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**A**: In Section VI, we present numerical examples to demonstrate the performance of
the proposed method**B**: Conclusions are drawn in Section VII.**C**: The application of C2-WORDsuperscriptC2-WORD\textrm{C}^{2}\textrm{-WORD}C start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT -WORD to pattern synthesis is presented in Se... | BCA | CBA | BAC | CAB | Selection 1 |
**A**: The utility of coverage size is denoted as**B**: Higher altitude indicates larger coverage size as shown in Fig. 1 (c)**C**:
In order to support as many users as possible, UAVs are required to enlarge coverage size, which is equal to enlarge the coverage proportion in the mission area | CBA | ABC | ACB | CAB | Selection 1 |
**A**: Pascal VOC datasets: The PASCAL Visual Object Classes (VOC) Challenge (Everingham et al., 2010) was an annual challenge that ran from 2005 through 2012 and had annotations for several tasks such as classification, detection, and segmentation**B**: The segmentation task was first introduced in the 2007 challenge ... | BAC | BCA | BAC | ABC | Selection 4 |
**A**: The sum SE calculated by (11) and (28) with different numbers of t-UAVs and the given transmit power is shown in Fig. 10, respectively, to verify the influence of the inter-UAV interference**B**:
In this paper, we mainly focus on the analog beam tracking without considering the inter-UAV interference**C**: It i... | BAC | ACB | CBA | CBA | Selection 1 |
**A**:
Motivated by distributed statistical learning over uncertain communication networks, we study the distributed stochastic convex optimization by networked local optimizers to cooperatively minimize a sum of local convex cost functions. The network is modeled by a sequence of time-varying random digraphs which ma... | ABC | CBA | BAC | BAC | Selection 1 |
**A**: MPC accounts for the real behavior of the machine and the axis drive dynamics can be excited to compensate for the contour error to a big extent, even without including friction effects in the model [4, 5]. High-precision trajectories or set points can be generated prior to the actual machining process following... | BCA | CAB | ABC | BCA | Selection 3 |
**A**: We show that B-CPP also achieves linear convergence for minimizing strongly convex and smooth objectives.
**B**: In the second part of this paper, we propose a broadcast-like CPP algorithm (B-CPP) that allows for asynchronous updates of the agents: at every iteration of the algorithm, only a subset of the agents... | CBA | CAB | CBA | ACB | Selection 2 |
**A**: We can further differentiate Bar(new) and Bar(cont), representing respectively the beginning of a new bar and a continuation of the current bar and always have one of them before a Sub-bar token. This way, the tokens would always occur in a group of four for MIDI scores.
For MIDI performances, six tokens would b... | CBA | CAB | ACB | CBA | Selection 2 |
**A**: 5, where the baseline is the result tested by feeding the speech sample sequence into the ASR module directly without considering communication problems**B**: The CER results of DeepSC-SR and two benchmarks under the AWGN channels and the Rayleigh channels are shown in Fig**C**: From the figure, DeepSC-SR obtain... | CAB | BAC | CBA | BCA | Selection 2 |
**A**: 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 images (10 %). All datasets have a 50/50 balance between positive (tumor present) and negative (tumor absent) samples**B**: The patches of 96 x 96 pixels images were automatically... | CAB | BCA | CAB | ACB | Selection 2 |
**A**: To this end, suppose we want to generate the étendue-expanded hologram of only a single scene.
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**B**: The ... | CAB | BCA | ACB | BAC | Selection 2 |
**A**: Here are some commonly used classic combinations of loss functions.
**B**: These combinations aim to balance the quality, details, and visual perception of the generated image**C**: Mixed Loss: In SISR, there are also some classic combinations of loss functions that are widely used to guide the network towards g... | ACB | BCA | ACB | CBA | Selection 4 |
**A**:
We have hence set out to explore explainability in anti-spoofing. Our goals are to better understand what artefacts are being captured or discarded by different solutions based upon different input features, different machine learning solutions or different components of ensemble systems [11, 12]**B**: We are a... | BAC | BAC | BAC | ABC | Selection 4 |
**A**: We discuss the algorithmic implementation of our framework to account for assumptions of our work in practice. For instance, our framework crucially relies on obtaining “unsafe” data which is hard to obtain in practice, and we propose a new algorithm to obtain unsafe datapoints as boundary points from the set of... | ACB | CAB | ABC | ACB | Selection 2 |
**A**: Tx and Rx have co-located dual-polarized antennas, such that two antenna ports are available for each antenna element at a distinct spatial location**B**: Compared to the case where the same number of antenna elements with
only a single polarization is available, this leads to an increase in diversity and capaci... | ABC | ACB | BCA | ACB | Selection 1 |
**A**: Another possibility is to try to perform a regularization by infimal regularization [8] for lower semicontinuous objective functionals. In [29], in a function space setting, Pock et al**B**: propose a high dimensional lifting of the Lagrangian formulation of (2) where the data-fit functional is non-convex. In th... | ABC | BCA | ABC | BAC | Selection 2 |
**A**:
Figure 1: The distribution of the mean radial error (MRE) when choosing a different image as a template in one-shot medical landmark detection task**B**: The x-axis refers to MRE and the y-axis refers to the percentage of MRE lying in the corresponding ranges**C**: Evidently, the choice of template affects the ... | CBA | CAB | CAB | ABC | Selection 4 |
**A**: A request from one of the participating teams to evaluate a second version of their container after the completion of the challenge was accommodated and the method is suffixed with the term post-challenge (pc) to distinguish it from other methods.
Table 4 gives an overview of the participating methods**B**: Thes... | CAB | BAC | ABC | CBA | Selection 2 |
**A**: Figure 1(a) presents the evolution of the smallest eigenvalue of the time-varying symmetric matrix M˙˙𝑀\dot{M}over˙ start_ARG italic_M end_ARG and it shows that the sufficient condition for continuous differentiability of τs(.)\tau_{s}(.)italic_τ start_POSTSUBSCRIPT italic_s end_POSTSUBSCRIPT ( **B**: ), given ... | CBA | BAC | BCA | BAC | Selection 3 |
**A**: If the controller gains are chosen such that the following inequalities are satisfied,**B**: Let us also consider the unsafe set for this system to be (12) and the metric measuring the distance from this unsafe set to be given by (13)**C**:
Consider the system (4) with boundary conditions (8) | CAB | BAC | CBA | BCA | Selection 3 |
**A**: In SS-Setting, a training sample is comprised of spectrum sensors’ received power readings. The location of entities is available by using a GPS dongle connected to the laptops as described below, and the sensor’s received power is computed as follows**B**: First, we compute an FFT on the I/Q samples collected w... | CAB | ACB | BAC | BCA | Selection 4 |
**A**: Similar algorithms have also been extended to cases where zeroth order [6, 30, 36, 26, 32] and second order [16] oracles are used instead of (sub)gradients. The works [6, 36] on bandit feedback consider the situation where there are time-varying inequality constraints. In such cases the algorithms proposed in [4... | ABC | CAB | BAC | BCA | Selection 4 |
**A**: The results demonstrate a significant improvement in the discriminative performance of the model compared to our previous experiments. Specifically, the substantial improvement in model performance when moving from 1,000 images to 99,000 images with large training epochs indicates that data quantity plays a crit... | BAC | CBA | ACB | ACB | Selection 2 |
**A**: However, the target of these datasets is the classification of a generic set of emotions instead of focusing on fear detection. This strategy makes it hard to obtain a robust model due to the lack of fear samples**B**: One of the main shortcomings of training this specific fear detection system for Bindi is the ... | BAC | BCA | BCA | ABC | Selection 1 |
**A**:
Table 1 presents the FID values for each GAN-based architecture**B**: StyleGAN2-ADA achieved the lowest FID value of 166166166166, while EBGAN was placed in last and its FID value of 380380380380 was the highest**C**: The smaller the FID value, the better is the quality of the generated image. Therefore, all fu... | BAC | ABC | BAC | CAB | Selection 2 |
**A**: If the simplicial partition-based implementation is considered, then one has also to account for the complexity of the resulting invariant set, which is typically high 6, 8, 49, 10, 2, 9. These methods can therefore require significant memory to store the vectors and/or matrices describing every simplicial parti... | CAB | BCA | BAC | CBA | Selection 1 |
**A**: However, as highlighted by [26], deploying DL models for privacy-preserving inference nowadays is predominantly achievable through Multi-Party Computation (MPC). This process necessitates multiple servers and incurs significant overhead, primarily attributed to the size of the DL model, especially when handling ... | BAC | CAB | ABC | ABC | Selection 2 |
**A**: In particular, the state transition of a low-rank MDP aligns with that in our low-rank POMDP model. Nevertheless, we remark that such states are observable in a low-rank MDP but are unobservable in POMDPs with the low-rank transition. Such unobservability makes solving a low-rank POMDP much more challenging than... | CAB | BCA | ACB | BAC | Selection 1 |
**A**: in 2s+12𝑠12s+12 italic_s + 1
variables xjsubscript𝑥𝑗x_{j}italic_x start_POSTSUBSCRIPT italic_j end_POSTSUBSCRIPT at a point η=0𝜂0\eta=0italic_η = 0 where xi=0subscript𝑥𝑖0x_{i}=0italic_x start_POSTSUBSCRIPT italic_i end_POSTSUBSCRIPT = 0, for 1≤i≤s1𝑖𝑠1\leq i\leq s1 ≤ italic_i ≤ italic_s**B**: Ex. 59 corr... | ABC | ACB | ACB | CBA | Selection 1 |
**A**: 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**B**: Based on which, Theorem 1 proves the almost sure convergence of the algorithm. Then, Theorem 2 gives intuitive conv... | BAC | CBA | ABC | CBA | Selection 3 |
**A**: Middle column: Reconstructions using SDGLR for regularization. Right column: Reconstructions using SDGGLR for regularization.**B**: Left column: Corrupted images**C**:
Figure 10: Image patch denoising and interpolation results (PSNR) using SDGLR versus SDGGLR | ACB | BAC | ACB | CBA | Selection 4 |
**A**: For both phases, we used Adam optimizer [20] and weight initialization as proposed by [21]. Mean squared error (MSE) loss was used for both phases.
**B**: For the second phase, we train the Param-Net on MRiLab dataset. The weights of the auto-encoder are frozen during this phase**C**: The training process compri... | CBA | ABC | BCA | CAB | Selection 1 |
**A**: On the receiver side, since the receiving light intensity of SiPM needed to be accurately measured, an integrating sphere Thorlabs IS200-4 was used to generate equal photon flux among the LED, MicroFC-10010 SiPM, and optical power meter. As the LED light enters the integrating sphere, it experiences numerous dif... | CBA | CBA | ABC | ACB | Selection 4 |
**A**: Section 5.2 shows results considering the close-approach, which are further analyzed in Section 5.3 in Monte Carlo simulations. Finally, Section 5.4 considers a different shape onboard to assess how the proposed autonomous GN&C behaves.**B**: In Section 5.1 we make some comments on the far-approach phase of an a... | BCA | BAC | BCA | CBA | Selection 4 |
**A**: Given the increasing attention being paid to the integration of UAVs into 5G networks [18, 19], for the sake of completeness of this tutorial, we next discuss briefly the modeling of UAV wireless channels, which is currently an active research topic [95, 96].
Before presenting the UAV channel models, let us disc... | BAC | ACB | CBA | ACB | Selection 3 |
**A**: Assumption 12 requires boundedness on the sum of two storage functions in terms of parts (but not all) of their arguments**B**: An alternative assumption where the lower and upper bounds are functions of ‖z‖norm𝑧\|z\|∥ italic_z ∥ will be considered later.
**C**: This resembles boundedness on a time-varying Lyap... | CAB | BAC | CBA | ACB | Selection 4 |
**A**: The RCBF has a form that is easy to imagine as a barrier, while the ZCBF is defined outside the safe set, allowing the design of control laws with robustness.**B**:
In the context of a CBF, the control objective is to make a specific subset, which is said to be a safe set, on the state space invariance forward ... | ABC | BAC | CAB | BAC | Selection 3 |
**A**: Further, we will derive the closed-form relationship between the gSCR and the capacity ratio between the GFM and the GFL converters to simplify the analysis of how large the capacity should be to meet certain stability margins.
**B**: On this basis, we will show in the next section that the integration of GFM co... | BCA | CBA | BCA | BAC | Selection 2 |
**A**:
The first and simplest way is to enlarge the model capacity by training the network with larger datasets and better strategies**B**: Specifically, based on ImageNet [10], IPT [4] and HAT [6] conducted a sophisticated pre-training to excavate the capability of transformers in image processing**C**: LSDIR [29] in... | ACB | CAB | ACB | ABC | Selection 4 |
**A**:
Figure 5: Trajectories for a confidence-parameterized FRT**B**: This causes the human’s FRT to update and expand, which leads the robot to deviate from its nominal trajectory (gray) to an adjusted trajectory (orange) to avoid collision with the human or the obstacle.**C**: The human moves towards its target (sh... | ACB | BCA | BAC | BAC | Selection 1 |
**A**: In addition, any attempt to intercept the transmission, by placing for instance an object in the first Fresnel zone, will lead to a channel distortion/disruption or a detectable change of the spatial channel experienced by the receiver. Such propagation anomaly can be detected during the channel estimation proce... | BCA | CAB | BCA | ABC | Selection 2 |
**A**: Specifically, the parallel visual and haptic feedback transmissions should be aligned with each other when arriving at the manipulator, and consecutive C&C and feedback transmissions should be within the motion-to-photon delay constraint, which is defined as the delay between the movement of the user’s head and ... | BAC | CAB | CAB | ACB | Selection 1 |
**A**:
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 o... | CBA | ABC | BAC | ACB | Selection 2 |
**A**: We replaced the original second dense layer in CNN-MLC with B𝐵Bitalic_B parallel BiLSTM layers, which results in the CNN-Mask backbone network. The B𝐵Bitalic_B BiLSTM layers take the sigmoid function as the activations**B**: They are designed to learn B𝐵Bitalic_B ratio masks. See the following for the details... | BCA | CAB | ABC | BAC | Selection 1 |
**A**: 10(c)**B**: For our second example, we observed that if the CNN were fed with images containing obstacles with a small apparent height (such as glass gates), it would often ignore the obstacle as if it were a traversable area.
A simulation at one of these failure states exposed a glass door, as shown in fig**C**... | CBA | ACB | BAC | CBA | Selection 3 |
**A**: This makes it non-trivial to implement exact batched inference for multi-blank RNN-Ts, since different utterances in the same batch might output blanks with different durations, making it hard to fully parallelize the computation.**B**: The multi-blank RNN-T method is ideal for on-device speech recognition in th... | ACB | ABC | CAB | ACB | Selection 3 |
**A**: Enhancing the real speech involving a scene, such as “Airport”. 2**B**: Adding another scene to the enhanced speech, such as “Street”. The signal noise ratio (SNR) of the real utterance is denoted by SNRreal. The SNR of the fake utterance is referred to as SNRfake. The SNRreal and SNRfake are both 5dB in the ex... | BCA | ACB | BAC | CAB | Selection 1 |
**A**: 4 that the non-zero initial conditions, in general, don’t decay to zero in finite time. Next, we discuss the performance comparison of TV-OKID and the Information-state approach**B**: First, we show the importance of having a system identification technique that is immune to non-zero initial conditions. We show ... | ABC | BAC | CBA | CBA | Selection 2 |
**A**:
Figure 6: Average inference latency per frame under different communication rates in the multi-camera pedestrian occupancy prediction task**B**: The overall latency comprises on-device computation time, transmission latency, and server-side computation time**C**: All the methods achieve a MODA of around 87%. | ABC | CAB | CBA | BCA | Selection 1 |
**A**: 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... | BAC | ACB | CBA | BAC | Selection 2 |
**A**: (2021) presented a nonlinear optimization model where 1111d Euler equations were used to derive a model of the water flow in a DHG.
On top of that, Machado et al**B**: (2022) built and described a modeling approach of DHGs, whose passivity properties were shown.**C**: To model DHGs in great physical detail, Krug... | ACB | BAC | BCA | ABC | Selection 3 |
**A**: This is because by synergistically deploying charging and battery swapping stations, the platform can effectively reduce the charging cost and thus address this bottleneck. As shown in Figure 3b, compared to charging-only deployment, joint planning yields a charging cost reduction of up to 44.4% under a sufficie... | CAB | CAB | CBA | BAC | Selection 3 |
**A**: It is noteworthy that DEviS exhibits superior robustness in uncertainty estimation compared to the conference version of the TBraTS method. Additionally, as depicted in Fig. 5 (c), the segmentation results of different methods show that under Gaussian noise, introducing the DEviS model can slightly improve the s... | BCA | CAB | CBA | ACB | Selection 3 |
**A**:
Due to the fact that the onset feature represents the beginning of a cardiac activity, IBIs sequence from the onset feature is selected as the baseline for segmentation**B**: Firstly, the IBIs sequence of the onset feature is divided into q segments where each segment contains three consecutive IBIs**C**: The t... | BCA | BAC | BAC | ABC | Selection 4 |
**A**: Each RRDB contains multiple densely connected residual blocks, which enables features to be fully reused and propagated efficiently within each RRDB to extract richer features and utilize correlations between channels. Compared to standard residual blocks, RRDB achieve superior performance with significantly few... | CAB | ABC | CBA | ABC | Selection 3 |
**A**: In this paper, we consider DoA estimation in a reverberant enclosure consisting of desired sources along with interference sources**B**: Consequently, their identification as desired or interference is unknown as well. The power of the different sources is also unknown, and the interference sources could, in fac... | CAB | ACB | BAC | ABC | Selection 2 |
**A**:
Researchers have been applying machine learning and deep learning techniques on human respiration data collected through various technologies for anomaly detection**B**: Some of the common categories of features used in the literature were statistical features from the data (mean, standard deviation, skewness, ... | ABC | BAC | ACB | BAC | Selection 3 |
**A**: Moreover, O-RAN operation is divided into three different control loops [7]: the real-time (RT), near-RT, and non-RT loops executing at different time-scales**B**: O-RAN adopts the functional split defined in 3GPP [6] and defines three distinct units [7]: the open central unit (O-CU), open distributed unit (O-DU... | BAC | BCA | ACB | CAB | Selection 1 |
**A**:
In the reminder of this study, we focus on the partial dual problem (14)**B**: These problems are known to be very challenging, even if convex [29, Chapter 2]. In contrast, as we will see later in the manuscript, the use of the partial dual problem leads to a tractable inner minimization problem. This is one of... | BAC | BCA | ACB | BCA | Selection 3 |
**A**: The suboptimality analysis of LQR, where a modeling error is present, has been considered in [18, 19, 20]**B**: As we consider the LQ setting, the performance analysis of LQ regulator (LQR) for unknown systems is also relevant, which has received renewed attention from the perspective of learning-based control [... | BCA | CBA | ACB | BAC | Selection 4 |
**A**: Although they are commonly employed for data assimilation in numerical weather prediction, they require large computational resources since they involve repeated solutions of the high-dimensional dynamics (1). Thus, they are not applicable in the context of embedded control systems, whose limited resources call ... | CAB | BCA | CBA | ACB | Selection 3 |
**A**: This kind of integration aids in capturing both local and global features and reducing the neglect of potentially diseased areas. Specifically, varying medical datasets may contain lesions of varying sizes. By capturing and understanding features across a broader range of scales, the hybrid model improves its ab... | BCA | CAB | CBA | BCA | Selection 2 |
**A**: The left and right imagers embed depth information to aid the generation of depth maps (with the help of a filter), and using these images in stereo-based localization algorithms might give lower accuracy compared to non-filtered images**B**: While using these data, the effect of the rolling shutter camera can b... | CAB | CBA | CBA | BAC | Selection 4 |
**A**: In this step, we are able to measure slowly and precisely, meaning repeated measurement is possible to increase the dynamic range of the diffraction pattern and reduce noise. So we can have a precise, high quality reference known before the reconstruction of the sample.**B**:
The first step is referenced patter... | CAB | ABC | CBA | BCA | Selection 1 |
**A**: While this does not affect the cost of the transfer itself, the additional round trip requires additional propellant and payload to be brought and stored at the depot**B**:
The demand D𝐷Ditalic_D reflects the number of round trips to be conducted between a depot and its clients**C**: As such, as D𝐷Ditalic_D i... | ABC | CAB | BCA | BAC | Selection 4 |
**A**: It supports easy benchmarking of different speech representation models. In this paper, we chose to experiment with two pre-trained models, wav2vec [28] and HuBERT [11] to see how different augmentation techniques impact the performance of both PR and ASR tasks.**B**:
Phoneme Recognition (PR) and Automatic Spee... | BCA | CBA | CAB | BAC | Selection 3 |
**A**: In S-Band, we already have existing terrestrial communication from 4G LTE devices. With the advent of mm-wave technology, terrestrial communication is also using the Ka-band in 5G**B**: So the satellite users will suffer from co-channel interference with the terrestrial users in both bands. To avoid this interfe... | BAC | BCA | CBA | CAB | Selection 2 |
**A**: To some degree, they represent distinct oscillation modes within the same hybrid system, triggered solely by initial conditions like forward speed and the torso’s height**B**: This work not only provides crucial insights into the rationale behind utilizing multiple gaits at varying speeds but also holds the prom... | ABC | BAC | BCA | ACB | Selection 3 |
**A**: The pure Python implementation implements the transform using matrix multiplication. Therefore, the speed of DCT-ResNets and BWT-ResNets is the same as in this implementation.
**B**: Other layers are built in PyTorch so they are always accelerated by CUDA**C**: Note: “CUDA” and “Pure Python” represent whether CU... | BCA | BAC | ABC | CBA | Selection 4 |
**A**: During training, no additional bias corrections were applied to the network output, thus we expected GT values close to zero to be mapped to predicted up-to-scale depths close to zero (i.e. zero offset) (see Figure 4A).**B**: For each trained model the relationship between the GT and the predicted up-to-scale de... | ABC | ABC | CBA | BAC | Selection 3 |
**A**: Crude Control**B**: A naïve system that forcefully modifies predicted values individually, this straw-man represents a controllable model that is entirely inconsistent**C**: Its default prosody is generated with NoControl, and then modified manually with the control points111Samples demonstrating TTS for these s... | ABC | CBA | BAC | ACB | Selection 1 |
**A**: Therefore, extended comparison of the ideal baseline scenario with several association policies is provided, and evaluation of diverse dominant interferer-based scenarios are highlighted.
**B**: In summary, even though angular coordinates have appeared in the stochastic geometry-based analysis, their manifestati... | CAB | BAC | ACB | BCA | Selection 1 |
**A**: The proposed filters exploit the cubature and quadrature rules to approximate the recursive Bayesian integrals. The I-CKF’s stability conditions are easily achieved for a stable forward CKF. The developed inverse filters are also consistent, provided that the initial estimate pair is consistent**B**: Numerical e... | CAB | ABC | BCA | ACB | Selection 3 |
**A**: We, therefore, turn to the dual problem and derive the SDP using the theories of positive hyperoctant trigonometric polynomials (PhTP) [28]. In the non-blind case, this approach has been previously employed for high-dimensional super-resolution (SR) [17] and bivariate radar parameter estimation [29]. We demonstr... | CBA | ABC | ABC | ABC | Selection 1 |
**A**:
Since we are comparing upper bounds, strictly speaking we cannot conclude that the actual Lipschitz constant of the SR-LASSO is larger than that of the LASSO**B**: However, the fact that these two upper bounds arise from the application of analogous proof techniques suggests this to be a reasonable conjecture**... | ABC | CAB | BCA | BCA | Selection 1 |
**A**: Some studies upsample each 2D LR medical slice to acquire the corresponding HR one, such as [8, 43, 47]**B**: [5] and Wang et al. [36] use 3D DenseNet-based networks to generate HR volumetric patches from LR ones.
Yu et al. [45] build a transformer-based MISR network to address volumetric MISR challenges.**C**: ... | ACB | BCA | BCA | CBA | Selection 1 |
**A**: For SZO-QQ, in case an infeasible iterate appears, which is a result of underestimated Lipschitz and smoothness constants, we can easily recover feasibility by using the last feasible iterate. Then, we need to enlarge the constants according to Remark 3.**B**:
We also notice that SZO-QQ and LB-SGD satisfy sampl... | BAC | BCA | CBA | CAB | Selection 4 |
**A**: For the same power constraint, the computational complexities of the RSD or RCG method are nearly the same and are lower than those of the RTR and comparable methods.
**B**: With the Riemannian ingredients derived in Section III, we propose three precoder design methods using the RSD, RCG and RTR in this section... | CBA | BCA | CAB | BAC | Selection 3 |
**A**: A SinGAN generative model is then used to realistically blend the contrast-altered tumor to the original image. The cascaded SinGAN generators are trained on the central slice and applied to every slice containing the tumor. **B**: For each tumor slice, tumor intensity values are linearly scaled by a factor λ𝜆\... | CBA | ABC | BCA | BAC | Selection 1 |
**A**: The implementation of the method is described in Section 3.2 and the results are presented and discussed in Section 4, including a comparison with other localization methods from the literature**B**: Finally, conclusions are drawn in Section 5.**C**:
The theoretical basis of the method is derived in Section 2 w... | ACB | CAB | BCA | ACB | Selection 3 |
**A**: By exploiting existing results on rewriting signal temporal logic specifications to MILP constraints, we can efficiently solve an optimization problem to automatically synthesize a control policy that ensures**B**:
In this paper, we have developed a direct data-driven controller synthesis method for linear time... | CBA | CAB | BCA | BAC | Selection 2 |
**A**: Specifically,
the MQA framework naturally allows users to examine the response changes to different combinations of imaging and clinical data, and thus, the informative clinical elements can be identified as those contributing to statistically high prediction accuracy.**B**: It is achieved with the MQA framework... | CAB | CBA | ACB | ACB | Selection 2 |
**A**: (6) and (4) indicates the importance of fixing codebook during finetuning to protect the pre-trained clean speech prior.
Comparison between Exp**B**: (6) and (5) demonstrates that the pre-trained prior knowledge in codebook is important to the clean speech restoration and downstream ASR.**C**: Furthermore, compa... | CBA | CAB | BCA | BAC | Selection 3 |
**A**: In order to decompose the strong coupling, we present the following representation of an arbitrary point located on the TX (RX) surface via using the center coordinates.
Since the TX (RX) surface is divided into discrete antenna elements, an arbitrary point located on the TX (RX) surface belongs to a certain ant... | CBA | BCA | ABC | ABC | Selection 1 |
**A**: The second consists of a synchronous machine described by a third order model, to which a governor and automatic voltage regulator (AVR) representations are added. The grid side is represented by an infinite bus [10, 13].
**B**: The first one is the well known synchronisation loop of classic grid-following conve... | ACB | BAC | BCA | CBA | Selection 4 |
**A**: ResNet. ResNet was introduced in 2015 by Microsoft Research**B**: The model is designed to handle the vanishing-gradient problem in deep networks, which can lead to underfitting during training. The pre-trained ResNet model is used as a meaningful extractor of residuals, instead of features, from the raw data us... | CBA | ABC | CAB | ACB | Selection 2 |
**A**: The proposed hybrid controller is run in Matlab R2020a on another computer running Windows 10, equipped with an Intel(R) i5-5200U CPU with a clock speed of 2.20 GHz and 12 GB RAM, referred to as Computer 2.**B**:
The next simulation is performed using the Turtlebot3 Burger model in Gazebo**C**: The simulation r... | ACB | ACB | CBA | CAB | Selection 4 |
**A**: The training settings followed the default configurations of 2D nnU-Net. Each model was trained on one A100 GPU with 1000 epochs and the last checkpoint was used as the final model.
The DeepLabV3+ specialist models used ResNet50 [38] as the encoder. Similar to [3], the input images were resized to 224×224×32242... | CBA | BAC | CAB | CBA | Selection 2 |
**A**: An illustration of the proposed model is shown in Fig. 1. In this study, the gNBs and IAB-nodes formed two different tiers. Multi-hop backhaul IAB networks will not be considered because of the challenge of network configuration, and the feasibility of using stochastic geometry in this scenario [16].
**B**: The ... | CBA | ACB | CAB | ACB | Selection 3 |
**A**: Therefore, applying this result to the PMB density
of the form (13) yields**B**: This type of single-target space was obtained when we introduced auxiliary variables to the PMB in (12), resulting in (13)**C**: A set-density defined in a single-target space that is the disjoint union of different sub-spaces can b... | CBA | BCA | CAB | ABC | Selection 1 |
**A**: For semantic communications, the accuracy can be measured by the task performance and easily quantified by semantic similarity for text transmission, character-error-rate for speech recognition, answer accuracy for VQA task, and so forth. However, the efficiency of semantic communications is usually hard to meas... | BCA | CBA | CAB | CBA | Selection 3 |
**A**: Furthermore, a stability-preserving, adaptive rational Krylov (SPARK) algorithm [33] was developed to maintain model stability and is usually embedded in the CURE scheme to generate a family of stable ROMs whose orders are increased by sequential accumulation in a single MOR process. This embedded CURE scheme wi... | BAC | CAB | CBA | ACB | Selection 3 |
**A**: Calibration-aware training improves the match between accuracy and confidence, resulting in a lower ECE, for both frequentist and Bayesian learning, with CA-BNN achieving the lowest ECE.
**B**: Bayesian learning is observed to yield better calibrated decisions than frequentist learning, as also manifest in the l... | BCA | ACB | CAB | CBA | Selection 4 |
**A**: In this section, we perform a case study on decentralized sparse soft-max regression to verify the theoretical results and show the convergence performance of Prox-DBRO-SAGA and Prox-DBRO-LSVRG, where three kinds of Byzantine attacks (zero-sum attacks, Gaussian attacks, and same-value attacks) are considered**B*... | ABC | BAC | ACB | CBA | Selection 3 |
**A**: The comparison between the proposed MaxViT-UNet and previous methodologies is presented in Table 3 on the MoNuSeg18 dataset and Table 4 on the MoNuSAC20 dataset**B**: For the MoNuSeg18 dataset, we performed binary semantic segmentation. Whereas the MoNuSAC20 challenge contains four types of nuclei, we performed ... | ABC | ACB | BCA | CBA | Selection 2 |
**A**: However, securing labels for new tasks is not always feasible. Furthermore, the success of these methods heavily depends on the number of supportive examples provided**B**:
Another way towards the universal medical image segmentation is few/one-shot learning [8, 51, 42, 41]. In this setting, a pre-trained found... | ABC | BAC | ACB | ABC | Selection 2 |
**A**: The solution can account for the aggregate volume across all current Web3 Node RPC providers and comes equipped with an inbuilt difficulty modulation to dynamically adapt to the growing traffic in the Web3 industry.
**B**: This paper introduces Relay Mining, a novel algorithm designed to scale efficiently to han... | ACB | ACB | BCA | CAB | Selection 4 |
**A**: We also performed AB preference tests by 15 listeners resulting in a total of 100 utterances to verify the generalization to different datasets conveniently. All the tests listed below are done in speaker F1, except the AB preference tests are done for both F1 and F2.
**B**: We calculate the average RMSE of the ... | CAB | CBA | CAB | BCA | Selection 2 |
**A**: There are two main criteria for optimizing speech pre-training: contrastive loss Oord et al. (2018); Chung and Glass (2020); Baevski et al. (2020) and masked prediction loss Devlin et al. (2018)**B**: (2021). Some recent work Chung et al. (2021) has combined the two approaches, achieving good performance for dow... | ABC | BAC | ACB | BCA | Selection 3 |
**A**: where δ=ℜ(𝐙SS(1,1))/M𝛿ℜsubscript𝐙SS11𝑀\delta=\mathfrak{R}(\mathbf{Z}_{\text{SS}}(1,1))/Mitalic_δ = fraktur_R ( bold_Z start_POSTSUBSCRIPT SS end_POSTSUBSCRIPT ( 1 , 1 ) ) / italic_M and 𝜽𝜽\boldsymbol{\theta}bold_italic_θ
is calculated according to [13, Eq**B**: (24)]**C**: First, | BCA | ACB | ABC | CBA | Selection 3 |
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