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shuffled_text stringlengths 267 3.44k	A stringclasses 6 values	B stringclasses 6 values	C stringclasses 6 values	D stringclasses 6 values	label stringclasses 4 values
A: In this paper, we use properties of distributive latticesB: Here we introduce basic concepts and results on posets and lattices while making an effort to minimize new terminologyC: For a more detailed introduction to lattice theory see e.g., [Bir37, DP02, Gra09].	ABC	CAB	ACB	BCA	Selection 1
A: This section shows the limitations of the algorithmic content of [ure states and their measurementsB: Given a measurement apparatus E𝐸Eitalic_E, there is only a tiny fraction of quantum pure states on which E𝐸Eitalic_E’s application produces coherent information. This is independent of the number of measur...	CBA	ABC	BCA	CBA	Selection 3
A: Table VII demonstrates the significant performance improvement of context normalization over batch normalization (BN) when using the ViT architecture trained from scratch on CIFAR-100B: The train and test loss comparison in Figure 4 further supports this observation, showing that CN-Patches and CN-Channels a...	BAC	ACB	CBA	CBA	Selection 2
A: 7 presents the detection performance of DFB using ImageNet-1k as in-distribution dataset and on four OOD datasets, including two new high resolution datasets, ImageNet-O (Hendrycks et al., 2021) and SUN (Xiao et al., 2010)B: To examine the impact of the number of classes, we show the results using C∈{200,300...	BAC	BCA	CAB	ABC	Selection 2
A: The LLFlow framework learns the conditional distribution between low-light and normally-exposed images via the generative paradigm of normalizing flow [34]B: Another relevant state-of-the-art approach with respect to this work is the DL model LLFlow [15]C: The LLFlow architecture consists of an encoder a...	ACB	ACB	BAC	ACB	Selection 3
A: Encoding the participants’ answers to the questions in the survey (see encoding details in the Supplementary Material), we end up with 18 control variables characterizing the participants by the six groups of questions specified above, 5 control variables indicating the game, game type (Speed-race or Least-click...	ACB	ABC	CAB	BAC	Selection 2
A: The intermediate state score computations offer a direct means to evaluate the multi-modality alignment level.B: Hence, we advocate for quantifying the sequence-structure retrieval power to gauge the alignment prowess of the pretrained model. Reflecting on the contrastive alignment loss employed during pretr...	BAC	BAC	CAB	CBA	Selection 4
A: The experiment is conducted on the largest FB15K237 dataset, with accuracy measured by MRR. Specifically, we include LiftNet variants of 2 to 4 FC layers, and the results are shown in Table IXB: We see that in most cases, LiftNet with 2 TC layers achieves more accurate link prediction results than LiftNet wi...	BCA	BAC	ACB	ACB	Selection 1
A: One has to test all possible source nodes, which suggests that the run time of such an approach will grow linearly with the number of nodes in the networkB: Assuming that we already found the optimal fidelity curves between any two nodes in the network, one can easily find the fidelity curve associated with ...	ACB	CAB	BAC	CAB	Selection 3
A: Meanwhile, the MWD is an effective metric to evaluate ML performanceB: The SCL decoding is widely used for polar codes and its performance can approach the ML performance with limited list size [3]C: Thus, in this paper, we focus on the construction methods based on MWD to improve the performance of pola...	BAC	BCA	BCA	CAB	Selection 1
A: So for this tree we have that the mean depth is given byB: Thus we see that when there are signals the weights on leaf nodes are no longer proportional, but skewed further towards the longer branchesC: where the inequality follows from our assumption 1/2<p<112𝑝11/2<p<11 / 2 < italic_p < 1	ABC	CBA	ABC	ABC	Selection 2
A: To explore the potential benefits of a diffusion-based approach over a GAN-based approach, we include the state-of-the-art StyleGAN3 as a baseline Karras et alB: To allow a fair comparison, we fine-tune a pre-trained StyleGAN3 on the same hardware for the same number of steps. A blind comparison between Sta...	CAB	BAC	ACB	BAC	Selection 3
A: Our architecture advances scene reconstruction by providing an intuitive interface for layout manipulationB: Here, the input panel allows for adjustments in the attributes of bounding boxes, such as modifying the position and scale of the ’apple’ bounding box prior to composition. The refinement process fur...	ACB	CAB	CAB	CAB	Selection 1
A: Unfortunately, setting up benchmarks with synthetic data can be laboriousB: The process typically involves creating data sets for a number of different scenariosC: For example, on benchmarks with convex clusters drawn from probabilistic mixture models, the scenarios may involve “clusters of very differen...	ABC	ACB	CBA	CBA	Selection 1
A: Post-generation re-ranking is usually applied in two-stage systems, that is, generation and re-ranking, to re-score the output from the first stage by training an additional re-ranking module. This technique has been widely used in neural translation and summarization. For example, Ng et alB: Both methods u...	BCA	BAC	ACB	BAC	Selection 3
A: At the end of this section, we list a dictionary of nations in related literatureB: The eigenvalue decay rate (also known as the capacity condition or effective dimension condition) and source condition are mentioned in almost all related literature studying the convergence behaviors of kernel methods but a...	ABC	ABC	BCA	BAC	Selection 4
A: There has been recent work on inducing point optimization on discrete domains [10], however such methods only obtain comparable performance to methods based on greedy selection of the inducing points from the input domain, which are considerably conceptually simplerB: The main disadvantage of a greedy approa...	BAC	BCA	CAB	ACB	Selection 2
A: As shown in Figs. 5-8, we visualize the experiment results on all datasets with a 20% client participating ratio. It is evident that FedAgg dominates other state-of-the-art baseline methods with a faster convergence rate, higher model accuracy, lower training loss, and faster loss descending rate, which demonstr...	BAC	ACB	ABC	ACB	Selection 1
A: We utilize a pre-trained RoBERTalarge (Liu et al., 2019) and adopt SQuAD (Rajpurkar et al., 2016) v1.1 and v2.0 benchmarks for extractive question answering evaluationB: Exact Match (EM) and F1 scores on the dev set are reportedC: We refer to the Appendix for other language tasks. As shown in Table 4.2, ...	BAC	ABC	ACB	ACB	Selection 2
A: PAL (Zhang et al., 2022) aligns features of pasted pseudo action regions from two synthetic videosB: BSP (Xu et al., 2021c) introduces a novel boundary-sensitive pretext task via classifying the boundary types of synthetic videos. These techniques are elaborately designed for training models on long videos s...	BAC	CBA	BCA	BAC	Selection 3
A: However, they all perform mediocrely on standard benchmarks. Thus, we might question the reliability of their similarity scores, as well as the validity of interpretability insights derived from them. B: They all share a similar methodology: given a pair of feature representations of the same input, they est...	ACB	CAB	CBA	ABC	Selection 3
A: The major contributions of our study are summarized as follows: B: To investigate the effectiveness of the proposed methods, we conducted extensive experiments on three large-scale datasets [10, 41, 75] as well as off-the-shelf camerasC: This evaluation demonstrated that our method notably outperforms co...	ABC	BCA	ABC	CAB	Selection 4
A: Then, we prove the following chain of implications: (iii) ⟹⟹\Longrightarrow⟹ (iv), followed by (iv) ⟹\implies⟹ (v), (v) ⟹\implies⟹ (vi), and finally (vi) ⟹\implies⟹ (ii). This ensures that all the six statements are equivalent.B: We first prove the equivalence among statements (i), (ii) and (iii)C: To sh...	CAB	BAC	CBA	ABC	Selection 3
A: They created a fault localization and repair pipeline to localize and fix discrepancies (Louloudakis et al., 2023b).B: Louloudakis et alC: studied behavioral issues resulting from framework-to-framework conversion (Louloudakis et al., 2023a). They found failures in 10 out of 36 conversions	CAB	BCA	BAC	BAC	Selection 1
A: In this case, the Web GUI is the component in charge of generating and uploading the mission that each drone is going to perform. We used the fused GPS signal for state estimation and a PID is used as the high-level controller. B: For this mission, since we will use HITL simulation, all the modules remained ...	ABC	ABC	ABC	CBA	Selection 4
A: Value refers to utility, performance, and attractiveness (Maher, 2010)B: Due to the large impact LLMs are already having (Bommasani et al., 2021) and the quality of outputs of the systems based on them (Stevenson et al., 2022b), it is possible to argue that the artifacts produced by them are indeed valuable....	ACB	CBA	CBA	CAB	Selection 1
A: However, medical data often involve private information, which makes them not shareableB: In this setting, we are confined to utilizing only a pre-trained source model along with unlabeled samples from the target domain, thereby circumventing the need for direct access to the sensitive source data.C: Con...	ACB	CBA	CBA	BAC	Selection 1
A: The scale of the problems can grow quickly as the size of the system and the number of scenarios growB: Therefore, an affine policy is often used to approximate (2) and (5)C: However, finding a good policy that satisfies the constraints (3c),(3d), (3e), (6f), (6g), and (6h) can be difficult. In the next ...	ABC	BAC	ACB	BCA	Selection 1
A: Note that our goal in this experiment is mainly to compare our self-supervised BNNs against other BNN methods on equal grounds, not necessarily to reach state-of-the-art performance on the used benchmark datasets. Indeed, reaching higher performances usually requires computationally expensive hyperparameter tuni...	ABC	CBA	BCA	BAC	Selection 1
A: Indeed, under more restrictive assumption on the measure, a mmpds can equivalently be completely characterized by the process of ball volumes of the finite-dimensional distributions (fidis), via Kolmogorov’s extension theoremB: This result is particularly useful because even though the underlying metric meas...	BCA	CAB	CAB	BAC	Selection 1

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