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**A**:
We propose Subgoal Search, a search algorithm based on subgoal generator**B**: We present two practical implementations MCTS-kSubS and BF-kSubS meant to be effective in complex domains requiring reasoning**C**: We confirm that indeed our implementations excel in Sokoban, Rubik’s Cube, and inequality benchmark INT. Interestingly, a simple k𝑘kitalic_k step ahead mechanism of generating subgoals backed up by transformer-based architectures performs surprisingly well. This evidence, let us hypothesize, that our methods (and related) can be further scaled up to even harder reasoning tasks. | ABC | CBA | CBA | ACB | Selection 1 |
**A**: Chinese use ‘Pinyin’, a special phonetic system, to represent the pronunciation of Chinese characters. In the phonetic system of ‘Pinyin’, we have four tunes, six single vowels, several plural vowels, and auxiliaries. Every Chinese character has its expression, also known as a syllable, in the ‘Pinyin’ system**B**: A complete syllable is usually made of an auxiliary, a vowel, and a tune. Typically, vowels appear on the right side of a syllable and can exist without auxiliaries, while auxiliaries appear on the left side and must exist with vowels.
However, the ‘Pinyin’ system has an important defect. Some similar pronunciations are denoted by totally different phonetic symbols**C**: For the example in Figure 4, the pronunciations of ‘cao3’ (grass) and ‘zao3’ (early) are quite similar because the two auxiliaries ‘c’ and ‘z’ sound almost the same that many native speakers may confuse them. This kind of similarity can not be represented by phonetic symbols in the ‘Pinyin’ system, where ‘c’, and ‘z’ are independent auxiliaries. In this situation, we have to develop a method to combine ‘Pinyin’ with another standard phonetic system, which can better describe characters’ phonetic similarities. Here, the international phonetic system seems the best choice, where different symbols have relatively different pronunciations so that people will not confuse them. | CBA | BAC | ABC | CBA | Selection 3 |
**A**: We note that existing methods on étendue expanded holography has focused on monochromatic 3D holograms[7, 28, 29]. Photon sieves[21] only achieves 3D color holography for sparse points. See Supplementary Note 4 for a discussion of these findings.
**B**: We find that neural étendue expansion also enables higher fidelity étendue expanded 3D color holograms**C**: Finally, we also investigate 3D étendue expanded holograms | CBA | CAB | CAB | ABC | Selection 1 |
**A**: A total of 4,560 samples are collected by a template-based method. The language modeling task is to predict the pronoun of a sentence**B**: For NLI and coreference resolution, three variations of each sentence are used to construct entailment pairs. For machine translation, sentences with two variations of third-person pronouns in English are used as source sentences.
**C**: ABC (Gonzalez et al., 2020), the Anti-reflexive Bias Challenge, is a multi-task benchmark dataset designed for evaluating gender assumptions in NLP models. ABC consists of 4 tasks, including language modeling, natural language inference (NLI), coreference resolution, and machine translation | CBA | BCA | ABC | CAB | Selection 2 |
**A**: Be sure to use the \\\backslash\IEEEmembership command to identify IEEE membership status.
Please see the “IEEEtran_HOWTO.pdf” for specific information on coding authors for Conferences and Computer Society publications**B**: Note that the closing curly brace for the author group comes at the end of the thanks group**C**: This will prevent you from creating a blank first page. | ABC | CAB | CBA | BCA | Selection 1 |
**A**: We set that 𝒞∗superscript𝒞\mathcal{C}^{*}caligraphic_C start_POSTSUPERSCRIPT ∗ end_POSTSUPERSCRIPT agrees with**B**: M′superscript𝑀′M^{\prime}italic_M start_POSTSUPERSCRIPT ′ end_POSTSUPERSCRIPT**C**: We construct a triplet G,ℓ,𝒞∗𝐺ℓsuperscript𝒞G,\ell,\mathcal{C}^{*}italic_G , roman_ℓ , caligraphic_C start_POSTSUPERSCRIPT ∗ end_POSTSUPERSCRIPT as follows: let Cj∈M′subscript𝐶𝑗superscript𝑀′C_{j}\in M^{\prime}italic_C start_POSTSUBSCRIPT italic_j end_POSTSUBSCRIPT ∈ italic_M start_POSTSUPERSCRIPT ′ end_POSTSUPERSCRIPT and
f′superscript𝑓′f^{\prime}italic_f start_POSTSUPERSCRIPT ′ end_POSTSUPERSCRIPT be the isomorphism from Cjsubscript𝐶𝑗C_{j}italic_C start_POSTSUBSCRIPT italic_j end_POSTSUBSCRIPT to C1subscript𝐶1C_{1}italic_C start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPT | CBA | CAB | ABC | ABC | Selection 2 |
**A**:
The characteristic that we describe as overall reciprocity consists of positive weights on the answers to all of the questions in the reciprocity questionnaire. This includes both questions about positive reciprocity (e.g**B**: At the onset of the treatment, they also shift more weight toward direct reciprocity. However, this shift toward direct reciprocity is potentially offset by a decrease in altruism (measured by additional weight placed on the costs of contributing) coupled with a strong decrease in generalized reciprocity. This suggests that individuals who have a high overall reciprocity attribute use new information to discriminate between collaborators as a mechanism for punishment.**C**: “If someone does me a favor, I am prepared to return it”), as well as negative reciprocity (“If someone puts me in a difficult position, I will do the same to them”). Estimates of the interaction between this characteristic and the behavioral utility terms suggest that these individuals are more altruistic in the baseline and behave more in line with generalized reciprocity | ABC | ACB | CBA | BAC | Selection 2 |
**A**: By using different loss functions, the model can achieve different performance**B**:
Among them, ℒℒ\mathcal{L}caligraphic_L is the loss function that is used to minimize the distance between the reconstructed image and the ground-truth image**C**: Therefore, an effective loss function is also crucial for SISR. | BAC | ACB | CBA | ABC | Selection 1 |
**A**: We demonstrate the capabilities of Neural Knitworks by utilizing a similar model with only minor adjustments for several tasks commonly investigated in the field of computer vision: 1) image inpainting 2) super-resolution and 3) denoising**B**: The following section describes the key implementation details for each task and presents corresponding qualitative results**C**: Furthermore, quantitative measures are provided by applying each method to Set5 [33] and Set14 [34].
| BCA | CBA | ABC | BCA | Selection 3 |
**A**: We identify a potential issue in the application of IDS to contextual problems, or those with non-stationary expected information gains**B**: We explain how a tunable variant avoids this issue.
**C**: Namely, that it may fail to take account of the magnitude of the information gain and make counter-intuitive selections as a result | ABC | ACB | BAC | BCA | Selection 2 |
**A**: In spite of the welcome leap in performance, however,
a typical criticism transformer architectures share with most deep learning models is their lack of interpretability**B**: Sure, the attention mechanism [4] could offer cues as to how to interpret the behavior of such models**C**: Nevertheless, whether attention could be meaningfully used as an analysis tool, especially from the perspective of a layman or end-user, is a matter of discussion [5, 6]. | ABC | BAC | ACB | CBA | Selection 1 |
**A**: The yellow cluster contains areas where the activity is high on the working days and lower on the weekends, with an intraday peak around noon**B**: The area corresponding to this cluster is Porta Romana, which contains the train station with the same name, a station primarily used by commuters into the city.**C**: Typical locations in this cluster are university centers or the city center, where most office buildings are situated.
The green cluster is the smallest one, with the characteristic that the activity plummets during the weekend | BCA | ACB | BAC | BCA | Selection 2 |
**A**: That adjoint situation is comonadic.
This fact not only reveals the coalgebraic nature of equality, but provides a universal construction yielding elementary doctrines from primary ones.**B**: It shows an adjoint situation between 𝐏𝐃𝐏𝐃\mathbf{PD}bold_PD and 𝐄𝐃𝐄𝐃\mathbf{ED}bold_ED, i.e**C**: the 2-categories of primary doctrines and that of elementary ones that is, primary doctrines with equality | ABC | CAB | CBA | ABC | Selection 2 |
**A**:
Figure 5 shows the Average Precision@K𝐾Kitalic_K of four studied measures on six labeled networks**B**: Note that the difference between ForestSim-EX and ForestSim-AP is marginal though the latter requires less time and space**C**: Moreover, ForestSim achieves comparable performance to RoleSim, the state-of-art role similarity metric, and it clearly outperforms StructSim in most cases. | CAB | CBA | ABC | CBA | Selection 3 |
**A**: (2021) employ type-aware GCN to distinguish different relations in the graph, achieving promising results.
Similarly, Li et al**B**: (2021a) propose SynGCN and SemGCN for different dependency information.**C**: For refining syntax structure quality in sentiment dependency learning, Tian et al | CBA | BAC | BCA | CAB | Selection 3 |
**A**: However, for the exposition in this section it sufficient to know what the properties of the operators 𝐋𝐋\mathbf{L}bold_L and 𝐖𝐖\mathbf{W}bold_W are.
**B**: The operator 𝐋𝐋\mathbf{L}bold_L and 𝐖𝐖\mathbf{W}bold_W are constructed from a multilevel decomposition of the location of predictors**C**: This process is somewhat elaborate and the reader is referred to [31] and [32] for all of the details | ACB | ACB | ABC | CAB | Selection 4 |
**A**: We use QNN as the benchmark PQC in this work. Figure 2 shows the QNN architecture. The inputs are classical data such as image pixels, and the outputs are classification results**B**: The QNN consists of multiple blocks. Each has three components: encoder encodes the classical values to quantum states with rotation gates such as RY; trainable quantum layers contain parameterized gates that can be trained to perform certain ML tasks; measurement part measures each qubit and obtains a classical value. The measurement outcomes of one block are passed to the next block**C**: For the MNIST-4 example in Figure 2, the first encoder takes the pixels of the down-sampled 4×\times× 4 image as rotation angles θ𝜃\thetaitalic_θ of 16 rotation gates. The measurement results of the last block are passed through a Softmax to output classification probabilities. QuantumNAT overview is in Figure 3.
| ACB | CAB | ABC | BAC | Selection 3 |
**A**: Therefore, how to simultaneously associate the event and image data to improve the performance of event-based methods is also one of the directions we will focus on in the future.
**B**: Besides, optical image data can provide additional visual information and may play a positive role in a hybrid information fusion with the event data**C**: In future work, we plan to explore more delicate model fitting approaches for event association, and this approach will also be extended for a variety of motion-related computer vision tasks | CAB | CBA | BAC | BAC | Selection 2 |
**A**:
We now prove our main result, that there are no ugly perfect graphs**B**: This generalizes the same fact which was previously proved for Meyniel graphs [22] (a class which contains chordal graphs, HHD-free graphs, Gallai graphs, parity graphs, distance-hereditary graphs…) and line graphs of bipartite graphs [3]**C**: Our proof is a generalization of the proof of the latter result by Bonamy, Groenland, Muller, Narboni, Pekárek and | CAB | CAB | ABC | ACB | Selection 3 |
**A**:
This work was supported by the National Key R&D Program of China (No**B**: 2022ZD0115100), the National Natural Science Foundation of China Project (No**C**: U21A20427), and Project (No. WU2022A009) from the Center of Synthetic Biology and Integrated Bioengineering of Westlake University. | BAC | ABC | CAB | ACB | Selection 2 |
**A**: Note that MCUNetV2-M4 shares a similar computation with MCUNet (172M vs**B**: This is because the expanded search space from patch-based inference allows us to choose a better configuration of larger input resolution and smaller models.
**C**: 168M) but a much better mAP | CBA | BAC | ABC | ACB | Selection 4 |
**A**: Finally, we conclude our proposed methods in Section V.**B**: In Section III and Section IV , we show the experiments and results of our model**C**: The rest of the paper is organized as follows: Section II describes
our solution which contains the model details | ABC | ABC | BCA | CBA | Selection 4 |
**A**: Within CGCL, multiple graph encoders observe input graphs to yield contrastive views. Ideally, these encoders should exhibit complementarity to enhance fitting capability. Specifically, an assembly with encoders possessing non-redundant observation angles demonstrates high complementarity**B**: For clarification, we refer the training loss upon completion as the stopping loss. Given a consistent dataset, a smaller stopping loss signifies enhanced assembly fitting ability. This capability is directly proportional to the non-redundant parameters across all encoders. As previously highlighted, complementarity is evident through non-redundant observation angles. With the above intuition, we define the Complementarity Coefficient of a certain CGCL’s assembly as follows:**C**: Redundancies in observation angles can be inferred from overlapping encoder parameters. This notion of complementarity in CGCL mirrors the diversity imperative of base learners in ensemble learning, where distinct learners better capture varied information.
As discussed in Section 3.3.1, CGCL generates contrastive views from encoder perspective, distinguishing it from the data augmentations in traditional GCL methods. Inspired by [5], we introduce a loss-centric metric to measure the complementarity of CGCL’s encoders | BCA | BAC | ACB | BAC | Selection 3 |
**A**: The inductive bias can be imposed into the architecture of the agents or the training procedure. For instance,**B**: The topic of communication is actively studied in multi-agent RL, see Hernandez-Leal et al., (2020, Table 2) for a recent survey. Compositionality is often investigated in the context of signaling games (Fudenberg and Tirole, (1991), Lewis, (1969), Skyrms, (2010), Lazaridou et al., (2018))**C**: Recent research has shown that strong inductive biases or grounding of communication protocols are necessary for the protocol to be compositional
(see e.g. Kottur et al., (2017), Słowik et al., 2020b ) | ACB | CAB | BAC | ACB | Selection 2 |
**A**: Learning with CBFs: Approaches that use CBFs during learning typically assume that a valid CBF is already given, while we focus on constructing CBFs so that our approach can be viewed as complementary. In [19], it is shown how safe and optimal reward functions can be obtained, and how these are related to CBFs. The authors in [20] use CBFs to learn a provably correct neural network safety guard for kinematic bicycle models**B**: The authors in [21] consider that uncertainty enters the system dynamics linearly and propose to use robust adaptive CBFs, as originally presented in [22], in conjunction with online set membership identification methods. In [23], it is shown how additive and multiplicative noise can be estimated online using Gaussian process regression for safe CBFs. The authors in [24] collect data to episodically update the system model and the CBF controller**C**: A similar idea is followed in [25] where instead a projection with respect to the CBF condition is episodically learned. Imitation learning under safety constraints imposed by a Lyapunov function was proposed in [26]. Further work in this direction can be found in
[27, 28, 29]. | ABC | CBA | BCA | BCA | Selection 1 |
**A**: Aaronson and Ambainis [AA14] showed that this related conjecture implies 13. it remains open to this day. Theorem 12 could be seen as the analogue of 13 for sparse oracles—an analogue that, because of the sparseness, turns out to be much easier to prove.**B**: [Mon12, OZ16]**C**:
While 13 has become influential in Fourier analysis of Boolean functions,888In the context of Fourier analysis, the Aaronson-Ambainis Conjecture usually refers to a closely-related conjecture about influences of bounded low-degree polynomials; see e.g | ACB | CBA | CAB | ACB | Selection 2 |
**A**: For visualization, Figure 7 shows adjacency matrices of the first two weighted networks**B**: Table 4 summaries basic information for the five networks. Detailed information of the five networks can be found below.**C**:
In this section, we apply nDFA and DFA to five real-world weighted networks Karate club weighted network (Karate-weighted for short), Gahuku-Gama subtribes network, the Coauthorships in network science network (CoauthorshipsNet for short), Condensed matter collaborations 1999 (Con-mat-1999 for short) and Condensed matter collaborations 2003 (Con-mat-2003 for short) | CAB | BCA | CAB | CAB | Selection 2 |
**A**:
Table 1**B**: In 3s_vs_5z, our agent discovers that keeping the opponents alive leads to higher rewards than killing them. This strategy, however, yields a low win rate. See Appendix F.1 for a detailed study.**C**: Median win rate of MA-Trace (obs) compared with other algorithms | BAC | CBA | ACB | BAC | Selection 3 |
**A**: The box plots are sorted according to the average values of all active models, visible as a number in teal. The difference to all models being active is shown with arrows facing up for increase or down for decrease in per-feature importance**B**: For both algorithms, we compute feature importance as the mean and standard deviation of accumulation of the impurity decrease within each tree. This is a measurement that can be calculated directly from RF Rogers2006Identifying and AB Wang2012AdaBoost algorithms, cf. Section Random Forest vs. Adaptive Boosting.
**C**: The box plots which aggregate per-algorithm importance (see Figure 1(b)) provide a holistic view of the performance of the models. Each pair of boxes is related to a unique feature, summarizing the active models’ normalized importance per feature (from 0 to 1, i.e., worst to best) | BCA | ABC | CBA | BAC | Selection 1 |
**A**: However, this also leads to doubling the number of antenna ports,**B**: Compared to the case where the same number of antenna elements with
only a single polarization is available, this leads to an increase in diversity and capacity, although the gain depends significantly on the XPD [15, 6]**C**: Tx and Rx have co-located dual-polarized antennas, such that two antenna ports are available for each antenna element at a distinct spatial location | CBA | ABC | BAC | ACB | Selection 1 |
**A**: Most existing theoretical research on packing, and all research on online translational packing that we are aware of, is concerned with axis-parallel rectangular pieces.
In this paper, we study online translational packing of convex polygons**B**: The pieces arrive one by one and have to be placed irrevocably into a horizontal strip (or into bins, a square, the plane) before the next piece is revealed, and only translations of the pieces are allowed**C**: The aim is to minimize the used space depending on the specific problem at hand, e.g., the used length of the strip, the number of bins, etc. | ABC | BAC | ACB | CBA | Selection 1 |
**A**: Cephalometric Xray:
It is a widely-used public dataset for cephalometric landmark detection, containing 400 radiographs, and is provided in IEEE ISBI 2015 Challenge [14, 37]**B**: The averaged version of annotations by two doctors is set as the ground truth. The image size is 1935×2400193524001935\times 24001935 × 2400 and the pixel spacing is 0.1mm. The dataset is split into 150 and 250 for training and testing respectively, referring to the official division.**C**: There are 19 landmarks of anatomical significance labeled by 2 expert doctors in each radiograph | ACB | BAC | BAC | CBA | Selection 1 |
**A**:
More than the above distributions, the distribution-free property of MMDF allows ℱℱ\mathcal{F}caligraphic_F to be any other distribution as long as Equation (5) holds**B**: For example, ℱℱ\mathcal{F}caligraphic_F can be Binomial, Double exponential, Exponential, Gamma, and Laplace distributions in http://www.stat.rice.edu/~dobelman/courses/texts/distributions.c&b.pdf**C**: Details on the probability mass function or probability density function on distributions discussed in this paper can also be found in the above URL link. Generally speaking, the distribution-free property guarantees the generality of our model MMDF, the DFSP algorithm, and our theoretical results. | ABC | ACB | BAC | BAC | Selection 1 |
**A**: Through extensive experimental analysis, we show the tremendous potential of this viewpoint**B**:
In this work, we study Class Incremental Learning (CIL) from a previously underexplored viewpoint — improving CIL by mimicking the oracle model representation at the initial phase**C**: We propose a novel CwD regularization term for improving the representation of the initial phase. Our CwD regularizer yields consistent and significant performance improvements over three previous SOTA methods across multiple benchmark datasets with different scales. | BCA | CAB | BAC | CBA | Selection 3 |
**A**: Similarly, the T1-CE follow-up scan shows the resection cavity, whereas the edema is visible in the FLAIR scan.**B**:
Figure 1: Example of a pre-operative baseline and its corresponding follow-up MRI scan**C**: The contrast-enhanced T1-weighted (T1-CE), and the T2 Fluid Attenuated Inversion Recovery (FLAIR) baseline scan clearly show the tumor and the edema, respectively | CBA | CAB | CBA | ACB | Selection 2 |
**A**: Let us further note that the case of primary keys is closely related to the block-independent-disjoint probabilistic databases [9, 10], where the repairs coincide with the possible worlds of the probabilistic database (see [25] for further details)**B**: However, as discussed in [25], these are different models that require different techniques**C**: Moreover, it remains unclear how the block-independent-disjoint probabilistic model can be adapted to align with the case of an LHS chain.
| BAC | ACB | CAB | ABC | Selection 4 |
**A**: Absorption-scaled graphs provide a useful way to adapt community-detection methods (including ones that are not based on random walks) to account for heterogeneous node-absorption rates. Community structure depends not only on network structure but also on network dynamics (see, e.g., [17]), and it is important to use a variety of perspectives to examine the “effective community structure” that is associated with different dynamical processes.**B**:
The community-detection algorithm InfoMap is based on random walks, so it is natural to adapt it to absorbing random walks**C**: However, there are numerous approaches to community detection [12, 33], and it is worthwhile to adapt other approaches, such as modularity maximization [29] and statistical influence using stochastic block models [31], to account for node-absorption rates | ABC | BAC | CAB | ABC | Selection 3 |
**A**: EP generation rate decay only polynomially in L𝐿Litalic_L**B**: Their path-based metric**C**:
More recently, Caleffi [18] formulated the entanglement generation rate on a given path between two nodes, under the more realistic condition where the intermediate nodes in the path may not all be equidistant, but still considered only balanced trees | BCA | BAC | CAB | ACB | Selection 4 |
**A**: As AI approaches provide the foundation for real-time driving actions, there is an inherent need and expectation from consumers, general society, and regulatory bodies that AI-based action decisions of AVs should be explainable to build confidence in these vehicles [3, 7, 8, 9] (e.g., Figure 1). **B**: While the potential impact and benefits of AVs in everyday life are promising, there is a major societal concern about functional safety of such vehicles**C**: This issue, as a major drawback, originates mainly from reports of recent traffic accidents with the presence
of AVs, primarily owing to their “black-box” decision-making [3, 4, 5, 6] | ACB | BCA | CAB | ACB | Selection 3 |
**A**: MobileNetV3 is cropped at the last stage with the encoder dimension of 960 before adaptive average pooling layer.
**B**: We used three representative CNN architectures (i.e., AlexNet, VGG-16 and MobileNetV3) and our proposed GhostCNN as front-ends to extract feature maps**C**: AlexNet and VGG-16 are cropped at the last convolution layer (conv5) with the encoder dimension (i.e., D𝐷Ditalic_D-dimension) of 256 and 512 before ReLU, respectively | BCA | BCA | CAB | ACB | Selection 3 |
**A**: In this paper, we propose a new form of algebraic attack, which is especially effective against nonlinear filter generators**B**: We show with two toy examples how the attack can be performed in practice**C**: We also apply our attack to WG-PRNG and we provide a complexity estimate that shows a fatal weakness of this cipher. We also report previous attempts at breaking WG-PRNG with algebraic attacks and we discuss their shortcomings.
| CAB | CBA | CAB | ABC | Selection 4 |
**A**:
Figure 6**B**: Gain comparison of best response (BR), local best response (LBR - only poker), and continual depth-limited best response (CDBR) in Leduc Hold’em (top) and IIGoofspiel 5 (bottom) against strategies from CFR using a small number of iterations (left) and random strategies (right)**C**: The a stands for the average of the other values in the plot. The number after CDBR stands for the number of actions CDBR was allowed to look at in the future, and CDBRNN is a one-step CDBR with a neural network as a value function. | CAB | ACB | CBA | ABC | Selection 4 |
**A**: However, there no theoretical guarantees for either that the partition found is near optimal, though recently [10] showed that a Louvain-like algorithm recovers the communities in the stochastic block model for a wide parameter range.
**B**: The algorithms are fast and have had success in recovering ground truth communities on real world networks**C**: Louvain [4] and Leiden [44] are examples of this | ABC | CAB | CBA | BCA | Selection 3 |
**A**: Effect sizes in Cluster 2, instead, are not affected by the source of data used.**B**:
Regarding the heterogeneity produced by the fact that studies use different sources of data for migration, we add dummies for sources used**C**: All estimated coefficients of this set of controls are statistically significant in Cluster 1: the use of different databases might influence the wide variety of findings | CAB | ABC | CBA | ACB | Selection 1 |
**A**:
This research was supported by NSFC (61921006, 62361146852), JiangsuSF (BK20220776), National Postdoctoral Program for Innovative Talent, and China Postdoctoral Science Foundation (2023M731597)**B**: The authors would like to thank Mengxiao Zhang and Ashok Cutkosky for helpful discussions. We are also grateful for anonymous reviewers and the action editor for their invaluable comments, in particular, we sincerely thank Reviewer #2 of JMLR for carefully reviewing the paper and providing many constructive suggestions.**C**: Peng Zhao was supported in part by the Xiaomi Foundation | CBA | ABC | CAB | ACB | Selection 4 |
**A**: Indeed, if for example, σ′(a)=cwsuperscript𝜎′𝑎𝑐𝑤\sigma^{\prime}(a)=cwitalic_σ start_POSTSUPERSCRIPT ′ end_POSTSUPERSCRIPT ( italic_a ) = italic_c italic_w, then σ′(ccca)superscript𝜎′𝑐𝑐𝑐𝑎\sigma^{\prime}(ccca)italic_σ start_POSTSUPERSCRIPT ′ end_POSTSUPERSCRIPT ( italic_c italic_c italic_c italic_a )**B**: Next, σ′(a)superscript𝜎′𝑎\sigma^{\prime}(a)italic_σ start_POSTSUPERSCRIPT ′ end_POSTSUPERSCRIPT ( italic_a ) (resp**C**: σ′(b)superscript𝜎′𝑏\sigma^{\prime}(b)italic_σ start_POSTSUPERSCRIPT ′ end_POSTSUPERSCRIPT ( italic_b ))
cannot begin or end with c𝑐citalic_c | ACB | CBA | BAC | CAB | Selection 4 |
**A**: (2017)**B**: More precisely,
these authors established the third term on the right-hand side in**C**: The result in Theorem 4 for s≥1/2𝑠12s\geq 1/2italic_s ≥ 1 / 2 (that is, 2k+2≥d2𝑘2𝑑2k+2\geq d2 italic_k + 2 ≥ italic_d) was already derived in Sadhanala et al | BCA | CAB | ABC | ABC | Selection 1 |
**A**: The study investigates whether the dynamic pattern of brain networks is a genetically influenced trait, an area previously underexplored**B**: By examining the state change patterns in twin brain networks, we make significant strides in understanding the genetic factors underlying dynamic brain network features. Furthermore, the paper makes its method accessible by providing MATLAB codes, contributing to reproducibility and broader application.**C**:
In addition to the methodological advancement, the paper applies the proposed technique to analyze the heritability of overall brain network topology using a twin study design | ACB | BCA | ACB | ABC | Selection 2 |
**A**: Figure 2(e) shows that the sufficient condition, given in Theorem 12, for non-convergence of inter-event times to a steady-state value is satisfied. Hence, we**B**: Therefore the ϕitalic-ϕ\phiitalic_ϕ map in
Figure 2(e) has no fixed point**C**: is always positive | CBA | CAB | ABC | BCA | Selection 1 |
**A**: On the other hand, trajectories starting in the unsafe region will be brought close of the safety boundary where closeness is proportional to the size of the input.**B**: Specifically, trajectories moving from safe zone towards unsafe region will violate safety boundary only in a sense proportional to the size of input**C**:
Input-to-state safety (ISSf) [4, 5, 6]: Here the objective is to ensure that the system state trajectories stay away from a predefined unsafe region, or in other words, stay close to safe region | CBA | BAC | ACB | CAB | Selection 1 |
**A**:
In another study of 50 hospital workers which also uses PANAS, Nadarajan et al. [23] find that speech activity can explain some variance in predicting positive affect measure. Employees wear a specifically designed audio badge during their work-shift hours**B**: The authors extract several features from the audio to identify foreground speech. They then use a linear mixed effects model to estimate positive and negative affect from foreground activation (i.e., the percentage of recording time that foreground speech is present)**C**: Similarly, in another study, Robles-Granda et al. [16] utilize multiple sensing modalities to assess anxiety, sleep and affect of 757 information workers. Sensing modalities include wearable, phone application, Bluetooth beacons and social media. Models trained on the fusion of all the features from different sensing modalities leads to up to 13.9% improvement in the symmetric mean absolute percentage error (SMAPE) when predicting affect, anxiety and sleep quality scores. | ABC | ACB | ACB | BCA | Selection 1 |
**A**: We thus assume a single channel and instant for now, and discuss multiple channels and request duration in §III-F.
**B**: The general spectrum allocation problem is to allocate optimal power to an SU’s request across spatial, frequency, and temporal domains**C**: We focus on the core function approximation problem, which is to determine the optimal power allocation to an SU for a given location, channel, and time instant—since frequency and temporal domains are essentially “orthogonal” dimensions of the problem and thus can be easily handled independently (as done in §III-F) | CBA | CAB | BAC | CBA | Selection 2 |
**A**: In this paper, we considered practical aspects of reconstructing planar curves with prescribed Euclidean or affine curvatures**B**: An immediate extension of the current work would be the reconstruction of planar curves with prescribed projective curvatures, and obtaining distance estimates between curves, modulo a projective transformation, compared to the distance between the projective curvatures**C**: Indeed, the projective group, containing both the special Euclidean and the special affine groups, plays a crucial role in computer vision (see, for, instance [5] and [13]). Extension to space curves is another direction with immediate applications.
| BAC | CAB | ABC | BCA | Selection 3 |
**A**: The online coordinate descent algorithm considered in this paper is given in Section 3. Regret bounds for random online coordinate descent algorithms are given in Section 4 followed by regret bounds for deterministic online coordinate descent algorithms in Section 5**B**:
The rest of paper is organized as follows. The problem formulation is presented in Section 2**C**: The numerical simulation is given in Section 6. Finally the results presented in this paper is summarized in Section 7. | BCA | BAC | CBA | ABC | Selection 2 |
**A**: To ensure the generality of our results, we included results using both the Support Vector Classifier (SVC) and the Euclidean distance classifier (Eucl.)**B**: and Noisy Eucl. Each network was trained on the same training sets of N-MNIST data and tested on the same randomly chosen N-MNIST test sets. We also measured classification performance for both layers of the architecture.
**C**: Thus, we considered four cases: Ideal SVC, Noisy SVC, Ideal Eucl | ACB | CAB | BCA | CAB | Selection 1 |
**A**: This also holds for a slightly weaker stability concept than perfect stability: in all future steps an agent’s opinion will not move further than by a given distance δ𝛿\deltaitalic_δ**B**: To show their result the authors construct a HKS with infinitely many oscillating states.
Their stability notion is also different to the one considered in this paper**C**: We analyze the time to reach a δ𝛿\deltaitalic_δ-stable state which is defined as a state where any edge in the influence network has length at most δ𝛿\deltaitalic_δ (see Section 1.2). | BAC | ABC | BAC | CBA | Selection 2 |
**A**: We employed deep learning architecture as explained in Section 3.2 to develop a model that can predict the presence or absence of the 14 pathological conditions based on the input Chest X-ray images**B**: The training process involved feeding the model with the labeled images and adjusting its parameters to learn the patterns and features associated with each condition.**C**:
We addressed the data leakage as explained in Section 3.1.1 and created a train and test set | CBA | CBA | BCA | ACB | Selection 3 |
**A**: The fact that the Bayes optimal algorithm is suboptimal means that even if we enhance KG and EI to plan more than one step ahead, their performance in a frequentist measure might not improve.**B**: A Bayesian measure requires it to optimize the performance averaged over the prior, whereas a frequentist measure requires an algorithm to optimize the performance against any case**C**:
On the contrary, we show that the Bayes optimal algorithm performs sub-optimally with some of the worst model parameters, which implies that maximizing the Bayesian objective differs substantially from maximizing the frequentist objective | ABC | ABC | ABC | CBA | Selection 4 |
**A**: For all other rotations, we see slight variations in the latent code, which, however, is to be expected due to interpolation artifacts for rotations on a discretized grid**B**: In contrast, the latent code of a classical autoencoder exhibits multiple clusters for different orientations of the same digit class.
**C**: Still, inspecting the 2d-projection of the latent code of our proposed model in Figure 2, we see distinct clusters for each digit class for the different images from the test dataset, independent of the orientation of the digits in the images | BCA | ACB | BCA | ABC | Selection 2 |
**A**: The result is given as the average over 100 runs on each of the data snapshots in each subset**B**: For BO one run is used. The metrics are calculated on the pre-processed data, i.e. after log transform and standardisation / mean-centring.
**C**: At each iteration a location is chosen at random from those available | CBA | CBA | BCA | CAB | Selection 3 |
**A**: We report the average ELBO (±1plus-or-minus1\pm 1± 1 standard error) on the training set after 1M steps over 5 independent runs**B**: Training binary latent VAEs with K=2,3𝐾23K=2,3italic_K = 2 , 3 (except for RELAX which uses 3333 evaluations) on MNIST, Fashion-MNIST, and Omniglot**C**: Test data bounds are reported in Table 4.
| CBA | BCA | BCA | BAC | Selection 4 |
**A**: Our framework follows the line of work started with CRDSA/IRSA in which each randomly accessing device sends multiple packet replicas [10, 11], rather than a single one as in plain ALOHA. The model involves a shared pool of resources - a short, periodic frame composed of limited number of slots, that makes our contribution relevant in scenarios with tight latency constrains. We focus on
comparing fully random selection of slots to**B**: sequences consisting of multiple redundant transmissions, to achieve higher communication reliability**C**: GF multiple access in which users apply access patterns, i.e | ABC | CAB | CAB | CBA | Selection 4 |
**A**: Here and for the rest of this paper, we refer to any of these two variations as the ATSP algorithm. The purpose of this note is to show that the ATSP algorithm, in the case that V𝑉Vitalic_V is finite, has polynomial time complexity.**B**:
Later, Schul [Sch07] provided a modification of the algorithm so that the ratio of the length of the yielded path over the length of the optimal path is bounded by a constant C𝐶Citalic_C independent of the dimension N𝑁Nitalic_N**C**: Variation of this algorithm also appears in [BNV19] | BAC | CBA | CAB | ACB | Selection 3 |
**A**: In section 2 we present a short overview of Chow forms**B**: Section 3 is on the computation of the Chow form in ℙnsuperscriptℙ𝑛\mathbb{P}^{n}blackboard_P start_POSTSUPERSCRIPT italic_n end_POSTSUPERSCRIPT and the extension of techniques to compute Hurwitz forms**C**: Section 4 presents algorithms for computing multiprojective Chow forms. Section 5 explores connections between multiprojective Chow & Hurwitz forms and matroid theory.
| ABC | BCA | BCA | ACB | Selection 1 |
**A**: The data is hosted encrypted in the UC3M4Safety Repository in the ’Consorcio Madroño’ online platform athttps://edatos.consorciomadrono.es/dataverse/empatia, which includes one folder per dataset described in Table 1**B**: Instructions to decrypt the data will be provided after fulfilling the EULA form located on https://www.uc3m.es/institute-gender-studies/DATASETS, which should be signed and emailed to the UC3M4Safety Team (uc3m4safety@uc3m.es).**C**:
The use of the WEMAC dataset is licensed under a Creative Commons Attribution 4.0 International License (CC-BY-4.0) | ACB | BAC | BCA | ACB | Selection 3 |
**A**: By repeating three steps: querying Oracle for a label, training a substitute model, and generating a synthetic dataset, an adversary can create a substitute model that can mimic the Oracle decision behavior. Adversarial samples are generated using any attacks on the substitute model. Such samples can evade the classifier of the Oracle following the transferability property of adversarial attacks.
**B**: demonstrate the first black-box attack that fooled a remotely hosted model (Oracle)**C**: Practical black-box attack (Papernot et al., [n. d.]): Papernot et al | ABC | BCA | CBA | ACB | Selection 3 |
**A**: Roughly speaking, we consider a zero-sum game between an adversary and a statistician, in which the adversary chooses a deviation and the statistician, after observing the realization s𝑠sitalic_s, has to guess the deviator if s∉D𝑠𝐷s\notin Ditalic_s ∉ italic_D**B**: We use the minimax theorem to establish that the statistician has a strategy that guarantees high payoff.
However, for the minimax theorem to apply, we need to make some modifications to the game.111Blackwell [5] gives an example of a statistical game without a value.**C**: A strategy for the statistican in this game is a blame function | CAB | BAC | ACB | ABC | Selection 3 |
**A**: Physical-layer attack vectors refer to those tampering the sensor inputs to the AI components via physical means**B**: Existing attacks on AD systems leverage a diverse set of attack vectors and we broadly categorize them in two categories: physical-layer and cyber-layer**C**: We further decompose physical-layer attack vectors into physical-world attack and sensor attack vectors, where the former modifies the physical-world driving environment and the latter leverages unique sensor properties to inject erroneous measurements. Cyber-layer attack vector refers to those that require internal access to the AD system, its computation platform, or even the system development environment.
| BAC | ABC | CBA | ACB | Selection 1 |
**A**: The sixth buffer contains a Blue join gadget in [18,21]1821[18,21][ 18 , 21 ]. Its long intervals terminate at 18181818 and start from 21212121**B**: It also contains a Red join gadget in [1.5,13.5]1.513.5[1.5,13.5][ 1.5 , 13.5 ]. Its long intervals terminate at 1.5,7.51.57.51.5,7.51.5 , 7.5 and start from 10.5,13.510.513.510.5,13.510.5 , 13.5.
**C**: It also contains the second part of the third switch gadget in [14.5,17.5]14.517.5[14.5,17.5][ 14.5 , 17.5 ]. Its long intervals terminate at 14.514.514.514.5 and start from 16.516.516.516.5 | ACB | BAC | BAC | ABC | Selection 1 |
**A**: [47] show the effectiveness of end-to-end strategies for the related temporal action detection task. They use FrozenBN but do not discuss BatchNorm issues.**B**: Although not mentioned as motivation for this model choice, BN would likely have caused both train-test discrepancy and “cheating” since single-sequence batches are used.
Recently, Liu et al**C**: The only end-to-end anticipation model for natural video of which we are aware (AVT, [26]) does not use BatchNorm as it is entirely Transformer-based | BCA | BCA | BCA | CBA | Selection 4 |
**A**: ResNet-34 (R34) and ResNet-100 (R100) were used as backbone models. We re-implemented the state-of-the-art models: CosFace[34], ArcFace[4], and MagFace[18].**B**:
Training. For preprocessing, face images were resized to 112×112112112112\times 112112 × 112 and normalized using the mean (0.485, 0.456, 0.406) and standard deviations (0.229, 0.224, 0.225)**C**: For data augmentation, a horizontal flip was applied with a 50% of chance. All experiments were performed using two NVIDIA-RTX A6000 GPUs with a mini-batch size of 512 | BCA | CAB | BAC | CBA | Selection 2 |
**A**: While this study and Burlina et al. [8] utilized the expertise of two clinical professionals for image evaluation, it’s important to acknowledge that having more experts such as three experts can make the study stronger and tests for the generalisability of the model. Further, three experts permits the disagreements being resolved by voting which tests the strength of the study.**B**: However, medical images are often non-binary with more classes**C**:
One limitation of our study is that it has only handled the binary problem, i.e., AMD versus non-AMD images | CBA | BCA | ACB | ABC | Selection 1 |
**A**: Early, most of FER researches [16, 17, 18] focused on lab-collected expression datasets, such as CK+ [19], MMI [20], JAFFE [21], Oulu-CASIA [22]**B**: For lab-collected datasets, facial expressions images were collected from several or dozens of individuals under similar conditions (such as illumination, angle, posture, et al.), generally with a few uncontrollable factors.**C**:
In view of the significance of FCRs, many studies [12, 13, 14, 15] have been proposed based on applying the information of facial local regions, where the facial landmarks are employed as the prior information of facial crucial regions, whereas the landmarks are given by manually annotating for facial expression images | CAB | ABC | BCA | ABC | Selection 3 |
**A**: These rating points are somewhat analogous to poker chips: when player A𝐴Aitalic_A and player B𝐵Bitalic_B play a game, they each place some of their rating points into a pot**B**: In the case of a draw, the players split the pot evenly. If one player wins, they take the entire pot.
The heart of the Elo system is dictating how many points each player must ante up.**C**: Each player is given some ‘rating’ value (measured in ‘points’ or simply ‘Elo’), which updates as they play games | CAB | ACB | ABC | BCA | Selection 4 |
**A**: Throughout the whole HardVis system, the visual encodings propagate from one view to the others. For example, the common grayscale denotes the four distinct types of instances in all views**B**: Tightly connected views—such as the UMAP projection and the inverse polar chart—share identical encodings, i.e., label class mapped to filled-in color, data type as outline color, and US/OS represented with symbols. The inverse polar chart is compact and uses the available space effectively due to its inherent design; it spares more area for the misclassified instances**C**: For the table heatmap view, the diverging color scale emphasizes the extreme values and allows users to notice more differences on the left- and right-hand sides of the middle point, with five colors having the same origin. For example, this middle point is crucial for the breast cancer data set, because instances with values closer to 1 for all features should be classified as malignant, while samples with values around 0 should be benign cancer. Finally in this view, hovering over a specific cell interaction partly resolves the ambiguity problem introduced due to distributing the normalized values into 10 distinct bins.
| ACB | CBA | ABC | ACB | Selection 3 |
**A**:
FIRST’s intuitive plug-and-play framework seamlessly integrates projects like Eigenlayer, aiming to minimize trust dependencies and match Ethereum’s renowned fault tolerance [46]**B**: Just as with Ethereum’s PoS system, any lapse in ensuring protocol security results in a corresponding slash of their stakes. Furthermore, we take into account the case where a subset of malicious verifiers attempt to leak the transaction details to Mallory, and show how FIRST prevents it in Section V.**C**: Eigenlayer offers Ethereum validators the opportunity to restake their ETH, thereby channeling Ethereum’s security prowess to additional protocols | ABC | BAC | ABC | ACB | Selection 4 |
**A**: (2021); Wager and Xu (2021) and use gradient-based optimization with policy gradient estimator to learn policies.**B**:
In this section, we define the policy gradient, the gradient of the equilibrium policy value with respect to the selection criterion, give an estimator of the policy gradient, and use the estimator to learn policies**C**: In particular, we give a method for estimating the policy gradient in finite samples in a unit-level randomized experiment as in Munro et al | BCA | CAB | BAC | CBA | Selection 2 |
**A**: We chose JTT over EIL for its simplicity. OccamNets, of course do not require such group labels to be specified.
**B**: BAR does not specify oracle group labels, so we adopt the JTT method. Specifically, we train an ERM model for single epoch, reserving 20% of the samples with the highest losses as the difficult group and the rest as the easy group**C**: For Biased MNISTv2, all the samples having the same class and the same value for all of the spurious factors are placed in a single group. For COCO-on-Places, objects placed on spuriously correlated backgrounds form the majority group, while the rest form the minority group | CAB | ACB | CBA | ABC | Selection 3 |
**A**: First, our exploration of contextual information for VSS focuses on simultaneously learning temporal contexts for all semantic categories**B**:
For future work, in addition to the above two aspects (local and global temporal contexts), the following two directions are promising**C**: Considering the relationships amongst various categories (e.g., horses is often related to the grassland), the explicit modeling of class-specific temporal contexts is also an interesting direction to explore. Second, it would be also interesting to extend our methods to other video tasks that require the learning of temporal contexts. | CAB | BAC | CBA | CAB | Selection 2 |
**A**: However, due to the heavy-tailed distribution of accesses, over 75% of these inputs are popular. As shown in Section VII-F1, this high proportion of popular inputs adequately conceals the parameter gathering latency for non-popular μ𝜇\muitalic_μ-batches.**B**:
We generated synthetic models and datasets with multi-hot encoded inputs to understand the efficacy of Hotline to model size increase**C**: Multi-hot encoded lookups influence the frequency of popular μ𝜇\muitalic_μ-batches | ACB | CBA | BAC | CAB | Selection 4 |
**A**: Flat cells in the PL category should be viewed as the appropriate analogues of critical points in the smooth category, with the caveat that not every flat cell is critical**B**: The flat cells of F𝐹Fitalic_F are, by definition, the cells of 𝒞(F)𝒞𝐹\mathcal{C}(F)caligraphic_C ( italic_F ) on which F𝐹Fitalic_F is constant**C**: Explicitly, we prove:
| ABC | BAC | CAB | CAB | Selection 2 |
**A**: Specifically, we study the time evolutions of the energy, universal statistics and correlations of the topological defects formed in the TFQIM after quantum phase transition induced by a quench. In particular, we quench the strength of the transverse magnetic field to drive the system from a paramagnetic state into a ferromagnetic state, during which the topological defects, i.e. the kinks where the polarization of the spins changes their directions, will form due to the KZM. In the machine learning we introduce the Restricted Boltzmann Machine (RBM) as a representation of the quantum state for TFQIM. RBM is a kind of neural networks with two layers of neurons, i.e. visible layer and hidden layer (see Fig.1)**B**:
In [15] the machine learning methods was merely applied in the unitary dynamics without phase transitions. Critical dynamics, i.e., the dynamics across the critical point of a phase transition is more complex and has richer phenomena [39]. Critical slowing down near the phase transition point may invalidate the applicability of this method. In this paper, we extend the machine learning methods introduced in [15] to study the nonequilibrium process of critical dynamics in a one-dimensional transverse-field quantum Ising model (TFQIM). TFQIM is a widely used model to study the phase transitions of one-dimensional spin chain and has been extensively studied analytically or experimentally such as in [40, 41, 42]. Therefore, TFQIM is a very suitable testbed to check the representing accuracy of neural networks and the robustness of machine learning methods**C**: In order to solve the ground state and the time evolution of the system, the stochastic reconfiguration (SR) method and time-dependent variational Monte Carlo (VMC) approach [43] are utilized, respectively. We find that time evolutions of the energy expectation value from the neural networks are perfectly consistent with the results reported in [40]. After the quench, the excited energy of the system are found to satisfy a power-law relation against the quench rate, which reveals the proportional relationship between the excitation energy and the kink numbers. Besides, the counting statistics of the kink numbers satisfy the Poisson binomial distributions introduced previously in [30, 44]. By computing the first three cumulants of the kink pair numbers, we find that they satisfy a universal power-law scalings to the quench rate consistent with the theoretical predictions. Additionally, we compute the kink-kink correlations at the end of the quench. The numerical data match the analytic formula presented in [41] very well. Therefore, our results show a very high accuracy of neural networks to investigate the critical dynamics of TFQIM. | BCA | BCA | CAB | BAC | Selection 4 |
**A**: Experiments show that our scheme preserves the helicity orders of magnitude better with a simple modification in the definition of the vorticity**B**: Besides, when the interval of mesh becomes larger, the helicity fails to conserve as well as the refining one.
**C**: Figure 8 show that the helicity of our model conserves better | ACB | CAB | ABC | BAC | Selection 1 |
**A**:
Figure 13: data coverage on data set 𝒟𝒟\mathcal{D}caligraphic_D in Figure 10(a) fails to capture the unreliability associated with the query points in uncertain regions**B**: The regions highlighted in red and green comprise the uncovered and covered regions respectively. Any query point belonging to the green (red) region is considered (un)covered.**C**: The training data (𝒟𝒟\mathcal{D}caligraphic_D) are highlighted as black dots | CAB | CAB | BCA | ACB | Selection 4 |
**A**: For example, a parent, P15, explained that some apps do not function properly otherwise**B**: In these cases, participants often said they either decided not to install an app or grant the permissions, so that they could use the app for its intended purpose.
**C**: In terms of how parents and teens decided on whether an app permission was safe to accept or not, we also found that most of the participants (79%, N=15 parents and 89%, N=17 teens) said they would simply accept the app permission requests because the apps otherwise would not work | ACB | ACB | BAC | BCA | Selection 4 |
**A**: Thus, the two square holes
give two nearby (overlapping) blue circular points in the persistence diagrams in Figure 4.**B**: The same number of points are sampled randomly from two square annuli, which are scaled versions of each other**C**: We illustrate the scale invariance property with the “Antman" example in Figure 4 | CAB | BAC | CBA | CAB | Selection 3 |
**A**: Unlike previous works that made attempt to deal with this problem through subject-specific learning or domain adaptation, we proposed a plug-in causal intervention module named CIS to remove the adverse effect brought by confounder Subject in a straightforward way, which could be inserted into almost all frame-based AU recognition model and boost them to a new state-of-the-art**B**: Extensive experiments prove the effectiveness of our CIS module, and vanilla backbones with CIS module inserted achieve state-of-the-art results.
**C**: This paper focuses on explaining the why and wherefores of subject variation problem in AU recognition with the help of causal inference theory and providing a solution for subject-invariant facial action unit recognition by deconfounding variable S𝑆Sitalic_S in the causal diagram via causal intervention | BAC | BCA | ABC | ACB | Selection 2 |
**A**: As a result, BBP is the first scheme to completely remove blockbody transmission (ignore retransmission probability) and block validation time. Thus, we call it truly scalable since each block duration is independent of the transaction volume in the block. Also, BBP achieves such significant improvement without any sacrifice of security or decentralization.
**B**: At first glance, our BBP scheme appears to combine compact blocks and simplified validation**C**: However, BBP fundamentally alters the block transmission and validation workflow by introducing the concept of pre-packed blockbody | BCA | ABC | ACB | CAB | Selection 4 |
**A**: (2021) establish sample complexity guarantees for searching the optimal policy in POMDPs whose models are identifiable and can be estimated by spectral methods. However, Azizzadenesheli et al. (2016) and Guo et al. (2016) add extra assumptions such that efficient exploration of the POMDP can always be achieved by running arbitrary policies**B**: Our work is related to a line of recent work on the sample efficiency of reinforcement learning for POMDPs. In detail, Azizzadenesheli et al. (2016); Guo et al. (2016); Xiong et al**C**: In contrast, the upper bound confidence (UCB) method is used in Xiong et al. (2021) for adaptive exploration. However, they require strictly positive state transition and observation emission kernels to ensure fast convergence to the stationary distribution. The more related work is Jin et al. (2020a), which considers undercomplete POMDPs, in other words, the observations are more than the latent states. Their proposed algorithm can attain the optimal policy without estimating the exact model, but an observable component (Jaeger, 2000; Hsu et al., 2012), which is the same for our algorithm design, while only applies to tabular POMDPs.
| ACB | CBA | ACB | BAC | Selection 4 |
**A**: We further report the geographical distribution of the included studies based on the
location of the study indicated in the paper (see Figure 7)**B**: We looked at the author’s affiliation and funding agency when required**C**: Most papers reported on studies which | ABC | CBA | CAB | BCA | Selection 1 |
**A**: We focus on single-cell data, as it is both high dimensional (20,000−40,000200004000020,000-40,00020 , 000 - 40 , 000 dimension)
and noisy [KAH19], using datasets from a popular benchmark database [DRS20] with ground truth community labels**B**: We first show that the average intra-community compression ratio is higher than the average inter-community compression ratio in all of the datasets. We then show that removing outliers in these datasets via our variance of compression technique improves the performance of clustering algorithms, such as PCA+K-Means, where we again outperform standard outlier detection methods.**C**: Finally, we test the relevance of compression ratio as a metric and the outlier detection method in real-world data | BAC | CBA | CAB | BCA | Selection 4 |
**A**: As the primary information source for various computer vision tasks, the visual input data play a significant role in most existing works to achieve competitive and promising performance**B**: To tackle the problem, we propose to supplement the missing visual data from another information source: the natural language dialog. Intuitively, humans rely on the multi-sensory systems from various modalities (e.g., vision, audio, and language) to understand the surrounding world, and it is intuitive for them to ask questions about the insufficient information given a specific task to fulfill.
To implement the proposed idea of supplementing the insufficient visual input via the natural language dialog, we introduce a model-agnostic interactive dialog framework, which can be jointly learned with most existing models and endows the models with the capability to communicate in the form of natural language question-answer interactions.**C**: It is reasonable to expect the performance drop under the task setting with incomplete visual input | ACB | BCA | CAB | CBA | Selection 1 |
**A**: [8] depicts state of the art**B**: Here, we mention some of the models: obnoxious facility games where every agent wants to stay away from the facility [11, 13];
heterogeneous facility games where the acceptable set of facilities for each agent could be different [25, 17, 12, 16];**C**: The recent survey by Chan et al | CBA | ACB | BCA | BAC | Selection 3 |
**A**: There is a paper [16] where the authors describe ILP formulations for the PED problem, together with some experimental results.
**B**: There is some more bibliography to add to the already vast literature [3, 10, 23, 31, 33, 35] on dominating induced matchings**C**: As we have seen before the papers on perfect edge domination are less frequent | BAC | CAB | ACB | BAC | Selection 2 |
**A**: Unlike (4), however, it is not immediately obvious whether the controller Φ(⋅)Φ⋅\Phi(\cdot)roman_Φ ( ⋅ ) defined by (5) will be PWA**B**: We will prove in §4 that this remains the case.
**C**: Under standard assumptions the problem (5) will always be feasible given the definition of a CLF, and its optimal solution will be unique | BCA | CAB | CAB | BAC | Selection 1 |
**A**: While many approaches work with trajectories in state space, there are also several works that operate directly on videos**B**: In this case, the information about physical quantities is substantially more abstract, so that uncovering
dynamics from video data is a significantly more difficult problem**C**: In their seminal work [55] consider objects sliding down a plane. By tracking the objects, they estimate velocity vectors that are used to supervise a rigid body simulation of the respective object. | CBA | ABC | CBA | CAB | Selection 2 |
**A**: At low noise, to achieve a quantum semantic fidelity of 0.70.70.70.7, QSC requires around 50%percent5050\%50 % quantum communication resources compared to semantic-agnostic QCNs using pruning data compression without any semantic concept extraction**B**:
In Figure 3, we show the quantum semantic fidelity achieved against the amount of quantum communication resources used for |𝒳|=500𝒳500\lvert\mathcal{X}\rvert=500| caligraphic_X | = 500**C**: This demonstrate the advantages of QSC accurately sending and reconstructing semantic information. | CAB | BCA | BAC | BCA | Selection 3 |
**A**:
Cooperation is fundamental to the effective learning of the agents formulated above**B**: Simply applying independent SARL algorithms to train individual agents interprets the other agents’ decisions as part of the environment, which would be, in turn, non-stationary as the other agents’ policies constantly change as well during the learning process**C**: Therefore, the MARL algorithm is utilized for training purposes. | ACB | ABC | CBA | CAB | Selection 2 |
**A**: These considerations include: additional evidence standards that the FDA may impose, the reluctance of insurers to compensate generously for a drug with marginal evidence of efficacy, lawsuits and liability costs, and the risk of a company developing a negative reputation among consumers, insurers, and regulators.
**B**: In particular, our calculation omits additional regulatory checks against approving ineffective drugs and punishments for agents who intentionally run clinical trials for drugs they believe to be ineffective**C**: There are important limitations to the above analysis | CBA | BCA | ACB | BAC | Selection 1 |
**A**: In Table 1 is reported an overview of the datasets used specifying their dimensions, modality, registration type, loss functions, and the Privacy Enhancing Technologies (PETs) employed.
**B**: We demonstrate and assess the different versions of PPIR illustrated in Section 3 on a variety of image registration problem, namely: (i) SSD for rigid transformation of point cloud data, (ii) SSD with linear and non-linear alignment of whole body positron emission tomography (PET) data; (iii) SSD and MI for mono- and multimodal linear alignment of MRI and PET brain scans; (iv) diffeomorphic non-linear registration with CC of multimodal abdomen data from CT and MRI scans**C**: Experiments are carried out on 2D (mainly for the SSD case) and 3D imaging data | CAB | BAC | ABC | ACB | Selection 1 |
Subsets and Splits