Split (1)

train · 2.93k rows

shuffled_text stringlengths 267 3.82k	A stringclasses 6 values	B stringclasses 6 values	C stringclasses 6 values	D stringclasses 6 values	label stringclasses 4 values
A: see [4]). The topology-free approach takes into account the cardinality of the basic set A𝐴Aitalic_A and the arity of operationsB: 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....	CAB	BCA	CAB	ACB	Selection 2
A: [GGK+22] recently developed frameworks to design approximation algorithms for diverse variants of combinatorial problemsB: Along the same line, Hanaka et al. [HKK+22] and Gao et alC: On the positive side, diverse variants of other classic problems are known to be polynomially solvable when considering c...	BAC	ACB	ABC	CBA	Selection 1
A: We also show how to lower bound information between probabilities over general spaces with information between probabilities over finite sequences using uniformly enumerable disjoint open setsB: We look at the average information between measures by using probability measures over spaces of measures.C: ...	BAC	BCA	CBA	ACB	Selection 4
A: Our short-term perspective consists in merging seamlessly a gradient free optimization algorithm with a gradient-based error optimizer in order to reach global convergenceB: This precision margin gained allows gaining insight into the neuron activation level sensitivity. C: We believe that this objective...	ACB	CBA	CBA	BCA	Selection 1
A: 4 and 5 respectively. We can see in FigB: The Reasons behind the Effectiveness of DFB. We aim to understand the effectiveness of DFB from two perspectives, including the foreground and background OOD scoring, and the latent features learned in DFB, with the results on the Textures dataset reported in Figs**C...	CAB	CAB	BAC	ACB	Selection 3
A: Existing denoising techniques can be applied to denoise low-light images either before or after contrast enhancement [9, 10]B: These denoising techniques range from low-pass filters and algorithms such as block matching and 3D filtering (BM3D) [11], to state-of-the-art DL denoisers [9, 12]C: Despite den...	CBA	CAB	ABC	BCA	Selection 3
A: JK and MZ were partially supported through ERC grant No. 810115-DYNASETB: We are grateful to Csaba Pleh, Peter Kardos, and Markus Strohmaier for their valuable advice. This project was supported by the Humboldt Foundation within the Research Group Linkage ProgramC: MZ acknowledges further support from 10...	CAB	BCA	ABC	BAC	Selection 4
A: Protein structure encoders face two main challenges: 1) Data scarcity. The number of reported protein structures is significantly lower than datasets in other machine learning fields due to the challenges of experimental protein structure determinationB: Unlike sequences, traditional self-supervised languag...	BCA	ACB	BAC	BAC	Selection 2
A: The input dimensions highly affect the parameter efficiency of LiftNet-based methods; thus we analyze its influence on the model performanceB: We evaluate LN-TransE, LN-TransH, LN-DistMult, and LiftNet-ComplEx with four input dimensions {4,16,64,256}41664256\{4,16,64,256\}{ 4 , 16 , 64 , 256 } on WN18RR dat...	ABC	CBA	CBA	CAB	Selection 1
A: Nevertheless, the proposed approach allows for the addition of as much detail as needed, provided that monotonic and isotonic routing metrics can still be defined. An interesting way to merge these two directions would be to reformulate this work as a non-linear programming problem. In its present form, the prop...	BCA	CAB	ABC	ACB	Selection 2
A: FigB: The required SNRs of MWUB equal to the target BLER are also provided.C: 5 shows the minimum required SNRs of polar codes constructed by polar sequence [4], GA algorithm [7] and MWD sequence to achieve the target BLER under the AWGN channel with the code rate range R=0.0625∼0.9375𝑅0.0625similar-to...	BAC	ACB	BAC	ABC	Selection 2
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	BCA	BAC	BCA	ABC	Selection 2
A: Pre-trained models are often trained on 2D RGB datasets, but many medical imaging modalities are 3D. Recently, studies such as Khader et alB: (2023) and Pinaya et alC: (2022) have trained diffusion models from scratch on 3D data or even on 4D data Kim and Ye (2022), and Han et al. (2023) use diffusion mo...	BAC	CAB	ABC	BCA	Selection 3
A: In our investigations, demonstrated in Fig. 17, we identified a ’floating’ issue when bounding box overlaps are absentB: Such non-interaction can pose challenges, as it does not provide the necessary contiguous context for the global semantics to incorporate these objects seamlessly. To rectify this, one str...	BAC	BAC	ABC	ACB	Selection 4
A: First, the algorithm samples a new probabilistic mixture model whose geometric structure matches the archetypeB: Second, we draw i.i.d. samples from this mixture model to generate a data set. Figure 1 illustrates this flow.C: Once an archetype has been defined, sampling concrete data sets proceeds in tw...	BAC	CBA	CBA	BCA	Selection 4
A: BART-Gen Li et alB: Constrained generation is applied for argument extraction that requires event-specific templates. C: (2021) is designed for document-level event extraction that can deal with the long-distance dependence issue and co-reference problem	ACB	CBA	ABC	CAB	Selection 1
A: Specifically, Steinwart and Scovel (2012, Theorem 4.6) reveals that for 0<s<10𝑠10<s<10 < italic_s < 1,B: It is worth pointing out the relation between the definition (5) and the interpolation space defined through the real method (real interpolation)C: For details of interpolation of Banach spaces thro...	CAB	ACB	ACB	ACB	Selection 1
A: However, as discussed in Section 2, tuning the user-specified HC parameters make striking a balance between feature preservation and retaining a sufficient density of points across the cloud relatively challenging. Moreover, there is no control over the size of the simplified cloud, as discussed by the authors [...	BCA	CAB	ABC	ACB	Selection 1
A: Liu et al. [25] design AAFL mechanism, which adaptively determines the optimal fraction of clients participating in global aggregation based on resource constraints. Liu et al. [26] introduce Fed2A which allows clients to upload shallow and deep layers of DNNs adaptively to improve their performance in a hetero...	CAB	BCA	ACB	CBA	Selection 3
A: To promote the open source of instruction models, Stanford Alpaca (Taori et al., 2023) fine-tunes all the 7B parameters of LLaMA (Touvron et al., 2023) with 52K self-instruct data.B: These methods normally enhance the pre-trained LLMs by fine-tuning them with high-quality instruction-output data pairsC: ...	CBA	BAC	BAC	CAB	Selection 4
A: TAL aims at predicting the temporal extent and the labels of action instancesB: Temporal action localization (TAL)C: 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.	BCA	CBA	BCA	BAC	Selection 4
A: However, using FAISS (red boxes) causes a big decrease in DeepCKA accuracy, while ContraSim maintains high accuracy. Furthermore, in 3 of 4 pairs we tested, FAISS sampling yielded better CKA accuracy than random samplingB: This might indicate that CKA suffers from stability issues.C: This contradicts the...	ABC	BCA	ACB	BAC	Selection 3
A: The VP estimator is trained using the modified HRNet loss function described aboveB: In addition, the distortion estimator is trained using the harmonic non-grid bearing loss [59].C: Training. Using the generated fisheye images with ground-truth camera parameters in Section 4.1 and VP/ADP labels in Secti...	BAC	CAB	ACB	BCA	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: To show the equivalence of the six statements in Theorem 1, the proof is struct...	ACB	ABC	ABC	CAB	Selection 4
A: Behavioural Differences: We observed a large fraction of behavioural differences (incorrect output) with synthetic modelsB: Compared to real models, which had 20 instances, synthetic models had 320 instances where the inference results exceeded the thresholdC: The majority of these instances were observe...	ACB	BAC	BAC	ABC	Selection 4
A: In Table I some relevant flight controller projects are listed. These projects may cover both hardware and software development of these controllersB: In 2018 Ebeid et al. presented a survey of open-source hardware and software comparing their main features [7]C: They range from Open Source Hardware (OSH...	CAB	BAC	CAB	CBA	Selection 2
A: (2012)). Some approaches combine all of them together (Gervás, 2013). B: Examples include the Computerized Haiku by Margaret Masterman555http://www.in-vacua.com/cgi-bin/haiku.pl, the storyteller TALE-SPIN (Meehan, 1977), Racter and its poems’ book (Racter, 1984), and UNIVERSE, which was able to generate cohe...	BCA	CBA	ABC	CAB	Selection 2
A: This can be seen from the recall rates at the large average numbers of false positives per CT image in Table VI. However, if the β𝛽\betaitalic_β is too large, then the teacher model will barely change. These results demonstrate the necessity of dealing with the label noise for the teacher-student mutual learnin...	BCA	CAB	ABC	ACB	Selection 2
A: In this section, we provide the experimental results of using the proposed algorithm in Table I to solve two-stage DCOPF problemsB: 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 Pytor...	ABC	BAC	ACB	BAC	Selection 3
A: We also report the accuracy and expected calibration error (ECE) in Appendix Table D.1. We further assess out-of-distribution (OOD) generalisation from CIFAR10 to CIFAR10-C (Hendrycks & Dietterich, 2019)B: Evaluation. To evaluate the predictive performance of these BNNs, we report the negative log-likeliho...	BCA	BAC	ACB	ABC	Selection 2
A: In particular, the lack of a vector space structure means that concise summaries of the distribution such as moments are not available, and generalized notions such as Fréchet means are not unique and hard to computeB: In fact, an important issue from the point of view of inference is that sophisticated shap...	BAC	CBA	CBA	CBA	Selection 1

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