Split (1)

train · 2.93k rows

shuffled_text stringlengths 267 3.81k	A stringclasses 6 values	B stringclasses 6 values	C stringclasses 6 values	D stringclasses 6 values	label stringclasses 4 values
A: Such an approach is useful when the number of optimal solutions is small, but in most cases (as in the Minimum s𝑠sitalic_s-t𝑡titalic_t Cut problem) the number of optimal solutions can be exponential in the input size, rendering the approach infeasibleB: Another approach is to present only a small number k�...	ABC	BAC	BCA	ACB	Selection 3
A: We provide an means to upper bound the probabilities between general spaces using computable non-probabilistic measure coversB: We also show how to lower bound information between probabilities over general spaces with information between probabilities over finite sequences using uniformly enumerable disjoi...	CBA	ACB	BAC	BCA	Selection 3
A: It is important to mention that the baseline models (ViT with standard preprocessing and ViT with batch normalization) collapsed in this blended dataset as the two datasets have different structures, and simple normalization does not allow a suitable representation of the data. Context normalization, on the othe...	ABC	ACB	CBA	CBA	Selection 1
A: To examine the impact of the number of classes, we show the results using C∈{200,300,500,1000}𝐶2003005001000\mathit{C}\in\{200,300,500,1000\}italic_C ∈ { 200 , 300 , 500 , 1000 } randomly selected ID classes from ImageNet-1k (C=1000𝐶1000C=1000italic_C = 1000 is the full ImageNet-1k data).B: Another challen...	BCA	CAB	ABC	ACB	Selection 2
A: In this paper, we present a framework for post-processing images which have undergone low-light image enhancement. The enhancement of low-light images often reveals a variety of degradations which are hidden in the dark, and thus a need for post-processing is introduced. Furthermore, each low-light enhancement t...	ABC	CAB	CAB	BAC	Selection 1
A: Another motivation for our analysis stems from the connection between navigation in the physical space and knowledge spaceB: Previous research has demonstrated that the same neural regions that are responsible for navigation in physical space are also involved in navigating the knowledge space: the hippocam...	ABC	ACB	BAC	CBA	Selection 1
A: We introduce the protein sequence design task (also known as protein inverse folding), which involves predicting protein sequences based on corresponding protein backbone atomic coordinatesB: Key metrics for this task include perplexity and accuracy of sequence recovery. Additionally, we incorporate protein ...	CBA	BCA	BAC	CAB	Selection 2
A: We implement all the methods with OpenKE [33], which is a pytorch-based open-source framework for knowledge embedding111Codes are available at https://github.com/brcai/LiftNet. We run TransE, TransH, DistMult, and ComplEx with low-dimensional (16) and high-dimensional (512) embedding dimensions to show the diff...	ABC	BAC	BAC	CBA	Selection 1
A: All discovered nodes are added to a priority queue that orders them according to the priority value. The algorithm then moves to the path with the highest priority that connects the source node to a node i𝑖iitalic_i. This path is then extended to connect the source node to all neighbors of node i𝑖iitalic_i, ca...	BAC	CBA	BAC	CAB	Selection 2
A: II describes the preliminaries of polar codes, MWD and SCL decodingB: The properties of MWUB are shown in Section III. In Section IV, we introduce the concept of partial MWD and propose the MWD sequenceC: The ECBS algorithm is proposed in Section V. Section VI shows the performance of polar codes constru...	CBA	CAB	ABC	CAB	Selection 3
A: Thus we see that when there are signals the weights on leaf nodes are no longer proportional, but skewed further towards the longer branchesB: where the inequality follows from our assumption 1/2<p<112𝑝11/2<p<11 / 2 < italic_p < 1C: So for this tree we have that the mean depth is given by	ABC	CBA	CAB	BAC	Selection 4
A: 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 Stable Diffusion and StyleGAN3 was made by an expert prostate radiologist, who compared 50 pairs of images generated by the two methods, shown side-to-side and randomized...	BAC	CBA	ACB	ACB	Selection 2
A: In contrast to the mesh-based method in Fantasia3D, which requires considerable human effort in mesh modification and graphics engine support for editing, CompoNeRF offers a more streamlined processB: CompoNeRF’s distinctive feature lies in its capability to recompose scenes by interfacing with decomposed Ne...	CAB	CBA	BAC	CAB	Selection 3
A: We use a large language model to map a user’s verbal description of a scenario onto these parameters. Although our approach is based on ellipsoidal clusters, we have implemented two post-processing functions for generating more irregular cluster shapesB: The first makes clusters non-convex by passing them th...	BCA	ABC	CBA	ACB	Selection 1
A: Building upon previous works (Wadden et al., 2019; Lin et al., 2020) that split and preprocess this dataset, we use two variants for the event extraction dataset, namely ACE05-E and ACE05-E+B: Detailed split and statistics of the two datasets can be found in Table 1. C: In this work, we evaluate our COFF...	ABC	CBA	ABC	BCA	Selection 4
A: As mentioned in Fischer and Steinwart (2020), the empirical process and the integral operator techniques are the two main techniques used to derive the learning rates of kernel methods. Steinwart et alB: (2009) firstly introduced the embedding property of RKHS for the empirical process technique. Fischer and...	BAC	ACB	ABC	CAB	Selection 3
A: Section 2 briefly reviews a number of existing point cloud simplification techniques which are relevant to our workB: Section 3 provides background details regarding the computation of surface variation, GPs with kernels defined on non-Euclidean domains and a greedy subset-of-data scheme for GP inference**C...	CAB	ACB	CAB	ABC	Selection 4
A: As depicted in Fig. 12, the decrement of the hyperparameter α𝛼\alphaitalic_α demonstrates that the FL framework accentuates the optimization of the discrepancy between the local model of client i𝑖iitalic_i and the average local model, which in turn, bolsters the precision of the global model and expedites the ...	BCA	CBA	CBA	CBA	Selection 1
A: In Table 2 for the ScienceQA performance, our single-modal ‘LLaMA-AdapterT’ attains 78.31% accuracy, surpassing several traditional VQA methods with large parametersB: This is because, LLaVA requires fine-tuning the entire 7B LLM, and Mini-GPT4 adopts Vicuna (Chiang et al., 2023) that also fully fine-tunes L...	ACB	ABC	CAB	BCA	Selection 1
A: It takes about one day for the whole pre-training stage.B: We implement S-ViLM in JAX and train all models on TPU acceleratorsC: During pre-training, SGD with momentum 0.9 and initial learning rate 0.1 is used for optimization. We train S-ViLM for 10 epochs with a batch size 1024 and adopt a cosine learn...	BCA	CBA	CAB	BCA	Selection 3
A: We then sample 10 different caption batches, either randomly or using FAISS (as before), and compute the similarity between the image representation and each random/FAISS-retrieved caption representation. If the highest similarity is between the image representation and the original caption representation, we ma...	CBA	BCA	BCA	BAC	Selection 1
A: Figure 8 shows the qualitative results of indoor scenes obtained by our methodB: The indoor image degraded the performance of our method because of the domain gap between indoor and outdoor environments. In this paper, we focused on outdoor scenes following the studies of conventional learning-based methods ...	ABC	CAB	BAC	ACB	Selection 4
A: Moreover, the proposed conditions also apply to systems where a direct input-to-output channel is present.B: We have provided necessary and sufficient conditions for the synchronization of identical linear SISO systems, with a guaranteed convergence rate, both in the continuous-time and in the discrete-time ...	CAB	BAC	CBA	BCA	Selection 1
A: Louloudakis et alB: studied behavioral issues resulting from framework-to-framework conversion (Louloudakis et al., 2023a). They found failures in 10 out of 36 conversionsC: They created a fault localization and repair pipeline to localize and fix discrepancies (Louloudakis et al., 2023b).	BAC	ABC	CBA	BCA	Selection 2
A: Generic FCUs offer versatility, accommodating various frames and components for customizable configurations, which is beneficial for developersB: FCUs can be classified into two blocks: generic or embedded, each presenting distinct advantages and limitationsC: However, their integration and calibration ...	BAC	ACB	CAB	ACB	Selection 1
A: However, finding the correct input and high-quality demonstrations for solving this type of task can be challenging (Liu et al., 2022). Certain domains might require more fine-grained knowledge than that acquired during pre-training (Peng et al., 2023). Because of this, other methods to adapt a pre-trained model...	CBA	CAB	ABC	ACB	Selection 2
A: In [25], the authors incorporate a deconvolutional structure in Faster RCNN for detecting the nodules on axial slices. In [26], the multi-view ConvNets is proposed for pulmonary nodule detection, which take a set of 2D patches from differently oriented planes as input. The final results are obtained by delicatel...	CBA	CAB	BAC	BCA	Selection 2
A: This allows it to solve a simplified version of the original problem and save solving time. Further information on how to implement the affine policies for each specific application can be found in Appendix G. B: We also compare the solutions produced by our method to that by using the affine policy method, ...	BAC	CAB	ABC	BCA	Selection 2
A: Unlike BNNs, we can condition the parameters of a DGM on unlabelled data. In contrast, our approach does not model the data distribution—the unlabelled data is used to construct pseudo-labelled tasks that encode our prior beliefs. Self-supervised BNNs are discriminative models, which tend to be more scalable and...	CAB	CBA	BAC	BCA	Selection 2
A: This result is particularly useful because even though the underlying metric measure dynamical system need not satisfy the necessary assumptions, (e.g., is not finite-dimensional or approximable by finite-dimensional spaces) the target spaces for most stable shape descriptors used in practice do.B: Indeed, u...	ABC	BAC	BCA	CBA	Selection 4

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