Split (1)

train · 2.93k rows

shuffled_text stringlengths 267 3.92k	A stringclasses 6 values	B stringclasses 6 values	C stringclasses 6 values	D stringclasses 6 values	label stringclasses 4 values
A: The polymorphism clone of a first-order structure, containing all finitary operations that preserve the structure, holds valuable information and serves as a powerful analytical tool. Clones are also essential in theoretical computer science, especially in the context of constraint satisfaction problems (CSPs) (...	CAB	BAC	ACB	CBA	Selection 2
A: In general, to solve a real-world problem, one typically formulates the problem as an instance of a computational problem and proceeds to find a solution with the help of an optimization algorithmB: However, this is not always an easy task, and the abstraction to a mathematical formulation is usually just a ...	CAB	ABC	CBA	BCA	Selection 4
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...	ABC	CBA	CBA	BAC	Selection 4
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: Both CN-Patches and CN-Channels approaches outperform BN by approximately 10% and 18% in terms of accuracy and top-5 accura...	CBA	BCA	CBA	ABC	Selection 4
A: In the other three OOD datasets without prominent foreground objects, the foreground branch of DFB focuses on fewer areas compared to the vanilla classifier while its background branch recognizes most of the background areas, in which the background OOD scores would exert greater influence on the OOD detection. ...	BAC	ABC	CBA	BCA	Selection 3
A: The LLFlow framework learns the conditional distribution between low-light and normally-exposed images via the generative paradigm of normalizing flow [34]B: The LLFlow architecture consists of an encoder as well as an invertible network, trained by minimizing negative log likelihood. The encoder of LLFlow p...	ACB	BCA	CBA	CAB	Selection 2
A: Specifically, among participants who chose to play the games under time constraints, younger and left-handed participants (third principal component of reported spatial abilities) tend to navigate through more unique paths to reach the target (Table 1)B: Our regression analysis on the uniqueness scores of n...	CAB	ACB	BAC	CAB	Selection 3
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...	ABC	ABC	CAB	CBA	Selection 4
A: In this paper, we propose a simple LiftNet method that helps improve the parameter efficiency of conventional KGE modelsB: LiftNet adopts a multi-layer neural network to enhance the expressiveness of low-dimensional entity representationsC: Experiments conducted on three public knowledge graph datasets ...	CBA	ABC	CAB	CAB	Selection 2
A: For example, one may apply link purification such that from a larger number of qubits having a low fidelity one can create a lower number of qubits holding a higher fidelity. The technology considered in this paper is based on photon entanglement, and, in this case, the achieved fidelity of a qubit pair is chief...	CBA	BCA	ACB	CAB	Selection 1
A: 8 are shown in Table III.B: FigC: 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 Fig	CAB	BAC	BAC	ABC	Selection 1
A: where the inequality follows from our assumption 1/2<p<112𝑝11/2<p<11 / 2 < italic_p < 1B: So for this tree we have that the mean depth is given byC: Thus we see that when there are signals the weights on leaf nodes are no longer proportional, but skewed further towards the longer branches	CAB	ACB	CAB	CAB	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: (2021)C: To allow a fair comparison, we fine-tune a pre-trained StyleGAN3 on the same hardware for the same number of steps. A blind compariso...	BAC	ABC	CBA	BAC	Selection 2
A: We provided four groups of generated 3D assets (refer to the supplementary material) to each participantB: Generative Quality EvaluationC: For each group, the 3D assets were created using Latent-NeRF, SJC, and our method, all based on the same text prompt. Participants were then asked to assess the quali...	CAB	BAC	BCA	ACB	Selection 2
A: Existing data generators do not cater directly to such high-level scenariosB: Instead, the user must carefully tune simulation parameters to arrive at the desired scenarios (Steinley and Henson, (2005), Schubert and Zimek, (2019), Iglesias et al., (2019))C: While some generators make it easy to control ...	ABC	CAB	BCA	BAC	Selection 1
A: In order to demonstrate the ability of our model to select event candidates, we analyze the results of two instances selected from the test setB: As shown in Table 3, our proposed model successfully extracts the missing events not detected by the baselines. The re-ranking mechanism enables the model to selec...	ACB	CBA	CAB	BAC	Selection 1
A: We believe that studying the misspecified case in our paper is a crucial step to remove the Gaussian design assumption and draw complete conclusions about the learning curves of kernel ridge regression (or further, general spectral algorithms). B: In addition, we also notice a line of work which studies the ...	ABC	ABC	CAB	ABC	Selection 3
A: We use the aforementioned evaluation procedure to compare our method (denoted GP) empirically to a number of competing simplification techniques discussed in Section 2B: We compare our approach to PC-Simp, AIVS, Potamias et al., HC and WLOP, with the latter two approaches implemented using the CGAL library**...	CBA	CBA	ABC	CBA	Selection 3
A: System model and problem formulation are given in Section III. In Section IV, an analysis of the adaptive learning rate is presentedB: We provide the convergence analysis in Section V. Experimental results are shown in Section VI. We conclude this paper in Section VII.C: The rest of this paper is organi...	BCA	BAC	ACB	BAC	Selection 1
A: Finally, we extend our approach for efficient fine-tuning of language and vision models in Section LABEL:s4. B: Then, we present the details of zero-initialized attention mechanisms with zero gating in Section 3.2, and generalize LLaMA-Adapter for multi-modal reasoning in Section 3.3C: In Section 3.1, we...	ABC	ACB	CBA	ACB	Selection 3
A: 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 such as movies or TV dramas, which contains natural scene changes.B: Specifically, TSP (Alwassel et al...	CAB	CBA	BAC	CBA	Selection 1
A: For language experiments, we use the MultiBERTs (Sellam et al., 2021), a set of 25 BERT models, differing only in their weights initializationB: For vision experiments, we pre-train 10 visual transformer (ViT) models (Dosovitskiy et al., 2020) on the ImageNet-1k dataset (Russakovsky et al., 2015)C: Then...	ACB	ABC	BCA	ACB	Selection 2
A: Following [59], we also evaluated calibration methods using off-the-shelf cameras to validate the effectiveness of our methodB: Figure 7 shows the qualitative results using off-the-shelf fisheye cameras using SL-MH for trainingC: Our method meaningfully outperformed Lochman et al.’s method [35] in terms ...	BAC	ABC	BCA	CAB	Selection 2
A: 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 caseB: Our conditions do not require any assumption on the graph, whose topology is just assumed to be time-i...	BAC	ABC	CBA	BAC	Selection 2
A: Compared to real models, which had 20 instances, synthetic models had 320 instances where the inference results exceeded the thresholdB: Behavioural Differences: We observed a large fraction of behavioural differences (incorrect output) with synthetic modelsC: The majority of these instances were observe...	CAB	BAC	CBA	ABC	Selection 2
A: These are components that serve as interfaces with multiple kinds of aerial platforms and sensorsB: Aerostack2 has defined standard interfaces that allow operating with both physical platforms and simulated platforms indistinctly. C: Sensor-Actuator interface	ACB	ABC	CAB	BCA	Selection 4
A: However, paraphrasing Chalmers (Chalmers, 1996), these appear as easy problems to solve in order to achieve creativity, since solutions to them can be identified by taking into consideration the underlying training and inference processes. The hard problem in machine creativity is about the intentionality and th...	BCA	ACB	ABC	BAC	Selection 3
A: (a) Existing SFUDA object detection works utilize feature alignment or sample generation to help with the pseudo labelingB: (b) Our proposed SUP-ICI utilizes instance-level contrastive learning (CL) to make use of the foreground-background semantic information of the unlabeled target images. Our weighted ent...	CBA	CBA	ACB	ABC	Selection 3
A: Our algorithm learns the first-stage solutions to the scenario-based problems in (3) and (6), respectively. We implement our learning algorithm in Google Colab [54] using Pytorch and all codes and data of our experiments are available at https://github.com/zhang-linnng/two-stage-dcopf-neural-solver.git.B: P...	ACB	CBA	BAC	ACB	Selection 2
A: The methods that incorporate unlabelled data perform best by far, with our method slightly outperforming SimCLR.B: We use predictive entropy for SimCLR, which does not provide epistemic uncertainty estimates. Mean and std. shown (3 seeds)C: For the self-supervised BNN and the ensemble, we acquire points ...	ABC	CBA	BCA	CAB	Selection 2
A: In fact, an important issue from the point of view of inference is that sophisticated shape descriptors such as those in TDA take values in Polish spaces that lack a vector space structure. Unfortunately, Polish spaces arising as targets for shape descriptors are hard to work with directlyB: In particular, t...	CBA	ABC	CAB	CAB	Selection 2

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