggreg... | 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) ... | C |
This section discusses the advancements in semantic image segmentation using convolutional neural networks (CNNs), which have been applied to interpretation tasks on both natural and medical images (Garcia-Garcia et al., 2018; Litjens et al., 2017). Although artificial neural network-based image segmentation approaches... | Next, encoder-decoder segmentation networks (Noh et al., 2015) such as SegNet, were introduced (Badrinarayanan et al., 2015). The role of the decoder network is to map the low-resolution encoder feature to full input resolution feature maps for pixel-wise classification. The novelty of SegNet lies in the manner in whic... | In order to preserve the contextual spatial information within an image as the filtered input data progresses deeper into the network, Long et al. (2015) proposed to fuse the output with shallower layers’ output. The fusion step is visualized in Figure 4. | The quantitative evaluation of segmentation models can be performed using pixel-wise and overlap based measures. For binary segmentation, pixel-wise measures involve the construction of a confusion matrix to calculate the number of true positive (TP), true negative (TN), false positive (FP), and false negative (FN) pix... | As one of the first high impact CNN-based segmentation models, Long et al. (2015) proposed fully convolutional networks for pixel-wise labeling. They proposed up-sampling (deconvolving) the output activation maps from which the pixel-wise output can be calculated. The overall architecture of the network is visualized i... | D |
The UAVs’ trajectory on the xy𝑥𝑦xyitalic_x italic_y-plane is assumed to follow the Smooth-Turn mobility model [34] that can capture the mobility of UAVs in the scenarios like patrolling. In this model, the UAV circles around a certain point on the horizontal plane (xy-plane) for an exponentially distributed duration... | A conceptual frame structure is designed which contains two types of time slots. One is the exchanging slot (e-slot) and the other is the tracking slot (t-slot). Let us first focus on the e-slot. It is assumed that UAVs exchange MSI every T𝑇Titalic_T t-slots, i.e., in an e-slot, to save resource for payload transmissi... | Moreover, the data block of MSI is set as BMSI=nMSI×T×BMSIsubscript𝐵MSIsubscript𝑛MSI𝑇subscript𝐵MSIB_{\text{MSI}}=n_{\text{MSI}}\times T\times B_{\text{MSI}}italic_B start_POSTSUBSCRIPT MSI end_POSTSUBSCRIPT = italic_n start_POSTSUBSCRIPT MSI end_POSTSUBSCRIPT × italic_T × italic_B start_POSTSUBSCRIPT MSI end_POSTSU... | Thanks to the integrated sensors, such as inertial measurement unit (IMU) and global position system (GPS), the UAV is able to derive its own MSI. However, the r-UAV also needs the MSI of all t-UAVs and each t-UAV needs the r-UAV’s MSI for beam tracking, which is challenging for the r-UAV/t-UAVs. | Specifically, the r-UAV/t-UAV’s historical MSI is first exchanged with the t-UAV/r-UAV over a lower-frequency band and then the t-UAV will predict the future MSI of the r-UAV based on the historical MSI by using the GP-based MSI prediction model. | C |
The inner product of the subgradients and the error between local optimizers’ states and the global optimal solution inevitably exists in the recursive inequality of the conditional mean square error. This leads the nonnegative supermartingale convergence theorem not to be applied directly | III. The co-existence of random graphs, subgradient measurement noises, additive and multiplicative communication noises are considered. 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... | We first estimate the mean square increasing rate of the states in Lemma III.2, and then substitute this rate into the recursive inequality (11) of the conditional mean square error between the state and the global optimal solution. | The inner product of the subgradients and the error between local optimizers’ states and the global optimal solution inevitably exists in the recursive inequality of the conditional mean square error. This leads the nonnegative supermartingale convergence theorem not to be applied directly | To this end, we estimate the upper bound of the mean square increasing rate of the local optimizers’ states at first (Lemma 3.2). Then we substitute this upper bound into the Lyapunov function difference inequality of the consensus error, and obtain the estimated convergence rate of mean square consensus (Lemma 3.3). F... | D |
H1,H2subscript𝐻1subscript𝐻2H_{1},H_{2}italic_H start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPT , italic_H start_POSTSUBSCRIPT 2 end_POSTSUBSCRIPT and H𝐻Hitalic_H are defined as H1(s)=KvKpG(s)subscript𝐻1𝑠subscript𝐾𝑣subscript𝐾𝑝𝐺𝑠H_{1}(s)=K_{v}K_{p}G(s)italic_H start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPT ( italic_s ) =... | One can easily obtain the transfer function from the reference trajectories to the actual position and velocity as | where vs,ksubscript𝑣𝑠𝑘v_{s,k}italic_v start_POSTSUBSCRIPT italic_s , italic_k end_POSTSUBSCRIPT is the sampled velocity along the path at time step k𝑘kitalic_k and T𝑇Titalic_T is the sampling time. | Given (3), one can obtain a discrete time model with sampling time T=2.5ms𝑇2.5msT=2.5\mathrm{ms}italic_T = 2.5 roman_ms as | Following (4), (5), (6) and (7), we obtain a linear time varying system of the form 𝐳k+1=Ak𝐳k+Bk𝐮k+𝐝ksubscript𝐳𝑘1subscriptA𝑘subscript𝐳𝑘subscriptB𝑘subscript𝐮𝑘subscript𝐝𝑘\mathbf{z}_{k+1}=\mathrm{A}_{k}\mathbf{z}_{k}+\mathrm{B}_{k}\mathbf{u}_{k}+% | C |
This indicates that as the compression accuracy becomes smaller, its impact exhibits “marginal effects”. | In other words, when the compression errors are not the bottleneck for the convergence, sacrificing the communication costs for faster convergence will reduce the communication efficiency. | In decentralized optimization, efficient communication is critical for enhancing algorithm performance and system scalability. One major approach to reduce communication costs is considering communication compression, which is essential especially under limited communication bandwidth. | 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. | The existence of compression errors may result in inferior convergence performance compared to uncompressed or centralized algorithms. For example, the methods considered by [41, 42, 43, 44, 45, 46] can only guarantee to reach a neighborhood of the desired solutions when the compression errors exist. | A |
Moreover, a smaller batch size degrades overall performance, including downstream classification accuracy. | In our experiments, we will use the same pre-trained model parameters to initialise the models for different downstream tasks. During fine-tuning, we fine-tune the parameters of all the layers, including the self-attention and token embedding layers. | (b), (c) the fine-tuning procedure for note-level and sequence-level classification. Apart from the last few output layers, both pre-training and fine-tuning use the same architecture. | To train Transformers, it is required that all input sequences have the same length. For both REMI and CP, we divide the token sequence for each entire piece into a number of shorter sequences with equal sequence length 512, zero-padding those at the end of a piece to 512 with an appropriate number of Pad tokens. | For fine-tuning, we create training, validation and test splits for each of the three datasets of the downstream tasks with the 8:1:1 ratio at the piece level (i.e., all the 512-token sequences from the same piece are in the same split). | D |
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. | H. Sun, X. Chen, Q. Shi, M. Hong, X. Fu, and N. D. Sidiropoulos, “Learning to optimize: Training deep neural networks for interference management,” IEEE Trans. Signal Process., vol. 66, no. 20, pp. 5438–5453, Oct. 2018. | H. Sun, X. Chen, Q. Shi, M. Hong, X. Fu, and N. D. Sidiropoulos, “Learning to optimize: Training deep neural networks for interference management,” IEEE Trans. Signal Process., vol. 66, no. 20, pp. 5438–5453, Oct. 2018. | M. Schuster and K. Paliwal, “Bidirectional recurrent neural networks,” IEEE Trans. Signal Process., vol. 45, no. 11, pp. 2673–2681, Nov. 1997. | M. Schuster and K. Paliwal, “Bidirectional recurrent neural networks,” IEEE Trans. Signal Process., vol. 45, no. 11, pp. 2673–2681, Nov. 1997. | C |
The computational running time was analysed for the for B2, B6 and the more complex InceptionV3 (IV3) model, both fully re-trained (F) and with transfer learning (TL) on the PCAM dataset. The results are shown in Table 2. Note that the time corresponds to the average time observed for one epoch. We can compare the mode... | Figure 4: Boxplots showing the AUC score for different CNN models for fully re-trained models (F) or with transfer learning (TL). | 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 2: Run time in seconds for one epoch on different GPU architectures. NbCU: number of CUDA cores. Pp: processing power in GFlops. TL: transfer learning. F: full retraining. | The computational running time was analysed for the for B2, B6 and the more complex InceptionV3 (IV3) model, both fully re-trained (F) and with transfer learning (TL) on the PCAM dataset. The results are shown in Table 2. Note that the time corresponds to the average time observed for one epoch. We can compare the mode... | C |
Then, the optimal complex wavefront modulation for the neural étendue expander would be the inverse Fourier transform of the target scene, and, as such, we do not require any additional modulation on the SLM. The SLM therefore can be set to zero-phase modulation. | To assess whether the optimized neural étendue expander ℰℰ\mathcal{E}caligraphic_E, shown in Fig. 1b, has learned the image statistics of the training set we evaluate the virtual frequency modulation ℰ~~ℰ\widetilde{\mathcal{E}}over~ start_ARG caligraphic_E end_ARG, defined as the spectrum of the generated image with th... | To further understand this property of a neural étendue expander, we consider the reconstruction loss ℒTsubscriptℒ𝑇\mathcal{L}_{T}caligraphic_L start_POSTSUBSCRIPT italic_T end_POSTSUBSCRIPT for a specific target image T𝑇Titalic_T. | If we generalize this single-image case to diverse natural images, the neural étendue expander is expected to preserve the common frequency statistics of natural images, while the SLM fills in the image-specific residual frequencies to generate a specific target image. | Therefore, obtaining the optimal neural étendue expander, which minimizes the reconstruction loss ℒTsubscriptℒ𝑇\mathcal{L}_{T}caligraphic_L start_POSTSUBSCRIPT italic_T end_POSTSUBSCRIPT, results in the virtual frequency modulation ℰ~~ℰ\widetilde{\mathcal{E}}over~ start_ARG caligraphic_E end_ARG that resembles the nat... | C |
Medical imaging methods such as Computational Tomography (CT) and Magnetic Resonance Imaging (MRI) are essential to clinical diagnoses and surgery planning. Hence, high-resolution medical images are desirable to provide necessary visual information about the human body. In recent years, many DL-based methods have also ... | et al., 2018) believed that low-resolution images in the real world constitute a specific distribution in high-dimensional space, and use a generative adversarial network to generate low-resolution images consistent with this distribution from high-resolution images. After that, Yuan et al. (Yuan | In recent years, more and more Transformer-based models have been proposed. For example, Chen et al. proposed the Image Processing Transformer (IPT (Chen et al., 2021)) which was pre-trained on large-scale datasets. In addition, contrastive learning is introduced for different image-processing tasks. Therefore, the pre... | et al., 2023c) proposed a Cross-receptive Focused Inference Network (CFIN) that can incorporate contextual modeling to achieve good performance with limited computational resources. Zhu et al. (Zhu et al., 2023) designed an Attention Retractable Frequency Fusion Transformer (ARFFT) to strengthen the representation abil... | For instance, Chen et al. proposed a Multi-level Densely Connected Super-Resolution Network (mDCSRN (Chen et al., 2018)) with GAN-guided training to generate high-resolution MR images, which can train and infer quickly. In (Wang | D |
SHAP visualisations such as that in Fig. 1(c) can be sparse, indicating that only few spectro-temporal bins contribute to the classifier output. A comparison of the time waveform in Fig. 1(a) and the SHAP values in Fig. 1(c) shows that this particular classifier essentially ignores information contained in non-speech r... | It shows the degree to which each spectro-temporal bin contributes to the classifier output. Darker red points indicate the spectro-temporal bins which lend stronger support for the positive class (here bona fide). In contrast, darker blue points indicate greater support for the negative class (here, spoofed speech). | 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. 2 shows the results of SHAP analysis for the ‘LA_E_1832578’ utterance and the PC-DARTS classifier. The plot shows the time waveform (a) and the temporal variation in SHAP values averaged across the full spectrum (b). This first example shows that the classifier has learned to focus predominantly upon non-speech in... | 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... | D |
CBFs that account for uncertainties in the system dynamics have been considered in two ways. The authors in [10] and [11] consider input-to-state safety to quantify possible safety violation. Conversely, the work in [12] proposes robust CBFs to guarantee robust safety by accounting for all permissible errors within an ... | Control barrier functions (CBFs) were introduced in [3, 4] to render a safe set controlled forward invariant. A CBF defines a set of safe control inputs that can be used to find a minimally invasive safety-preserving correction to a nominal control law by solving a convex quadratic program. Many variations and extensio... | CBFs that account for uncertainties in the system dynamics have been considered in two ways. The authors in [10] and [11] consider input-to-state safety to quantify possible safety violation. Conversely, the work in [12] proposes robust CBFs to guarantee robust safety by accounting for all permissible errors within an ... | Learning with CBFs: Approaches that use CBFs during learning typically assume that a valid CBF is already given, while we focus on constructing CBFs so that our approach can be viewed as complementary. In [19], it is shown how safe and optimal reward functions can be obtained, and how these are related to CBFs. The aut... | 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 d... | C |
90∘superscript9090^{\circ}90 start_POSTSUPERSCRIPT ∘ end_POSTSUPERSCRIPT difference in Tx- or Rx-polarization angles, as described | For the low SNR regime such as 5 dB SNR, the theoretically derived optimal Tx-polarization angles themselves have insignificant differences from numerically derived optimal Tx-polarization angles. The simulation results for the low SNR regime are omitted owing to the page limit. | The differences between theoretically and numerically obtained optimal Tx-polarization angles are considerable. This is due to the fact that the approximation (8) is less accurate at higher SNRs. | high SNR regime, utilizing our joint polarization pre-post coding improves PR-MIMO channel capacity with around 5 dB, 4 dB, and 3dB SNR gains in | and receiver and uses random polarization, in the low SNR regime (below 3 dB). The degrees of freedom (slop at high SNR) are the same in all three cases, since they are determined by the number of antenna ports. | A |
\end{split}start_ROW start_CELL italic_A start_POSTSUBSCRIPT roman_Σ end_POSTSUBSCRIPT start_POSTSUPERSCRIPT italic_U end_POSTSUPERSCRIPT ( italic_λ italic_R ) end_CELL start_CELL = italic_A start_POSTSUBSCRIPT roman_Σ end_POSTSUBSCRIPT start_POSTSUPERSCRIPT italic_U end_POSTSUPERSCRIPT ( italic_R ) , end_CELL end_ROW ... | We also verify that multiplying a regularizer by a scalar does not change the compliance measure which is consistent with recovery guarantees. | Consider a cone Σ⊂ℋΣℋ\Sigma\subset\mathcal{H}roman_Σ ⊂ caligraphic_H and assume that Σ−ΣΣΣ\Sigma-\Sigmaroman_Σ - roman_Σ is a union of subspaces, (Σ−Σ)∩S(1)ΣΣ𝑆1(\Sigma-\Sigma)\cap S(1)( roman_Σ - roman_Σ ) ∩ italic_S ( 1 ) is compact, and Σ≠span(x)Σspan𝑥\Sigma\neq\mathrm{span}(x)roman_Σ ≠ roman_span ( italic_x ) fo... | First γz∈𝒯R(FΣ)𝛾𝑧subscript𝒯𝑅𝐹Σ\gamma z\in\mathcal{T}_{R}(F\Sigma)italic_γ italic_z ∈ caligraphic_T start_POSTSUBSCRIPT italic_R end_POSTSUBSCRIPT ( italic_F roman_Σ ) if, and only if, there exists x∈Σ𝑥Σx\in\Sigmaitalic_x ∈ roman_Σ such that | Let x∈Σ𝑥Σx\in\Sigmaitalic_x ∈ roman_Σ. We remark that, the tangent cone is invariant by scalar multiplication: | D |
The above optimization is combinatorial in nature as there are (NM)binomial𝑁𝑀\binom{N}{M}( FRACOP start_ARG italic_N end_ARG start_ARG italic_M end_ARG ) possible combinations, which are nearly impossible to exhaust in practice except for very small M𝑀Mitalic_M. Therefore, we randomly sample a large number (say 10,0... | To implement template selection per Eq. (6), the knowledge of landmarks is assumed. However, even such knowledge is nonexistent before template selection. Therefore, we proposed to utilize potential key points to substitute landmarks. In particular, we utilize the classical multi-scale detector, SIFT, to find key point... | Figure 5: Similarities of potential key points vs. landmarks. The correlation coefficient (CC) of potential key points and landmarks is 0.462, thus we think it is feasible to replace landmarks with potential key points when estimating similarities. | Q: How good is the use of SIFT key points as substitutes for landmarks? Figure 5 demonstrate the relationship between landmarks and potential key points from handcraft methods in feature level (Eq. (9)). | In this paper, we propose a framework named Sample Choosing Policy (SCP) to find the most annotation-worthy images as templates. First, to handle the situation of no landmark label, we choose handcrafted key points as substitutes for landmarks of interest. Second, to replace the MRE, we proposed to use a similarity sco... | A |
This may be because task 3 was the only task where registration was performed between two follow-up time points. | The presence of similar deformations and structures in these scans likely rendered the registration between these two time points comparatively easier than the other three tasks. | Following close coordination with the clinical experts of the organizing committee (H.A., M.B., B.W., J.S., E.C., J.R., S.A., M.M.), the time-window between the two paired scans of each patient was decided to be selected such that i) the scans of the two time-points had sufficient apparent tissue deformations, and ii) ... | The presence of similar deformations and structures in these scans likely rendered the registration between these two time points comparatively easier than the other three tasks. | The presence of similar deformations and structures in these scans likely rendered the registration between these two time points comparatively easier than the other three tasks. | A |
θ∈[θ¯,θ⋆)𝜃¯𝜃superscript𝜃⋆\theta\in[\bar{\theta},\theta^{\star})italic_θ ∈ [ over¯ start_ARG italic_θ end_ARG , italic_θ start_POSTSUPERSCRIPT ⋆ end_POSTSUPERSCRIPT ). | If there does not exist a neighborhood of θ⋆superscript𝜃⋆\theta^{\star}italic_θ start_POSTSUPERSCRIPT ⋆ end_POSTSUPERSCRIPT in | there exists a neighborhood of θ⋆superscript𝜃⋆\theta^{\star}italic_θ start_POSTSUPERSCRIPT ⋆ end_POSTSUPERSCRIPT in which | the ϵ−limit-fromitalic-ϵ\epsilon-italic_ϵ -neighborhood of θ⋆superscript𝜃⋆\theta^{\star}italic_θ start_POSTSUPERSCRIPT ⋆ end_POSTSUPERSCRIPT for some | of convergence of θ⋆superscript𝜃⋆\theta^{\star}italic_θ start_POSTSUPERSCRIPT ⋆ end_POSTSUPERSCRIPT. | A |
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]). For example, PDE ISSt have been explored for reaction-diffusion systems [20], hyperbolic systems [21], [22], parabolic syst... | In this paper, we have explored safe control of a class of linear Parabolic PDEs under disturbances. First, we defined unsafe sets and distance of the system states from such unsafe sets. Next, we constructed both control barrier and Lyapunov functional in order to develop a design framework for the controller under sp... | In the subsequent sections, our approach of finding the control gains are as follows. First, in Section 3, we find the conditions on control gains that satisfy the pISSf criterion in (9). Next, in Section 4, we show that the pISSf conditions on control gains additionally guarantee ISSt for the system in the sense of (1... | In this section, we have derived the conditions on control gains for which the system is pISSf. In the following section, we will show that the derived conditions for pISSf ensures ISSt for the system. | In light of the aforementioned discussion, the main contributions of this paper is the following: Building upon the existing literature, we extend PDE safety research by designing a feedback based control that satisfies both pISSf and ISSt under disturbances, utilizing pISSf barrier functional characterization and ISSt... | D |
In this section, we implement and evaluate a complete testbed system for our spectrum allocation system. We use the testbed to collect training samples, which are then used | 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. | The inference time complexity of all our ML approaches is linear in the size of the input, and thus, the inference time in practice is minimal (a fraction of a second). The training time complexity of most ML models depends on the training samples and the resulting convergence, and is thus, uncertain. The actual traini... | Overall, we implemented a Python repository running on Linux that transmits and receives signals and measures and collects relevant parameters in real-time at | The general spectrum allocation problem is to allocate optimal power to an SU’s request across spatial, frequency, and temporal domains. We focus on the core function approximation problem, which is to determine the optimal power allocation to an SU for a given location, channel, and time instant—since frequency and te... | C |
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 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 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 according to Definition 1. Then, the static regret (4) achieved by Algorithm 1 satisfies | D |
In 2015, Bar et al. (2015) used a pre-trained image classifier for classifying pathologies in chest radiographs, demonstrating the feasibility of detecting X-ray pathologyDonahue et al. (2014). In 2017, Cicero et al. (2017) presented a similar CNN classifier that achieved an AUC of 0.964 using a medium-sized dataset of... | Limitations: Most disease prediction models focus on single-label classification, where the model only detects the presence of a single pathology. However, multi-label disease classification can offer several advantages over single-label classification. Multi-label diagnosis is akin to realistic representation since, i... | In Table 3 and Table 4, we compare the performance of our proposed model against single and multi-label prediction models for selected pathologies. Table 3 shows that our proposed multi-label approach was able to outperform single-label models. In Table 4, the results indicate that our proposed architecture outperforms... | Given a medical image of a patient as input, a disease prediction system provides the probability of the occurrence of a disease. This approach represents a single-label classification problem. Examples of such diagnoses include diabetic retinopathy in eye fundus images, skin cancer in skin lesion images, and pneumonia... | 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 patholog... | A |
A discrete emotion out of a total of 12121212 (joy, sadness, surprise, contempt, hope, fear, attraction, disgust, tenderness, anger, calm, and tedium) [21]. | Physiological signals [17]: BVP, GSR, and SKT physiological signals captured during the experimentation by the BioSignalPlux research toolkit are provided in a binary MATLAB® file (.mat). It contains a cell array with 100100100100 rows (one per volunteer) and 14141414 columns (one per video). Each cell contains four fi... | The signals being released are the ones acquired by the BioSignalPlux research toolkit. Specifically, the raw and filtered BVP, GSR, and SKT signals captured during every video visualization are provided. The preprocessing is as follows: | Additionally, two in-house sensory systems are employed. On the one hand, the Bindi’s bracelet [28] measures dorsal wrist BVP, ventral wrist GSR, and forearm SKT. The hardware and software particularities of this device are detailed in [29, 30, 31]. The previously mentioned BioSignalPlux toolkit is employed as a golden... | The BioSignalPlux333https://biosignalsplux.com/products/kits/researcher.html research toolkit system. It is a commonly used device to acquire different physiological signals in the literature [23, 24, 25, 26]. More specifically, we capture finger Blood Volume Pulse (BVP), ventral wrist Galvanic Skin Response (GSR), for... | D |
We have made available an online system with this trained network so that anyone can use it and test it, simply by uploading images. The software automatically labels the images as positive or negative to AMD. We have also provided the source code of the entire software and it is available publicly to facilitate resear... | Figure 5 provides examples of real and synthetic images that are from eyes, positive and negative to AMD. One can observe the high-quality images that were generated for both, AMD and non-AMD images. | We have made the source code for generating the synthetic images publicly available to facilitate joint research in the field. We have also provided free access through this paper for the online use of the AMD detection model. This will facilitate future work to broaden the scope for detecting the severity of AMD, and ... | Evaluating the quality of synthetic images is important for establishing their usability in practical applications, such as training deep learning models. It can significantly influence the training of these models. If the data does not accurately represent reality or lacks diversity, the synthetic data may introduce n... | The potential of diffusion models [63], known for their advanced capabilities in generating high-quality and diverse images, presents exciting future research in AMD and other ophthalmology diagnoses. These models should be considered for future development. | D |
Our approach parallels the development in 17, where we addressed the approximation of model predictive control policies for deterministic systems. We ask whether the training of a ReLU-based neural network to approximate a controller Φ(⋅)Φ⋅\Phi(\cdot)roman_Φ ( ⋅ ) has been sufficient to ensure that the network’s outpu... | Our approach parallels the development in 17, where we addressed the approximation of model predictive control policies for deterministic systems. We ask whether the training of a ReLU-based neural network to approximate a controller Φ(⋅)Φ⋅\Phi(\cdot)roman_Φ ( ⋅ ) has been sufficient to ensure that the network’s outpu... | The first quantity is precisely of the type required to apply the stability result of §3.2, thus supplying a condition on the optimal value of an MILP sufficient to certify the uniform ultimate boundedness of the closed-loop system (1) under the action of ΦNN(⋅)subscriptΦNN⋅\Phi_{\textrm{NN}}(\cdot)roman_Φ start_POSTS... | By analyzing the results in Tab. 3 – specifically, by contrasting the third and fourth column – we notice that we have always succeeded in the design of a minimum complexity, stabilizing ReLU-based surrogate ΦNN(⋅)subscriptΦNN⋅\Phi_{\textrm{NN}}(\cdot)roman_Φ start_POSTSUBSCRIPT NN end_POSTSUBSCRIPT ( ⋅ ) of Φ(⋅)Φ⋅\P... | We will obtain a condition on the optimal value of \pglsMILP sufficient to assure that the closed-loop system (1) under the action of ΦNN(⋅)subscriptΦNN⋅\Phi_{\textrm{NN}}(\cdot)roman_Φ start_POSTSUBSCRIPT NN end_POSTSUBSCRIPT ( ⋅ ) is (uniformly) ultimately bounded within a set of adjustable size and (exponential) co... | D |
Specifically, the E𝐸Eitalic_E-verifier can be used to obtain, with polynomial complexity, one necessary and one sufficient condition for C𝐶Citalic_C-enforceability; in case that the sufficient condition is satisfied, the trimmed version of the E𝐸Eitalic_E-verifier leads to a strategy to enforce concealability, also ... | These developments should be contrasted against constructions with exponential complexity [12] (the latter, however, provide a necessary and sufficient condition). | Specifically, the E𝐸Eitalic_E-verifier can be used to obtain, with polynomial complexity, one necessary and one sufficient condition for C𝐶Citalic_C-enforceability; in case that the sufficient condition is satisfied, the trimmed version of the E𝐸Eitalic_E-verifier leads to a strategy to enforce concealability, also ... | It is worth mentioning that the focus of this paper is on the use of reduced complexity constructions (with polynomial complexity) to provide one necessary condition and one sufficient condition for C𝐶Citalic_C-enforceability. | Taking advantage of the special structure of the concealability problem, we propose a verifier-like structure of polynomial complexity to obtain one necessary condition and one sufficient condition for enforceability of the defensive function with polynomial complexity. | A |
In this section we review typical loss functions used in image registration, and analyze the related requirements for privacy-preserving optimization. | Since the registration gradient is generally driven mainly by a fraction of the image content, such as the image boundaries in the case of SSD cost, a reasonable approximation of Equations (4) and (6) can be obtained by evaluating the cost only on relevant image locations. | The loss f𝑓fitalic_f can be any similarity measure, e.g., the Sum of Squared Differences (SSD), the negative Mutual Information (MI), or normalized cross correlation (CC). | A typical loss function to be optimized during the registration process is the sum of squared intensity differences (SSD) evaluated on the set of image coordinates: | Thanks to the privacy and security guarantees of these cryptographic tools, during the entire registration procedure, the content of the image data S𝑆Sitalic_S and J𝐽Jitalic_J is never disclosed to the opposite party. | C |
1})over^ start_ARG italic_b end_ARG start_POSTSUPERSCRIPT italic_t end_POSTSUPERSCRIPT start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPT ( italic_τ start_POSTSUPERSCRIPT italic_H end_POSTSUPERSCRIPT start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPT ) ← over^ start_ARG blackboard_P end_ARG start_POSTSUPERSCRIPT italic_t end_POSTSUPERSCRIPT... | 12: Update the confidence set 𝒞tsuperscript𝒞𝑡\mathcal{C}^{t}caligraphic_C start_POSTSUPERSCRIPT italic_t end_POSTSUPERSCRIPT by (4.4). | To conduct optimistic planning, we seek for the policy that maximizes the return among all parameters θ∈𝒞t𝜃superscript𝒞𝑡\theta\in\mathcal{C}^{t}italic_θ ∈ caligraphic_C start_POSTSUPERSCRIPT italic_t end_POSTSUPERSCRIPT and the corresponding features. The update of policy takes the following form, | \in\mathcal{C}^{t}}V^{\pi}(\theta),italic_π start_POSTSUPERSCRIPT italic_t end_POSTSUPERSCRIPT ← roman_argmax start_POSTSUBSCRIPT italic_π ∈ roman_Π end_POSTSUBSCRIPT roman_max start_POSTSUBSCRIPT italic_θ ∈ caligraphic_C start_POSTSUPERSCRIPT italic_t end_POSTSUPERSCRIPT end_POSTSUBSCRIPT italic_V start_POSTSUPERSCRIP... | }^{t}}V^{\pi}(\theta)italic_π start_POSTSUPERSCRIPT italic_t end_POSTSUPERSCRIPT ← roman_argmax start_POSTSUBSCRIPT italic_π ∈ roman_Π end_POSTSUBSCRIPT roman_max start_POSTSUBSCRIPT italic_θ ∈ caligraphic_C start_POSTSUPERSCRIPT italic_t end_POSTSUPERSCRIPT end_POSTSUBSCRIPT italic_V start_POSTSUPERSCRIPT italic_π end... | A |
𝒪Y1,B′=0subscript𝒪subscript𝑌1superscript𝐵′0\mathcal{O}_{Y_{1},B^{\prime}}=0caligraphic_O start_POSTSUBSCRIPT italic_Y start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPT , italic_B start_POSTSUPERSCRIPT ′ end_POSTSUPERSCRIPT end_POSTSUBSCRIPT = 0. | to be a variant that returns the set of columns Y1subscript𝑌1Y_{1}italic_Y start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPT and the set of | Y1subscript𝑌1Y_{1}italic_Y start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPT, with the notations of the above lemma. | R[K]subscript𝑅delimited-[]KR_{\rm[K]}italic_R start_POSTSUBSCRIPT [ roman_K ] end_POSTSUBSCRIPT, with the notations of lem. 61. | notations and hypotheses as in lemma 53, with A:=AΣassign𝐴subscript𝐴ΣA:=A_{\Sigma}italic_A := italic_A start_POSTSUBSCRIPT roman_Σ end_POSTSUBSCRIPT, | B |
\mathcal{H}^{T}\mathcal{H})=\frac{1}{2k+2}italic_λ start_POSTSUBSCRIPT roman_min end_POSTSUBSCRIPT ( italic_b ( italic_k ) over^ start_ARG caligraphic_L end_ARG start_POSTSUBSCRIPT caligraphic_G end_POSTSUBSCRIPT + italic_a ( italic_k ) caligraphic_H start_POSTSUPERSCRIPT italic_T end_POSTSUPERSCRIPT caligraphic_H ) = ... | For the special case without regularization, we directly obtain the following corollary by Theorem 1. | The convergence and performance analysis of the algorithm (6) are presented in this section. First, Lemma 1 gives a nonnegative supermartingale type inequality of the squared estimation error. Based on which, Theorem 1 proves the almost sure convergence of the algorithm. Then, Theorem 2 gives intuitive convergence cond... | Then, we give intuitive convergence conditions for the case with balanced conditional digraphs. We first introduce the following definitions. | Whereafter, we give more intuitive convergence conditions for the case with Markovian switching graphs and regression matrices. We first make the following assumption. | A |
Graph signal variations can also be computed in ℓ1subscriptℓ1\ell_{1}roman_ℓ start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPT-norm as graph total variation (GTV) [10, 11]. | Graph signal variations can also be computed in ℓ1subscriptℓ1\ell_{1}roman_ℓ start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPT-norm as graph total variation (GTV) [10, 11]. | Though convex, minimization of ℓ1subscriptℓ1\ell_{1}roman_ℓ start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPT-norm like GTV requires iterative algorithms like proximal gradient (PG) [24] that are often computation-expensive. | Its generalization, total generalized variation (TGV) [17, 18], better handles the known staircase effect, but retains the non-differentiable ℓ1subscriptℓ1\ell_{1}roman_ℓ start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPT-norm that requires iterative optimization. | Total variation (TV) [16] was a popular image prior due to available algorithms in minimizing convex but non-differentiable ℓ1subscriptℓ1\ell_{1}roman_ℓ start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPT-norm. | B |
In summary, our simulation study showed that DL-based methods can be used for MR image re-parameterization. Based on our preliminary results, we suggest that DL-based methods hold the potential to generate via simulations MR imaging scans with a new set of parameters. | In summary, our simulation study showed that DL-based methods can be used for MR image re-parameterization. Based on our preliminary results, we suggest that DL-based methods hold the potential to generate via simulations MR imaging scans with a new set of parameters. | Future work can focus on varying larger number of acquisition parameters. This approach could also be utilized for T1/T2 mapping, based on the availability of sufficient training data. | Brainweb is a simulated brain database that contains a set of realistic MRI data volumes produced by an MRI Simulator. We used this tool to generate test scans in 5 different parameter settings. The results can be seen in Figures 6 and 6 for both models. The evaluation metrics on this test-set can be found in Table 2. | 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.... | B |
1) To the best of our knowledge, this design represents the first real-time photon counting receiver implementation on a conventional SiPM and an FPGA, enhancing its potential for IoT applications compared to previous offline approaches [10],[11], [12], [26], [27]. | 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... | To optimize the real-world performance of the real-time SiPM-based receiver for IoT applications, the power consumption of its components was measured. Table II presents the power consumption measurements for the prototyped receiver under a data rate of up to 1 Mbps. It is observed that the SiPM’s power consumption inc... | 2) By conducting numerical simulations, this study assessed the GBP of the post-readout circuit within the SiPM-based optical receiver. This assessment complements previous research findings and offers insights into the circuit’s suitability for future low-power consumption applications. | The previous section designed the receiver based on the ideal setup to investigate the SiPM performance. However, the receiver components often contain amplifier blocks and lowpass or bandpass hardware filters, which affect the shape of the SiPM output pulses to the FPGA. To ensure the best transmission performance of ... | C |
Suppose we extrapolate the ≈\approx≈0.05 m/s spent by the spacecraft in the Hohmann-like transfer plus orbital maintenance in the 800 m orbit (tighter than the tightest 1 km orbit of OSIRIS-REx [54]). In that case, the spacecraft could still orbit Bennu, and make similar orbital transfers, for about 227 days before rea... | It is also crucial to emphasize that the comparison of these magnitudes with the OSIRIS-REx mission and other missions hereafter serves only to provide a notion of the order of magnitude of the ΔVΔ𝑉\Delta Vroman_Δ italic_V budget in real mission cases. The intention is only to showcase that the architecture proposed ... | In addition to these benefits, and more importantly, an autonomous and rapid approach to exploration can shape current scientific asteroid missions to be more cost-effective and time-efficient. Current missions have a conservative and cautious operational profile, often taking months of surveying and slowly approaching... | We would like to emphasize that our intention is not to advocate for a universal approach of “rapid exploration” in all asteroid missions. Instead, our objective is to illustrate the lack of necessity in minimizing uncertainties to an excessively low level for autonomous robotic spacecraft. We aim to demonstrate that a... | Well-designed guidance and control laws can allow an autonomous spacecraft to have a bolder operation, even with a higher level of uncertainty in the navigation. On top of that, there is not a significant compromise in budget ΔVΔ𝑉\Delta Vroman_Δ italic_V as one could expect. Therefore, a fully autonomous mission in c... | D |
Consider a multirotor UAV with an antenna on the top surface (i.e. the UAV’s surface facing the sky) that is communicating with a ground node. Assume that the UAV moves away from the node. To do this, the multirotor UAV has to tilt in such a way that its bottom surface (i.e. the UAV’s surface facing the ground) is slig... | iii. Mathematical model available: in this case, we only dispose of a mathematical model of the communications channel. In our previous work [4], we considered the problem of a multirotor UAV that must reach some goal while transmitting data to a BS. The only information about the communications channel used for solvin... | where Thovermsuperscriptsubscript𝑇hover𝑚T_{\rm hover}^{m}italic_T start_POSTSUBSCRIPT roman_hover end_POSTSUBSCRIPT start_POSTSUPERSCRIPT italic_m end_POSTSUPERSCRIPT is the time that the UAV hovers over the m𝑚mitalic_mth HL, which depends on the number of HLs, the channel capacity, and the probability of receiving ... | Consider a multirotor UAV with an antenna on the top surface (i.e. the UAV’s surface facing the sky) that is communicating with a ground node. Assume that the UAV moves away from the node. To do this, the multirotor UAV has to tilt in such a way that its bottom surface (i.e. the UAV’s surface facing the ground) is slig... | The communications channel gain depends on the relative orientation of the transmitting and receiving antennas. During the flying phase, a multirotor UAV must tilt, thus changing its antenna orientation. As a consequence, the communication channel observed when a multirotor UAV hovers is different than when they move [... | D |
In the case where Σ2subscriptΣ2\Sigma_{2}roman_Σ start_POSTSUBSCRIPT 2 end_POSTSUBSCRIPT is static or stability of x2∗=0superscriptsubscript𝑥20x_{2}^{*}=0italic_x start_POSTSUBSCRIPT 2 end_POSTSUBSCRIPT start_POSTSUPERSCRIPT ∗ end_POSTSUPERSCRIPT = 0 is of no concern, the dissipativity conditions (i)-(iv) in Theorem 2... | Interestingly, asymptotic stability of the feedback system may be established using a type of strict dissipativity where the strictness is derived from | Feedback stability in the sense of Lyapunov often leaves much to be desired. Next, we examine the stronger notion of asymptotic feedback stability via dissipativity. | IQCs, whereas the dynamics of the auxiliary system facilitate the verification of the dissipativity of the system with respect to the supply rate in | of dissipativity so that the stronger notion of asymptotic stability of Σ1∥Σ2conditionalsubscriptΣ1subscriptΣ2\Sigma_{1}\|\Sigma_{2}roman_Σ start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPT ∥ roman_Σ start_POSTSUBSCRIPT 2 end_POSTSUBSCRIPT can be established. It is worth noting that | A |
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... | 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... | 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 Section 4, first, we propose an AS-RCBF and an AS-ZCBF ensuring the invariance of a safe set with probability one. Second, we design a safety-critical control ensuring the existence of an AS-RCBF and an AS-ZCBF and show that the controller diverges towards the boundary of a safe set. Third, we construct a new type o... | 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... | B |
\approx\bar{\sigma}({\bf Z}_{\rm PI}(s))\approx-40~{}{\rm dB}=0.01over¯ start_ARG italic_σ end_ARG ( bold_Z start_POSTSUBSCRIPT roman_droop end_POSTSUBSCRIPT ( italic_s ) ) ≈ over¯ start_ARG italic_σ end_ARG ( bold_Z start_POSTSUBSCRIPT roman_GFM end_POSTSUBSCRIPT ( italic_s ) ) ≈ over¯ start_ARG italic_σ end_ARG ( bol... | In this paper, to ensure the generality of the proposed approach, we consider GFM converters with different implementations, such as droop control, power synchronization control, and VSMs (w/wo reactive power droop control [23], virtual impedance [24], and damping enhancement [25, 26]). We focus on the voltage source b... | Rather than changing the power network, we use GFM converters under power synchronization control or VSMs (w/wo reactive power droop control), respectively, to improve the power grid strength and stabilize the system according to Proposition IV.1. Fig. 8, Fig. 9, and Fig. 10 show the responses of the system with differ... | We consider the scenario where the system is unstable with gSCR=gSCR0=1.1gSCRsubscriptgSCR01.1{\rm gSCR}={\rm gSCR}_{0}=1.1roman_gSCR = roman_gSCR start_POSTSUBSCRIPT 0 end_POSTSUBSCRIPT = 1.1 (i.e., γ=0𝛾0\gamma=0italic_γ = 0) at the 35 kV bus. The other settings for the power grid in Fig. 7 are the same as those desc... | In this paper, to test the generality and effectiveness of the proposed approach when considering GFM converters under different implementations, we will consider power synchronization control and VSMs w/wo reactive power droop control in the analysis and simulation studies to quantify how they improve the small signal... | D |
Table 7: Quantitative comparison (average PSNR/SSIM) with state-of-the-art approaches for tiny/light image SR on benchmark datasets (×\times×4). The best and second best performances are highlighted and underlined, respectively. | In Fig. 7, we also exhibit the visual results of several tiny/lightweight models on Urban100 (×\times×4). For img_078, the tiny and light models are tested with the patches framed by green and red boxes, respectively. Generally, MANs can restore the texture better and clearer than other methods. | To validate the effectiveness of our MAN, we compare our normal model to several SOTA classical ConvNets [58, 8, 59, 41, 40, 37]. We also add SwinIR [30] for reference. In Tab. 6, the quantitative results show that our MAN exceeds other convolutional methods to a large extent. The maximum improvement on PSNR reaches 0.... | Overall study on components of MAN. In Tab. 2, we present the results of deploying the proposed components on our tiny and light networks. In general, the best performances are achieved by employing all proposed modules. Specifically, 0.25 dB and 0.29 dB promoting on Urban100 [18] can be observed in MAN-tiny and MAN-li... | To verify the efficiency and scalability of our MAN, we compare MAN-tiny and MAN-light to some state-of-the-art tiny [12, 26, 56, 44, 27] and lightweight [19, 36, 52, 30, 57] SR models. Tab. 7 presents the numerical results that our MAN-tiny/light outperforms all other tiny/lightweight methods. Specifically, MAN-tiny e... | D |
For the system safety analysis, we are interested in computing the BRT of ℒ(βL)ℒsubscript𝛽𝐿\mathcal{L}(\beta_{L})caligraphic_L ( italic_β start_POSTSUBSCRIPT italic_L end_POSTSUBSCRIPT ) given dynamics in (1). | BRT is the set of states such that the system trajectories that start from this set will eventually reach the given target set despite the worst-case disturbance (or an exogenous, adversarial input more generally). | Backward Reachable Tube (BRT): the set of initial states of the system for which the agent acting optimally and under worst-case disturbances will eventually reach the target set ℒℒ\mathcal{L}caligraphic_L within the time horizon [t,T]𝑡𝑇[t,T][ italic_t , italic_T ] : | The BRT for this collision set corresponds to all the states from which the pursuer can drive the system trajectory into the collision set within the time horizon [t,T]𝑡𝑇[t,T][ italic_t , italic_T ], despite the best efforts of the evader to avoid a collision. | First, a target function l(x)𝑙𝑥l(x)italic_l ( italic_x ) is defined whose sub-zero level set is the target set ℒℒ\mathcal{L}caligraphic_L, i.e. ℒ={x:l(x)≤0}ℒconditional-set𝑥𝑙𝑥0\mathcal{L}=\{x:l(x)\leq 0\}caligraphic_L = { italic_x : italic_l ( italic_x ) ≤ 0 }. Typically, l(x)𝑙𝑥l(x)italic_l ( italic_x ) is de... | B |
Another approach is to intentionally use broken (zig-zag) multi-hop trajectories to mislead the attacker or avoid risk areas. | The use of distributed antennas is a common approach to address the coverage issue. The fronthaul connection that is needed between the central node and the remote radio heads is highly challenging due to its high bandwidth and stringent latency requirements. It is generally implemented by an optical network. RIS-based... | After highlighting several advantages of the directive RIS architecture, we shall discuss its disadvantages as compared to the reflective RIS configuration. In addition, to the need for a (metasurface) lens for analog DFT processing, the major issue is the need for longer RF interconnections (see Fig. 7) and a multista... | 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 seq... | We introduced the concept of nonlocal or redirective reconfigurable surfaces with low-rank scattering as an artificial wave-guiding structure for wireless wave propagation at high frequencies. We showed multiple functionalities that can be implemented, including beam bending, multi-beam data forwarding, wave amplificat... | D |
In the VR display task, the central server transmits virtual 360∘superscript360360^{\circ}360 start_POSTSUPERSCRIPT ∘ end_POSTSUPERSCRIPT video streaming to the user. To avoid the transmission of the whole 360∘superscript360360^{\circ}360 start_POSTSUPERSCRIPT ∘ end_POSTSUPERSCRIPT video, the central server can predict... | 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... | To implement a closed-loop XR-aided teleoperation system, the wireless network is required to support mixed types of data traffic, which includes control and command (C&C) transmission, haptic information feedback transmission, and rendered 360∘superscript360360^{\circ}360 start_POSTSUPERSCRIPT ∘ end_POSTSUPERSCRIPT vi... | 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... | In the scenario of a swarm of (autonomous) robots where they need to perform a collaborative task (or a set of tasks) within a deadline over a wireless network, an effective communication protocol that takes into account the peculiarities of such a scenario is needed. Considering the simple case of two robots, let’s sa... | C |
The second test case is the 33-bus system case33bw, which has multiple branches. In this example, we demonstrate the efficacy of our approach in handling a system with complex components through the implementation of volt-VAR control, which represents smarter inverter behavior (whose characteristics are described in os... | The first test case is the 10-bus system case10ba, a simple single-branch network. We consider a variant where the nominal loads are 60%percent6060\%60 % of the values in the Matpower file. The results from each formulation place a sensor at the end of the branch (furthest bus from the substation) with an alarm thresho... | The second test case is the 33-bus system case33bw, which has multiple branches. In this example, we demonstrate the efficacy of our approach in handling a system with complex components through the implementation of volt-VAR control, which represents smarter inverter behavior (whose characteristics are described in os... | 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... | Table 4.1 shows both the computation times and the results of randomly drawing sampled power injections within the specified range of variability, computing the associated voltages by solving the power flow equations, and finding the number of false positive alarms (i.e., the voltage at a bus with a sensor is outside t... | D |
We have employed an advanced classification-based DOA estimation algorithm that is free of quantization errors. The backbone network is CNN, where a mask layer is used to enhance the robustness of the DOA estimation. Furthermore, to improve the accuracy of the DOA estimation of the CNN-based classification model, we in... | Consider a room with an ad-hoc microphone array of N𝑁Nitalic_N nodes and B𝐵Bitalic_B speakers, where each node comprises a conventional array of M𝑀Mitalic_M microphones. | We recorded a real-world dataset named Libri-adhoc-node10. It contains a conference room and an office room. Each room has 10 ad-hoc nodes and a loudspeaker. Each node contains a 4-channel linear array with an aperture of 8cm. Fig. 4 shows the recording environment of the two rooms. The size of the office room is appro... | We have recorded a real-world dataset named Libri-adhoc-nodes10. The Libri-adhoc-nodes10 dataset is a 432-hour collection of replayed speech of the “test-clean” subset of the Librispeech corpus [32], where an ad-hoc microphone array with 10 nodes were placed in an office and a conference room respectively. Each node is... | For the test sets, we need to generate simulated data for ad-hoc microphone arrays, whose ad-hoc nodes are either circular arrays or linear arrays. Specifically, for each randomly generated room, we repeated the procedure of constructing the training data, except that (i) we randomly placed 10 ad-hoc nodes in the room ... | C |
The even coding model also has the potential to adapt to binocular vision data by incorporating an additional input dimension of size two. | As a result, the question of whether these methods are principled or reflect crucial features of biological systems is often sidelined or deemed irrelevant. | Investigating whether the model can detect binocular disparity or even construct a 3D model of the world would be fascinating. | The even coding model also has the potential to adapt to binocular vision data by incorporating an additional input dimension of size two. | after the model has been trained the vast majority of the output values are either at 0 or 1, signifying that our model encoded the images using binary representation. | B |
\hat{\uppi}(\mathbf{x})\quad\vspace{-0.65em}italic_u = roman_π ( italic_I , bold_x , italic_E ) = roman_π ( italic_S ( bold_x , italic_E ) , bold_x , italic_E ) ⟹ italic_u = over^ start_ARG roman_π end_ARG ( bold_x ) | Specifically, given the set of undesirable states 𝒪𝒪\mathcal{O}caligraphic_O, the sensor mapping can be composed with the vision-based controller to obtain the closed-loop, state-feedback policy, π^^π\hat{\uppi}over^ start_ARG roman_π end_ARG for a given environment: | The complement of the BRAT thus represents the unsafe states for the robot under π^^π\hat{\uppi}over^ start_ARG roman_π end_ARG. | Given the policy π^^π\hat{\uppi}over^ start_ARG roman_π end_ARG, we compute the BRT 𝒱𝒱\mathcal{V}caligraphic_V by solving the HJB-VI in (7). | Finally, a model-based spline planner P𝑃Pitalic_P takes in the predicted waypoint to produce a smooth control profile for the robot. Hence, the closed-loop policy π^^π\hat{\uppi}over^ start_ARG roman_π end_ARG is given by π^:=P∘C∘S(𝐱,g,E)assign^π𝑃𝐶𝑆𝐱𝑔𝐸\hat{\uppi}:=P\circ C\circ S(\mathbf{x},g,E)over^ start_ARG... | C |
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... | In standard decoding algorithms for RNN-Ts, the emission of a blank symbol advances input by one frame. | 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. | C |
The utterances of training, development and seen test set in the noisy LA dataset are generated based upon that of training, development and test set from the LA dataset, respectively. The utterances in these three sets are generated by using six scenes: Airport, Bus, Park, Public, Shopping, Station. The voices of unse... | The acoustic scenes are randomly sampled to mix with the bona fide and spoofed utterances at 6 different SNRs each: -5dB, 0dB, 5dB, 10dB, 15dB and 20dB. | The fake utterances are generated by mixing another randomly sampled acoustic scenes with the enhanced utterances each mixed with 6 different SNRfake -5dB, 0dB, 5dB, 10dB, 15dB and 20dB. Fake utterances are also generated by using an open-source toolkit Augly. | The real utterances of our training, development and test sets are generated based upon the bona fide ones of training, development and test sets from the LA dataset, respectively. They are generated by randomly adding acoustic scenes to clean utterances at 6 different SNRfake each -5dB, 0dB, 5dB, 10dB, 15dB and 20dB. | The statistics of real and fake utterances in our SceneFake dataset at different SNRs are reported in Tables 4 and 5, where #-5dB, #0dB, #5dB, #10dB, #15dB and #20dB denote the number of real or fake utterances at 6 different SNRs each -5dB, 0dB, 5dB, 10dB, 15dB and 20dB. | A |
[4, 5]. The technique discussed in this paper, building upon the preliminary idea introduced in [1], uses a system realization that is based on the “information-state” as the state vector. An ARMA model which can represent the current output in terms of inputs and outputs from q𝑞qitalic_q steps in the past, is found b... | The pioneering work in system identification for LTI systems is the Ho-Kalman realization theory [6] of which the Eigensystem Realization Algorithm (ERA) algorithm is one of the most popular [4]. Another system identification method, namely, q𝑞qitalic_q-Markov covariance equivalent realization, generates a stable LTI... | 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... | 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 idea of using an ARMA model to describe the input-output data of an LTI system was first introduced in a series of papers related to the Observer/Kalman filter identification (OKID) algorithm [9, 18, 13], and the time-varying case was later considered in [11]. The credit for using an ARMA model for system identific... | A |
In many cases, the transmission process is the main bottleneck causing delays in edge inference, especially when the communication rate is low. | The extra feature extraction step in our method increases the complexity on the device side, but it effectively removes the task-irrelevant information and largely reduces the communication overhead. | While our method introduces additional complexity on the device side due to the complex feature extraction process, the proposed TOCOM-TEM method still enables low-latency inference. | In this paper, we develop a task-oriented communication framework for edge video analytics, which effectively extracts task-relevant features and reduces both the spatial and temporal redundancy in the feature domain. | Thus, it addresses the objective of reducing communication overhead by discarding task-irrelevant information. | A |
The Connectome 1.0 human brain DW-MRI data used in this study is part of the MGH Connectome Diffusion Microstructure Dataset (CDMD)(Tian et al., 2022), which is publicly available on the figshare repository https://doi.org/10.6084/m9.figshare.c.5315474. MATLAB codes generated for simulation study, parameter fitting, an... | The utility of diffusional kurtosis imaging for inferring information on tissue microstructure was described decades ago. Continued investigations in the DW-MRI field have led to studies clearly describing the importance of mean kurtosis mapping to clinical diagnosis, treatment planning and monitoring across a vast ran... | The direct link between the sub-diffusion model parameter β𝛽\betaitalic_β and mean kurtosis is well established (Yang et al., 2022; Ingo et al., 2014, 2015). An important aspect to consider is whether mean β𝛽\betaitalic_β used to compute the mean kurtosis is alone sufficient for clinical decision making. While benefi... | Instead of attempting to improve an existing model-based approach for kurtosis estimation, as has been considered by many others, we considered the problem from a different perspective. In view of the recent generalisation of the various models applicable to DW-MRI data (Yang et al., 2022), the sub-diffusion framework ... | For DKI to become a routine clinical tool, DW-MRI data acquisition needs to be fast and provides a robust estimation of kurtosis. The ideal protocol should have a minimum number of b-shells and diffusion encoding directions in each b-shell. The powder averaging over diffusion directions improves the signal-to-noise rat... | A |
Variance of σ02superscriptsubscript𝜎02\sigma_{0}^{2}italic_σ start_POSTSUBSCRIPT 0 end_POSTSUBSCRIPT start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT and γ02superscriptsubscript𝛾02\gamma_{0}^{2}italic_γ start_POSTSUBSCRIPT 0 end_POSTSUBSCRIPT start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT | \mathbf{{G}}}^{\prime}_{j}}\right\|^{2}_{\mathrm{F}}start_OVERACCENT ( italic_a ) end_OVERACCENT start_ARG ≤ end_ARG ∥ bold_F start_POSTSUPERSCRIPT roman_H end_POSTSUPERSCRIPT bold_G start_POSTSUPERSCRIPT ′ end_POSTSUPERSCRIPT start_POSTSUBSCRIPT italic_i end_POSTSUBSCRIPT ∥ start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT ... | \mathrm{T}}\in\mathbb{C}^{(M+1)\times 1}bold_italic_θ start_POSTSUPERSCRIPT ′ end_POSTSUPERSCRIPT ≜ [ bold_italic_θ start_POSTSUPERSCRIPT roman_T end_POSTSUPERSCRIPT , italic_t ] start_POSTSUPERSCRIPT roman_T end_POSTSUPERSCRIPT ∈ blackboard_C start_POSTSUPERSCRIPT ( italic_M + 1 ) × 1 end_POSTSUPERSCRIPT | Indoor region size (m3superscriptm3\mathrm{m}^{3}roman_m start_POSTSUPERSCRIPT 3 end_POSTSUPERSCRIPT) | \mathrm{F}}= ∥ bold_F start_POSTSUPERSCRIPT roman_H end_POSTSUPERSCRIPT roman_Δ bold_G start_POSTSUPERSCRIPT ′ end_POSTSUPERSCRIPT start_POSTSUBSCRIPT italic_i end_POSTSUBSCRIPT - bold_F start_POSTSUPERSCRIPT roman_H end_POSTSUPERSCRIPT roman_Δ bold_G start_POSTSUPERSCRIPT ′ end_POSTSUPERSCRIPT start_POSTSUBSCRIPT ital... | C |
By (2) and (3), the spatial temperature profiles are omitted and a coherent temperature profile between all nodes and edges is ensured, see also e.g. Krug et al. (2021). | We regard the internal energy of water as the main energy carrier and neglect other energy forms. Furthermore, as in Machado et al. (2022), we assume a linear dependency between the internal energy and the temperature of water. | The power-to-heat (P2H) connection of the two layers is implemented by heat pumps that couple nodes from the electrical layer with edges from the thermal layer. | Typically, the dynamics of the electrical layer and the heat pumps are fast compared to the thermal layer, | The thermal edges, i.e., the simple pipes and heat exchanger are modeled as pipes transporting water as thermal energy carrier that exchanges heat flow with its environment, due to thermal losses, heat injection or extraction. | D |
Step 4: Combine subproblems’ solutions to establish a valid upper bound for (29). Evaluate the bound performance by measuring the gap between lower and upper bounds. | Table 1 reports the optimality gap and the computation time of Step 3 after one iteration, which is the most time-consuming component in the proposed method. The results demonstrate the consistent performance of our approach across different settings. Using the multipliers obtained in Step 2 without further updates, we... | We establish a tight upper bound for the joint deployment problem despite its nonconcavity. A decomposable problem is developed through proper model relaxations. By leveraging the favorable structures of the relaxed problem, we are able to obtain an accurate estimation of the globally optimal solution to the original p... | Step 5: Terminate the procedure if the optimality gap is satisfactorily tight. Otherwise, update the multipliers according to (43c) and go to Step 3. | where ζ𝜁\zetaitalic_ζ is the step size. Due to the model relaxation, the established upper bound is an overestimation of the globally optimal solution to the original problem (29). In other words, the bound is also a theoretical upper bound for the original problem, which allows us to quantify the optimality of its so... | C |
2) Image quality indicator: As shown in Fig. 2 e, it demonstrates that DEviS can serve as an indicator for representing the quality of medical images. Uncertainty estimation is an intuitive and quantitative way to inform clinicians or researchers about the quality of medical images. DEviS guides image quality quantitat... | 6) FIVES dataset. In the second application, the Fundus Image Vessel Segmentation (FIVES) dataset is used for the quality indicator. In the FIVES dataset, each image was evaluated for four qualities: normal, lighting and color distortion, blurring, and low-contrast distortion. In this experiment, we define normal image... | 2) Image quality indicator: As shown in Fig. 2 e, it demonstrates that DEviS can serve as an indicator for representing the quality of medical images. Uncertainty estimation is an intuitive and quantitative way to inform clinicians or researchers about the quality of medical images. DEviS guides image quality quantitat... | We conducted OOD experiments on the Johns Hopkins OCT dataset and Duke OCT dataset with Diabetic Macular Edema (DME). As shown in Fig. 6 a, we first observed a slight improvement in results for mixed ID and OOD data after using DEviS. Then, we found significant differences in the performance of the segmentation between... | In what follows, we apply DEviS with UAF to indicate the quality of data for real-world applications. The FIVES datasets are used for quality assessment experiments. We initially classified samples into three categories based on their quality labels: high quality, high & low quality, and low quality. We observed distin... | D |
IF=IF++IFpI_{F}=\hskip 2.0ptI_{F}+\!\!\!+I_{Fp}italic_I start_POSTSUBSCRIPT italic_F end_POSTSUBSCRIPT = italic_I start_POSTSUBSCRIPT italic_F end_POSTSUBSCRIPT + + italic_I start_POSTSUBSCRIPT italic_F italic_p end_POSTSUBSCRIPT | 0.95∗superscript0.95\textbf{0.95}^{*}0.95 start_POSTSUPERSCRIPT ∗ end_POSTSUPERSCRIPT |||| 3.7 %∗superscript3.7 %\textbf{3.7 \%}^{*}3.7 % start_POSTSUPERSCRIPT ∗ end_POSTSUPERSCRIPT | 0.98∗superscript0.98\textbf{0.98}^{*}0.98 start_POSTSUPERSCRIPT ∗ end_POSTSUPERSCRIPT |||| 2.2 %∗superscript2.2 %\textbf{2.2 \%}^{*}2.2 % start_POSTSUPERSCRIPT ∗ end_POSTSUPERSCRIPT | 0.96∗superscript0.96\textbf{0.96}^{*}0.96 start_POSTSUPERSCRIPT ∗ end_POSTSUPERSCRIPT |||| 3.2 %∗superscript3.2 %\textbf{3.2 \%}^{*}3.2 % start_POSTSUPERSCRIPT ∗ end_POSTSUPERSCRIPT | ,i_{Op}^{1},i_{Op}^{2},i_{Op}^{3}\}{ italic_i start_POSTSUBSCRIPT italic_S italic_p end_POSTSUBSCRIPT start_POSTSUPERSCRIPT 1 end_POSTSUPERSCRIPT , italic_i start_POSTSUBSCRIPT italic_S italic_p end_POSTSUBSCRIPT start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT , italic_i start_POSTSUBSCRIPT italic_S italic_p end_POSTSUBSCR... | C |
To further validate the effectiveness and reliability of the system, we deployed the system at both the transmitting and receiving end and conducted real-channel image transmission using hardware. As shown in Fig. 9, YunSDR Y750111Introduction website : https://www.v3best.com/y750s devices were used at both the transmi... | At a bpp value of 0.1 and an SNR of around 0, the image metrics obtained from the hardware experiment exhibit fluctuations around the results obtained from software simulation. In such low SNR scenarios, our STSCI still performs well both in terms of image metrics and visual effects. The average values are slightly low... | These results demonstrate that STSCI is capable of performing well in real hardware deployment and transmitting over real channels. It also confirms the reliability of the software simulation results obtained earlier. | Meanwhile, Fig. 10 provides a visual example of hardware transmission along with its corresponding image metrics. According to Fig. 10, even at SNR around 0dB, the image metrics of the final image are still relatively high, without significant distortion or deformation. In contrast to the blurry and unclear version of ... | To further validate the effectiveness and reliability of the system, we deployed the system at both the transmitting and receiving end and conducted real-channel image transmission using hardware. As shown in Fig. 9, YunSDR Y750111Introduction website : https://www.v3best.com/y750s devices were used at both the transmi... | A |
Note that this statement holds under condition (30), which implies that the received power of the desired source is stronger than the received power of each interference source, considering the attenuation stemming from the activity duration. | The proofs of Proposition 1 and Proposition 2 rely on the following lemma, which is important in its own right. | Third, following the same techniques in the proof of Proposition 1 and Proposition 2, similar results are derived for an alternative definition of the SIR: SIRtot(𝚪)≡𝒅0H𝚪𝒅0∑j=1NI𝒅jH𝚪𝒅jsubscriptSIRtot𝚪superscriptsubscript𝒅0𝐻𝚪subscript𝒅0superscriptsubscript𝑗1subscript𝑁Isuperscriptsubscript𝒅𝑗𝐻𝚪subsc... | Since we established that the Riemannian approach is better than the Euclidean one in terms of the SIR in Proposition 1, Proposition 2 implies that increasing the SNR further increases the gap between the two approaches. Nevertheless, it also indicates that the performance of the Riemannian approach in terms of the SIR... | Similarly to Proposition 1, the following Proposition 3 examines the performance in terms of the SIR defined in (43). Here, assumptions 2-4 are not required, and therefore, the ATFs of the interference sources could be correlated, and the number of sources is not limited by the number of microphones in the array. | A |
The remainder of this manuscript is organized as follows. Section II includes an overview of related works from the literature on breathing anomaly detection using various sensing technologies and machine learning. Section III describes various human breathing patterns from the literature to be used as breathing classe... | Some past classification efforts involved one-class classification or outlier detection, as in [30] where the model was trained using human breathing data in resting condition to predict if the person was exercising in new examples. Binary classification between normal breathing and apnea were performed in [29] to dete... | Feature extraction is an important step in machine learning-based data classification. After detrending, four handcrafted features were extracted from the collected data using MATLAB code for the following three cases: | Researchers have been applying machine learning and deep learning techniques on human respiration data collected through various technologies for anomaly detection. Most of these efforts made use of handcrafted features to perform breathing data classification for anomaly detection. Some of the common categories of fea... | The features for each data were saved in separate rows in CSV files along with the class label for each row. Thus, labeled features were prepared for the subsequent classification task. The details of extracted handcrafted features are provided as follows. | C |
\mathsf{UE}}_{k}}}(\widehat{g}^{(i)}_{km})^{*}w^{\mathsf{u}}_{m}.over¯ start_ARG italic_x end_ARG start_POSTSUPERSCRIPT sansserif_u end_POSTSUPERSCRIPT start_POSTSUBSCRIPT italic_k end_POSTSUBSCRIPT = ∑ start_POSTSUBSCRIPT italic_m ∈ caligraphic_M start_POSTSUPERSCRIPT sansserif_UE end_POSTSUPERSCRIPT start_POSTSUBSCRI... | Based on (5) and the formulation in [18], the effective uplink signal to interference plus noise ratio (SINR) of user k𝑘kitalic_k is given by | For uplink transmission, each user k𝑘kitalic_k transmits a data signal xk𝗎subscriptsuperscript𝑥𝗎𝑘x^{\mathsf{u}}_{k}italic_x start_POSTSUPERSCRIPT sansserif_u end_POSTSUPERSCRIPT start_POSTSUBSCRIPT italic_k end_POSTSUBSCRIPT. | From Theorem 18, we conclude that learning based on our Markov games model is equivalent to performing the pilot update which minimizes the interference due to PC at each near-RT PA. | The received signal y¯k𝖽subscriptsuperscript¯𝑦𝖽𝑘\bar{y}^{\mathsf{d}}_{k}over¯ start_ARG italic_y end_ARG start_POSTSUPERSCRIPT sansserif_d end_POSTSUPERSCRIPT start_POSTSUBSCRIPT italic_k end_POSTSUBSCRIPT for user k𝑘kitalic_k is then given by | A |
\mathsf{H}}\mathbbm{t}_{k}]\right)roman_ℜ ( italic_b start_POSTSUBSCRIPT italic_k end_POSTSUBSCRIPT ( blackboard_T ) ) = roman_ℜ ( sansserif_E [ blackboard_h start_POSTSUBSCRIPT italic_k end_POSTSUBSCRIPT start_POSTSUPERSCRIPT sansserif_H end_POSTSUPERSCRIPT blackboard_t start_POSTSUBSCRIPT italic_k end_POSTSUBSCRIPT ]... | is readily given by combining Lemma 3, Lemma 4, Lemma 5, and by noticing that the unique solution 𝕋′superscript𝕋′\mathbbm{T}^{\prime}blackboard_T start_POSTSUPERSCRIPT ′ end_POSTSUPERSCRIPT to Problem (32) is also a solution to Problem (10) (note: the converse does not hold in general). | Let 𝛌⋆superscript𝛌⋆\bm{\lambda}^{\star}bold_italic_λ start_POSTSUPERSCRIPT ⋆ end_POSTSUPERSCRIPT be a solution to Problem (14). Then, a solution to Problem (10) is given by any solution to | Problem (32) admits a unique solution 𝕋′∈𝒯superscript𝕋′𝒯\mathbbm{T}^{\prime}\in\mathcal{T}blackboard_T start_POSTSUPERSCRIPT ′ end_POSTSUPERSCRIPT ∈ caligraphic_T. Furthermore, strong duality holds for Problem (32), i.e., Problem (33) and Problem (32) have the same optimum, and there exist Lagrangian multipliers (�... | The next simple lemma can be used to relate Problem (10) to Problem (32), following a similar idea in [30, 23]. | D |
In this subsection, we first obtain an estimate (A^,B^)^𝐴^𝐵(\hat{A},\hat{B})( over^ start_ARG italic_A end_ARG , over^ start_ARG italic_B end_ARG ) offline from measured data of the unknown real system (2), and then synthesize a controller (2.1) with zero terminal matrix P=0𝑃0P=0italic_P = 0. This is the classical r... | There are many recent studies on linear system identification and its finite-sample error bounds [22, 23, 18]. | In this work, the obtained bounds hold regardless of whether εmsubscript𝜀m\varepsilon_{\mathrm{m}}italic_ε start_POSTSUBSCRIPT roman_m end_POSTSUBSCRIPT and εpsubscript𝜀p\varepsilon_{\mathrm{p}}italic_ε start_POSTSUBSCRIPT roman_p end_POSTSUBSCRIPT are coupled. The presence of coupling, e.g., εp=h(εm)subscript𝜀pℎsu... | In this work, the true model (A⋆,B⋆)subscript𝐴⋆subscript𝐵⋆(A_{\star},B_{\star})( italic_A start_POSTSUBSCRIPT ⋆ end_POSTSUBSCRIPT , italic_B start_POSTSUBSCRIPT ⋆ end_POSTSUBSCRIPT ) is unknown, and we only have access to an approximate model (A^,B^)^𝐴^𝐵(\hat{A},\hat{B})( over^ start_ARG italic_A end_ARG , over^ st... | where the regret is linear in T𝑇Titalic_T. This observation matches the result in [34], where the regret of a linear unconstrained RHC controller, with a fixed prediction horizon and an exact system model, is linear in T𝑇Titalic_T. This linear regret is caused by the fact that even if the model is perfectly identifie... | A |
Remark. Since the policy (7) is conditioned on a partial observation 𝒐ksubscript𝒐𝑘{\bm{o}}_{k}bold_italic_o start_POSTSUBSCRIPT italic_k end_POSTSUBSCRIPT of the state 𝒔ksubscript𝒔𝑘{\bm{s}}_{k}bold_italic_s start_POSTSUBSCRIPT italic_k end_POSTSUBSCRIPT, the stationary MDP we have defined in this section is, in f... | In this paper, we have introduced the reinforcement learning reduced-order estimator (RL-ROE), a new state estimation methodology for parametric PDEs. Our approach relies on the construction of a computationally inexpensive reduced-order model (ROM) to approximate the dynamics of the system. The novelty of our contribu... | The RL-ROE exhibits robust performance across the entire parameter range μ∈[0,1]𝜇01\mu\in[0,1]italic_μ ∈ [ 0 , 1 ], including when estimating trajectories corresponding to previously unseen parameter values. Finally, Figure 4 (right) displays the average over time and over μ𝜇\muitalic_μ of the normalized L2subscript�... | A big challenge is that ROMs provide a simplified and imperfect description of the dynamics, which negatively affects the performance of the state estimator. One potential solution is to improve the accuracy of the ROM through the inclusion of additional closure terms (Ahmed et al., 2021). In this paper, we leave the R... | We evaluate the state estimation performance of the RL-ROE for systems governed by the Burgers equation and Navier-Stokes equations. For each system, we first compute various solution trajectories corresponding to different physical parameter values, which we use to construct the ROM and train the RL-ROE following the ... | D |
The massive presence of networked systems in many areas is making distributed optimization more and more attractive for a wide range of tasks. | convergence of the network systems to a steady-state configuration corresponding to a stationary point of the problem. | These tasks often involve dynamical systems (e.g., teams of robots or electric grids) that need to be controlled while optimizing a cost index. | The massive presence of networked systems in many areas is making distributed optimization more and more attractive for a wide range of tasks. | In [19], algebraic systems are controlled by relying on gradient information affected by random errors. As for feedback optimization in multi-agent systems, the early reference [20] proposes an approach based on saddle point flows, while [21] addresses a partition-based scenario. | B |
End of preview. Expand in Data Studio
README.md exists but content is empty.
- Downloads last month
- 3