context stringlengths 250 4.88k | A stringlengths 250 4.73k | B stringlengths 250 3.79k | C stringlengths 250 8.2k | D stringlengths 250 4.17k | label stringclasses 4
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This is f′′(x)/f′(x)superscript𝑓′′𝑥superscript𝑓′𝑥f^{\prime\prime}(x)/f^{\prime}(x)italic_f start_POSTSUPERSCRIPT ′ ′ end_POSTSUPERSCRIPT ( italic_x ) / italic_f start_POSTSUPERSCRIPT ′ end_POSTSUPERSCRIPT ( italic_x ) of the generic formula, and can be quickly | Rnm′′/Rnm′superscriptsuperscriptsubscript𝑅𝑛𝑚′′superscriptsuperscriptsubscript𝑅𝑛𝑚′{R_{n}^{m}}^{\prime\prime}/{R_{n}^{m}}^{\prime}italic_R start_POSTSUBSCRIPT italic_n end_POSTSUBSCRIPT start_POSTSUPERSCRIPT italic_m end_POSTSUPERSCRIPT start_POSTSUPERSCRIPT ′ ′ end_POSTSUPERSCRIPT / italic_R start_POSTSUBSCRIPT it... | computed from Rnm(x)/Rnm′(x)=f(x)/f′(x)superscriptsubscript𝑅𝑛𝑚𝑥superscriptsuperscriptsubscript𝑅𝑛𝑚′𝑥𝑓𝑥superscript𝑓′𝑥R_{n}^{m}(x)/{R_{n}^{m}}^{\prime}(x)=f(x)/f^{\prime}(x)italic_R start_POSTSUBSCRIPT italic_n end_POSTSUBSCRIPT start_POSTSUPERSCRIPT italic_m end_POSTSUPERSCRIPT ( italic_x ) / italic_R sta... | \prime},+ 12 ( 1 + italic_m ) italic_x start_POSTSUPERSCRIPT italic_m + 1 end_POSTSUPERSCRIPT italic_F start_POSTSUPERSCRIPT ′ ′ end_POSTSUPERSCRIPT + 8 italic_x start_POSTSUPERSCRIPT italic_m + 3 end_POSTSUPERSCRIPT italic_F start_POSTSUPERSCRIPT ′ ′ ′ end_POSTSUPERSCRIPT ,
| {n,n^{\prime}}.∫ start_POSTSUBSCRIPT 0 end_POSTSUBSCRIPT start_POSTSUPERSCRIPT 1 end_POSTSUPERSCRIPT italic_x start_POSTSUPERSCRIPT italic_D - 1 end_POSTSUPERSCRIPT italic_R start_POSTSUBSCRIPT italic_n end_POSTSUBSCRIPT start_POSTSUPERSCRIPT italic_m end_POSTSUPERSCRIPT ( italic_x ) italic_R start_POSTSUBSCRIPT italic... | B |
The lower-unitriangular matrices u1subscript𝑢1u_{1}italic_u start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPT and u2subscript𝑢2u_{2}italic_u start_POSTSUBSCRIPT 2 end_POSTSUBSCRIPT are returned as words in the Leedham-Green–O’Brien standard generators [11] for SL(d,q)SL𝑑𝑞\textnormal{SL}(d,q)SL ( italic_d , italic_q ) define... |
There are several well-known generating sets for classical groups. For example, special linear groups are generated by the subset of all transvections [21, Theorem 4.3] or by two well chosen matrices, such as the Steinberg generators [19]. Another generating set which has become important in algorithms and application... |
Therefore, we decided to base the procedures we present on a set of generators very close to the LGO standard generators. Note, that the choice of the generating set has no impact on the results as it is always possible to determine an MSLP which computes the LGO standard generators given an arbritary generating set a... | Note that a small variation of these standard generators for SL(d,q)SL𝑑𝑞\textnormal{SL}(d,q)SL ( italic_d , italic_q ) are used in Magma [14] as well
as in algorithms to verify presentations of classical groups, see [12], where only the generator v𝑣vitalic_v is slightly different in the two scenarios when d𝑑ditali... | The LGO generating set offers a variety of advantages. In practice it is the generating set produced by the constructive recognition algorithms from [10, 11] as implemented in MAGMA. Consequently, algorithms in the composition tree data structure, both in MAGMA and in GAP, store elements in classical groups as words in... | A |
where Ω⊂ℝdΩsuperscriptℝ𝑑\Omega\subset\mathbb{R}^{d}roman_Ω ⊂ blackboard_R start_POSTSUPERSCRIPT italic_d end_POSTSUPERSCRIPT with d=2𝑑2d=2italic_d = 2 or 3333 for simplicity, and is an open bounded domain with polyhedral boundary ∂ΩΩ\partial\Omega∂ roman_Ω, the symmetric tensor 𝒜∈[L∞(Ω)]symd×d𝒜superscriptsubscrip... | In [MR2718268] is shown that the number of eigenvalues that are very large is related to the number of connected sub-regions on τ¯∪τ¯′¯𝜏superscript¯𝜏′\bar{\tau}\cup{\bar{\tau}}^{\prime}over¯ start_ARG italic_τ end_ARG ∪ over¯ start_ARG italic_τ end_ARG start_POSTSUPERSCRIPT ′ end_POSTSUPERSCRIPT with large coefficien... | One difficulty that hinders the development of efficient methods is the presence of high-contrast coefficients [MR3800035, MR2684351, MR2753343, MR3704855, MR3225627, MR2861254]. When LOD or VMS methods are considered, high-contrast coefficients might slow down the exponential decay of the solutions, making the method ... | It is hard to approximate such problem in its full generality using numerical methods, in particular because of the low regularity of the solution and its multiscale behavior. Most convergent proofs either assume extra regularity or special properties of the coefficients [AHPV, MR3050916, MR2306414, MR1286212, babuos85... |
As in many multiscale methods previously considered, our starting point is the decomposition of the solution space into fine and coarse spaces that are adapted to the problem of interest. The exact definition of some basis functions requires solving global problems, but, based on decaying properties, only local comput... | C |
We think Alg-A is better in almost every aspect. This is because it is essentially simpler.
Among other merits, Alg-A is much faster, because it has a smaller constant behind the asymptotic complexity O(n)𝑂𝑛O(n)italic_O ( italic_n ) than the others: |
Our experiment shows that the running time of Alg-A is roughly one eighth of the running time of Alg-K, or one tenth of the running time of Alg-CM. (Moreover, the number of iterations required by Alg-CM and Alg-K is roughly 4.67 times that of Alg-A.) | Comparing the description of the main part of Alg-A (the 7 lines in Algorithm 1) with that of Alg-CM (pages 9–10 of [8]),
Alg-A is conceptually simpler. Alg-CM is claimed “involved” by its authors as it contains complicated subroutines for handling many subcases. |
Alg-A has simpler primitives because (1) the candidate triangles considered in it have all corners lying on P𝑃Pitalic_P’s vertices and (2) searching the next candidate from a given one is much easier – the code length for this is 1:7 in Alg-A and in Alg-CM. | Alg-A computes at most n𝑛nitalic_n candidate triangles (proof is trivial and omitted) whereas Alg-CM computes at most 5n5𝑛5n5 italic_n triangles (proved in [8]) and so as Alg-K.
(by experiment, Alg-CM and Alg-K have to compute roughly 4.66n4.66𝑛4.66n4.66 italic_n candidate triangles.) | D |
We observe that at certain points in time, the volume of rumor-related tweets (for sub-events) in the event stream surges. This can lead to false positives for techniques that model events as the aggregation of all tweet contents; that is undesired at critical moments. We trade-off this by debunking at single tweet le... |
As observed in [19, 20], rumor features are very prone to change during an event’s development. In order to capture these temporal variabilities, we build upon the Dynamic Series-Time Structure (DSTS) model (time series for short) for feature vector representation proposed in [20]. We base our credibility feature on t... |
In this work, we propose an effective cascaded rumor detection approach using deep neural networks at tweet level in the first stage and wisdom of the “machines”, together with a variety of other features in the second stage, in order to enhance rumor detection performance in the early phase of an event. The proposed ... |
Most relevant for our work is the work presented in [20], where a time series model to capture the time-based variation of social-content features is used. We build upon the idea of their Series-Time Structure, when building our approach for early rumor detection with our extended dataset, and we provide a deep analys... | at an early stage. Our fully automatic, cascading rumor detection method follows
the idea on focusing on early rumor signals on text contents; which is the most reliable source before the rumors widely spread. Specifically, we learn a more complex representation of single tweets using Convolutional Neural Networks, tha... | B |
In a follow-up work Nacson et al. (2018) provided partial answers to these questions. They proved that the exponential tail has the optimal convergence rate, for tails for which ℓ′(u)superscriptℓ′𝑢\ell^{\prime}(u)roman_ℓ start_POSTSUPERSCRIPT ′ end_POSTSUPERSCRIPT ( italic_u ) is of the form exp(−uν)superscript𝑢𝜈... | Perhaps most similar to our study is the line of work on understanding AdaBoost in terms its implicit bias toward large L1subscript𝐿1L_{1}italic_L start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPT-margin solutions, starting with the seminal work of Schapire et al. (1998). Since AdaBoost can be viewed as coordinate descent on th... | decreasing loss, as well as for multi-class classification with cross-entropy loss. Notably, even though the logistic loss and the exp-loss behave very different on non-separable problems, they exhibit the same behaviour for separable problems. This implies that the non-tail
part does not affect the bias. The bias is a... | The convergence of the direction of gradient descent updates to the maximum L2subscript𝐿2L_{2}italic_L start_POSTSUBSCRIPT 2 end_POSTSUBSCRIPT margin solution, however is very slow compared to the convergence of training loss, which explains why it is worthwhile
continuing to optimize long after we have zero training ... | The follow-up paper (Gunasekar et al., 2018) studied this same problem with exponential loss instead of squared loss. Under additional assumptions on the asymptotic convergence of update directions and gradient directions, they were able to relate the direction of gradient descent iterates on the factorized parameteriz... | D |
For analysing the employed features, we rank them by importances using RF (see 4). The best feature is related to sentiment polarity scores. There is a big bias between the sentiment associated to rumors and the sentiment associated to real events in relevant tweets. In specific, the average polarity score of news even... | To construct the training dataset, we collected rumor stories from the rumor tracking websites snopes.com and urbanlegends.about.com. In more detail, we crawled 4300 stories from these websites. From the
story descriptions we manually constructed queries to retrieve the relevant tweets for the 270 rumors with highest i... | We use the same dataset described in Section 4.1. In total –after cutting off 180 events for pre-training single tweet model – our dataset contains 360 events and 180 of them are labeled as rumors. As a rumor is often of a long circurlating story (friggeri2014rumor, ), this results in a rather long time span. In this w... |
Training data for single tweet classification. An event might include sub-events for which relevant tweets are rumorous. To deal with this complexity, we train our single-tweet learning model only with manually selected breaking and subless events from the above dataset. In the end, we used 90 rumors and 90 news assoc... | The time period of a rumor event is sometimes fuzzy and hard to define. One reason is a rumor may have been triggered for a long time and kept existing, but it did not attract public attention. However it can be triggered by other events after a uncertain time and suddenly spreads as a bursty event. E.g., a rumor999htt... | B |
Evaluating methodology.
For RQ1, given an event entity e, at time t, we need to classify them into either Breaking or Anticipated class. We select a studied time for each event period randomly in the range of 5 days before and after the event time. In total, our training dataset for AOL consists of 1,740 instances of b... |
RQ2. Figure 4 shows the performance of the aspect ranking models for our event entities at specific times and types. The most right three models in each metric are the models proposed in this work. The overall results show that, the performances of these models, even better than the baselines (for at least one of the ... | We further investigate the identification of event time, that is learned on top of the event-type classification. For the gold labels, we gather from the studied times with regards to the event times that is previously mentioned. We compare the result of the cascaded model with non-cascaded logistic regression. The res... | RQ3. We demonstrate the results of single models and our ensemble model in Table 4. As also witnessed in RQ2, SVMall𝑆𝑉subscript𝑀𝑎𝑙𝑙SVM_{all}italic_S italic_V italic_M start_POSTSUBSCRIPT italic_a italic_l italic_l end_POSTSUBSCRIPT, will all features, gives a rather stable performance for both NDCG and Recall... |
Results. The baseline and the best results of our 1stsuperscript1𝑠𝑡1^{st}1 start_POSTSUPERSCRIPT italic_s italic_t end_POSTSUPERSCRIPT stage event-type classification is shown in Table 3-top. The accuracy for basic majority vote is high for imbalanced classes, yet it is lower at weighted F1. Our learned model achie... | D |
RL [Sutton and Barto, 1998] has been successfully applied to a variety of domains,
from Monte Carlo tree search [Bai et al., 2013] and hyperparameter tuning for complex optimization in science, engineering and machine learning problems [Kandasamy et al., 2018; Urteaga et al., 2023], | SMC weights are updated based on the likelihood of the observed rewards:
wt,a(m)∝pa(yt|xt,θt,a(m))proportional-tosuperscriptsubscript𝑤𝑡𝑎𝑚subscript𝑝𝑎conditionalsubscript𝑦𝑡subscript𝑥𝑡superscriptsubscript𝜃𝑡𝑎𝑚w_{t,a}^{(m)}\propto p_{a}(y_{t}|x_{t},\theta_{t,a}^{(m)})italic_w start_POSTSUBSCRIPT italic_t , it... | The techniques used in these success stories are grounded on statistical advances on sequential decision processes and multi-armed bandits.
The MAB crystallizes the fundamental trade-off between exploration and exploitation in sequential decision making. | we propagate forward the sequential random measure pM(θt,a|ℋ1:t)subscript𝑝𝑀conditionalsubscript𝜃𝑡𝑎subscriptℋ:1𝑡p_{M}(\theta_{t,a}|\mathcal{H}_{1:t})italic_p start_POSTSUBSCRIPT italic_M end_POSTSUBSCRIPT ( italic_θ start_POSTSUBSCRIPT italic_t , italic_a end_POSTSUBSCRIPT | caligraphic_H start_POSTSUBSCRIPT 1 : ... | the fundamental operation in the proposed SMC-based MAB Algorithm 1
is to sequentially update the random measure pM(θt,a|ℋ1:t)subscript𝑝𝑀conditionalsubscript𝜃𝑡𝑎subscriptℋ:1𝑡p_{M}(\theta_{t,a}|\mathcal{H}_{1:t})italic_p start_POSTSUBSCRIPT italic_M end_POSTSUBSCRIPT ( italic_θ start_POSTSUBSCRIPT italic_t , itali... | B |
These are also the patients who log glucose most often, 5 to 7 times per day on average compared to 2-4 times for the other patients.
For patients with 3-4 measurements per day (patients 8, 10, 11, 14, and 17) at least a part of the glucose measuremtents after the meals is within this range, while patient 12 has only t... | For time delays between carb entries and the next glucose measurements we distinguish cases where glucose was measured at most 30 minutes before logging the meal, to account for cases where multiple measurements are made for one meal – in such cases it might not make sense to predict the glucose directly after the meal... | In order to have a broad overview of different patients’ patterns over the one month period, we first show the figures illustrating measurements aggregated by days-in-week.
For consistency, we only consider the data recorded from 01/03/17 to 31/03/17 where the observations are most stable. | Table 2 gives an overview of the number of different measurements that are available for each patient.111For patient 9, no data is available.
The study duration varies among the patients, ranging from 18 days, for patient 8, to 33 days, for patient 14. | The insulin intakes tend to be more in the evening, when basal insulin is used by most of the patients. The only difference happens to patient 10 and 12 whose intakes are earlier at day.
Further, patient 12 takse approx. 3 times the average insulin dose of others in the morning. | B |
We propose a new CNN architecture with modules adapted from the semantic segmentation literature to predict fixation density maps of the same image resolution as the input. Our approach is based on a large body of research regarding saliency models that leverage object-specific features and functionally replicate human... | To restore the original image resolution, extracted features were processed by a series of convolutional and upsampling layers. Previous work on saliency prediction has commonly utilized bilinear interpolation for that task Cornia et al. (2018); Liu and Han (2018), but we argue that a carefully chosen decoder architect... | Later attempts addressed that shortcoming by taking advantage of classification architectures pre-trained on the ImageNet database Deng et al. (2009). This choice was motivated by the finding that features extracted from CNNs generalize well to other visual tasks Donahue et al. (2014). Consequently, DeepGaze I Kümmerer... | Image-to-image learning problems require the preservation of spatial features throughout the whole processing stream. As a consequence, our network does not include any fully-connected layers and reduces the number of downsampling operations inherent to classification models. We adapted the popular VGG16 architecture S... |
Figure 2: An illustration of the modules that constitute our encoder-decoder architecture. The VGG16 backbone was modified to account for the requirements of dense prediction tasks by omitting feature downsampling in the last two max-pooling layers. Multi-level activations were then forwarded to the ASPP module, which... | C |
The procedure which, for each vertex v∈V𝑣𝑉v\in Vitalic_v ∈ italic_V, constructs αesubscript𝛼𝑒\alpha_{e}italic_α start_POSTSUBSCRIPT italic_e end_POSTSUBSCRIPT for some e∈E𝑒𝐸e\in Eitalic_e ∈ italic_E adjacent to v𝑣vitalic_v in O(h)Oℎ\operatorname{O}(h)roman_O ( italic_h ), runs 𝒜𝒜\mathcal{A}caligraphic_A in O... | In the following, we obtain an approximation algorithm for the locality number by reducing it to the problem of computing the pathwidth of a graph. To this end, we first describe another way of how a word can be represented by a graph. Recall that the reduction to cutwidth from Section 4 also transforms words into grap... |
In the following, we discuss the lower and upper complexity bounds that we obtain from the reductions provided above. We first note that since Cutwidth is NP-complete, so is Loc. In particular, note that this answers one of the main questions left open in [15]. | The main results are presented in Sections 4, 5 and 6. First, in Section 4, we present the reductions from Loc to Cutwidth and vice versa, and we discuss the consequences of these reductions. Then, in Section 5, we show how Loc can be reduced to Pathwidth, which yields an approximation algorithm for computing the local... |
In this section, we introduce polynomial-time reductions from the problem of computing the locality number of a word to the problem of computing the cutwidth of a graph, and vice versa. This establishes a close relationship between these two problems (and their corresponding parameters), which lets us derive several u... | B |
The first network is a six layer CNN that detects the slice located within heart limits, and segments the thoracic and epicardial-paracardial masks.
The second network is a five layer CNN that detects the pericardium line from the CT scan in cylindrical coordinates. | The literature phrase search is the combined presence of each one of the cardiology terms indicated by (∗*∗) in Table I with each one of the deep learning terms related to architecture, indicated by (+++) in Table II using Google Scholar111https://scholar.google.com, Pubmed222https://ncbi.nlm.nih.gov/pubmed/ and Scopus... | First, optimal paths in a computed flow field are found and then a CNN classifier is used for removing extraneous paths in the detected centerlines.
The method was enhanced using a model-based detection of coronary specific territories and main branches to constrain the search space. | These predictions formed a vector field which was then used for evolving the contour using the Sobolev active contour framework.
Anh et al.[130] created a non-rigid segmentation method based on the distance regularized level set method that was initialized and constrained by the results of a structured inference using ... | A graph was then constructed from the retinal vascular network where the nodes are defined as the vessel branches and each edge gets associated to a cost that evaluates whether the two branches should have the same label.
The CNN classification was propagated through the minimum spanning tree of the graph. | B |
This demonstrates that SimPLe excels in a low data regime, but its advantage disappears with a bigger amount of data.
Such a behavior, with fast growth at the beginning of training, but lower asymptotic performance is commonly observed when comparing model-based and model-free methods (Wang et al. (2019)). As observed ... | The iterative process of training the model, training the policy, and collecting data is crucial for non-trivial tasks where random data collection is insufficient. In a game-by-game analysis, we quantified the number of games where the best results were obtained in later iterations of training. In some games, good pol... | Finally, we verified if a model obtained with SimPLe using 100100100100K is a useful initialization for model-free PPO training.
Based on the results depicted in Figure 5 (b) we can positively answer this conjecture. Lower asymptotic performance is probably due to worse exploration. A policy pre-trained with SimPLe was... | We focused our work on learning games with 100100100100K interaction steps with the environment. In this section we present additional results for settings with 20202020K, 50505050K, 200200200200K, 500500500500K and 1111M interactions; see Figure 5 (a).
Our results are poor with 20202020K interactions. For 50505050K th... |
The primary evaluation in our experiments studies the sample efficiency of SimPLe, in comparison with state-of-the-art model-free deep RL methods in the literature. To that end, we compare with Rainbow (Hessel et al., 2018; Castro et al., 2018), which represents the state-of-the-art Q-learning method for Atari games, ... | B |
Here we also refer to CNN as a neural network consisting of alternating convolutional layers each one followed by a Rectified Linear Unit (ReLU) and a max pooling layer and a fully connected layer at the end while the term ‘layer’ denotes the number of convolutional layers.
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For the purposes of this paper we use a variation of the database111https://archive.ics.uci.edu/ml/datasets/Epileptic+Seizure+Recognition in which the EEG signals are split into segments with 178178178178 samples each, resulting in a balanced dataset that consists of 11500115001150011500 EEG signals. | A high level overview of these combined methods is shown in Fig. 1.
Although we choose the EEG epileptic seizure recognition dataset from University of California, Irvine (UCI) [13] for EEG classification, the implications of this study could be generalized in any kind of signal classification problem. | For the spectrogram module, which is used for visualizing the change of the frequency of a non-stationary signal over time [18], we used a Tukey window with a shape parameter of 0.250.250.250.25, a segment length of 8888 samples, an overlap between segments of 4444 samples and a fast Fourier transform of 64646464 sampl... | This is achieved with the use of multilayer networks, that consist of million parameters [1], trained with backpropagation [2] on large amount of data.
Although deep learning is mainly used in biomedical images there is also a wide range of physiological signals, such as Electroencephalography (EEG), that are used for ... | A |
A major obstacle in achieving seamless autonomous locomotion transition lies in the need for an efficient sensing methodology that can promptly and reliably evaluate the interaction between the robot and the terrain, referred to as terramechanics. These methods generally involve performing comprehensive on-site measure... |
In the literature review, Gorilla [2] is able to switch between bipedal and quadrupedal walking locomotion modes autonomously using criteria developed based on motion efficiency and stability margin. WorkPartner [8] demonstrated its capability to seamlessly transition between two locomotion modes: rolling and rolking.... | In this section, we explore the autonomous locomotion mode transition of the Cricket robot. We present our hierarchical control design, which is simulated in a hybrid environment comprising MATLAB and CoppeliaSim. This design facilitates the decision-making process when transitioning between the robot’s rolling and wal... | There are two primary technical challenges in the wheel/track-legged robotics area [2]. First, there’s a need to ensure accurate motion control within both rolling and walking locomotion modes [5] and effectively handle the transitions between them [6]. Second, it’s essential to develop decision-making frameworks that ... | Figure 11: The Cricket robot tackles a step of height 2h, beginning in rolling locomotion mode and transitioning to walking locomotion mode using the rear body climbing gait. The red line in the plot shows that the robot tackled the step in rolling locomotion mode until the online accumulated energy consumption of the ... | A |
As argued in detail in [9], there are compelling reasons to study the advice complexity of online computation.
Lower bounds establish strict limitations on the power of any online algorithm; there are strong connections between randomized online algorithms and online algorithms with advice (see, e.g., [27]); online alg... | Notwithstanding such interesting attributes, the known advice model has certain drawbacks. The advice is always assumed to be some error-free information that may be used to encode some property often explicitly connected to the optimal solution. In many settings, one can argue that such information cannot be readily a... |
Under the current models, the advice bits can encode any information about the input sequence; indeed, defining the “right” information to be conveyed to the algorithm plays an important role in obtaining better online algorithms. Clearly, the performance of the online algorithm can only improve with larger number of ... |
In future work, we would like to expand the model so as to incorporate, into the analysis, the concept of advice error. More specifically, given an advice string of size k𝑘kitalic_k, let η𝜂\etaitalic_η denote the number of erroneous bits (which may be not known to the algorithm). In this setting, the objective would... | It should be fairly clear that such assumptions are very unrealistic or undesirable. Advice bits, as all information, are prone to transmission errors. In addition, the known advice models often allow
information that one may arguably consider unrealistic, e.g., an encoding of some part of the offline optimal solution.... | A |
Another (more elaborated) policy could have taken into account how fast the positive value grows (the slope) in relation with the negative one, and if a given threshold was exceeded, classify subjects as depressed —in such case our subject could have been classified as depressed, for instance, after reading his/her 92n... | This brief subsection describes the training process, which is trivial. Only a dictionary of term-frequency pairs is needed for each category.
Then, during training, dictionaries are updated as new documents are processed —i.e. unseen terms are added and frequencies of already seen terms are updated. | Otherwise, it can be omitted since, during classification, gv𝑔𝑣gvitalic_g italic_v can be dynamically computed based on the frequencies stored in the dictionaries.
It is worth mentioning that this algorithm could be easily parallelized by following the MapReduce model as well —for instance, all training documents co... | In the rest of this subsection, we will exemplify how the SS3 framework carries out the classification and training process and how the early classification and explainability aspects are addressed. The last subsection goes into more technical details and we will study how the local and global value of a term is actual... | Note that with this simple training method there is no need neither to store all documents nor to re-train from scratch every time a new training document is added, making the training incremental101010Even new categories could be dynamically added.. Additionally, there is no need to compute the document-term matrix be... | A |
We run DMSGD, DGC (w/ mfm), DGC (w/o mfm) and GMC respectively to solve the optimization problem: min𝐰∈ℝdF(𝐰)subscript𝐰superscriptℝ𝑑𝐹𝐰\min_{{\bf w}\in{\mathbb{R}}^{d}}F({\bf w})roman_min start_POSTSUBSCRIPT bold_w ∈ blackboard_R start_POSTSUPERSCRIPT italic_d end_POSTSUPERSCRIPT end_POSTSUBSCRIPT italic_F ( bol... | Table 2 and Figure 4 show the performance under non-IID data distribution. We can find that GMC can achieve much better test accuracy and faster convergence speed compared to other methods. Furthermore, we can find that the momentum factor masking trick will severely impair the performance of DGC under non-IID data dis... | Figure 2(b), 2(c) and 2(d) show the distances to the global optimal point when using different s𝑠sitalic_s for the case when d=20𝑑20d=20italic_d = 20. We can find that, compared with the local momentum methods, the global momentum method GMC converges faster and more stably.
| process. As for global momentum, the momentum term −(𝐰t−𝐰t−1)/ηsubscript𝐰𝑡subscript𝐰𝑡1𝜂-({\bf w}_{t}-{\bf w}_{t-1})/\eta- ( bold_w start_POSTSUBSCRIPT italic_t end_POSTSUBSCRIPT - bold_w start_POSTSUBSCRIPT italic_t - 1 end_POSTSUBSCRIPT ) / italic_η contains global information from all the workers. Since we are... | We can find that after a sufficient number of iterations, the parameter in DGC (w/o mfm) can only oscillate within a relatively large neighborhood of the optimal point. Compared with DGC (w/o mfm), the parameter in GMC converges closer to the optimal point and then remains stable.
Figure 2(a) shows the distances to the... | D |
For the purposes of this paper we use a variation of the database444https://archive.ics.uci.edu/ml/datasets/Epileptic+Seizure+Recognition in which the EEG signals are split into segments with 178178178178 samples each, resulting in a balanced dataset that consists of 11500115001150011500 EEG signals in total.
| During supervised learning the weights of the kernels are frozen and a one layer fully connected network (FNN) is stacked on top of the reconstruction output of the SANs.
The FNN is trained for an additional 5555 epochs with the same batch size and model selection procedure as with SANs and categorical cross-entropy as... | We use one signal from each of 15 signal datasets from Physionet listed in the first column of Table I.
Each signal consists of 12000120001200012000 samples which in turn is split in 12121212 signals of 1000100010001000 samples each, to create the training (6666 signals), validation (2222 signals) and test datasets (44... | We then split the 11500115001150011500 signals into 76%percent7676\%76 %, 12%percent1212\%12 % and 12%percent1212\%12 % (8740,1380,13808740138013808740,1380,13808740 , 1380 , 1380 signals) as training, validation and test data respectively and normalize in the range [0,1]01[0,1][ 0 , 1 ] using the global max and min.
F... | The first two fully connected layers are followed by a ReLU while the last one produces the predictions.
The CNN is trained for an additional 5555 epochs with the same batch size and model selection procedure as with SANs and categorical cross-entropy as the loss function. | C |
A new algorithm which can learn from previous experiences is required, and the algorithm with faster learning speed is more desirable. Existing algorithms’ learning method is learning by prediction. It means that UAV knows current strategies with corresponding payoff and it can randomly select another strategy and calc... | A new algorithm which can learn from previous experiences is required, and the algorithm with faster learning speed is more desirable. Existing algorithms’ learning method is learning by prediction. It means that UAV knows current strategies with corresponding payoff and it can randomly select another strategy and calc... |
Compared with other algorithms, novel algorithm SPBLLA has more advantages in learning rate. Various algorithms have been employed in the UAV networks in search of the optimal channel selection [31][29], such as stochastic learning algorithm [30]. The most widely seen algorithm–LLA is an ideal method for NE approachin... |
The learning rate of the extant algorithm is also not desirable [13]. Recently, a new fast algorithm called binary log-linear learning algorithm (BLLA) has been proposed by [14]. However, in this algorithm, only one UAV is allowed to change strategy in one iteration based on current game state, and then another UAV ch... | In the literatures, most works search PSNE by using the Binary Log-linear Learning Algorithm (BLLA). However, there are limitations of this algorithm. In BLLA, each UAV can calculate and predict its utility for any si∈Sisubscript𝑠𝑖subscript𝑆𝑖s_{i}\in S_{i}italic_s start_POSTSUBSCRIPT italic_i end_POSTSUBSCRIPT ∈ it... | C |
resistivity, η[m2/s]=η′/μ0𝜂delimited-[]superscriptm2ssuperscript𝜂′subscript𝜇0\eta[\mbox{m}^{2}/\mbox{s}]=\eta^{\prime}/\mu_{0}italic_η [ m start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT / s ] = italic_η start_POSTSUPERSCRIPT ′ end_POSTSUPERSCRIPT / italic_μ start_POSTSUBSCRIPT 0 end_POSTSUBSCRIPT is magnetic
diffusivi... | \perp}|_{\Gamma}=\mathbf{0},\,(\nabla_{\perp}\psi)|_{\Gamma}=0\mbox{ and }(%
\nabla_{\perp}f)|_{\Gamma}=0bold_v | start_POSTSUBSCRIPT roman_Γ end_POSTSUBSCRIPT = bold_0 , bold_q start_POSTSUBSCRIPT italic_i ⟂ end_POSTSUBSCRIPT | start_POSTSUBSCRIPT roman_Γ end_POSTSUBSCRIPT = bold_q start_POSTSUBSCRIPT italic_e ⟂ end_P... | are standard. The boundary conditions and closure for this model (namely,
definitions of thermal fluxes 𝐪isubscript𝐪𝑖\mathbf{q}_{i}bold_q start_POSTSUBSCRIPT italic_i end_POSTSUBSCRIPT and 𝐪esubscript𝐪𝑒\mathbf{q}_{e}bold_q start_POSTSUBSCRIPT italic_e end_POSTSUBSCRIPT, | With reference to the definitions of the discrete forms for the thermal
flux ∇¯^⋅𝐪^α⋅^¯∇subscript^𝐪𝛼\widehat{\overline{\nabla}}\cdot\widehat{\mathbf{q}}_{\alpha}over^ start_ARG over¯ start_ARG ∇ end_ARG end_ARG ⋅ over^ start_ARG bold_q end_ARG start_POSTSUBSCRIPT italic_α end_POSTSUBSCRIPT | terms 𝐪^isubscript^𝐪𝑖\widehat{\mathbf{q}}_{i}over^ start_ARG bold_q end_ARG start_POSTSUBSCRIPT italic_i end_POSTSUBSCRIPT and 𝐪^esubscript^𝐪𝑒\widehat{\mathbf{q}}_{e}over^ start_ARG bold_q end_ARG start_POSTSUBSCRIPT italic_e end_POSTSUBSCRIPT.
For the viscous terms, we use, for simplicity, the unmagnetised versi... | B |
Let r𝑟ritalic_r be the relation on 𝒞Rsubscript𝒞𝑅\mathcal{C}_{R}caligraphic_C start_POSTSUBSCRIPT italic_R end_POSTSUBSCRIPT given to the left of Figure 12.
Its abstract lattice ℒrsubscriptℒ𝑟\mathcal{L}_{r}caligraphic_L start_POSTSUBSCRIPT italic_r end_POSTSUBSCRIPT is represented to the right. | The tuples t1subscript𝑡1t_{1}italic_t start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPT, t4subscript𝑡4t_{4}italic_t start_POSTSUBSCRIPT 4 end_POSTSUBSCRIPT represent a counter-example to BC→A𝐵𝐶→𝐴BC\operatorname{\rightarrow}Aitalic_B italic_C → italic_A for g1subscript𝑔1g_{1}italic_g start_POSTSUBSCRIPT 1 end_POSTSUBSCRI... | First, remark that both A→B𝐴→𝐵A\operatorname{\rightarrow}Bitalic_A → italic_B and B→A𝐵→𝐴B\operatorname{\rightarrow}Aitalic_B → italic_A are possible.
Indeed, if we set g=⟨b,a⟩𝑔𝑏𝑎g=\langle b,a\rangleitalic_g = ⟨ italic_b , italic_a ⟩ or g=⟨a,1⟩𝑔𝑎1g=\langle a,1\rangleitalic_g = ⟨ italic_a , 1 ⟩, then r⊧gA→... | If no confusion is possible, the subscript R𝑅Ritalic_R will be omitted, i.e., we will use
≤,∧,∨\leq,\operatorname{\land},\operatorname{\lor}≤ , ∧ , ∨ instead of ≤R,∧R,∨Rsubscript𝑅subscript𝑅subscript𝑅\leq_{R},\operatorname{\land}_{R},\operatorname{\lor}_{R}≤ start_POSTSUBSCRIPT italic_R end_POSTSUBSCRIPT , ∧ start_P... | For convenience we give in Table 7 the list of all possible realities
along with the abstract tuples which will be interpreted as counter-examples to A→B𝐴→𝐵A\operatorname{\rightarrow}Bitalic_A → italic_B or B→A𝐵→𝐴B\operatorname{\rightarrow}Aitalic_B → italic_A. | D |
The sources of DQN variance are Approximation Gradient Error(AGE)[23] and Target Approximation Error(TAE)[24]. In Approximation Gradient Error, the error in gradient direction estimation of the cost function leads to inaccurate and extremely different predictions on the learning trajectory through different episodes b... |
The results in Figure 3 show that using DQN with different Dropout methods result in better-preforming policies and less variability as the reduced standard deviation between the variants indicate to. In table 1, Wilcoxon Sign-Ranked test was used to analyze the effect of Variance before applying Dropout (DQN) and aft... | To that end, we ran Dropout-DQN and DQN on one of the classic control environments to express the effect of Dropout on Variance and the learned policies quality. For the Overestimation phenomena, we ran Dropout-DQN and DQN on a Gridworld environment to express the effect of Dropout because in such environment the optim... |
To evaluate the Dropout-DQN, we employ the standard reinforcement learning (RL) methodology, where the performance of the agent is assessed at the conclusion of the training epochs. Thus we ran ten consecutive learning trails and averaged them. We have evaluated Dropout-DQN algorithm on CARTPOLE problem from the Class... |
Reinforcement Learning (RL) is a learning paradigm that solves the problem of learning through interaction with environments, this is a totally different approach from the other learning paradigms that have been studied in the field of Machine Learning namely the supervised learning and the unsupervised learning. Rein... | B |
Chaichulee et al. (2017) extended the VGG16 architecture (Simonyan and Zisserman, 2014) to include a global average pooling layer for patient detection and a fully convolutional network for skin segmentation. The proposed model was evaluated on images from a clinical study conducted at a neonatal intensive care unit, ... | V-Net (Milletari et al., 2016) and FCN (Long et al., 2015). Sinha and Dolz (2019) proposed a multi-level attention based architecture for abdominal organ segmentation from MRI images. Qin et al. (2018) proposed a dilated convolution base block to preserve more detailed attention in 3D medical image segmentation. Simil... | Bischke et al. (2019) proposed a cascaded multi-task loss to preserve boundary information from segmentation masks for segmenting building footprints and achieved state-of-the-art performance on an aerial image labeling task. He et al. (2017) extended Faster R-CNN (Ren et al., 2015) by adding a new branch to predict th... | Mask R-CNN has also been used for segmentation tasks in medical image analysis such as automatically segmenting and tracking cell migration in phase-contrast microscopy (Tsai et al., 2019), detecting and segmenting nuclei from histological and microscopic images (Johnson, 2018; Vuola et al., 2019; Wang et al., 2019a, b... |
Chaichulee et al. (2017) extended the VGG16 architecture (Simonyan and Zisserman, 2014) to include a global average pooling layer for patient detection and a fully convolutional network for skin segmentation. The proposed model was evaluated on images from a clinical study conducted at a neonatal intensive care unit, ... | C |
The red line indicates the number of edges that remain in 𝐀¯¯𝐀\bar{{\mathbf{A}}}over¯ start_ARG bold_A end_ARG after sparsification.
It is possible to see that for small increments of ϵitalic-ϵ\epsilonitalic_ϵ the spectral distance increases linearly, while the number of edges in the graph drops exponentially. | We notice that the coarsened graphs are pre-computed before training the GNN.
Therefore, the computational time of graph coarsening is much lower compared to training the GNN for several epochs, since each MP operation in the GNN has a cost 𝒪(N2)𝒪superscript𝑁2\mathcal{O}(N^{2})caligraphic_O ( italic_N start_POSTSUP... |
The proposed spectral algorithm is not designed to handle very dense graphs; an intuitive explanation is that 𝐯maxssubscriptsuperscript𝐯𝑠max{\mathbf{v}}^{s}_{\text{max}}bold_v start_POSTSUPERSCRIPT italic_s end_POSTSUPERSCRIPT start_POSTSUBSCRIPT max end_POSTSUBSCRIPT can be interpreted as the graph signal with the... | The GNN is then trained to fit its node representations to these pre-determined structures.
Pre-computing graph coarsening not only makes the training much faster by avoiding to perform graph reduction at every forward pass, but it also provides a strong inductive bias that prevents degenerate solutions, such as entire... | The reason can be once again attributed to the low information content of the individual node features and in the sparsity of the graph signal (most node features are 0), which makes it difficult for the feature-based pooling methods to infer global properties of the graph by looking at local sub-structures.
| C |
The following analyses are shown exemplarily on the Soybean dataset. This dataset has 35353535 features and 19191919 classes. First, we analyze the generated data with a fixed number of decision trees, i.e., the number of sampled decision trees in RFsub𝑅subscript𝐹subRF_{\text{sub}}italic_R italic_F start_POSTSUBSCRI... | Probability distribution of the predicted confidences for different data generation settings on Soybean with 5555 (top) and 50505050 samples per class (bottom). Generating data with different numbers of decision trees is visualized in the left column. Additionally, a comparison between random sampling (red), NRFI unifo... | This shows that neural random forest imitation is able to generate significantly better data samples based on the knowledge in the random forest.
NRFI dynamic improves the performance by automatically optimizing the decision tree sampling and generating the largest variation in the data. | The analysis shows that random data samples and uniform sampling have a bias to generate data samples that are classified with high confidence.
NRFI dynamic automatically balances the number of decision trees and archives an evenly distributed data distribution, i.e., generates the most diverse data samples. |
NRFI uniform and NRFI dynamic sample the number of decision trees for each data point uniformly, respectively, optimized via automatic confidence distribution (see Section 4.1.4). The confidence distributions for both sampling modes are visualized in the second column of Figure 5. Additionally, sampling random data po... | D |
Coupled with powerful function approximators such as neural networks, policy optimization plays a key role in the tremendous empirical successes of deep reinforcement learning (Silver et al., 2016, 2017; Duan et al., 2016; OpenAI, 2019; Wang et al., 2018). In sharp contrast, the theoretical understandings of policy opt... |
Broadly speaking, our work is related to a vast body of work on value-based reinforcement learning in tabular (Jaksch et al., 2010; Osband et al., 2014; Osband and Van Roy, 2016; Azar et al., 2017; Dann et al., 2017; Strehl et al., 2006; Jin et al., 2018) and linear settings (Yang and Wang, 2019b, a; Jin et al., 2019;... |
A line of recent work (Fazel et al., 2018; Yang et al., 2019a; Abbasi-Yadkori et al., 2019a, b; Bhandari and Russo, 2019; Liu et al., 2019; Agarwal et al., 2019; Wang et al., 2019) answers the computational question affirmatively by proving that a wide variety of policy optimization algorithms, such as policy gradient... | for any function f:𝒮→ℝ:𝑓→𝒮ℝf:{\mathcal{S}}\rightarrow\mathbb{R}italic_f : caligraphic_S → blackboard_R. By allowing the reward function to be adversarially chosen in each episode, our setting generalizes the stationary setting commonly adopted by the existing work on value-based reinforcement learning (Jaksch et al.... | Our work is based on the aforementioned line of recent work (Fazel et al., 2018; Yang et al., 2019a; Abbasi-Yadkori et al., 2019a, b; Bhandari and Russo, 2019; Liu et al., 2019; Agarwal et al., 2019; Wang et al., 2019) on the computational efficiency of policy optimization, which covers PG, NPG, TRPO, PPO, and AC. In p... | B |
Both training and inference have extremely high demands on their targeted platform and certain hardware requirements can be the deciding factor whether an application can be realized.
This section briefly introduces the most important hardware for deep learning and discusses their potentials and limitations. | Jacob et al. (2018) proposed a quantization scheme that accurately approximates floating-point operations using only integer arithmetic to speed up computation.
During training, their forward pass simulates the quantization step to keep the performance of the quantized DNN close to the performance of using single-preci... | In Huang and Wang (2018), the outputs of different structures are scaled with individual trainable scaling factors.
By using a sparsity enforcing ℓ1superscriptℓ1\ell^{1}roman_ℓ start_POSTSUPERSCRIPT 1 end_POSTSUPERSCRIPT-norm regularizer on these scaling factors, the outputs of the corresponding structures are driven t... | Quantized DNNs with 1-bit weights and activations are the worst performing models, which is due to the severe implications of extreme quantization on prediction performance.
As can be seen, however, the overall performance of the quantized models increases considerably when the bit width of activations is increased to ... | CPUs were originally designed to optimize single-thread performance in order to execute an individual computation within the shortest possible latency.
Unfortunately, single-thread performance is stagnating since the end of Dennard scaling (Dennard et al., 1974), and now performance scaling usually requires paralleliza... | D |
In Section 3, we construct a category of metric pairs. This category will be the natural setting for our extrinsic persistent homology. Although being functorial is trivial in the case of Vietoris-Rips persistence, the type of functoriality which one is supposed to expect in the case of metric embeddings is a priori no... | One main contribution of this paper is establishing a precise relationship (i.e. a filtered homotopy equivalence) between the Vietoris-Rips simplicial filtration of a metric space and a more geometric (or extrinsic) way of assigning a persistence module to a metric space, which consists of first isometrically embedding... |
In Section 4, we show that the Vietoris-Rips filtration can be (categorically) seen as a special case of persistent homology obtained through metric embeddings via the isomorphism theorem (Theorem 1). In this section, we also we also establish the stability of the filtration obtained via metric embeddings. | In Section 3, we construct a category of metric pairs. This category will be the natural setting for our extrinsic persistent homology. Although being functorial is trivial in the case of Vietoris-Rips persistence, the type of functoriality which one is supposed to expect in the case of metric embeddings is a priori no... | In Section 8, we reprove Rips and Gromov’s result about the contractibility of the Vietoris-Rips complex of hyperbolic geodesic metric spaces, by using our method consisting of isometric embeddings into injective metric spaces. As a result, we will be able to bound the length of intervals in Vietoris-Rips persistence b... | B |
The difference line plot (d), on the other hand, builds on the standard plot by highlighting the differences between the selection and the global average, shown as positive and negative values around the 0 value of the y-axis.
It provides a clearer overall picture of the difference in preservation among all the shown s... | After choosing a projection, users will proceed with the visual analysis using all the functionalities described in the next sections. However, the hyper-parameter exploration does not necessarily stop here. The top 6 representatives (according to a user-selected quality measure) are still shown at the top of the main ... | Adaptive PCP vs. PCP
Although it is not uncommon to find tools that use PCP views together with DR-based scatterplots (e.g., iPCA [69]) with various schemes for re-ordering and prioritizing the axes (e.g., [70, 71]), the arrangement and presentation of these PCP’s are usually static in order to reflect attributes of ... | Apart from the adaptive filtering and re-ordering of the axes, we maintained a rather standard visual presentation of the PCP plot, to make sure it is as easy and natural as possible for users to inspect it. The colors reflect the labels of the data with the same colors as in the overview (Subsection 4.2), when availab... |
Adaptive Parallel Coordinates Plot Our first proposal to support the task of interpreting patterns in a t-SNE projection is an Adaptive PCP [59], as shown in Figure 1(k). It highlights the dimensions of the points selected with the lasso tool, using a maximum of 8 axes at any time, to avoid clutter. The shown axes (... | B |
The correct design of a bio-inspired algorithm involves the execution of a series of steps in a conscientious and organized manner, both at the time of algorithm development and during subsequent experimentation and application to real-world optimization problems. In [5], a complete tutorial on the design of new bio-in... |
In such work, an analysis is conducted from a critical yet constructive point of view, aiming to correct misconceptions and bad methodological habits. Each phase of the analysis includes the prescription of application guidelines and recommendations intended for adoption by the community. These guidelines are intended... | The correct design of a bio-inspired algorithm involves the execution of a series of steps in a conscientious and organized manner, both at the time of algorithm development and during subsequent experimentation and application to real-world optimization problems. In [5], a complete tutorial on the design of new bio-in... | The rest of this paper is organized as follows. In Section 2, we examine previous surveys, taxonomies, and reviews of nature- and bio-inspired algorithms reported so far in the literature. Section 3 delves into the taxonomy based on the inspiration of the algorithms. In Section 4, we present and populate the taxonomy b... | As we have mentioned in the introduction, we revisit a triple study of evolutionary and bio-inspired algorithms from a triple perspective, where we stand and what’s next from a perspective published in 2020, but still valid in terms of the need to address important problems and challenges in optimization for EAs and po... | A |
Figure 1: Framework of AdaGAE. k0subscript𝑘0k_{0}italic_k start_POSTSUBSCRIPT 0 end_POSTSUBSCRIPT is the initial sparsity. First, we construct a sparse graph via the generative model defined in Eq. (7). The learned graph is employed to apply the GAE designed for the weighted graphs. After training the GAE, we update ... |
In recent years, GCNs have been studied a lot to extend neural networks to graph type data. How to design a graph convolution operator is a key issue and has attracted a mass of attention. Most of them can be classified into 2 categories, spectral methods [24] and spatial methods[25]. | However, the existing methods are limited to graph type data while no graph is provided for general data clustering. Since a large proportion of clustering methods are based on the graph, it is reasonable to consider how to employ GCN to promote the performance of graph-based clustering methods.
In this paper, we propo... | As well as the well-known k𝑘kitalic_k-means [1, 2, 3], graph-based clustering [4, 5, 6] is also a representative kind of clustering method.
Graph-based clustering methods can capture manifold information so that they are available for the non-Euclidean type data, which is not provided by k𝑘kitalic_k-means. Therefore,... | (1) Via extending the generative graph models into general type data, GAE is naturally employed as the basic representation learning model and weighted graphs can be further applied to GAE as well. The connectivity distributions given by the generative perspective also inspires us to devise a novel architecture for dec... | B |
Since the Open Resolver and the Spoofer Projects are the only two infrastructures providing vantage points for measuring spoofing - their importance is immense as they facilitated many research works analysing the spoofability of networks based on the datasets collected by these infrastructures. Nevertheless, the studi... | Limitations of filtering studies. The measurement community provided indispensable studies for assessing “spoofability” in the Internet, and has had success in detecting the ability to spoof in some individual networks using active measurements, e.g., via agents installed on those networks (Mauch, 2013; Lone et al., 20... |
Network Traces. To overcome the dependency on vantage points for running the tests, researchers explored alternatives for inferring filtering of spoofed packets. A recent work used loops in traceroute to infer ability to send packets from spoofed IP addresses, (Lone et al., 2017). |
(Lichtblau et al., 2017) developed a methodology to passively detect spoofed packets in traces recorded at a European IXP connecting 700 networks. The limitation of this approach is that it requires cooperation of the IXP to perform the analysis over the traffic and applies only to networks connected to the IXP. Allow... | Vantage Points. Measurement of networks which do not perform egress filtering of packets with spoofed IP addresses was first presented by the Spoofer Project in 2005 (Beverly and Bauer, 2005). The idea behind the Spoofer Project is to craft packets with spoofed IP addresses and check receipt thereof on the vantage poin... | B |
Sensor drift in industrial processes is one such use case. For example, sensing gases in the environment is mostly tasked to metal oxide-based sensors, chosen for their low cost and ease of use [1, 2]. An array of sensors with variable selectivities, coupled with a pattern recognition algorithm, readily recognizes a b... |
More specifically, natural odors consist of complex and variable mixtures of molecules present at variable concentrations [4]. Sensor variance arises from environmental dynamics of temperature, humidity, and background chemicals, all contributing to concept drift [5], as well as sensor drift arising from modification ... | An alternative approach is to emulate adaptation in natural sensor systems. The system expects and automatically adapts to sensor drift, and is thus able to maintain its accuracy for a long time. In this manner, the lifetime of sensor systems can be extended without recalibration.
| While natural systems cope with changing environments and embodiments well, they form a serious challenge for artificial systems. For instance, to stay reliable over time, gas sensing systems must be continuously recalibrated to stay accurate in a changing physical environment. Drawing motivation from nature, this pape... |
The purpose of this study was to demonstrate that explicit representation of context can allow a classification system to adapt to sensor drift. Several gas classifier models were placed in a setting with progressive sensor drift and were evaluated on samples from future contexts. This task reflects the practical goal... | B |
Now we can define the tables A(1)superscript𝐴1A^{(1)}italic_A start_POSTSUPERSCRIPT ( 1 ) end_POSTSUPERSCRIPT, A(2)superscript𝐴2A^{(2)}italic_A start_POSTSUPERSCRIPT ( 2 ) end_POSTSUPERSCRIPT and A(3)superscript𝐴3A^{(3)}italic_A start_POSTSUPERSCRIPT ( 3 ) end_POSTSUPERSCRIPT that our algorithm uses.
Recall that for... | A(2)[i,B]:={A representative set containing pairs (M,x), where M is a perfect matching on B∈ℬi(2) and x is a real number equal to the minimum total length of a path cover of P0∪⋯∪Pi−1∪B realizing the matching M.assignsuperscript𝐴2𝑖𝐵casesA representative set containing pairs (M,x), where M is a perfect matching on B... |
A[i,B]:={A representative set containing pairs (M,x), where M is a perfect matching on B and x is a real number equal to the minimum total length of a path cover of P0∪⋯∪Pi−1∪B realizing the matching M.assign𝐴𝑖𝐵casesA representative set containing pairs (M,x), where M is a perfect matching on B and x is a real num... |
A[i,B]:={A representative set containing pairs (M,x), where M is a perfect matching on B∈ℬi and x is a real number equal to the minimum total length of a path cover of P0∪⋯∪Pi−1∪B realizing the matching M.assign𝐴𝑖𝐵casesA representative set containing pairs (M,x), where M is a perfect matching on B∈ℬi and x is a re... | A(1)[i,B]:={A representative set containing pairs (M,x), where M is a perfect matching on B∈ℬi(1) and x is a real number equal to the minimum total length of a path cover of P0∪⋯∪Pi−1∪B realizing the matching Massignsuperscript𝐴1𝑖𝐵casesA representative set containing pairs (M,x), where M is a perfect matching on B∈... | C |
Let S𝑆Sitalic_S be a (completely) self-similar semigroup and let T𝑇Titalic_T be a finite or free semigroup. Then S⋆T⋆𝑆𝑇S\star Titalic_S ⋆ italic_T is (completely) self-similar. If furthermore S𝑆Sitalic_S is a (complete) automaton semigroup, then so is S⋆T⋆𝑆𝑇S\star Titalic_S ⋆ italic_T.
| While our main result significantly relaxes the hypothesis for showing that the free product of self-similar semigroups (or automaton semigroups) is self-similar (an automaton semigroup), it does not settle the underlying question whether these semigroup classes are closed under free product. It is possible that there ... | By Corollaries 10 and 11, we have to look into idempotent-free automaton semigroups without length functions in order to find a pair of self-similar (or automaton) semigroups not satisfying the hypothesis of Theorem 6 (or 8), which would be required in order to either relax the hypothesis even further (possibly with a ... | from one to the other, then their free product S⋆T⋆𝑆𝑇S\star Titalic_S ⋆ italic_T is an automaton semigroup (8). This is again a strict generalization of [19, Theorem 3.0.1] (even if we only consider complete automata).
Third, we show this result in the more general setting of self-similar semigroups111Note that the c... | The construction used to prove Theorem 6 can also be used to obtain results which are not immediate corollaries of the theorem (or its corollary for automaton semigroups in 8). As an example, we prove in the following theorem that it is possible to adjoin a free generator to every self-similar semigroup without losing ... | B |
SCR divides the region proposals into influential and non-influential regions and penalizes the model if: 1) 𝒮(agt)𝒮subscript𝑎𝑔𝑡\mathcal{S}(a_{gt})caligraphic_S ( italic_a start_POSTSUBSCRIPT italic_g italic_t end_POSTSUBSCRIPT ) of a non-influential region is higher than an influential region, and 2) the regio... | Here, we study these methods. We find that their improved accuracy does not actually emerge from proper visual grounding, but from regularization effects, where the model forgets the linguistic priors in the train set, thereby performing better on the test set. To support these claims, we first show that it is possible... | As observed by Selvaraju et al. (2019) and as shown in Fig. 2, we observe small improvements on VQAv2 when the models are fine-tuned on the entire train set. However, if we were to compare against the improvements in VQA-CPv2 in a fair manner, i.e., only use the instances with visual cues while fine-tuning, then, the p... | We test our regularization method on random subsets of varying sizes. Fig. A6 shows the results when we apply our loss to 1−100%1percent1001-100\%1 - 100 % of the training instances. Clearly, the ability to regularize the model does not vary much with respect to the size of the train subset, with the best performance o... | We probe the reasons behind the performance improvements of HINT and SCR. We first analyze if the results improve even when the visual cues are irrelevant (Sec. 4.2) or random (Sec. 4.3) and examine if their differences are statistically significant (Sec. 4.4). Then, we analyze the regularization effects by evaluating ... | D |
A privacy policy is a legal document that an organisation uses to disclose how they collect, analyze, share, and protect users’ personal information. Legal jurisdictions around the world require organisations to make their privacy policies readily available to their users, and laws such as General Data Protection Regul... |
For the question answering task, we leveraged the PrivacyQA corpus (Ravichander et al., 2019). PrivacyQA consists of 1,750 questions about the contents of privacy policies from 35 privacy documents. While crowdworkers were asked to come up with privacy related questions based on public information about an application... |
Prior collections of privacy policy corpora have led to progress in privacy research. Wilson et al. (2016) released the OPP-115 Corpus, a dataset of 115 privacy policies with manual annotations of 23k fine-grained data practices, and they created a baseline for classifying privacy policy text into one of ten categorie... | Other corpora similar to OPP-115 Corpus have enabled research on privacy practices. The PrivacyQA corpus contains 1,750 questions and expert-annotated answers for the privacy question answering task (Ravichander et al., 2019). Similarly, Lebanoff and Liu (2018) constructed the first corpus of human-annotated vague word... |
Natural language processing (NLP) provides an opportunity to automate the extraction of salient details from privacy policies, thereby reducing human effort and enabling the creation of tools for internet users to understand and control their online privacy. Existing research has achieved some success using expert ann... | D |
We answered that the per-class performance is also a very important component, and exploratory visualization can assist in the selection process, as seen in Figure 2(b and c.1).
The expert understood the importance of visualization in that situation, compared to not using it. | Interpretability and explainability is another challenge (mentioned by E3) in complicated ensemble methods, which is not necessarily always a problem depending on the data and the tasks. However, the utilization of user-selected weights for multiple validation metrics is one way towards interpreting and trusting the re... | Workflow. E1, E2, and E3 agreed that the workflow of StackGenVis made sense.
They all suggested that data wrangling could happen before the algorithms’ exploration, but also that it is usual to first train a few algorithms and then, based on their predictions, wrangle the data. |
Figure 4: Our feature selection view that provides three different feature selection techniques. The y-axis of the table heatmap depicts the data set’s features, and the x-axis depicts the selected models in the current stored stack. Univariate-, permutation-, and accuracy-based feature selection is available as long ... | Another positive opinion from E3 was that, with a few adaptations to the performance metrics, StackGenVis could work with regression or even ranking problems.
E3 also mentioned that supporting feature generation in the feature selection phase might be helpful. Finally, E1 suggested that the circular barcharts could onl... | D |
We thus have 3333 cases, depending on the value of the tuple
(p(v,[010]),p(v,[323]),p(v,[313]),p(v,[003]))𝑝𝑣delimited-[]010𝑝𝑣delimited-[]323𝑝𝑣delimited-[]313𝑝𝑣delimited-[]003(p(v,[010]),p(v,[323]),p(v,[313]),p(v,[003]))( italic_p ( italic_v , [ 010 ] ) , italic_p ( italic_v , [ 323 ] ) , italic_p ( italic_v... | p(v,[013])=p(v,[313])=p(v,[113])=1𝑝𝑣delimited-[]013𝑝𝑣delimited-[]313𝑝𝑣delimited-[]1131p(v,[013])=p(v,[313])=p(v,[113])=1italic_p ( italic_v , [ 013 ] ) = italic_p ( italic_v , [ 313 ] ) = italic_p ( italic_v , [ 113 ] ) = 1.
Similarly, when f=[112]𝑓delimited-[]112f=[112]italic_f = [ 112 ], | Then, by using the adjacency of (v,[013])𝑣delimited-[]013(v,[013])( italic_v , [ 013 ] ) with each of
(v,[010])𝑣delimited-[]010(v,[010])( italic_v , [ 010 ] ), (v,[323])𝑣delimited-[]323(v,[323])( italic_v , [ 323 ] ), and (v,[112])𝑣delimited-[]112(v,[112])( italic_v , [ 112 ] ), we can confirm that | By using the pairwise adjacency of (v,[112])𝑣delimited-[]112(v,[112])( italic_v , [ 112 ] ), (v,[003])𝑣delimited-[]003(v,[003])( italic_v , [ 003 ] ), and
(v,[113])𝑣delimited-[]113(v,[113])( italic_v , [ 113 ] ), we can confirm that in the 3333 cases, these | {0¯,1¯,2¯,3¯,[013],[010],[323],[313],[112],[003],[113]}.¯0¯1¯2¯3delimited-[]013delimited-[]010delimited-[]323delimited-[]313delimited-[]112delimited-[]003delimited-[]113\{\overline{0},\overline{1},\overline{2},\overline{3},[013],[010],[323],[313],%
[112],[003],[113]\}.{ over¯ start_ARG 0 end_ARG , over¯ start_ARG 1 end... | C |
In Experiment I: Text Classification, we use FewRel [Han et al., 2018] and Amazon [He and McAuley, 2016]. They are datasets for 5-way 5-shot classification, which means 5 classes are randomly sampled from the full dataset for each task, and each class has 5 samples. FewRel is a relation classification dataset with 65/... |
In Experiment II: Dialogue Generation, we use Persona [Zhang et al., 2018] and Weibo, regarding building a dialogue model for a user as a task. Persona is a personalized dialogue dataset with 1137/99/100 users for meta-training/meta-validation/meta-testing. Each user has 121 utterances on average. Weibo is a personali... |
In Experiment I: Text Classification, we use FewRel [Han et al., 2018] and Amazon [He and McAuley, 2016]. They are datasets for 5-way 5-shot classification, which means 5 classes are randomly sampled from the full dataset for each task, and each class has 5 samples. FewRel is a relation classification dataset with 65/... | Task similarity. In Persona and Weibo, each task is a set of dialogues for one user, so tasks are different from each other. We shuffle the samples and randomly divide tasks to construct the setting that tasks are similar to each other. For a fair comparison, each task on this setting also has 120 and 1200 utterances o... | In meta-learning, we have multiple tasks T𝑇Titalic_T sampled from distribution p(𝒯)𝑝𝒯p(\mathcal{T})italic_p ( caligraphic_T ) [Ravi and Larochelle, 2017, Andrychowicz et al., 2016, Santoro et al., 2016]. For each task Tisubscript𝑇𝑖T_{i}italic_T start_POSTSUBSCRIPT italic_i end_POSTSUBSCRIPT, we train a base mode... | A |
The rest of this paper is as follows. In Section II, the system model is introduced. In Section III, the CCA codebook design and the codebook-based joint subarray partition and AWV selection algorithms are proposed. Next, the TE-aware codebook-based beam tracking with 3D beamwidth control is further proposed in Sectio... | In addition, the AOAs and AODs should be tracked in the highly dynamic UAV mmWave network.
To this end, in Section IV we will further propose a novel predictive AOA/AOD tracking scheme in conjunction with tracking error treatment to address the high mobility challenge, then we integrate these operations into the codebo... | Note that directly solving the above beam tracking problem is very challenging, especially in the considered highly dynamic UAV mmWave network. Therefore, developing new and efficient beam tracking solution for the CA-enabled UAV mmWave network is the major focus of our work. Recall that several efficient codebook-base... |
A CCA-enabled UAV mmWave network is considered in this paper. Here, we first establish the DRE-covered CCA model in Section II-A. Then the system setup of the considered UAV mmWave network is described in Section II-B. Finally, the beam tracking problem for the CA-enabled UAV mmWave network is modeled in Section II-C. |
The rest of this paper is as follows. In Section II, the system model is introduced. In Section III, the CCA codebook design and the codebook-based joint subarray partition and AWV selection algorithms are proposed. Next, the TE-aware codebook-based beam tracking with 3D beamwidth control is further proposed in Sectio... | C |
The case of 1111-color is characterized by a Presburger formula that just expresses the equality of the number of edges calculated from
either side of the bipartite graph. The non-trivial direction of correctness is shown via distributing edges and then merging. | To conclude this section, we stress that although the 1111-color case contains many of the key ideas, the multi-color case requires a finer
analysis to deal with the “big enough” case, and also may benefit from a reduction that allows one to restrict | The case of 1111-color is characterized by a Presburger formula that just expresses the equality of the number of edges calculated from
either side of the bipartite graph. The non-trivial direction of correctness is shown via distributing edges and then merging. | After the merging the total degree of each vertex increases by tδ(A0,B0)2𝑡𝛿superscriptsubscript𝐴0subscript𝐵02t\delta(A_{0},B_{0})^{2}italic_t italic_δ ( italic_A start_POSTSUBSCRIPT 0 end_POSTSUBSCRIPT , italic_B start_POSTSUBSCRIPT 0 end_POSTSUBSCRIPT ) start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT.
We perform the... | The case of fixed degree and multiple colors is done via an induction, using merging and then swapping to eliminate parallel edges.
The case of unfixed degree is handled using a case analysis depending on whether sizes are “big enough”, but the approach is different from | D |
Deep reinforcement learning achieves phenomenal empirical successes, especially in challenging applications where an agent acts upon rich observations, e.g., images and texts. Examples include video gaming (Mnih et al., 2015), visuomotor manipulation (Levine et al., 2016), and language generation (He et al., 2015). Suc... |
Moreover, soft Q-learning is equivalent to a variant of policy gradient (O’Donoghue et al., 2016; Schulman et al., 2017; Nachum et al., 2017; Haarnoja et al., 2017). Hence, Proposition 6.4 also characterizes the global optimality and convergence of such a variant of policy gradient. |
In this paper, we study temporal-difference (TD) (Sutton, 1988) and Q-learning (Watkins and Dayan, 1992), two of the most prominent algorithms in deep reinforcement learning, which are further connected to policy gradient (Williams, 1992) through its equivalence to soft Q-learning (O’Donoghue et al., 2016; Schulman et... | In this section, we extend our analysis of TD to Q-learning and policy gradient. In §6.1, we introduce Q-learning and its mean-field limit. In §6.2, we establish the global optimality and convergence of Q-learning. In §6.3, we further extend our analysis to soft Q-learning, which is equivalent to policy gradient.
| Szepesvári, 2018; Dalal et al., 2018; Srikant and Ying, 2019) settings. See Dann et al. (2014) for a detailed survey. Also, when the value function approximator is linear, Melo et al. (2008); Zou et al. (2019); Chen et al. (2019b) study the convergence of Q-learning. When the value function approximator is nonlinear, T... | B |
Regarding parameter efficiency for NMT, Wu et al. (2019a) present lightweight and dynamic convolutions. Ma et al. (2021) approximate softmax attention with two nested linear attention functions. These methods are orthogonal to our work and it should be possible to combine them with our approach. |
In this paper, we replace residual connections of the Transformer with depth-wise LSTMs, to selectively manage the representation aggregation of layers benefiting performance while ensuring convergence of the Transformer. Specifically, we show how to integrate the computation of multi-head attention networks and feed-... |
We use depth-wise LSTM rather than a depth-wise multi-head attention network Dou et al. (2018) with which we can build the NMT model solely based on the attention mechanism for two reasons: 1) we have to compute the stacking of Transformer layers sequentially as in sequential token-by-token decoding, and compared to t... | We suggest that selectively aggregating different layer representations of the Transformer may improve the performance, and propose to use depth-wise LSTMs to connect stacked (sub-) layers of Transformers. We show how Transformer layer normalization and feed-forward sub-layers can be absorbed by depth-wise LSTMs, while... | Directly replacing residual connections with LSTM units will introduce a large amount of additional parameters and computation. Given that the task of computing the LSTM hidden state is similar to the feed-forward sub-layer in the original Transformer layers, we propose to replace the feed-forward sub-layer with the ne... | A |
define compact sets in X𝑋Xitalic_X for the topology generated by ℒ′superscriptℒ′\mathcal{L}^{\prime}caligraphic_L start_POSTSUPERSCRIPT ′ end_POSTSUPERSCRIPT. We usually instantiate the theorem with
X⊆Struct(σ)𝑋StructσX\subseteq\operatorname{Struct}(\upsigma)italic_X ⊆ roman_Struct ( roman_σ ) ℒ=⟦𝖥𝖮[σ]⟧X\mathcal{L... | 𝒪∩⟦𝖥𝖮[σ]⟧X=⟦𝖥⟧X.\mathcal{O}\cap\llbracket\mathsf{FO}[\upsigma]\rrbracket_{X}=\llbracket\mathsf%
{F}\rrbracket_{X}\;.caligraphic_O ∩ ⟦ sansserif_FO [ roman_σ ] ⟧ start_POSTSUBSCRIPT italic_X end_POSTSUBSCRIPT = ⟦ sansserif_F ⟧ start_POSTSUBSCRIPT italic_X end_POSTSUBSCRIPT . | instantiated with ℒ=⟦𝖥𝖮[σ]⟧X\mathcal{L}=\llbracket\mathsf{FO}[\upsigma]\rrbracket_{X}caligraphic_L = ⟦ sansserif_FO [ roman_σ ] ⟧ start_POSTSUBSCRIPT italic_X end_POSTSUBSCRIPT and ℒ′=⟦𝖥⟧X\mathcal{L}^{\prime}=\llbracket\mathsf{F}\rrbracket_{X}caligraphic_L start_POSTSUPERSCRIPT ′ end_POSTSUPERSCRIPT = ⟦ sansserif_F ... | and ℒ′=⟦𝖥⟧X\mathcal{L}^{\prime}=\llbracket\mathsf{F}\rrbracket_{X}caligraphic_L start_POSTSUPERSCRIPT ′ end_POSTSUPERSCRIPT = ⟦ sansserif_F ⟧ start_POSTSUBSCRIPT italic_X end_POSTSUBSCRIPT where 𝖥𝖥\mathsf{F}sansserif_F is a
fragment of 𝖥𝖮[σ]𝖥𝖮delimited-[]σ\mathsf{FO}[\upsigma]sansserif_FO [ roman_σ ]. | that ⟦𝖥⟧X\llbracket\mathsf{F}\rrbracket_{X}⟦ sansserif_F ⟧ start_POSTSUBSCRIPT italic_X end_POSTSUBSCRIPT is a base of
⟨τ≤∩⟦𝖥𝖮[σ]⟧X⟩\left\langle\uptau_{\leq}\cap\llbracket\mathsf{FO}[\upsigma]\rrbracket_{X}\right\rangle⟨ roman_τ start_POSTSUBSCRIPT ≤ end_POSTSUBSCRIPT ∩ ⟦ sansserif_FO [ roman_σ ] ⟧ start_POSTSUBSCRI... | C |
Relationship to Distortion Distribution: We first emphasize the relationship between two learning representations and the realistic distortion distribution of a distorted image. In detail, we train a learning model to estimate the distortion parameters and the ordinal distortions separately, and the errors of estimate... |
Relationship to Distortion Distribution: We first emphasize the relationship between two learning representations and the realistic distortion distribution of a distorted image. In detail, we train a learning model to estimate the distortion parameters and the ordinal distortions separately, and the errors of estimate... |
To exhibit the performance fairly, we employ three common network architectures VGG16, ResNet50, and InceptionV3 as the backbone networks of the learning model. The proposed MDLD metric is used to express the distortion estimation error due to its unique and fair measurement for distortion distribution. To be specific... | (1) Overall, the ordinal distortion estimation significantly outperforms the distortion parameter estimation in both convergence and accuracy, even if the amount of training data is 20% of that used to train the learning model. Note that we only use 1/4 distorted image to predict the ordinal distortion. As we pointed o... | Distortion Learning Evaluation: Then, we introduce three key elements for evaluating the learning representation: training data, convergence, and error. Supposed that the settings such as the network architecture and optimizer are the same, a better learning representation can be described from the less the training da... | D |
Please note that EXTRAP-SGD has two learning rates for tuning and needs to compute two mini-batch gradients in each iteration. EXTRAP-SGD requires more time than other methods to tune hyperparameters and train models.
Similarly, CLARS needs to compute extra mini-batch gradients to estimate the layer-wise learning rate ... | First, we use the dataset CIFAR-10 and the model ResNet20 [10] to evaluate SNGM. We train the model with eight GPUs. Each GPU will compute a gradient with the batch size being B/8𝐵8B/8italic_B / 8. If B/8≥128𝐵8128B/8\geq 128italic_B / 8 ≥ 128, we will use the gradient accumulation [28]
with the batch size being 128. ... |
To further verify the superiority of SNGM with respect to LARS, we also evaluate them on a larger dataset ImageNet [2] and a larger model ResNet50 [10]. We train the model with 90 epochs. As recommended in [32], we use warm-up and polynomial learning rate strategy. | Hence, with the same number of gradient computations, SNGM can adopt a larger batch size than MSGD to converge to the ϵitalic-ϵ\epsilonitalic_ϵ-stationary point.
Empirical results on deep learning further verify that SNGM can achieve better test accuracy than MSGD and other state-of-the-art large-batch training methods... | We don’t use training tricks such as warm-up [7]. We adopt the linear learning rate decay strategy as default in the Transformers framework.
Table 5 shows the test accuracy results of the methods with different batch sizes. SNGM achieves the best performance for almost all batch size settings. | B |
Clustering is a fundamental task in unsupervised and self-supervised learning. The stochastic setting models situations in which decisions must be made in the presence of uncertainty and are of particular interest in learning and data science. The black-box model is motivated by data-driven applications where specific ... | An outbreak is an instance from 𝒟𝒟\mathcal{D}caligraphic_D, and after it actually happened, additional testing and vaccination locations were deployed or altered based on the new requirements, e.g., [20], which corresponds to stage-II decisions.
To continue this example, there may be further constraints on FIsubscrip... | For instance, during the COVID-19 pandemic, testing and vaccination centers were deployed at different kinds of locations, and access was an important consideration [18, 20]; access can be quantified in terms of different objectives including distance, as in our work. Here,
ℱℱ\mathcal{F}caligraphic_F and 𝒞𝒞\mathcal{C... | We are given a set of clients 𝒞𝒞\mathcal{C}caligraphic_C and a set of facilities ℱℱ\mathcal{F}caligraphic_F, in a metric space with a distance function d𝑑ditalic_d. We let n=|𝒞|𝑛𝒞n=|\mathcal{C}|italic_n = | caligraphic_C | and m=|ℱ|𝑚ℱm=|\mathcal{F}|italic_m = | caligraphic_F |. Our paradigm unfolds in two stages... |
There is an important connection between our generalization scheme and the design of our polynomial-scenarios approximation algorithms. In Theorem 1.1, the sample bounds are given in terms of the cardinality |𝒮|𝒮|\mathcal{S}|| caligraphic_S |. Our polynomial-scenarios algorithms are carefully designed to make |𝒮|𝒮... | B |
In addition to uncertainties in information exchange, different assumptions on the cost functions have been discussed.
In the most of existing works on the distributed convex optimization, it is assumed that the subgradients are bounded if the local cost | Both (sub)gradient noises and random graphs are considered in [11]-[13]. In [11], the local gradient noises are independent with bounded second-order moments and the graph sequence is i.i.d.
In [12]-[14], the (sub)gradient measurement noises are martingale difference sequences and their second-order conditional moments... |
Motivated by distributed statistical learning over uncertain communication networks, we study the distributed stochastic convex optimization by networked local optimizers to cooperatively minimize a sum of local convex cost functions. The network is modeled by a sequence of time-varying random digraphs which may be sp... | Besides, the network graphs may change randomly with spatial and temporal dependency (i.e. Both the weights of different edges in the network graphs at the same time instant and the network graphs at different time instants may be mutually dependent.) rather than i.i.d. graph sequences as in [12]-[15],
and additive and... | However, a variety of random factors may co-exist in practical environment.
In distributed statistical machine learning algorithms, the (sub)gradients of local loss functions cannot be obtained accurately, the graphs may change randomly and the communication links may be noisy. There are many excellent results on the d... | C |
Typically, the attributes in microdata can be divided into three categories: (1) Explicit-Identifier (EI, also known as Personally-Identifiable Information), such as name and social security number, which can uniquely or mostly identify the record owner; (2) Quasi-Identifier (QI), such as age, gender and zip code, whi... | Specifically, there are three main steps in the proposed approach. First, MuCo partitions the tuples into groups and assigns similar records into the same group as far as possible. Second, the random output tables, which control the distribution of random output values within each group, are calculated to make similar ... | Generalization [8, 26] is one of the most widely used privacy-preserving techniques. It transforms the values on QI attributes into general forms, and the tuples with equally generalized values constitute an equivalence group. In this way, records in the same equivalence group are indistinguishable. k𝑘kitalic_k-Anonym... |
Although the generalization for k𝑘kitalic_k-anonymity provides enough protection for identities, it is vulnerable to the attribute disclosure [23]. For instance, in Figure 1(b), the sensitive values in the third equivalence group are both “pneumonia”. Therefore, an adversary can infer the disease value of Dave by mat... | However, despite protecting against both identity disclosure and attribute disclosure, the information loss of generalized table cannot be ignored. On the one hand, the generalized values are determined by only the maximum and the minimum QI values in equivalence groups, causing that the equivalence groups only preserv... | B |
Bells and Whistles. MaskRCNN-ResNet50 is used as baseline and it achieves 53.2 mAP. For PointRend, we follow the same setting as Kirillov et al. (2020) except for extracting both coarse and fine-grained features from the P2-P5 levels of FPN, rather than only P2 described in the paper. Surprisingly, PointRend yields 62.... | HTC is known as a competitive method for COCO and OpenImage. By enlarging the RoI size of both box and mask branches to 12 and 32 respectively for all three stages, we gain roughly 4 mAP improvement against the default settings in original paper. Mask scoring head Huang et al. (2019) adopted on the third stage gains an... | Deep learning has achieved great success in recent years Fan et al. (2019); Zhu et al. (2019); Luo et al. (2021, 2023); Chen et al. (2021). Recently, many modern instance segmentation approaches demonstrate outstanding performance on COCO and LVIS, such as HTC Chen et al. (2019a), SOLOv2 Wang et al. (2020), and PointRe... |
We implement PointRend using MMDetection Chen et al. (2019b) and adopt the modifications and tricks mentioned in Section 3.3. Both X101-64x4d and Res2Net101 Gao et al. (2019) are used as our backbones, pretrained on ImageNet only. SGD with momentum 0.9 and weight decay 1e-4 is adopted. The initial learning rate is set... | Bells and Whistles. MaskRCNN-ResNet50 is used as baseline and it achieves 53.2 mAP. For PointRend, we follow the same setting as Kirillov et al. (2020) except for extracting both coarse and fine-grained features from the P2-P5 levels of FPN, rather than only P2 described in the paper. Surprisingly, PointRend yields 62.... | C |
I(f)<1,andH(|f^|2)>nn+1logn.formulae-sequence𝐼𝑓1and𝐻superscript^𝑓2𝑛𝑛1𝑛I(f)<1,\ \ {\mbox{and}}\ \ H(|\hat{f}|^{2})>\frac{n}{n+1}\log n.italic_I ( italic_f ) < 1 , and italic_H ( | over^ start_ARG italic_f end_ARG | start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT ) > divide start_ARG italic_n end_ARG start_ARG ita... | For the significance of this conjecture we refer to the original paper [FK], and to Kalai’s blog [K] (embedded in Tao’s blog) which reports on all significant results concerning the conjecture. [KKLMS] establishes a weaker version of the conjecture. Its introduction is also a good source of information on the problem.
|
In version 1 of this note, which can still be found on the ArXiv, we showed that the analogous version of the conjecture for complex functions on {−1,1}nsuperscript11𝑛\{-1,1\}^{n}{ - 1 , 1 } start_POSTSUPERSCRIPT italic_n end_POSTSUPERSCRIPT which have modulus 1111 fails. This solves a question raised by Gady Kozma s... |
Here we give an embarrassingly simple presentation of an example of such a function (although it can be shown to be a version of the example in the previous version of this note). As was written in the previous version, an anonymous referee of version 1 wrote that the theorem was known to experts but not published. Ma... | (0log0:=0assign0000\log 0:=00 roman_log 0 := 0). The base of the log\logroman_log does not really matter here. For concreteness we take the log\logroman_log to base 2222. Note that if f𝑓fitalic_f has L2subscript𝐿2L_{2}italic_L start_POSTSUBSCRIPT 2 end_POSTSUBSCRIPT norm 1111 then the sequence {|f^(A)|2}A⊆[n]subsc... | C |
^{l},a_{h}^{l})+\max_{a\in\mathcal{A}}Q_{h+1}^{k-1}(s_{h+1}^{l},a)-\langle\bm{%
\phi}(s_{h}^{l},a_{h}^{l}),\bm{w}\rangle]^{2}+\left\lVert\bm{w}\right\rVert_{2}.bold_italic_w start_POSTSUBSCRIPT italic_h end_POSTSUBSCRIPT start_POSTSUPERSCRIPT italic_k end_POSTSUPERSCRIPT = roman_arg roman_min start_POSTSUBSCRIPT bold_i... |
In practice, the transition function ℙℙ\mathbb{P}blackboard_P is unknown, and the state space might be so large that it is impossible for the learner to fully explore all states. If we parametrize the action-value function in a linear form as ⟨ϕ(⋅,⋅),𝒘⟩bold-italic-ϕ⋅⋅𝒘\langle\bm{\phi}(\cdot,\cdot),\bm{w}\rangle⟨ bo... |
Finally, we use epoch restart strategy to adapt to the drifting environment, which achieves near-optimal dynamic regret notwithstanding its simplicity. Specifically, we restart the estimation of 𝒘𝒘\bm{w}bold_italic_w after WH𝑊𝐻\frac{W}{H}divide start_ARG italic_W end_ARG start_ARG italic_H end_ARG episodes, all il... | From Figure 1, we see LSVI-UCB-Restart with the knowledge of global variation drastically outperforms all other methods designed for stationary environments , in both abruptly-changing and gradually-changing environments, since it restarts the estimation of the Q𝑄Qitalic_Q function with knowledge of the total variatio... |
One might be skeptical since simply applying least-squares method to solve 𝒘𝒘\bm{w}bold_italic_w does not take the distribution drift in ℙℙ\mathbb{P}blackboard_P and r𝑟ritalic_r into account and hence, may lead to non-trivial estimation error. However, we show that the estimation error can gracefully adapt to the n... | D |
Fake news is news articles that are “either wholly false or containing deliberately misleading elements incorporated within its content or context” (Bakir and McStay, 2018). The presence of fake news has become more prolific on the Internet due to the ease of production and dissemination of information online (Shu et a... | Singapore is a city-state with an open economy and diverse population that shapes it to be an attractive and vulnerable target for fake news campaigns (Lim, 2019). As a measure against fake news, the Protection from Online Falsehoods and Manipulation Act (POFMA) was passed on May 8, 2019, to empower the Singapore Gover... | While fake news is not a new phenomenon, the 2016 US presidential election brought the issue to immediate global attention with the discovery that fake news campaigns on social media had been made to influence the election (Allcott and Gentzkow, 2017). The creation and dissemination of fake news is motivated by politic... |
In general, respondents possess a competent level of digital literacy skills with a majority exercising good news sharing practices. They actively verify news before sharing by checking with multiple sources found through the search engine and with authoritative information found in government communication platforms,... | Fake news is news articles that are “either wholly false or containing deliberately misleading elements incorporated within its content or context” (Bakir and McStay, 2018). The presence of fake news has become more prolific on the Internet due to the ease of production and dissemination of information online (Shu et a... | A |
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