shuffled_text stringlengths 267 3.39k | A stringclasses 6
values | B stringclasses 6
values | C stringclasses 6
values | D stringclasses 6
values | label stringclasses 4
values |
|---|---|---|---|---|---|
**A**: This is due to the fact that SlotUsagePattern was handed a well-designed SLP**B**: When faced with an SLP not designed to be memory efficient, one might not expect such drastic improvements.
**C**: We note that after applying the function SlotUsagePattern, the resulting SLP only required 12121212 memory slots an... | CBA | ACB | BCA | CAB | Selection 3 |
**A**: The exact definition of some basis functions requires solving global problems, but, based on decaying properties, only local computations are required, although these are not restricted to a single element**B**: It is interesting to notice that, although the formulation is based on hybridization, the final numer... | ABC | CAB | CBA | BCA | Selection 4 |
**A**: Alg-CM uses an involved subroutine (far more complicated than ours given in Algorithm 1) to update the coordinates in each iteration, which accumulates the inaccuracy of coordinates. Even worse, this subroutine computes three angles and selects the smallest to decide how to proceed each time, and due to float is... | ABC | CBA | CAB | BAC | Selection 2 |
**A**: (non-rumor) news classification.
**B**: These feature vectors are used to train the classifier for rumor vs**C**: In the lower part of the pipeline, we extract features from tweets and combine them with the creditscore to construct the feature vector in a time series structure called Dynamic Series Time Model | CAB | ABC | ACB | CBA | Selection 4 |
**A**: We might improve the validation and test errors even when when the decrease in the training loss is tiny and even when the validation loss itself increases.
**B**: Instead, we should look at the 00–1111 error on the validation dataset**C**: We should not rely on plateauing of the training loss or on the loss (lo... | CBA | CAB | BCA | BCA | Selection 1 |
**A**: As we can see except for the first one, the fitting results of other three are not good enough.
**B**: We show the performance of fitting these two model with only the first 10 hours tweets’ volume in Figure 4**C**: But if we fit the models of the first few hours with limited data, the result of learning paramet... | ABC | CAB | ACB | CBA | Selection 4 |
**A**: We further investigate the identification of event time, that is learned on top of the event-type classification**B**: For the gold labels, we gather from the studied times with regards to the event times that is previously mentioned**C**: We compare the result of the cascaded model with non-cascaded logistic re... | BCA | ABC | ACB | CAB | Selection 2 |
**A**: The only difference happens to patient 10 and 12 whose intakes are earlier at day.
Further, patient 12 takse approx**B**: 3 times the average insulin dose of others in the morning.**C**: The insulin intakes tend to be more in the evening, when basal insulin is used by most of the patients | ABC | BAC | BCA | BAC | Selection 3 |
**A**: Image-level context was represented as the output after global average pooling (i.e**B**:
In this work, we laid out three convolutional layers with kernel sizes of 3×3333\times 33 × 3 and dilation rates of 4, 8, and 12 in parallel, together with a 1×1111\times 11 × 1 convolutional layer that could not learn new... | CAB | ABC | ACB | BAC | Selection 4 |
**A**: Firstly, it is known that, assuming the Small Set Expansion Conjecture (denoted SSE; see [44]), there exists no constant-ratio approximation for MinCutwidth (see [52])**B**: Consequently, approximating MinLoc within any constant factor is also SSE-hard. In particular, we point out that stronger inapproximability... | ABC | CBA | BCA | ABC | Selection 3 |
**A**: Lower asymptotic performance is probably due to worse exploration**B**: Finally, we verified if a model obtained with SimPLe using 100100100100K is a useful initialization for model-free PPO training.
Based on the results depicted in Figure 5 (b) we can positively answer this conjecture**C**: A policy pre-traine... | BCA | BCA | BAC | ACB | Selection 3 |
**A**: This design facilitates the decision-making process when transitioning between the robot’s rolling and walking locomotion modes. Through energy consumption analyses during step negotiations of varied heights, we establish energy criterion thresholds that guide the robot’s transition from rolling to walking mode.... | CBA | CAB | ABC | ACB | Selection 2 |
**A**: The advice is always assumed to be some error-free information that may be used to encode some property often explicitly connected to the optimal solution**B**: Notwithstanding such interesting attributes, the known advice model has certain drawbacks**C**: In many settings, one can argue that such information ca... | BCA | CAB | BAC | BCA | Selection 3 |
**A**: In Section 4 the proposed framework is compared to state-of-the-art methods used in a recent early depression detection task**B**: Section 5 goes into details of the main contributions of our approach by analyzing quantitative and qualitative aspects of the proposed framework**C**: Finally, Section 6 summarizes ... | ACB | BCA | ABC | BAC | Selection 3 |
**A**: But in practice, even when the constraint is relaxed, e.g., β=0.9𝛽0.9\beta=0.9italic_β = 0.9,
GMC still converges well**B**: Note that we impose a constraint on the momentum coefficient β𝛽\betaitalic_β during the theoretical proof**C**: More details about the convergence performance of GMC are provided in Sect... | ABC | ABC | BAC | ACB | Selection 3 |
**A**:
, where ∗*∗ is the convolution333We use convolution instead of cross-correlation only as a matter of compatibility with previous literature and computational frameworks**B**: Using cross-correlation would produce the same results and would not require flipping the kernels during visualization**C**: operation. | ACB | BCA | ABC | BAC | Selection 3 |
**A**:
Fig. 12 shows how the number of UAVs affect the computation complexity of SPBLLA. Since the total number of UAVs is diverse, the goal functions are different**B**: The goal functions’ value in the optimum states increase with the growth in UAVs’ number. Since goal functions are the summation function of utility... | ABC | CBA | ACB | CAB | Selection 1 |
**A**: italic_g **B**: , (a¯/b¯)i=ai/bisubscript¯𝑎¯𝑏𝑖subscript𝑎𝑖subscript𝑏𝑖(\overline{a}\,/\,\overline{b})_{i}=a_{i}/b_{i}( over¯ start_ARG italic_a end_ARG / over¯ start_ARG italic_b end_ARG ) start_POSTSUBSCRIPT italic_i end_POSTSUBSCRIPT = italic_a start_POSTSUBSCRIPT italic_i end_POSTSUBSCRIPT / italic_b sta... | BCA | BAC | CBA | ABC | Selection 1 |
**A**:
A fully connected neural network architecture was used**B**: ADAM optimizer for the minimization[25].**C**: It was composed of two hidden layers of 128 neurons and two Dropout layers between the input layer and the first hidden layer and between the two hidden layers | BCA | ABC | BCA | ACB | Selection 4 |
**A**: (2015) discussed weighting the cross-entropy loss (WCE) for each class in order to counteract a class imbalance present in the dataset. WCE was defined as:**B**: This can be problematic if the various classes have unbalanced representation in the image, as the most prevalent class can dominate training. Long et ... | ABC | CBA | BCA | CAB | Selection 2 |
**A**: This accelerates the process and enables parallelization via GPUs.**B**: (1) We enable the generation of neural networks with very few training examples.
(2) The resulting network can be used as a warm start, is fully differentiable, and allows further end-to-end fine-tuning**C**: (3) The generated network can b... | CBA | BAC | CBA | CAB | Selection 4 |
**A**: Such a notion of robustness partially justifies the empirical advantages of KL-regularized policy optimization (Neu et al., 2017; Geist et al., 2019). To the best of our knowledge, OPPO is the first provably sample-efficient policy optimization algorithm that incorporates exploration.
**B**: In comparison, exist... | BCA | CBA | ACB | ABC | Selection 2 |
**A**: Wu et al**B**: (2018a) performed mixed-precision quantization using similar NAS concepts to those used by Liu et al**C**: (2019a) and Cai et al. (2019).
They introduce gates for every layer that determine the number of bits used for quantization, and they perform continuous stochastic optimization of probability... | BAC | CAB | CAB | ABC | Selection 4 |
**A**: One main contribution of this paper is establishing a precise relationship (i.e**B**: These neighborhoods, being also metric (and thus topological) spaces, permit giving a short proof of the Künneth formula for Vietoris-Rips persistent homology.
**C**: a filtered homotopy equivalence) between the Vietoris-Rips s... | ABC | ACB | BAC | CAB | Selection 2 |
**A**: In t-viSNE, we focus on bringing forward hidden information about the DR algorithm that is usually lost, with all the interactions occurring in a single main scatterplot view (and some additional auxiliary views)**B**: SIRIUS [49] focuses on the concurrent exploration of similarity relationships between instance... | BCA | ABC | BAC | CAB | Selection 3 |
**A**: This fact was expected for the lack of good methodological practices when comparing nature- and bio-inspired algorithms, which was pointed out previously in our analysis**B**: This issue has not encouraged participants in competitions to embrace them as reference algorithms to design better solvers. The rising t... | BAC | ACB | BAC | BCA | Selection 4 |
**A**: The adaptive learning will induce the model to exploit the high-level information. In particular, AdaGAE is stable on all datasets.
**B**: Classical clustering models work poorly on large scale datasets. Instead, DEC and SpectralNet work better on the large scale datasets**C**: Although GAE-based models (GAE, MG... | CAB | BCA | BAC | ACB | Selection 1 |
**A**: Furthermore, we find that when a Web server is not available (“N/A”), both Email and DNS servers cannot be tested, either. This also results in much higher N/A outcomes for tests against Email and DNS servers as opposed to Web servers.
**B**: In general, tests against Web servers have a higher applicability rate... | BAC | CAB | ACB | ACB | Selection 2 |
**A**: The system expects and automatically adapts to sensor drift, and is thus able to maintain its accuracy for a long time**B**: An alternative approach is to emulate adaptation in natural sensor systems**C**: In this manner, the lifetime of sensor systems can be extended without recalibration.
| BAC | CAB | ACB | ABC | Selection 1 |
**A**: In fact, the construction to generate these semigroups is quite simple [4, Proposition 4.1] (compare also to 3). The same construction can also be used to generate free monoids as automaton semigroups or monoids. Here, the main difference is that the free monoid in one generator can indeed be generated by an aut... | ACB | BAC | BCA | CAB | Selection 4 |
**A**:
Based on these observations, we hypothesize that controlled degradation on the train set allows models to forget the training priors to improve test accuracy**B**: To test this hypothesis, we introduce a simple regularization scheme that zeros out the ground truth answers, thereby always penalizing the model, w... | CBA | ACB | CAB | ABC | Selection 4 |
**A**: While Wilson et al**B**: For each topic, we identified a corresponding entry from the OPP-115 annotation scheme (Wilson et al., 2016), which was created by legal experts to label the contents of privacy policies**C**: (2016) followed a bottom-up approach and identified different categories from analysis of data ... | ABC | ABC | ABC | BAC | Selection 4 |
**A**: In storytelling, for example, the annotation is considered as a key element [58].**B**: According to the insights gained from the exploration process, users are able to formulate new hypotheses that can be efficiently tested with the help of interactive visualization.
This goal is valuable especially for ensembl... | CAB | ABC | BCA | ABC | Selection 1 |
**A**: Model-Agnostic Meta-Learning (MAML) [Finn et al., 2017] is one of the most popular meta-learning methods**B**: It is trained on plenty of tasks (i.e**C**: small data sets) to get a parameter initialization which is easy to adapt to target tasks with a few samples. As a model-agnostic framework, MAML is successfu... | ACB | CBA | ABC | BCA | Selection 3 |
**A**: The position and attitude compose the UAV’s motion state information (MSI). In this section, the MSI prediction based AOAs and AODs estimation scheme and the protocol for beam tracking are introduced in Section IV-A**B**: Then the TE estimation algorithm which exploits the MSI prediction error is proposed in Sec... | CBA | ABC | CBA | BCA | Selection 4 |
**A**: We**B**: Note that the 1111-color case with the completeness requirement is not very interesting, and also not useful for the general case: completeness states that every node on
the left must be connected, via the unique edge relation, to every node on the right – regardless of the matrix**C**: This will be boo... | ABC | ABC | CBA | BAC | Selection 3 |
**A**: On the other hand, when the value function approximator in TD is an overparameterized multi-layer neural network, which is required to be properly scaled, such a feature representation stabilizes at the initial one (Cai et al., 2019), making the explicit local linearization in nonlinear gradient TD unnecessary. ... | ACB | BCA | CBA | ABC | Selection 3 |
**A**: Thus, in the other experiments, we bind parameters for the computation of LSTM gates across stacked layers by default.**B**:
Table 5 shows that: 1) Sharing parameters for the computation (Equation 6) of the depth-wise LSTM hidden state significantly hampers performance, which is consistent with our conjecture**... | BAC | BCA | CAB | BAC | Selection 3 |
**A**: In particular, as
b∈W⊆V2(a′,y′)𝑏𝑊superscriptsubscript𝑉2superscript𝑎′superscript𝑦′b\in W\subseteq V_{2}^{(a^{\prime},y^{\prime})}italic_b ∈ italic_W ⊆ italic_V start_POSTSUBSCRIPT 2 end_POSTSUBSCRIPT start_POSTSUPERSCRIPT ( italic_a start_POSTSUPERSCRIPT ′ end_POSTSUPERSCRIPT , italic_y start_POSTSUPERSCRIPT... | BAC | BCA | ACB | CAB | Selection 2 |
**A**: Unlike the semantic information, the distortion information is redundant in images, showing the central symmetry and mirror symmetry to the principal point**B**: Consequently, the efficiency of rectification algorithms can be significantly improved when taking the ordinal distortion estimation as a learning targ... | ABC | BCA | ACB | BAC | Selection 2 |
**A**: The initial learning rate is selected from {0.001,0.01,0.1}0.0010.010.1\{0.001,0.01,0.1\}{ 0.001 , 0.01 , 0.1 } according to the performance on the validation set**B**: We do not adopt any learning rate decay or warm-up strategies.
The model is trained with 10 epochs.**C**: The momentum coefficient is set as 0.9... | ABC | ACB | BCA | CBA | Selection 3 |
**A**: The black-box model is motivated by data-driven applications where specific knowledge of the distribution is unknown but we have the ability to sample or simulate from the distribution. To our knowledge, radius minimization has not been previously considered in the two-stage stochastic paradigm**B**: Most prior ... | BCA | BAC | BAC | ACB | Selection 1 |
**A**: graph sequences as in [12]-[15],
and additive and multiplicative communication noises may co-exist in communication links ([21]).**B**: Both the weights of different edges in the network graphs at the same time instant and the network graphs at different time instants may be mutually dependent.) rather than i.i.... | BAC | CBA | BCA | BAC | Selection 2 |
**A**: For instance, a smaller ϵitalic-ϵ\epsilonitalic_ϵ for ϵitalic-ϵ\epsilonitalic_ϵ-differential privacy provides better protection but worse information utility.
**B**: However, differential privacy also faces the contradiction between privacy protection and data analysis [9]**C**: Differential privacy [6, 38], whi... | CAB | BAC | CBA | ABC | Selection 3 |
**A**: X101-64x4d Xie et al. (2017) is then used as large backbone and it boosts 6 mAP against ResNet50. DCN and More Points Train. We adopt more interpolated points during training, by increasing the number of sampled points from original 14 to 26 for coarse prediction head, and from 14 to 24 for fine-grained point he... | CBA | BAC | CBA | ABC | Selection 2 |
**A**:
In version 1 of this note, which can still be found on the ArXiv, we showed that the analogous version of the conjecture for complex functions on {−1,1}nsuperscript11𝑛\{-1,1\}^{n}{ - 1 , 1 } start_POSTSUPERSCRIPT italic_n end_POSTSUPERSCRIPT which have modulus 1111 fails**B**: More specifically, we proved**C**... | ACB | CAB | BAC | CAB | Selection 1 |
**A**:
Compared to OPT-WLSVI and MASTER, our proposed algorithms achieve comparable empirical performance. More specifically, MASTER outperforms our proposed algorithm which agrees with its dynamic regret upper bound**B**: However, the variance of MASTER is larger due to the random scheduling of multiple base algorith... | BCA | CAB | ABC | CBA | Selection 3 |
**A**: Trust is built on transparency and truthfulness, and the presence of fake news, which is deceptive and usually meant to serve hidden agendas, may erode trust. It is worthwhile to consider whether the trust in media items is due to people’s own encounters with fake news, or because of secondary factors**B**:
The... | ACB | BCA | BAC | CAB | Selection 3 |
**A**: They usually leverage existing GNN models, such as GCN and GAT [43, 44], to aggregate an entity’s neighbors**B**: GNN-based methods [13, 37, 38, 39, 40, 41, 42] introduce relation-specific composition operations to combine neighbors and their corresponding relations before performing neighborhood aggregation**C*... | CAB | BCA | BCA | BAC | Selection 4 |
**A**: To evaluate the adaptability, we further adopt the policies learned from the Level 1111 to other levels. More specifically, for each method, we first save the last policy when training in the Level 1111, and then fine-tune such a policy in the Levels 2222 and 3333**B**: We observe that VDM performs similarly to ... | ACB | ABC | ABC | BCA | Selection 1 |
**A**: We complement the established notion of unisolvent nodes by the dual notion of unisolvence**B**: That is: For given arbitrary nodes P𝑃Pitalic_P, determine the polynomial space ΠΠ\Piroman_Π such that
P𝑃Pitalic_P is unisolvent with respect to ΠΠ\Piroman_Π**C**: In doing so, we revisit earlier results by Carl de ... | ABC | BAC | BAC | BAC | Selection 1 |
**A**: In the Appendix we present such implementations. where we significantly constrain the capacity of the learned representation and heavily regularize the model to produce independent factors. As we explained above, such a model will likely learn a good disentangled representation, however, its reconstruction will ... | BCA | CBA | ABC | ACB | Selection 1 |
**A**:
The structural computer used an inverted signal pair to implement the reversal of a signal (NOT operation) as a structural transformation, i.e. a twist, and four pins were used for AND and OR operations as a series and parallel connection were required. However, one can think about whether the four pin designs ... | ACB | CBA | ABC | CAB | Selection 3 |
**A**: Given a finite subset of such permutations, we can compute a group generated by this set**B**: In this paper, we propose a representation of such a group using the concept of linear representation defined through the Koopman operator.**C**:
A finite field, by definition, is a finite set, and the set of all perm... | BAC | BCA | ACB | BAC | Selection 2 |
**A**: For this purpose, one would ideally like to use an algorithm that provides sparsity, but also algorithmic stability in the sense that given two very similar data sets, the set of selected views should vary little. However, sparse algorithms are generally not stable, and vice versa (Xu \BOthers., \APACyear2012).
... | CAB | ACB | BCA | BCA | Selection 2 |
**A**: It is noteworthy that FBED-CART-PS is the same algorithm proposed in [4].**B**: Among them, FBED-CART-PS and FBED-CART-Sum are considered good choices as they exhibit favorable performance in both ROC AUC and AP**C**:
In summary, the DepAD methods FBED-CART-RZPS, FBED-CART-PS, and FBED-CART-Sum generally demons... | BCA | CBA | ACB | CAB | Selection 2 |
**A**: [2021] recently proposed the idea of convex relaxation of the confidence set for the more straightforward logistic bandit setting. Our work can be viewed as an extension of their construction to the MNL setting.
**B**: [2021] Abeille et al**C**: Comparison with Abeille et al | CAB | BCA | CBA | ACB | Selection 3 |
**A**: Here, we refer to action instances that are shorter than 30 seconds as short actions**B**: On ActivityNet, there are 54.4% short actions, whereas on THUMOS, there are 99.7% short actions. We can see that our performance gains on short actions over other methods are even more evident.**C**:
Besides evaluating al... | CAB | BCA | ACB | CBA | Selection 2 |
**A**: Figure 4(c) suggests that M2+M3 are better for the Healthy class, while M1 is better for the Diseased class. M4 is somewhere in-between but very powerful overall. By keeping the balance in this ensemble, we achieve the highest recorded performance for our analysis (cf. horizontal bar chart in Figure 4(d))**B**: ... | BAC | ABC | BCA | BAC | Selection 3 |
**A**: Graph temporal logic (GTL) is introduced in [16] to impose high-level task specifications as a constraint to the Markov chain synthesis**B**: Markov chain synthesis is formulated as mixed-integer nonlinear programming (MINLP) feasibility problem and the problem is solved using a coordinate descent algorithm**C**... | ACB | CAB | ABC | CAB | Selection 3 |
**A**: However, a major disadvantage is that such an approach has a strong bias due to the choice of the reference.**B**:
In principle, any pairwise shape matching method can be used for matching a shape collection**C**: To do so, one can select one of the shapes as reference, and then solve a sequence of pairwise sha... | ABC | CAB | BAC | ABC | Selection 2 |
**A**: In Section 4 we present our recognition algorithm for path graphs, we prove its correctness, we report some implementation details and we compute its time complexity. Finally, in Section 5 we provide a similar analysis for directed path graphs.
**B**: In Section 2 we present the characterization of path graphs a... | CBA | CAB | CAB | ACB | Selection 1 |
**A**: They suggest that estimating the memberships becomes harder as the purity of mixed nodes decreases**B**: Mixed-SLIM and Mixed-SCORE perform similarly and both two approaches perform better than OCCAM and GeoNMF under the MMSB setting. Meanwhile, Mixed-SLIM significantly outperforms the other three methods under ... | BCA | BAC | BAC | ACB | Selection 1 |
**A**: (2017); Agarwal et al. (2018); Zhang et al. (2018); Tripuraneni et al**B**: (2018); Boumal et al. (2018); Bécigneul and Ganea (2018); Zhang and Sra (2018); Sato et al. (2019); Zhou et al. (2019); Weber and Sra (2019) and the references therein.
Also see recent reviews (Ferreira et al., 2020; Hosseini and Sra, 20... | ABC | ACB | BCA | CAB | Selection 3 |
**A**: The difference from the mixedl setting is that the arrival rate of vehicles during 1200-1800s increased from 0.33 vehicles/s to 4.0 vehicles/s. The data statistics are listed in Tab. II.**B**:
Mixedh**C**: The mixedh is a mixed high traffic flow with a total flow of 4770 in one hour, in order to simulate a heav... | ABC | ACB | ABC | CAB | Selection 4 |
**A**:
We first present and analyze an algorithm called ProfilePacking, that achieves optimal consistency, and is also efficient if the prediction error is relatively small**B**: This is a natural concept that, perhaps surprisingly, has not been exploited in the long history of competitive analysis of bin packing, and... | ACB | BCA | BAC | BCA | Selection 1 |
**A**: In this section, we describe the experimental results of the proposed method. First, we evaluate the generative capabilities of the model**B**: Second, we provide the reconstruction result with respect to reference approaches. Finally, we check the quality of generated meshes, comparing our results to baseline m... | BCA | BCA | ABC | CBA | Selection 3 |
**A**: Paper organization. This paper is organized as follows**B**: In Section 4, we present the lower complexity bounds for saddle point problems without individual variables. Finally in Section 5, we show how the proposed algorithm can be applied to the problem computing Wasserstein barycenters .
**C**: Section 2 pre... | ACB | ABC | BAC | BCA | Selection 1 |
**A**: In [5] a unified perspective of the problem is presented. The authors show that the MCB problem is different in nature for each class. For example in [10] a remarkable reduction is constructed to prove that the MCB problem is NP-hard for the strictly fundamental class, while in [11] a polynomial time algorithm i... | CAB | CBA | CBA | BCA | Selection 1 |
**A**: This technique, which we briefly outline here, was specifically designed for complete intersection patterns**B**:
The proof of Theorem 2.1 is quite involved and builds on the method of constrained chain maps developed in [18, 35] to study intersection patterns via homological minors [37]**C**: A major part of t... | ACB | CBA | BAC | BCA | Selection 3 |
**A**: Also, users could possibly want to refer to the history of the actions they performed to identify the key spots that might have corresponded to improvements in the outcome**B**: VA systems must thus be able to provide ways of monitoring the performance, as described in G5.**C**: T5: Evaluate the results of the f... | CAB | CAB | BAC | BCA | Selection 4 |
**A**: The BO progress is shown in Figure 5, right pannel, for the optimization with constraints on the jerk and on the tracking error**B**: For the initialization phase needed to train the GPs in the Bayesian optimization, we select 20 samples over the whole range of MPC parameters, using Latin hypercube design of exp... | BAC | ABC | ACB | ABC | Selection 1 |
**A**: SD and GDRO obtain the highest accuracies. As discussed previously, we observe trade-offs between blond and non-blond classes with the improvements in the rare blond class incurring degradations in the non-blond class.
**B**: CelebA**C**: We show accuracy for each group of CelebA in Table. A3 | BAC | ACB | CBA | CAB | Selection 4 |
**A**: Many regression functions have been used to regress gaze from appearance, e.g., local linear interpolation [21] and adaptive linear regression [19].
3) A large number of training samples to learn the regression function. They usually collect personal samples with a time-consuming personal calibration, and learn ... | ACB | BCA | BCA | CBA | Selection 4 |
**A**:
We have tested the face recognizer presented in luttrell2018deep that achieved a good recognition accuracy on two subsets of the FERET database phillips1998feret **B**: The reported results in Table 3 show that the proposed method outperformed the TL-based method on the RMFRD and SMFRD datasets.**C**: This tec... | ABC | CAB | ACB | BCA | Selection 3 |
**A**: Sized types are a type-oriented formulation of size-change termination [LJBA01] for rewrite systems [TG03, BR09]. Sized (co)inductive types [BFG+04, Bla04, Abe08, AP16] gave way to sized mixed inductive-coinductive types [Abe12, AP16]**B**: In parallel, linear size arithmetic for sized inductive types [CK01, Xi0... | CAB | ABC | CBA | BAC | Selection 2 |
**A**: FairCMS-II has an increase in user-side overhead compared to FairCMS-I, because in FairCMS-II, the user transmits and decrypts the media content while in FairCMS-I, the user transmits and decrypts the D-LUTs. Nevertheless, this user-side efficiency level is substantially the same as the current mainstream media ... | BCA | BAC | CAB | ACB | Selection 3 |
**A**: Note that although we did not present the statistics here, we also tested the influence of number of attention heads H𝐻Hitalic_H**B**: This is reasonable, as without the interaction between features, the neighborhood aggregation operation will only make the neighboring features similar.**C**: The performance of... | CBA | BCA | ACB | CBA | Selection 3 |
**A**: The oracles listed under the Requirements column are the additional oracles required, other than the first-order oracle (FOO) and the linear minimization oracle (LMO) which all algorithms use.**B**: Table 1:
Number of iterations needed to achieve an ε𝜀\varepsilonitalic_ε-optimal solution for Problem 1.1**C**: ... | CBA | CAB | BCA | BCA | Selection 2 |
**A**: Informal description: Extend-Active-Paths can be seen as performing a Depth First Search (DFS) along active paths**B**: The backtracking is not applied to the structures that are on hold, as those structures did not attempt extending their active paths in the corresponding streaming pass.
**C**: When an active p... | ACB | BAC | BCA | ABC | Selection 1 |
**A**: B-CPP further reduces the communicated data per iteration and is also provably linearly convergent over directed graphs for minimizing strongly convex and smooth objective functions**B**: Numerical experiments demonstrate the advantages of B-CPP in saving communication costs.**C**:
We consider an asynchronous b... | CAB | CAB | BAC | BCA | Selection 4 |
**A**: It contains 50,0005000050,00050 , 000 and 10,0001000010,00010 , 000 images in the training and validation sets, respectively, equally distributed over 10101010 classes. To emulate the distributed scenario, we partition the dataset into N𝑁Nitalic_N non-overlapping subsets in a heterogeneous manner. For each subs... | CBA | CBA | BAC | CAB | Selection 4 |
**A**: It consists of bargaining rounds between a smuggler, who is motivated to import contraband without getting caught, and a sheriff, who is motivated to find contraband or accept bribes**B**: Figure 2(c) shows that JPSRO is capable of finding the optimal value.
**C**: Sheriff (Farina et al., 2019b) is a two-player,... | ABC | BAC | CAB | BCA | Selection 4 |
**A**: Bayes stability captures the concept that the results returned by a mechanism and the queries selected by the adaptive adversary are such that the queries behave similarly on the true data distribution and on the posterior distribution induced by those results**B**: In order to complete the triangle inequality, ... | BAC | ACB | CBA | ACB | Selection 1 |
**A**: We use reduction steps inspired by the kernelization algorithms [12, 46] for Feedback Vertex Set to bound the size of 𝖺𝗇𝗍𝗅𝖾𝗋𝖺𝗇𝗍𝗅𝖾𝗋\mathsf{antler}sansserif_antler in the size of 𝗁𝖾𝖺𝖽𝗁𝖾𝖺𝖽\mathsf{head}sansserif_head, by analyzing an intermediate structure called feedback vertex cut**B**: After s... | BAC | ACB | BCA | CAB | Selection 3 |
**A**: Backward adjustment: In contrast with manually adjusting the foreground of composite image to create harmonized image, some other works [156, 22, 18] adopted an inverse approach, i.e., adjusting the foreground of real image to create synthetic composite image. Specifically, they treat a real image as harmonized ... | BAC | ABC | CBA | ACB | Selection 4 |
**A**: This limitation underscores the urgent need for a comprehensive and spatio-temporally aligned dataset in urban computing to facilitate more precise algorithms and insightful analyses. Such a dataset would enable researchers to study the complexities of urban data arising from multiple entities and their intercon... | ABC | BAC | ABC | CAB | Selection 4 |
**A**: Because this choice of nonconformity measure only considers differences in the target space, i.e. there is no 𝒳𝒳\mathcal{X}caligraphic_X-dependent scaling, the algorithm produces prediction intervals of constant size**B**:
The nonconformity measures defined in (31) are important due to the fact that they allo... | BAC | CAB | ACB | ABC | Selection 1 |
**A**: Einaudi (contemporary), “J”: H. Joe (contemporary), “S”: R. Sakamoto (contemporary), “M”: Bethel Music (religious) and “W”: Hillsong Worship (religious).
**B**: Each row shows the percentage of sequences of a class predicted as another class. Notation—“C”: R**C**: Clayderman (pop), “Y”: Yiruma (pop), “H”: H. Han... | CBA | ACB | CAB | BCA | Selection 3 |
**A**: This description draws a comparison e.g**B**: [10] for a survey), where the colors of any two adjacent vertices have to differ by at least k𝑘kitalic_k and the colors of any two vertices within distance 2222 have to be distinct.
**C**: to L(k,1)𝐿𝑘1L(k,1)italic_L ( italic_k , 1 )-labeling problem (see e.g | BAC | ACB | BCA | ABC | Selection 2 |
**A**: 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**B**:
The first benchmark is a tradit... | CBA | CAB | BAC | CAB | Selection 3 |
**A**: The existing 3D WSSS methods formulate the problem in different directions. [10] utilize dense 2D segmentation labels to supervise the training in 3D by projecting the 3D predictions onto the corresponding 2D labels. However, each 3D sample is projected to 2D in several views and each projected 2D image needs pi... | ACB | BCA | ABC | CBA | Selection 1 |
**A**: We use black box for ground-truth, red box for baseline results, and blue box for our results**B**: All the illustrated images are from the KITTI val set. Zoom in on the circles for more detailed comparison.
**C**: Qualitative results of our method for Bird’s-Eye-View | CBA | BCA | ACB | CAB | Selection 2 |
**A**: The GCN now has the ability to classify the type of segments, which will benefit the following FPNS step. In these figures, Non-Text Segments are not displayed for clarity.**B**: The Interval Segments and Char Segments are shown with different colors**C**:
Figure 5: The results of weakly supervised annotation (... | ACB | ABC | CBA | BCA | Selection 3 |
**A**: The hash function assigns each key to a unique bucket for each IP address. Unfortunately, the hash function can generate the same hash code for more than one IP address**B**: With the increase in the generation of big data, millions or tens of millions of records have become ubiquitous in network traffic. Theref... | CAB | BAC | BCA | ABC | Selection 3 |
**A**: The work of G. Ju is supported in part by the National Key R & D Program of China (2017YFB1001604). The work of J**B**: Li is partially supported by the National Natural Science Foundation of China No. 11971221 and the Shenzhen Sci-Tech Fund No. RCJC20200714114556020, JCYJ20170818153840322 and JCYJ20190809150413... | ACB | CAB | BCA | BAC | Selection 3 |
**A**: MIMIC-III:
We train an LSTM model using the MIMIC-III (Medical Information Mart for Intensive Care) dataset, (Johnson et al., 2016), which consists of anonymized information of patients admitted to critical care units in a hospital**B**: We follow the data processing steps from (Harutyunyan et al., 2019) to obta... | ABC | ACB | BAC | CAB | Selection 1 |
**A**: Given the significance of pseudospectra in solving matrix problems, we aim to extend this tool to tensors based on the theoretical analysis in Subsection 4.1.**B**:
The study of spectra and pseudospectra in matrix cases indicates that while eigenvalues are successful tools for solving mathematical problems in v... | CAB | ABC | BAC | ABC | Selection 1 |
**A**: To highlight the structure priors, we build a single-stream network as baseline, which fills missing regions by solely modeling texture features, and the discriminator is single-stream accordingly**B**: As shown in Figure 7 (b), the baseline method does not well deal with complex structures and tends to smooth o... | ACB | BAC | BCA | BAC | Selection 3 |
**A**: In a binary erasure channel (BEC), a binary symbol is either received correctly or totally erased with probability ε𝜀\varepsilonitalic_ε**B**: The concept of BEC was first introduced by Elias in 1955 InfThe **C**: Together with the binary symmetric channel (BSC), they are frequently used in coding theory and in... | ABC | CBA | BAC | CAB | Selection 1 |
End of preview. Expand in Data Studio
README.md exists but content is empty.
- Downloads last month
- 3