context stringlengths 250 4.37k | A stringlengths 250 8.2k | B stringlengths 250 4.23k | C stringlengths 250 4.99k | D stringlengths 250 3.54k | label stringclasses 4
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to the weight such that a Gauss-Legendre integration for moments xD+m−1superscript𝑥𝐷𝑚1x^{D+m-1}italic_x start_POSTSUPERSCRIPT italic_D + italic_m - 1 end_POSTSUPERSCRIPT
is engaged and the wiggly remainder of Rnmsuperscriptsubscript𝑅𝑛𝑚R_{n}^{m}italic_R start_POSTSUBSCRIPT italic_n end_POSTSUBSCRIPT start_POSTSUPE... | Rnm(x)=∑s=0(n−m)/2(−1)s(n−m2s)(D2+n−s−1n−m2)xn−2s.superscriptsubscript𝑅𝑛𝑚𝑥superscriptsubscript𝑠0𝑛𝑚2superscript1𝑠binomial𝑛𝑚2𝑠binomial𝐷2𝑛𝑠1𝑛𝑚2superscript𝑥𝑛2𝑠\displaystyle R_{n}^{m}(x)=\sum_{s=0}^{(n-m)/2}(-1)^{s}\binom{\frac{n-m}{2}}{s%
}\binom{\frac{D}{2}+n-s-1}{\frac{n-m}{2}}x^{n-2s}.italic_R st... | that adds the results of 1+(n−m)/21𝑛𝑚21+(n-m)/21 + ( italic_n - italic_m ) / 2
Gaussian integrations for moments xD−1+n−2ssuperscript𝑥𝐷1𝑛2𝑠x^{D-1+n-2s}italic_x start_POSTSUPERSCRIPT italic_D - 1 + italic_n - 2 italic_s end_POSTSUPERSCRIPT. The disadvantage |
Gaussian integration rules for integrals ∫01xD−1Rnm(x)f(x)𝑑xsuperscriptsubscript01superscript𝑥𝐷1superscriptsubscript𝑅𝑛𝑚𝑥𝑓𝑥differential-d𝑥\int_{0}^{1}x^{D-1}R_{n}^{m}(x)f(x)dx∫ start_POSTSUBSCRIPT 0 end_POSTSUBSCRIPT start_POSTSUPERSCRIPT 1 end_POSTSUPERSCRIPT italic_x start_POSTSUPERSCRIPT italic_D - 1 ... | to the weight such that a Gauss-Legendre integration for moments xD+m−1superscript𝑥𝐷𝑚1x^{D+m-1}italic_x start_POSTSUPERSCRIPT italic_D + italic_m - 1 end_POSTSUPERSCRIPT
is engaged and the wiggly remainder of Rnmsuperscriptsubscript𝑅𝑛𝑚R_{n}^{m}italic_R start_POSTSUBSCRIPT italic_n end_POSTSUBSCRIPT start_POSTSUPE... | B |
In other words, our algorithm initialises w:=gassign𝑤𝑔w:=gitalic_w := italic_g, u1:=1assignsubscript𝑢11u_{1}:=1italic_u start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPT := 1 and u2:=1assignsubscript𝑢21u_{2}:=1italic_u start_POSTSUBSCRIPT 2 end_POSTSUBSCRIPT := 1 and multiplies w𝑤witalic_w, u1subscript𝑢1u_{1}italic_u start... |
For the purposes of determining the cost of Taylor’s algorithm in terms of matrix operations, namely determining the length of an MSLP for the algorithm, we assume that the field elements −gicgrc−1subscript𝑔𝑖𝑐superscriptsubscript𝑔𝑟𝑐1-g_{ic}g_{rc}^{-1}- italic_g start_POSTSUBSCRIPT italic_i italic_c end_POSTSU... | does not yield an upper bound for the memory requirement in a theoretical analysis.
Moreover, the result of SlotUsagePattern improves the memory usage but it is not necessarily optimized overall and, hence, the number of slots can still be greater than the number of slots of a carefully computed MSLP. It should also be... |
As for the simpler examples considered in the previous section, here to keep the presentation clear we do not write down explicit MSLP instructions, but instead determine the cost of Algorithm 3 while keeping track of the number of elements that an MSLP for this algorithm would need to keep in memory at any given time... | The cost of the subroutines is determined with this in mind; that is, for each subroutine we determine the maximum length and memory requirement for an MSLP that returns the required output when evaluated with an initial memory containing the appropriate input.
| C |
The key to approximate (25) is the exponential decay of Pw𝑃𝑤Pwitalic_P italic_w, as long as w∈H1(𝒯H)𝑤superscript𝐻1subscript𝒯𝐻w\in{H^{1}({\mathcal{T}_{H}})}italic_w ∈ italic_H start_POSTSUPERSCRIPT 1 end_POSTSUPERSCRIPT ( caligraphic_T start_POSTSUBSCRIPT italic_H end_POSTSUBSCRIPT ) has local support. That al... |
It is essential for the performing method that the static condensation is done efficiently. The solutions of (22) decay exponentially fast if w𝑤witalic_w has local support, so instead of solving the problems in the whole domain it would be reasonable to solve it locally using patches of elements. We note that the ide... |
Solving (22) efficiently is crucial for the good performance of the method, since it is the only large dimensional system of (21), in the sense that its size grows with order of h−dsuperscriptℎ𝑑h^{-d}italic_h start_POSTSUPERSCRIPT - italic_d end_POSTSUPERSCRIPT. | mixed finite elements. We note the proposal in [CHUNG2018298] of generalized multiscale finite element methods based on eigenvalue problems inside the macro elements, with basis functions with support weakly dependent of the log of the contrast. Here, we propose eigenvalue problems based on edges of macro element remov... | 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 ... | A |
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.) | The difference is mainly due to the degenerate case (where a chord of P𝑃Pitalic_P is parallel to an edge of P𝑃Pitalic_P) and the float issue of both programs.
Our implementations of Alg-K and Alg-CM have logical difference in handling degenerate cases. |
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. |
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. | A |
It has to be noted here that even though we obtain reasonable results on the classification task in general, the prediction performance varies considerably along the time dimension. This is understandable, since tweets become more distinguishable, only when the user gains more knowledge about the event. |
Training data for single tweet classification. Here we follow our assumption that 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 333the terminology subless indicates an eve... |
We use the same dataset described in Section 5.1. 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. Those rumors and news fall comparatively evenly in 8 different categories, namely Politics, Science, Attacks, Disaster, A... |
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... | story descriptions we manually constructed queries to retrieve the relevant tweets for 270 rumors with high impact. Our approach to query construction mainly follows [11]. For the news event instances (non-rumor examples), we make use of the manually constructed corpus from Mcminn et al. [21], which covers 500 real-wor... | B |
\left(\sqrt{\frac{\log\log t}{\log t}}\right)∥ divide start_ARG bold_w ( italic_t ) end_ARG start_ARG ∥ bold_w ( italic_t ) ∥ end_ARG - divide start_ARG over^ start_ARG bold_w end_ARG end_ARG start_ARG ∥ over^ start_ARG bold_w end_ARG ∥ end_ARG ∥ = italic_O ( square-root start_ARG divide start_ARG roman_log roman_log i... | In some non-degenerate cases, we can further characterize the asymptotic behavior of 𝝆(t)𝝆𝑡\boldsymbol{\rho}\left(t\right)bold_italic_ρ ( italic_t ). To do so, we need to refer to the KKT conditions (eq. 6)
of the SVM problem (eq. 4) and the associated | 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... | where 𝝆(t)𝝆𝑡\boldsymbol{\rho}\left(t\right)bold_italic_ρ ( italic_t ) has a bounded norm for almost all datasets, while in zero measure case 𝝆(t)𝝆𝑡\boldsymbol{\rho}\left(t\right)bold_italic_ρ ( italic_t ) contains additional O(loglog(t))𝑂𝑡O(\log\log(t))italic_O ( roman_log roman_log ( italic_t ) ) componen... |
where the residual 𝝆k(t)subscript𝝆𝑘𝑡\boldsymbol{\rho}_{k}(t)bold_italic_ρ start_POSTSUBSCRIPT italic_k end_POSTSUBSCRIPT ( italic_t ) is bounded and 𝐰^ksubscript^𝐰𝑘\hat{\mathbf{w}}_{k}over^ start_ARG bold_w end_ARG start_POSTSUBSCRIPT italic_k end_POSTSUBSCRIPT is the solution of the K-class SVM: | A |
The performance of this feature group is not so convincing. The feature Pasubscript𝑃𝑎P_{a}italic_P start_POSTSUBSCRIPT italic_a end_POSTSUBSCRIPT from SpikeM model is the best one of them. The problem of these two models which we have already figured out in Section 3.2.3 is that two models need substantial data to f... | . As shown in Table 11, CreditScore is the best feature in general. Figure 10 shows the result of models learned with the full feature set with and without CreditScore. Overall, adding CreditScore improves the performance, significantly for the first 8-10 hours. The performance of all-but-CreditScore jiggles a bit afte... |
Text feature set contains totally 16 features. The feature ranking are shown in Table 7. The best one is NumOfChar which is the average number of different characters in tweets. PolarityScores is the best feature when we tested the single tweets model, but its performance in time series model is not ideal. It is true ... | As we can see in Figure 9 the best result on average over 48 hours is the BestSet. Second one is All features. Except those two, the best group feature is Text features. One reason is the text feature set has the largest group of feature with totally 16 features. But if look into each feature in text feature group, we ... | The performance of user features is similar with the Twitter features, they are both quite stable from the first hour to the last hour. As shown in Table 9, the best feature over 48 hours of the user feature group is UserTweetsPerDays and it is the best feature overall in the first 4 hours, but its rank decreases with ... | A |
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 ... |
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... | 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... | 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... | B |
\right)\;.\\
\end{cases}where { start_ROW start_CELL roman_Θ start_POSTSUBSCRIPT italic_t start_POSTSUBSCRIPT 0 end_POSTSUBSCRIPT : italic_t start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPT , italic_a end_POSTSUBSCRIPT = [ italic_θ start_POSTSUBSCRIPT italic_t start_POSTSUBSCRIPT 0 end_POSTSUBSCRIPT , italic_a end_POSTSUBSCRIPT... | —i.e., the dependence on past samples decays exponentially, and is negligible after a certain lag—
one can establish uniform-in-time convergence of SMC methods for functions that depend only on recent states, see [Kantas et al., 2015] and references therein. | the combination of Bayesian neural networks with approximate inference has also been investigated.
Variational methods, stochastic mini-batches, and Monte Carlo techniques have been studied for uncertainty estimation of reward posteriors of these models [Blundell et al., 2015; Kingma et al., 2015; Osband et al., 2016; ... | The use of SMC in the context of bandit problems was previously considered for probit [Cherkassky and Bornn, 2013] and softmax [Urteaga and Wiggins, 2018c] reward models,
and to update latent feature posteriors in a probabilistic matrix factorization model [Kawale et al., 2015]. | More broadly, one can establish uniform-in-time convergence for path functionals that depend only on recent states,
as the Monte Carlo error of pM(θt−τ:t|ℋ1:t)subscript𝑝𝑀conditionalsubscript𝜃:𝑡𝜏𝑡subscriptℋ:1𝑡p_{M}(\theta_{t-\tau:t}|\mathcal{H}_{1:t})italic_p start_POSTSUBSCRIPT italic_M end_POSTSUBSCRIPT ( ital... | A |
Overall, the distribution of all three kinds of values throughout the day roughly correspond to each other.
In particular, for most patients the number of glucose measurements roughly matches or exceeds the number of rapid insulin applications throughout the days. | 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. | Patient 17 has more rapid insulin applications than glucose measurements in the morning and particularly in the late evening.
For patient 15, rapid insulin again slightly exceeds the number of glucose measurements in the morning. Curiously, the number of glucose measurements match the number carbohydrate entries – it i... | Overall, the distribution of all three kinds of values throughout the day roughly correspond to each other.
In particular, for most patients the number of glucose measurements roughly matches or exceeds the number of rapid insulin applications throughout the days. | Likewise, the daily number of measurements taken for carbohydrate intake, blood glucose level and insulin units vary across the patients.
The median number of carbohydrate log entries vary between 2 per day for patient 10 and 5 per day for patient 14. | B |
This representation constitutes the input to an Atrous Spatial Pyramid Pooling (ASPP) module Chen et al. (2018). It utilizes several convolutional layers with different dilation factors in parallel to capture multi-scale image information. Additionally, we incorporated scene content via global average pooling over the... | To quantify the contribution of multi-scale contextual information to the overall performance, we conducted a model ablation analysis. A baseline architecture without the ASPP module was constructed by replacing the five parallel convolutional layers with a single 3×3333\times 33 × 3 convolutional operation that result... |
In this work, we laid out three convolutional layers with kernel sizes of 3×3333\times 33 × 3 and dilation rates of 4, 8, and 12 in parallel, together with a 1×1111\times 11 × 1 convolutional layer that could not learn new spatial dependencies but nonlinearly combined existing feature maps. Image-level context was rep... |
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... | 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... | B |
There is a polynomial-time O(log(opt)log(n))Oopt𝑛\operatorname{O}(\sqrt{\log(\operatorname{\textsf{opt}})}\log(n))roman_O ( square-root start_ARG roman_log ( opt ) end_ARG roman_log ( italic_n ) )-approximation algorithm and a polynomial-time O(log(opt)opt)Ooptopt\operatorname{O}(\sqrt{\log(\operatorname{\texts... |
In this section, we discuss some examples that illustrate the concepts of marking sequences and the locality number, and we also discuss some word combinatorial properties related to the locality number. Note that for illustration purposes, the example words considered in this section are not necessarily condensed. | 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... |
As mentioned several times already, our reductions to and from the problem of computing the locality number also establish the locality number for words as a (somewhat unexpected) link between the graph parameters cutwidth and pathwidth. We shall discuss in more detail in Section 6 the consequences of this connection.... |
In Section 2, we give basic definitions (including the central parameters of the locality number, the cutwidth and the pathwidth). In the next Section 3, we discuss the concept of the locality number with some examples and some word combinatorial considerations. The purpose of this section is to develop a better under... | D |
Besides solving the data and interpretability problems, researchers in cardiology could utilize the already established deep learning architectures that have not been widely applied in cardiology such as capsule networks.
Capsule networks[265] are deep neural networks that require less training data than CNNs and its l... | They have been used by a number of publications in cardiology in medical history prediction[70], ECG beat classification[86] and CVD prediction using fundus[192].
Another simpler tool for interpretability is saliency maps[264] that uses the gradient of the output with respect to the input which intuitively shows the re... | Amongst their experiments they found that rotational and scaling data augmentations did not help increase accuracy, attributing it to interpolation altering pixel intensities which is problematic due to the sensitivity of CNN to pixel distribution patterns.
| However an important constraint they currently have which limits them from achieving wider use, is the high computational cost compared to CNNs due to the ‘routing by agreement’ algorithm.
Amongst their recent uses in medicine include brain tumor classification[266] and breast cancer classification[267]. | Lessman et al.[195] method for coronary calcium scoring utilizes three independently trained CNNs to estimate a bounding box around the heart, in which connected components above a Hounsfield unit threshold are considered candidates for CACs.
Classification of extracted voxels was performed by feeding two-dimensional p... | C |
An important step in this direction was made by Leibfried et al. (2016), which extends the work of Oh et al. (2015) by including reward prediction, but does not use the model to learn policies that play the games.
Most of these approaches, including ours, encode knowledge of the game in implicit way. Unlike this, there... | Using models of environments, or informally giving the agent ability to predict its future, has a fundamental appeal for reinforcement learning. The spectrum of possible applications is vast, including learning policies
from the model (Watter et al., 2015; Finn et al., 2016; Finn & Levine, 2017; Ebert et al., 2017; Haf... | have incorporated images into real-world (Finn et al., 2016; Finn & Levine, 2017; Babaeizadeh et al., 2017a; Ebert et al., 2017; Piergiovanni et al., 2018; Paxton et al., 2019; Rybkin et al., 2018; Ebert et al., 2018) and simulated (Watter et al., 2015; Hafner et al., 2019) robotic control.
Our video models of Atari en... | Notable exceptions are the works of
Oh et al. (2017), Sodhani et al. (2019), Ha & Schmidhuber (2018), Holland et al. (2018), Leibfried et al. (2018) and Azizzadenesheli et al. (2018). Oh et al. (2017) use a model of rewards to augment model-free learning with good results on a number of Atari games. However, this metho... | Atari games gained prominence as a benchmark for reinforcement learning with the introduction of the Arcade Learning Environment (ALE) Bellemare et al. (2015). The combination of reinforcement learning and deep models then enabled RL algorithms to learn to play Atari games directly from images of the game screen, using... | C |
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.
| 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 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 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. | 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... | C |
The track tip positioning was the key parameter controlled during the creation of these climbing gaits. To assure seamless locomotion, trajectories for each joint of the robot were defined through a fifth-order polynomial along with their first and second derivatives. The trajectory design took into account six constra... | 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 ... |
Figure 10: The Cricket robot tackles a step of height h using rolling locomotion mode, negating the need for a transition to the walking mode. The total energy consumed throughout the entire step negotiation process in rolling locomotion stayed below the preset threshold value. This threshold value was established bas... |
The whole-body climbing gait involves utilizing the entire body movement of the robot, swaying forwards and backwards to enlarge the stability margins before initiating gradual leg movement to overcome a step. This technique optimizes stability during the climbing process. To complement this, the rear-body climbing ga... | The evaluation of energy consumption for the walking locomotion mode encompassed the entire step negotiation process, from the commencement of the negotiation until its completion. Fig. 8 reveals minimal discrepancies in energy consumption for the whole-body climbing gait, which can be attributed to the thoughtful desi... | C |
Suppose that you have an investment account with a significant amount in it, and that your financial institution advises you periodically on investments. One day, your banker informs you that company X will soon receive a big boost, and advises to use the entire account to buy stocks. If you were to completely trust th... |
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... | We introduced a new model in the study of online algorithms with advice, in which the online algorithm can leverage information about the request sequence that is not necessarily foolproof. Motivated by advances in learning-online algorithms, we studied tradeoffs between the trusted and untrusted competitive ratio, as ... |
In this work we focus on the online computation with advice. Our motivation stems from observing that, unlike the real world, the advice under the known models is often closer to “fiat” than “recommendation”. Our objective is to propose a model which allows the possibility of incorrect advice, with the objective of ob... | D |
With the aim of avoiding cases of misclassification like in (d), we decided to implement the second classifier, SS3Δ, whose policy also takes into account the changes in both slopes.
As it can be seen from Algorithm 3 and as mentioned before, SS3Δ additionally classifies a subject as positive if the positive slope chan... | the accumulated negative confidence value starts being greater than the positive one, but as more chunks are read (specifically starting after reading the 3rd chunk), the positive value starts and stays growing until it exceeds the other one. In this case, this subject is classified as depressed after reading the 6th c... | This problem can be detected in this subject by seeing the blue dotted peek at around the 60th writing, indicating that “the positive slope changed around five times faster than the negative” there, and therefore misclassifying the subject as positive. However, note that this positive change was in fact really small (l... | In Figure 7 is shown again the subject 1914, this time including information about the changes in the slopes.
Note that this subject was previously misclassified as not depressed because the accumulated positive value never exceeded the negative one, but by adding this new extra policy, this time it is correctly classi... |
the subject is misclassified as positive since the positive accumulated exceeded the negative one. When we manually analyzed cases like these we often found out that the classifier was correctly accumulating positive evidence since the users were, in fact, apparently depressed. | C |
Stochastic gradient descent (SGD) and its variants (Robbins and Monro, 1951; Bottou, 2010; Johnson and Zhang, 2013; Zhao et al., 2018, 2020, 2021) have been the dominating optimization methods for solving (1).
In each iteration, SGD calculates a (mini-batch) stochastic gradient and uses it to update the model parameter... | Furthermore, when we distribute the training across multiple workers, the local objective functions may differ from each other due to the heterogeneous training data distribution. In Section 5, we will demonstrate that the global momentum method outperforms its local momentum counterparts in distributed deep model trai... | With the rapid growth of data, distributed SGD (DSGD) and its variant distributed MSGD (DMSGD) have garnered much attention. They distribute the stochastic gradient computation across multiple workers to expedite the model training.
These methods can be implemented on distributed frameworks like parameter server and al... | Recently, parameter server (Li et al., 2014) has been one of the most popular distributed frameworks in machine learning. GMC can also be implemented on the parameter server framework.
In this paper, we adopt the parameter server framework for illustration. The theories in this paper can also be adapted for the all-red... | GMC can be easily implemented on the all-reduce distributed framework in which each worker sends the sparsified vector 𝒞(𝐞t+12,k)𝒞subscript𝐞𝑡12𝑘\mathcal{C}({\bf e}_{t+\frac{1}{2},k})caligraphic_C ( bold_e start_POSTSUBSCRIPT italic_t + divide start_ARG 1 end_ARG start_ARG 2 end_ARG , italic_k end_POSTSUBSCRIPT )... | B |
The three separate clusters which are depicted in Fig. 3 and the aggregated density plot in Fig. 4LABEL:sub@subfig:crrl_density_plot between the Identity activation function, the ReLU and the rest show the effect of a sparser activation function on the representation. | Figure 1: Visualization of the activation maps of five activation functions (Identity, ReLU, top-k absolutes, Extrema-Pool indices and Extrema) for 1D and 2D input in the top and bottom row respectively.
The 1D input to the activation functions is denoted with the continuous transparent green line using an example from... | Imposing a med𝑚𝑒𝑑meditalic_m italic_e italic_d on the extrema detection algorithm makes 𝜶𝜶\bm{\alpha}bold_italic_α sparser than the previous cases and solves the problem of double extrema activations that Extrema-Pool indices have (as shown in Fig. 1LABEL:sub@subfig:extrema).
The sparsity parameter in this case ... | The sparser an activation function is the more it compresses, sometimes at the expense of reconstruction error.
However, by visual inspection of Fig. 5 one could confirm that the learned kernels of the SAN with sparser activation maps (Extrema-Pool indices and Extrema) correspond to the reoccurring patterns in the data... |
The three separate clusters which are depicted in Fig. 3 and the aggregated density plot in Fig. 4LABEL:sub@subfig:crrl_density_plot between the Identity activation function, the ReLU and the rest show the effect of a sparser activation function on the representation. | C |
Figure 1: The topological structure of UAV ad-hoc networks. a) The UAV ad-hoc network supports user communications. b) The coverage of a UAV depends on its altitude and field angle. c) There are two kinds of links between users, and the link supported by UAV is better. |
Fig. 12 shows how the number of UAVs affect the computation complexity of SPBLLA. Since the total number of UAVs is diverse, the goal functions are different. The goal functions’ value in the optimum states increase with the growth in UAVs’ number. Since goal functions are the summation function of utility functions, ... |
Figure 1: The topological structure of UAV ad-hoc networks. a) The UAV ad-hoc network supports user communications. b) The coverage of a UAV depends on its altitude and field angle. c) There are two kinds of links between users, and the link supported by UAV is better. |
We construct a UAV ad-hoc network in a post-disaster scenario with M𝑀Mitalic_M identical UAVs being randomly deployed, in which M𝑀Mitalic_M is a huge number compared with normal Multi-UAV system. All the UAVs have the same volume of battery E𝐸Eitalic_E and communication capability. The topological structure of Mult... | Since the UAV ad-hoc network game is a special type of potential game, we can apply the properties of the potential game in the later analysis. Some algorithms that have been applied in the potential game can also be employed in the UAV ad-hoc network game. In the next section, we investigate the existing algorithm wit... | C |
Π¯rsubscript¯Π𝑟\displaystyle\overline{\Pi}_{r}over¯ start_ARG roman_Π end_ARG start_POSTSUBSCRIPT italic_r end_POSTSUBSCRIPT
=[−2Dr¯^∗(μ^r^(Dr^¯∗v¯r))−Dz¯^∗(μ^r^(Dr^¯∗v¯z+Dz^¯∗v¯r))]/r¯absentdelimited-[]absent2^¯𝐷𝑟^𝜇^𝑟¯^𝐷𝑟subscript¯𝑣𝑟absent^¯𝐷𝑧^𝜇^𝑟¯^𝐷𝑟subscript¯𝑣𝑧¯^𝐷𝑧subscript¯𝑣𝑟¯𝑟\displ... | }}\,\,\widehat{r}\,\,\left(\overline{\widehat{Dr}}*\overline{v}_{z}+\overline{%
\widehat{Dz}}*\overline{v}_{r}\right)\right)}\biggr{]}\,/\,\overline{r}= [ start_UNDERACCENT end_UNDERACCENT start_ARG - 2 over^ start_ARG over¯ start_ARG italic_D italic_r end_ARG end_ARG ∗ ( over^ start_ARG italic_μ end_ARG over^ start_AR... | \widehat{Dz}}*\overline{v}_{r}\right)\right)}\biggr{]}\,/\,\overline{r}= [ start_UNDERACCENT end_UNDERACCENT start_ARG - 2 over^ start_ARG over¯ start_ARG italic_D italic_z end_ARG end_ARG ∗ ( over^ start_ARG italic_μ end_ARG over^ start_ARG italic_r end_ARG ( over¯ start_ARG over^ start_ARG italic_D italic_z end_ARG e... | \overline{\psi}\right)\,\,\left(\overline{\widehat{Dz}}*\overline{f}\right)%
\right)\,/\,\widehat{r}\right\}= divide start_ARG 2 italic_π end_ARG start_ARG italic_μ start_POSTSUBSCRIPT 0 end_POSTSUBSCRIPT end_ARG ( over^ start_ARG italic_s end_ARG over^ start_ARG italic_r end_ARG ) start_POSTSUPERSCRIPT italic_T end_PO... | }_{r}\,/\,\overline{r}^{2}}start_UNDERACCENT end_UNDERACCENT start_ARG + divide start_ARG 2 end_ARG start_ARG 3 end_ARG ( over^ start_ARG over¯ start_ARG italic_D italic_r end_ARG end_ARG ∗ ( over^ start_ARG italic_μ end_ARG ( over¯ start_ARG over^ start_ARG ∇ end_ARG end_ARG ⋅ over¯ start_ARG bold_v end_ARG ) ) ) end_... | A |
When using the framework, one can further require reflexivity on the comparability functions, i.e. f(xA,xA)=1A𝑓subscript𝑥𝐴subscript𝑥𝐴subscript1𝐴f(x_{A},x_{A})=1_{A}italic_f ( italic_x start_POSTSUBSCRIPT italic_A end_POSTSUBSCRIPT , italic_x start_POSTSUBSCRIPT italic_A end_POSTSUBSCRIPT ) = 1 start_POSTSUBSCRIP... | Indeed, in practice the meaning of the null value in the data should be explained by domain experts, along with recommendations on how to deal with it.
Moreover, since the null value indicates a missing value, relaxing reflexivity of comparability functions on null allows to consider absent values as possibly | When using the framework, one can further require reflexivity on the comparability functions, i.e. f(xA,xA)=1A𝑓subscript𝑥𝐴subscript𝑥𝐴subscript1𝐴f(x_{A},x_{A})=1_{A}italic_f ( italic_x start_POSTSUBSCRIPT italic_A end_POSTSUBSCRIPT , italic_x start_POSTSUBSCRIPT italic_A end_POSTSUBSCRIPT ) = 1 start_POSTSUBSCRIP... | fA(u,v)=fB(u,v)={1if u=v≠nullaif u≠null,v≠null and u≠vbif u=v=null0otherwise.subscript𝑓𝐴𝑢𝑣subscript𝑓𝐵𝑢𝑣cases1if 𝑢𝑣null𝑎formulae-sequenceif 𝑢null𝑣null and 𝑢𝑣𝑏if 𝑢𝑣null0otherwise.f_{A}(u,v)=f_{B}(u,v)=\begin{cases}1&\text{if }u=v\neq\texttt{null}\\
a&\text{if }u\neq\texttt{null},v\neq\texttt{null}... | Intuitively, if an abstract value xAsubscript𝑥𝐴x_{A}italic_x start_POSTSUBSCRIPT italic_A end_POSTSUBSCRIPT of ℒAsubscriptℒ𝐴\mathcal{L}_{A}caligraphic_L start_POSTSUBSCRIPT italic_A end_POSTSUBSCRIPT is interpreted as 1111 (i.e., equality)
by hAsubscriptℎ𝐴h_{A}italic_h start_POSTSUBSCRIPT italic_A end_POSTSUBSCRIPT... | A |
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... |
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... | For the experiments, fully connected neural network architecture was used. It was composed of two hidden layers of 128 neurons and two Dropout layers between the input layer and the first hidden layer and between the two hidden layers. To minimize the
DQN loss, ADAM optimizer was used[25]. |
It’s the original Dropout method. It was introduced in 2012. Standard Dropout provides a simple technique for avoiding over-fitting in fully connected neural networks[12]. During each training phase, each neuron is excluded from the network with a probability p. Once trained, in the testing phase the full network is u... |
A fully connected neural network architecture was used. It was composed of two hidden layers of 128 neurons and two Dropout layers between the input layer and the first hidden layer and between the two hidden layers. ADAM optimizer for the minimization[25]. | B |
In medical image segmentation works, researchers have converged toward using classical cross-entropy loss functions along with a second distance or overlap based functions. Incorporating domain/prior knowledge (such as coding the location of different organs explicitly in a deep model) is more sensible in the medical d... |
Going beyond pixel intensity-based scene understanding by incorporating prior knowledge, which have been an active area of research for the past several decades (Nosrati and Hamarneh, 2016; Xie et al., 2020). Encoding prior knowledge in medical image analysis models is generally more possible as compared to natural im... | Deep learning has had a tremendous impact on various fields in science. The focus of the current study is on one of the most critical areas of computer vision: medical image analysis (or medical computer vision), particularly deep learning-based approaches for medical image segmentation. Segmentation is an important pr... |
Exploring reinforcement learning approaches similar to Song et al. (2018) and Wang et al. (2018c) for semantic (medical) image segmentation to mimic the way humans delineate objects of interest. Deep CNNs are successful in extracting features of different classes of objects, but they lose the local spatial information... |
For image segmentation, sequenced models can be used to segment temporal data such as videos. These models have also been applied to 3D medical datasets, however the advantage of processing volumetric data using 3D convolutions versus the processing the volume slice by slice using 2D sequenced models. Ideally, seeing ... | A |
From Fig. 9(b) we notice that the graphs 𝐀(1)superscript𝐀1{\mathbf{A}}^{(1)}bold_A start_POSTSUPERSCRIPT ( 1 ) end_POSTSUPERSCRIPT and 𝐀(2)superscript𝐀2{\mathbf{A}}^{(2)}bold_A start_POSTSUPERSCRIPT ( 2 ) end_POSTSUPERSCRIPT in GRACLUS have additional nodes that are disconnected.
As discussed in Sect. V, these are ... | Fig. 9(c) shows that NMF produces graphs that are very dense, as a consequence of the multiplication with the dense soft-assignment matrix to construct the coarsened graph.
Finally, Fig. 9(d) shows that NDP produces coarsened graphs that are sparse and preserve well the topology of the original graph. | Fig. 12 shows for the result of the NDP coarsening procedure on the 6 types of graphs.
The first column shows the subset of nodes of the original graph that are selected (𝒱+superscript𝒱\mathcal{V}^{+}caligraphic_V start_POSTSUPERSCRIPT + end_POSTSUPERSCRIPT, in red) and discarded (𝒱−superscript𝒱\mathcal{V}^{-}calig... | Fig. 12 shows for the result of the NDP coarsening procedure on the 6 types of graphs.
The first column shows the subset of nodes of the original graph that are selected (𝒱+superscript𝒱\mathcal{V}^{+}caligraphic_V start_POSTSUPERSCRIPT + end_POSTSUPERSCRIPT, in red) and discarded (𝒱−superscript𝒱\mathcal{V}^{-}calig... | From Fig. 9(b) we notice that the graphs 𝐀(1)superscript𝐀1{\mathbf{A}}^{(1)}bold_A start_POSTSUPERSCRIPT ( 1 ) end_POSTSUPERSCRIPT and 𝐀(2)superscript𝐀2{\mathbf{A}}^{(2)}bold_A start_POSTSUPERSCRIPT ( 2 ) end_POSTSUPERSCRIPT in GRACLUS have additional nodes that are disconnected.
As discussed in Sect. V, these are ... | A |
For real-world applications, the dependency on large amounts of labeled data represents a significant limitation (Breiman et al., 1984; Hekler et al., 2019; Barz & Denzler, 2020; Qi & Luo, 2020; Phoo & Hariharan, 2021; Wang et al., 2021). Frequently, there is little or even no labeled data for a particular task and hun... | Transfer learning and regularization methods are usually applied to reduce overfitting.
However, for training with little data, the networks still have a considerable number of parameters that have to be fine-tuned – even if just the last layers are trained. | Random forests and neural networks share some similar characteristics, such as the ability to learn arbitrary decision boundaries; however, both methods have different advantages.
Random forests are based on decision trees. Various tree models have been presented – the most well-known are C4.5 (Quinlan, 1993) and CART ... | Additionally, the experiment shows that the training is very robust to overfitting even when the number of parameters in the network increases.
When combining the generated data and original data, the accuracy on Car and Covertype improves with an increasing number of training examples. | First, we analyze the performance of state-of-the-art methods for mapping random forests into neural networks and neural random forest imitation. The results are shown in Figure 4 for different numbers of training examples per class.
For each method, the average number of parameters of the generated networks across all... | A |
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;... | 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.... |
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... | 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... | C |
In this section, we review approaches that aim to reduce the model size by employing efficient matrix representations.
There exist several methods using low-rank decompositions which represent a large matrix (or a large tensor) using only a fraction of the parameters. | Several works have investigated special matrix structures that require fewer parameters and allow for faster matrix multiplications—the main workload in fully connected layers.
Furthermore, there exist several manually designed architectures that introduced lightweight building blocks or modified existing building bloc... | In this section, we review approaches that aim to reduce the model size by employing efficient matrix representations.
There exist several methods using low-rank decompositions which represent a large matrix (or a large tensor) using only a fraction of the parameters. | In most cases, the implicitly represented matrix is never computed explicitly such that also a computational speed-up is achieved.
Furthermore, there exist approaches using special matrices that are specified by only few parameters and whose structure allows for extremely efficient matrix multiplications. | In Cheng et al. (2015), the weight matrices of fully connected layers are restricted to circulant matrices 𝐖∈ℝn×n𝐖superscriptℝ𝑛𝑛\mathbf{W}\in\mathbb{R}^{n\times n}bold_W ∈ blackboard_R start_POSTSUPERSCRIPT italic_n × italic_n end_POSTSUPERSCRIPT, which are fully specified by only n𝑛nitalic_n parameters.
While thi... | C |
(iλ,λ′)∗(ω0)=ω1+ω2subscriptsubscript𝑖𝜆superscript𝜆′subscript𝜔0subscript𝜔1subscript𝜔2(i_{\lambda,\lambda^{\prime}})_{*}(\omega_{0})=\omega_{1}+\omega_{2}( italic_i start_POSTSUBSCRIPT italic_λ , italic_λ start_POSTSUPERSCRIPT ′ end_POSTSUPERSCRIPT end_POSTSUBSCRIPT ) start_POSTSUBSCRIPT ∗ end_POSTSUBSCRIPT ( ita... |
ω2 is the degree-1 homology class induced bysubscript𝜔2 is the degree-1 homology class induced by\displaystyle\omega_{2}\text{ is the degree-1 homology class induced by }italic_ω start_POSTSUBSCRIPT 2 end_POSTSUBSCRIPT is the degree-1 homology class induced by | and seeks the infimal r>0𝑟0r>0italic_r > 0 such that the map induced by ιrsubscript𝜄𝑟\iota_{r}italic_ι start_POSTSUBSCRIPT italic_r end_POSTSUBSCRIPT at n𝑛nitalic_n-th homology level annihilates the fundamental class [M]delimited-[]𝑀[M][ italic_M ] of M𝑀Mitalic_M. This infimal value defines FillRad(M)FillRad𝑀\m... | ω1 is the degree-1 homology class induced bysubscript𝜔1 is the degree-1 homology class induced by\displaystyle\omega_{1}\text{ is the degree-1 homology class induced by }italic_ω start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPT is the degree-1 homology class induced by
|
ω0 is the degree-1 homology class induced bysubscript𝜔0 is the degree-1 homology class induced by\displaystyle\omega_{0}\text{ is the degree-1 homology class induced by }italic_ω start_POSTSUBSCRIPT 0 end_POSTSUBSCRIPT is the degree-1 homology class induced by | D |
The remaining costs are one aspect of estimating the projection quality. This means that projected points with high remaining costs can be moved by an additional optimization step. Akin to this idea, t-viSNE might show a preview of the data points in the next optimization step. In consequence, users could determine whe... |
Clustervision [51] is a visualization tool used to test multiple batches of a varying number of clusters and allows the users to pick the best partitioning according to their task. Then, the dimensions are ordered according to a cluster separation importance ranking. As a result, the interpretation and assessment of t... | The goals of the comparative study presented in this paper were to provide initial evidence of the acceptance of t-viSNE by analysts, the consistency of their results when exploring a t-SNE projection using our tool, and the improvement over another state-of-the-art tool.
The tasks of the study were designed to test ho... | we present t-viSNE, a tool designed to support the interactive exploration of t-SNE projections (an extension to our previous poster abstract [17]). In contrast to other, more general approaches, t-viSNE was designed with the specific problems related to the investigation of t-SNE projections in mind, bringing to light... | In this paper, we introduced t-viSNE, an interactive tool for the visual investigation of t-SNE projections. By partly opening the black box of the t-SNE algorithm, we managed to give power to users allowing them to test the quality of the projections and understand the rationale behind the choices of the algorithm whe... | D |
Nature inspired optimization algorithms or simply variations of metaheuristics? - 2021 [15]: This overview focuses on the study of the frequency of new proposals that are no more than variations of old ones. The authors critique a large set of algorithms based on three criteria: (1) whether there is a physical analogy... |
Initialization of metaheuristics: comprehensive review, critical analysis, and research directions - 2023 [35]: This review addresses a gap in the literature by developing a taxonomy of initialization methods for metaheuristics. This classification is based on the initialization of metaheuristics according to random t... |
50 years of metaheuristics - 2024 [40]: This overview traces the last 50 years of the field, starting from the roots of the area to the latest proposals to hybridize metaheuristics with machine learning. The revision encompasses constructive (GRASP and ACO), local search (iterated local search, Tabu search, variable n... |
In the last update of this report, which is herein released 4 years after its original version, we note that there has been an evolution within the nature and bio-inspired optimization field. There is an excessive use of the biological approach as opposed to the real problem-solving approach to tackle real and complex... |
An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges - 2023 [34]: This taxonomy provides a large classification of metaheuristics based on the number of control parameters of the algorithm. In this work, the authors question the novelty of new pr... | D |
}).italic_Z = italic_φ start_POSTSUBSCRIPT italic_m end_POSTSUBSCRIPT ( over^ start_ARG italic_A end_ARG italic_φ start_POSTSUBSCRIPT italic_m - 1 end_POSTSUBSCRIPT ( ⋯ italic_φ start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPT ( over^ start_ARG italic_A end_ARG italic_X italic_W start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPT ) ⋯ ) ita... | To study the impact of different parts of the loss in Eq. (12), the performance with different λ𝜆\lambdaitalic_λ is reported in Figure 4.
From it, we find that the second term (corresponding to problem (7)) plays an important role especially on UMIST. If λ𝜆\lambdaitalic_λ is set as a large value, we may get the trivi... |
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 ... |
Figure 2: Visualization of the learning process of AdaGAE on USPS. Figure 2(b)-2(i) show the embedding learned by AdaGAE at the i𝑖iitalic_i-th epoch, while the raw features and the final results are shown in Figure 2(a) and 2(j), respectively. An epoch corresponds to an update of the graph. | To illustrate the process of AdaGAE, Figure 2 shows the learned embedding on USPS at the i𝑖iitalic_i-th epoch. An epoch means a complete training of GAE and an update of the graph. The maximum number of epochs, T𝑇Titalic_T, is set as 10. In other words, the graph is updated 10 times. Clearly, the embedding becomes mo... | C |
We also want to understand the types of networks that we could test via domains-wide scans. To derive the business types we use the PeeringDB. We classify the ASes according to the following business types: content, enterprise, Network Service Provider (NSP), Cable/DSL/ISP, non-profit, educational/research, route serve... |
SMap (The Spoofing Mapper). In this work we present the first Internet-wide scanner for networks that filter spoofed inbound packets, we call the Spoofing Mapper (SMap). We apply SMap for scanning ingress-filtering in more than 90% of the Autonomous Systems (ASes) in the Internet. The measurements with SMap show that ... |
Domain-scan and IPv4-scan both show that the number of spoofable ASes grows with the overall number of the ASes in the Internet, see Figure 1. Furthermore, there is a correlation between fraction of scanned domains and ASes. Essentially the more domains are scanned, the more ASes are covered, and more spoofable ASes a... | There is a strong correlation between the AS size and the enforcement of spoofing, see Figure 13. Essentially, the larger the AS, the higher the probability that our tools identify that it does not filter spoofed packets. The reason can be directly related to our methodologies and the design of our study: the larger th... | Identifying servers with global IPID counters. We send packets from two hosts (with different IP addresses) to a server on a tested network. We implemented probing over TCP SYN, ping and using requests/responses to Name servers and we apply the suitable test depending on the server that we identify on the tested networ... | C |
While context did introduce more parameters to the model (7,57575757{,}5757 , 575 parameters without context versus 14,3151431514{,}31514 , 315 including context), the model is still very small compared to most neural network models, and is trainable in a few hours on a CPU. When units were added to the “skill” layer ... | The estimation of context by learned temporal patterns should be most effective when the environment results in recurring or cyclical patterns, such as in cyclical variations of temperature and humidity and regular patterns of human behavior generating interferents. In such cases, the recurrent pathway can identify use... | This paper builds upon previous work with this dataset [7], which used support vector machine (SVM) ensembles. First, their approach is extended to a modern version of feedforward artificial neural networks (NNs) [8]. Context-based learning is then introduced to utilize sequential structure across batches of data. The ... |
The current design of the context-based network relies on labeled data because the odor samples for a given class are presented as ordered input to the context layer. However, the model can be modified to be trained on unlabeled data, simply by allowing arbitrary data samples as input to the context layer. This design... |
One prominent feature of the mammalian olfactory system is feedback connections to the olfactory bulb from higher-level processing regions. Activity in the olfactory bulb is heavily influenced by behavioral and value-based information [19], and in fact, the bulb receives more neural projections from higher-level regio... | A |
For the second change, we need to take another look at how we place the separators tisubscript𝑡𝑖t_{i}italic_t start_POSTSUBSCRIPT italic_i end_POSTSUBSCRIPT.
We previously placed these separators in every second nonempty drum σi:=[iδ,(i+1)δ]×Balld−1(δ/2)assignsubscript𝜎𝑖𝑖𝛿𝑖1𝛿superscriptBall𝑑1𝛿2\sigma_{i}:=... | We generalize the case of integer x𝑥xitalic_x-coordinates to the case where the drum [x,x+1]×Balld−1(δ/2)𝑥𝑥1superscriptBall𝑑1𝛿2[x,x+1]\times\mathrm{Ball}^{d-1}(\delta/2)[ italic_x , italic_x + 1 ] × roman_Ball start_POSTSUPERSCRIPT italic_d - 1 end_POSTSUPERSCRIPT ( italic_δ / 2 ) contains O(1)𝑂1O(1)italic_O ( ... | However, in order for our algorithm to meet the requirements of Lemma 5.7, we would like to avoid having a point enter a drum after the x𝑥xitalic_x-coordinates are multiplied by some factor λ>1𝜆1\lambda>1italic_λ > 1.
Furthermore, since the proof of Lemma 4.3 requires every drum to be at least δ𝛿\deltaitalic_δ wide,... | It would be interesting to see whether a direct proof can be given for this fundamental result.
We note that the proof of Theorem 2.1 can easily be adapted to point sets of which the x𝑥xitalic_x-coordinates of the points need not be integer, as long as the difference between x𝑥xitalic_x-coordinates of any two consecu... | Finally, we will show that the requirements for Lemma 5.7 hold, where we take 𝒜𝒜\mathcal{A}caligraphic_A to be the algorithm described above.
The only nontrivial requirement is that T𝒜(Pλ)⩽T𝒜(P)subscript𝑇𝒜subscript𝑃𝜆subscript𝑇𝒜𝑃T_{\mathcal{A}}(P_{\lambda})\leqslant T_{\mathcal{A}}(P)italic_T start_POSTSUBS... | B |
There are quite a few results on free (and related) products of self-similar or automaton groups (again see [15] for an overview) but many of them present the product as a subgroup of an automaton/self-similar group and, thus, loose the self-similarity property. An exception here is a line of research based on the Bel... | 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... |
There are quite a few results on free (and related) products of self-similar or automaton groups (again see [15] for an overview) but many of them present the product as a subgroup of an automaton/self-similar group and, thus, loose the self-similarity property. An exception here is a line of research based on the Bel... | 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 ... | However, there do not seem to be constructions for presenting arbitrary free products of self-similar groups in a self-similar way. For semigroups, on the other hand, such results do exist. In fact, the free product of two automaton semigroups S𝑆Sitalic_S and T𝑇Titalic_T is always at least
very close to being an auto... | D |
Here, we showed that existing visual grounding based bias mitigation methods for VQA are not working as intended. We found that the accuracy improvements stem from a regularization effect rather than proper visual grounding. We proposed a simple regularization scheme which, despite not requiring additional annotations,... | 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... | This work was supported in part by AFOSR grant [FA9550-18-1-0121], NSF award #1909696, and a gift from Adobe Research. We thank NVIDIA for the GPU donation. The views and conclusions contained herein are those of the authors and should not be interpreted as representing the official policies or endorsements of any spon... | It is also interesting to note that the drop in training accuracy is lower with this regularization scheme as compared to the state-of-the-art methods. Of course, if any model was actually visually grounded, then we would expect it to improve performances on both train and test sets. We do not observe such behavior in ... | Since Wu and Mooney (2019) reported that human-based textual explanations Huk Park et al. (2018) gave better results than human-based attention maps for SCR, we train all of the SCR variants on the subset containing textual explanation-based cues. SCR is trained in two phases. For the first phase, which strengthens the... | B |
For each topic, we identified a corresponding entry from the OPP-115 annotation scheme (Wilson et al., 2016), which was created by legal experts to label the contents of privacy policies. While Wilson et al. (2016) followed a bottom-up approach and identified different categories from analysis of data practices in priv... |
Topic Modelling. Topic modelling is an unsupervised machine learning method that extracts the most probable distribution of words into topics through an iterative process (Wallach, 2006). We used topic modelling to explore the distribution of themes of text in our corpus. Topic modelling using a large corpus such as P... |
We found that two LDA topics contained vocabulary corresponding with the OPP-115 category First Party Collection/Use, one dealing with purpose and information type collected and the other dealing with collection method. Two LDA topics corresponded with the OPP-115 category Third Party Sharing and Collection, one detai... |
It is likely that the divergence between OPP-115 categories and LDA topics comes from a difference in approaches: the OPP-115 categories represent themes that privacy experts expected to find in privacy policies, which diverge from the actual distribution of themes in this text genre. Figure 2 shows the percentage of ... |
For the data practice classification task, we leveraged the OPP-115 Corpus introduced by Wilson et al. (2016). The OPP-115 Corpus contains manual annotations of 23K fine-grained data practices on 115 privacy policies annotated by legal experts. To the best of our knowledge, this is the most detailed and widely used da... | B |
T5: Inspect the same view with alternative techniques and visualizations. To eventually avoid the appearance of cognitive biases, alternative interaction methods and visual representations of the same data from another perspective should be offered to the user (G5).
| As in the data space, each point of the projection is an instance of the data set. However, instead of its original features, the instances are characterized as high-dimensional vectors where each dimension represents the prediction of one model. Thus, since there are currently 174 models in \raisebox{-.0pt} {\tiny\bfS... | Figure 2(a.2) displays overlapping barcharts for depicting the per-class performances for each algorithm, i.e., two colors for the two classes in our example. The more saturated bar in the center of each class bar represents the altered performance when the parameters of algorithms are modified. Note that the view only... |
Figure 2: The exploration process of ML algorithms. View (a.1) summarizes the performance of all available algorithms, and (a.2) the per-class performance based on precision, recall, and f1-score for each algorithm. (b) presents a selection of parameters for KNN in order to boost the per-class performance shown in (c.... | Figure 6: The process of exploration of distinct algorithms in hypotheticality stance analysis. (a) presents the selection of appropriate validation metrics for the specification of the data set. (b) aggregates the information after the exploration of different models and shows the active ones which will be used for th... | C |
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 | 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 | cannot be adjacent to 2¯¯2\overline{2}over¯ start_ARG 2 end_ARG nor 3¯¯3\overline{3}over¯ start_ARG 3 end_ARG,
and so f′superscript𝑓′f^{\prime}italic_f start_POSTSUPERSCRIPT ′ end_POSTSUPERSCRIPT is [013]delimited-[]013[013][ 013 ] or [010]delimited-[]010[010][ 010 ]. | 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 | (E𝐂,(2¯,(u2,[013])))superscript𝐸𝐂¯2subscript𝑢2delimited-[]013(E^{\mathbf{C}},(\overline{2},(u_{2},[013])))( italic_E start_POSTSUPERSCRIPT bold_C end_POSTSUPERSCRIPT , ( over¯ start_ARG 2 end_ARG , ( italic_u start_POSTSUBSCRIPT 2 end_POSTSUBSCRIPT , [ 013 ] ) ) ),
(E𝐂,((u1,[112]),(u2,[010])))superscript𝐸𝐂subscr... | C |
When applying MAML to NLP, several factors can influence the training strategy and performance of the model. Firstly, the data quantity within the datasets used as ”tasks” varies across different applications, which can impact the effectiveness of MAML [Serban et al., 2015, Song et al., 2020]. Secondly, while vanilla ... | The finding suggests that parameter initialization at the late training stage has strong general language generation ability, but performs comparative poorly in task-specific adaptation.
Although in the early training stage, the performance improves benefiting from the pre-trained general language model, if the languag... | In this paper, we take an empirical approach to systematically investigating these impacting factors and finding when MAML works the best. We conduct extensive experiments over 4 datasets. We first study the effects of data quantity and distribution on the training strategy:
RQ1. Since the parameter initialization lear... |
When applying MAML to NLP, several factors can influence the training strategy and performance of the model. Firstly, the data quantity within the datasets used as ”tasks” varies across different applications, which can impact the effectiveness of MAML [Serban et al., 2015, Song et al., 2020]. Secondly, while vanilla ... |
To answer RQ1, we compare the changing trend of the general language model and the task-specific adaptation ability during the training of MAML to find whether there is a trade-off problem. (Figure 1) We select the trained parameter initialization at different MAML training epochs and evaluate them directly on the met... | B |
Activated Subarray with Limited DREs: As shown in Fig. 1, given a certain azimuth angle, there are limited DREs that can be activated. Due to the directivity, the DREs of the CCA subarray at different positions are anisotropic, and this phenomenon is different from the UPA. If an inappropriate subarray is activated, t... | After the discussion on the characteristics of CCA, in this subsection, we continue to explain the specialized codebook design for the DRE-covered CCA. Revisiting Theorem 1 and Theorem 3, the size and position of the activated CCA subarray are related to the azimuth angle; meanwhile, the beamwidth is determined by the ... | The r-UAV needs to select multiple appropriate AWVs 𝒗(ms,k,ns,k,ik,jk,𝒮k),k∈𝒦𝒗subscript𝑚𝑠𝑘subscript𝑛𝑠𝑘subscript𝑖𝑘subscript𝑗𝑘subscript𝒮𝑘𝑘𝒦\boldsymbol{v}(m_{s,k},n_{s,k},i_{k},j_{k},\mathcal{S}_{k}),k\in\mathcal{K}bold_italic_v ( italic_m start_POSTSUBSCRIPT italic_s , italic_k end_POSTSUBSCRIPT , ital... | Multiuser-resultant Receiver Subarray Partition: As shown in Fig. 3, the r-UAV needs to activate multiple subarrays to serve multiple t-UAVs at the same time. Assuming that an element can not be contained in different subarrays, then the problem of activated CCA subarray partition rises at the r-UAV side for the fast m... |
In the considered UAV mmWave network, the r-UAV needs to activate multiple subarrays and select multiple combining vectors to serve multiple t-UAVs at the same time. Hence, the beam gain of the combining vector maximization problem for r-UAV with our proposed codebook can be rewritten as | C |
Thus,
a¯|b¯conditional¯𝑎¯𝑏\bar{a}|\bar{b}over¯ start_ARG italic_a end_ARG | over¯ start_ARG italic_b end_ARG-regular digraphs with size M¯¯𝑀\bar{M}over¯ start_ARG italic_M end_ARG can be characterized as a¯|b¯conditional¯𝑎¯𝑏\bar{a}|\bar{b}over¯ start_ARG italic_a end_ARG | over¯ start_ARG italic_b end_ARG-biregula... | This will be bootstrapped to the multi-color case in later sections. Note that the 1111-color case with the completeness requirement is not very interesting, and also not useful for the general case: completeness states that every node on
the left must be connected, via the unique edge relation, to every node on the ri... | We start in this section by giving proofs only for the 1111-color case, without the completeness requirement. While this case does not directly correspond to any formula used in the proof of Theorem 3.7 (since matrices (4) have 2 rows even when there are no binary predicates), this case gives the flavor of the argument... | 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 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 | C |
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... | Although Assumption 6.1 is strong, we are not aware of any weaker regularity condition in the literature, even in the linear setting (Melo et al., 2008; Zou et al., 2019; Chen et al., 2019b) and the NTK regime (Cai et al., 2019). Let the initial distribution ν0subscript𝜈0\nu_{0}italic_ν start_POSTSUBSCRIPT 0 end_POSTS... | Assumption 4.1 can be ensured by normalizing all state-action pairs. Such an assumption is commonly used in the mean-field analysis of neural networks (Chizat and Bach, 2018b; Mei et al., 2018, 2019; Araújo et al., 2019; Fang et al., 2019a, b; Chen et al., 2020). We remark that our analysis straightforwardly generalize... | Meanwhile, our analysis is related to the recent breakthrough in the mean-field analysis of stochastic gradient descent (SGD) for the supervised learning of an overparameterized two-layer neural network (Chizat and Bach, 2018b; Mei et al., 2018, 2019; Javanmard et al., 2019; Wei et al., 2019; Fang et al., 2019a, b; Che... | 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... | C |
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. | 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... |
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... |
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-... | D |
\upsigma_{i}]\rrbracket_{X_{i}}caligraphic_K start_POSTSUPERSCRIPT ∘ end_POSTSUPERSCRIPT ( italic_X start_POSTSUBSCRIPT italic_i end_POSTSUBSCRIPT ) = roman_τ start_POSTSUBSCRIPT italic_i end_POSTSUBSCRIPT ∩ ⟦ sansserif_FO [ roman_σ start_POSTSUBSCRIPT italic_i end_POSTSUBSCRIPT ] ⟧ start_POSTSUBSCRIPT italic_X start_P... | lpps is indeed a pre-spectral space. Conversely, ⟨X,τ,𝒦∘(X)⟩𝑋τsuperscript𝒦𝑋\left\langle X,\uptau,\mathcal{K}^{\circ}\!\left(X\right)\right\rangle⟨ italic_X , roman_τ , caligraphic_K start_POSTSUPERSCRIPT ∘ end_POSTSUPERSCRIPT ( italic_X ) ⟩
is well-defined whenever (X,τ)𝑋τ(X,\uptau)( italic_X , roman_τ ) is a pre... | definition, this map is surjective. Notice that this map is actually
a logical map from ⟨Y,τY,𝒦∘(Y)⟩𝑌subscriptτ𝑌superscript𝒦𝑌\left\langle Y,\uptau_{Y},\mathcal{K}^{\circ}\!\left(Y\right)\right\rangle⟨ italic_Y , roman_τ start_POSTSUBSCRIPT italic_Y end_POSTSUBSCRIPT , caligraphic_K start_POSTSUPERSCRIPT ∘ end_POS... | {U∣U∈⟨τY∩⟦𝖥𝖮[σ]⟧Y⟩}\left\{U\mid U\in\langle\uptau_{Y}\cap\llbracket\mathsf{FO}[\upsigma]%
\rrbracket_{Y}\rangle\right\}{ italic_U ∣ italic_U ∈ ⟨ roman_τ start_POSTSUBSCRIPT italic_Y end_POSTSUBSCRIPT ∩ ⟦ sansserif_FO [ roman_σ ] ⟧ start_POSTSUBSCRIPT italic_Y end_POSTSUBSCRIPT ⟩ } | pre-spectral space. Recall that ⟨Y,τY,𝒦∘(Y)⟩𝑌subscriptτ𝑌superscript𝒦𝑌\langle Y,\uptau_{Y},\mathcal{K}^{\circ}\!\left(Y\right)\rangle⟨ italic_Y , roman_τ start_POSTSUBSCRIPT italic_Y end_POSTSUBSCRIPT , caligraphic_K start_POSTSUPERSCRIPT ∘ end_POSTSUPERSCRIPT ( italic_Y ) ⟩ is a lpps. We are going to exhibit
a su... | D |
In particular, we redesign the whole pipeline of deep distortion rectification and present an intermediate representation based on the distortion parameters. The comparison of the previous methods and the proposed approach is illustrated in Fig. 1. Our key insight is that distortion rectification can be cast as a probl... | (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... |
Figure 1: Method Comparisons. (a) Previous learning methods, (b) Our proposed approach. We aim to transfer the traditional calibration objective into a learning-friendly representation. Previous methods roughly feed the whole distorted image into their learning models and directly estimate the implicit and heterogeneo... | Previous learning methods directly regress the distortion parameters from a distorted image. However, such an implicit and heterogeneous representation confuses the distortion learning of neural networks and causes the insufficient distortion perception. To bridge the gap between image feature and calibration objective... |
In this part, we compare our approach with the state-of-the-art methods in both quantitative and qualitative evaluations, in which the compared methods can be classified into traditional methods [23][24] and learning methods [8][11][12]. Note that our approach only requires a patch of the input distorted image to esti... | B |
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. | 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. ... | We compare SNGM with four baselines: MSGD, ADAM [14], LARS [34] and LAMB [34]. LAMB is a layer-wise adaptive large-batch optimization method based on ADAM, while LARS is based on MSGD.
The experiments are implemented based on the DeepCTR 888https://github.com/shenweichen/DeepCTR-Torch framework. | We use a pre-trained ViT 555https://huggingface.co/google/vit-base-patch16-224-in21k [4] model and fine-tune it on the CIFAR-10/CIFAR-100 datasets.
The experiments are implemented based on the Transformers 666https://github.com/huggingface/transformers framework. We fine-tune the model with 20 epochs. |
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. | C |
When the algorithm terminates with Cs=∅subscript𝐶𝑠C_{s}=\emptysetitalic_C start_POSTSUBSCRIPT italic_s end_POSTSUBSCRIPT = ∅, Lemma 5.2 ensure the solution zfinalsuperscript𝑧finalz^{\text{final}}italic_z start_POSTSUPERSCRIPT final end_POSTSUPERSCRIPT is integral. By Lemma 5.5, any client j𝑗jitalic_j with d(j,S)>... | FAs¯←{ijA|j∈HA and FI∩GπIj=∅}←subscriptsuperscript𝐹¯𝑠𝐴conditional-setsubscriptsuperscript𝑖𝐴𝑗𝑗subscript𝐻𝐴 and subscript𝐹𝐼subscript𝐺superscript𝜋𝐼𝑗F^{\bar{s}}_{A}\leftarrow\{i^{A}_{j}~{}|~{}j\in H_{A}\text{ and }F_{I}\cap G_{%
\pi^{I}j}=\emptyset\}italic_F start_POSTSUPERSCRIPT over¯ start_ARG italic_s... | Brian Brubach was supported in part by NSF awards CCF-1422569 and CCF-1749864, and by research awards from Adobe. Nathaniel Grammel and Leonidas Tsepenekas were supported in part by NSF awards CCF-1749864 and CCF-1918749, and by research awards from Amazon and Google. Aravind Srinivasan was supported in part by NSF awa... | 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... |
do FA←{ijA|j∈HA and FI∩GπIj=∅}←subscript𝐹𝐴conditional-setsubscriptsuperscript𝑖𝐴𝑗𝑗subscript𝐻𝐴 and subscript𝐹𝐼subscript𝐺superscript𝜋𝐼𝑗F_{A}\leftarrow\{i^{A}_{j}~{}|~{}j\in H_{A}\text{ and }F_{I}\cap G_{\pi^{I}j}=\emptyset\}italic_F start_POSTSUBSCRIPT italic_A end_POSTSUBSCRIPT ← { italic_i star... | B |
In real networked systems, the information exchange among nodes is often affected by communication noises, and the structure of the network often changes randomly due to packet dropouts, link/node failures and recreations, which are studied in [8]-[10].
| such as the economic dispatch in power grids ([1]) and the traffic flow control in intelligent transportation networks ([2]), et al. Considering the various uncertainties in practical network environments, distributed stochastic optimization algorithms have been widely studied. The (sub)gradients of local cost function... |
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... | D |
In recent years, local differential privacy [12, 4] has attracted increasing attention because it is particularly useful in distributed environments where users submit their sensitive information to untrusted curator. Randomized response [10] is widely applied in local differential privacy to collect users’ statistics... | Differential privacy [6, 38], which is proposed for query-response systems, prevents the adversary from inferring the presence or absence of any individual in the database by adding random noise (e.g., Laplace Mechanism [7] and Exponential Mechanism [24]) to aggregated results. However, differential privacy also faces ... |
In recent years, local differential privacy [12, 4] has attracted increasing attention because it is particularly useful in distributed environments where users submit their sensitive information to untrusted curator. Randomized response [10] is widely applied in local differential privacy to collect users’ statistics... | The advantages of MuCo are summarized as follows. First, MuCo can maintain the distributions of original QI values as much as possible. For instance, the sum of each column in Figure 3 is shown by the blue polyline in Figure 2, and the blue polyline almost coincides with the red polyline representing the distribution i... | Note that, the application scenarios of differential privacy and the models of k𝑘kitalic_k-anonymity family are different. Differential privacy adds random noise to the answers of the queries issued by recipients rather than publishing microdata. While the approaches of k𝑘kitalic_k-anonymity family sanitize the origi... | D |
Table 3: PointRend’s performance on testing set (trackB). “EnrichFeat” means enhance the feature representation of coarse mask head and point head by increasing the number of fully-connected layers or its hidden sizes. “BFP” means Balanced Feature Pyramid. Note that BFP and EnrichFeat gain little improvements, we guess... | 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... | PointRend performs point-based segmentation at adaptively selected locations and generates high-quality instance mask. It produces smooth object boundaries with much finer details than previously two-stage detectors like MaskRCNN, which naturally benefits large object instances and complex scenes. Furthermore, compared... | 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... | 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.... | B |
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... | We denote by εi:{−1,1}n→{−1,1}:subscript𝜀𝑖→superscript11𝑛11\varepsilon_{i}:\{-1,1\}^{n}\to\{-1,1\}italic_ε start_POSTSUBSCRIPT italic_i end_POSTSUBSCRIPT : { - 1 , 1 } start_POSTSUPERSCRIPT italic_n end_POSTSUPERSCRIPT → { - 1 , 1 } the projection onto the i𝑖iitalic_i-s coordinate: εi(δ1,…,δn)=δisubscript𝜀𝑖subsc... |
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... | 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.
| A |
Corollary 1 shows that if local variations are known, we can achieve near-optimal dependency on the the total variation B𝛉,B𝛍subscript𝐵𝛉subscript𝐵𝛍B_{\bm{\theta}},B_{\bm{\mu}}italic_B start_POSTSUBSCRIPT bold_italic_θ end_POSTSUBSCRIPT , italic_B start_POSTSUBSCRIPT bold_italic_μ end_POSTSUBSCRIPT and time horizo... | Reinforcement learning (RL) is a core control problem in which an agent sequentially interacts with an unknown environment to maximize its cumulative reward (Sutton & Barto, 2018). RL finds enormous applications in real-time bidding in advertisement auctions (Cai et al., 2017), autonomous driving (Shalev-Shwartz et al.... | The definition of total variation B𝐵Bitalic_B is related to the misspecification error defined by Jin et al. (2020). One can apply the Cauchy-Schwarz inequality to show that our total variation bound implies that misspecification in Eq. (4) of Jin et al. is also bounded (but not vice versa). However, the regret analys... | The last relevant line of work is on dynamic regret analysis of nonstationary MDPs mostly without function approximation (Auer et al., 2010; Ortner et al., 2020; Cheung et al., 2019; Fei et al., 2020; Cheung et al., 2020). The work of Auer et al. (2010) considers the setting in which the MDP is piecewise-stationary and... | Motivated by empirical success of deep RL, there is a recent line of work analyzing the theoretical performance of RL algorithms with function approximation (Yang & Wang, 2019; Cai et al., 2020; Jin et al., 2020; Modi et al., 2020; Ayoub et al., 2020; Wang et al., 2020; Zhou et al., 2021; Wei et al., 2021; Neu & Olkhov... | B |
Many studies worldwide have observed the proliferation of fake news on social media and instant messaging apps, with social media being the more commonly studied medium. In Singapore, however, mitigation efforts on fake news in instant messaging apps may be more important. Most respondents encountered fake news on inst... |
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,... |
There is a very strong, negative correlation between the media sources of fake news and the level of trust in them (ref. Figures 1 and 2) which is statistically significant (r(9)=−0.81𝑟90.81r(9)=-0.81italic_r ( 9 ) = - 0.81, p<.005𝑝.005p<.005italic_p < .005). Trust is built on transparency and truthfulness, and t... | 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... | Many studies worldwide have observed the proliferation of fake news on social media and instant messaging apps, with social media being the more commonly studied medium. In Singapore, however, mitigation efforts on fake news in instant messaging apps may be more important. Most respondents encountered fake news on inst... | B |
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