context stringlengths 250 6.18k | A stringlengths 250 3.82k | B stringlengths 250 8.2k | C stringlengths 250 4.99k | D stringlengths 250 4.17k | label stringclasses 4
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}\sum^{M}_{m=1}(1-u_{j,m})\ell_{\text{ce}}(D_{m}(G(\mathbf{z}_{j})),y).caligraphic_L start_POSTSUBSCRIPT ne end_POSTSUBSCRIPT start_POSTSUPERSCRIPT italic_G end_POSTSUPERSCRIPT ( over~ start_ARG bold_x end_ARG ) = ∑ start_POSTSUPERSCRIPT italic_N start_POSTSUBSCRIPT italic_g end_POSTSUBSCRIPT end_POSTSUPERSCRIPT start_... | In practice, our specialized discriminators to the subsets of training data outperforms independent training of discriminators on the whole dataset (as in GMAN) or different discriminator assignment strategies such as minimum-score discriminator selections, opposite to MCL-GAN, and random selections.
Also, the performa... | A weakness of MCL is the possible mistakes caused by overconfidence issues.
If non-specialized models make wrong predictions with high confidence in the score aggregation process, the average scores are misleading and the ensemble model may result in poor-quality outputs. | Each discriminator learns to be specialized in a subset of reference data space, which is identified automatically during training, so the ensemble of discriminators provides not only the differentiation of fake data but also more accurate predictions over the clusters of real data.
In this respect, a generator is enco... |
The non-expert model training is effective to handle the overconfidence issue, but the model may still suffer from the data deficiency problem frequently happening in the standard MCL algorithms because each discriminator can observe only a subset of the whole dataset. | D |
The communication systems utilizing DL techniques are typically designed to transmit digital bit sequences and optimized by minimizing the bit-error rate (BER) or symbol-error rate (SER), which achieves the first level communications according to the categorization by Shannon and Weaver[6]. Inspired by potentially high... |
Semantic information is relevant to the transmission goal at the receiver, which could be either source massage recovery or more intelligent tasks. In the cases of intelligent tasks, the semantic information only contains the task-related features while the other irrelative features will not be extracted or transmitte... | Regarding the semantic commutations for speech information, our previous work developed an attention mechanism-based semantic communication system to restore the source message, i.e., reconstruct the speech signals[18]. However, in this paper, we consider an intelligent task at the receiver to recover the text informat... |
Based on the spectrum and transcription of the original speech sample sequence, the proposed system model is shown in Fig. 1. From the figure, the transmitter consists of two individual components: the semantic encoder and the channel encoder, each component is implemented by an independent NN. At the transmitter, the... |
The semantic communication system for speech recognition aims to transmit and recover the information-related semantic features. In this section, we introduce the details of the considered system model and the adopted performance metrics are presented. | A |
We provide visualizations of our final segmentation predictions in Figure5. We compare our results with the ground truth and the predictions from the fully supervised counterpart. We can see that our method performs even better in label consistency and in hard examples like the fourth row. | The existing 3D WSSS methods formulate the problem in different directions. [10] utilize dense 2D segmentation labels to supervise the training in 3D by projecting the 3D predictions onto the corresponding 2D labels. However, each 3D sample is projected to 2D in several views and each projected 2D image needs pixel-lev... | We narrowed the performance gap between 3D weakly supervised semantic segmentation and current fully supervised methods while significantly reducing the human effort for annotation.
Our proposed method with 10% and 1% of the points being labeled can produce compatible results with its fully supervised counterpart in S3... | Existing 3D WSSS methods utilize different kinds of weak supervisions.[10] utilize dense 2D segmentation labels to supervise the training in 3D by projecting the 3D predictions onto the corresponding 2D labels. [11] proposes to generate pseudo point-level label using 3D class activation map[12] from subcloud-level anno... | Comparison with existing 3D WSSS methods:
We compare our proposed method with existing 3D WSSS methods[13, 51, 41]. For One-Thing-One-Click[48] and MulPro[8], we use the results reported from the MulPro[8] paper. [10] utilizes 2D dense labels on 2D projections of the 3D point clouds and [13] utilize the same weak supe... | D |
Table 1: Monocular 3D object detection results on the KITTI test set for the Pedestrian and Cyclist categories with the evaluation metric of AP40subscriptAP40\rm{AP}_{40}roman_AP start_POSTSUBSCRIPT 40 end_POSTSUBSCRIPT. The IoU threshold is set to 0.5. The bold black/blue color indicates the best/the second best perf... |
Table 1: Monocular 3D object detection results on the KITTI test set for the Pedestrian and Cyclist categories with the evaluation metric of AP40subscriptAP40\rm{AP}_{40}roman_AP start_POSTSUBSCRIPT 40 end_POSTSUBSCRIPT. The IoU threshold is set to 0.5. The bold black/blue color indicates the best/the second best perf... | As mentioned in the main paper, the KITTI [11] official data set contains 7,481 training and 7,518 test images with 2D and 3D bounding box annotations for pedestrian and cyclist categories.
We report our quantitative results in Table 1, using the official settings with IoU ≥0.5absent0.5\geq 0.5≥ 0.5 for pedestrians and... |
Figure 5: Qualitative results of our method for multi-class 3D object detection. We use orange box for cars, purple box for pedestrians, and green box for cyclists. All illustrated images are from the KITTI test set. Zoom in the image for more details. | Setup. The KITTI dataset [11] provides widely used benchmarks for various visual tasks in the autonomous driving, including 2D Object detection, Average Orientation Similarity (AOS), Bird’s Eye View (BEV), and 3D Object Detection. The official data set contains 7481 training and 7518 test images with 2D and 3D bounding... | B |
In this ablation experiment, the TR map was obtained from the original FPN layer, whose weight is shared with the TCL map, without the guidance of GCN.
The results listed under the heading ‘GCN’ show that even without GCN, our proposed FPNS and SAp strategies can still produce F-measure improvements of 1.1%percent1.11.... | We argue that GCN makes limited contributions when dealing with text instances that are spatially close enough to each other.
Such text instances are more common in multi-oriented texts, because most text segments in these texts do not have many spatial changes and have relatively small character and word spaces. Moreo... | Moreover, thanks to our proposed FPNS strategy, our method has achieved the highest recall rate of 83.8%percent83.883.8\%83.8 %. This shows the effectiveness of our idea of depicting the streamline of the texts, which helps to retrieve some misdetected texts.
As our approach focuses on detecting arbitrary-shape text, i... | For example, the arbitrary-shape text detectors [36, 9, 12, 8, 11, 29, 20, 37, 21] can only achieve SOTA results on at most one multi-oriented detection benchmark.
The arbitrary-shape text detectors focus more on the space variation of texts, whereas multi-oriented text detectors focus more on scale variation. | This is because the dense design of the text segments can effectively depict the ‘characterness’ property that non-Latin texts also exhibit.
Moreover, our proposed FPNS and SAp strategies enable the network to accurately identify connectivity within multi-lingual texts. | A |
\cal E}})\;.italic_ρ start_POSTSUPERSCRIPT ′ end_POSTSUPERSCRIPT ⋉ italic_ρ start_POSTSUPERSCRIPT ′ ′ end_POSTSUPERSCRIPT = italic_π start_POSTSUPERSCRIPT - 1 end_POSTSUPERSCRIPT ( ( italic_π start_POSTSUBSCRIPT caligraphic_S ( caligraphic_V × caligraphic_V ) end_POSTSUBSCRIPT ( italic_ϱ start_POSTSUPERSCRIPT ′ end_POS... | \mid{\cal V},{\cal E}}]=\varpi_{U}^{-1}(\mathcal{R})\subset U({{\cal V},{\cal E%
}})\;,∀ caligraphic_R ⊂ caligraphic_V × caligraphic_V , italic_D start_POSTSUBSCRIPT italic_U end_POSTSUBSCRIPT [ caligraphic_R ∣ caligraphic_V , caligraphic_E ] = italic_ϖ start_POSTSUBSCRIPT italic_U end_POSTSUBSCRIPT start_POSTSUPERSCRI... | \mid{\cal V},{\cal E}}]=\varpi^{-1}(\mathcal{R})\subset P({{\cal V},{\cal E}})\;,∀ caligraphic_R ⊂ caligraphic_V × caligraphic_V , italic_D start_POSTSUBSCRIPT italic_P end_POSTSUBSCRIPT [ caligraphic_R ∣ caligraphic_V , caligraphic_E ] = italic_ϖ start_POSTSUPERSCRIPT - 1 end_POSTSUPERSCRIPT ( caligraphic_R ) ⊂ italic... | ℛ⊂𝒱×𝒱ℛ𝒱𝒱\mathcal{R}\subset{\cal V}{\times}{\cal V}caligraphic_R ⊂ caligraphic_V × caligraphic_V, we associate the
subset DP[ℛ∣𝒱,ℰ]subscript𝐷𝑃delimited-[]conditionalℛ𝒱ℰD_{P}[{\mathcal{R}\mid{\cal V},{\cal E}}]italic_D start_POSTSUBSCRIPT italic_P end_POSTSUBSCRIPT [ caligraphic_R ∣ caligraphic_V , caligraphic_E... | relation ℛ⊂𝒱×𝒱ℛ𝒱𝒱\mathcal{R}\subset{\cal V}\times{\cal V}caligraphic_R ⊂ caligraphic_V × caligraphic_V, we associate the
subset DU[ℛ∣𝒱,ℰ]subscript𝐷𝑈delimited-[]conditionalℛ𝒱ℰD_{U}[{\mathcal{R}\mid{\cal V},{\cal E}}]italic_D start_POSTSUBSCRIPT italic_U end_POSTSUBSCRIPT [ caligraphic_R ∣ caligraphic_V , caligr... | D |
Bubble sort is a classical sorting algorithm in which each element in a list is compared with its neighboring elements and swapped until they are in the desired order Shutler2008SA . Bubble sort leads to (n−1)𝑛1\left({n-1}\right)( italic_n - 1 ) number of passes and n(n−1)2𝑛𝑛12\frac{{n\left({n-1}\right)}}{2}divide... |
A number of statistical algorithms for solving this problem have been studied in the last few decades Jing05DLBS ; Kapur2013SA ; Klein2013Sorting ; Fredman2014Sorting ; Agapitos2016RSA . A classic divide-and-conquer strategy has been proposed in which IP addresses are first divided into multiple subsets. Then, each su... |
The hardware architecture of modern processors usually consists of more than two independent central processing units (CPUs) or graphics processing units (GPUs). Parallel software platforms can be implemented using high-level programming frameworks for specific hardware architectures Chen2009SA . The Compute Unified D... |
The statistics collection algorithm should be stable, effective, and efficient for large-scale records. To overcome the disadvantages of general statistics collection methods, a number of parallel techniques have been developed for large-scale records by optimizing the efficiency and complexity. For example, these alg... | In this paper, we present two efficient algorithms for collecting the statistics of large-scale IP address data. We can obtain the frequently occurring IP addresses from the statistics, which can be regarded as a pre-processing step of user behavior analysis in network traffic management. Because of the increasing volu... | B |
-\mathcal{T}_{1}^{2}-2\mathcal{T}_{1}+I)=0.( caligraphic_T start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPT - italic_I ) ( caligraphic_T start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPT start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT - caligraphic_T start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPT - italic_I ) ( caligraphic_T start_POSTSUBSCRIPT 1 e... | We conclude that the entries of the first row of RTksubscript𝑅subscript𝑇𝑘R_{T_{k}}italic_R start_POSTSUBSCRIPT italic_T start_POSTSUBSCRIPT italic_k end_POSTSUBSCRIPT end_POSTSUBSCRIPT change values from 1111 to −11-1- 1 alternatingly. Therefore,
the number of sign changes in the first column is k𝑘kitalic_k. | problems with n−1𝑛1n-1italic_n - 1 subdomains. In many references, these linear systems are assumed to be symmetric. No matter whether it is symmetric or not, 𝒜𝒜\mathcal{A}caligraphic_A is generally indefinite. For solving such a system in large-scale computations, Krylov subspace methods with preconditioners are us... | Thus, according to the Routh-Hurwitz stability criterion [5, 20] and the fact that there is no zero root,
the number of roots of p¯ksubscript¯𝑝𝑘\bar{p}_{k}over¯ start_ARG italic_p end_ARG start_POSTSUBSCRIPT italic_k end_POSTSUBSCRIPT which are lying in the right half-plane equals k𝑘kitalic_k. | It follows that the eigenvalues of 𝒯1subscript𝒯1\mathcal{T}_{1}caligraphic_T start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPT must be among these values.
One can use the method of contradiction to prove that there is no polynomial of degree less than 6666 such that | D |
Throughout the training process, each hub maintains the block of model parameters for its silo.
To form the full global model, each hub’s model definition and parameters can be copied to a central server for downstream inference purposes. This can be done periodically for model checkpointing or at the end of the traini... | We denote the label for a sample p𝑝pitalic_p by y(p)superscript𝑦𝑝y^{(p)}italic_y start_POSTSUPERSCRIPT ( italic_p ) end_POSTSUPERSCRIPT. We assume that each client stores a copy of the sample labels for each of its sample IDs. We denote the copy of sample labels for 𝐗k,jsubscript𝐗𝑘𝑗\mathbf{X}_{k,j}bold_X start_P... | However, in cases when the labels are sensitive and sharing the labels for a sample ID across silos is not feasible, the label information for a sample ID may only be present in a client in one silo. In this case, we could modify our algorithm in the following way, similar to (Liu et al., 2020a): the clients in all sil... | For CIFAR-10, we increase the number of clients in each silo from K=50𝐾50K=50italic_K = 50 to K=100𝐾100K=100italic_K = 100, resulting in total 200 participants with N=2𝑁2N=2italic_N = 2 vertical partitions. We show the test performance results vs. the training latency in Fig. 3(c), Fig. 3(d). As in the previous sect... | However, such frequent information exchanges can lead to high training latency, especially when the communication latency is high and the intermediate information is large (Liu et al., 2020a).
The authors in a more recent work (Liu et al., 2020a) proposed an algorithm that addresses the communication bottleneck by perf... | B |
\mathrm{H}}E_{y}\operatorname{unfold}(\mathcal{X}).italic_γ = divide start_ARG ( roman_unfold ( caligraphic_X ) ) start_POSTSUPERSCRIPT roman_H end_POSTSUPERSCRIPT [ roman_bcirc ( caligraphic_B ) - roman_bcirc ( caligraphic_B ) start_POSTSUPERSCRIPT roman_H end_POSTSUPERSCRIPT ] roman_unfold ( caligraphic_X ) end_ARG s... | In this section, we delve into the study of pseudospectra for third-order tensors within the tensor-tensor multiplication framework. Specifically, we explore different formulations of pseudospectra for third-order tensors in Subsection 4.1. Subsection 4.2 is dedicated to the examination of various properties of pseudos... | The second main contribution of this paper is the development of pseudospectra theory for third-order tensors. We present four different definitions of tensor ε𝜀\varepsilonitalic_ε-pseudospectra (cf. Definitions 9 and 10) and establish their equivalence under certain conditions. We also provide various pseudospectral ... | The pseudospectra of finite-dimensional matrices and their extension to linear operators in Banach space have been extensively investigated and summarized in the classical book by Trefethen and Embree trefethen2005spectra . In the book, four different definitions of matrix pseudospectra are introduced and shown to be e... | Due to many scholars have focused their attentions on the matrix perturbation analysis Bauer-Fike ; 1986Generalization ; Rellich1969 ; shi2012sharp ; Sun1987 ; trefethen2005spectra , a wealth of results have been developed up to now. These include the Gershgorin disc theorem, the Bauer-Fike theorem, and the Kahan theor... | C |
As with most computer vision problems, image inpainting has been largely advanced by the widespread use of deep learning during the past decade. Different from the traditional methods [2, 5] that gradually fill in missing areas by searching for the most similar patches from known regions, the deep generative ones [19,... |
We also show additional comparison to EdgeConnect [18] and PRVS [10] in Figure 6 as these methods all claim to improve results by reconstructing image structures. Comparatively, the proposed model recovers more reasonable and sharper structures, leading to better results. | The generator is a two-stream architecture, modeled by a U-Net variant, as shown in Figure 2 (a). At the encoding stage, the corrupted image and its corresponding edge map are individually projected into the latent space, where the left branch focuses on texture features and the right branch targets structure features.... | a number of multi-stage methods that serially incorporate additional structural priors are proposed, producing more impressive results. EdgeConnect [18] extracts image structures by edges, based on which the holes are filled. Xiong et al. [28] show a similar model while it employs foreground object contours as structur... |
To deal with this problem, a number of multi-stage methods are proposed to explicitly incorporate structure modeling, which hallucinate structures of missing regions in the first stage and use them to guide pixel generation in the second stage. For instance, EdgeConnect [18] encodes such structures by edges, while [20... | D |
In a binary erasure channel (BEC), a binary symbol is either received correctly or totally erased with probability ε𝜀\varepsilonitalic_ε. The concept of BEC was first introduced by Elias in 1955 InfThe . Together with the binary symmetric channel (BSC), they are frequently used in coding theory and information theory ... | In this paper we carried out an in-depth study on the average decoding error probabilities of the random parity-check matrix ensemble ℛm,nsubscriptℛ𝑚𝑛\mathcal{R}_{m,n}caligraphic_R start_POSTSUBSCRIPT italic_m , italic_n end_POSTSUBSCRIPT over the erasure channel under three decoding principles, namely unambiguous de... | In particular in FFW , upon improving previous results, the authors provided a detailed study on the decoding error probabilities of a general q𝑞qitalic_q-ary linear code over the erasure channel under the three decoding principles. Via the notion of qℓsuperscript𝑞ℓq^{\ell}italic_q start_POSTSUPERSCRIPT roman_ℓ end_P... |
The problem of decoding linear codes over the erasure channel has received renewed attention in recent years due to their wide application in the internet and the distributed storage system in analyzing random packet losses Byers ; Luby ; Lun . Three important decoding principles, namely unambiguous decoding, maximum ... |
First recall that the error exponents of the average decoding error probability of the ensemble ℛ(1−R)n,nsubscriptℛ1𝑅𝑛𝑛\mathcal{R}_{(1-R)n,n}caligraphic_R start_POSTSUBSCRIPT ( 1 - italic_R ) italic_n , italic_n end_POSTSUBSCRIPT over the erasure channel under the three decoding principles are defined by | C |
We check also that MCTS-kSubS vastly outperforms the baseline - AlphaZero algorithm, see Figure 1 (top, left). An MCTS-based agent was also evaluated in [54]. Its implementation uses graph neural networks architectures and achieves 92929292% success rate for L=5𝐿5L=5italic_L = 5. Our transformed-based baseline is str... | In Figure 1, we present the performance of Subgoal Search. We measure the success rate as a function of the search budget. The success rate is measured on 1000100010001000 instances of a given problem (which results in confidence intervals within ±0.03plus-or-minus0.03\pm 0.03± 0.03). For BF-kSubS the search budget is ... | Sokoban Using BF-kSubS allows for significantly higher success rates rates within the same computational budget, see Table 3. Our solution scales well to the board size as big as 20×20202020\times 2020 × 20; note that 10×10101010\times 1010 × 10 boards are typically used in deep RL research [16, 37]. Importantly, we ob... |
INT The difficulty of the problems in INT increases fast with the proof length L𝐿Litalic_L and the number of accessible axioms. W used K=18𝐾18K=18italic_K = 18; all of available axioms. We observe, that BF-kSubS scales to proofs of length L=10𝐿10L=10italic_L = 10 and L=15𝐿15L=15italic_L = 15, which are significant... |
Figure 1: The performance of Subgoal Search. (top, left) comparison on INT (with the proof length 15) to AlphaZero. (top, right) BF-kSubS consistently achieves high performance even for small computational budgets. (bottom, left) similarly on Sokoban (board size 12x12 with 4 boxes) the advantage of BF-kSubS is clearly... | B |
Meanwhile, in order to relieve character substitution problems and enhance the robustness of NER models, researchers have also paid attention to utilizing glyph and phonetic features of Chinese characters. Jiang Yang and Hongman Wang suggested using the ‘Four-corner’ code, a radical-based encoding method for Chinese ch... |
our MFE-NER is a lightweight Named Entity Recognition method fusing the glyph and phonetic feature embeddings for Chinese character substitution, which is complementary to pre-trained language models in the representation of Chinese characters. As shown in Figure 2, MFE-NER introduces an extra module, fusing glyph emb... | Nowadays, the informal language environment created by social media has deeply changed the way that people express their thoughts. Using character substitution to generate new named entities becomes a common linguistic phenomenon which is a big challenge for NER. In this paper, we propose a lightweight method fusing th... | In this section, we first show why our method is lightweight and explain the improvement that MFE-NER achieved in recognizing substitution forms of named entities. Then, we will analyse the overall performance enhancement by applying MFE-NER. Here, the embeddings without glyph and phonetic features are named ‘pure’ emb... | In this paper, we propose a lightweight method, Multi-feature Fusion Embedding for Chinese Named Entity Recognition (MFE-NER), which fuses extra glyph and phonetic features to detect possible substitution forms of named entities in Chinese. On top of using pre-trained models to represent the semantic feature, we choose... | A |
In addition to field-of-view, we also investigate the eyebox that is produced with neural étendue expansion. By initializing the learning process with a uniform random expander we bias the optimized solution towards expanders that distribute energy throughout the eyebox, in contrast to a quadratic phase profiles[28] t... | The uniform random expander is constructed by assigning each pixel a phase that is uniformly randomly chosen within [0,2π]02𝜋[0,2\pi][ 0 , 2 italic_π ]. To ensure at least 2π2𝜋2\pi2 italic_π phase is available for all wavelengths the [0,2π]02𝜋[0,2\pi][ 0 , 2 italic_π ] phase range is defined for 660 nmtimes660nm6... |
To characterize the hologram reconstruction with the proposed neural étendue expander we simulate a Fourier holographic setup that has been augmented with a neural étendue expander. Fig. 3a reports qualitative examples of trichromatic and monochromatic reconstructions achieved with neural étendue expanders, binary ran... | The experimental findings on the display prototype verify that conventional non-étendue expanded holography can produce high-fidelity content but at the cost of a small FOV. Increasing the étendue via a binary random expander will increase the FOV but at the cost of low image fidelity, even at the design wavelength of ... | Finally, we also investigate 3D étendue expanded holograms. We find that neural étendue expansion also enables higher fidelity étendue expanded 3D color holograms. 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 ... | D |
To counter data scarcity in the multi-choice question answering task, \addedJin
et al. (2020) propose a multi-stage MTL model that is first coarsely pre-trained using a large out-of-domain natural language inference dataset and then fine-tuned on an in-domain dataset. | In some settings where MTL is used to improve the performance of a primary task, the introduction of auxiliary tasks at different levels could be helpful. Several works integrate a language modeling task on lower-level encoders for better performance on simile detection (Rei, 2017), sequence labeling (Liu
et al., 2018a... | For text generation tasks, MTL is brought in to improve the quality of the generated text.
It is observed in (Domhan and Hieber, 2017) that adding a target-side language modeling task on the decoder of a neural machine translation (NMT) model brings moderate but consistent performance gain. | et al., 2020) where speech recognition and text translation are learned jointly. Similarly for video captioning (Pasunuru and
Bansal, 2017), the video prediction task and text entailment generation task are used to enhance the encoder and decoder of the model, respectively. A multimodal representation space also makes ... | et al. (2019) add an unsupervised auxiliary task that learns continuous bag-of-words embeddings on the retrieval corpus in addition to the sentence-level parallel data. \deletedRecently, \addedWang
et al. (2020d) build a \replacedmultilingualmulti-lingual NMT system with source-side language modeling and target-side de... | B |
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| D |
G2,ℓ2,𝒞2subscript𝐺2subscriptℓ2subscript𝒞2G_{2},\ell_{2},\mathcal{C}_{2}italic_G start_POSTSUBSCRIPT 2 end_POSTSUBSCRIPT , roman_ℓ start_POSTSUBSCRIPT 2 end_POSTSUBSCRIPT , caligraphic_C start_POSTSUBSCRIPT 2 end_POSTSUBSCRIPT are isomorphic if there exists an isomorphism between them.
| Suppose we are given a triplet
G,ℓ,𝒞𝐺ℓ𝒞G,\ell,\mathcal{C}italic_G , roman_ℓ , caligraphic_C. We say that two sets C1⊆V(G)subscript𝐶1𝑉𝐺C_{1}\subseteq V(G)italic_C start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPT ⊆ italic_V ( italic_G ) and C2⊆V(G)subscript𝐶2𝑉𝐺C_{2}\subseteq V(G)italic_C start_POSTSUBSCRIPT 2 end_POSTS... | Suppose we are given a triplet G,ℓ,𝒞𝐺ℓ𝒞G,\ell,\mathcal{C}italic_G , roman_ℓ , caligraphic_C having q+1𝑞1q+1italic_q + 1 disjoint vertex sets C1,C2,…,Cq+1subscript𝐶1subscript𝐶2…subscript𝐶𝑞1C_{1},C_{2},\ldots,C_{q+1}italic_C start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPT , italic_C start_POSTSUBSCRIPT 2 end_POSTSUBSCRIP... | v∈C1𝑣subscript𝐶1v\in C_{1}italic_v ∈ italic_C start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPT. Since C1subscript𝐶1C_{1}italic_C start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPT and C2subscript𝐶2C_{2}italic_C start_POSTSUBSCRIPT 2 end_POSTSUBSCRIPT have the same type on G,ℓ,𝒞𝐺ℓ𝒞G,\ell,\mathcal{C}italic_G , roman_ℓ , caligraphic_C... | Suppose we are given a triplet G,ℓ,𝒞𝐺ℓ𝒞G,\ell,\mathcal{C}italic_G , roman_ℓ , caligraphic_C, a vertex v∈V(G)𝑣𝑉𝐺v\in V(G)italic_v ∈ italic_V ( italic_G ) is said to be unlabeled if there does not exist ui∈Usubscript𝑢𝑖𝑈u_{i}\in Uitalic_u start_POSTSUBSCRIPT italic_i end_POSTSUBSCRIPT ∈ italic_U such
that ℓ(ui)... | A |
This characterization is inspired by the literature on group size effects in voluntary contributions environments (e.g. Isaac and Walker, 1988). Pure congestion describes a situation in which players generate fixed efficiency gains by sharing, and gains from their investment are divided between the sharing player and t... | This characterization is inspired by the literature on group size effects in voluntary contributions environments (e.g. Isaac and Walker, 1988). Pure congestion describes a situation in which players generate fixed efficiency gains by sharing, and gains from their investment are divided between the sharing player and t... |
The estimation in column (3) of Table 1 shows the effects of the treatment on subjects’ abilities to coordinate on efficient structure. By efficient structure, we refer to a network topology that satisfies the conditions required for efficiency by Proposition 2, without necessarily satisfying the requirement of full c... |
This special case lends itself to a convenient characterization of efficient structure, based on how a player’s MPCR scales with their out-degree. That is, the efficient outcome depends on the curvature of the MPCR function. It transitions between these phases when the game is purely congestive, at the boundary betwee... |
In another relevant study by Rand et al. (2011), the authors conducted an experiment to gauge the effects of endogenous networks on cooperation in a repeated prisoner’s dilemma. By varying the opportunity for network updates, they showed that subjects are able to take advantage of their ability to change social ties i... | C |
As shown in Fig. 4 (a), researchers usually consider two kinds of transposed convolution operations: one adds padding around the input matrix and then applies the convolution operation, and the other adds padding between the values of the input matrix followed by the direct convolution operation. The latter is also cal... |
In ESPCN (Shi et al., 2016), Shi et al. proposed an efficient sub-pixel convolutional layer. Instead of increasing the resolution by directly increasing the number of LR feature maps, sub-pixel first increases the dimension of LR feature maps, i.e., the number of the LR feature maps, and then a periodic shuffling oper... | As shown in Fig. 4 (a), researchers usually consider two kinds of transposed convolution operations: one adds padding around the input matrix and then applies the convolution operation, and the other adds padding between the values of the input matrix followed by the direct convolution operation. The latter is also cal... | where 𝒫𝒮𝒫𝒮\mathcal{PS}caligraphic_P caligraphic_S denotes the periodic shuffling operator, which transfers an h×w×C⋅r2⋅ℎ𝑤𝐶superscript𝑟2h\times w\times C\cdot r^{2}italic_h × italic_w × italic_C ⋅ italic_r start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT tensor to a tensor of shape rh×rw×C𝑟ℎ𝑟𝑤𝐶rh\times rw\times... | et al., 2020), the authors utilized the gradient maps to guide image recovery to solve the problem of structural distortions in the GAN-based methods. Among them, the gradient maps are obtained from a gradient branch and integrated into the SR branch to provide structure prior. With the help of gradient maps, we know w... | A |
Table 2: We compare the blind super-resolution performance achieved by a conventional coordinate , a -based internal learning framework of SinGAN and our method. We compute PSNR (↑↑\uparrow↑) and SSIM (↑↑\uparrow↑) for a number of upscaling factors and downsampling kernels. | Figure 3: Ablation study of Neural Knitwork components. Conventional does not produce coherent inpainted region and this is improved with the introduction of patches. Further, imposing cross-patch consistency constraint increases the quality of the synthesized region while employing a approach ensures patches of high... | Reconstructed Pixel Loss The transition from predicting isolated pixel colors to patches introduces a new trade-off between imposing spatial relationships of the pixel colors and obtaining a high fidelity image with accurate detail. In practice, there will be some disagreement between the predictions for the same pixel... | As we demonstrate in Figure 8, a standard network has limited denoising capability because it attempts to fit all pixel colors with no additional constraints. In contrast, a Neural Knitwork ensures that both patches and pixel colors are reliably reconstructed while imposing additional consistency constraint on the der... | The reconstruction quality of the whole image is comparable for the three tested methods. However, when inpainted region is concerned, we observe a significant improvement of over 4 dB for the Neural Knitwork compared to the conventional coordinate and 2 dB less than the -based technique. For some of the results, the ... | C |
In this paper we have explored the use of heuristic Bayesian decision-making rules for logistic contextual apple tasting problems. We have shown that both Thompson Sampling and Information Directed Sampling methods are highly efficient for such problems, and indeed more so than confidence bound based approach of Bartók... | The phenomenon of a greedy approach sometimes outperforming more complex attempts to balance exploration and exploitation in contextual problems is not unprecedented. Indeed, in the contextual bandit literature, a number of recent works (Bastani et al.,, 2021; Kannan et al.,, 2018; Raghavan et al.,, 2023; Jedor et al.,... |
The present paper is the first work we aware of that specifically applies TS to apple tasting, but previous work has considered its use for logistic bandits. For logistic contextual bandits, the implementation of exact TS (i.e. the policy that draws its sample from the exact posterior) is infeasible due to the intract... |
We have shown that the existence of a non-informative action in apple tasting can inhibit the performance of traditional Information Directed Sampling, and it would be of interest to explore further more complex settings (in partial monitoring, reinforcement learning, etc.) where this effect could be even more pronoun... | In this paper we have explored the use of heuristic Bayesian decision-making rules for logistic contextual apple tasting problems. We have shown that both Thompson Sampling and Information Directed Sampling methods are highly efficient for such problems, and indeed more so than confidence bound based approach of Bartók... | C |
However, these are structured methods that require translating expert knowledge into some knowledge representation formalism. Typically, that implies a costly abstraction process and a compromise in terms of conceptual simplifications imposed by the limitations of the representation language.
To illustrate the point, l... | In a legal analytics scenario [11] where the identification of unfair clauses is done automatically, a system’s output of “potential unfairness” could be explained by the distribution of attention mass on specific segments of text. However, this in itself is not the type of explanation that a legal expert would provide... | 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. Sure, the attention mechanism [4] could offer cues as to how to interpret the behavior of such models. Nevertheless, whether attention could be mean... | Here, the identification of relevant rationales is the result of an abstraction process carried out by the legal expert in an effort to explain an opinion. Similarly, an automatic system for unfair clause detection could use a shortlist of legal rationales to justify a prediction of unfairness, as they would define the... | Online Terms of Service (ToS) often contain potentially unfair clauses to the consumer.
Unfair clause detection in online ToS is a binary classification task where each clause is labeled as either fair (negative class) or unfair (positive class) [10]. Multiple unfairness categories result in multiple binary classificat... | A |
In this section, we propose some informal properties that the crowdedness level in a big city should satisfy to robustly withstand critical events.
Let c𝑐citalic_c represent a crowdedness threshold for all the areas of the city. Note, however, that the framework can accommodate for, e.g., area-specific threshold value... | In addition to the previous requirements that are related to general aspects of the mobile network,
for the evaluation of the city in terms of safety and quality of life, it is interesting to look at how the city is performing with respect to the reachability of some key points of interest. For example, in an emergency... |
One possible stakeholder of our proposed framework is a telecommunications company, which would like to have a predictive alert system to ensure that their mobile network does not get overcrowded. The following three properties could be of interest to the telecommunications company: | In this section, we propose some informal properties that the crowdedness level in a big city should satisfy to robustly withstand critical events.
Let c𝑐citalic_c represent a crowdedness threshold for all the areas of the city. Note, however, that the framework can accommodate for, e.g., area-specific threshold value... | Especially in high-dimensional, complex models, these requirements or properties relevant for decision-making are typically highly nonlinear functions of the random variables, and one is interested in their predictive distribution. Their verification ex-ante as well as their evaluation ex-post (as part of the posterior... | B |
2:enumerate over all kpoly(kϵ−1)superscript𝑘poly𝑘superscriptitalic-ϵ1k^{\operatorname{poly}(k\epsilon^{-1})}italic_k start_POSTSUPERSCRIPT roman_poly ( italic_k italic_ϵ start_POSTSUPERSCRIPT - 1 end_POSTSUPERSCRIPT ) end_POSTSUPERSCRIPT k𝑘kitalic_k-partitions of S𝑆Sitalic_S
to find k𝑘kitalic_k-partition 𝒫={P1,... | 3:∀x∈Xfor-all𝑥𝑋\forall x\in X∀ italic_x ∈ italic_X, let px←σx∑y∈Xσy←subscript𝑝𝑥subscript𝜎𝑥subscript𝑦𝑋subscript𝜎𝑦p_{x}\leftarrow\frac{\sigma_{x}}{\sum_{y\in X}{\sigma_{y}}}italic_p start_POSTSUBSCRIPT italic_x end_POSTSUBSCRIPT ← divide start_ARG italic_σ start_POSTSUBSCRIPT italic_x end_POSTSUBSCRIPT end_ARG ... |
∑i=1k1|Pi|∑x,y∈Pi‖x−y‖2superscriptsubscript𝑖1𝑘1subscript𝑃𝑖subscript𝑥𝑦subscript𝑃𝑖superscriptnorm𝑥𝑦2\sum_{i=1}^{k}\frac{1}{|P_{i}|}\sum_{x,y\in P_{i}}\|x-y\|^{2}∑ start_POSTSUBSCRIPT italic_i = 1 end_POSTSUBSCRIPT start_POSTSUPERSCRIPT italic_k end_POSTSUPERSCRIPT divide start_ARG 1 end_ARG start_ARG | italic... | {\operatorname{cost}_{z}^{\varphi}(X,C^{\star})}+\frac{w_{X}(x)}{w_{X}(C^{%
\star}(x))}italic_σ start_POSTSUBSCRIPT italic_x end_POSTSUBSCRIPT ← divide start_ARG italic_w start_POSTSUBSCRIPT italic_X end_POSTSUBSCRIPT ( italic_x ) ⋅ ( roman_dist ( italic_x , italic_C start_POSTSUPERSCRIPT ⋆ end_POSTSUPERSCRIPT ) ) star... | where each cisubscript𝑐𝑖c_{i}italic_c start_POSTSUBSCRIPT italic_i end_POSTSUBSCRIPT is represented as 1|Pi|∑x∈Piφ(x)1subscript𝑃𝑖subscript𝑥subscript𝑃𝑖𝜑𝑥\frac{1}{|P_{i}|}\sum_{x\in P_{i}}\varphi(x)divide start_ARG 1 end_ARG start_ARG | italic_P start_POSTSUBSCRIPT italic_i end_POSTSUBSCRIPT | end_ARG ∑ start_... | B |
Here, we give more background on doctrines (Section 2.1) and more details on standard equality in the substructural setting (Section 2.2).
Moreover, we provide a categorical comparison between our quantitative equality and the usual one by left adjoints (Section 5.2). | Doctrines provide a simple categorical framework to study several kinds of logics.
In this section, we first recall basic notions about doctrines for standard (i.e. non-linear) logics, then we introduce the class of doctrines modelling the fragment of linear logic we will be concerned with. | As we have seen in the previous section, R𝑅Ritalic_R-Lipschitz doctrines are the objects of the 2-category 𝐋𝐋𝐃Rsubscript𝐋𝐋𝐃𝑅\mathbf{LLD}_{R}bold_LLD start_POSTSUBSCRIPT italic_R end_POSTSUBSCRIPT.
In this section we study its properties, relating it with with other 2-categories of doctrines. | how does this (standard) notion of equality relate to our quantitative equality?
To answer this question in a precise way, first of all we observe that also elementary R𝑅Ritalic_R-graded doctrines can be organised in a 2-category, and then we compare it with the 2-category of R𝑅Ritalic_R-Lipschitz doctrines. | which are doctrines modelling the (⊗,𝟏)tensor-product1(\otimes,\mathbf{1})( ⊗ , bold_1 )-fragment of Linear Logic enriched by R𝑅Ritalic_R-graded modalities, where R𝑅Ritalic_R is an ordered semiring of resources,
and introduce R𝑅Ritalic_R-Lipschitz doctrines, namely, R𝑅Ritalic_R-graded doctrines with quantitative e... | A |
Figure 5 shows the Average Precision@K𝐾Kitalic_K of four studied measures on six labeled networks. Note that the difference between ForestSim-EX and ForestSim-AP is marginal though the latter requires less time and space. Moreover, ForestSim achieves comparable performance to RoleSim, the state-of-art role similarity... | In this section, we first define a new role similarity measure, namely ForestSim, based on spanning rooted forests. We then show that the ForestSim score can be expressed in terms of the diagonal elements in the forest matrix and prove that ForestSim is an admissible role similarity metric. After that, we propose Fores... |
Efficient top-k similarity search algorithm : We devise ForestSimSearch for the top-k similarity search. ForestSimSearch can handle a top-k query in O(k)𝑂𝑘O(k)italic_O ( italic_k ) time once the precomputation is finished. Furthermore, we use the fast approximate algorithm to compute the diagonal entries of the for... |
In this paper, on the basis of spanning rooted forests, we propose ForestSim, a new node similarity metric. ForestSim uses the average size of the trees rooted at the node u𝑢uitalic_u in spanning rooted forests of the graph, denoted by s(u)𝑠𝑢s(u)italic_s ( italic_u ), to capture its structural properties. Two node... |
In this paper, we propose a novel node similarity metric, namely ForestSim, to quickly and effectively process top-k similarity search on large networks. Different from previous frameworks, ForestSim, based on spanning rooted forests of graphs, adopts the average size of all trees rooted at node u𝑢uitalic_u in spanni... | D |
TGCN-BERT Tian et al. (2021) introduces a type-aware GCN that uses an attention mechanism to measure the importance of each edge in the syntax structure graph.
SARL-RoBERTa Wang et al. (2021) employs adversarial training to mitigate sentiment bias and align aspects with opinion words using span-based dependency. | However, the progress of sentiment dependency-based methods, such as the work by Zhang et al. (2019); Zhou et al. (2020); Tian et al. (2021); Li et al. (2021a); Dai et al. (2021), has contributed to the improvement of coherent sentiment learning.
These studies explored the effectiveness of syntax information in ABSC, w... | Finally, dotGCN-BERT Chen et al. (2022), SSEGCN-BERT Zhang et al. (2022), and TGCN-BERT Li et al. (2021a) are also included in our comparison.
These models represent the current landscape of ABSC research, allowing us to assess the effectiveness of LSA against well-established approaches. |
Aspect-based sentiment classification Pontiki et al. (2014, 2015, 2016) (ABSC) aims to identify sentiments associated with specific aspects within a text, as highlighted in several studies Ma et al. (2017); Fan et al. (2018); Zhang et al. (2019); Yang et al. (2021). | However, sentiment dependency remains a somewhat ambiguous concept in the current research landscape.
Furthermore, previous methods Zhou et al. (2020); Zhao et al. (2020); Tang et al. (2020); Li et al. (2021a, a) tend to model context topological dependency (e.g., context syntax structure) rather than sentiment depende... | B |
𝐗𝐗\mathbf{X}bold_X. Suppose that there exist orthogonal projections 𝐋:ℝN→𝒫p(𝕊):𝐋→superscriptℝ𝑁superscript𝒫𝑝𝕊\mathbf{L}:\mathbb{R}^{N}\rightarrow{\mathcal{P}}^{p}(\mathbb{S})bold_L : blackboard_R start_POSTSUPERSCRIPT italic_N end_POSTSUPERSCRIPT → caligraphic_P start_POSTSUPERSCRIPT italic_p end_POSTSUPERSCR... | gradient estimate of 𝜸𝐖subscript𝜸𝐖\bm{\gamma}_{\mathbf{W}}bold_italic_γ start_POSTSUBSCRIPT bold_W end_POSTSUBSCRIPT, where 𝜸𝐖0superscriptsubscript𝜸𝐖0\bm{\gamma}_{\mathbf{W}}^{0}bold_italic_γ start_POSTSUBSCRIPT bold_W end_POSTSUBSCRIPT start_POSTSUPERSCRIPT 0 end_POSTSUPERSCRIPT is the initial
guess. The main ... | In Figure 4, we can observe a comparison of performance among different methods for the totchg (total charge) variable: Kriging/BLUP, KNN-Reg, KNN, GLS, and DDL. This comparison is conducted across varying training/validation proportions of the dataset (from (10%/90%) to (90%/10%) of the data. The horizontal axis depic... | The operator 𝐋𝐋\mathbf{L}bold_L and 𝐖𝐖\mathbf{W}bold_W are constructed from a multilevel decomposition of the location of predictors. This process is somewhat elaborate and the reader is referred to [31] and [32] for all of the details. However, for the exposition in this section it sufficient to know what the prop... |
Figure 4: Performance comparison for imputation of the total charge variable among Kriging/BLUP, KNN-Reg, KNN, GLS and DDL for different training/validation proportions of the data. On the horizontal axis we have the percentage proportion for the training dataset. The vertical axis corresponds to the rMSE, MAPE and me... | C |
\mathrm{Var}(\bm{y})}=\hat{y_{i}}over^ start_ARG italic_f ( italic_y start_POSTSUBSCRIPT italic_i end_POSTSUBSCRIPT ) end_ARG = ( italic_f ( italic_y start_POSTSUBSCRIPT italic_i end_POSTSUBSCRIPT ) - blackboard_E [ italic_f ( bold_italic_y ) ] ) / square-root start_ARG roman_Var ( italic_f ( bold_italic_y ) ) end_ARG ... | Figure 3. QuantumNAT Overview. (1) Post-measurement normalization matches the distribution of measurement results between noise-free simulation and real QC. (2) Based on realistic noise models, noise-injection inserts quantum error gates to the training process to increase the classification margin between classes. (3)... |
Figure 4 compares the noise-free measurement result distribution of 4 qubits (blue) with their noisy counterparts (yellow) for MNIST-4. Qualitatively, we can clearly observe that the post-measurement normalization reduces the mismatch between two distributions. | Visualization of QNN extracted features. MNIST-2 classification result is determined by which feature is larger between the two: feature one is the sum of measurement outcomes of qubit 0 and 1; feature 2 is that of qubit 2 and 3. We visualize the two features obtained from experiments on Belem in a 2-D plane as in Figu... |
Compatibility with existing noise mitigation. QuantumNAT is orthogonal to existing noise mitigation such as extrapolation method. It can be combined with post-measurement normalization (Table 4). The QNN model has 2 blocks, each with three U3+CU3 layers. For “Normalization only”, the measurement outcomes of the 3-laye... | B |
However, gallego2018unifying and other event-based data association methods zhu2017event ; gallego2019focus ; peng2022globally show that the events triggered by the same edge in the scene can be associated with each other using an event trajectory. Furthermore, they also show that the event trajectories, triggered a... | We extensively evaluate the proposed EDA on object tracking. The experimental results demonstrate the superiority of EDA over other state-of-the-art event-based tracking methods and several popular conventional tracking methods. In addition, the estimated true event trajectories corresponding to object motions are also... | For the object tracking task, since the event data is associated between two adjacent frames, we employ an evaluation protocol of frame-wise tracking to evaluate the performance of the proposed EDA approach. For the frame-wise tracking, the evaluation of these competing methods is based on object pairs, each of which i... |
To evaluate the proposed EDA on visual tracking, we need to calculate the corresponding object bounding box based on the event trajectories associated by EDA. According to the frame-wise tracking protocol, for each tracking instance, we have the ground truth bounding box of the tracked object at the current frame. EDA... |
However, gallego2018unifying and other event-based data association methods zhu2017event ; gallego2019focus ; peng2022globally show that the events triggered by the same edge in the scene can be associated with each other using an event trajectory. Furthermore, they also show that the event trajectories, triggered a... | A |
As a connected vertex-ordering of H𝐻Hitalic_H can be obtained in linear time using a standard graph traversal algorithm, and a colouring of G′superscript𝐺′G^{\prime}italic_G start_POSTSUPERSCRIPT ′ end_POSTSUPERSCRIPT may be computed in O(mn)𝑂𝑚𝑛O(mn)italic_O ( italic_m italic_n ) time [12, Chapter 5.7], we concl... |
We now prove our main result, that there are no ugly perfect graphs. 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]. Our proof ... | class of perfect graphs. We also give a simple and constructive proof for comparability graphs (which are perfect). Note that there exist bad graphs in these graph classes, consider for example the fish graph, which is K4subscript𝐾4K_{4}italic_K start_POSTSUBSCRIPT 4 end_POSTSUBSCRIPT-minor-free and comparability; see... | Concerning other subclasses of interest, as mentioned in the introduction, the same task can be done in time O(m+n)𝑂𝑚𝑛O(m+n)italic_O ( italic_m + italic_n ) for chordal graphs using the LexBFS algorithm [21], for Meyniel graphs this can be done in time O(n2)𝑂superscript𝑛2O(n^{2})italic_O ( italic_n start_POSTSUP... | Recently, connected greedy edge-colourings (equivalently, connected greedy colourings of line graphs) have been studied in [3], and it was proved that there is no line graph of a bipartite graph that is ugly.444Moreover, a careful analysis of the proof of [3] gives an algorithm running in time O(n4)𝑂superscript𝑛4O(n... | A |
In contrast to SSL tasks, the input distance in KD tasks is assumed to be well-defined for both positive and negative sample pairs. Thus, we adopt the static p~Xsubscript~𝑝𝑋\tilde{p}_{X}over~ start_ARG italic_p end_ARG start_POSTSUBSCRIPT italic_X end_POSTSUBSCRIPT and try to mine relationships among different sub-m... | Firstly, we compare the effects of hyper-parameters in GenURL for DR and SSL tasks. As shown in Figure 12, GenURL prefers smaller νZsubscript𝜈𝑍\nu_{Z}italic_ν start_POSTSUBSCRIPT italic_Z end_POSTSUBSCRIPT, i.e., using ν=0.01𝜈0.01\nu=0.01italic_ν = 0.01 to balance the local and global structures. Figure 5 shows that... | Unsupervised graph embedding experiments are conducted on three graph network datasets (Cora, CiteSeer, and PubMed), and we evaluate the learned embeddings by the node classification task. We compare GE methods that utilize both features and graph structures, including AGC [25], AGE [28], GIC [88], and ARGA [27]. The l... | We perform DR experiments on MNIST, FMNIST, and COIL-20 datasets. We compare the current leading methods, including non-parametric methods (t-SNE [13] and UMAP [4]) and parametric methods (P-UMAP [15], GRAE [89], TopoAE [11], and DMT [16]). Besides the linear classification top-1 accuracy (Acc) with logistic regression... | Unlike previous DR and GE methods, the proposed GenURL can import extra prior knowledge and is robust to highly redundant data; additionally, different from GE and SSL methods, GenURL is agnostic to network structures and predefined proxy tasks.
Extensive experiments conducted on benchmarks of four URL tasks (self-supe... | B |
(2) We also study on-device profiling to report the measured SRAM and Flash usage when executing the deep model on MCU. The number is usually larger than the analytic results since we need to account for temporary buffers storing partial weights, Im2Col buffer, etc. |
We analyze the advantage of our method on image classification datasets: ImageNet [11] as the standard benchmark, and Visual Wake Words [10] to reflect TinyML applications. We further validate our method on object detection datasets: Pascal VOC [13] and WIDER FACE [51] to show our advantage: be able to fit larger reso... |
Patch-based inference effectively reduces the peak memory usage of existing networks by 4-8×\times× (Figure 5). The results are further improved when co-designing neural architecture with inference scheduling. On ImageNet [11], we achieve a record accuracy of 71.8% on MCU (Table 4.2); on visual wake words dataset [10]... | To show the advantage of our method, we conduct experiments on MobileNetV3 [23] space by extending it to support different r𝑟ritalic_r’s and w𝑤witalic_w’s. We compared it with state-of-the-art methods under different computation budgets in Table 6.
Our NAS method consistently outperforms existing techniques for tiny ... | To show the advantage of our method, we conduct experiments on MobileNetV3 [23] space by extending it to support different r𝑟ritalic_r’s and w𝑤witalic_w’s. We compared it with state-of-the-art methods under different computation budgets in Table 6.
Our NAS method consistently outperforms existing techniques for tiny ... | A |
The ICDM 2020 Knowledge Graph Contest is a competition-style event co-located with the leading ICDM conference. This paper describes our solution for the consumer event-cause extraction task, and we won 1st place in the first stage leaderboard and 3rd place in the final stage leaderboard. Extracting causes of consumer... |
The ICDM 2020 Knowledge Graph Contest is a competition-style event co-located with the leading ICDM conference. This paper describes our solution for the consumer event-cause extraction task, and we won 1st place in the first stage leaderboard and 3rd place in the final stage leaderboard. Extracting causes of consumer... | In the 2020 ICDM Competition 333https://www.biendata.xyz/competition/icdm_2020_kgc/,
the task adds judgments on multiple event types, which is difficult to solve with a reading comprehension framework. The goal of the competition is to extract multiple event types and event-causes for each text and brand/product. To th... | The Consumer Event Cause Extraction (CECE) task aims to extract consumer events and the cause of the event from the text of a given brand or product. Traditional methods use a model structure similar to extract machine reading comprehension (MRC) [7]. Most of the related work [6] extracted events type and events-cause ... |
In this competition, we introduce a fresh perspective to revisit the relational event-cause extraction task and propose a novel sequence tagging [8] framework, instead of extracting event types and events-causes separately. Experiments show our framework outperforms baseline methods even when its encoder module uses a... | C |
DGCNN [37],
and Set2Set [30] in this paper. Technically, any GNN working on graph-level tasks is viable as a graph encoder candidate. Graph encoders can be combined into what we term the CGCL assembly, and subsequently, they can be trained by the collaborative contrastive learning framework | As described in Section 3.2, the proposed CGCL consists of multiple graph encoders. Naturally, the assembly of different types of graph encoders have a significant impact on the performance of CGCL. Therefore, in this section we first explain the essence of collaborative framework. Subsequently, we delve into the two c... | In this study, we introduce CGCL, a novel collaborative graph contrastive learning framework, designed to address the invariance challenge encountered in current GCL methods. Unlike the conventional practice of constructing augmented graphs by hand, CGCL employs multiple GNN-based encoders to generate multiple contrast... | To cope with the problem of model collapse, we devise the asymmetric structure for CGCL. The asymmetry lies in the differences of GNN-based encoders’ message-passing schemes. Besides, graph encoders in CGCL are supposed to be complementary for a stronger fitting ability. Specifically, high complementarity indicate that... |
We propose the concepts of asymmetric structure and complementary encoders as foundational principles for the collaborative learning paradigm. To provide a more comprehensive theoretical analysis, we put forth two quantitative metrics to assess both the asymmetry and complementarity inherent in the collaborative frame... | A |
We use two datasets: shapes3d (used in Burgess and Kim, (2018) and included in the TensorFlow datasets package) and a dataset used by Choi et al., (2018)333The dataset is available at https://github.com/benbogin/obverter. We used the code provided in the repository to generate 1000100010001000 images for each color-sha... | Each element is a (64,64,3)64643(64,64,3)( 64 , 64 , 3 ) RGB image, and is characterized by multiple features, such as shape or object hue. We choose images with values for both features ranging in {0,1,2,3}0123\{0,1,2,3\}{ 0 , 1 , 2 , 3 }.
The obverter dataset contains images of four shapes (box, cylinder, ellipsoid, ... | This experiment was intended to check how much the CNN-backed input is relevant in the compositionality context. We could conjecture that CNN may facilitate shape recognition and therefore be the driving force in the emergence of languages compositional with respect to the canonical shape, color split. To check this we... | permuting the set of (color,shape)colorshape(\text{color},\text{shape})( color , shape ) and factorizing them into new labels (see Appendix D.8). We use a random permutation, so the new labels are abstract and correspond to some joint color-shape concepts.
In our experiments, we show that languages, which emerge are co... | We use two datasets: shapes3d (used in Burgess and Kim, (2018) and included in the TensorFlow datasets package) and a dataset used by Choi et al., (2018)333The dataset is available at https://github.com/benbogin/obverter. We used the code provided in the repository to generate 1000100010001000 images for each color-sha... | A |
The previous result can be used to guarantee that the set 𝒞𝒞\mathcal{C}caligraphic_C defined in equation (5) contains the set 𝒟¯¯𝒟\underline{\mathcal{D}}under¯ start_ARG caligraphic_D end_ARG, i.e., 𝒟¯⊆𝒞¯𝒟𝒞\underline{\mathcal{D}}\subseteq\mathcal{C}under¯ start_ARG caligraphic_D end_ARG ⊆ caligraphic_C. Hence, ... | We first learn a ROCBF controller in the case that the state x𝑥xitalic_x is perfectly known, i.e., the model of the output measurement map is such that X^(y)=X(y)=x^𝑋𝑦𝑋𝑦𝑥\hat{X}(y)=X(y)=xover^ start_ARG italic_X end_ARG ( italic_y ) = italic_X ( italic_y ) = italic_x and the error is ΔX(y):=0assignsubscriptΔ𝑋... |
Propositions 1 and 2 guarantee that the level-sets of the learned function h(x)ℎ𝑥h(x)italic_h ( italic_x ) satisfy the desired geometric safety properties. We now derive conditions that ensure that h(x)ℎ𝑥h(x)italic_h ( italic_x ) is a ROCBF, i.e., that the ROCBF constraint (4) is also satisfied. | where ⊕direct-sum\oplus⊕ is the Minkowski sum operator. The set 𝒩𝒩{\mathcal{N}}caligraphic_N should be thought of as a layer of width σ𝜎\sigmaitalic_σ surrounding the set 𝒟𝒟\mathcal{D}caligraphic_D, see Fig. 3 (right) for a graphical depiction. As will be made clear in the sequel, by enforcing that the value of th... | The previous section provides safety guarantees when h(x)ℎ𝑥h(x)italic_h ( italic_x ) is a ROCBF. However, one is still left with the potentially difficult task of constructing a twice continuously differentiable function h(x)ℎ𝑥h(x)italic_h ( italic_x ) such that (i) the set 𝒞𝒞\mathcal{C}caligraphic_C defined in e... | B |
Consider a quantum algorithm Q𝑄Qitalic_Q that makes T𝑇Titalic_T queries to x∈{0,1}N𝑥superscript01𝑁x\in\{0,1\}^{N}italic_x ∈ { 0 , 1 } start_POSTSUPERSCRIPT italic_N end_POSTSUPERSCRIPT. Then for any ε,δ>0𝜀𝛿0\varepsilon,\delta>0italic_ε , italic_δ > 0, there exists a deterministic classical algorithm that makes p... | Having said that, very few of our results will follow from Raz-Tal in any straightforward way. Most often we need to develop other lower bound tools, in addition to or instead of Raz-Tal. Our new tools, which seem likely to be of independent interest, include a random restriction lemma for quantum query algorithms, a c... |
It is also worth exploring whether our random restriction lemma could be generalized to other classes of functions. Our argument works for functions of low quantum query complexity, so it is natural to ask: is there a comparable random restriction lemma for bounded low-degree polynomials, and thus functions of low app... | We remark that Theorem 12 bears similarity to a well-known conjecture that involves simulation of quantum query algorithms by classical algorithms. A decade ago, motivated by the question of whether 𝖯=𝖡𝖰𝖯𝖯𝖡𝖰𝖯\mathsf{P}=\mathsf{BQP}sansserif_P = sansserif_BQP relative to a random oracle with probability 1111, Aa... |
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. [Mon12, OZ16]. Aaronson and Ambainis [AA14] showed that this relat... | D |
The arc spaces may also have a rich scheme (i.e.. nilpotent) structure (see [27, 16, 14]) reflecting the geometry of the original scheme [35, 9].
In the case of a fat point ℐm=⟨xm⟩⊂k[x]subscriptℐ𝑚delimited-⟨⟩superscript𝑥𝑚𝑘delimited-[]𝑥\mathcal{I}_{m}=\langle x^{m}\rangle\subset k[x]caligraphic_I start_POSTSUBSCRI... | Note that the series does not depend on the multiplicity m𝑚mitalic_m of the point.
One way to capture the scheme structure of ℒ(X)ℒ𝑋\mathcal{L}(X)caligraphic_L ( italic_X ) could be to take the components of the projections in (3) with their multiplicities. | The arc spaces may also have a rich scheme (i.e.. nilpotent) structure (see [27, 16, 14]) reflecting the geometry of the original scheme [35, 9].
In the case of a fat point ℐm=⟨xm⟩⊂k[x]subscriptℐ𝑚delimited-⟨⟩superscript𝑥𝑚𝑘delimited-[]𝑥\mathcal{I}_{m}=\langle x^{m}\rangle\subset k[x]caligraphic_I start_POSTSUBSCRI... | Our result (2) suggests that one possibility is to define the multiplicity of a solution as the growth rate of multiplicities of its truncations, and this definition will be consistent with the usual algebraic multiplicity for the case of a fat point on a line.
| From the point of view of the algebraic geometry, I(∞)superscript𝐼I^{(\infty)}italic_I start_POSTSUPERSCRIPT ( ∞ ) end_POSTSUPERSCRIPT defines the arc space ℒ(X)ℒ𝑋\mathcal{L}(X)caligraphic_L ( italic_X ) [13] of the scheme X𝑋Xitalic_X.
Geometrically, the points of the arc space correspond to the Taylor coefficients... | A |
We apply nDFA and DFA on Karate-weighted and Gahuku-Gama subtribes, and find that error rates for both methods on both data are zero, suggesting that nDFA and DFA perform perfect on this two networks. For visualization, Figures 9, 10 and 11 show community detection results by applying nDFA on these weighted networks e... | (c) To measure performances of different methods on real-world weighted network with unknown information on nodes labels, we propose a general modularity as an extension of classical Newman’s modularity [23]. For weighted network in which all edge weights are nonnegative, the general modularity is exactly the Newman’s ... | For above setting, two different adjacency matrices are generated under DFM in Figure 2 where we also report error rates for DFA and nDFA. Meanwhile, since A𝐴Aitalic_A and Z𝑍Zitalic_Z are known here, one can run DFA and nDFA directly to A𝐴Aitalic_A in Figure 2 with two communities to check the error rates of DFA and... |
Figure 10: Panel (a) and panel (b) show nDFA’s detection results of CoauthorshipsNet1589 and CoauthorshipsNet379, respectively. Here, different colors are used to distinguish different communities. For visualization, two nodes are connected by a line if there is a positive edge weight between them, and we do not show ... | In CoauthorshipsNet, node means scientist and weights mean coauthorship, where weights are assigned by the original papers. For this network, there is no ground truth about nodes labels, and the numbers of communities are unknown. The CoauthorshipsNet has 1589 nodes, however its adjacency matrix is disconnected. Among ... | C |
Banerjee (2016); Oliehoek and
Amato (2016); Foerster et al. (2016) is based on using the centralized information during training. During execution, the agents act using only their respective observations. Following this scheme, Foerster et al. (2016) introduces the RIAL and DIAL algorithms in the context of Q𝑄Qitalic_... | Reinforcement learning has witnessed impressive development in recent years. Famously, superhuman performance has been achieved in games Go Silver et al. (2016), StarCraft II Vinyals et al. (2019b), Dota 2 Berner et al. (2019) and other applications. These successes are the result of rapid algorithmic development. Rese... | et al. (2018), a distributed single-agent algorithm. The idea of extending RL algorithms to the multi-agent setting has been successfully executed multiple times. Lowe et al. (2017) propose a multi-agent actor-critic algorithm MADDPG, which is based on the DDPG algorithm Lillicrap et al. (2016). Yu
et al. (2020) introd... | Following this idea, Rashid et al. (2018) introduced QMIX, which learns a complex state-dependent decomposition by using monotonic mixing hypernetworks. Extensions of QMIX include MAVEN Mahajan et al. (2019), COMIX de Witt et al. (2020), SMIX(λ𝜆\lambdaitalic_λ) Wen
et al. (2020), and QTRAN Son |
In this work, we take a step towards amending this situation. We propose MA-Trace, a new on-policy actor-critic algorithm, which adheres to the centralized training and decentralized execution paradigm Lowe et al. (2017); Foerster et al. (2018); Rashid et al. (2018). The key component of MA-Trace is the usage of impor... | C |
Suppose there are N𝑁Nitalic_N instances in our training data set. If we consider a binary classification problem, then we will have: xi∈ℝnsubscript𝑥𝑖superscriptℝ𝑛{x}_{i}\in\mathbb{R}^{n}italic_x start_POSTSUBSCRIPT italic_i end_POSTSUBSCRIPT ∈ blackboard_R start_POSTSUPERSCRIPT italic_n end_POSTSUPERSCRIPT, yi∈{−1,... | The panel in Figure 1(e) contains interactive views that help users find outliers, borderline cases, and misclassified cases in the test set. The first main view supports extracting the manual decisions (MD) from the previous phase (see Section Manual Decisions). This output is stored in a JSON format where the boundar... | Random Forest. This algorithm works in two stages. The first stage involves integrating numerous decision trees to construct the RF, and the second stage involves making predictions for each tree created in the first stage, followed by a majority voting strategy.
| Boosting attaches weak classifiers (e.g., decision stumps or shallow decision trees) sequentially, each improving the predictions made by the previous models. Freund1996Experiments ; Schapire1990Strength
Stacking involves fitting many base models from different algorithms on the same data set and using a metamodel to ... |
From the analyses and the overall score of the RF and AB models, we observe that the most performant models for RF consider only 2 features when splitting the nodes (i.e., max_features hyperparameter). The PCPs in Figure 7(d) enable us to scan the internal regions of the hyperparameters’ solution space for RF. As for ... | B |
An electromagnetic wave that propagates from the transmitter (Tx) to the receiver (Rx) of a wireless communications system is characterized, inter alia, by its polarization, i.e., orientation of the field vector. Interaction of a wave (multipath component, MPC) with environmental objects may change the orientation, an... | Various other aspects of polarization in MIMO systems have been investigated as well. Ref. [16] showed that space-time block coding (STBC) with single polarization outperforms STBC with dual polarization in Rayleigh and Ricean fading channels. A MIMO system with dual-polarized antenna elements can have lower spatial di... |
In particular, several recent research works present the benefit of utilizing the polarization domain in recently proposed communication schemes including, but not limited to, MIMO spatial multiplexing [1]; spatial modulation (SM) [2, 3, 4]; non-orthogonal multiple access (NOMA) [5]; and beamforming [6, 7]. It is vali... | In this section, we present simulation results focusing on the system performance when applying the proposed PR-HS-MIMO scheme. The results include improvement in channel capacity; and the comparison of EW and global polarization reconfiguration schemes in terms of channel capacity and selected Tx antenna indices. In t... | It has been demonstrated that utilizing the polarization domain may increase channel capacity and spectral efficiency; and improve symbol error rate (SER) [15, 16, 17, 1, 18]. For this reason, impact of polarization on the wireless communication systems has been regarded as a promising research topic [1, 19, 20, 21, 22... | D |
In particular, for any algorithm and infinitely many n𝑛nitalic_n, there exists a stream of n𝑛nitalic_n pieces of total area O(loglogn/logn)𝑂𝑛𝑛O(\sqrt{\log\log n/\log n})italic_O ( square-root start_ARG roman_log roman_log italic_n / roman_log italic_n end_ARG ) that the algorithm cannot pack in the unit square... | As indicated above, in the offline setting, all the problems in Theorem 2 have O(1)𝑂1O(1)italic_O ( 1 )-approximations (Theorem 6) and also allow for O(1)𝑂1O(1)italic_O ( 1 )-competitive algorithms in the online setting if the arriving pieces are axis-parallel rectangles or if rotations are allowed (see Section 1.2... | Then the density of the piece in its axis-parallel bounding box is at least 1/2121/21 / 2, and the algorithm for rectangles can be applied to the bounding box, again leading to O(1)𝑂1O(1)italic_O ( 1 )-competitive algorithms.
We also note that using known results, it is easy to show that the offline versions of the t... | Below we survey the most important results related to the packing problems studied in this paper.
Let us highlight that when the pieces are restricted to axis-parallel rectangles, there are online algorithms with constant competitive ratios solving all the problems of Theorem 2. | We note that these problems have known O(1)𝑂1O(1)italic_O ( 1 )-competitive algorithms if the arriving pieces are axis-parallel rectangles; see Section 1.2 for references. In other words, online algorithms exists which produce solutions that are only a constant factor worse than the offline optimum. If the arriving p... | A |
WFLW:
This dataset is from [38] containing 10,000 faces with 7500 and 2500 in training and test sets, respectively. All images are collected from the WIDER FACE dataset [40] and manually labeled with 98 landmarks. The dataset contains different test subsets where the image appearances vary due to variations in pose, ex... |
Figure 1: The distribution of the mean radial error (MRE) when choosing a different image as a template in one-shot medical landmark detection task. The x-axis refers to MRE and the y-axis refers to the percentage of MRE lying in the corresponding ranges. Evidently, the choice of template affects the performance signi... | 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]. There are 19 landmarks of anatomical significance labeled by 2 expert doctors in each radiograph. The averaged version of annotations by two doct... | However, during our research following the work of [42], we observe an interesting phenomenon (see Figure 1). The template choice highly impacts the final performance. The mean radial error (MRE) of our trained model varies from 2.9mm under the “best” template to 4.5mm under the “worst" template. It is evident that the... | Metrics: Following the official challenge [14, 37], mean radial error (MRE) and successful detection rate (SDR) in four radii (2mm, 2.5mm, 3mm, and 4mm) are applied, based on the Euclidean distance between prediction and ground truth. In addition, similarity via Eq. (9) is demonstrated for comparison. For the WFLW data... | D |
(2) We use a spectral algorithm to fit MMDF. We show that the proposed algorithm stably yields consistent community detection under MMDF. Especially, theoretical results when edge weights follow a specific distribution can be obtained immediately from our results.
|
(3) We provide fuzzy weighted modularity to evaluate the quality of mixed membership community detection for overlapping weighted networks. We then provide a method to determine the number of communities for overlapping weighted networks by increasing the number of communities until the fuzzy weighted modularity does ... | MMDF is a generative model and fuzzy weighted modularity is a general modularity for overlapping weighted networks. We expect that our model MMDF and fuzzy weighted modularity proposed in this paper will have wide applications in learning and understanding the latent structure of overlapping weighted networks, just as ... |
In this paper, we have proposed a general, flexible, and identifiable mixed membership distribution-free (MMDF) model to capture community structures of overlapping weighted networks. An efficient spectral algorithm, DFSP, was used to conduct mixed membership community detection and shown to be consistent under mild c... |
In the DFSP algorithm, the number of communities K𝐾Kitalic_K should be known in advance, which is usually impractical for real-world networks. Here, we introduce fuzzy weighted modularity, then we combine it with DFSP to estimate K𝐾Kitalic_K for overlapping weighted networks. | A |
A¯=1N+1∑i=0NAi.¯𝐴1𝑁1superscriptsubscript𝑖0𝑁subscript𝐴𝑖\bar{A}=\frac{1}{N+1}\sum_{i=0}^{N}A_{i}.over¯ start_ARG italic_A end_ARG = divide start_ARG 1 end_ARG start_ARG italic_N + 1 end_ARG ∑ start_POSTSUBSCRIPT italic_i = 0 end_POSTSUBSCRIPT start_POSTSUPERSCRIPT italic_N end_POSTSUPERSCRIPT italic_A start_POSTSU... | In this section, we add our proposed CwD on three previous SOTA methods, namely LUCIR[12], PODNet[8] and AANet[19], to validate the effectiveness of our method.
We denote the number of classes learned at initial phase by B𝐵Bitalic_B and number of new classes learned per phase after the initial one by S𝑆Sitalic_S. | Next, in Sec. 4.2, we add our proposed Class-wise Decorrelation (CwD) on some State-Of-the-Art (SOTA) methods [12, 8, 19] to validate its effectiveness.
Finally, in Sec. 4.3, we provide ablation study on how factors such as number of classes of the initial phase, number of exemplars for each class, and CwD coefficient ... | Table 1: Comparison of average incremental accuracy (%) with or without Class-wise Decorrelation (CwD) at the initial phase.
B𝐵Bitalic_B denotes number of classes learned at initial phase and S𝑆Sitalic_S denotes number of classes learned per phase after the initial one. | In this section, we study how the following factors affect the effectiveness of CwD: (1) number of classes for the initial phase, (2) number of exemplars, and (3) CwD coefficient (η𝜂\etaitalic_η in Eqn. (9)). Experiments in this section are based on LUCIR[12], with the ResNet18 model and the ImageNet100 dataset.
| A |
For each team, we started with the list of observed subject-level cumulative ranks, i.e., the actual ranking based on the BraTS-Reg score. For each pair of teams, we repeatedly randomly permuted (100,000 times) the cumulative ranks for each subject.
For each permutation, we calculated the difference in the BraTS-Reg sc... | For each team, we started with the list of observed subject-level cumulative ranks, i.e., the actual ranking based on the BraTS-Reg score. For each pair of teams, we repeatedly randomly permuted (100,000 times) the cumulative ranks for each subject.
For each permutation, we calculated the difference in the BraTS-Reg sc... | For each permutation, we calculated the difference in the BraTS-Reg score between this pair of teams.
The statistical significance of the relative rankings was determined by assessing the proportion of occasions when the difference in BraTS-Reg score, calculated using randomly permuted data, surpassed the observed diff... | The statistical significance of the relative rankings was determined by assessing the proportion of occasions when the difference in BraTS-Reg score, calculated using randomly permuted data, surpassed the observed difference in BraTS-Reg score (i.e., using the actual data).
This proportion was reported as a p-value. | For each permutation, we calculated the difference in the BraTS-Reg score between this pair of teams.
The statistical significance of the relative rankings was determined by assessing the proportion of occasions when the difference in BraTS-Reg score, calculated using randomly permuted data, surpassed the observed diff... | C |
As said above, we have that β∈𝖼𝗈𝗆𝗉Σ(Q)𝛽subscript𝖼𝗈𝗆𝗉Σ𝑄\beta\in\mathsf{comp}_{\Sigma}(Q)italic_β ∈ sansserif_comp start_POSTSUBSCRIPT roman_Σ end_POSTSUBSCRIPT ( italic_Q ) if and only if βy↦x∈𝖼𝗈𝗆𝗉Σ(Q′)subscript𝛽maps-to𝑦𝑥subscript𝖼𝗈𝗆𝗉Σsuperscript𝑄′\beta_{y\mapsto x}\in\mathsf{comp}_{\Sigma}(Q^{\p... |
If the condition of line 7 is satisfied by Q′superscript𝑄′Q^{\prime}italic_Q start_POSTSUPERSCRIPT ′ end_POSTSUPERSCRIPT, then it is also satisfied by Q𝑄Qitalic_Q as we have not changed constants or removed variables from primary-lhs positions, which contradicts Lemma 29. | If the condition of line 1 is satisfied by Q′superscript𝑄′Q^{\prime}italic_Q start_POSTSUPERSCRIPT ′ end_POSTSUPERSCRIPT, then it is also satisfied by Q𝑄Qitalic_Q as we have only removed an orphan variable, which does not affect the complex part of the query. This is a contradiction to Lemma 29.
|
If the condition of line 7 is satisfied by Q′superscript𝑄′Q^{\prime}italic_Q start_POSTSUPERSCRIPT ′ end_POSTSUPERSCRIPT, then it is also satisfied by Q𝑄Qitalic_Q as we have not changed constants or removed variables from primary-lhs positions, which contradicts Lemma 29. | If the condition of line 7 is satisfied by Q′superscript𝑄′Q^{\prime}italic_Q start_POSTSUPERSCRIPT ′ end_POSTSUPERSCRIPT, then it is also satisfied by Q𝑄Qitalic_Q as we have not changed constants or removed variables from primary-lhs positions, which contradicts Lemma 29.
| D |
Inspired by an example in Salathé and Jones [38] about the impact of community structure on disease spread, we explore the impact of the effective community structure of a contact network on the outbreak duration, final size, and outbreak peak of a disease in simulations of a susceptible–infected–recovered (SIR) model... |
Inspired by an example in Salathé and Jones [38] about the impact of community structure on disease spread, we explore the impact of the effective community structure of a contact network on the outbreak duration, final size, and outbreak peak of a disease in simulations of a susceptible–infected–recovered (SIR) model... | Salathé and Jones [38] explored the effect of differences in community structure on the outbreak duration, final size, and outbreak peak in simulations of an SIR model on networks. In our work, we consider a modified version of one of the examples in [38].
Salathé and Jones rewired edges to consider networks with diffe... |
Community structure can greatly influence disease dynamics on networks [26, 30]. Salathé and Jones [38] illustrated that changes in community structure are correlated with changes in disease quantities for susceptible–infected–recovered (SIR) dynamics on a network. One of their findings is that outbreak duration can a... |
In the present paper, we examine absorbing random walks on graphs in which different nodes can have different absorption rates, inducing an “effective” network structure that is reflected only partially by the edge weights of a network. Many notions of network community structure arise from the analysis of random walk... | B |
(1) The 3/2 Factor; Throttled Trees.
As mentioned in §III, the 3/2 factor is an accurate estimate if the corresponding T𝑇Titalic_T’s are equal. However, in the above equation, T[i,k,h−1]𝑇𝑖𝑘ℎ1T[i,k,h-1]italic_T [ italic_i , italic_k , italic_h - 1 ] and T[k,j,h−1]𝑇𝑘𝑗ℎ1T[k,j,h-1]italic_T [ italic_k , italic_j , ... | latency (tgsubscript𝑡𝑔t_{g}italic_t start_POSTSUBSCRIPT italic_g end_POSTSUBSCRIPT) and probability of success (pgsubscript𝑝𝑔p_{g}italic_p start_POSTSUBSCRIPT italic_g end_POSTSUBSCRIPT). Generation latency
is the time between successive attempts by the node to excite the | Consider two siblings (A,B)𝐴𝐵(A,B)( italic_A , italic_B ) and (B,C)𝐵𝐶(B,C)( italic_B , italic_C ) at a depth777Defined as the distance of a node
from the root; depth of the root is 0. of i𝑖iitalic_i (i>0𝑖0i>0italic_i > 0) from 𝒯𝒯\mathcal{T}caligraphic_T’s root. | less555We note that, in our context, the storage time as well as the memory coherence
time are statistical quantities due to the underlying statistical mechanisms. However, for the purposes of selecting a swapping tree, we use a fixed decoherence threshold τdsubscript𝜏𝑑\tau_{d}italic_τ start_POSTSUBSCRIPT italic_d en... | In our overall methodology, to conserve node and link resources, we post-process or ”throttle” the swapping-tree obtained from the DP algorithm by increasing the generation latencies of some of the non-root nodes such that (i) the latencies of siblings are equalized, and (ii) the parents latency is related to the child... | D |
Various organizations have recently proposed guidelines on the regulation of AVs to monitor their compliance with law enforcement. NACTO’s (National Association of City
Transportation Officials) statement on automated vehicles proposes nine principles to shape a policy on regulation of future generation AVs [34]. NHTSA... |
The issues and growing concerns caused by AI systems create the need to scrutinize the regulation of this technology. As a result, public institutions have initiated the development of regulatory frameworks to monitor the activities of data-driven systems at both a country level and internationally. The focal points o... | The general principles for acceptable functional safety of road vehicles are defined by the ISO 26262 standard [43]. According to this standard, there should be a safety certification development with evidence-based rationales: the vehicle should be able to meet the established functional safety requirement in its oper... | While the regulations have been set out to ensure legislative norms and user demands are met, some standards provide specifications to achieve a high safety level, quality assurance, efficiency, and environmentally friendly transportation systems. The International Organization for Standardization (ISO) has adopted sev... | We can expect that the potential evolution of the srC𝑠𝑟𝐶srCitalic_s italic_r italic_C processes will ultimately rely on the automation of regulatory compliance testing against all eeC𝑒𝑒𝐶eeCitalic_e italic_e italic_C systems. The complexity of srC𝑠𝑟𝐶srCitalic_s italic_r italic_C systems lies within the sc... | C |
Pre-train In NetVLAD [9], VGG-16 and AlexNet are pre-trained by ImageNet [37] for classification, which demonstrates that the end-to-end model achieves higher accuracy and training speed [38, 39]. The ImageNet dataset has more than 14 million images, covering more than 20000 categories, and it is widely used for deep l... | GhostCNN ensures a lightweight architecture and low computational cost by replacing part of convolution operations with a series of linear transformations to generate ghost feature maps. Though the FLOPs of Ghost-dil-NetVLAD is only 1%percent11\%1 % of that of VGG16-NetVLAD and Patch-NetVLAD, and the parameters is 17%p... |
In this section, six models including Alex-NetVLAD, VGG16-NetVLAD, Patch-NetVLAD (Considering our limited computational resources, we only use its built-in storage mode. In this paper, we uniformly call this method Patch-NetVLAD.), MobileNetV3-NetVLAD (lightweight CNN + NetVLAD), Ghost-NetVLAD (the Ghost module does n... | Tokyo 24/7 dataset. The experimental results demonstrate that Patch-NetVLAD achieves the best performance on the Pitts30k test dataset and Tokyo 24/7 dataset, while Ghost-dil-NetVLAD performs the best on TJU-Location test dataset because most Recall@N of Ghost-dil-NetVLAD are greater than those of the remaining models.... |
Table 2: Recall@N of Alex-NetVLAD, VGG16-NetVLAD, Patch-NetVLAD, MobileNetV3-NetVLAD, Ghost-NetVLAD and Ghost-dil-NetVLAD on the Pitts30k test dataset, Tokyo 24/7 and TJU-Location test dataset. We report all results for each of them, including the best, second-best and third-best results. | B |
Stream ciphers[16] are one of the main cryptographic primitives used in symmetric cryptography. Historically, the first stream ciphers were built with “linear” registers, where linearity is meant both in the register update function (which sends one state to the next) and in the output function, which computes the keys... | The main idea behind this attack is to decrease the degree of the original system by multiplying each equation in (3), that are usually of high degree, by a well chosen g∈𝔹n𝑔subscript𝔹𝑛g\in\mathbb{B}_{n}italic_g ∈ blackboard_B start_POSTSUBSCRIPT italic_n end_POSTSUBSCRIPT. The resulting equations are
| Stream ciphers[16] are one of the main cryptographic primitives used in symmetric cryptography. Historically, the first stream ciphers were built with “linear” registers, where linearity is meant both in the register update function (which sends one state to the next) and in the output function, which computes the keys... | In this paper, we propose a new form of algebraic attack, which is especially effective against nonlinear filter generators. We show with two toy examples how the attack can be performed in practice. We also apply our attack to WG-PRNG and we provide a complexity estimate that shows a fatal weakness of this cipher. We ... | Traditionally, stream ciphers are attacked with two approaches: correlation attacks, that exploit possible correlations between some part of the keystream and a portion of the initial state, and approximation attacks, where the nonlinear part is approximated by a linear component. The design defenses against these type... | D |
\mathcal{I}_{TR}|+tN_{S}\epsilon_{S}roman_max start_POSTSUBSCRIPT italic_σ start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPT start_POSTSUPERSCRIPT ∗ end_POSTSUPERSCRIPT ∈ roman_Σ start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPT end_POSTSUBSCRIPT italic_u start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPT ( italic_σ start_POSTSUBSCRIPT 1 end_POSTSUB... | The opponent modeling and exploitation process consists of two steps: opponent modeling and model exploitation. Opponent modeling requires building a model from previous data or actions observed during an online play. Model exploitation is finding a good strategy against the given model and is the main focus of this pa... |
When we exploit an opponent model, we need to worsen the strategy in terms of exploitability. We must limit how much the strategy worsens if we want a safe response. Gadgets are used to ensure exploitability does not increase Moravcik et al. (2016); Burch |
This work explores the full model exploitation and proposes continual depth-limited best response (CDBR). CDBR relies on the value function used in the standard limited look-ahead solving, and we prove theoretical guarantees on the performance. A drawback of using the same value function is decreased performance, and ... |
While CDBR maximizes the exploitation of the fixed opponent model, it allows a player to be exploited. When we face an opponent unsure if our model is perfect we must limit our exploitability. For example, when we gradually build a model during play, we must limit our exploitability in the initial game rounds when the... | D |
(a) For fixed ρ𝜌\rhoitalic_ρ with 0<ρ<10𝜌10<\rho<10 < italic_ρ < 1, modularity is estimable for graphs with density at least ρ𝜌\rhoitalic_ρ.
(b) For any given function ρ(n)=o(1)𝜌𝑛𝑜1\rho(n)=o(1)italic_ρ ( italic_n ) = italic_o ( 1 ), modularity is not estimable for n𝑛nitalic_n-vertex graphs with density at leas... |
The plan of the rest of the paper is as follows. In Section 2, we first show an application of our results to the stochastic block model in Section 2.1, then provide an overview of the further results of this paper in Section 2.2 and lastly give some background and the relation of this paper to previous results in Sec... | Sections 3 to 5 give the proofs of the main results of the paper: Section 3 gives a crucial preliminary lemma for the proofs, the ‘fattening lemma’; and Theorem 1.1 and Theorem 1.2 are proven in Section 4 and Section 5 respectively.
(Indeed we also prove versions of these results which take into account the number of p... |
The later sections, Section 8 and 9 contain further related results. In Section 8 we see that under-sampling tends to lead to over-estimation of modularity (using Theorem 1.1). In Section 9 we show that Theorem 1.1 implies results on the expected modularity of random graphs Gpsubscript𝐺𝑝G_{p}italic_G start_POSTSUBSC... |
As in Section 4.2, recall that q≤k∗(G)=max|𝒜|≤kq𝒜(G)subscriptsuperscript𝑞absent𝑘𝐺subscript𝒜𝑘subscript𝑞𝒜𝐺q^{*}_{\leq k}(G)=\max_{|{\mathcal{A}}|\leq k}q_{\mathcal{A}}(G)italic_q start_POSTSUPERSCRIPT ∗ end_POSTSUPERSCRIPT start_POSTSUBSCRIPT ≤ italic_k end_POSTSUBSCRIPT ( italic_G ) = roman_max start_POSTS... | A |
Note: PRISMA Diagram (Page et al., 2021a, ) of identification, screening, eligibility and inclusion stages of academic contributions. The resulting sample is obtained through a search on Scopus, Web of Science, Google Scholar, IDEAS RePEc, and previous meta-analyses (Hoffmann et al., , 2020; Beine and Jeusette, , 2021)... | The PRISMA flow diagram (Moher et al., , 2009) in Figure 1 shows the process of identifica-tion, screening, eligibility, and inclusion of contributions in the final sample. It is important to note that there are two levels of inclusion: the first level identifies the sample of contributions included in our network anal... | This first step provides the most comprehensive sample of economic contributions on the relationship between climatic variations (and natural hazards) and human mobility, in all its different forms. We implement a systematic review aimed at mapping the body of literature and defining the boundaries of our focus. System... | screening, eligibility, and inclusion of contribution based on PRISMA guideline (see Figure 1 in the next section) - a unique dataset that synthesises the estimated coefficients of 96 empirical papers released between 2003 and 2020, published in academic journals, working papers series, or unpublished studies, providin... | The obtained sample only includes published documents, however since we perform a MA, it is important to take into account also non-published documents, as a way to control for a well-known publication bias in meta-analytic methodology (see Section 4). Therefore, we use the bibliographic database IDEAS, based on RePEc ... | A |
A pivotal technique in our paper is the collaborative online ensemble framework, which effectively facilitates the collaboration between meta and base layers by incorporating correction terms in the algorithm design and exploiting negative terms in the regret analysis. We have found this collaboration crucial for a va... |
A pivotal technique in our paper is the collaborative online ensemble framework, which effectively facilitates the collaboration between meta and base layers by incorporating correction terms in the algorithm design and exploiting negative terms in the regret analysis. We have found this collaboration crucial for a va... |
In this paper, we exploit the easiness of problem instances to enhance the universal dynamic regret. We propose two novel online ensemble algorithms, Sword and Sword++, for convex and smooth online learning. Both algorithms achieve a best-of-both-worlds dynamic regret of order 𝒪((1+PT+min{VT,FT})(1+PT))𝒪1subscrip... | Zhang et al. (2022a) investigate time-varying zero-sum games, introducing individual regret, dynamic NE-regret, and duality gap as the joint performance measures to guide algorithmic design. To handle multiple performance requirements, they deploy a two-layer algorithm for each player, demonstrating that the overall al... | We resolve the question affirmatively. The new algorithm, called Sword++, also implements an online ensemble structure. Compared to Sword presented in Section 4.2, the key novel ingredient is the framework of collaborative online ensemble. We carefully introduce correction terms to the online loss and optimism, forming... | C |
For n≥m(K)𝑛𝑚𝐾n\geq m(K)italic_n ≥ italic_m ( italic_K ), we have by (8.4), σn(wn)⊂ℒ(y)superscript𝜎𝑛subscript𝑤𝑛ℒ𝑦\sigma^{n}(w_{n})\subset\mathcal{L}(y)italic_σ start_POSTSUPERSCRIPT italic_n end_POSTSUPERSCRIPT ( italic_w start_POSTSUBSCRIPT italic_n end_POSTSUBSCRIPT ) ⊂ caligraphic_L ( italic_y ). | we may assume that ℒ(y)ℒ𝑦\mathcal{L}(y)caligraphic_L ( italic_y ) is not contained in ℒ(x)ℒ𝑥\mathcal{L}(x)caligraphic_L ( italic_x ).
We choose a word w(k+1)∈ℒ(y)∖ℒ(x)superscript𝑤𝑘1ℒ𝑦ℒ𝑥w^{(k+1)}\in\mathcal{L}(y)\setminus\mathcal{L}(x)italic_w start_POSTSUPERSCRIPT ( italic_k + 1 ) end_POSTSUPERSCRIPT ∈ caligr... | σm(ℒk(x(n)))⊂ℒ(y).superscript𝜎𝑚subscriptℒ𝑘superscript𝑥𝑛ℒ𝑦\sigma^{m}(\mathcal{L}_{k}(x^{(n)}))\subset\mathcal{L}(y).italic_σ start_POSTSUPERSCRIPT italic_m end_POSTSUPERSCRIPT ( caligraphic_L start_POSTSUBSCRIPT italic_k end_POSTSUBSCRIPT ( italic_x start_POSTSUPERSCRIPT ( italic_n ) end_POSTSUPERSCRIPT ) ) ⊂ c... | This shows that w𝑤witalic_w is in ℒ(y)ℒ𝑦\mathcal{L}(y)caligraphic_L ( italic_y ). Since ℒk(x)⊂ℒ(y)subscriptℒ𝑘𝑥ℒ𝑦\mathcal{L}_{k}(x)\subset\mathcal{L}(y)caligraphic_L start_POSTSUBSCRIPT italic_k end_POSTSUBSCRIPT ( italic_x ) ⊂ caligraphic_L ( italic_y )
for all k𝑘kitalic_k, we conclude that x𝑥xitalic_x is per... | σn(ℒk(x(n)))⊂ℒ(y).superscript𝜎𝑛subscriptℒ𝑘superscript𝑥𝑛ℒ𝑦\sigma^{n}(\mathcal{L}_{k}(x^{(n)}))\subset\mathcal{L}(y).italic_σ start_POSTSUPERSCRIPT italic_n end_POSTSUPERSCRIPT ( caligraphic_L start_POSTSUBSCRIPT italic_k end_POSTSUBSCRIPT ( italic_x start_POSTSUPERSCRIPT ( italic_n ) end_POSTSUPERSCRIPT ) ) ⊂ c... | C |
{n}}{n}+\bigg{(}\frac{C_{n}}{n}\bigg{)}^{\frac{2}{2s+1}}\Bigg{)}.italic_R ( caligraphic_T start_POSTSUBSCRIPT italic_n , italic_d end_POSTSUBSCRIPT start_POSTSUPERSCRIPT italic_k end_POSTSUPERSCRIPT ( italic_C start_POSTSUBSCRIPT italic_n end_POSTSUBSCRIPT ) ) = roman_Ω ( divide start_ARG 1 end_ARG start_ARG italic_n e... | smoothness index k+1𝑘1k+1italic_k + 1 in each coordinate direction, and any third index q≥1𝑞1q\geq 1italic_q ≥ 1, is indeed n−2s/(2s+1)superscript𝑛2𝑠2𝑠1n^{-2s/(2s+1)}italic_n start_POSTSUPERSCRIPT - 2 italic_s / ( 2 italic_s + 1 ) end_POSTSUPERSCRIPT for s>1/2𝑠12s>1/2italic_s > 1 / 2 (or 2k+2>d2𝑘2𝑑2k+2>d2 it... | 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. (2017). More precisely,
these authors established the third term on the right-hand side in | ℋdk(1)superscriptsubscriptℋ𝑑𝑘1\mathcal{H}_{d}^{k}(1)caligraphic_H start_POSTSUBSCRIPT italic_d end_POSTSUBSCRIPT start_POSTSUPERSCRIPT italic_k end_POSTSUPERSCRIPT ( 1 ). This matches the optimal rate for estimation over Holder
classes (see Sadhanala et al. (2017) for a formal statement and proof for | This paper is a unification and extension of Sadhanala et al. (2016, 2017) (more will be said about the relationship to these papers
in Section 1.3). The models of smoothness for f0subscript𝑓0f_{0}italic_f start_POSTSUBSCRIPT 0 end_POSTSUBSCRIPT that we | B |
For each subject, we computed the average correlation of each state, where the average is taken over all time points within each state. The correlation matrices are then used as the input to the transposition based twin correlations (Chung et al., 2019b). Subsequently, we computed the MZ- and DZ-twin correlations with... | The MZ-twin correlations (Figure 12-top) are densely observed in many connections while there is no DZ-twin correlations (Figure 12-middle) observed above 0.3. We then computed the heritability index (HI) of each state (Figure 12-bottom). The heritability of the first state is characterized by strong lateralization of ... |
For each subject, we computed the average correlation of each state, where the average is taken over all time points within each state. The correlation matrices are then used as the input to the transposition based twin correlations (Chung et al., 2019b). Subsequently, we computed the MZ- and DZ-twin correlations with... | Figure 13: MZ-correlation (top) and DZ-correlation (middle) in each state obtained through topological clustering in Figure 9. There is no MZ-correlation above 0.3 and not displayed. The heritability index (HI) is determined by the twice the difference in twin correlations. HI of each state shows extensive genetic cont... |
Figure 12: MZ-correlation (top) and DZ-correlation (middle) in each state obtained through topological clustering in Figure 9. There is no DZ-correlation above 0.3 and not displayed. The heritability index (HI) is determined by the twice the difference in twin correlations. HI of each state shows the extensive genetic... | A |
tracing out the τs(θ)subscript𝜏𝑠𝜃\tau_{s}(\theta)italic_τ start_POSTSUBSCRIPT italic_s end_POSTSUBSCRIPT ( italic_θ ) function, there are finitely many
jumps between the branches of the level set fs(θ,τ)=0subscript𝑓𝑠𝜃𝜏0f_{s}(\theta,\tau)=0italic_f start_POSTSUBSCRIPT italic_s end_POSTSUBSCRIPT ( italic_θ , ita... | fs(θ,τ)=0subscript𝑓𝑠𝜃𝜏0f_{s}(\theta,\tau)=0italic_f start_POSTSUBSCRIPT italic_s end_POSTSUBSCRIPT ( italic_θ , italic_τ ) = 0 has finitely many connected branches in the
set {(θ,τ)∈[0,π)×[0,τmax]}𝜃𝜏0𝜋0subscript𝜏\{(\theta,\tau)\in[0,\pi)\times[0,\tau_{\max}]\}{ ( italic_θ , italic_τ ) ∈ [ 0 , italic_π ) × [ 0 ... | in the set {(θ,τ)∈[0,π)×[0,τmax]}𝜃𝜏0𝜋0subscript𝜏\{(\theta,\tau)\in[0,\pi)\times[0,\tau_{\max}]\}{ ( italic_θ , italic_τ ) ∈ [ 0 , italic_π ) × [ 0 , italic_τ start_POSTSUBSCRIPT roman_max end_POSTSUBSCRIPT ] }. Second, for all θ∈[0,π)𝜃0𝜋\theta\in[0,\pi)italic_θ ∈ [ 0 , italic_π ) except at finitely many
θ𝜃\theta... | {(θ,τ)∈[0,π)×[0,τmax]}𝜃𝜏0𝜋0subscript𝜏\{(\theta,\tau)\in[0,\pi)\times[0,\tau_{\max}]\}{ ( italic_θ , italic_τ ) ∈ [ 0 , italic_π ) × [ 0 , italic_τ start_POSTSUBSCRIPT roman_max end_POSTSUBSCRIPT ] }. We formally
state this claim in the following result. | Lemma 6 allows us to apply the implicit function
theorem on fs(θ,τ)=0subscript𝑓𝑠𝜃𝜏0f_{s}(\theta,\tau)=0italic_f start_POSTSUBSCRIPT italic_s end_POSTSUBSCRIPT ( italic_θ , italic_τ ) = 0 at all (θ,τs(θ))∈[0,π)×[0,τmax]𝜃subscript𝜏𝑠𝜃0𝜋0subscript𝜏(\theta,\tau_{s}(\theta))\in[0,\pi)\times[0,\tau_{\max}]( italic... | B |
Control of PDE systems has been widely explored over the years [15, 16, 17, 18]. Similar to ODEs, notions of ISSt for PDE systems have garnered a lot of attention recently (see survey paper [19]). For example, PDE ISSt have been explored for reaction-diffusion systems [20], hyperbolic systems [21], [22], parabolic syst... |
In this paper, we have explored safe control of a class of linear Parabolic PDEs under disturbances. First, we defined unsafe sets and distance of the system states from such unsafe sets. Next, we constructed both control barrier and Lyapunov functional in order to develop a design framework for the controller under s... |
In light of the aforementioned discussion, the main contributions of this paper is the following: Building upon the existing literature, we extend PDE safety research by designing a feedback based control that satisfies both pISSf and ISSt under disturbances, utilizing pISSf barrier functional characterization and ISS... | Control of PDE systems has been widely explored over the years [15, 16, 17, 18]. Similar to ODEs, notions of ISSt for PDE systems have garnered a lot of attention recently (see survey paper [19]). For example, PDE ISSt have been explored for reaction-diffusion systems [20], hyperbolic systems [21], [22], parabolic syst... | In the subsequent sections, our approach of finding the control gains are as follows. First, in Section 3, we find the conditions on control gains that satisfy the pISSf criterion in (9). Next, in Section 4, we show that the pISSf conditions on control gains additionally guarantee ISSt for the system in the sense of (1... | B |
To ensure the relevance and rigor of our research, we conducted searches for terms like “passive sensing & future of work”, “wearables & worker productivity”, and “wearables & employee health”, leading us to review over 25,000 articles on platforms like Google Scholar. Our selection criteria favored recent papers publi... | To ensure the relevance and rigor of our research, we conducted searches for terms like “passive sensing & future of work”, “wearables & worker productivity”, and “wearables & employee health”, leading us to review over 25,000 articles on platforms like Google Scholar. Our selection criteria favored recent papers publi... |
Passive sensing is increasingly involved in various aspects of our daily lives. Within the workplace, it has been used to monitor physiological factors of workers, promote work safety, enhance efficiency among other things [8]. Recently, the Tesserae [9] project involved over 700 information workers to investigate how... | As ubiquitous devices become more embedded in our lives, they offer an unprecedented ability to passively capture our daily behavior at a high resolution via multiple sensors. Researchers have been leveraging passive sensing techniques to model users’ behavior and their environment from various perspectives.
Before the... | RealityMining [5] uses location signals and Bluetooth log data to recognize social patterns in daily user activity, identify socially significant locations and model organizational rhythms.
CenceMe [6] uses a wider range of sensors – including embedded cameras, microphones, accelerometer, GPS, temperature, light, humid... | C |
This paper addresses a realistic federated learning scenario, where a large number of clients with heterogeneous data and limited participation constraints hinder the convergence and performance of trained models.
To tackle these issues, we proposed a novel federated learning framework that aggregates past global gradi... | There exists a long line of research on client-side optimization aimed at reducing the divergence of clients from the global model.
FedProx [25] penalizes the difference between the server and client parameters, while FedDyn [1] and FedPD [48] adopt cumulative gradients of each client for dynamic regularization of loca... |
We test the accuracy of our algorithm for the Dirichlet (0.3) and i.i.d. splits by varying the values of λ𝜆\lambdaitalic_λ and β𝛽\betaitalic_β, which control the momentum integration of the server model and the weight of the proximal term, respectively. | This paper addresses a realistic federated learning scenario, where a large number of clients with heterogeneous data and limited participation constraints hinder the convergence and performance of trained models.
To tackle these issues, we proposed a novel federated learning framework that aggregates past global gradi... |
This work was partly supported by Samsung Advanced Institute of Technology (SAIT), and by the National Research Foundation of Korea grant [No.2022R1A2C3012210] and the Institute for Information & communications Technology Planning & Evaluation (IITP) grants [2022-0-00959; 2021-0-02068; 2021-0-01343] funded by the Kore... | D |
Other works have addressed spectrum allocation with other optimization objectives. e.g., researchers have considered throughput maximization as an objective [40, 49] under various constraints such as
maximum allocated power [31], given QoS requirements [29], etc. |
Paper Organization. The rest of the paper is organized as follows. In the following section, we develop our spectrum allocation model and setting, discuss related work, and give a high-level overview of our approach. In §III, we develop our CNN-based deep learning model and associated techniques for spectrum allocatio... | Second, in our context, an unsupervised approach is meaningless as unlabelled samples have minimal information (actually, zero information in the PU-Setting), and as explained
in §III, a reinforcement-learning approach is also not suitable for our setting. | context.222If we use the RL technique in our setting by considering
actions as power allocations, we’ll need to provide training examples for every possible system state, due to the lack of an underlying MDP, making the approach infeasible. Note that in a setting with no underlying MDP, the RL approach learns the polic... | See Fig. 10(b), which plots the 𝒜errsubscript𝒜err\mathrm{\mathcal{A}_{err}}caligraphic_A start_POSTSUBSCRIPT roman_err end_POSTSUBSCRIPT metric in PU-Setting for the above models compared with our DeepAlloc.
We see that our approach of pre-training using log-normal model-based images in DeepAlloc yields a notable per... | B |
\left<g\gamma_{1}-\gamma_{2}\right>(\alpha)}\leq\sqrt{2}\frac{\delta L}{\hat{c%
}}(e^{\hat{c}L}-1).italic_d ( italic_g caligraphic_C start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPT , caligraphic_C start_POSTSUBSCRIPT 2 end_POSTSUBSCRIPT ) ≤ square-root start_ARG 2 end_ARG | | italic_g italic_γ start_POSTSUBSCRIPT 1 end_POSTSUB... | In this paper, we used the Hausdorff distance between curves when considering both the SE(2)𝑆𝐸2SE(2)italic_S italic_E ( 2 )- and the SA(2)𝑆𝐴2SA(2)italic_S italic_A ( 2 )-actions on the plane. However, while the Hausdorff distance is SE(2)𝑆𝐸2SE(2)italic_S italic_E ( 2 )-invariant, it is not SA(2)𝑆𝐴2SA(2)... |
To a human eye, two figures look the same if they are related by a rigid motion. However, since a reflection changes the orientation of an object, a group of orientation-preserving rigid motions, consisting of rotations and translations only, is often considered. This group is called the special Euclidean group and is... | In this paper, we considered practical aspects of reconstructing planar curves with prescribed Euclidean or affine curvatures. 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 projecti... | In this work, we consider congruence of planar curves relative to the special Euclidean group SE(2)𝑆𝐸2SE(2)italic_S italic_E ( 2 ) and the special affine group SA(2)𝑆𝐴2SA(2)italic_S italic_A ( 2 ). The latter group consists of compositions of area and orientation preserving (i.e. unimodular) linear transformati... | C |
In the paper, we consider the case where only one coordinate is allowed to change per iteration. The results on random coordinate descent can be extended to other cases where probabilities of selections are unequal with potentially overlapping components [20], such as the random sleep scheme [34]. Intuitively speaking,... |
In the seminal work of [41], the method of online gradient descent is proposed for OCO problems, where at each time step the decision maker performs one gradient descent step using the latest available information. A static regret upper bound that is sublinear in T𝑇Titalic_T is proved, where T𝑇Titalic_T is the lengt... |
A substantial review of variants of coordinate descent algorithms can be found in [4, Section 6.5.1]. The cyclic selection of coordinates is normally assumed to ensure convergence of the algorithm. On the other hand, the use of an irregular order is then considered by researchers to accelerate convergence. Particularl... | To the best of our knowledge, Coordinate descent [31], as an important class of optimization algorithms, is not sufficiently analyzed by researchers in the online optimization community. In coordinate descent algorithms, most components of the decision variable are fixed during one iteration while the cost function is ... |
The main contributions of the paper can be summarized as follows. First, we extend the coordinate descent algorithms considered in [31] to the online case and provide their regret analysis. To the best of our knowledge, this is the first attempt to look at possibilities of using coordinate descent methods to solve OCO... | B |
Hybrid substrates present an intriguing design space that can leverage the advantages of both analog and digital domains [61]. The most prevalent hybrid model utilizes analog circuits for computation and digital circuits for communication [62, 63, 64]. This approach recognizes that digital circuits operate much faster... | In this context, we advocate for a hybrid model that employs a different partitioning in the abstraction. Rather than dividing along the lines of function (computation vs. communication), we propose a partitioning based on dimension (time vs. space). We argue that analog implementation is optimal for temporal integrati... |
The memristive network we study moves in the direction of the proposed partitioning. It exhibits space-time separability, as it integrates information over time, pixel by pixel, or more generally, neuron by neuron, followed by spatial integration across pixels or neurons. Space-time separability is a well-known princi... |
Nevertheless, we argue that a large and compelling class of algorithms, operating at an abstract scale without relying on spiking neurons, specifically those utilizing time surfaces and hierarchies of time surfaces (HOTS) [28, 48, 68], naturally exhibit space-time separability as described earlier. These algorithms ar... |
Hybrid substrates present an intriguing design space that can leverage the advantages of both analog and digital domains [61]. The most prevalent hybrid model utilizes analog circuits for computation and digital circuits for communication [62, 63, 64]. This approach recognizes that digital circuits operate much faster... | A |
On the other hand, there exist n2(n2−2)𝑛2𝑛22\frac{n}{2}\left(\frac{n}{2}-2\right)divide start_ARG italic_n end_ARG start_ARG 2 end_ARG ( divide start_ARG italic_n end_ARG start_ARG 2 end_ARG - 2 ) edges with length longer than ε𝜀\varepsilonitalic_ε and hence Φ(S0)=Θ(ε2n2)Φsubscript𝑆0Θsuperscript𝜀2superscript�... | In the following lemma, we prove that the projected system behaves similarly to the original system in the sense that the length of the edge e𝑒eitalic_e stays the same and the influence network does not change.
Furthermore, the agents in the original HKS move at least as much as the agents in the projected state, when... | In this section, we will prove two improved upper bounds, each for a more restricted set of graph classes.
The first result holds when the social network is a complete graph, while the second holds when, in each step of the HKS, the influence network is the same as the social network. | Our result transfers to their notion of convergence as follows. Assume δ≤ε/2𝛿𝜀2\delta\leq\varepsilon/2italic_δ ≤ italic_ε / 2 and that the length of the longest edge is at most δ.𝛿\delta.italic_δ .
If the social network is the complete graph, each connected component in the influence network must be a complete sub-g... | For these systems, we can prove a better upper bound on the expected number of steps needed to reach a δ𝛿\deltaitalic_δ-stable state.
Examples of such graphs are the path, where all the nodes are positioned with equal distance of at most ε𝜀\varepsilonitalic_ε, as well as the graph from Theorem 9 if the social network... | B |
The ROC curve is a graphical representation of the true positive rate (sensitivity) versus the false positive rate (1-specificity) at different threshold settings for classification. The area under the ROC curve (AUC) is a threshold-independent measure of the model’s goodness of fit and its ability to distinguish betwe... | In particular, the model achieved high sensitivity and accuracy on all conditions, indicating its ability to correctly identify positive cases. However, it is important to note that the positive predictive value (PPV) of the predictions can still be low. For example, for the Pneumonia condition, the sensitivity is 0.6,... |
Table 1 shows the evaluation metrics for the model trained on 1000 X-ray images and tested on a test dataset. The results indicate poor generalization of the model, as reflected in its performance across all metrics. Although accuracy can be deceptive, particularly for conditions such as Pneumothorax, Hernia, and Pleu... |
The F1 score is computed by taking the harmonic mean of precision and recall. It is a single metric that balances both precision and recall. The best possible value of the F1 score is 1 (perfect precision and recall), while the worst value is 0. If a single F1 score is required for multiclass classification, a micro-a... |
We computed several metrics to assess the generalization of our diagnostic models. These metrics include sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), the receiver operating characteristic (ROC) curve, and the F1 score. | C |
The KG algorithm (Gupta and Miescke, 1994) also adopts a Bayesian approach. However, the analytical properies of KG are little understood.
The results of Ryzhov et al. (2012) show the discounted version of KG most frequently drawing the best arm. Elsewhere, Wang and Powell (2018) have further characterized KG without g... | Popular algorithms for fixed-budget identification include successive rejects (SR; Audibert et al. 2010) and successive halving (Karnin et al., 2013). There are also several Bayesian algorithms that utilize a prior, such as top-two Thompson sampling (Russo, 2020), knowledge gradient (KG; Gupta and Miescke 1994), and ex... |
Several known results demonstrate the suboptimality of KG and EI in K𝐾Kitalic_K-armed BAI, which we describe in Section 7. In this paper, the convergence rate is characterized according to the Bayes optimal algorithm rather than its one-step approximations KG and EI. We show that the Bayes optimal algorithm performs ... | Sometimes, the probability of error ℙ[J(T)≠i∗]ℙdelimited-[]𝐽𝑇superscript𝑖\mathbb{P}[J(T)\neq i^{*}]blackboard_P [ italic_J ( italic_T ) ≠ italic_i start_POSTSUPERSCRIPT ∗ end_POSTSUPERSCRIPT ], which has the same exponential rate as the frequentist simple regret (Audibert et al. 2010, Section 2), is used to measur... | Another well-known one-step lookahead algorithm is EI (Jones et al., 1998; Bull, 2011). Ryzhov (2016) showed that EI draws suboptimal arms Θ(logT)Θ𝑇\Theta(\log T)roman_Θ ( roman_log italic_T ) times, implying the suboptimality of EI in terms of the frequentist simple regret (or probability of error). Qin et al. (201... | D |
Due to the symmetric setup, each agent is potentially blocked by its left and right neighbors at about t=2.0𝑡2.0t=2.0italic_t = 2.0s.
Via the proposed resolution scheme, the repulsive force from the left hand is increased and thus larger than the right-hand force, yielding a right-hand rotation. | This pattern continues for about 7777s such that most of the underlying agents are close to their targets.
A slight violation of the safety constraint (less than 0.030.030.030.03m) happens around 2.62.62.62.6s due to the strong air turbulence in the confined space, which however can be compensated by enlarging the safe... | This however can lead to unpredictable behavior and even safety issue in practice as the magnitude of such perturbations is hard to determine.
Other works in [32, 33, 34] instead propose to select a detour point on the right-hand side of each robot as the temporary target. | As shown in Fig. 6, four robots located in a 2m×2m2m2m2{\rm m}\times 2{\rm m}2 roman_m × 2 roman_m square transit to their antipodal positions.
The robots approach the center point initially at t=0.2𝑡0.2t=0.2italic_t = 0.2s and the terminal positions have entered the warning band. | These two conditions in Theorem 2 are not restrictive as the first condition requires that the targets are at least separated by the minimum safety distance; and the second condition that more than three targets are strictly collinear is quite rare to happen and can be easily avoided by slightly adjusting the target po... | A |
Such choice, however, can significantly restrict the functional form of the network. A more expressive variation of this approach consists of an equivariant neural network, followed by a symmetric function.
This allows the network to leverage the benefits of invariance while having a larger capacity due to the less res... | Another line of related work is concerned with group equivariant autoencoders. Such models utilize specific network architectures to encode and decode data in an equivariant way, resulting into equivariant representations only Hinton et al. (2011); Sabour et al. (2017); Kosiorek et al. (2019); Guo et al. (2019).
Feige ... | involves an increased dimensionality of the feature space, due to the lifting to G𝐺Gitalic_G. Equivariance has been explored in a variety of architecture and data structures: Convolutional Neural Networks Cohen & Welling (2016a); Worrall et al. (2017); Weiler et al. (2018c); Bekkers et al. (2018); Thomas et al. (2018)... | In fact, in many real-world application, equivariance is beneficial if not necessary Smidt (2020); Miller et al. (2020). For example, the interaction of a molecule (per se rotational invariant) with an external magnetic field is an intrinsically equivariant problem.
| An increasing body of work has shown that incorporating knowledge about underlying symmetries in neural networks as inductive bias can drastically improve the performance and reduce the amount of data needed for training Cohen & Welling (2016a); Bronstein et al. (2021). For example, the equivariant design with respect ... | C |
At each iteration, the problem maxxa(m(x))subscript𝑥𝑎𝑚𝑥\max_{x}a(m(x))roman_max start_POSTSUBSCRIPT italic_x end_POSTSUBSCRIPT italic_a ( italic_m ( italic_x ) ) is solved instead of maxxf(x)subscript𝑥𝑓𝑥\max_{x}f(x)roman_max start_POSTSUBSCRIPT italic_x end_POSTSUBSCRIPT italic_f ( italic_x ). While evaluat... | These pitfalls can be avoided using a prior that is informed by relevant data from other contexts. To that end, our model is structured hierarchically as described in Fig. 1, and the prior inferred from a related tuning set (𝒟1,⋯,𝒟N)subscript𝒟1⋯subscript𝒟𝑁(\mathcal{D}_{1},\cdots,\mathcal{D}_{N})( caligraphic_D sta... | Hierarchical Bayesian modelling is the principle of having layers of random variables built on top of each other (Shiffrin et al. 2008).
The advantage of using a hierarchical model for BO is that it can be used to transfer information about the distribution of GP hyperparameters across different contexts – such as citi... |
Figure 1: Visualisation of the hierarchical structure adopted. The bottom level is the observed data 𝒟𝒟\mathcal{D}caligraphic_D. It is modelled using GPs, which are defined by their hyperparameters θ𝜃\thetaitalic_θ. The distribution of the GP hyperparameters is captured by η𝜂\etaitalic_η. The hyperparameters θ𝜃\t... | This is advantageous in applications like pollution monitoring where there are plentiful observations for some contexts but not others, e.g. cities with and without extensive pollution monitoring programs, and sample efficiency is paramount as each sample is expensive to collect. Hierarchical Bayesian modelling has bee... | B |
The two main classes of gradient estimators used in machine learning are the pathwise or reparameterization gradient estimators [29, 47, 58] and the REINFORCE or score function estimators [65, 18].
The pathwise estimators have shown great success in training variational autoencoders [29] but are only applicable to cont... |
Although the Double CV framework points to a promising new direction for developing better REINFORCE estimators, one only obtains significant reduction in variance when bksubscript𝑏𝑘b_{k}italic_b start_POSTSUBSCRIPT italic_k end_POSTSUBSCRIPT is strongly correlated with f𝑓fitalic_f. | As we have seen in Section 2, there is a long history of designing CVs for REINFORCE estimators using “baselines” [64, 7, 45, 37].
Recent progress is mostly driven by leave-one-out [49, 38, 30, 48] and sample-dependent baselines [43, 22, 60, 62, 20]. | The benefits of using Stein operators to construct discrete CVs are twofold. First, the operator structure permits us to learn CVs with a flexible functional form such as those parameterized by neural networks.
Second, since our operators are derived from Markov chains on the discrete support, they naturally incorporat... | We then develop a gradient estimation framework—RODEO—that augments REINFORCE estimators with mean-zero CVs generated from Stein operators.
Finally, inspired by Double CV [60], we extend our method to develop CVs for REINFORCE leave-one-out estimators [49, 30] to further reduce the variance. | D |
Until now, the implicit assumption was that perfect channel estimates have been available. In reality channel estimates are never perfect, however, the quality of estimation can be improved by making the pilot sequences longer and/or investing in them more transmit power, as long as the pilot sequences of transmitting ... | Since users are free to select any of the (MK)binomial𝑀𝐾\binom{M}{K}( FRACOP start_ARG italic_M end_ARG start_ARG italic_K end_ARG ) possible random access patterns and do so independently from each other (with replacement), the probability that L𝐿Litalic_L out of potentially U−1𝑈1U-1italic_U - 1 interfering users ... | In a slot in which an arbitrary user is active, there are only D−1𝐷1D-1italic_D - 1 other patterns that could cause a collision and the selection is done without replacement due to the unique preassignments.
Hence, the probability that L𝐿Litalic_L out of U−1𝑈1U-1italic_U - 1 devices select one of them while the rest... |
Unlike Random selection, the Steiner system guarantees that at most D𝐷Ditalic_D users can be active in any given slot. Furthermore, since each user has to be assigned a specific access pattern for their packet replicas, it can be simultaneously instructed which pilot sequence to use in which slot, thus eliminating an... | This is a significant challenge for random access schemes that rely on fully random selection of access patterns.
Since any user can be active in any slot, the only way to avoid pilot collisions, would be to assign a unique orthogonal sequence to each of the N𝑁Nitalic_N devices. | D |
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. Variation of this algorithm also appears in [BNV19]. Here and for the rest of this paper... | We remark that although time-wise this algorithm is fast, the ratio constant of the yielded path over the optimal path has not been computed and is much larger that Christofides’ 3/2323/23 / 2 ratio. In fact, in our algorithm, the yielded path has length at
most (300)9/2log300superscript30092300(300)^{9/2}\log{300}( ... |
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. Variation of this algorithm also appears in [BNV19]. Here and for the rest of this paper... | In lieu of the above discussion, “nearly-optimal” algorithms have been explored. That is, algorithms that produce a path which may not be the optimal one but is comparable (or even arbitrarily close) in length to the optimal one. The nearest insertion algorithm [RSL74] computes in O(n2)𝑂superscript𝑛2O(n^{2})italic_O... |
It should be noted that the work of Jones [Jon90], Okikiolu [Oki92], and Schul [Sch07] provides the sets for which a solution exists but not the solution itself when the set is infinite. That is, they classify the sets which are contained in curves of finite length, but do not provide the parametrization of the curves... | A |
If α,β∈𝒫𝛼𝛽𝒫\alpha,\beta\in\mathcal{P}italic_α , italic_β ∈ caligraphic_P with |β|<|α|𝛽𝛼|\beta|<|\alpha|| italic_β | < | italic_α |, then there exists an index i∈[l]𝑖delimited-[]𝑙i\in[l]italic_i ∈ [ italic_l ] such that β+𝒆i∈𝒫𝛽subscript𝒆𝑖𝒫\beta+\bm{e}_{i}\in\mathcal{P}italic_β + bold_italic_e start_POSTSU... | In addition, we discuss a multihomogeneous generalization of the Hurwitz form. To the best of our knowledge, our paper provides the first result in this area. Contrary to the homogeneous case, multigraded Chow forms and Hurwitz form require a choice of a non-degenerate multidimension vector for the linear subspace, in ... | For the remainder of the section, the chief example of a polymatroid to us is the support of a multiprojective variety (and its downward closure). The polymatroids of this form are now called Chow polymatroids,333This naming is unfortunate for us because we will later associate a polymatroid to the formats of non-degen... | In [46], Osserman and Trager gave a generalization of Chow forms to multiprojetive varieties, i.e., varieties in the multiprojective space ℙ𝒏≔∏i=1lℙni≔superscriptℙ𝒏superscriptsubscriptproduct𝑖1𝑙superscriptℙsubscript𝑛𝑖\mathbb{P}^{\bm{n}}\coloneqq\prod_{i=1}^{l}\mathbb{P}^{n_{i}}blackboard_P start_POSTSUPERSCRIPT b... |
As demonstrated in Figure 1, the set of formats α𝛼\alphaitalic_α for which 𝒞𝒵V,α𝒞subscript𝒵𝑉𝛼\mathcal{CZ}_{V,\alpha}caligraphic_C caligraphic_Z start_POSTSUBSCRIPT italic_V , italic_α end_POSTSUBSCRIPT is a hypersurface equals to the set of lattice points that lie “below” supp(V)supp𝑉\operatorname{supp}(V)ro... | B |
2) a high number of volunteers (100100100100), higher than any other public dataset of this type to the best of our knowledge;
3) a specifically designed modification of the labeling methodology to consider gender perspective by changing the design of the original Self-Assessment Manikins (SAMs) [20]; |
The submission to the Ethical Committee covered essential topics for the development of the experiments. Among others, the adequacy of the volunteers’ informed consent, the research goals and plans, the data management and de-identification procedures, and the compliance with the European General Data Protection Regul... | The study was conducted between October 2020202020202020 and April 2021202120212021. It took place in the
Electronics Technology Department at the School of Engineering of the Universidad Carlos III de Madrid, Spain. The experimental methodology designed to be applied for each volunteer is schematized in Figure 1. Duri... | This work has been supported by the Dept. of Research and Innovation of Madrid Regional Authority, in the EMPATIA-CM research project (reference Y2018/TCS-5046), SAPIENTAE4Bindi Project from Grant PDC2021-121071-I00 funded by MCINAEI10.13039/501100011033 and by the European Union ”NextGenerationEU/PRTR”, grant PID2021-... | The experimentation was approved by and performed following the guidelines and regulations of the Ethics in Research Committee of University Carlos III Madrid. The approval was granted considering the circumstances of the research project entitled Integral protection of gender-based violence victims using multimodal af... | D |
where, wklb≤wk≤wkubsubscriptsuperscript𝑤𝑙𝑏𝑘subscript𝑤𝑘subscriptsuperscript𝑤𝑢𝑏𝑘w^{lb}_{k}\leq w_{k}\leq w^{ub}_{k}italic_w start_POSTSUPERSCRIPT italic_l italic_b end_POSTSUPERSCRIPT start_POSTSUBSCRIPT italic_k end_POSTSUBSCRIPT ≤ italic_w start_POSTSUBSCRIPT italic_k end_POSTSUBSCRIPT ≤ italic_w start_POS... | Defensive Distillation: Hinton et al. (Hinton et al., 2014) proposed distillation for deep learning to utilize the knowledge acquired by a large and complex neural model and transfer it to a smaller model. Papernot et al. (Papernot et al., 2016c) apply the intuition of distillation for improving the resilience of deep ... |
Yang et al. (Yang et al., 2017) also evaluate different defense mechanisms to improve the robustness of classifiers. Using adversarial training, they were able to improve the robustness of classifiers. They also evaluate weight bounding, similar to Demontis et al. (Demontis et al., 2017), to improve the robustness of ... | Grosse et al.(Grosse et al., 2017b) investigate the performance of defense methods introduced in the context of computer vision in malware detection. They experiment with defensive distillation (Papernot et al., 2016c) and adversarial training (Goodfellow et al., 2015).
Overall, adversarial training improved the robust... |
Adversarial training: Adversarial training (Goodfellow et al., 2015) is a popular defense method using a mixture of normal and adversarial examples for training. The additional set of adversarial examples provides information regarding adversarial ‘outliers’ to improve model performance against adversarial attacks. Eq... | C |
The algorithm above can be adapted to this case.
Indeed, supposing that the realization s𝑠sitalic_s is such that the origin is visited finitely many times, then applying the above algorithm to the suffix of the realization after the last visit to the origin identifies Deviator with probability 1. | Since sn2superscriptsubscript𝑠𝑛2s_{n}^{2}italic_s start_POSTSUBSCRIPT italic_n end_POSTSUBSCRIPT start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT increases by 1111 in expectation on Honest’s moves, it should decrease on Deviator’s moves to keep the walk close to 00, and this discrepancy is what’s detected in Step 3.
|
The idea of the proof is that since Deviator must move to the right during periods where Honest moves substantially to the left (to avoid going below zero), Deviator must thus move to the left when Honest moves to the right to avoid being clearly right-biased (Steps 1 and 2 detect right-biased behavior). Thus Deviator... | The algorithm above can be adapted to this case.
Indeed, supposing that the realization s𝑠sitalic_s is such that the origin is visited finitely many times, then applying the above algorithm to the suffix of the realization after the last visit to the origin identifies Deviator with probability 1. | Almost surely either a player was chosen on Step 1 or 2 or the sum of just the odd-numbered terms (given by B’s moves) of expression (2) diverges to ∞\infty∞, by Lemma 3.6. In the latter case, the sum of the even-numbered terms must diverge to −∞-\infty- ∞ (as the sum of all terms is convergent), and therefore cannot d... | B |
Besides considering the currently-undefended AI components, there are also possibly-applicable defense strategies that have not been explored yet, for example certified robustness [40]. While unexplored (§III-C1), such a defense is actually highly desired in the AD AI security domain since it can provide strong theore... |
Based on the systematization of existing works on AD AI security, we summarize a list of scientific gaps that we observed and also discuss possible solution directions. To avoid subjective opinions and bias, such observations are drawn from quantitative comparisons vertically among the choices made in existing AD AI s... | In the general CPS (Cyber-Physical System) area, there are also SoKs on the security of technologies related to AD AI, for example on drones [24] that share similar controller designs, on Automatic Speech Recognition and Speaker Identification (ASR/SI) [52] that are also AI-enabled CPS, and on sensor technology [53] th... | As shown in Table I, there are only 11.1% (6/54) AD AI attack works that leverage cyber-layer attack vectors in their attack designs, assuming ML backdoors [93], malware [75], remote exploitation [91], and compromised ROS nodes [59], respectively. Among these four, only the last one is relatively domain-specific to AD.... |
As identified in §III-D, currently it is not a common practice for AD AI security works to perform system-level evaluation: overall only 25.4% of existing works perform that, and such a number is especially low (7.4%) for the most extensively-studied AI component, camera object detection. In fact, the vast majority (7... | C |
For every edge gigj∈E(G)subscript𝑔𝑖subscript𝑔𝑗𝐸𝐺g_{i}g_{j}\in E(G)italic_g start_POSTSUBSCRIPT italic_i end_POSTSUBSCRIPT italic_g start_POSTSUBSCRIPT italic_j end_POSTSUBSCRIPT ∈ italic_E ( italic_G ), where i<j𝑖𝑗i<jitalic_i < italic_j, we choose a link chain starting from 𝒱isubscript𝒱𝑖\mathcal{V}_{i}cali... | 2η2l2superscript𝜂2𝑙2\eta^{2}l2 italic_η start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT italic_l – the cut between the gadgets of the chain and the long intervals of H𝐻Hitalic_H covering them (the number of gadgets in the chain also equals l𝑙litalic_l).
| On each of the figures, this gadget is colored in Red and Blue.
After we construct the whole graph H𝐻Hitalic_H of interval count two, we will argue that, for every Maximum Cut partition of H𝐻Hitalic_H, the coloring of each of its gadgets is similar to one displayed on the corresponding figure. | All these five ways are displayed on Figure 1 on fig. 1.
Let ℒ1,…,ℒ5subscriptℒ1…subscriptℒ5\mathcal{L}_{1},\ldots,\mathcal{L}_{5}caligraphic_L start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPT , … , caligraphic_L start_POSTSUBSCRIPT 5 end_POSTSUBSCRIPT be the corresponding sets of long intervals that intersect a given 3-block wi... | If a chain does not participate in some switch procedure, then, during this procedure, the long intervals of this chain are joined by join gadgets that look the same as vertex gadgets.
The composition of H𝐻Hitalic_H is displayed on Figure 3 on fig. 3. | D |
Fig. 1: Two surgical workflow tasks on the Cholec80 dataset [70]: phase recognition, a special case of temporal action segmentation, and instrument anticipation, defined as predicting the time until occurrence of an instrument within a specified horizon.
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Multi-stage: Recently, methods have shifted towards more complex temporal models on top of pretrained visual features. BN-related issues are circumvented by first training a CNN on randomly sampled image batches, followed by the temporal model trained on frozen image features. | Methods in this style have been proposed for phase recognition [13, 14, 24, 85, 91], duration prediction [2, 8, 71], tracking [55] or anticipation [86]. Most notably, TeCNO [13], an MS-TCN [19] trained on ResNet features, is the popular approach for 2-stage learning and Trans-SVNet [24], a 3-stage method which trains a... | The focus has shifted towards developing sophisticated temporal models to operate on extracted image features similar to the natural-video domain, replacing end-to-end learning with complex multi-stage training procedures, where each component (CNN, LSTM [30], TCN [19], Transformer [73], etc.) is trained individually [... |
Multi-stage: Multi-stage training is the most popular approach and effectively avoids BN’s issues by training backbones without temporal context. Yet, this has several disadvantages. Firstly, it increases the number of hyperparameters since learning rate, number of epochs etc. have to be tuned for each training stage.... | A |
Geometrical Interpretation of Feature Space We interpret the role of UNPG by associating a feature space, as shown in Fig. 3. To form WDFS satisfying inf𝒮p>sup𝒮ninfimumsuperscript𝒮𝑝supremumsuperscript𝒮𝑛\inf{\mathcal{S}^{p}}>\sup{\mathcal{S}^{n}}roman_inf caligraphic_S start_POSTSUPERSCRIPT italic_p end_POSTSUPERS... | Results on IJB-B and IJB-C. IJB-B consists of 21.8 K images of 1,845 subjects and 55 K frames of 7,011 videos. IJB-C, an extended version of IJB-B, contains 31.3 K images of 3,531 subjects and 117.5 K frames of 11,799 videos. 10 k / 8 M and 19 K / 15 M of positive / negative pairs in IJB-B and IJB-C were used for 1:1 v... |
This paper proposes unified negative pair generation (UNPG) by combining two PG strategies (i.e., MLPG and CLPG) from a unified perspective to alleviate the mismatch. Moreover, it includes filtering noise-negative pairs, such as too-easy/hard negative pairs, in order to guarantee reliable convergence and improve perfo... | Datasets. For training, MS1M-V2[4] and K-FACE:T4[11] datasets were employed. MS1M-V2, a semi-automatically refined version of MS-Celeb-1M[5], has 5.8M images and 85K identities. K-FACE:T4 is a preprocessed version of K-FACE[2] utilized in MixFace[11] and has 3.8M images and 370 identities. For testing, several benchmar... | Test. Cosine similarity was used as a similarity score. Different evaluation metrics were applied depending on the FR tasks. In the verification task (1:1), verification accuracy using the best threshold was exploited for a dataset that has a small number of test images with the same ratio between positive and negative... | C |
In retinal imaging, GANs have been used to create synthetic data. Li et al. [27] highlighted the importance of enhancing the quality of synthetic retinal images in their review, emphasizing that using synthetic images in training can improve performance and help mitigate overfitting. | The second experiment determines whether clinical experts, who are very experienced with the analysis of eye fundus images, can distinguish between synthetic and real images. Such a step is essential to evaluate the effectiveness of StyleGAN2-ADA for generating synthetic eye fundus images. The experts were provided wit... | Anh et al [30] tested the FundusGAN to generate eye-fundus images for two eye disease: Age-related macular degeneration and Diabetic retinopathy and demonstrated its ability for the synthetic images to be generalisable for the two disease.However, Anh et al. [30] work was confined to a single dataset in which most part... | Bellemo et al. [28] described the possible advantages and limitations towards synthetic retina image generation using GANs. The authors highlighted the potential clinical applications of GANs concerning early- and late-stage AMD classification.
Burlina et al. [8] trained a Progressive GAN [29] on 133,821133821133,82113... | Burlina et al have successfully developed a method for [8, 10] generating the synthetic images. Their method is based on GAN, and they tested their method by showing that experts were unable to identify the synthetic images. However, their method has been patented and hence not available for being used by others. We h... | C |
In Table VII, it shows accuracies obtained by the proposed method based on different number (N𝑁Nitalic_N) of overlapping pixels. From the results, it is seen that the performance on the test set increases slowly to plateau as the number of overlapping pixels increases. It illustrates that the more the overlapping pix... | Experimental results also demonstrate that some local crucial regions can be effectively enhanced in feature learning by LNLAttenNet while there are not any given information of landmarks in the training model.
Moreover, the proposed method focuses on enhancing facial crucial regions in FER without any landmark informa... | In this paper, we propose the LNLAttenNet method to effectively explore the significance of facial crucial regions in feature learning for FER, without any landmark information.
In LNLAttenNet, the global information of the facial expression is utilized to construct the non-local attention network, and meanwhile the lo... |
Based on the above analyses, we propose a new method of facial expression recognition in this paper, which constructs a local non-local joint network to adaptively enhance the facial crucial regions in the process of deep feature learning, shortened for LNLAttenNet. | In LNLAttenNet, the local and the non-local information of facial expressions are simultaneously considered to construct two parts of the network respectively: a local multi-network ensemble and a non-local attention network, and then the generated local and non-local feature vectors are integrated and jointly optimize... | B |
Why is there a jump in the upper bound from f(k)1/3𝑓superscript𝑘13f(k)^{1/3}italic_f ( italic_k ) start_POSTSUPERSCRIPT 1 / 3 end_POSTSUPERSCRIPT to f(k/n)𝑓𝑘𝑛f(k/n)italic_f ( italic_k / italic_n ) at n=k1/3𝑛superscript𝑘13n=k^{1/3}italic_n = italic_k start_POSTSUPERSCRIPT 1 / 3 end_POSTSUPERSCRIPT? Can one find... | The highest Elo problem also bares a passing resemblance to the Toda lattice: players are particles whose positions on the real line is given by their ratings; they exhibit a repulsive force when a higher rated player beats a lower rated player and an attractive force if vice versa. Is it possible to use tools from sol... | Each player is given some ‘rating’ value (measured in ‘points’ or simply ‘Elo’), which updates as they play games. 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. In the case of a draw, the p... | Pick any pair of players whose ratings are within δ𝛿\deltaitalic_δ of each other and have the higher rated player beat the lower rated player. Repeat until no two players are within δ𝛿\deltaitalic_δ rating points or until k𝑘kitalic_k games have been played. Note for δ=0𝛿0\delta=0italic_δ = 0, one recovers the origi... | There are additional complications in real-world implementations of Elo. For legibility and practicality, fractional and negative rating points are avoided by scaling and shifting points up and rounding to the nearest integer, and by imposing an artificial floor on possible ratings (by gifting a player points if they w... | A |
The identification of conditions for the efficient use of a particular method is an open research problem [NS16]. A user should be competent in judging the influence of a suggestion on the whole data set. For example, what if, by removing too many rare cases, the model overfits the training data but generalizes poorly ... | A user should have the ability to partially confirm the proposal of the automated methods based on the analysis he/she has performed earlier in the preceding task. How will the data distribution change due to the acceptance of such a suggestion? VA systems should envision these future steps and enhance users’ decision-... | After the extraction of evidence as defined in G3, users should see how the distribution of instances will change due to the undersampling and/or oversampling phases. Next, the system should give a prediction for a data point and juxtapose it to all other points. With this, users should be able to estimate the impact o... | Examining unsafe instances proposed for removal.
Afterwards, she activates the NCR algorithm with the default settings (k-value is synchronized to 13 due to the previously-selected projection) from the Undersampling (US) tab. Cluster 1 (C1) in Figure 5(c) is interesting because 7 benign cases (mostly marked as outliers... | Limitations identified by the experts.
E1 and E2 were concerned about the scalability of the system. The former concentrated on the problem of visualizing hundreds of features, while the latter on the exploration of more than three classes. E1 acknowledged that the box plots and the table heatmap view are interactive w... | A |
We evaluate the performance of FIRST on real Ethereum traces over a month long period of observation.
We analyze FIRST’s suggested 𝐹𝐼𝑅𝑆𝑇_𝐹𝐸𝐸𝐹𝐼𝑅𝑆𝑇_𝐹𝐸𝐸\mathit{FIRST\_FEE}italic_FIRST _ italic_FEE during our experiment and show the effectiveness of FIRST in terms of the percentage of frontrunnable transa... | To evaluate the cost of FIRST transaction verifications by the smart contract (Protocol 7) we deployed the aggregated signature [19] verification function on a smart contract using the Solidity programming language.
We used elliptic curve pairing operations, such as addition, multiplication, and pairing checks introduc... | As we discussed before, on Ethereum, the chance of transactions being frontrun is a bit higher on account of higher volatility (we theorize, due to NFT transactions and slower block confirmation time) compared to BSC, which is more stable on account of the faster settling of transactions. For example, from our data, in... | In order to get the most accurate waiting times of transactions in the pending pool, we deployed a Geth333https://github.com/ethereum/go-ethereum full node (v.1.11.0) running on an Amazon AWS Virtual Machine located in North Virginia. The AWS node had an AMD EPYC 7R32 CPU clocked at 3.30 GHz with 8 dedicated cores, 32 ... | Out of the total 30.6M transactions our node was able to detect the wait time for 29.65M transactions. Since our node did not receive a total of 944807 transactions (roughly 3.08%percent3.083.08\%3.08 %), we conclude that these transactions were either never sent to the P2P layer because of the use of relayers (e.g., F... | C |
For an agent i𝑖iitalic_i with unobservables in the support of F𝐹Fitalic_F, xi∗(β,s)∈Int(𝒳)subscriptsuperscript𝑥𝑖𝛽𝑠Int𝒳x^{*}_{i}(\beta,s)\in\text{Int}(\mathcal{X})italic_x start_POSTSUPERSCRIPT ∗ end_POSTSUPERSCRIPT start_POSTSUBSCRIPT italic_i end_POSTSUBSCRIPT ( italic_β , italic_s ) ∈ Int ( caligraphic_X )... |
We describe some of the extensions of our model and learning procedure. First, our model assumes that the decision maker’s policy is fixed over time. Dynamic treatment rules, where the policy is time-varying, would extend this work and would likely require new equilibrium definitions. Second, we consider linear polici... | In the context of college admissions, Assumption 4 requires that 𝒳𝒳\mathcal{X}caligraphic_X is large enough that agents’ raw covariates do not “bunch” at the boundaries of 𝒳𝒳\mathcal{X}caligraphic_X, and that 𝒞𝒞\mathcal{C}caligraphic_C is large enough that it contains cost functions that are linear offsets of the... |
In the context of college admission, Assumption 2 requires a finite number of student types. Realistically, this assumption may not hold because there could be an infinite number of types, but this assumption is made for technical convenience and we conjecture that similar results will hold when F𝐹Fitalic_F has conti... | In Section 3, we give conditions on our model that guarantee existence and uniqueness of equilibria in the mean-field regime, the limiting regime where at each time step, an infinite number of agents are considered for the treatment. Furthermore, we show that under additional conditions, the mean-field equilibrium aris... | C |
In Table 4, we compare how ResNet and OccamResNet are affected by the different bias variables in Biased MNISTv2. For this, we present majority and minority group accuracies for each variable. Bias variables with large differences between the majority and the minority groups, i.e., large majority/minority group discrep... |
To examine if the proposed inductive biases improve bias-resilience in other architectures too, we created OccamEfficientNet-B2 and OccamMobileNet-v3 by modifying EfficientNet-B2 [65] and MobileNet-v3 [28, 29]. OccamNet variants outperform standard architectures on both Biased MNISTv2 (OccamEfficientNet-B2: 59.2 vs. E... | Apart from ResNet, we also tested the proposed inductive biases on EfficientNet and MobileNet. The results are presented in Table A13. For both Biased MNISTv2 and COCO-on-Places, Occam variants outperform the standard architectures, showing the efficacy of the proposed modifications.
| Modifications for COCO-on-Places.
For COCO-on-Places, the images are small (64×64646464\times 6464 × 64), so for ResNet-18 and OccamResNet-18, we replace the first convolutional layer (kernel size=7777, padding=3333, stride=2222), with a smaller layer (kernel size=3333, padding=1111 and stride=1111) and also remove the... | Exit Details.
For convenience, we specify the exit locations with reference to PyTorch 1.7.1 implementations of the architectures. For ResNet, the residual layers that yield the same number of output channels are grouped together and we refer to each of those groups as a ‘block’. ResNet-18 consists of 4 blocks and we a... | A |
Next, we tokenize the extracted features and treat the feature vector at each pixel as a token, resulting in tokens 𝒐∈ℝNo×c𝒐superscriptℝsubscript𝑁𝑜𝑐\bm{o}\in\mathbb{R}^{N_{o}\times c}bold_italic_o ∈ blackboard_R start_POSTSUPERSCRIPT italic_N start_POSTSUBSCRIPT italic_o end_POSTSUBSCRIPT × italic_c end_POSTSUPER... | For global temporal contexts, few VSS methods [17, 53] have exploited the contexts from the whole video. The modeling of global temporal contexts is usually achieved by a memory module in the form of a memory bank [17] or a tiny network [53] which is updated during inference. Although promising results have been achiev... | Due to the selection of video frames across the whole video and the use of GPU-based k𝑘kitalic_k-means clustering, the process of generating global temporal contextual prototypes is fast and does not significantly decrease the speed, which will be shown in our experiments (§5.2).
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Influence of the number of global temporal contextual prototypes. In our experiments, we set the number (Npsubscript𝑁𝑝N_{p}italic_N start_POSTSUBSCRIPT italic_p end_POSTSUBSCRIPT) of contextual prototypes as 100 when extracting global temporal information. Here, we study the influence of this parameter. The results... | Figure 3: Overview of the proposed CFFM++ for additionally mining global temporal contexts. Due to the large number of frames in the video, we uniformly sample frames by a fixed step. The sampled video frames go through the encoder trained by CFFM and corresponding features are generated. After tokenizing the feature m... | B |
Figure 12: The execution pipeline of Hotline involves the accelerator actively classifying a mini-batch into popular and non-popular μ𝜇\muitalic_μ-batches, then scheduling the popular μ𝜇\muitalic_μ-batch onto the GPU(s). Simultaneously, the accelerator gathers the working parameters for the non-popular μ𝜇\muitalic_... |
B. Efficient Embedding Tracking: The EAL design is motivated by two observations: the size of frequently-accessed embeddings is ≤\leq≤512 MB and their access skews are extremely high. Thus, EAL is designed as a cache-like structure that tracks frequently-accessed embedding indices using a 4 MB SRAM cache with 2 millio... | C. Multi-Banked SRAM for Parallel Lookup: Hotline enables parallel lookups by dividing the EAL into multiple banks. Figure 16 shows our empirical design space exploration, which reveals the average number of requests issued as the number of banks (n𝑛nitalic_n) and input queue size (m𝑚mitalic_m) vary. On average, a 51... | 1. Access-aware Embedding Layout in Memory: Hotline leverages the observation that real-world recommender systems exhibit a high skew in popularity, causing certain embedding entries to be accessed significantly more frequently than others [10, 11, 12, 13, 14]. These frequently accessed embeddings, referred to as frequ... | The Embedding Access Logger (EAL) actively utilizes a counter to track the frequency of access to embedding entries. EAL stores only the indices of embedding entries with valid bits and access counts. Figure 14 depicts the components of EAL, which include a multi-banked Static Random Access Memory (SRAM), a controller,... | D |
Let F:|M|→ℝ:𝐹→𝑀ℝF:|M|\rightarrow\mathbb{R}italic_F : | italic_M | → blackboard_R be a map that is linear on cells of a finite polyhedral complex M𝑀Mitalic_M, let a∈ℝ𝑎ℝa\in\mathbb{R}italic_a ∈ blackboard_R be a nontransversal threshold, and K𝐾Kitalic_K a connected component of the intersection of its corresponding ... |
If there is an n𝑛nitalic_n–cell of 𝒞(F)𝒞𝐹\mathcal{C}(F)caligraphic_C ( italic_F ) with ternary labeling (−1,…,−1)1…1(-1,\ldots,-1)( - 1 , … , - 1 ), Lemma 3.12 tells us that F𝐹Fitalic_F is not PL Morse. If there is no n𝑛nitalic_n–cell with ternary labeling (−1,…,−1)1…1(-1,\ldots,-1)( - 1 , … , - 1 ), then the o... | 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. 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 critica... | In [7], the authors define the notion of H-criticality and H-regularity only for flat 00–cells and not for (connected unions of) flat higher-dimensional cells. Since the functions of interest to us have a non-zero probability of having higher-dimensional flat cells, we extend the definition.
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It is immediate that any face of a flat cell will also be flat, so the set of flat cells relative to a map F:|M|→ℝ:𝐹→𝑀ℝF:|M|\rightarrow\mathbb{R}italic_F : | italic_M | → blackboard_R forms a subcomplex of M𝑀Mitalic_M, which we will denote Mflat(F)subscript𝑀flat𝐹M_{\tiny\mbox{flat}}(F)italic_M start_POSTSUBSCRIP... | C |
One of the most challenging problems in modern physics is the so-called many-body problem. In its quantum version – quantum many-body physics, the exponential complexity of the states in the Hilbert space makes the strongly correlated systems difficult to deal with [1]. Only limited analytical solutions are amenable to... | The recent state-of-the-art neural networks have been shown to provide high efficient representations of such complex states, making the overwhelming complexity computationally tractable [6, 7]. Except for the success in the industrial applications, such as the image and speech recognitions [8], the autonomous driving,... |
The states of quantum system can be characterized by wave functions. One can approximate the wave functions with neural-network quantum states (NQS) as ΨNN(s,𝒲)subscriptΨ𝑁𝑁𝑠𝒲\Psi_{\!N\!N}(s,\mathcal{W})roman_Ψ start_POSTSUBSCRIPT italic_N italic_N end_POSTSUBSCRIPT ( italic_s , caligraphic_W ), where s=(s1,s2…... |
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 applic... |
Fortunately, neural network methods have more universalities. The same neural network can be used to represent the states or to study the dynamical processes for various systems, such as those with different dimensions or with different interactions. | D |
This theorem tells that for the ideal case when Re=∞subscript𝑅𝑒R_{e}=\inftyitalic_R start_POSTSUBSCRIPT italic_e end_POSTSUBSCRIPT = ∞ and 𝒇=𝟎𝒇0\bm{f}=\bm{0}bold_italic_f = bold_0, we obtain the helicity conservation. On the other hand, the natural pollution term for the helicity conservation is the effect of diff... | The rest of the paper is organized as follows. In Section 2, we provide preliminaries, notation and helicity-conservative finite element scheme. In Section 3, we present a PINN-based algorithm that preserves the helicity. In Section 4, we present numerical results on the convergence and helicity-preserving properties o... | where QNNsubscript𝑄𝑁𝑁Q_{NN}italic_Q start_POSTSUBSCRIPT italic_N italic_N end_POSTSUBSCRIPT is the L2superscript𝐿2L^{2}italic_L start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT projection onto the space of neural network functions chosen in the model. This in general can not guarantee the divergence free property of 𝝎... | In this section, we shall consider Helicity-conservative finite element methods defined on contractible domains. One can extend the definition of helicity to nontrivial topology and different space dimensions [3, Chapter 3]. We use the standard notation for the inner product and the norm of the
L2superscript𝐿2L^{2}ita... | Numerical modeling and simulation for the incompressible Navier-Stokes system is critical in a number of applications. Therefore, there have been a lot of efforts in designing numerical methods for solving the incompressible Navier-Stokes equations. It is well-known that the Navier-Stokes system has various conserved q... | C |
In the following, we provide the complimentary experiments by choosing alternative settings compared to the previous experiments.
In short, in the complimentary experiments, we observed consistency with our previous results, further validating and verifying our proposal. |
We used Gaussian as the underlying distribution of our synthetic datasets. In this experiment, we study whether the underlying distribution of the data would affect the capacity of the RU measures in revealing unreliability. To do so, we follow the same procedure outlined in the construction of synthetic datasets in §... | Validation on Regression:
In this experiment, we study the effectiveness of our RU measures in the regression tasks. Accordingly, we used RN and HS data sets and computed strongRU and weakRU values for all the query points in the uniform sample. Thereafter, we repeated the bucketization process as we did in the last ex... | We repeat this experiment for two classifications (AD and DCC) and two regression (HS and DI) data sets. The results are illustrated in Figure 20. In summary, the results are consistent with the previous observations, confirming the validity of our previous experiment and the minimal impact of removing the outliers fro... | Having provided the visual validation results, we next validate our RU measures on classification tasks. In this regard, using SYN data set, we first computed the RU measures for all the query points in the uniform sample and bucketized the points w.r.t. their RU values in ranges of length 0.1. We repeated this for bot... | A |
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