Split (1)

train · 2.93k rows

shuffled_text stringlengths 267 2.96k	A stringclasses 6 values	B stringclasses 6 values	C stringclasses 6 values	D stringclasses 6 values	label stringclasses 4 values
A: This approach is widely followed in the literature (e.gB: Bruno Poizat fully developed the topology-free approach in [19]. In [20, 21] R. Pöschel characterised the Galois closed sets of relations with the help of infinitary operations.C: see [4]). The topology-free approach takes into account the cardina...	ACB	BCA	CAB	CBA	Selection 1
A: The algorithm works by traversing the graph from left to right in iterations while marking the vertices it visitsB: Each iteration consists of two parts: a marking step, and a cut-finding step. In the marking step (Lines 3-9), the algorithm identifies currently invalid edges by marking the non-path neighbor...	CBA	BCA	BCA	ACB	Selection 4
A: 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 measurement outcomes of E𝐸Eitalic_E. B: This section shows the limitations of the algorithmic content of [u...	BAC	CAB	CBA	ACB	Selection 3
A: Despite the good performance, the effect of BN is dependent on the mini-batch size, andB: If the samples within the mini-batch are from the same distribution, the transformation in Equation (1) generates a zero mean and unit variance distributionC: This zero-mean and unit-variance constraint allows stabi...	ABC	ACB	CAB	ABC	Selection 3
A: There are generally two types of post-hoc OOD detection approaches, including raw logit-based and softmax probability-based methodsB: Our background-based OOD score is based on an unbounded logit value, which can dominant the overall OOD score when combining with the foreground-based OOD score using the soft...	CBA	BAC	BAC	ABC	Selection 4
A: The U-Net of the LPDM consists of 4 downsampling stages (encoder) and 4 upsampling stages (decoder), with 2 residual blocks per stage. Between the encoder and the decoder is a middle block which processes the latent encodingB: The middle block contains 2 residual blocks which surround a scaled dot-product at...	BCA	CAB	BAC	ABC	Selection 4
A: Regarding other traits, distinctions emerge when considering the two categories of constraintsB: Among participants who chose to play Speed-race games involving time constraints, superior performance is exhibited by male participants with an Asian ethnic background who do not speak a foreign language at a na...	ACB	BCA	ABC	BCA	Selection 3
A: Based on this observation, we pose the question: Can we augment protein structure model training supervised by robust pretrained protein language models? B: Considering there is a natural pairing relationship between structure and sequence, establishing this relationship can help guide structural learning wi...	BAC	CBA	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	BCA	BAC	BAC	Selection 1
A: Note that we did not specify how the priority value of each node is computed simply because we do not know what the optimal way to compute such a value isB: This process continues until the priority queue is emptyC: Prioritizing paths with a low likelihood of being dominated will reduce the number of rec...	ABC	ABC	BAC	CAB	Selection 3
A: FigB: 8 provides the BLER performance of (512,256)512256\left(512,256\right)( 512 , 256 ) and (512,384)512384\left(512,384\right)( 512 , 384 ) polar codes with different construction methods. The MWDs of the polar codes in FigC: 8 are shown in Table III.	ABC	CAB	CBA	ACB	Selection 1
A: where the inequality follows from our assumption 1/2<p<112𝑝11/2<p<11 / 2 < italic_p < 1B: Thus we see that when there are signals the weights on leaf nodes are no longer proportional, but skewed further towards the longer branchesC: So for this tree we have that the mean depth is given by	CBA	BAC	ABC	CAB	Selection 3
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...	ACB	BAC	ACB	CBA	Selection 4
A: This supplementary global oversight guarantees a harmonious rendering that upholds the unique identities of the objects while fostering overall compositional unityB: Its vital role is underscored by its absence in (c), where its omission brings collapsed results. We refer readers to our suppl. for more ablat...	CBA	CBA	BAC	BCA	Selection 4
A: Version 1.0.0B: In the rare case that the natural language workflow fails, repliclust throws an error. We release all few-shot examples and prompt templates in the natural_language module of our code base. For convenience, they are reproduced in Appendix F. C: of repliclust uses 22-shot prompting to map ...	ABC	ABC	ACB	ABC	Selection 3
A: For example, ‘home’ is an entity that serves as both the ‘Place’ argument of the ‘Transport’ event and the ‘Destination’ argument of the ‘Attack’ event in Figure 1. B: Event Argument: Event arguments identify the entities involved in events and their roles based on their relationships with the event triggers...	CAB	CBA	BAC	ABC	Selection 1
A: Recall that in this paper we denote the eigenvalue decay rate as β𝛽\betaitalic_β and denote the source condition as s𝑠sitalic_s. Table 1 summarizes the notations used in some of the references.B: The eigenvalue decay rate (also known as the capacity condition or effective dimension condition) and source c...	CBA	BCA	CAB	BCA	Selection 3
A: Furthermore, we validate our technique’s feature-sensitive approach on real-world scanning datasets captured using different acquisition devices. Firstly, we use a desk scene point cloud from the NYU Depth V2 dataset, derived from RGBD data acquired using RGB and Depth cameras from Microsoft Kinect. This cloud ...	ACB	BAC	BAC	CAB	Selection 1
A: Wang et al. [27] propose AsyncFedED, considering the staleness of the arrived gradients measured by the Euclidean distance between the stale and the current global model, as well as the number of local epochs. Zhang et al. [28] propose a semi-asynchronous clustered FL framework, named FedMDS, which adopts a clus...	CAB	BCA	BAC	CAB	Selection 2
A: Starting from 175 human-written instruction-output pairs (Wang et al., 2022a), Alpaca leverages GPT-3.5 (Brown et al., 2020) to expand the training data to 52K in a self-instruct mannerB: Supervised by this, Alpaca fine-tunes the entire 7B parameters in LLaMA, producing an exceptional instruction model that ...	BCA	ACB	ABC	CAB	Selection 1
A: We evaluate the performance on ActivityNet (Heilbron et al., 2015), an action understanding dataset of 19,994 temporally annotated untrimmed videos with 200 action categories.B: Temporal action localization (TAL)C: TAL aims at predicting the temporal extent and the labels of action instances	BCA	BCA	CAB	CBA	Selection 3
A: None of the similarity measures commonly used in NNs analysis uses negative examples to estimate the similarity of a given pair (Section 2)B: However, based on knowledge from other examples, we can construct a better similarity measure.C: Given two examples, these measures output a scalar that represent...	CAB	BCA	ACB	CBA	Selection 3
A: In addition, Yan et al. [68] also proposed a distortion-aware loss for the distortion of equirectangular projection and an uncertainty-aware loss for the inaccuracy in non-smooth regions. The proposed method using these loss functions achieved high accuracy for panoramic depth completion. These panoramic depth c...	CBA	CBA	CBA	CAB	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	CBA	ACB	CBA	Selection 1
A: The majority of these instances were observed in the tf2onnx converter. For both converters, we disclosed such instances to the respective engineering teams (we have not heard a response yet).B: Compared to real models, which had 20 instances, synthetic models had 320 instances where the inference results ex...	BAC	CBA	CAB	ABC	Selection 2
A: Regarding the level of user expertise, we can differentiate two blocks of tools:B: The components of this layer are designed to ease the definition of a mission to a human operator or to help with the supervision of the systemC: The top layer of the proposed architecture is mainly dedicated to the user ...	ABC	ABC	CBA	BCA	Selection 3
A: In particular, deep language models, i.e., probabilistic models of in-context token occurrences trained on a corpus of text with deep learning, easily allow the sampling of new text, facilitating and automating natural language generationB: For instance, recurrent neural networks with long-short term memory ...	BAC	CAB	BAC	ABC	Selection 4
A: [47] propose a two-stage method named CPGA, which first utilizes the classifier of the source model to generate source prototypes via contrastive learning, and then align each pseudo-labeled target data to the corresponding source prototypes. Differently, in [48, 49], the authors define the local affinity of the...	BCA	ACB	BAC	CBA	Selection 3
A: To take uncertainties in the net-load into account,111In this paper, we use the term net-load to capture both renewable generation in the system [5] and the loadB: In these problems, decisions are made sequentially at each stage, based on the forecast of the net-load and the fact that additional adjustments ...	BAC	ACB	ABC	ABC	Selection 2
A: At a high level, self-supervised BNNs allow unlabelled data to be incorporated by using it to learn a powerful prior that captures known similarities between inputs.B: To overcome this limitation, we introduce Self-Supervised BNNsC: Conventional BNNs are unable to use unlabelled data to improve their pr...	ABC	CAB	BAC	CBA	Selection 4
A: A consequence of the preceding theorems is that one can often characterize the geometric features as captured by shape descriptors of a Polish-valued process X𝑋Xitalic_X via the ball volume processes of the fidisB: This is convenient for hypothesis testingC: For example, note that the ball volume corres...	CBA	ABC	CAB	ACB	Selection 2

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