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**A**: Thus recording the row and and column operations required to transform a diagonal matrix into the identity, allows us to write the input matrix as a product of transvections. **B**:
The key idea is to transform the diagonal matrix with the help of row and column operations into the identity matrix in a way simi... | ABC | CAB | BAC | ACB | Selection 2 |
**A**: Most convergent proofs either assume extra regularity or special properties of the coefficients [AHPV, MR3050916, MR2306414, MR1286212, babuos85, MR1979846, MR2058933, HMV, MR1642758, MR3584539, MR2030161, MR2383203, vs1, vs2, MR2740478]**B**: It is hard to approximate such problem in its full generality using n... | CBA | ABC | ABC | BAC | Selection 4 |
**A**: These coordinates are computed somehow and their true values can differ from their values stored in the computer**B**: Moreover, Alg-A is more stable than the alternatives.
During the iterations of Alg-CM, the coordinates of three corners and two midpoints of a P-stable triangle (see Figure 37) are maintained**C... | ACB | CAB | CBA | BAC | Selection 4 |
**A**: . As shown in Table 5, CreditScore is the best feature in overall. In Figure 4 we show the result of models learned with the full feature set with and without CreditScore**B**: Overall, adding CreditScore improves the performance, especially for the first 8-10 hours. The performance of all-but-CreditScore jiggle... | ACB | ABC | CAB | BAC | Selection 2 |
**A**: Unlike the case of squared loss, the result for exponential loss are independent of initialization and with only mild conditions on the step size. Here again, we see the asymptotic nature of exponential loss on separable data nullifying the initialization effects thereby making the analysis simpler compared to s... | CAB | BAC | ABC | BCA | Selection 1 |
**A**: We trade-off this by debunking at single tweet level and let each tweet vote for the credibility of its event. We show the CreditScore measured over time in Figure 13(a)**B**: It can be seen that although the credibility of some tweets are low (rumor-related), averaging still makes the CreditScore of Munich shoo... | ABC | CAB | BCA | CAB | Selection 3 |
**A**: We compare the result of the cascaded model with non-cascaded logistic regression. The results are shown in Table 3-bottom, showing that our cascaded model, with features inherited from the performance of SVM in previous task, substantially improves the single model. However, the overall modest results show the ... | BCA | ACB | CAB | CBA | Selection 4 |
**A**: The insulin intakes tend to be more in the evening, when basal insulin is used by most of the patients**B**: The only difference happens to patient 10 and 12 whose intakes are earlier at day.
Further, patient 12 takse approx**C**: 3 times the average insulin dose of others in the morning. | BCA | BCA | CBA | ABC | Selection 4 |
**A**: When computing the cumulative rank (i.e. the sum of ranks according to the standard competition ranking procedure) on a subset of weakly correlated measures (sAUC, CC, KLD) Riche et al. (2013); Bylinskii et al. (2018), we ranked third behind the two architectures DenseSal and DPNSal from Oyama and Yamanaka (2018... | BCA | BAC | CBA | ACB | Selection 2 |
**A**:
As mentioned several times already, our reductions to and from the problem of computing the locality number also establish the locality number for words as a (somewhat unexpected) link between the graph parameters cutwidth and pathwidth**B**: Next, we conclude this section by providing a formal proof of Lemma 5... | CAB | CBA | ACB | ABC | Selection 3 |
**A**: To that end, we experiment with several stochastic video prediction techniques, including a novel model based on discrete latent variables**B**: We present an approach, called Simulated Policy Learning (SimPLe), that utilizes these video prediction techniques and trains a policy to play the game within the learn... | CBA | CBA | BAC | BCA | Selection 4 |
**A**: This feat was accomplished through the implementation of devised criteria that took into account a comprehensive analysis of energy utilization, wheel slip percentage, and the intricate dynamics between the wheels and the demanding terrain. However, it’s noteworthy that Gorilla only has walking locomotion mode a... | ABC | BAC | CBA | BCA | Selection 4 |
**A**:
Intuitively, Rrc works similarly to Reserved-Critical except that it might not open as many critical bins as suggested by the advice**B**: The algorithm is more “conservative” in the sense that it does not keep two thirds of many (critical) bins open for critical items that might never arrive**C**: The smaller ... | ACB | ABC | BCA | BCA | Selection 2 |
**A**: It is important to note that, as it is described in Section 2.2 of [Losada & Crestani, 2016], to construct the depression group, authors first collected users by doing specific searches on Reddit (e.g**B**: They only included a user into the depression group when there was a clear and explicit mention of a diagn... | ABC | ABC | BCA | ACB | Selection 4 |
**A**: Xu and Huang (2022) propose DEF-A to solve the convergence problem by using detached error feedback (DEF) technique 111Xu and Huang (2022) proposes two algorithms: DEF and DEF-A**B**: Due to the larger compressed error introduced by RBGS compared with top-s𝑠sitalic_s when selecting the same number of components... | BCA | CBA | CBA | BAC | Selection 4 |
**A**: Using cross-correlation would produce the same results and would not require flipping the kernels during visualization**B**: operation.**C**:
, where ∗*∗ is the convolution333We use convolution instead of cross-correlation only as a matter of compatibility with previous literature and computational frameworks | BCA | ACB | BAC | BAC | Selection 1 |
**A**:
We establish a multi-factor system model based on large-scale UAV networks in highly dynamic post-disaster scenarios**B**: The main contributions of this paper are as follows:**C**: Considering the limitations in existing algorithms, we devise a novel algorithm which is capable of updating strategies simultaneo... | CBA | ACB | BAC | CBA | Selection 2 |
**A**: italic_e **B**: , superimposition
of the ψcompsubscript𝜓𝑐𝑜𝑚𝑝\psi_{comp}italic_ψ start_POSTSUBSCRIPT italic_c italic_o italic_m italic_p end_POSTSUBSCRIPT boundary conditions, scaled by experimentally**C**: tcomp=45μsubscript𝑡𝑐𝑜𝑚𝑝45μt_{comp}=45\upmuitalic_t start_POSTSUBSCRIPT italic_c italic_o i... | CAB | BAC | BCA | CAB | Selection 3 |
**A**: Additionally, the noise from approximation methods and, in some cases, environmental factors can exacerbate the expected overestimation. This often results in a more pronounced bias in states where Q-values for different actions are similar, or in states that mark the beginning of a long trajectory.
**B**: This ... | BCA | BCA | BAC | CAB | Selection 4 |
**A**:
Attention can be viewed as using information transferred from several subsequent layers/feature maps to select and localize the most discriminative (or salient) part of the input signal. Wang et al. (2017a) added an attention module to the deep residual network (ResNet) for image classification**B**: Their prop... | BCA | BAC | CBA | ABC | Selection 4 |
**A**:
Welbl (2014) and Biau et al. (2019) follow a similar strategy**B**: Independent training fits all networks one after the other and creates an ensemble of networks as a final classifier. Joint training concatenates all tree networks into one single network so that the output layer is connected to all leaf neuron... | BAC | BAC | ABC | ACB | Selection 4 |
**A**: To answer this question, we propose the first policy optimization algorithm that incorporates exploration in a principled manner. In detail, we develop an Optimistic variant of the PPO algorithm, namely OPPO**B**: As is shown subsequently, solving such a subproblem corresponds to one iteration of infinite-dimens... | CAB | CBA | ACB | CAB | Selection 3 |
**A**: (2015) is suboptimal since it is difficult to compare individual layers of different DNNs.
Therefore, they propose a method to match more understandable factors extracted from the intermediate layers of the student and the teacher DNNs.**B**: Kim et al**C**: (2018) argue that matching the raw features of certain... | CAB | BAC | CBA | ACB | Selection 1 |
**A**: We thank Prof. Henry Adams and Dr. Johnathan Bush for very useful feedback about a previous version of this article**B**: We also thank Prof. Mikhail Katz and Prof. Michael Lesnick for explaining to us some aspects of their work**C**: We thank Dr. Qingsong Wang for bringing to our attention the paper [76] which ... | ABC | CBA | CBA | CAB | Selection 1 |
**A**: Such insights, found only through the visual analysis, also contribute to the investigation of the quality of the projection, and in t-viSNE they can be used to trigger a search for an improved projection before the visual analysis proceeds.**B**: It appears that the variation of the other clusters was prioritiz... | BCA | BAC | BCA | CBA | Selection 4 |
**A**: In [3], the authors show a full discussion of the status of the field from both descriptive (where we stand) and prescriptive (what’s next) points of view**B**: We should pause and reflect on which research directions should be pursued in the future in regard to bio-inspired optimization and related areas, as th... | ACB | BAC | BCA | BCA | Selection 2 |
**A**: (1) Via extending the generative graph models into general type data, GAE is naturally employed as the basic representation learning model and weighted graphs can be further applied to GAE as well**B**: The connectivity distributions given by the generative perspective also inspires us to devise a novel architec... | ABC | BCA | BCA | CBA | Selection 1 |
**A**: In IPv4 scan to locate the services SMap probes every IP, checking for open ports that correspond to the services that we need; for instance, port 25 for Email, 53 for DNS, 80/443 for Web. To reduce the traffic volume of the scan, instead of probing each IP address for target ports, SMap enables also query of th... | BAC | CBA | BCA | ABC | Selection 2 |
**A**: The first preprocessing step was to remove all samples taken for gas 6, toluene, because there were no toluene samples in batches 3, 4, and 5**B**: Data was too incomplete for drawing meaningful conclusions. Also, with such data missing it was not possible to construct contexts from odor samples from each class ... | ABC | BCA | BAC | ABC | Selection 2 |
**A**: While these constructions and the involved proofs are generally deemed quite complicated, the situation for semigroups turns out to be much simpler. While it is known that the free semigroup of rank one is not an automaton semigroup [4, Proposition 4.3], the free semigroups of higher rank can be generated by an ... | ABC | CAB | CBA | BAC | Selection 4 |
**A**: (2019) and as shown in Fig. 2, we observe small improvements on VQAv2 when the models are fine-tuned on the entire train set**B**: However, if we were to compare against the improvements in VQA-CPv2 in a fair manner, i.e., only use the instances with visual cues while fine-tuning, then, the performance on VQAv2 ... | BCA | ABC | ABC | ABC | Selection 1 |
**A**: (2019) released a set of over 400k URLs to Android app privacy policy pages collected by crawling the Google Play store. Amos et al. (2020) collected privacy policies from around 130,000 websites from over two decades and analysed the evolution of the online privacy landscape. Finally, Nokhbeh Zaeem and Barber (... | BAC | ABC | CBA | CAB | Selection 3 |
**A**: Log loss penalizes outliers, and in our case, we should be aware of outliers as we have sensitive healthcare data. Finally, four of the performance metrics include one more option—they are marked with an asterisk in StackGenVis: Alignment of Data, Algorithms, and Models for Stacking Ensemble Learning Using Perfo... | BAC | CAB | CBA | CBA | Selection 2 |
**A**:
When applying MAML to NLP, several factors can influence the training strategy and performance of the model. Firstly, the data quantity within the datasets used as ”tasks” varies across different applications, which can impact the effectiveness of MAML [Serban et al., 2015, Song et al., 2020]**B**: Secondly, wh... | ACB | BCA | ACB | ABC | Selection 4 |
**A**: Note that conventional ULA/UPA-oriented codebook designs mainly focus on the beam direction/width controlling via the random-like subarray activation/deactivation without specific subarray localization. In contrast, the codebook design for DRE-covered CA should emphasize the location of the activated subarray to... | BCA | BCA | ACB | CBA | Selection 4 |
**A**: This will be bootstrapped to the multi-color case in later sections**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 no... | BAC | BCA | CAB | ABC | Selection 4 |
**A**: Szepesvári, 2018; Dalal et al., 2018; Srikant and Ying, 2019) settings. See Dann et al. (2014) for a detailed survey. Also, when the value function approximator is linear, Melo et al. (2008); Zou et al**B**: See Geist and Pietquin (2013); Bertsekas (2019) for a detailed survey. When the value function approximat... | ACB | ABC | BCA | BCA | Selection 1 |
**A**: (2016) with 32k32𝑘32k32 italic_k merging operations on all data sets to address the unknown word issue**B**: We applied joint Byte-Pair Encoding Sennrich et al**C**: We only kept sentences with a maximum of 256256256256 subword tokens for training. For fair comparison, we did not tune any hyperparameters but f... | BCA | BAC | CBA | ACB | Selection 2 |
**A**: However, notice that the T0subscript𝑇0T_{0}italic_T start_POSTSUBSCRIPT 0 end_POSTSUBSCRIPT quotient of
Struct(σ)Structσ\operatorname{Struct}(\upsigma)roman_Struct ( roman_σ ) is sober when τ=τ⊆iτsubscriptτsubscript𝑖\uptau=\uptau_{\subseteq_{i}}roman_τ = roman_τ start_POSTSUBSCRIPT ⊆ start_POSTSUBSCRIPT itali... | ACB | BCA | BAC | ACB | Selection 3 |
**A**: As shown in Fig. 5, we visualize the scatter diagram of two learning representations using 1,000 test distorted images**B**:
Relationship to Distortion Distribution: We first emphasize the relationship between two learning representations and the realistic distortion distribution of a distorted image. In detail... | BAC | CBA | CAB | CAB | Selection 1 |
**A**: As recommended in [32], we use warm-up and polynomial learning rate strategy.**B**:
To further verify the superiority of SNGM with respect to LARS, we also evaluate them on a larger dataset ImageNet [2] and a larger model ResNet50 [10]**C**: We train the model with 90 epochs | BAC | BCA | ABC | CAB | Selection 4 |
**A**: See Appendix A for an in-depth discussion.**B**: On a high level, the obstacle is that radius-minimization requires estimating the cost of each approximate solution; counter-intuitively, this may be harder than optimizing the cost (which is what is done in previous results)**C**:
Unfortunately, standard SAA app... | ACB | CBA | ACB | BCA | Selection 2 |
**A**: However, this can not be obtained for the case with the linearly growing subgradients**B**: Also, different from [15], the subgradients are not required to be bounded and the inequality (28) in [15] does not hold.**C**: That is, the mean square error at the next time can be controlled by that at the
previous tim... | BCA | CAB | CBA | BAC | Selection 1 |
**A**: However, differential privacy also faces the contradiction between privacy protection and data analysis [9]**B**: Differential privacy [6, 38], which is proposed for query-response systems, prevents the adversary from inferring the presence or absence of any individual in the database by adding random noise (e.g... | BAC | ABC | ACB | BCA | Selection 1 |
**A**: PointRend performs point-based segmentation at adaptively selected locations and generates high-quality instance mask**B**: Furthermore, compared to HTC’s mask head, PointRend’s lightweight segmentation head alleviates both memory and computation costs dramatically, thus enables larger input image resolutions du... | BAC | ABC | BAC | ACB | Selection 4 |
**A**: For the significance of this conjecture we refer to the original paper [FK], and to Kalai’s blog [K] (embedded in Tao’s blog) which reports on all significant results concerning the conjecture**B**: [KKLMS] establishes a weaker version of the conjecture**C**: Its introduction is also a good source of information... | ACB | BAC | BAC | ABC | Selection 4 |
**A**: As discussed before, we are the first one to perform numerical experiments on online exploration for non-stationary MDPs and demonstrate the effectiveness of proposed algorithms.
**B**: In this section, we perform empirical experiments on synthetic datasets to illustrate the effectiveness of LSVI-UCB-Restart and... | BCA | ABC | ACB | CAB | Selection 4 |
**A**:
The survey was written in English and made available to anyone with the hyperlink**B**: Participation was fully voluntary**C**: For dissemination, various channels were employed including a mailing list of students from a local Singapore university, an informal Telegram supergroup joined by students, alumni, an... | ABC | BCA | BAC | CAB | Selection 1 |
**A**: Unlike inductive relation prediction, these methods address unseen relations**B**: The task setting differs from conventional entity prediction, where a support triplet set specific to a relation r𝑟ritalic_r is provided to predict the missing entities in the query set related to r𝑟ritalic_r. Each training/test... | CAB | CAB | BAC | BCA | Selection 4 |
**A**: State preprocessing.
In Atari games, the observations are raw images. The images are resized to 84×84848484\times 8484 × 84 pixels and converted to grayscale**B**: The state stacks 4444 recent observations as a frame of shape 84×84×48484484\times 84\times 484 × 84 × 4. In both Super Mario and Atari games, we use... | ABC | BAC | CAB | CAB | Selection 1 |
**A**: It is therefore inappropriate for
approximating strongly varying functions, such as the Runge function**B**: Further, we recognize that the Vandermonde approach is inaccurate and even becomes numerically unstable (rising errors) for higher degrees**C**: As expected, (Chebyshev) polynomial interpolation on unifor... | CBA | CAB | CAB | BAC | Selection 4 |
**A**: If the latent space is heavily regularized, not allowing enough capacity for the nuisance variables, reconstruction quality is diminished**B**: While the aforementioned models made significant progress on the problem, they suffer from an inherent trade-off between learning DR and reconstruction quality**C**: On ... | CBA | BAC | ACB | ABC | Selection 2 |
**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 | CAB | CAB | Selection 1 |
**A**:
A finite field, by definition, is a finite set, and the set of all permutation polynomials over the finite field forms a group under composition**B**: In this paper, we propose a representation of such a group using the concept of linear representation defined through the Koopman operator.**C**: Given a finite ... | ACB | ABC | BCA | CBA | Selection 1 |
**A**: Our implementation of the nonnegative adaptive lasso produced slightly sparser models than the regular nonnegative lasso. This did not appear to substantially reduce classification accuracy in our simulations, although there were some minor reductions in some low sample size cases**B**: In both gene expression d... | BCA | ABC | ACB | BAC | Selection 2 |
**A**: However, the percentage of anomalies can negatively affect their effectiveness.**B**:
Sensitivity Experiments: DepAD algorithms are not sensitive to the average correlation, sparseness, or dimensionality of datasets**C**: DepAD methods exhibit stability when data contains noisy variables | ABC | CAB | ABC | BCA | Selection 2 |
**A**: [2010]. Optimistic parameter search provides a cleaner description of the learning strategy. In non-linear reward models, both approaches may not follow similar trajectory but may have overlapping analysis styles (see Filippi et al. [2010] for a short discussion).**B**: [2011]), which is in contrast to the use o... | BCA | CBA | ABC | BAC | Selection 2 |
**A**: We train 10 epochs at learning rate 0.00005 for THUMOS and 15 epochs at learning rate 0.0001 for ActivityNet**B**: We directly predict the 20 action categories for THUMOS; we conduct binary classification and then fuse our prediction scores with video-level classification scores from [41] for ActivityNet followi... | BAC | BCA | ACB | ABC | Selection 2 |
**A**: This widespread automation does not stop in supervised classification problems, but also includes dimensionality reduction (DR) algorithms (e.g., t-SNE) [BCA∗19, KB19].
**B**: Indeed, several packages exist that focus on automatically optimizing Bayesian methods with the use of a single performance measurement [... | ACB | ABC | CBA | BAC | Selection 3 |
**A**: This technique formulates the objective function and constraints using linear matrix inequalities (LMIs) to synthesize a Markov chain capable of achieving a desired distribution while adhering to specified transition constraints. Notably, this study does not impose any assumptions on the Markov chain, rendering ... | BAC | CBA | ABC | BCA | Selection 2 |
**A**: Throughout this section we also report results of the initialisation methods ZoomOut and ZoomOut+Sync. Further details can be found in the supplementary material.
**B**: By doing so, we obtain the initial U𝑈Uitalic_U and Q𝑄Qitalic_Q. We refer to this method of synchronising the ZoomOut results as ZoomOut+Sync,... | ABC | CBA | BAC | BCA | Selection 2 |
**A**: Both graph classes are characterized very similarly in [18], and we extended the simpler characterization of path graphs in [1] to include directed path graphs as well; this result can be of interest itself**B**: We presented the first recognition algorithm for both path graphs and directed path graphs**C**: Thu... | BAC | CAB | CBA | ACB | Selection 1 |
**A**: In this paper, we extend the symmetric Laplacian inverse matrix (SLIM) method (SLIM, ) to mixed membership networks and call this proposed method as mixed-SLIM. As mentioned in SLIM , the idea of using the symmetric Laplacian inverse matrix to measure the closeness of nodes comes from the first hitting time in a... | CAB | CAB | BAC | ABC | Selection 4 |
**A**: First, utilizing the optimal transport framework and the variational form of the objective functional, we propose a novel variational transport algorithmic framework for solving the distributional optimization problem via particle approximation.
In each iteration, variational transport first solves the variation... | BAC | CAB | BCA | BAC | Selection 2 |
**A**: We conduct extensive experiments and demonstrate the superior performance of our method over the state-of-the-art. We have collected and released more complex scenarios containing different structures 777https://github.com/zhuliwen/RoadnetSZ, and will improve the method based on these scenarios in the future. In... | CBA | BAC | BCA | CAB | Selection 4 |
**A**:
In the experiments that we discussed in Section 6.3, we reported the performance of the algorithm on a typical sequence**B**: More precisely, we considered a single randomly generated sequence, as opposed to averaging the cost of the algorithm over multiple input sequences, because each input sequence is associ... | ABC | CBA | ACB | CBA | Selection 1 |
**A**:
The proposed framework overcomes the limitations of previous methods**B**: First, we theoretically solve the problem of stitching partial meshes since every chart is informed about its local neighborhood**C**: Second, our method can easily fill the missing spaces in the final mesh by adding a new mapping for th... | ABC | BAC | ACB | BAC | Selection 1 |
**A**: Our reformulation is built upon moving the consensus constraints into the problem by adding Lagrangian multipliers**B**: As a result, we get a common saddle point problem that includes both primal and dual variables. After that, we employ the Mirror-Prox algorithm and bound the norms of dual variables at solutio... | ABC | BAC | CBA | BCA | Selection 4 |
**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... | BAC | CAB | CBA | BAC | Selection 2 |
**A**: of Patáková [35, Theorem 2.3] into:
**B**: One immediate application of Theorem 1.2 is the reduction of fractional Helly numbers**C**: For instance, it easily improves a theorem444[35, Theorem 2.3] was not phrased in terms of (K,b)𝐾𝑏(K,b)( italic_K , italic_b )-free covers but readily generalizes to that setti... | CBA | CAB | ACB | BAC | Selection 2 |
**A**: It supports the investigation of interdependencies between different features and entity subsets. Their main idea is to find features with the strongest correlation with instance partitions of the data set. Alternatively, our approach groups instances based on the predicted probability of an ML algorithm. It als... | CBA | BAC | ABC | ACB | Selection 1 |
**A**: We use a contouring predictive control approach to optimize the input to a low level controller**B**: This paper demonstrated a hierarchical contour control implementation for the increase of productivity in positioning systems**C**: This control framework requires tuning of multiple parameters associated with a... | BAC | BCA | BCA | ACB | 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 | CBA | ABC | ACB | CAB | Selection 4 |
**A**: They build a discriminator to judge the source of images from the extracted feature. The feature extractor has to confuse the discriminator, i.e., the generated feature should be domain-invariant. Guo et al. [137] use source samples to form a locally linear representation of each target domain prediction in gaz... | ACB | CBA | ABC | ACB | Selection 2 |
**A**: It consists of applying a TL technique to fine-tune the pre-trained models to the problem of masked face recognition using an SVM classifier. We have tested the this strategy on the masked faces. The results in Table 3 further demonstrate the efficiency of the BoF paradigm compared to the use of a machine learni... | BAC | CBA | CAB | BCA | Selection 3 |
**A**: Briefly, inductive calls must occur on data smaller than the input and, dually, coinductive calls must be guarded by further codata output**B**: In either case, we are concerned with the decrease of (co)data size—height of data and observable depth of codata—in a sequence of recursive calls. Since inferring this... | ACB | BCA | CAB | CAB | Selection 2 |
**A**: There are two extra challenges that need to be addressed**B**: For one thing, considering that the original purpose of cloud’s involvement is to help resource-constrained owners efficiently share their media contents, the owner-side overhead needs to be carefully controlled to ensure that owners can obtain sig-n... | CAB | BCA | ABC | CAB | Selection 3 |
**A**: Since its introduction, several variants of FM have been proposed, including Field-aware factorization machine (FFM) Juan et al. (2016) which takes into account field information and introduces field-aware embeddings, and AFM Xiao et al**B**: Modeling feature interactions is a crucial aspect of predictive analyt... | BAC | CAB | ACB | ACB | Selection 1 |
**A**: [2022] is in essence the Frank-Wolfe algorithm with a modified version of the backtracking line search of Pedregosa et al**B**: [2020]. In the next section, we provide improved convergence guarantees for various cases of interest for this algorithm, which we refer to as the Frank-Wolfe algorithm with Backtrack (... | BAC | ACB | CBA | BCA | Selection 4 |
**A**: We begin by describing what procedures our framework requires the access to**B**:
In this section we describe how to generalize our algorithm to other computation models**C**: The input contains a graph G𝐺Gitalic_G and an approximation parameter ε𝜀\varepsilonitalic_ε. | ACB | CBA | CAB | BAC | Selection 4 |
**A**: CPP is introduced and analyzed in Section III. In Section IV, we consider the algorithm B-CPP. Numerical examples are presented in Section V, and we conclude the paper in Section VI.**B**: We provide necessary notation and assumptions in Section II**C**:
The rest of this paper is organized as follows | ACB | CBA | BCA | BCA | Selection 2 |
**A**: The problem (4) is divided into two minimization subproblems, by X𝑋Xitalic_X, and by Y𝑌Yitalic_Y. Hence, the problem (4) is solved by Fast Gradient Descent. Further, we note that the algorithm’s steps in lines 3, 6, and 7 are local and separable on each machine. The following theorem states the convergence rat... | BAC | CBA | ABC | ACB | Selection 2 |
**A**: Figure 2(c) shows that JPSRO is capable of finding the optimal value.
**B**: 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**C**: Sheriff (Farina et al., 2019b) is a two-player,... | CAB | CAB | CBA | ABC | Selection 3 |
**A**: Part this work was completed while Ligett was visiting Princeton University’s Center for Information Technology Policy.
**B**: Shenfeld’s work was also partly supported by the Apple Scholars in AI/ML PhD Fellowship**C**: This work was supported in part by a gift to the McCourt School of Public Policy and Georget... | ACB | BCA | CBA | ACB | Selection 3 |
**A**: Hence NP-hard problems do not admit such parameter-decreasing algorithms. To formalize a meaningful line of inquiry, we take our inspiration from the Vertex Cover problem, the fruit fly of parameterized algorithms.
**B**: We therefore propose the following novel research direction: to investigate how preprocessi... | CBA | ABC | CAB | BCA | Selection 3 |
**A**: [52] constructed paired data by manually removing the foreground shadows from real shadow images in SOBA dataset [162] to produce synthetic composite images without foreground shadows, leading to DESOBA dataset. This strategy to create synthetic composite image is similar to the backward adjustment for construct... | BCA | ABC | ABC | ABC | Selection 1 |
**A**: Table I provides details on the properties of the collected data, including data range, size, and availability**B**: It is important to note that due to limitations in data availability, not all types of data are accessible for each city**C**: For ease of reference, we have compiled a list of notations used in t... | BAC | ABC | CBA | BAC | Selection 2 |
**A**: Different modifications to obtain heteroscedastic models have been proposed in the literature, the main one being to normalize papadopoulos2008normalized the nonconformity measure by a dispersion function σ:𝒳→ℝ:𝜎→𝒳ℝ\sigma:\mathcal{X}\rightarrow\mathbb{R}italic_σ : caligraphic_X → blackboard_R. This modificat... | BCA | ABC | BAC | CAB | Selection 1 |
**A**: 3 presents the normalised confusion tables for three-class melody classification, illustrating distinct performance characteristics among the models**B**: We note that the baseline exhibits a tendency to conflate vocal melody (M1) and instrumental melody (M2), whereas our model outperforms the RNN-based model, e... | CAB | CBA | CBA | BCA | Selection 4 |
**A**: [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.
**B**: to L(k,1)𝐿𝑘1L(k,1)italic_L ( italic_k , 1 )-labeling problem (see e.g**C**: This description draws a comparison e.g | CBA | CAB | ABC | BCA | Selection 1 |
**A**: The WER scores comparison of different approaches are compared in Fig**B**: From the figure, the proposed DeepSC-SR can provide lower WER scores and outperform the speech transceiver under various channel conditions, as well as the text transceiver under the Rayleigh channels when SNR is lower than around 8 dB. ... | BCA | ACB | ABC | BAC | Selection 2 |
**A**: The encoder is composed by bottleneck ResNet blocks[47] with KP convolution layers. The decoder part is composed of the nearest upsampling layers with unary convolution layers**B**: We put the CSFR and ISFR modules after the first upsampling layer for larger spatial resolution. Due to the limitation in computati... | CBA | BCA | ABC | CBA | Selection 2 |
**A**: For instance, M3D-RPN [3] employs the consistency between the 2D projected and the predicted 2D bounding boxes to optimize orientation parameters in a post-processing process**B**: There are several recent methods considering utilizing the geometric information for monocular 3D object detection [15, 31, 29, 5, 1... | BAC | ABC | ACB | BCA | Selection 1 |
**A**: FPNS (Node) rectifies false detections by measuring attributes of the text segments in local graph structures and upgrades GCNs to a multiple-task network rather than only linkage reasoning, modifications which support each other**B**: Both node classification and link prediction utilize the same relational feat... | BCA | ACB | BCA | ABC | Selection 4 |
**A**:
We formally present a storage strategy for IP addresses that consists of two layers that consist of a limited number of memory blocks. The first layer contains 256×256256256256\times 256256 × 256 memory blocks**B**: Each element of a memory block in this layer stores the number of occurrences of the correspondi... | CBA | ACB | BAC | BCA | Selection 2 |
**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**: The authors would like to thank Mingjian Ding, and Baoxuan Zhu for providing an alternative proof of the Hurwitz stability of polynomials (25). They also thank Jarle Sogn for communicating on S... | CAB | CBA | BCA | BAC | Selection 4 |
**A**: The hubs orchestrate this information exchange every time they update their parameter blocks**B**: To execute the local iterations, clients need embeddings from the data from clients in other silos**C**: By waiting for several training iterations to exchange this information, the communication cost of the algori... | BAC | ABC | ABC | BCA | Selection 1 |
**A**: These include the Gershgorin disc theorem, the Bauer-Fike theorem, and the Kahan theorem, as well as the development of pseudospectra theory for matrices.
In this paper, we generalize these classical results and pseudospectra theory to the tensor case**B**: Due to many scholars have focused their attentions on t... | BCA | ACB | BAC | CAB | Selection 3 |
**A**: The generator is a two-stream architecture, modeled by a U-Net variant, as shown in Figure 2 (a)**B**: At the encoding stage, the corrupted image and its corresponding edge map are individually projected into the latent space, where the left branch focuses on texture features and the right branch targets structu... | BAC | CBA | ABC | 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 | BAC | CBA | CBA | Selection 1 |
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