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**A**: This is done in Magma [14] using the results of Elliot Costi [6] and in GAP using the results of this paper see Section 6**B**: Other rewriting algorithms also exist, for example Cohen et al. [26] present algorithms to compute with elements of finite Lie groups.
**C**: One important task in this context is writi... | CBA | CBA | BCA | CAB | Selection 3 |
**A**: From now on, we concentrate on approximating P𝑃Pitalic_P so that (25) can be accurately and efficiently solved.
**B**: Except for (ii), all steps above above can be performed efficiently as the matrices involved are sparse and either local or independent of hℎhitalic_h**C**: Solving (25) on the other hand invol... | ABC | BAC | CAB | ACB | Selection 3 |
**A**: 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**B**: These coordinates are computed somehow and their true values can differ from their values stored in the computer**C... | BCA | ABC | BAC | BCA | Selection 2 |
**A**: The processing pipeline of our classification approach is shown in Figure 2**B**: Subsequently, in the upper part of the pipeline,
we predict tweet credibilty with our pre-trained credibility model and aggregate the prediction probabilities on single tweets (CreditScore).**C**: In the first step, relevant tweets... | ACB | ABC | BCA | CAB | Selection 1 |
**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... | BCA | CAB | CBA | ACB | Selection 4 |
**A**: Subsequently, in the upper part of the pipeline,
we predict tweet credibilty with our pre-trained credibility model and aggregate the prediction probabilities on single tweets (CreditScore).**B**: The processing pipeline of our clasification approach is shown in Figure 1**C**: In the first step, relevant tweets ... | ABC | BCA | BCA | CAB | Selection 4 |
**A**: We model 𝖽(e)𝖽𝑒\mathsf{d}(e)sansserif_d ( italic_e ) as the corresponding Wikipedia article text**B**: Language Model-based, how likely aspects are generated by as stastical LM based on the textual representation of the entity 𝖽(e)𝖽𝑒\mathsf{d}(e)sansserif_d ( italic_e )**C**: We use the unigram model wit... | ABC | CAB | BAC | CAB | Selection 3 |
**A**: Body weight, according to BMI, is normal for half of the patients, four are overweight and one is obese. The mean BMI value is 26.9**B**:
Table 1 shows basic patient information. Half of the patients are female and ages range from 17 to 66, with a mean age of 41.8 years**C**: Only one of the patients suffers fr... | BAC | CBA | BCA | CAB | Selection 1 |
**A**: Such initialization schemes are demonstrably important for training deep neural networks successfully from scratch Sutskever et al. (2013)**B**:
Weight values from the ASPP module and decoder were initialized according to the Xavier method by Glorot and Bengio (2010). It specifies parameter values as samples dr... | CBA | BAC | ABC | CAB | Selection 2 |
**A**: In the next Section 3, we discuss the concept of the locality number with some examples and some word combinatorial considerations**B**:
In Section 2, we give basic definitions (including the central parameters of the locality number, the cutwidth and the pathwidth)**C**: The purpose of this section is to devel... | BAC | ACB | BCA | ABC | Selection 1 |
**A**: 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 learned model. With several iterations of dataset aggregation, where the policy is deployed to collect more data in the original game, we learn a poli... | ABC | CBA | CAB | ABC | Selection 3 |
**A**:
During the step negotiation simulations, it was noticed that the rolling locomotion mode encountered constraints when attempting to cross steps with a height greater than thrice the track height (h being the track height as shown in Fig. 3)**B**: For evaluating the energy expenditure during step negotiation, en... | ACB | BCA | BCA | CBA | Selection 1 |
**A**: Motivated by advances in learning-online algorithms, we studied tradeoffs between the trusted and untrusted competitive ratio, as function of the advice size**B**: We introduced a new model in the study of online algorithms with advice, in which the online algorithm can leverage information about the request seq... | ACB | CBA | BAC | ACB | Selection 3 |
**A**: As we will see, these values are the basis of the entire classification process.
**B**: In the rest of this subsection, we will exemplify how the SS3 framework carries out the classification and training process and how the early classification and explainability aspects are addressed**C**: The last subsection g... | CAB | ABC | ACB | BCA | Selection 1 |
**A**: Each worker computes stochastic gradients locally and communicates with the server or other workers to obtain the aggregated stochastic gradients for updating the model parameter**B**: In large-scale model training tasks, the communicated messages become high-dimensional vectors. Due to the latency and limited b... | CBA | ABC | BAC | ACB | Selection 4 |
**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 | ABC | ACB | CBA | Selection 2 |
**A**: The topological structure of Multi-UAV network is shown in Fig. 1 (a).**B**: All the UAVs have the same volume of battery E𝐸Eitalic_E and communication capability**C**:
We construct a UAV ad-hoc network in a post-disaster scenario with M𝑀Mitalic_M identical UAVs being randomly deployed, in which M𝑀Mitalic_M ... | CAB | CBA | BCA | ACB | Selection 2 |
**A**: of τLRsubscript𝜏𝐿𝑅\tau_{LR}italic_τ start_POSTSUBSCRIPT italic_L italic_R end_POSTSUBSCRIPT can be chosen as a simulation input when shorter-lived
CTs (e.g.,formulae-sequence𝑒𝑔e.g.,italic_e **B**: italic_g **C**: , for simulations with relatively high thermal diffusion | ABC | CBA | CAB | ACB | Selection 1 |
**A**: Over course of time a wide range of Dropout techniques inspired by the original method have been proposed. The term Dropout methods was used to refer to them in general[14]. They include variational Dropout[15], Max-pooling Dropout[16], fast Dropout[17], Cutout[18], Monte Carlo Dropout[19], Concrete Dropout[20] ... | CBA | ABC | ACB | ABC | Selection 1 |
**A**:
Exploring reinforcement learning approaches similar to Song et al**B**: (2018) and Wang et al**C**: (2018c) for semantic (medical) image segmentation to mimic the way humans delineate objects of interest. Deep CNNs are successful in extracting features of different classes of objects, but they lose the local sp... | CAB | BCA | ABC | BCA | Selection 3 |
**A**: To avoid overfitting, the data is generated on-the-fly so that each training example is unique**B**: For training, we generate input-target pairs (x,y)𝑥𝑦(x,y)( italic_x , italic_y ) as described in the last section.
These training examples are fed into the training process to teach the network to predict the s... | BAC | ABC | CBA | ACB | Selection 1 |
**A**: Such an exploration-exploitation tradeoff is better captured by the aforementioned statistical question regarding the regret or sample complexity, which remains even more challenging to answer than the computational question**B**: In a more practical setting, the agent sequentially explores the state space, and ... | ABC | BAC | BCA | CAB | Selection 2 |
**A**: Furthermore, in particular for the TPU, experimentation is often hindered due to limitations in the tool chain which is not flexible enough to support such optimizations**B**: They are not suited to execute generic compressed models and are therefore not included in the following experiments.
**C**: While domain... | CBA | ABC | BCA | ACB | Selection 3 |
**A**: We alert readers that, in this paper, the same notation can mean either a simplicial complex itself or its geometric realization, interchangeably**B**: In this section we cover the background needed for proving our main results**C**: The precise meaning will be made clear in each context.
| CBA | BAC | ACB | ACB | Selection 2 |
**A**: We then demonstrate the effectiveness of t-viSNE by describing two use cases with real data in Section 5. Thereafter in Section 6, we discuss the usability and applicability of t-viSNE by reporting the results of a user study**B**:
The rest of this paper is organized as follows. In the next two sections, we dis... | CBA | CAB | BAC | ACB | Selection 3 |
**A**: Table 26 compiles all algorithms that are classified in this subcategory**B**: Neighborhood based differential vector: In this subcategory, each solution is affected only by solutions in its local neighborhood**C**: A notable example in this list is BFOA [148], in which all solutions in the neighborhood impact o... | ABC | ACB | CAB | BAC | Selection 4 |
**A**: Classical clustering models work poorly on large scale datasets. Instead, DEC and SpectralNet work better on the large scale datasets**B**: Although GAE-based models (GAE, MGAE, and GALA) achieve impressive results on graph type datasets, they fail on the general datasets, which is probably caused by the fact th... | ABC | BCA | CAB | CAB | Selection 1 |
**A**:
Inferring spoofing. Given a DNS resolver at IP 1.2.3.7, we send a DNS query to 1.2.3.7 port 53 asking for a record in domain under our control**B**: The query is sent from a spoofed source IP address belonging to the tested network. We monitor for DNS requests arriving at our Name server**C**: If a query for th... | ACB | BAC | CAB | ABC | Selection 4 |
**A**:
The context pathway is based on a recurrent neural network (RNN) approach**B**: It reuses weights and biases across the steps of a sequence and can thus process variable-length sequences**C**: The alternative was to use a long-short term memory (LSTM), which employs gating variables to better remember informati... | CAB | BCA | CAB | ABC | Selection 4 |
**A**: 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⋆𝑆𝑇S\star Titalic_S ⋆ italic_T**B**: For semigroups, on the other hand, such results do exist**C**: However, there do not seem to be con... | CAB | BAC | ABC | CBA | Selection 4 |
**A**:
The VQA-CP dataset Agrawal et al. (2018) showcases this phenomenon by incorporating different question type/answer distributions in the train and test sets. Since the linguistic priors in the train and test sets differ, models that exploit these priors fail on the test set**B**: These approaches rely on additio... | ACB | CAB | BAC | CAB | Selection 1 |
**A**: To the best of our knowledge, this is the most detailed and widely used dataset of annotated privacy policies in the research community. The OPP-115 Corpus contains paragraph-sized segments annotated according to one or more of the twelve coarse-grained categories of data practices. We fine-tuned PrivBERT on the... | BAC | CBA | CAB | ACB | Selection 1 |
**A**: Compelling accuracy results [59] were also observed for text data, where stacking is better than alternative techniques such as voting ensembles [50]**B**: In spite of this challenge of hardly understanding why a specific configuration works [57], predicting the relation of supply-demand [60] and anomaly/bug rep... | ACB | ABC | ABC | BAC | Selection 4 |
**A**: Task similarity. In Persona and Weibo, each task is a set of dialogues for one user, so tasks are different from each other. We shuffle the samples and randomly divide tasks to construct the setting that tasks are similar to each other**B**: For a fair comparison, each task on this setting also has 120 and 1200 ... | ACB | ABC | CBA | BAC | Selection 2 |
**A**: Assuming that an element can not be contained in different subarrays, then the problem of activated CCA subarray partition rises at the r-UAV side for the fast multi-UAV beam tracking**B**: Multiuser-resultant Receiver Subarray Partition: As shown in Fig. 3, the r-UAV needs to activate multiple subarrays to serv... | ACB | CBA | BAC | 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**: We**C**: This will be boo... | ABC | BAC | ABC | BCA | Selection 4 |
**A**: Specifically, by exploiting the permutation invariance of the parameter, we associate the neural network and its induced feature representation with an empirical distribution, which, at the infinite-width limit, further corresponds to a population distribution**B**: The key to our analysis is a mean-field perspe... | CAB | ACB | BAC | CAB | Selection 3 |
**A**:
To test the effectiveness of depth-wise LSTMs in the multilingual setting, we conducted experiments on the challenging massively many-to-many translation task on the OPUS-100 corpus Tiedemann (2012); Aharoni et al. (2019); Zhang et al**B**: (2020) for fair comparison. We adopted BLEU Papineni et al. (2002) for ... | CBA | ABC | CBA | ACB | Selection 4 |
**A**: Therefore U=V∩Y𝑈𝑉𝑌U=V\cap Yitalic_U = italic_V ∩ italic_Y with V𝑉Vitalic_V a definable open 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**: definable closed set... | BCA | CBA | ACB | CAB | Selection 2 |
**A**: In contrast, our proposed approach only requires a part of a distorted image (distortion element) and estimates the ordinal distortion. Due to its explicit description and homogeneity, we can obtain more accurate distortion estimation and achieve better corrected results.**B**:
Figure 1: Method Comparisons. (a)... | ACB | ACB | CAB | BCA | Selection 3 |
**A**: We can observe that in the small-batch training, SNGM and other large-batch training methods achieve similar performance in terms of training loss and test accuracy as MSGD.
In large-batch training, SNGM achieves better training loss and test accuracy than the four baselines**B**: Furthermore, it achieves faster... | CBA | ACB | BCA | ABC | Selection 3 |
**A**: This new algorithm is intricate and may be of interest on its own.**B**:
We follow up with 3333-approximations for the homogeneous robust outlier MatSup and MuSup problems, which are slight variations on algorithms of [6] (specifically, our approach in Section 4.1 is a variation on their solve-or-cut methods)**... | BCA | CAB | ACB | ABC | Selection 2 |
**A**: Compared with the case with only a single random factor, the coupling terms of different random factors inevitably affect the mean square difference between optimizers’ states and any given vector. What’s more, multiplicative noises relying on the relative states between adjacent local optimizers make states, gr... | CBA | BAC | CAB | CAB | Selection 2 |
**A**: According to Figure 3, there are three records, namely Daphne, Helen, and Dean, may carry 28. Therefore, the probability that Helen is re-identified by matching her age value of 28 is calculated as
**B**: Suppose that an adversary aims to find the record of Helen in the anonymized table by matching her age value... | BAC | BAC | CAB | BAC | Selection 3 |
**A**: (2019) are used as our backbones, pretrained on ImageNet only. SGD with momentum 0.9 and weight decay 1e-4 is adopted. The initial learning rate is set to 0.01 for Res2Net101 and 0.02 for X101-64x4d defaultly and decayed by factor 0.1 at epoch 32**B**: During training process, the batch size is 8 (one image per ... | ACB | BAC | BCA | CBA | Selection 3 |
**A**: More specifically, we proved**B**: This solves a question raised by Gady Kozma some time ago (see [K], comment from April 2, 2011)**C**:
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\... | CBA | BAC | BCA | BAC | 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... | CAB | BAC | BAC | ABC | Selection 4 |
**A**: The survey contained 19 question items, 2 branching questions, and 3 demographic questions. Respondents were allowed to select multiple options for some question items while the branching questions served to direct them to different sections based on their answer**B**: Although branching means that respondents m... | ACB | ACB | BCA | ABC | Selection 3 |
**A**:
Table 6 and Table 7 present the results for conventional entity prediction**B**: decentRL demonstrates competitive or even superior performance when compared to state-of-the-art methods on the FB15K and WN18 benchmarks, showcasing its efficacy in entity prediction**C**: While on the FB15K-237 and WN18RR dataset... | ABC | CBA | BCA | BCA | Selection 1 |
**A**: For example, the popular deep learning models like VAEs and GANs are shown to be unstable since the introduce of stochasticity in latent space [51, 52]**B**:
Nevertheless, the introduce of latent variable often introduce instability to neural networks**C**: We find VDM performs generally well and shows small pe... | CBA | BAC | BCA | ABC | Selection 2 |
**A**: In doing so, we revisit earlier results by Carl de Boor and Amon Ros [28, 29] and answer their question from our perspective.**B**: We complement the established notion of unisolvent nodes by the dual notion of unisolvence**C**: That is: For given arbitrary nodes P𝑃Pitalic_P, determine the polynomial space ΠΠ\P... | CBA | CAB | CBA | ACB | Selection 2 |
**A**: The key observation that we make is that the DR learning problem can be cast as a style transfer task [DBLP:conf/cvpr/GatysEB16], thus allowing us to borrow techniques from this extensively explored area.
**B**: The framework is general and can utilize any DGM**C**: Furthermore, even though it involves two stage... | CAB | BAC | CBA | BCA | Selection 1 |
**A**: Thus, signal cables require one transistor for switching action at the end**B**: When introducing the concept of an inverted signal pair of digital signals into a structural computer, the signals are paired, so a total of four wires are required to process the two auxiliary signals. This is defined as a double p... | BCA | ABC | 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**: Given a finite subset of such permutations, we can compute a group generated by this set**C**: In this paper, we propose a representation of such a group using t... | ABC | CBA | BCA | BCA | Selection 1 |
**A**: (\APACyear2020). This regression weight is either 0.040.040.040.04 or −0.040.04-0.04- 0.04, each with probability 0.5. In the setting with 300 views, the number of features per view is reduced by a factor 10. To compensate for the reduction in the number of features, the aforementioned regression weights are mul... | CAB | CBA | CBA | BAC | Selection 1 |
**A**: The two DepAD methods learn and use the MB of a variable as its relevant variables, while ALSO, for each variable, uses all other variables as the variable’s relevant variables**B**:
It is worth noting that the key difference between the two DepAD methods (FBED-CART-PS and FBED-CAR-Sum) and ALSO lies in their r... | BAC | CBA | ACB | ABC | Selection 1 |
**A**:
Comparison with Amani & Thrampoulidis [2021] While the authors in Amani & Thrampoulidis [2021] also extend the algorithms of Faury et al**B**: They model various click-types for the same advertisement (action) via the multinomial distribution. further, they consider actions played at each round to be non-combin... | ABC | ACB | BCA | ABC | Selection 2 |
**A**: b) Long video is cut into multiple short clips. c) Each video clip is up-scaled along the temporal dimension**B**:
Figure 3: Video self-stitching (VSS). a) Snippet-level features are extracted for the entire video**C**: d) Original clip (green dots) and up-scaled clip (orange dots) are stitched into one feature... | ABC | CAB | CAB | BAC | Selection 4 |
**A**: Users can intervene in the running procedure to anchor a few hyperparameters and modify others. However, this could be hard to generalize for more than one algorithm at the same time**B**: One common focus of related work is the hyperparameter search for deep learning models. HyperTuner [LCW∗18] is an interactiv... | CBA | ABC | ABC | BAC | 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... | CAB | BCA | CAB | BAC | Selection 2 |
**A**: Moreover, for general non-rigid settings learning these basis functions has also been proposed [43].
A wide variety of extensions to make functional maps more robust or more flexible have been developed. This includes orientation-preservation [56], image co-segmentation [75], denoising [23, 55], partiality [58],... | BCA | CBA | CAB | CBA | Selection 3 |
**A**: Rooted path graphs can be recognized in linear time by using the algorithm by Dietz [7]. All inclusions between introduced classes of graphs are resumed in the following:**B**:
We now introduce a last class of intersection graphs**C**: A rooted path graph is the intersection graph of directed paths in a rooted ... | BCA | BCA | CAB | BAC | Selection 3 |
**A**: The network splits naturally into two large groups females and males dolphins1 ; dolphinnewman , which are seen as the ground truth in our analysis.**B**:
Dolphins: this network consists of frequent associations between 62 dolphins in a community living off Doubtful Sound**C**: In the Dolphins network, node den... | CBA | CAB | ABC | CBA | Selection 2 |
**A**: Our Contribution**B**: 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 fir... | ACB | BCA | ABC | CBA | Selection 1 |
**A**: For an intersection, the incoming lanes refer to the lanes where the vehicles are about to enter the intersection. In real world, most intersections are equipped with 4-way entering approaches, but some are 3-way or 5-way intersections**B**: Each approach consists of three types of lanes, representing "left-turn... | ACB | CAB | CBA | CBA | Selection 1 |
**A**: That is, no algorithm, online or offline, can perform better than this lower bound.**B**: As often in offline bin packing, we also report the L2 lower bound (?, ?) as a lower-bound estimation of the optimal offline bin packing solution**C**:
As explained earlier, FirstFit and BestFit perform very well in practi... | CAB | CAB | BAC | CBA | Selection 4 |
**A**: However, it does not consider the stitching process itself - no information is shared between patches**B**:
Using this formulation, charts are trained to approximate the target surface as closely as possible**C**: If one of them fails to cover the neighborhood of p𝑝pitalic_p properly, then no other patch will ... | CBA | BAC | CBA | ABC | Selection 2 |
**A**: Our paper technique can be generalized to non-smooth problems by using another variant of sliding procedure [34, 15, 23]**B**: Instead of the smooth convex-concave saddle-point problem we can consider general sum-type saddle-point problems with common variables in more general form. For each group of common vari... | ACB | BAC | BCA | BAC | Selection 1 |
**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**:
Different classes of cycle bases can be considered**C**: Among these classes we can find the strictly fundamental class. | BAC | CBA | ABC | ABC | Selection 1 |
**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**: This technique, which we briefly outline here, was specifically designed for complete intersection patterns**C**:
The proof of Theorem 2.1 is quite in... | CBA | ABC | ABC | ACB | Selection 1 |
**A**:
We derived the analytical tasks described in this section from the in-depth analysis of the related work in Section 2**B**: The three analytical tasks from Krause et al. [50], the three experts who expressed their requirements in Zhao et al. [32], and the user tasks acquired through expert interviews from Colla... | BAC | ABC | CAB | BCA | Selection 2 |
**A**:
We use two geometries to evaluate the performance of the proposed approach, an octagon geometry with edges in multiple orientations with respect to the two axes, and a curved geometry (infinity shape) with different curvatures, shown in Figure 4**B**: We compare three schemes: manual tuning of the MPCC paramete... | BCA | ACB | ABC | BCA | Selection 2 |
**A**: Because the implicit methods do not rely on the choice of explicit biases, we simply repeat the same accuracy across x-axis. Among the implicit methods, LFF obtains the highest improvement, whereas SD is close to StdM.**B**: As shown in Fig. 4, all explicit methods are below StdM on Biased MNISTv1. Barring LNL a... | ACB | CAB | CBA | ABC | Selection 3 |
**A**: They select a larger time range, e.g., 1∼2ssimilar-to12𝑠1\sim 2s1 ∼ 2 italic_s (30∼90similar-to309030\sim 9030 ∼ 90 frames), in the gaze prediction task.
We visualize a expample network architecture in Fig. 5.**B**: Besides, some methods utilize the past gaze trajectory for gaze prediction[129, 86]**C**: These... | ACB | BAC | CBA | ACB | Selection 3 |
**A**: Then we extract only the blocks including the non-masked region (blocks from number 1 to 50). Finally, we eliminate the rest of the blocks as presented in Fig. 3.
**B**: Next, we partition a face into blocks. The principle of this technique is to divide the image into 100 fixed-size square blocks (24 ×\times× 24... | BCA | CAB | BAC | CBA | Selection 4 |
**A**: Although we use infinite typing derivations, we explicitly avoid syntactic termination checking for its non-compositionality**B**: Nevertheless, we are interested in implementing such validity conditions as uses of sized types as future work. Relatedly, cyclic termination proofs for separation logic programs can... | CAB | BAC | BCA | CAB | Selection 3 |
**A**: Section III describes the system model, threat model, and design goals. Subsequently, Section IV introduces the involved fundamental techniques**B**: The two schemes are constructed in Section V. The performance of the two schemes regarding the three problems is evaluated in Section VI followed by the efficiency... | BCA | CBA | ACB | BAC | Selection 1 |
**A**: Graph-Convolved Factorization Machines (GCFM) Zheng et al. (2021) developed the Graph-Convolved Feature Crossing (GCFC) layer to traverse all features for each input example and leveraged the features of each sample to compute the corresponding multi-feature interaction graph and propagated its influence on othe... | ABC | CBA | BAC | BCA | Selection 2 |
**A**: Table 1:
Number of iterations needed to achieve an ε𝜀\varepsilonitalic_ε-optimal solution for Problem 1.1**B**: 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.**C**: ... | ACB | BCA | BAC | BAC | Selection 1 |
**A**: Indeed, this has been a key property in the qualification of efficiency in parametrized complexity**B**: However, to be considered an efficient approximation algorithm in theory, ideally the dependence on all relevant parameters should be polynomial**C**: The question whether there is a (1+ε)1𝜀(1+\varepsilon)( ... | ACB | BAC | ABC | CAB | Selection 2 |
**A**: In this paper, we consider decentralized optimization over general directed networks and propose a novel Compressed Push-Pull method (CPP) that combines Push-Pull/𝒜ℬ𝒜ℬ\mathcal{A}\mathcal{B}caligraphic_A caligraphic_B with a general class of unbiased compression operators**B**: We show CPP achieves linear conv... | ABC | BAC | ACB | ABC | Selection 3 |
**A**: It furthermore covers various communication topologies and hence goes beyond the centralized setting.
**B**: We present a new SPP formulation of the PFL problem (1) as the decentralized min-max mixing model**C**: This extends the classical PFL problem to a broader class of problems beyond the classical minimizat... | BCA | CAB | ACB | ABC | Selection 2 |
**A**: At action selection time only (C)CEs require further coordination. NEs are factorizable and therefore can sample independently without further coordination. (C)CEs rely on a central correlation device that will recommend actions from the equilibrium that was previously agreed upon.**B**:
There are two levels of... | CAB | ABC | BCA | ACB | Selection 1 |
**A**: In this section we propose such a notion and prove several key properties of it**B**: Missing proofs from this section can be found in Appendix D.**C**:
In order to leverage Lemma 3.5, we need a stability notion that implies Bayes stability of query responses in a manner that depends on the actual datasets and ... | CBA | CAB | BCA | ACB | Selection 3 |
**A**: We present structural properties of antlers and how they combine in Section 4. In Section 5 we show how color coding can be used to find a large feedback vertex cut, if one exists**B**: We also prove that, given a large feedback vertex cut, we can shrink it while preserving the antlers in the graph. Our main res... | CBA | CAB | BCA | CBA | Selection 3 |
**A**: [140] proposed an interesting way to construct GMS Dataset. Specifically, they place the same physical model (3D foreground object) in different lighting conditions to capture different images and align the foregrounds in different images. Nevertheless, the collection cost is still very high and the diversity of... | ACB | CBA | CAB | ABC | Selection 3 |
**A**: To address this challenge, we leverage the data available in CityNet and present benchmarks for the taxi dispatching task**B**: In this task, operators are responsible for dispatching available taxis to waiting passengers in real-time with the objective of maximizing the long-term total revenue of the taxi syste... | BCA | ACB | CBA | ABC | Selection 1 |
**A**: Moreover, some general conclusions that can be used in future applications or research are derived.
**B**: In this and the following section some of the models introduced above are experimentally investigated**C**: They are evaluated and compared based on some general performance measures | CBA | CAB | CBA | ABC | Selection 2 |
**A**: The authors showcased the efficacy of MusicBERT by applying it to two generative music tasks, melody completion and accompaniment suggestion and two sequence-level discriminative tasks, including genre and style classification. In comparison to non PTM-based baselines, MusicBERT consistently led to better perfor... | CBA | ACB | ACB | BAC | Selection 1 |
**A**: Next, let us count the total number of jumps necessary for finding central vertices over all loops in Algorithm 1**B**: As it was stated in the proof of Lemma 2.2, while searching for a central vertex we always jump from a vertex to its neighbor in a way that decreases the largest remaining component by one**C**... | BAC | ABC | BAC | CAB | Selection 2 |
**A**: Particularly, we jointly design the semantic and channel coding to learn and extract the features and mitigate the channel effects**B**:
In this article, we have investigated a DL-enabled semantic communication system for speech recognition, named DeepSC-SR, which aims to restore the text transcription by utili... | CBA | CBA | BAC | BCA | Selection 3 |
**A**: In inference, we can simply discard the two modules and use the basic segmentation network as a normal point cloud segmentation network to get the segmentation predictions**B**: Therefore, no extra memory and computational resources are introduced at test time.
**C**: The two modules implicitly guide the optimiz... | CBA | BCA | ACB | 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... | BCA | ABC | BAC | BCA | Selection 3 |
**A**: Hence, evaluating the two FPNS mechanisms (as shown in the Table II) is equivalent to the evaluation when LAT+FD are in place**B**: FD cannot be applied and tested independently without the LAT module because the LAT process is an essential step for fusing the visual features from FPN and the relational features... | CBA | BCA | CAB | CAB | Selection 2 |
**A**: A minimum heap of size k𝑘kitalic_k is shared among these computers. Hence, this greatly increases the computational efficiency of the task by q𝑞qitalic_q times.**B**: Further assume are q𝑞qitalic_q computers for parallel computation, where the statistics collection task of each subset can be performed by an i... | CAB | BAC | CBA | BCA | Selection 3 |
**A**: The outline of the remainder of this paper is as follows. In section 2, we briefly recall the classic saddle point problem and its Schur complement, and introduce the twofold saddle point problem and the form of Schur complement, we then construct and analyze the block-triangular and block-diagonal preconditione... | CAB | CAB | ABC | BCA | Selection 3 |
**A**: We observe that since each mini-batch is used for Q𝑄Qitalic_Q local iterations, this reuse leads to a bias in some of the stochastic partial derivatives**B**: We now provide the main theoretical result of this paper**C**: Nevertheless, with some care, it is possible to prove the algorithm convergence.
The proof... | CBA | BAC | ACB | CBA | Selection 2 |
**A**: We extend several classical theorems from the matrix domain to the tensor domain, offering insights into the perturbation behavior of tensors.**B**: In Section 2, we introduce some notations commonly used throughout the paper and review basic concepts and fundamental results.
Section 3 is dedicated to the pertur... | CAB | CBA | ACB | ACB | Selection 2 |
**A**: Correspondingly, a two-branch discriminator is developed to estimate the performance of this generation, which supervises the model to synthesize realistic pixels and sharp edges simultaneously for global optimization. In addition, we introduce a novel Bi-directional Gated Feature Fusion (Bi-GFF) module to integ... | CBA | ACB | CAB | 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 ... | BAC | BCA | CBA | ACB | Selection 3 |
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