robench-2024b
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48 items • Updated
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In summary, our work differs significantly from each of the above-mentioned works, and other literatures in UAV ad-hoc networks. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> c) There are two kinds of links between users, and the link supported by UAV is better.
. | **A**: a) The UAV ad-hoc network supports user communications.
**B**: b) The coverage of a UAV depends on its altitude and field angle.
**C**: As far as we know, our proposed algorithm is capable of learning previous utilities and strategies, achieve NE with restricted information and constrained strategies sets, and... | CAB | CAB | CBA | CAB | Selection 1 |
3.5 Sequenced Models
The Recurrent Neural Network (RNN) was designed for handling sequences. The long short-term memory (LSTM) network is a type of RNN that introduces self-loops to enable the gradient flow for long duration (Hochreiter and Schmidhuber, 1997). In the medical image analysis domain, RNNs have been used... | **A**: (2018) applied LSTM and CNN to model temporal relationship in brian MRI slices to improve segmentation performance in 4D volumes. Li et al.
**B**: (2018) proposed an image sequence segmentation algorithm by combining a fully convolutional network with a recurrent neural network, which incorporates both spatial ... | BAC | BAC | ABC | BAC | Selection 2 |
<|MaskedSetence|> <|MaskedSetence|> Recall that several efficient codebook-based beam training and tracking schemes have been proposed for conventional mmWave network with uniform ULA and UPA [22, 23]. These prior works inspire us to propose a specialized new codebook design and the corresponding codeword selection/p... | **A**: If an inappropriate subarray is activated, the beam angle may go beyond the radiation range of certain subarray elements, degrading the beam gain and SE.
.
**B**: Note that directly solving the above beam tracking problem is very challenging, especially in the considered highly dynamic UAV mmWave network.
**C*... | ACB | BCA | BCA | BCA | Selection 4 |
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... | **A**: Finally, we get an estimate of the mean square increasing rate of the local optimizers’ states in terms of the step sizes of the algorithm (Lemma 3.2).
.
**B**: What’s more, multiplicative noises relying on the relative states between adjacent local optimizers make states, graphs and noises coupled together.... | BCA | BCA | BCA | BCA | Selection 4 |
In this work, we use the model predictive contouring control (MPCC)
which is an MPC-based contouring approach to generate optimized tracking references. We account for model mismatch by automated tuning of both the MPC-related parameters and the low level cascade controller gains, to achieve precise contour tracking w... | **A**: In our approach the tracking error is coupled with the progression along the path through the cost function.
**B**: We demonstrate enhanced performance in simulation for a 2-axis gantry, for geometries of different nature.
.
**C**: The automated tuning of the parameters is performed using a cost that accounts... | ACB | BCA | ACB | ACB | Selection 1 |
The results show that MusicBERT achieves a testing accuracy of 37.25% for style classification and 77.78% for emotion classification. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> This finding is intriguing and suggests that the application of large-scale pre-training may yield substantial benefits in classi... | **A**: Conversely, in the emotion classification task, MusicBERT demonstrates impressive performance, surpassing our model (70.64%) by a significant margin.
**B**: Specifically, in the style classification task, MusicBERT exhibits clear signs of overfitting and falls short in performance when compared to our model (81... | BCA | BCA | BCA | BCA | Selection 1 |
<|MaskedSetence|> <|MaskedSetence|> Moreover, the transcription is obtained from the recovered speech signals after passing through an automatic speech recognition (ASR) module. For the system, the adaptive multi-rate wideband (AMR-WB)[21] is used for speech source coding and 64-QAM is utilized for modulation. <|Mas... | **A**: Polar codes with successive cancellation list (SCL) decoding algorithm[22] is employed for channel coding, in which the block length is 512 and the list size is 4.
**B**: The first benchmark is a traditional communication system to transmit speech signals, named speech transceiver.
**C**: Particularly, the inp... | BCA | BCA | BCA | BAC | Selection 2 |
The PCAM dataset was downloaded from the original website (https://github.com/basveeling/pcam). <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> The patches of 96 x 96 pixels images were automatically extracted from the CAMELYON dataset [13]. For each image, a positive label indicates that the 32 x 32 pixel cen... | **A**: All images have a size of 96 x 96 pixels, in three colors.
**B**: All datasets have a 50/50 balance between positive (tumor present) and negative (tumor absent) samples.
**C**: The training set has 262,144 images (80 % of the total), the validation set has 32,768 images (10 %) and the test set also has 32,768 ... | ACB | ACB | CAB | ACB | Selection 1 |
<|MaskedSetence|> By initializing the learning process with a uniform random expander we bias the optimized solution towards expanders that distribute energy throughout the eyebox, in contrast to a quadratic phase profiles[28] that concentrate the energy at fixed points. Thus, the viewer’s eye pupil can freely move wi... | **A**: See Supplementary Note 4 for a discussion of these findings.
.
**B**:
In addition to field-of-view, we also investigate the eyebox that is produced with neural étendue expansion.
**C**: We find that neural étendue expansion also enables higher fidelity étendue expanded 3D color holograms.
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2.5. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> In contrast, subjective methods are always based on human subjective judgments and are more related to evaluating the perceptual quality of the image. Based on the pros and cons of the two types of methods mentioned above, several assessment methods are br... | **A**: However, they can only reflect the recovery of image pixels from a numerical point of view and are difficult to accurately measure the true visual effect of the image.
**B**: Objective methods commonly use a specific formulation to compute the results, which are simple and fair, thus becoming the mainstream ass... | CBA | CBA | ABC | CBA | Selection 1 |
2 Related work
Several related explainability studies have been reported previously. <|MaskedSetence|> The use of Gradient-weighted Class Activation Mapping (Grad-CAM) [25] to explain spoofing classifier behaviour is reported in [26]. It is applied to generate a binary saliency map for the network input layer. <|Mas... | **A**: Using an approach based upon the attenuation of distinct spectral components, [24] shows that artefacts indicative of different spoofing attacks are located within different sub-band intervals, and hence that they can be detected more reliably with front-ends that emphasise the same frequency range.
**B**: Inpu... | ABC | ABC | CAB | ABC | Selection 1 |
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. <|Mas... | **A**: In [23], it is shown how additive and multiplicative noise can be estimated online using Gaussian process regression for safe CBFs.
**B**: The authors in [20] use CBFs to learn a provably correct neural network safety guard for kinematic bicycle models.
**C**: The authors in [21] consider that uncertainty ente... | ABC | BCA | BCA | BCA | Selection 4 |
<|MaskedSetence|> <|MaskedSetence|> [16] showed that space-time block coding (STBC) with single polarization outperforms STBC with dual polarization in Rayleigh and Ricean fading channels. A MIMO system with dual-polarized antenna elements can have lower spatial diversity but higher spatial multiplexing gain than a c... | **A**: Ref.
**B**:
Various other aspects of polarization in MIMO systems have been investigated as well.
**C**: Various channel sounding campaigns and channel models provide insights into the characteristics of wireless channel polarization [26, 21, 22, 20, 27, 28, 23, 29, 30].
.
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<|MaskedSetence|> In [7], adding a Lagrangian term to the regularization of a constrained non-convex minimization permits to build an equivalent minimization problem that is convex locally. <|MaskedSetence|> In [29], in a function space setting, Pock et al. <|MaskedSetence|> In the context of non-convex polynomial o... | **A**:
Many works intent to find a convex proxy to a non-convex objective function.
**B**: Another possibility is to try to perform a regularization by infimal regularization [8] for lower semicontinuous objective functionals.
**C**: propose a high dimensional lifting of the Lagrangian formulation of (2) where the d... | ABC | BCA | ABC | ABC | Selection 4 |
The predictions by one template from our method and random selection are visualized in Figure 4. Our predictions around ears and nose locate more closer to the ground-truth landmarks than those by random template, which has consistent performance in MRE, quantitatively.
Besides, another group of experiments are condu... | **A**: We speculate that the diversity of Hand Xray dataset could not be well "represented" by such small group of templates.
**B**: Results are listed in Table 1, showing reliable improvements (e.g., MRE reduced by 35.5%percent35.535.5\%35.5 % (4.1144.1144.1144.114mm to 2.6532.6532.6532.653mm)) .
**C**: So it makes ... | ABC | BAC | BAC | BAC | Selection 2 |
<|MaskedSetence|> The NICE-Net consists of a feature learning encoder and a coarse-to-fine registration decoder. The feature learning encoder has two identical, weight-shared paths to extract features from the fixed and moving images separately, which were then propagated to the coarse-to-fine registration decoder. Th... | **A**: As the provided training set was relatively small (140 intra-patient image pairs), the NICE-Net-ds was first pretrained with inter-patient image pairs (280 ×\times× 279 pairs) to avoid overfitting.
**B**: This team adopted the recently proposed Non-Iterative Coarse-to-fine registration Network (NICE-Net) (Meng ... | BAC | BAC | BAC | BAC | Selection 3 |
<|MaskedSetence|> <|MaskedSetence|> In Corollary 8, we also
provide a sufficient condition under which the function τs(.)\tau_{s}(.)italic_τ start_POSTSUBSCRIPT italic_s end_POSTSUBSCRIPT ( . ) is
continuous. Note also that the angle map ϕ(.)\phi(.)italic_ϕ ( . <|MaskedSetence|> | **A**: ) is continuous except at finitely many angles
θ𝜃\thetaitalic_θ.
**B**: assumptions on M(τ)𝑀𝜏M(\tau)italic_M ( italic_τ ), that in general the inter-event time
function τs(.)\tau_{s}(.)italic_τ start_POSTSUBSCRIPT italic_s end_POSTSUBSCRIPT ( .
**C**: ) is a continuous.
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Control of PDE systems has been widely explored over the years [15, 16, 17, 18]. Similar to ODEs, notions of ISSt for PDE systems have garnered a lot of attention recently (see survey paper [19]). For example, PDE ISSt have been explored for reaction-diffusion systems [20], hyperbolic systems [21], [22], parabolic sy... | **A**: Subsequently, utilizing ISSt Lyapunov functional characterization, we prove that such designed safety control is also an input-to-state stabilizing control under certain additional conditions.
**B**: Notions of practical ISSt for PDEs have been explored in [28].
**C**: In our current work, we attempt an altern... | BCA | BCA | BCA | ACB | Selection 1 |
Collecting Training Samples. Recall that a sample in PU-Setting is comprised of a sample of PUs’ parameters (location and power) and the optimal power allocated to the SU. In SS-Setting, a training sample is comprised of spectrum sensors’ received power readings. <|MaskedSetence|> <|MaskedSetence|> Then, we compute t... | **A**: To determine whether PU to PUR transmission is incurring any harmful interference from SU, we have PU continuously streaming ASCII messages over the 1 MHz bandwidth channel centered at frequency 915.8 MHz, and check if the messages are successfully received at the PUR.
**B**: The location of entities is availab... | BCA | ACB | BCA | BCA | Selection 4 |
To the best of our knowledge, Coordinate descent [31], as an important class of optimization algorithms, is not sufficiently analyzed by researchers in the online optimization community. In coordinate descent algorithms, most components of the decision variable are fixed during one iteration while the cost function is ... | **A**: Another situation where one may find coordinate descent useful is dual decomposition based methods for distributed optimization, see [21] and references therein.
**B**: Specifically, we consider both random and deterministic online coordinate descent algorithms under assumptions commonly used in the literature.... | ABC | ABC | CAB | ABC | Selection 2 |
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 outperf... | **A**: Irvin et al.
**B**: (2019) in multiple detection whereas betters performance of CheXNext Rajpurkar et al.
**C**: (2018), which is the state-of-the-art chest x-ray disease prediction model, for cardiomegaly condition only..
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Emotional elicitation and labeling is a complex task, and sometimes the expected (or targeted) emotions are not the ones the volunteers experienced (or reported). The agreement between the target class and the self-reported discrete emotion annotations by the volunteers in this experiment is shown in the matrix in Figu... | **A**: It is also observed that sadness, calm, joy and fear are the emotions best identified, being the agreement in the fear emotion especially relevant for the use case.
**B**: Analyzing this figure it can be found that the non-included emotions (attraction, contempt, hope and tedium) are very scarcely selected with... | CBA | CBA | CBA | ABC | Selection 1 |
<|MaskedSetence|> The authors highlighted the potential clinical applications of GANs concerning early- and late-stage AMD classification.
Burlina et al. [8] trained a Progressive GAN [29] on 133,821133821133,821133 , 821 color fundus images from 4,61346134,6134 , 613 age-related eye disease individuals to learn how t... | **A**: Bellemo et al. [28] described the possible advantages and limitations towards synthetic retina image generation using GANs.
**B**: Two retina specialists were asked to distinguish between images with and without AMD for original and synthetic images.
**C**: The accuracy differences between synthetic and real i... | ABC | ABC | ACB | ABC | Selection 1 |
Among the available approaches, the concept of control invariant set is one of the most exploited historically, since it ensures the existence of some feedback law able to steer the closed-loop trajectories of the uncertain system within a prescribed state set 25, 6, 8, 37. This is traditionally achieved by associating... | **A**: With a specific focus on discrete-time polytopic systems, an admissible control policy that actually makes a polyhedral CLF a suitable Lyapunov candidate for the closed-loop system is typically synthesized in two ways: through a variable structure 25, 46, 47, or a (minimal) selection-based controller 3.
**B**: ... | ABC | ABC | ABC | ABC | Selection 3 |
Fig. <|MaskedSetence|> <|MaskedSetence|> First and second column show respectively MRI and CT images. The third column shows the MRI transformed using Clear, while the fourth column shows the MRI transformed using PPIR(MPC). <|MaskedSetence|>
. | **A**: The transformed images are highlighted by red and green frames, respectively.
**B**: 3: Qualitative results for diffeomorphic registration with CC between 3D medical images from the AbdomenMRCT dataset [25].
**C**: The images are presented in a 3×4343\times 43 × 4 grid, with the first row representing the axia... | BCA | BCA | BCA | CAB | Selection 2 |
To this end, we identify a class of POMDPs with a low-rank structure on the state transition kernel (but not on the observation emission kernel), which allows prediction and control in a sample-efficient manner. More specifically, the transition admits a low-rank factorization into two unknown features, whose dimension... | **A**: To this end, we construct a confidence set of embeddings upon identifying and estimating the Bellman operator, which further allows efficient exploration via optimistic planning.
**B**: The Bellman operator allows us to further factorize the history across multiple steps to obtain its embedding, which assembles... | BCA | BCA | BCA | BCA | Selection 3 |
III Main Results
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. <|MaskedSetence|> <|MaskedSetence|> Whereafter, Corollary 2 gives more intuitive convergence conditions fo... | **A**: The proofs of theorems, Proposition 1 and Corollary 2 are in Appendix A, and those of the lemmas in this section are in Appendix B.
.
**B**: Then, Theorem 2 gives intuitive convergence conditions for the case with balanced conditional digraphs by Lemma 2.
**C**: Based on which, Theorem 1 proves the almost sure... | CBA | ACB | CBA | CBA | Selection 4 |
<|MaskedSetence|> One serves as input to the model, and the other as the ground truth corresponding to the desired parameter setting to compute the loss. <|MaskedSetence|> We generated these brain MRI scans for 200 random pairs of {TE, TR}. <|MaskedSetence|> The TE values ranged from 20 ms to 1s non-uniformly. The d... | **A**:
For our training, we require the MRI scans in two different parameter settings of {TE, TR}.
**B**: The TR values were chosen uniformly at random in the range 1.2 s to 10s.
**C**: We use MRiLab [7] which is an MRI Simulator to generate these synthetic brain scans in different parameter settings of {TE, TR}.
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<|MaskedSetence|> Considering the peripheral connection between SiPM output and FPGA board, the peripheral module (PMOD) interface provided by the board was used [35]. The PMOD interface was developed by Digilent Inc. <|MaskedSetence|> The expected bandwidth of the PMOD interface is tens of megahertz. <|MaskedSetenc... | **A**: Since the digital signal characteristics are not specified, the maximum speed for digital SiPM pulse detection was evaluated.
.
**B**:
To demonstrate the real-time optical receiver with SiPM, an AMD/Xilinx PYNQ-Z1 evaluation board with Zynq-7000 SoC XC7Z020-1CLG400C FPGA was chosen as the platform to charact... | BCA | BCA | CAB | BCA | Selection 2 |
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. <|MaskedSetence|> <|MaskedSetence|> This preliminary analysis aids in determinin... | **A**: For scenarios where solar radiation pressure dominates, a sun-terminator orbit would be suitable, while for elongated asteroids, a retrograde equatorial orbit in the asteroid’s inertial frame would be generally preferable [40, 38, 39].
**B**: Once the initial assessment of the environment is conducted, the spac... | CBA | BCA | CBA | CBA | Selection 4 |
The importance of such an interdisciplinary approach has also been recently recognized in [24, 25], where the authors proposed a simulation framework that allows for coordinating a robotics simulator (e.g. Robot Operating System (ROS)), a communications network simulator, and an antenna simulator. <|MaskedSetence|> I... | **A**: Some tutorials have recently been published on communications-aware robotics problems.
**B**: This enables to accurately simulate the dynamics of the robot and the communications channel.
In the literature, however, CaR and RaC problems are often not addressed with such an interdisciplinary approach.
**C**: ... | BAC | BAC | BAC | CAB | Selection 3 |
In this paper, a general notion of dissipativity with dynamic supply rates was introduced for nonlinear systems, extending the notion of classical dissipativity. <|MaskedSetence|> In these results, both dynamical systems are characterised by compatible dissipation inequalities with respect to “coupled”
dynamic supply... | **A**: A noteworthy specialisation of the results is a simple coupling test to verify whether the feedback interconnection of two nonlinear systems, each satisfying independent (Ψ,Π,Υ,Ω)ΨΠΥΩ(\Psi,\Pi,\Upsilon,\Omega)( roman_Ψ , roman_Π , roman_Υ , roman_Ω )-dissipation inequalities, is asymptotically stable.
**B**: Ly... | BAC | BAC | BAC | BAC | Selection 3 |
In this paper, we propose a way of analyzing safety probability for a stochastic system via a CBF approach. The contributions of this paper are as follows. First, we propose an almost sure reciprocal control barrier function (AS-RCBF) ensuring the safety of a set with probability one, which is considered as a stochas... | **A**: Our stochastic ZCBF satisfies an inequality, which differs from the previous results in [9, 10, 11, 12, 13, 14, 15] because the inequality directly includes the diffusion coefficients.
**B**: In the procedure, we also provide control design strategies using AS-RCBF/AS-ZCBF and our stochastic ZCBF.
**C**: In a... | ABC | CAB | ABC | ABC | Selection 1 |
This paper aims at answering the above questions. <|MaskedSetence|> Note that a large admittance (or equivalently, a small impedance) indicates that the converter’s behavior is closer to a voltage source. <|MaskedSetence|> We show that only GFM control can provide effective voltage source behaviors even under differ... | **A**: By explicitly deriving how the integration of GFM converters affects the power grid strength, we link the capacity of GFM converters to the stability of a GFM-GFL hybrid system.
Then, we give recommendations for the capacity ratio between GFM and GFL converters to satisfy a (prescribed) desired stability margin.... | CBA | CBA | CBA | ABC | Selection 3 |
<|MaskedSetence|> Many SR networks apply attention modules to exploit latent correlations among the immediate features. Following RCAN [58] that first adopted channel attention, SAN [8] leveraged second-order channel attention to adapt the channel-wise features through second-order statistics. Several works introduced... | **A**: 2.2 Attention in Super-Resolution
The attention mechanism can be viewed as a discriminative selection process that focuses on informative regions and ignores the irrelevant noise of pending features.
**B**: More recently, DAT [7] leveraged SA along both channel and spatial dimensions and enabled an effective i... | ACB | ACB | ACB | ACB | Selection 3 |
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... | **A**: This results in substantial initial access latency and a long beam-hopping period.
**B**: To allow for initial access, all potential beam directions are sequentially illuminated and scanned (beam sweeping) during multiple synchronization signal blocks (SSB).
**C**: Therefore, the reconfiguration needs to be do... | CBA | CBA | CBA | CAB | Selection 3 |
<|MaskedSetence|> However, during the testing phase, the whole learning model is transmitted between the server and devices. Federated pruning permanently removes neurons in either or both training and testing phases. <|MaskedSetence|> Thus, how to design federated pruning methods with low computation complexity need... | **A**: The pruning ratio should be carefully designed to guarantee learning accuracy, and extra computation latency is required to calculate the importance of parameters.
**B**: Thus, how to design a model compression algorithm with high learning accuracy still needs to be investigated.
.
**C**:
Federated dropout r... | CAB | BCA | CAB | CAB | Selection 3 |
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... | **A**: These linear approximations can also effectively incorporate the characteristics of more complex components (e.g., tap-changing transformers, smart inverters, etc.), only requiring the ability to apply a power flow solver to the system.
**B**: These linear approximations are called conservative linear approxima... | BCA | BAC | BAC | BAC | Selection 3 |
For the training and validation sets, we only need to generate simulation data for a single fixed array, either circular or linear, since that they will only be used to the training of the CNN-based DOA estimation. Specifically, each utterance was 2 seconds long. For each individual utterance, we generated a room. The... | **A**: We used Pyroomacoustics [38] to generate the room impulse response.
**B**: To study the effects of different types of microphone arrays on performance, for each randomly generated test room, we applied exactly the same environmental setting (including the speech source, room environment, speaker positions, micr... | CAB | CAB | CAB | CBA | Selection 2 |
<|MaskedSetence|> Our BRAT slices indicate that the robot is able to traverse through hallways reasonably well; however, sometimes, it fails.
Figure 10:
(a) Notice the highlighted area in the top-right location of the BRAT for the robot heading of −π/2𝜋2-\pi/2- italic_π / 2 radians. <|MaskedSetence|> (b) On simul... | **A**: Misunderstanding certain obstacles as traversable.
**B**: (c) Another situation was observed where the robot crashed into a glass door due to the low height of the wooden pane around it.
**C**: Even though the robot faces down (wrt the top view), it cannot escape from the recessed region.
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There are four sets in the SceneFake dataset: training, development, seen test and unseen test. We design an unseen test set to evaluate the generalization of the models. <|MaskedSetence|> <|MaskedSetence|> Our training, development and seen test sets are populated with utterances with 6 kinds of acoustic scenes: Air... | **A**: The acoustic scenes are randomly sampled to mix with the utterances at 6 different SNRs each: -5dB, 0dB, 5dB, 10dB, 15dB and 20dB.
The data structure and the detailed configurations of acoustic scene manipulation in the SceneFake dataset are illustrated in Figure 4.
.
**B**: The dataset consists of real and fa... | CBA | ACB | CBA | CBA | Selection 3 |
Figure 6: Results on the nonlinear models. <|MaskedSetence|> The identified observer in both cases is sensitive. Even small errors between the true response and the predicted response in the first few steps are amplified, leading to instability. Hence, we plot only the TV-OKID without the observer.
The results show... | **A**: The last q𝑞qitalic_q steps in OKID are ignored, as there is not sufficient data to calculate models for the last few steps, as discussed in Sec. 6.3.
**B**: The experiments for identifying the system were performed from zero-initial conditions and non-zero initial conditions.
**C**: More examples can be found... | CAB | BAC | BAC | BAC | Selection 4 |
<|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> Moreover, multiple b-shell, multiple direction high quality DW-MRI data can take many minutes to acquire, which poses challenges for clinical imaging protocols involving a multitude of MRI contrasts already taking tens of minutes to execute. Reduction of DKI data... | **A**: Recent clinical benefits of using kurtosis metrics over other DW-MRI derived measures have been demonstrated for grading hepatocellular carcinoma (Li et al., 2022b), prognosing chronic kidney disease (Liu et al., 2021), differentiating parotid gland tumours (Huang et al., 2021a), measuring response to radiothera... | ABC | ABC | CAB | ABC | Selection 1 |
Despite a large volume of literature on charging infrastructure planning for electric ride-hailing fleets, most existing works focus on either charging or battery swapping stations, without considering their complementary effects. Among the few early attempts, Zhang et al. [47] studied the joint planning of swapping an... | **A**: In contrast, this paper distinguishes itself from all previous studies in two key aspects.
**B**: To the best of our knowledge, these considerations have not been studied in the literature.
.
**C**: Firstly, we consider a multimodal charging network where charging stations and battery swapping stations are ... | ACB | ACB | ACB | ACB | Selection 1 |
<|MaskedSetence|> As shown in Fig. 3 (a), the comparison results between DEviS and other U-Net variants under different Gaussian noise levels and mask ratios are presented. Fig. 3 (a) indicates a gradual degradation in performance for U-Net, AU-Net, V-Net, and nnU-Net, particularly at higher mask ratios and noise leve... | **A**:
1) Comparison with U-Net based methods.
**B**: Additionally, the generated uncertainty estimates, as illustrated in Fig. 3 (c), can be utilized by researchers and clinicians to discern the unreliability of the data.
2) Comparison with uncertainty-based methods.
**C**: As shown in Fig. 3 (a), the comparison ... | ABC | ABC | ABC | BCA | Selection 3 |
<|MaskedSetence|> One fusion approach is signal switching, where candidate fiducial points from a signal modality with the best signal quality are selected as final fiducial points in a certain segment. Singh and Sunkaria (2017) uses the sample entropy to assess the noise content in multiple signal modalities, such as... | **A**: (2019) obtains the best set of IBI arrays from three PPG morphological features by selecting those segments with minimal standard deviation of IBI subarray.
**B**:
Fusion Methods of Physiological Signals.
Fusion approaches have been explored to enhance the accuracy of heartbeats detection by incorporating the... | BAC | BAC | BAC | BAC | Selection 2 |
<|MaskedSetence|> STSCI consists of two systems, the base system and the semantic enhancement system. <|MaskedSetence|> <|MaskedSetence|> The simulated-channel model is only used during the model training process to simulate a real-world wireless channel. This process is indicated by blue lines.
. | **A**: The semantic enhancement system with the process indicated by red lines, on the other hand, includes a YOLONet for identifying key semantic content and an enhancement CNN network that utilizes extra information to enhance the transmission quality of the key semantic information.
**B**: The base system consists ... | BCA | CBA | CBA | CBA | Selection 2 |
<|MaskedSetence|> The top subfigure presents the results for the practical implementation that includes estimating the dimension, whereas the bottom subfigure presents the results for the oracle. We see that the Riemannian approach outperforms its Euclidean counterpart, by approximately 20dB20dB20\text{dB}20 dB. <|M... | **A**: The reason is that the Riemannian mean better attenuates the interference sources, allowing for a better estimation of the signal subspace than the Euclidean mean.
.
**B**: In addition, the oracle SbSp method is better than the practical SbSp.
In comparison to Figure 3(top), it can be seen that the Riemannian ... | CAB | CBA | CBA | CBA | Selection 3 |
<|MaskedSetence|> The data included regular breathing and six different types of anomalous breathing. Since breathing is an involuntary activity, humans have limited control over their breathing rates and depths. <|MaskedSetence|> Using a breathing machine or robot eliminated the need to establish ground truth as the... | **A**: Therefore, it is extremely challenging for humans to consistently breathe at various prescribed rates and depths for the purpose of collecting training data, unless they have received specialized training to do so.
**B**: Moreover, the machine could consistently generate data for extended durations, resulting i... | CAB | CAB | CAB | BCA | Selection 1 |
<|MaskedSetence|> <|MaskedSetence|> In the cell-free massive MIMO literature, the best performing joint precoders are typically designed from joint uplink combiners, motivated by a known uplink-downlink duality principle for fading channels [19, Ch. 4] [20, Ch. 6]. However, optimal joint precoders are generally unkno... | **A**: Second, the known uplink-downlink duality principle for fading channels holds for a looser and somewhat less practical sum power constraint.
.
**B**:
Each AP must form its transmit signal as a function of the CSI and data bearing signals specified by the constraints, and no additional information exchange be... | CBA | BCA | BCA | BCA | Selection 2 |
<|MaskedSetence|> In this case, the model is estimated from data, and thus the modeling error is inevitable. Our suboptimality analysis can incorporate this modeling error to provide performance guarantees for these controllers.
Related work: When the model is exact, the suboptimality analysis of RHC controllers, wi... | **A**: The impact of the modeling error has been investigated in the above analysis; however, the effect of the prediction horizon and the terminal value function on the control performance is not considered [14, 13, 12]..
**B**:
Moreover, we demonstrate an application of our analysis in the performance analysis of l... | BCA | ACB | BCA | BCA | Selection 4 |
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... | **A**: The RL-ROE is constructed from the ROM in an analogous way to a Kalman filter, with the crucial difference that the linear filter gain function, which takes in the current measurement data, is replaced by a nonlinear policy trained through reinforcement learning (RL).
**B**: The flexibility of the nonlinear pol... | ABC | ABC | ABC | ACB | Selection 2 |
However, as the CNN- and transformer-based methods focus more on capturing local and global features respectively, marrying these two methods can further enrich the local and global features extracted. <|MaskedSetence|> Firstly, the CNN and transformer are ensembled in tandem or parallel. For example, Tummala et al. [... | **A**: The recent methods of integrating CNN and transformer can be primarily categorized in two-fold.
**B**: Contrasting with the approaches with sophisticated module design, our CECT exhibits enhanced effectiveness and generalization ability with straightforward yet effective architecture.
.
**C**: The convolution ... | ACB | BAC | ACB | ACB | Selection 3 |
<|MaskedSetence|> In our measurement campaign, 12121212 microphones (T-bone MM-1) are set up according to Fig. 2 and connected to a single sound card that is recording to a laptop. A stereo speaker is placed on top of the robot, as well as the 12121212th microphone.
The microphone on the robot is placed as close as po... | **A**: Microphones are placed asymmetrically on the floor and on different heights to avoid microphones being co-linear or co-planar, as this may cause degeneracies when solving for positions.
**B**: All microphones, except the one on the robot, have two markers placed, as seen on the left side of Fig. 4.
The sampli... | ABC | CBA | CBA | CBA | Selection 2 |
<|MaskedSetence|> We employ specaugment and gaussian noise for phoneme recognition [20]. <|MaskedSetence|> <|MaskedSetence|> To augment the LibriSpeech data used in training, we used the open-source torchaudio library [39]. Our goal in this data augmentation was to enhance the total number of samples while maintaini... | **A**: For ASR, we employ SpecAugment and Speed Perturbation [13].
**B**: We adopt these enhancements since prior research has demonstrated their effectiveness for the aforementioned tasks.
**C**:
3.3 Data Augmentation
Here, we employ three different types of task-specific augmentation.
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<|MaskedSetence|> So, intuitively, the CSI of this channel should contain information about the Doppler shift. This idea has been already explored in terrestrial networks to generate a model using ML[158, 113, 159]. The ground truth values or the labels are usually generated using the ephemeris information. Different ... | **A**:
In wireless communication systems, due to the mobility of the transceivers, the channel between the transceivers changes significantly resulting in received signal power variation and Doppler shift.
**B**: Complexity analysis is required to justify the
applicability of these DL architectures replacing the sta... | CBA | ACB | ACB | ACB | Selection 3 |
Training ResNet-20 and its revisions follow the implementation in [1]. In detail, we use an SGD optimizer with a weight decay of 0.0001 and momentum of 0.9. <|MaskedSetence|> The initial learning rate is 0.1, and the learning rate is reduced by a factor of 1/10 at epochs 82, 122, and 163, respectively. <|MaskedSeten... | **A**: During the training, the best models are saved based on the accuracy of the CIFAR-10 test dataset, and their accuracy numbers are reported in Table V.
TABLE V: CIFAR-10 Experimental Results..
**B**: Data augmentation is implemented as follows: First, we pad 4 pixels on the training images.
**C**: Models are ... | CAB | CBA | CBA | CBA | Selection 4 |
2 Related work
Over the years, various solutions were suggested to overcome the lack of target GT depth measurements for training MDEs to predict absolute depth from target images. <|MaskedSetence|> <|MaskedSetence|> A recent zero-shot model [28] successfully overcame the geometrical domain gap between the source an... | **A**: The first approach is implemented as zero-shot [44] (see Figure 2a), where a model is trained on source datasets, and used to infer depth on target images, in the hope of generalizing well on the new domain.
**B**: Here we cover the main approaches, that are also presented by category in Figure 2.
**C**: In ad... | BAC | CAB | BAC | BAC | Selection 3 |
<|MaskedSetence|> We explored two strategies: randomly selecting a certain number of PAFs or using iterative refinement (sequentially providing all the PAFs in descending order of reconstruction error). <|MaskedSetence|> 2) Providing a few PAFs at the beginning and the end of a sentence. <|MaskedSetence|> A detailed... | **A**: However, other strategies exist, including: 1) Providing PAFs for an entire word.
**B**: 3) Providing only F0 values.
**C**: 5 Limitations
We acknowledge that our work does not investigate all plausible strategies for selecting control points.
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With the rise of 5G and beyond communication systems, the use of multiple antennas at the BSs and the UEs has introduced beamforming capabilities as a central feature in 5G NR that leads to higher data rates. However, a series of beam management procedures are needed to ensure efficient handling and network operation. ... | **A**: However, the interference from other BSs is ignored.
**B**: Therefore, in [17], the authors study among others both the initial beam selection during BS handover and beam reselection technique in a mmWave cell.
**C**: However, either a binary valued antenna pattern, called flat-top pattern [4]−--[6], or ideal ... | CBA | CAB | CBA | CBA | Selection 4 |
Prior research on inverse Bayesian filtering includes inverse hidden Markov model[10] for finite state-space and inverse Kalman filter (I-KF)[5] for linear Gaussian state-space models. These works do not address the highly non-linear counter-adversarial systems encountered in practice. In this regard, our recent work ... | **A**: The basic intuition of unscented transform is that it is easier to approximate a probability distribution than it is to approximate an arbitrary non-linear function [15].
**B**: The corresponding inverse UKF (I-UKF) was proposed in our recent work [16, 17]..
**C**: However, even EKF performs poorly in case of ... | CAB | CAB | CAB | CAB | Selection 4 |
Preliminary results of our work appeared in [26], where the noiseless case of three-dimensional (3-D) SoMAN formulation was derived. In this paper, we apply our approach to estimate the communications messages and radar waveforms, formulate 3-D SoMAN in the presence of noise, provide detailed theoretical guarantees, ... | **A**: We demonstrate our approach through extensive numerical experiments..
**B**: Our main contributions are:
1) M-DBD with structured unknown continuous-valued parameters.
**C**: Following the approaches in [9, 27], we represent the unknown transmit radar signal (a periodic waveform) and communications messages in... | BCA | BCA | BCA | CBA | Selection 3 |
Initially, some conventional methods like [12, 40] and widely-used interpolation methods like bicubic and tricubic interpolations [18] were employed in the early research.
Inspired by [11], recent studies have shifted their focus towards using deep learning-based super-resolution networks in the medical domain.
Lim et ... | **A**: On the other hand, Chen et al.
**B**: [45] build a transformer-based MISR network to address volumetric MISR challenges..
**C**: [5] and Wang et al.
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<|MaskedSetence|> <|MaskedSetence|> Pattern search methods are extended in [13] to solve optimization problems with known constraints.
In case the explicit formulations of both objective and constraint functions are not available, the work [14] solves the problem by learning the functions using non-parametric model... | **A**: Classical techniques for zeroth-order optimization can be classified as direct-search-based (where a set of points around the current point is searched for a lower value of the objective function), gradient-descent-based (where the gradients are estimated based on samples), and model-based (where a local model o... | ABC | ABC | ACB | ABC | Selection 4 |
In general, the designs of WSR-maximization precoders under the power constraints mentioned above can be formulated as optimization problems with equality constraints. <|MaskedSetence|> In mathematics, a manifold is a topological space that locally resembles Euclidean space near each point. By revealing the inherent g... | **A**: Further, we present three Riemannian design methods using Riemannian steepest descent (RSD), Riemannian conjugate gradient (RCG) and Riemannian trust region (RTR), respectively.
**B**: Recently, manifold optimization has been extensively studied and successfully applied to many domains [18, 19, 20, 21], showing... | BAC | BCA | BAC | BAC | Selection 4 |
Deep domain adaptation (DA) methods are being increasingly studied in medical image segmentation to reduce the domain shift effects [1, 10, 11]. In the context of cross-modal segmentation, we focus in particular on unsupervised domain adaptation (UDA) methods that do not rely on any prior knowledge of the labels of the... | **A**: Although newer generative paradigms based on e.g.
**B**: The most popular models for unsupervised I2I translation are CycleGAN and its variants [29, 30, 31].
**C**: It is common to retain the best performing model (as measured subjectively) from several trainings, which is not satisfactory due to the aleatoric... | ACB | ABC | ABC | ABC | Selection 2 |
<|MaskedSetence|> <|MaskedSetence|> Based on three traveling wave measurements, the propagation medium is characterized and the origin of the event is localized. The method thus solves the problem of having to know the wave propagation characteristic of the transmission line in advance. This eliminates the setting as... | **A**:
5 Conclusion
In this paper, we propose a new online method for localizing events on transmission lines.
**B**: The method analyzes the traveling wave in the time-frequency domain of the wavelet transform.
**C**: At the same time, the characteristic of the transmission line is evaluated in a frequency domain,... | ABC | ABC | ABC | ABC | Selection 1 |
<|MaskedSetence|> Additionally, some part of the system is always left unmodeled due to limitations of first-principles-based modeling or uncertainty of the estimated model. These modeling errors propagate into the control design and result in unwanted behavior. A way to circumvent this issue is to directly synthesize... | **A**: reinforcement learning Sutton and Barto (1999).
**B**: Due to the increasing complexity of systems, obtaining an accurate model has become a challenging task in practice Hjalmarsson (2005).
**C**: The interest in such direct data-driven control synthesis techniques is increased by the huge success of e.g.
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4.6 Transfer Learning
M3FM is designed for adaptability and generalization, enabling the enhancement of out-of-distribution task performance through transfer learning. <|MaskedSetence|> <|MaskedSetence|> For diverse clinical datasets, we can simply describe involved clinical data in free text to the model, without ... | **A**: To accommodate different image dimensions, the addition of a linear embedding layer suffices.
**B**: This capability extends to new tasks with varying image input dimensions, clinical data types, and output dimensions.
**C**: Consequently, M3FM can be easily fine-tuned to enable new tasks by leveraging the pre... | BAC | BAC | ABC | BAC | Selection 4 |
In comparison, our Transformer-based code predictor yields better ASR performance thanks to its stronger ability of global context modeling, i.e., Exp. <|MaskedSetence|> <|MaskedSetence|> (6) and (4) indicates the importance of fixing codebook during finetuning to protect the pre-trained clean speech prior.
Compariso... | **A**: (3).
As shown in Fig. 4, the Transformer code predictor achieves higher prediction accuracy than CNN predictor and NN matching.
Furthermore, comparison between Exp.
**B**: (10)-(12)..
**C**: (6) vs.
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In channel modeling, the new features of H-MIMO inevitably introduce significant changes that should be addressed from a fundamental EM perspective. Specifically, the EM wave field, the actual transmission carrier in H-MIMO communications, is a spatial vector, and the communication distance tends to be in the near-fiel... | **A**: However, the former work focuses on deriving a measurement-efficient model with high flexibility and mathematical tractability, whereas sacrificing the depiction accuracy to some extent.
**B**: As shown recently in [18, 19, 20], the authors describe a wireless channel following EM principles, where they studied... | BCA | BCA | CBA | BCA | Selection 2 |
More precisely, the existing frameworks [21, 22, 23] require users to manually write and solve SoSP problems to obtain the RoA approximation. In contrast, the proposed SOStab Matlab toolbox fully automates the SoSP aspect, eliminating the need for users to possess knowledge of the Moment-SoS hierarchy. <|MaskedSetence... | **A**: SOStab has been developed and made publicly available which allows users to compute RoA of non-linear dynamical systems.
To the best of the authors’ knowledge, the only existing toolbox for RoA approximation in a finite time horizon setting is SparseDynamicSystem [18], coded in the Julia language.
**B**: A no... | CAB | ACB | CAB | CAB | Selection 1 |
Cloud computing, also known as on-demand computing, provides customers with a variety of services. Due to its rising popularity, it is susceptible to many intruders who can threaten the confidentiality and security of data stored in the cloud (Fig. 17). <|MaskedSetence|> The biggest issues of on-demand services are pr... | **A**: Because of their dispersed nature, the most difficult aspect of cloud-based solutions is security.
**B**: The experimental findings demonstrate that the suggested method can effectively move the encryption models from a number of SDs to the TD, and the accuracy rate can reach 93.01% in ciphertext with no apprec... | ABC | ABC | ABC | ACB | Selection 1 |
<|MaskedSetence|> On the other hand, in [18, Assumption 10], the obstacles are assumed to be smooth and sufficiently separated from each other. In [21], the authors proposed a discontinuous feedback control law for autonomous robot navigation in partially known two-dimensional environments. When a known obstacle is en... | **A**: However, when close to an unknown obstacle, the robot moves along its boundary, relying on the local curvature information of the obstacle.
**B**: This method is limited to point robots and, similar to [18], assumes smooth obstacle boundaries without sharp edges.
**C**: In [19, Definition 2], the proposed hybr... | CAB | CAB | CAB | BCA | Selection 3 |
We curated a comprehensive dataset by collating images from publicly available medical image segmentation datasets, which were obtained from various sources across the internet, including the Cancer Imaging Archive (TCIA) [34], Kaggle, Grand-Challenge, Scientific Data, CodaLab, and segmentation challenges in the Medica... | **A**: We incorporated these annotations directly for both model development and validation.
The original 3D datasets consisted of Computed Tomography (CT) and Magnetic Resonance (MR) images in DICOM, nrrd, or mhd formats.
**B**: To facilitate stable training, we performed intensity normalization across all images, ... | ACB | ACB | CAB | ACB | Selection 1 |
<|MaskedSetence|> For the multi-path scenario, we relax Assumption 1, and the power of the desired signal is calculated based on Lemma 1 in [5]. This lemma states that for a large number of antennas, when the number of paths is much smaller than the number of antennas, the power of the desired signal converges to the ... | **A**: Without Assumption 1, the performance analyzed in the previous subsections is degraded.
**B**:
VI-D Effect of Assumption 1
In Fig. 5, we simulate the ergodic capacity for different links per subcarrier to analyze the effect of Assumption 1 by simulations and provide the corresponding ergodic capacity for the... | CBA | BCA | BCA | BCA | Selection 2 |
<|MaskedSetence|> That is, vector-type BP-SLAM is obtained assuming models for single-feature dynamics, single-feature death, sensor dynamics, and measurement models, but no feature births. Instead, it is assumed that, at the current time step, we know the multi-Bernoulli density of previous features and the multi-Ber... | **A**: The resulting algorithm, with the minor modifications explained above, is similar to vector-BP SLAM with the external PHD filter in [13]..
**B**: Then, the factor graph is obtained.
**C**:
It is also important to realize that this paper provides an alternative derivation of the (slightly modified) vector-type... | CBA | BCA | CBA | CBA | Selection 1 |
Semantic information is regarded as the meaning of the source data underlying the concrete expression. <|MaskedSetence|> This indicates that (i) semantic information relies not only on the source data but also on the specific task, which is significantly different from the information defined by Shannon, and (ii) sema... | **A**: Consequently, the same data may contain different amounts of semantic information for different tasks.
**B**: More specifically, semantic information is the effective information contained in the source data for accomplishing a specific task.
**C**: For example, an image contains much more semantic information... | BAC | BAC | BAC | BAC | Selection 3 |
To assess the quality of the ROMs, we used the backward Euler method [7] to simulate the DPM and all its surrogate models, using the same solver and time steps. The simulation results are presented in Figures 7 through 7, where the abscissa denotes computation time and the ordinate denotes the average displacement of ... | **A**: This solver preserves system matrix sparsity, allowing it to use computationally efficient operations, such as sparse LU decompositions [41], that would otherwise be infeasible or time-consuming.
**B**: It is important to note that the simulations were only used to illustrate the accuracy of the surrogate model... | CAB | BCA | CAB | CAB | Selection 4 |
In light of the mentioned limitations of both approaches, this paper proposes an integrated training framework, referred to as calibration-aware Bayesian neural networks (CA-BNNs). As described in Sec. <|MaskedSetence|> 2 and Sec. 3, the proposed training criterion applies a data-dependent regularizer that penalizes c... | **A**: 6 validate the proposed approach.
II BACKGROUND.
**B**: 4, after providing the necessary background in Sec.
**C**: As a secondary contribution, in Sec.
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<|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> Section IV details the convergence results of the proposed algorithms. Case studies on decentralized learning problems with various Byzantine attacks to illustrate the effectiveness and performance of the proposed algorithms are carried out in Section V. Section ... | **A**: Section II presents the basic notation, problem statement, problem reformulation, and setup of its robust variant.
**B**: The connection of the proposed algorithms with existing methods and the algorithm development are elaborated in Section III.
**C**:
I-D Organization
We provide the remainder of the paper... | CAB | CAB | ABC | CAB | Selection 2 |
3.5 Proposed Multi-Axis Attention-based Hybrid Decoder Block
The proposed Hybrid Decoder is designed by stacking layers of Mutil-Axis Attention-based MaxViT-blocks in a hierarchical architecture, with a TransposeConv layer at the start of each stage, as shown in Figure 2. <|MaskedSetence|> <|MaskedSetence|> Similar ... | **A**: The MaxViT blocks further enhance them using MBConv, local attention, and global attention sub-block.
.
**B**: Similar to the encoder, we created a parameter-efficient decoder by using only two MaxViT blocks per stage.
**C**: The decoder also enjoys the global and local receptive fields at all stages and is a... | BCA | ACB | BCA | BCA | Selection 3 |
In Table 2, we compare the efficiency of our model with one/few-shot learning models across 14 test tasks. Additionally, we train 14 TransUNet on 14 test datasets to establish an upper-bound for the performance. <|MaskedSetence|> On average, it takes a user 27.47 seconds to annotate one image across 14 datasets, while... | **A**: Comparing with the fully-supervised upper-bound, One-Prompt only needs to train one time for all downstream tasks, which saves significant parameters, training run time, and user-cost time for the annotation.
Table 2: Model efficiency comparison with few/one-shot transfer learning models.
**B**: The One-Prom... | CBA | ACB | CBA | CBA | Selection 1 |
The network is composed of 20,000 nodes; however, this number does not correlate with the number of deployed blockchain nodes. The current version of the protocol cannot verifiably prove the real number of blockchain nodes, as the Servicers are permissionless, but a rough estimate can be obtained through the nodes’ ... | **A**: and that each node runner has at least two nodes per staked blockchain 555Accounting for redundancy or geographical distribution of blockchain nodes., the Pocket Network has more than 30 independent blockchain servers on each of the main served blockchains.
**B**: It is important to note that this is only an es... | ACB | CAB | CAB | CAB | Selection 2 |
<|MaskedSetence|> We choose the FastSpeech2 with an emphasis embedding and the hierarchical prosodic module [19] as the baseline to compare fairly. <|MaskedSetence|> We use bert-base-chinese available on HuggingFace666https://huggingface.co/bert-base-chinese as our pre-trained BERT and fine-tuned with our model train... | **A**: For our proposed model, the conformer encoder has 4 layers with both input and encoder dimensions of 256 and 2 attention heads, following the implementation and the default configurations of Espnet555https://github.com/espnet/espnet.
**B**:
3.2.1 TTS Training Configurations
We utilize the basic configuration ... | BAC | BAC | BCA | BAC | Selection 4 |
For instance, a room with hard surfaces like concrete or glass reflects sound waves, whereas a room with soft surfaces such as carpets or curtains absorbs them. <|MaskedSetence|> Recent years have seen a surge in significant research Li et al. <|MaskedSetence|> (2021); Li et al. (2023); Huang et al. (2023b) addressin... | **A**: This variance can drastically impact the clarity and quality of the sound we hear.
To ensure an authentic and captivating experience, it is imperative to accurately model the acoustics of a room, particularly in virtual reality (VR) and augmented reality (AR) applications.
**B**: (2022) have proposed a unifie... | ACB | CBA | ACB | ACB | Selection 3 |
According to Frank & Schönherr (2021), a \acgmm trained on top of \acLFCC features performed best on the original WaveFake dataset. The \acgmm outperformed the deep RawNet2 proposed by Jung et al. (2020). RawNet2 processes raw and unmodified waveforms. <|MaskedSetence|> After the encoder, RawNet2 employs a recurrent l... | **A**: M𝑀Mitalic_M denotes the number of output channels from the convolutional layers before..
**B**: Dilated convolutions enlarge the receptive field without downsampling (Yu & Koltun, 2015).
**C**: A convolution-based encoder computes feature vectors.
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Notably, the self-trained method is extraordinary with two round training processes. The incorrect manual picking is removed between the two training processes. <|MaskedSetence|> Multiple training processes used in the self-trained network can correct these error.
Thus, the self-trained network performs well in the HR... | **A**: However, the precision of the self-training network is insufficient, so both UPNet and CNNRNN exceed it on the HR@1px and HR@3px.
UPNet performs better than the self-trained on accuracy (MAE) and stability (RMSE) for another three folds.
Then, UPNet can outperform STA/LTA, the benchmark, and CNNRNN a lot in the ... | CBA | CAB | CAB | CAB | Selection 4 |
Figure 7 illustrates the reliability diagrams and prediction interval widths for 1-step ahead forecasts. <|MaskedSetence|> Notably, the reliability diagram of the proposed model fluctuates around the ideal case, albeit in close proximity. <|MaskedSetence|> While these constraints ensure that higher quantiles are no s... | **A**: This behavior is attributed to the monotonicity constraint imposed on the proposed model.
**B**: A more in-depth analysis is provided in the subsequent subsection.
**C**: In this case, DeepAR continues to demonstrate the least reliability among all models.
| CAB | CAB | CAB | CAB | Selection 3 |
The paper is organized as follows. <|MaskedSetence|> <|MaskedSetence|> Similarly, in Section 4 we expose the results in RKHS theory which we leverage in this work. Our main contributions are contained in Section 5, which in particular details the methodologies we exposed at the previous steps 1) and 2) and correspond... | **A**: Precision and computational complexity of our approach are discussed in Section 6.
**B**: To ease the reading of this section, we moved a more detailed description of the aforementioned results to Section 8 and their technical proofs to Appendix A.
**C**: After gathering basic notation and preliminary results ... | CBA | CBA | ACB | CBA | Selection 2 |
Besides the issue mentioned above, sometimes the importance scores given by the WPR algorithm may converge to an undesired distribution in some heads: (1) tokens at the edge of the input image may get very high importance scores; (2) the importance score distribution may become nearly uniform. We provide visual examp... | **A**: We compute the variance of the distribution in each head and set both a minimum and a maximum threshold for the variance.
**B**: Both heads in these cases do not provide helpful information and are even misleading.
**C**: Then the final importance score equation becomes:
.
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To perform PSD on the raw signal, simply run the “main.m” program using the default settings, which utilizes all PSD methods. The discrimination factors of each method are then calculated and used to generate histograms. An automatic double Gaussian distribution fitting process is then applied, where the neutron dist... | **A**: However, when using other datasets, these parameters must be adjusted accordingly.
**B**: The three-sigma points of each Gaussian distribution are used as the end of the distribution since they contain 99.74% of the distribution.
**C**: Additionally, HQC-SCM incorporates a genetic algorithm-based automatic par... | BAC | BAC | BAC | BAC | Selection 4 |
<|MaskedSetence|> This comparison underscores the differences in the overall quality of synthetic 3D brain MRIs produced by various methods. <|MaskedSetence|> 3D-α𝛼\alphaitalic_α-WGAN-GP results in even blurrier images with similar textures, while HA-GAN produces the blurriest images, which are consistently asymmetr... | **A**: 3.4 Generated Images
Fig. 2 presents coronal, sagittal, and axial slices of real brain MRI images alongside those generated by our proposed method, two conditional baseline models, and other unconditional 3D brain MRI synthesis models.
**B**: LDM occasionally generates images with uniform textures and clearer ... | BAC | ACB | ACB | ACB | Selection 2 |
We perform two experiments to analyze to what extent context contributes to lip synchronization. First, we mask out 0.14s (7 frames) of the source audio and generate frames of the masked time steps. As shown in Fig.2, our work can still generate well-synchronized lips even with the absence of audio because it is able ... | **A**: It reaches 1.162 in LMD at ±plus-or-minus\pm±15 frames.
**B**: The audio frames that lie outside the window are zero-padded and we measure the lip-sync quality of the generated target frame using LMD.
**C**: In contrast, the previous works cannot generate correct lip movements because they do not consider surr... | CBA | CBA | ACB | CBA | Selection 2 |
<|MaskedSetence|> In contrast, the BSMSD is an ideal high-pass filter that disregards all frequency components before the cutoff frequency (here λ15subscript𝜆15\lambda_{15}italic_λ start_POSTSUBSCRIPT 15 end_POSTSUBSCRIPT), and the LPF detector is based on minimum and maximum operations, which are sensitive to noise.... | **A**: 2.c, respectively.
**B**: 2.b and Fig.
**C**: In addition, the superiority of our methods compared to the BSMSD and the LPF detector can be explained by the fact that the proposed detectors employ a weighted average of the filtered graph frequency components, with greater weight given to higher graph frequency... | CBA | BCA | CBA | CBA | Selection 1 |
<|MaskedSetence|> As illustrated in Fig. 1(a), clinical readers are trained to look across many images to identify those that show the aortic valve at sufficient quality and then use these “relevant” images to assess the valve’s health. Training an algorithm to mimic this expert diagnostic process is difficult. <|Mas... | **A**: The challenge in developing an automated system for diagnosing AS is that each echocardiogram study consists of dozens of images or videos (typically 27-97 in our data) that show the heart’s complex anatomy from different acquisition angles.
**B**: Standard deep learning classifiers are designed to consume only... | ABC | ABC | BCA | ABC | Selection 2 |
The field of music information retrieval (MIR) has long been facing challenges in data availability due to the costs associated with music audio annotation and country-specific copyright laws (Chen et al.,, 2019; Castellon et al.,, 2021). <|MaskedSetence|> Existing acoustic music pre-trained models primarily focus on ... | **A**: To address this challenge, pre-trained language models (PLMs) for acoustic music have been proposed to provide reusable learned representations, enabling transfer learning for various downstream MIR tasks without the need for extensive data annotation (Castellon et al.,, 2021).
However, current acoustic music pr... | ABC | ABC | BAC | ABC | Selection 4 |
5 Visual Interpretability
We visualize the most discriminative regions of several representative methods using the gradient-weighted class activation map (Grad-CAM) [29] in the Messidor-1 dataset for the DR task. As shown in Fig. 6, the proposed method showed the most accurate localization of diabetic lesions compare... | **A**: Interestingly, The nn-mobilenet and ReXNet share the same model configuration, but the latter still struggles to accurately learn lesion representation.
**B**: We observed that this MIL attention mechanism tends to assign a uniform level of importance to all patch tokens, which disrupts the ability of ViT to le... | CBA | CBA | ABC | CBA | Selection 2 |
<|MaskedSetence|> <|MaskedSetence|> This is a crucial point of departure from multiparametric techniques and deserves to be underlined. <|MaskedSetence|> In contrast, nonlinear models with non-convex cost functions may lead to a high degree of structural complexity, over and beyond the piecewise affine regime, of th... | **A**: (B)
The QuIFS algorithm applies to nonlinear systems and non-convex cost functions whenever the underlying optimal control problem admits a unique solution.
**B**: It relies on coarse properties of the optimal feedback such as Lipschitz continuity, etc., rather than more detailed local structural properties; i... | ABC | ABC | ABC | BCA | Selection 3 |
<|MaskedSetence|> When annotators disagree, majority voting and averaging are commonly used to derive single ground truth labels for training supervised machine learning systems. However, in many subjective tasks, there is usually no single “correct” answer. By enforcing a single ground truth, there’s a potential risk... | **A**: This can cause minority views to be under-represented.
**B**:
In tasks involving subjective evaluations such as emotion recognition, it is common to employ multiple human annotators to give multiple annotations to each data instance.
**C**: Emotion recognition is at risk of exposing a person’s inner state to ... | BAC | BAC | ACB | BAC | Selection 2 |
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