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**A**:
The key idea is to transform the diagonal matrix with the help of row and column operations into the identity matrix in a way similar to an algorithm to compute the elementary divisors of an integer matrix, as described for example in [23, Chapter 7, Section 3]**B**: Note that row and column operations are effe... | BAC | BCA | CBA | ABC | Selection 4 |
**A**: Here in this paper, in the presence of rough coefficients, spectral techniques are employed to overcome such hurdle, and by solving local eigenvalue problems we define a space where the exponential decay of solutions is insensitive to high-contrast coefficients. Additionally, the spectral techniques remove
macro... | CBA | BCA | CAB | CBA | Selection 3 |
**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... | BAC | ABC | ACB | ABC | Selection 1 |
**A**: 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**B**: (non-rumor) news classification.
**C**: These feature vectors are used to train the classifier for rumor vs | CBA | ACB | BCA | ABC | Selection 2 |
**A**: The follow-up paper (Gunasekar et al., 2018) studied this same problem with exponential loss instead of squared loss**B**: 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... | BAC | BAC | ACB | ABC | Selection 3 |
**A**: Our task is, to a point, a reverse engineering task; to measure the probability a tweet refers to a news or rumor event; which is even trickier. We hence, consider this a weak learning process**B**:
Given a tweet, our task is to classify whether it is associated with either a news or rumor. Most of the previous... | ABC | BAC | BCA | ABC | Selection 2 |
**A**:
We propose two sets of features, namely, (1) salience features (taking into account the general importance of candidate aspects) that mainly mined from Wikipedia and (2) short-term interest features (capturing a trend or timely change) that mined from the query logs**B**: The features from the two categories ar... | BAC | BCA | ACB | CBA | Selection 3 |
**A**: The only difference happens to patient 10 and 12 whose intakes are earlier at day.
Further, patient 12 takse approx**B**: The insulin intakes tend to be more in the evening, when basal insulin is used by most of the patients**C**: 3 times the average insulin dose of others in the morning. | ABC | BAC | ACB | CAB | Selection 2 |
**A**: This makes our model suitable for applications in (virtual) robotic environments, as demonstrated by Bornet et al. (2019), and we developed a webcam-based interface for saliency prediction in the browser with only moderate hardware requirements (see https://storage.googleapis.com/msi-net/demo/index.html).
**B**:... | ABC | CAB | ACB | BCA | Selection 2 |
**A**: Regarding the question of how repetitions of a word affect its locality number, we can show the following result (see the Appendix for a proof).**B**: For example, note that tutustuttu from above is nearly a repetition**C**:
A repetitive structure often leads to high locality | BCA | ABC | ABC | CBA | Selection 4 |
**A**: At inference time, the latent bits will be generated by this auxiliary network in contrast to sampling from a prior**B**: To make the predictive model more robust to unseen latent bits, we add uniform noise to approximated latent values before discretization and apply dropout (Srivastava et al., 2014) on bits af... | BCA | CAB | CBA | BAC | Selection 1 |
**A**: The trajectory design took into account six constraints: initial and final position, velocity, and acceleration [23]. The Reflexxes Motion Library IV [24] was utilized to perform the inverse kinematics calculation.
**B**: To assure seamless locomotion, trajectories for each joint of the robot were defined throug... | CBA | BCA | CAB | ACB | Selection 1 |
**A**: Our objective is to propose a model which allows the possibility of incorrect advice, with the objective of obtaining more realistic and robust online algorithms.**B**: Our motivation stems from observing that, unlike the real world, the advice under the known models is often closer to “fiat” than “recommendatio... | CBA | BAC | ACB | CAB | Selection 1 |
**A**: “I was diagnosed with depression”) to obtain self-expressions of depression diagnoses, and then they manually reviewed the matched posts to verify that they were really genuine.
According to the authors, this manual review was strict, expressions like “I have depression”, “I think I have depression”, or “I am de... | CBA | BAC | BCA | CAB | Selection 3 |
**A**: The detail of GMC+ is shown in Algorithm 2.
We also adopt parameter server architecture for illustration**B**: GMC+ can also be easily implemented on all-reduce frameworks.**C**: We improve DEF-A by changing its local momentum to global momentum, getting a new method called GMC+ | BCA | BAC | ACB | CBA | Selection 1 |
**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. | BCA | ABC | BCA | CAB | Selection 2 |
**A**: Furthermore, the turbulence of upper air disrupts the stability of UAVs with more energy consumption. Thus, a suitable height is essential to determine the coverage area.**B**: The higher altitude it is, the larger coverage size a UAV has. A large coverage size means a substantial opportunity of supporting more ... | CBA | ACB | ABC | ABC | Selection 1 |
**A**: Here, we will look at the derivation of Dr¯¯¯¯𝐷𝑟\overline{\overline{Dr}}over¯ start_ARG over¯ start_ARG italic_D italic_r end_ARG end_ARG,
the derivation of Dz¯¯¯¯𝐷𝑧\overline{\overline{Dz}}over¯ start_ARG over¯ start_ARG italic_D italic_z end_ARG end_ARG is analogous**B**: The node-to-node**C**: in figure ... | CAB | ABC | BCA | ACB | Selection 3 |
**A**: Our main contribution is an extension to the DQN algorithm that incorporates Dropout methods to stabilize training and enhance performance**B**: The effectiveness of our solution is demonstrated through computer simulations in a classic control environment.
**C**: In this paper, we introduce and conduct an empir... | BCA | BAC | CBA | CBA | Selection 1 |
**A**: (2019) proposed to learn spatially adaptive weight maps to account for spatial variations in pixel-level annotations and used noisy annotations to train a segmentation model for skin lesions. Taghanaki et al. (2019d) proposed to learn spatial masks using only image-level labels with minimizing mutual information... | ACB | CAB | ABC | BAC | Selection 4 |
**A**: In practice, sparse weights require a special differentiable implementation, which can drastically decrease performance, especially when training on a GPU**B**: Full connectivity optimizes all parameters of the fully connected network.
Massiceti et al. (2017) extend this approach and introduce a network splittin... | CBA | BAC | BAC | BCA | 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**: Moreover, we prove t... | ACB | CAB | CBA | BAC | Selection 2 |
**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... | CBA | ABC | CAB | CBA | Selection 3 |
**A**: However, if FillRad(M)FillRad𝑀\mathrm{FillRad}(M)roman_FillRad ( italic_M ) were small, one would not be able to apply Wilhelm’s theorem**B**: To avoid that, we will invoke a result due to Liu [64].
**C**: The proof strategy for Propositions 9.8 and 9.9 is to invoke Wilhelm’s result [82, Main Theorem 2] and Le... | BCA | CAB | ABC | CAB | Selection 1 |
**A**: We give extra support to the user by providing the results of 5 quality measures for each representative projection: neighborhood hit (NH), trustworthiness (T), continuity (C), normalized stress (S), and Shepard diagram correlation (SDC), accompanied by the quality metrics average (QMA)**B**: For more details on... | ACB | CAB | CBA | BAC | Selection 1 |
**A**: This foraging behavior can in turn be observed in many flavors, from the tactics used by the animal at hand to surround its food source (as in the aforementioned GWO [240] and LA [263]), inspired in breeding nutrition (as Cuckoo Search [188, 390]), inspired in hunting techniques observed in grey wolves and lions... | BCA | ACB | CAB | ACB | Selection 1 |
**A**: Most of them can be classified into 2 categories, spectral methods [24] and spatial methods[25].**B**:
In recent years, GCNs have been studied a lot to extend neural networks to graph type data**C**: How to design a graph convolution operator is a key issue and has attracted a mass of attention | ABC | BCA | CAB | ACB | Selection 3 |
**A**: Limitations of filtering studies. The measurement community provided indispensable studies for assessing “spoofability” in the Internet, and has had success in detecting the ability to spoof in some individual networks using active measurements, e.g., via agents installed on those networks (Mauch, 2013; Lone et ... | BAC | CBA | BCA | ACB | Selection 4 |
**A**:
The purpose of this study was to demonstrate that explicit representation of context can allow a classification system to adapt to sensor drift**B**: Several gas classifier models were placed in a setting with progressive sensor drift and were evaluated on samples from future contexts**C**: This task reflects t... | ABC | BCA | CAB | BAC | Selection 1 |
**A**: However, there do not seem to be constructions for presenting arbitrary free products of self-similar groups in a self-similar way**B**: In fact, the free product of two automaton semigroups S𝑆Sitalic_S and T𝑇Titalic_T is always at least
very close to being an automaton semigroup: adjoining an identity to S⋆T⋆... | BAC | ACB | ABC | CAB | Selection 2 |
**A**: We find that their improved accuracy does not actually emerge from proper visual grounding, but from regularization effects, where the model forgets the linguistic priors in the train set, thereby performing better on the test set**B**: To support these claims, we first show that it is possible to achieve such g... | BAC | BCA | ACB | ABC | Selection 2 |
**A**: Due to the unbalanced nature of the dataset, we report the macro-average and micro-average scores. PrivBERT achieves state of the art results improving not only on the macro-average F1 score of RoBERTa by about 4% but also improving on the F1 score for every category in the task.
**B**: We report reproduced resu... | CBA | BCA | ACB | BCA | Selection 1 |
**A**: There is a large solution space of different learning methods and concrete models which can be combined in a stack**B**: One way to manage this is to keep track of the history of each model.
Analysts might also want to step back to a specific previous stage in case they reached a dead end in the exploration of a... | BCA | ACB | BCA | BAC | Selection 2 |
**A**: This variation manifests both between training tasks and between training and testing tasks, similarly affecting the performance of MAML. Few works have thoroughly studied these impact factors.**B**: Secondly, while vanilla MAML assumes that the data distribution is the same across tasks, in real-world NLP tasks... | CAB | CAB | CBA | BCA | Selection 3 |
**A**: For this goal, the CCA codebook design and the codebook-based joint Subarray Partition and Array-weighting-vector Selection (SPAS) algorithm will be first proposed in the next section.**B**: Noting the interdependent relationship between the beamformer/combiner (or AWV) and the activated subarray or subarray par... | BCA | CAB | CBA | BCA | Selection 3 |
**A**: 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**B**: This will be bootstrapped... | BAC | CAB | ABC | CAB | Selection 1 |
**A**: Meanwhile, our analysis is related to the recent breakthrough in the mean-field analysis of stochastic gradient descent (SGD) for the supervised learning of an overparameterized two-layer neural network (Chizat and Bach, 2018b; Mei et al., 2018, 2019; Javanmard et al., 2019; Wei et al., 2019; Fang et al., 2019a,... | CAB | BCA | BAC | ABC | Selection 4 |
**A**:
Notably, on the En-De task, the 12-layer Transformer with depth-wise LSTM already outperforms the 24-layer vanilla Transformer, suggesting efficient use of layer parameters**B**: Unlike in the En-De task, increasing depth over the 12-layer Transformer can still achieve some BLEU improvements, with the 18-layer ... | ACB | BAC | BCA | BAC | Selection 1 |
**A**: definable closed set of X𝑋Xitalic_X**B**: Remark that V≜U∪(X∖Y)≜𝑉𝑈𝑋𝑌V\triangleq U\cup(X\setminus Y)italic_V ≜ italic_U ∪ ( italic_X ∖ italic_Y ) is an open set of X𝑋Xitalic_X, and is still definable**C**: Therefore U=V∩Y𝑈𝑉𝑌U=V\cap Yitalic_U = italic_V ∩ italic_Y with V𝑉Vitalic_V a definable open set of... | CAB | CBA | ABC | BCA | Selection 3 |
**A**: 6 and Fig. 7.
**B**: Distortion Learning Evaluation: Then, we introduce three key elements for evaluating the learning representation: training data, convergence, and error. Supposed that the settings such as the network architecture and optimizer are the same, a better learning representation can be described f... | ACB | CAB | BAC | CBA | Selection 2 |
**A**: Hence, compared to other results, SNGM requires more training time for the batch size of 128. Furthermore, we can observe that the training time decreases with the increasing batch size.
**B**: When B=128𝐵128B=128italic_B = 128, SNGM has to execute communication frequently and each GPU only computes a mini-batc... | ABC | CAB | CBA | ABC | Selection 3 |
**A**: Our main goal is to develop algorithms for the black-box setting. As usual in two-stage stochastic problems, this has three steps**B**: First, we develop algorithms for the simpler polynomial-scenarios model. Second, we sample a small number of scenarios from the black-box oracle and use our polynomial-scenarios... | BAC | ACB | BAC | ABC | Selection 4 |
**A**: The local cost functions in this paper are not required to be differentiable and the subgradients only satisfy the linear growth condition.
The inner product of the subgradients and the error between local optimizers’ states and the global optimal solution inevitably exists in the recursive inequality of the con... | CAB | BCA | CAB | CBA | Selection 2 |
**A**: We randomly generate 1,000 queries and calculate the average relative error rate for comparison**B**: In this experiment, we use the approach of aggregate query answering [37] to check the information utility of MuCo**C**: The sequence of the query is expressed in the following form
SELECT SUM(salary) FROM Micro... | ABC | BCA | BAC | CAB | Selection 3 |
**A**: “BFP” means Balanced Feature Pyramid. Note that BFP and EnrichFeat gain little improvements, we guess that our PointRend baseline already achieves promising performance (77.38 mAP).
**B**: “EnrichFeat” means enhance the feature representation of coarse mask head and point head by increasing the number of fully-c... | CBA | ABC | ABC | ACB | Selection 1 |
**A**: As was written in the previous version, an anonymous referee of version 1 wrote that the theorem was known to experts but not published**B**: Maybe the presentation below is what was known. **C**:
Here we give an embarrassingly simple presentation of an example of such a function (although it can be shown to be... | BAC | ABC | BCA | CAB | Selection 3 |
**A**:
Our proposed algorithm LSVI-UCB-Restart has two key ingredients: least-squares value iteration with upper confidence bound to properly handle the exploration-exploitation trade-off (Jin et al., 2020), and restart strategy to adapt to the unknown nonstationarity**B**: From a high-level point of view, our algorit... | BAC | ACB | CBA | BCA | Selection 2 |
**A**: The details on the participant demographics of SG-75 are shown in Table 1. From SG-75, two more subsets were formed via the branching questions**B**: 75 of the 104 responses fulfilled the criterion of having respondents who were currently based in Singapore. This set was extracted for further analysis and will b... | CAB | ACB | BAC | BCA | Selection 3 |
**A**: While on the FB15K-237 and WN18RR datasets, the performance of decentRL is slightly below the best-performing methods, it does achieve the best Hits@10 on FB15K-237. It is worth noting that FB15K-237 and WN18RR pose greater challenges for methods not tailored to this task, such as RSN [34] and decentRL.**B**:
T... | CBA | CAB | ABC | BCA | Selection 2 |
**A**: Conducting exploration without the extrinsic rewards is called the self-supervised exploration. From the perspective of human cognition, the learning style of children can inspire us to solve such problems**B**: By extending such idea to RL domain, the ‘intrinsic’ rewards are used in RL to incentivize exploratio... | BCA | CBA | ACB | CBA | Selection 3 |
**A**: The observations made in 2D remain valid**B**: However, Floater-Hormann becomes indistinguishable from 5thsuperscript5𝑡ℎ5^{th}5 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT-order splines.
Further, when considering the amount of coefficients/nodes required to determine the interpolant, plotted in... | ACB | ABC | BAC | BAC | Selection 2 |
**A**: Prior work in unsupervised DR learning suggests the objective of learning statistically independent factors of the latent space as means to obtain DR. The underlying assumption is that the latent variables H𝐻Hitalic_H can be partitioned into independent components C𝐶Citalic_C (i.e**B**: They aren’t really sepa... | CBA | ABC | ACB | ABC | Selection 3 |
**A**: 4 is an implementation of an XOR gate combining NAND and OR, expressed in 33 vertices and 46 mains**B**: Graphs are expressed in red and blue numbers in cases where there is no direction of the main line (the main line that can be passed in both directions) and the direction of the main line (the main line that... | BAC | BAC | CAB | BCA | Selection 4 |
**A**: Conditions for such families of maps to define a permutation of the field 𝔽𝔽\mathbb{F}blackboard_F are well studied and established for special classes like Dickson polynomials [20], linearized polynomials [21] and few other specific forms [13, 14] to name a few.
**B**: There has been extensive study about a f... | CAB | ACB | CBA | ABC | Selection 1 |
**A**:
For each experimental condition, we simulate 100 multi-view data training sets. For each such data set, we randomly select 10 views. In 5 of those views, we determine all of the features to have a relationship with the outcome**B**: In the other 5 views, we randomly determine 50% of the features to have a relat... | ABC | BCA | CBA | ACB | Selection 1 |
**A**: Given that there has been an extensive collection of feature selection and prediction techniques available, a meaningful and effective way for anomaly detection is to make use of off-the-shelf supervised techniques, instead of reinventing the wheel by developing new methods.
**B**: Thus, dependency-based approac... | CBA | ACB | BCA | ACB | Selection 1 |
**A**: [2010], Li et al**B**: Next we show how using a global lower bound in form of κ𝜅\kappaitalic_κ (see Assumption 2) early in the analysis in the works Filippi et al**C**: [2017], Oh & Iyengar [2021] lead to loose prediction error upper bound. For this we first introduce a new notation:
| BAC | CBA | CBA | BCA | Selection 1 |
**A**: Recent literature shows the practice of re-scaling videos via linear interpolation before feeding into a network [3, 20, 21, 44, 48], but these methods actually down-scale rather than up-scale videos (e.g., using only 100 snippets on AcitivityNet-v1.3)**B**: Even if we can adapt a method to using a larger-scale ... | CAB | ACB | ACB | BCA | Selection 4 |
**A**:
ATMSeer [WMJ∗19] implements a multi-granularity visualization for model selection and hyperparameter tuning**B**: In contrast to VisEvol, it only supports a single performance measurement, and the output is a single optimized model.**C**: It is a visualization tool coupled with a backend framework, called ATM [... | CAB | ABC | BCA | ACB | Selection 4 |
**A**: This assumption means that the union of graphs over an infinite interval is strongly connected**B**: In [29], previous works are extended to solve the consensus problem on networks under limited and unreliable information exchange with dynamically changing interaction topologies. The convergence of the algorithm... | ACB | BCA | ACB | CBA | Selection 2 |
**A**: Although this way there is no bias, in general the resulting correspondences are not cycle-consistent**B**: Alternatively, one could solve pairwise shape matching problems between all pairs of shapes in the shape collection**C**: As such, matching shape A via shape B to shape C, may lead to a different correspon... | BAC | CBA | CAB | ACB | Selection 1 |
**A**: A graph is a chordal graph if it does not contain a hole as an induced subgraph, where a hole is a chordless cycle of length at least four**B**: Gavril [8] proves that a graph is chordal if and only if it is the intersection graph of subtrees of a tree. We can recognize chordal graphs in O(m+n)𝑂𝑚𝑛O(m+n)itali... | BCA | BAC | ABC | ACB | Selection 1 |
**A**: SLIM combined the SLIM with the spectral method based on DCSBM for community detection. And the SLIM method outperforms state-of-art methods in many real and simulated datasets**B**: Therefore, it is worth modifying this method to mixed membership networks. Numerical results of simulations and substantial empir... | CBA | ACB | BCA | CBA | Selection 3 |
**A**: See, e.g., Welling and Teh (2011); Chen et al. (2014); Ma et al. (2015); Chen et al. (2015); Dubey et al. (2016); Vollmer et al**B**: (2018); Cheng and Bartlett (2018); Chatterji et al. (2018); Wibisono (2018); Bernton (2018); Dalalyan and Karagulyan (2019); Baker et al. (2019); Ma et al. (2019a, b); Mou et al. ... | CAB | ACB | CBA | ABC | Selection 2 |
**A**: A number of prior works have explored how RL can be cast in the framework of variational inference. Latent variable could transform the dynamically updated task-related information such as trajectories into a continuous lower-dimensional space**B**: For example, [59] shows that exploring in latent space can enha... | CAB | ABC | BCA | BCA | Selection 2 |
**A**: There are two important issues to take into account. First, such simple distributions are often unrealistic and do not capture typical applications of bin packing such as resource allocation, as observed in (?). Second, in what concerns online algorithms, simple algorithms such as FirstFit and BestFit are very c... | CBA | BCA | ABC | CAB | Selection 1 |
**A**:
Recently proposed object representations address this pitfall of point clouds by modeling object surfaces with polygonal meshes (Wang et al., 2018; Groueix et al., 2018; Yang et al., 2018b; Spurek et al., 2020a, b). They define a mesh as a set of vertices that are joined with edges in triangles. These triangles... | CBA | ACB | ABC | ABC | Selection 2 |
**A**: However, in the Section 3 we obtained results in a general setup without additional knowledge about cost functions and sets. On the contrary, in this section we utilize the special structure of the WB problem and introduce slightly different norms. After that, we get a convergence guarantee by applying Theorem 3... | BCA | ABC | CAB | BAC | Selection 3 |
**A**: In [6] the authors characterize them in terms of their corresponding cycle matrices and present a Venn diagram that shows their inclusion relations**B**: Among these classes we can find the strictly fundamental class.**C**:
Different classes of cycle bases can be considered | ABC | CBA | BCA | ACB | Selection 3 |
**A**: A major part of this paper, all of Sections 3 and 4, is devoted to adapt it to handle the k𝑘kitalic_k-partite structure of colorful 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... | BCA | BCA | BAC | CAB | Selection 4 |
**A**: In Fig. 7(d), we acknowledged a case where, according to the target correlation measure, all logarithmic transformations were sufficient. However, F8_l2 and F8_l10 were better candidates for transformation since the correlation between features decreased (green lines instead of red-colored) when choosing these o... | ABC | CAB | CBA | BAC | Selection 2 |
**A**: The weights in the MPCC cost terms are manually tuned, the controller gains are kept at their nominal values, and the horizon length is set to 25252525 time steps.
**B**: This is enabled by the high repeatability of the system which results in run-to-run deviations of 3μm3𝜇𝑚3\mu m3 italic_μ italic_m, well be... | ABC | ABC | CBA | ABC | Selection 3 |
**A**:
Distributionally Robust Optimization (DRO): DRO [22] minimizes the worst-case expected loss over potential test distributions**B**: However, this lacks structured priors about the potential shifts, and instead hurts generalization [32].**C**: Often, such distributions are approximated by sampling from a uniform... | ABC | CBA | CBA | ACB | Selection 4 |
**A**: They define the gaze vector starting from the face center to the gaze target [56, 47, 50, 54]. Here we introduce a gaze origin conversion method to bridge the gap between these two types of gaze estimates.**B**: Recently, more attention has been paid to gaze estimation using the face images and they estimate gaz... | BAC | CBA | ACB | BCA | Selection 2 |
**A**:
The images of the used dataset are already cropped around the face, so we don’t need a face detection stage to localize the face from each image**B**: However, we need to correct the rotation of the face so that we can remove the masked region efficiently**C**: To do so, we detect 68 facial landmarks using Dlib... | CBA | BAC | ABC | BCA | Selection 3 |
**A**: Richer types: to mix linear [Pfe20], affine linear, non-linear, etc**B**: In that case, sizes could be related to the grades of the adjoint modalities [Som21]. Furthermore, we are interested in generalizing to substructural, polymorphic, higher-kinded [DDMP21], and dependent types [CP96, KPB15].
**C**: reference... | ABC | ACB | ABC | BAC | Selection 2 |
**A**: TTP-free**B**: The TTP mentioned here does not cover the judge, who is only responsible for handing down sentences in cases of suspected redistribution and is not involved in the media sharing process. The judge is an indispensable participant. In contrast, the existing schemes [26, 27, 3] that are not TTP-free ... | BCA | CBA | BAC | ACB | Selection 4 |
**A**:
Currently, Graph Neural Networks (GNN) Kipf and Welling (2017); Hamilton et al. (2017); Veličković et al**B**: (2018) have recently emerged as an effective class of models for capturing high-order relationships between nodes in a graph and have achieved state-of-the-art results on a variety of tasks such as com... | ACB | ABC | CBA | BCA | Selection 2 |
**A**:
Furthermore, with this simple step size we can also prove a convergence rate for the Frank-Wolfe gap, as shown in Theorem 2.6**B**: The idea of the proof is very similar to the one in Jaggi [2013]. In a nutshell, as the primal progress per iteration is directly related to the step size times the Frank-Wolfe gap... | CAB | ABC | ACB | BAC | Selection 3 |
**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. | BAC | ABC | BCA | BCA | Selection 1 |
**A**: Numerical experiments demonstrate the advantages of B-CPP in saving communication costs.**B**:
We consider an asynchronous broadcast version of CPP (B-CPP)**C**: B-CPP further reduces the communicated data per iteration and is also provably linearly convergent over directed graphs for minimizing strongly convex... | BCA | CAB | BCA | BCA | Selection 2 |
**A**: These methods (Algorithm 1 and Algorithm 2) are based on recent sliding technique [27, 28, 29] adapted to SPPs in a decentralized PFL**B**:
We develop multiple novel algorithms to solve decentralized personalized federated saddle-point problems**C**: In addition, we present Algorithm 3 which used the randomized... | ACB | BCA | BAC | CBA | Selection 3 |
**A**: To produce robust BRs, entropy maximizing MSs (such as MG(C)CE) have better empirical value and convergence than the uniform MS. For exploration, we can randomly select a valid equilibrium at each iteration which outperforms random joint and random Dirichlet by a significant margin (similar to AlphaStar’s “explo... | CAB | CBA | CBA | ACB | Selection 1 |
**A**: This work was supported in part by a gift to the McCourt School of Public Policy and Georgetown University, Simons Foundation Collaboration 733792, Israel Science Foundation (ISF) grant 2861/20, and a grant from the Israeli Council for Higher Education**B**: Part this work was completed while Ligett was visiting... | CAB | CAB | ACB | CAB | Selection 3 |
**A**: We also prove that, given a large feedback vertex cut, we can shrink it while preserving the antlers in the graph. Our main results are derived in Section 6, where we show how color coding can be used to efficiently find antlers when the size of their 𝖺𝗇𝗍𝗅𝖾𝗋𝖺𝗇𝗍𝗅𝖾𝗋\mathsf{antler}sansserif_antler part ... | BCA | BAC | ACB | CAB | Selection 4 |
**A**: Because the fusion network relies on ground-truth composite images obtained by using accurate alpha matte as supervision, the work [194] also proposed an easy-to-hard data-augmentation scheme to relieve the burden of annotating ground-truth alpha matte. Similarly, Xing et al**B**: [177] proposed to concatenate f... | ACB | ABC | CBA | BCA | Selection 4 |
**A**: By leveraging POI data for region matching, our proposed RegionTrans method achieves lower error rates than fine-tuning in most cases**B**:
Impact of Context Data**C**: This finding, coupled with the results presented in Section III-A, underscores the importance of multi-modal data in CityNet and verifies the c... | BCA | BCA | BAC | CBA | Selection 3 |
**A**: As originally introduced vapnik ; saunders1999transduction ; vovk1999machine , the conformal prediction (CP) framework allows for the construction of valid prediction regions given a data set and a nonconformity measure that evaluates how different a new instance is from the given data set**B**: The original imp... | BCA | CAB | CBA | BAC | Selection 1 |
**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... | ACB | ACB | ACB | CBA | Selection 4 |
**A**: Next, let us count the total number of jumps necessary for finding central vertices over all loops in Algorithm 1**B**: Thus, if in the next iteration we start at exactly the neighbor of the previous central vertex, there can be only O(n)𝑂𝑛O(n)italic_O ( italic_n ) such jumps in total.
**C**: As it was stated... | ACB | BAC | ABC | ABC | Selection 1 |
**A**: The CER results of DeepSC-SR and two benchmarks under the AWGN channels and the Rayleigh channels are shown in Fig**B**: From the figure, DeepSC-SR obtains lower CER scores than the speech transceiver and text transceiver under all tested channel environments. Moreover, DeepSC-SR performs steadily when coping wi... | BCA | ACB | CAB | CBA | Selection 2 |
**A**: The two modules implicitly guide the optimization of the basic network only at training time**B**: Therefore, no extra memory and computational resources are introduced at test time.
**C**: In inference, we can simply discard the two modules and use the basic segmentation network as a normal point cloud segmenta... | BAC | ABC | ACB | BAC | Selection 3 |
**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**: UR3D [39] uses estimated key points to post-optimize the predictions of physical sizes and yaw angles by minimizing the objective fu... | CBA | CAB | CAB | BCA | Selection 4 |
**A**: In this case, the dense overlapping design of the text segments and our shape-approximation strategy are sufficient to ensure connectivity of text segments**B**: This further demonstrates that the proposed FPNS and SAp strategies are able to boost the performance of bottom-up methods with visual reasoning cues w... | ABC | CAB | BAC | CAB | Selection 1 |
**A**: Each element of a memory block in this layer stores the number of occurrences of the corresponding IP address. Figure 2 shows an example of the relationship mapping between the memory blocks of two layers and an IP addresses. The positions of the first three dimensionalities of the sparse matrix can be mapped to... | CAB | BCA | BCA | BCA | Selection 1 |
**A**: We consider the following preconditioner:
**B**: We study both block-triangular and block-diagonal preconditioners for the system matrix (1)**C**: For block-triangular preconditioners, we focus on a lower triangular type with left preconditioning because an upper triangular one with right preconditioning can be ... | BAC | ABC | ACB | CAB | Selection 4 |
**A**: A hub itself does not contain data but facilitates training by coordinating clients’ information.
The goal is to jointly train a model on the features of the data contained across silos, without explicitly sharing raw data between the clients and the hubs and between clients across different silos.**B**: The hub... | ACB | ACB | CBA | CAB | Selection 4 |
**A**: The generalization of eigenvalues from matrices to tensors has been studied through the implementation of tensor-tensor multiplication**B**: Significant attention and extensive research have been devoted to this field, resulting in a substantial body of work focused on their variants, applications, and theoretic... | ABC | CBA | BCA | CBA | Selection 1 |
**A**:
On Multi-scale Feature Aggregation in CFA**B**: As our CFA module is updated from the contextual attention layer [35], we directly compare it with the original version to prove its effectiveness**C**: As shown in Figure 7 (f) and Table 2, we demonstrate that multi-scale feature aggregation obviously benefits th... | ABC | ACB | ACB | CAB | Selection 1 |
**A**: Together with the binary symmetric channel (BSC), they are frequently used in coding theory and information theory because they are among the simplest channel models, and many problems in communication theory can be reduced to problems in a BEC. Here we consider more generally a q𝑞qitalic_q-ary erasure channel ... | ABC | BCA | BCA | CBA | Selection 4 |
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