Working memory (Miyake and Shah, 1999) is a short-term temporary store with limited capacity.

thus as a new (p i , v i , k i )-triplet is added to the MEM, there are trade-offs to be made regarding computational complexity versus performance of the agent.


We hypothesize that in general the agent should perform the best in training, somewhat worse on the holdout-interpolation level and the worst on the holdout-extrapolation level.


To make it more computationally tractable, we place a stop-gradient in the memory write.

the write operation for the key in (1) becomes: where SG(â€¢) denote that the gradients are stopped.


Memory is an important aspect of intelligence and plays a role in many deep reinforcement learning models.

little progress has been made in understanding when specific memory systems help more than others and how well they generalize.


Using a similar analysis to [13], the preceding result can easily be strengthened to the strong converse, stating that P e is not only bounded away from zero when t is below the threshold given, but tends to one.

the proof based on Fano's inequality extends more easily to noisy settings.


Under adaptive testing (i.e., tests can be designed based on previous outcomes), this question is wellunderstood [30], as outlined below.

a standard implementation of COMP or DD yields decoding complexity O(n 2 t), which may be infeasible when n is large and decoding time is limited.


Taking the union bound over the B bundles, and noting that 2 Ã— 0.073 2 > 0.01, we find that we can classify all of the bundles correctly with probability approaching one when t mul = (100 log B)(1 + o(1)), so that the total number of tests used is t mul B = (100B log B)(1 + o(1)).

this leads to the SSS algorithm described in Algorithm 3.


Their idea is that -due to environmental circumstances-time-series often have heterogeneous lengths and their measurements are taken with different time-intervals in between.

we chose to build six polygonal curves with 2205 vertices each in the 6000-dimensional Euclidean space.


This problem, known as the discrete median, has a polynomial-time exhaustivesearch algorithm: calculate the cost of each possible curve by summing over all other input curves.

this only happens for line segments of length larger than 10 15 .


common approaches, where univariate time-series are represented by a point in a high-dimensional space, each dimension corresponding to one instant of time, become hard or even impossible to apply.


To the best of our knowledge, there is no tractable algorithm to compute an exact median polygonal curve.

we restrict the search space of feasible solutions to the input.


In this section, we illustrate how RTPs can be used to model categorical data.

the OP is not self-consistent, and so the binary space partitioning-tree (BSP) process [9] was introduced to modify the cut distribution of the OP in order to recover self-consistency.


Their analysis, however, introduces several sources of looseness.

the use of Lipschitz constants, which lead to distributionindependent bounds, eradicates any hope that these bounds will be non-vacuous for modern models and datasets.


The utility of using data-dependent priors to control disintegrated mutual information depends on the balance of two effects: On the one hand, I(W;S) ≤ I(W;S|F), and so conditioning never improves a theoretical bound and may make it looser.

i(W ; S) depends on the unknown data distribution and so distribution-independent bounds will often be very loose.


In the process of developing tighter distribution-dependent bounds, we also observe that, in some circumstances, one may obtain tighter estimates by working with conditional or disintegrated information-theoretic quantities.

doing so provides more opportunities to exchange expectation and concave functions than are available with previous mutual information bounds.


The use of data-dependent estimates leads to distribution-dependent bounds that naturally adapt to the model of interest and the data distribution.

using data-dependent estimates, we arrive at bounds in terms of the incoherence of gradients in the dataset.


We introduce the technique of data-dependent priors for bounding mutual information in datadependent estimates of expected generalization error.

we use data-dependent priors to forecast the dynamics of iterative algorithms using a randomly chosen subset of the data.


Deep neural networks have become an important tool in applied machine learning, achieving state-ofthe-art regression and classification accuracy in many domains.

quantifying and measuring uncertainty in these discriminative models, despite recent important advances, is still an open problem.


Finally, we demonstrate an interesting property of our method that learns a broader prior over Ï† as the training set size decreases.

a relation between the predictive uncertainty and the number of unique structures (connectivity patterns) encoded in a BRAINet model (exemplified in Appendix C-Figure 1).


Bayesian neural networks are a powerful solution, where the prior over network weights is a design choice, often a normal distribution or other distribution encouraging sparsity.

this prior is agnostic to the generative process of the input data, which might lead to unwarranted generalization for outof-distribution tested data.


It is a 10 frequency signal, where the frequencies at each sampling time-instant are generated uniformly at random in the audible frequency range 20 Hz to 20 kHz, and where the amplitudes Ar(t) are generated uniformly in the range [−180, 180];

in each trial among a total of N , at every sampling instant an independent realization of the sound-level signal is used.


When there is a large number of trials, the error in the estimation of empirical CDFs reduces further.

the simulation results validate our distribution-learning method with location-unaware samples.


In this case, an integration model will be selected so that multiple cues can be weighed and combined together to form a unified estimate of head direction of self-motion [7,8].

when we wear a goggle to navigate in a virtual reality world while sitting on a spinning chair, the visual and the vestibular signals would be quite discordant and it would be wrong to integrate them during inference [9].


A number of psychological studies have suggested that our brains indeed perform causal inference as an ideal observer (e.g., [10,[12][13][14]).

it has been challenging to come up with a simple and biologically plausible neural implementation for causal inference.


As such, we construct an error predictor (EP) and an iterative error correction machine (EC machine) as shown in Figure 2.

we freeze the autoencoder-decoder from the previous stage and reuse them to generate the states of the input nodes h k , k = 0, ..., K and output nodes h t , t = 0, ..., T .


On the one hand, malicious parties may recover interpretable source codes from the software products to gain commercial advantages.

binary decompilation can be leveraged for code vulnerability analysis and malware detection.


Lastly, it should not require any modification to existing model architectures so that we can achieve better flexibility and minimal engineering effort.

most of the methods that currently exist do not meet some of the above criteria.


Moreover, knowing uncertainty is important for fundamental machine learning research [19]; for example, many reinforcement learning algorithms (such as Thompson sampling [40]) requires estimating uncertainty of the distribution [39].

there has not been any well-established, effective and efficient method to assess prediction uncertainty of deep learning models.


In fact, trying to minimize the natural log loss for a classification task is the same as trying to maximize the doubling rate in a gambling problem.

in practice, we are often in a horse race where some information about the horse is known.


The rest four methods (SCML, DRML, SGDINC, and our M-FLRML) claim that there is no need for SVD preprocessing, which are compared using the original data matrices as input.

since the SVD calculation for datasets M10-100 and M40-100 has exceeded the memory limit of common PCs, only these four methods are tested on these two datasets.


Smaller matrix size theoretically means the ability to process larger datasets on the same size of memory.

in practice, we find that the bottleneck is not the optimization process of FLRML.


In addition to these generalizations, there is good reason to suspect the results to extend to much broader class of models and optimization procedures.

a wealth of recent literature suggests that the mean field theory governing the wide network limit of fully-connected models [32,33] extends naturally to residual networks [35], CNNs [34], RNNs [39], batch normalization [40], and to broad architectures [37].


[7], it is shown that the comparison of performance between finite-and infinite-width networks is highly architecture-dependent.

it was found that infinite-width networks perform as well as or better than their finite-width counterparts for many fully-connected or locally-connected architectures.


VAEs with non Euclidean latent space have been recently introduced, such as Davidson et al. (2018) making use of hyperspherical geometry and Falorsi et al. (2018) endowing the latent space with a SO(3) group structure.

gradients rµz can straightforwardly be computed thanks to the exponential map reparametrisation (Eq 3), and gradients w.r.t. the dispersion rz are readily available for the wrapped normal.


See a sample of the perturbed images produced by our method in Section B.

this is closely related to the standard approximation variant of polynomial maximization problem where the goal is to obtain, in polynomial time, an objective value as close to the optimal one, without violating the B n OE ball constraint.


A principled logic rule-based approach is the Markov Logic Network (MLN), which is able to leverage domain knowledge with first-order logic and meanwhile handle the uncertainty.

the inference in MLNs is usually very difficult due to the complicated graph structures.


We propose such an approach called the probabilistic Logic Neural Networks (pLogicNet).

logic rules can be imperfect or even contradictory.


Some recent work also studies using reinforcement learning for reasoning on knowledge graphs [6,23,38,47], where an agent is trained to search for reasoning paths.

the performance of these methods is not so competitive.


Such methods have been proven effective for reasoning on knowledge graphs.

lastly, there are also some recent studies trying to combine statistical relational learning and graph neural networks for semi-supervised node classification [33], or using Markov networks for visual dialog reasoning [32,51].


These graphs have been proven useful in many tasks, such as question answering [49], relation extraction [34] and recommender systems [4].

one big challenge of knowledge graphs is that their coverage is limited.


The level-2 model had a lower fitting accuracy compared to the level-1 model in almost all subjects (Figure 1b).

we used only the first two levels of ToM for further analysis.


As the level of ToM increases, the player free-rides in earlier rounds because others (modeled as optimal agents) would be expected to free ride in later rounds.

using higher levels of ToM shifts free-riding towards the first round, decreasing the contribution rate over all rounds to 0 (Figure 2d).


The accuracy of a model was determined by the similarity between the model's predicted action and the actual action of the subject in each round on average.

the idea of selecting actions that maximize the total expected reward transforms the model from one based on HMMs (previous section) to a Partially Observable Markov Decision Process (POMDP) [19].


We fit models based on conformity with different levels of ToM to the behavior of each subject and compared the accuracy of the different level models in explaining human behavior.

in our model fitting, we used a set of free parameters that explain all games of each subject across all the different conditions.


Specifically, in our model fitting, we used a set of free parameters that explain all games of each subject across all the different conditions.

different conditions are modelled naturally in our framework without any parameter tuning for each condition.


In terms of training objective, they use lower bound (5) with beam search over T * (Y ), which is different from our lower bound (6).

we found our lower bound to be beneficial in terms of quality and less prone to getting stuck in local optima.


This has already been used in previous works [10,11,12].

this leads to poor performance and unstable results.


Also we noticed that tokens which are generated first are often uninformative (e.g., punctuation, determiners, etc.).

wu et al. [7] suggest that left-to-right NMT models fit better for right-branching languages (e.g., English) and right-to-left NMT models fit better for left-branching languages (e.g., Japanese).


This generation procedure is inherently sequential.

it is challenging to effectively parallelize it on GPU accelerators.


As expected, the right-to-left generation order is better than the left-to-right for translation into Japanese.

our model significantly outperforms both baselines.


While in our model positional encodings of most of the tokens change after each insertion operation and, therefore, decoder self-attention is re-applied at each generation step, the model by Gu et al. [22] does not need this and has better theoretical time complexity of O(len(Y )2) in contrast to our O(len(Y )3).

in practice our decoding is on average only 50% times slower than the baseline; for the details, see Section 5.2.


Secondly, the decoder predicts the joint probability of a token and a position corresponding to a single insertion (rather than the probability of a token, as usually done in the standard setting).

the predicted probabilities should add up to 1 over all positions and tokens at each step.


However, if the entire probability mass is concentrated on a single trajectory, both lower bounds are tight.

when maximizing (6), we also expect most of the probability mass to be concentrated on one or a few "best" trajectories.


Instead of minimizing the exact loss function L∞, we can optimize its upper bound L∞- in a much more efficient way

the proposed ISDA boils down to a novel robust loss function, which can be easily adopted by most deep models.


To further improve the efficiency, we derive a closed-form upper bound of the expected cross-entropy (CE) loss with the proposed data augmentation scheme.

instead of performing the augmentation procedure explicitly, we can directly minimize the upper bound, which is, in fact, a novel robust loss function.


Our work is motivated by the intriguing property that deep networks are surprisingly good at linearizing features, such that certain directions in the deep feature space correspond to meaningful semantic transformations, e.g., adding sunglasses or changing backgrounds.

translating training samples along many semantic directions in the feature space can effectively augment the dataset to improve generalization.


In this paper, we propose an implicit semantic data augmentation (ISDA) algorithm for training deep image recognition networks.

these models generally suffer a performance reduction on CIFAR-100, which do not contain enough samples to learn a valid generator for each class.


This motivates us to augment the training set by applying such semantic transformations on deep features.

manually searching for semantic directions is infeasible for large scale problems.


When the feature of a person, who does not wear glasses, is translated along this direction, the new feature may correspond to the same person but with glasses (The new image can be explicitly reconstructed using proper algorithms as shown in [9]).

by searching for many such semantic directions, we can effectively augment the training set in a way complementary to traditional data augmenting techniques.


In case (a), ABQ will not allocate any points to this region A at all, after a finite number of iterations.

the information about the integrand f on this region A will not be obtained after a finite number of evaluations, which makes it difficult to guarantee the consistency of quadrature, unless f has a finite degree of freedom on A.


In fact, it is only recently that researchers have proposed stochastic gradient descent (SGD) algorithms for DRO with f -divergence-based ambiguity sets [17].

f -divergence measures can only compare distributions with the same support, while the Wasserstein distances do not have such a restriction.


Furthermore, we conduct three different experiments to show its superb performance on both synthetic and real-world datasets.

our method can achieve the same accuracy up to 800+ times faster than the standard off-the-shelf solver.


In this paper, we take a first step towards resolving the above difficulty by developing a first-order algorithmic framework for tackling a class of Wasserstein distance-based distributionally robust logistic regression (DRLR) problem.

we propose a novel linearized proximal ADMM to solve the DRLR problem, whose objective is convex but consists of a smooth term plus two non-separable non-smooth terms.


As standard ADMM-type algorithms are very sensitive to the choice of penalty parameters, it is hard for us to tune the optimal penalty parameter for each subproblem with different .

we use a constant penalty parameter for the non-adaptive case.


The latter makes our algorithmic framework numerically more robust in general.


On the numerical side, we demonstrate via extensive experiments that our proposed method can be sped up substantially by adopting a geometrically increasing step size strategy.

our method can achieve a hundred-fold speedup over the standard solver (which is the only other method that has been used so far to solve (1.1)) on both synthetic and real-world datasets without the need to tune an optimal penalty parameter in every iteration.


We observe our model consistently attend the head or tail on two attention maps respectively.

we compare the detected parts with head or tail ground truth annotations.


[25,5,26] train a multiple-part detector with groundtruth annotations to produce the bounding boxes of parts and learn the part features with conventional recognition tasks.

the heavy involvement of human labor for part annotations makes tasks costly in the real large-scale problems.


We find that the complexity of our model can be reduced by using the same CNN with shared weights for both image and part patches, but the zero-shot learning performance is slightly degraded, e.g., the score for CUB-PS and AWA-PS decreases by 3.6%, 2.7%, respectively.


However, the heavy involvement of human labor for part annotations makes tasks costly in the real large-scale problems.

learning part attentions in a weakly supervised way is desirable in the zero-shot recognition scenario.


Thus, recovering Fourier coefficients from the measurements of f i can be treated as recovering a sparse vector in R 2 i .

in this paper, we provide a method to learn the directed structure of a Bayesian network using data.


Our setting is similar to an interventional setting where a selection can be compared to an intervention and an assignment can be compared to an experiment, although our method never queries the 2 k distinct values, but a single random assignment instead.

we compare our results to the state-of-the-art interventional methods in Table 1.


However, when neural network function approximation is used, the effect of optimistic initialisation can disappear quickly with optimisation (Osband et al., 2018).

with non-orthogonal state-action embeddings, Q value estimates may decrease for yet unseen state-action pairs, and estimates for different state-action states can move in tandem.


to gain a better insight into the value prediction step, we examine its idealised implementation: Suppose we have access to a belief over MDPs, P T (as in PSRL), and want to compute the implied distribution P QÏ€ for a single policy Ï€.


Randomised value functions (RVF) can be viewed as a promising approach to scaling PSRL.

we show that most contemporary algorithms combining RVF with neural network function approximation do not possess the properties which make PSRL effective, and provably fail in sparse reward problems.


Figure 3 shows the scaling for Successor Uncertainties and Bootstrap+Prior for this problem.

the raw scores are reported in table 2 (appendix), and the difference in human normalised score between SU and the competing algorithms for individual games is charted in figure 4.


Propagation of uncertainty is a desirable property when using Upper Confidence Bounds (UCB; Auer, 2002) for exploration, since UCB methods rely only on the first two moments of PQˆπ .

propagation of uncertainty is not sufficient for effective exploration under posterior sampling.


In this paper, we study this in the classic setting where the unknown environment is modelled as a Markov Decision Process (MDP).

we focus on developing an algorithm that combines effective exploration with neural network function approximation.


For UBE, this is a consequence of proposition 1, and the similarly poor behaviour of BDQN suggests it may suffer from an analogous issue.

a family of methods called Randomised Value Functions (RVF; Osband et al., 2016b) attempt to overcome these issues by directly modelling a distribution over Q functions, P Q, instead of over MDPs, P T .


Examples of correlations are even found in stateless decision-making problems, such as multi-armed bandits, where prominent patterns in the reward mechanisms of different arms can translate into correlated action choices of the operating agent [7,9].

these statistical relationships become more pronounced in the case of contextual bandits, where effective decision-making strategies not only exhibit temporal correlation but also take into account the state context at each time point, introducing a second source of correlation [12].


First, we note that a difficulty of Problem (RC fair ) is the discontinuity of its objective function.

we show that (RC fair ) can be formulated equivalently as a two-stage robust optimization problem by introducing a fictitious counting phase after e  is revealed.


Here, imposing fairness is more challenging as we do not know a-priori which nodes may fail.

we must ensure that fairness constraints are satisfied under all failure scenarios.


Next, we show that between-community coverage is negligible compared to within-community coverage.

we determine the distribution of the monitors, in the presence and absence of fairness constraints.


These heuristics, although computationally efficient, are coverage-centered and do not take fairness into consideration.

they may lead to discriminatory outcomes, see Table 1.


Similarly, using Bender's decomposition is needed: even for moderate values of K (2 to 3), the K-adaptability MILP (5) could not be loaded in memory.

to the best of our knowledge, ours is the first paper enforcing fairness constraints in the context of graph covering subject to node failure.


To motivate our approach, consider deploying in the open world a state-of-the art algorithm for robust graph covering (which does not incorporate fairness considerations).

we apply the solutions provided by the algorithm from [45] on five real-world social networks.


We thus propose to augment the robust covering problem with fairness constraints.

we propose to achieve max-min fairness by imposing fairness constraints on each group's coverage: we require that at least a fraction W of nodes from each group be covered.


However, when considering uncertainty in node performance/availability, the objective function loses the submodularity property while exact techniques fail to scale to even moderate problem sizes.

existing (exact or approximate) approaches do not apply.


The symmetry results in significant slow down of the Brand-and-Bound procedure [18].

we introduce symmetry breaking constraints in the formulation (5) that stipulate the candidate covering schemes be lexicographically decreasing.


The steps of the proof are similar to those in the proof of Proposition 1 with the difference that, under uncertainty, monitors should be distributed such that the fairness constraints are satisfied even after J nodes fail.

we quantify a minimum number of monitors that should be allocated to each community.


Indeed, when deploying interventions in the open world (especially in sensitive domains impacting life and death like the ones that motivate this work), care must be taken to ensure that algorithms do not discriminate among people with respect to protected characteristics such as race, ethnicity, disability, etc.

we need to ensure that independently of their group, individuals have a high chance of being covered, a notion we refer to as group fairness.


Meanwhile, local minima are characterized by the condition that the gradient with respect to the parameters r âœ“ L(âœ“) is zero.

one way to avoid finding local minima that aren't global is to ensure that the parameter gradient is zero if and only if the output space gradient (for each case) is zero.


We show that natural gradient enables us to use a much larger step size, resulting in an even faster convergence rate.

the maximal step size of natural gradient descent is O (1) for (polynomially) wide networks.


In general, finding a global minimum of a general non-convex function is an NP-complete problem, and neural network training in particular is NP-complete [Blum and Rivest, 1992].

in the past two years, researchers have finally gained substantial traction in understanding the dynamics of gradient-based optimization of neural networks.


Intuitively, we would expect the smallest eigenvalue to be very small if all x i are similar to each other.

some notion of diversity of the training inputs is needed.


Note that strongly convexity is a very mild assumption since we can always add L 2 regularization to make the convex loss strongly convex.

this function class includes regularized cross-entropy loss (which is typically used in classification) and squared error (for regression).


The combination of dilated convolution and dense connection enables Pyramid-like multi-scale feature fusion instead of parallel convolution [15,35] while keep the depth of network.

one is CDNwithDC, which is implemented without dilated convolution and uses traditional one-step data consistency layer instead.


An example of sub-Nyquist sampled MR Image is given in Figure 1.

it stands for how large portion of image can be seen by a neuron.


[36] show that with careful initialization, residual networks can be trained as stable as those with normalization.

we also show the possibility of estimating RMS on a subset of the summed inputs, maintaining this invariance property.


For a fixed number of neurons, the network may follow either of these dynamics, depending on the initialization.

we show that kernel dynamics correspond to interpolation with cubic splines, whereas active dynamics yields adaptive linear splines, where neurons accumulate at the discontinuities and yield piecewise linear approximations.


For appropriate initial distributions of neurons, the solution to kernel learning is a smooth cubic spline (Theorem 5).

arbitrary initializations will locally interpolate between these two regimes.


According to [34], the mixing matrix in OICA can be estimated up to the permutation and scaling indeterminacies (including the sign indeterminacy) of the columns.

these indeterminacies stop us from comparing the estimated mixing matrices by different OICA algorithms.


Here we adopt MMD as the distributional distance as it does not require an explicit discriminator network, which simplifies the whole optimization procedure.

because conducting randomized controlled trials is usually expensive or infeasible, discovering causal relations from observational data, i.e.,causal discovery [1,2]) has received much attention in the past decades.


Causal discovery witnessed significant progress over the past decades.

many recent causal discovery methods make use of independent, non-Gaussian noise to achieve identifiability of the causal models.


Here we visualize the causal diagram estimated by LFOICA and the ground-truth in Figure 3(a) and 3(d).

we can readily extend our LFOICA algorithm to estimate the causal adjacency matrix B. Granger causal analysis has been shown to be sensitive to temporal frequency/resolution of time series.


For example, if assuming each IC follows a Mixture of Gaussian (MoG) distribution, we can simply derive the likelihood for the observed data.

the number of Gaussian mixtures increases exponentially in the number of ICs, which poses significant computational challenges.


In order to speed up optimization, we gradually increase the number of frames used.

we first optimize the parameters using only 1 simulated frame.


Regular simulators need to run one simulation for each input variable to compute the gradient, while our method only needs to run once for all gradients to be computed.

the more input variables there are during learning, the greater the performance gain that can be achieved by our method over finite-difference methods.


Collisions that actually happen in each step are very sparse compared to the complete set.

we use a dynamic approach that incorporates collision detection and response.


Each collision involves a vertex-face or edge-edge pair, which both have 4 vertices and 12 variables.

the original matrix size (n + m = 13m) should be about 13 times bigger than in our method (m).


We use a bounding volume hierarchy for collision detection [27], and non-rigid impact zones [12] to compute the collision response.

we solve a cubic equation to detect the collision time t of each vertex-face or edge-edge pair that is sufficiently close to contact: where x k and v k (k = 1, 2, 3) are the relative position and velocity to the first vertex.


Somewhat analogously, in the sequential machine learning setting, modern machine learning methods, such as artificial neural networks, can be drastically disrupted when presented with new information from different domains, which leads to catastrophic interference and forgetting [19,14].

to retain long-term memory (for both human and machine learners), it is crucial to devise teaching strategies that adapt to the underlying forgetting mechanisms of the learner.


Our theoretical framework is inspired by recent results on sequence submodular function maximization [43,36] and adaptive submodular optimization [10].

[43] introduced the notion of string submodular functions, which, analogous to the classical notion of submodular set functions [15], enjoy similar performance guarantees for maximization of deterministic sequence functions.


Overall, GR achieved higher gains compared to the baselines.

dataset We simulated concepts of two different types: "easy" and "difficult".


Here, we highlight the differences with some recent work in the machine learning literature involving forgetful learners.

[44] aimed to teach the learner a binary classifier by sequentially providing training examples, where the learner has an exponential decaying memory of the training examples.


From the ablation study, we observe that each component plays its own role in a complementary way.

recently, gradient-based meta-learning methods [10,36] have been successfully applied to few-shot learning, with a procedure purely leveraging gradient descent.


In line with our goal of domain generalization, the model is trained on a sequence of simulated episodes with domain shift.

at each iteration, the available domains D are randomly split into sets of meta-train D tr and meta-test D te domains.


Because of this delay, we want a model which predicts brain activity to have access to the words that precede the timepoint at which the fMRI image is captured.

we use the 20 words (which cover the 10 seconds of time) leading up to each fMRI image as input to our model, irrespective of sentence boundaries.


Our observations that the fine-tuning reduces [CLS] attention to the [SEP] token can be interpreted in these terms.

further analysis is needed to understand whether this reduction in attention is specifically due to the task of predicting fMRI recordings or generally arises during fine-tuning on any task.


Fu et al. (2017) proposes a way to estimate a latent space that is high-dimensional both in time and space from simulated fMRI and MEG activity.

effectively combining fMRI and MEG/EEG remains an open research problem.


The summation and elimination of inner indices is called contraction.

the basic idea is that tensors are distinguished only by the indices they own and we consider them as vertices.


For example, molecular systems biology studies molecules (e.g., gene products, proteins) in a living cell that interact according to biochemical laws.

structural causal models support counterfactual inference, but do not identify the mechanisms.


Predicting the outcome of an intervention requires us to model the system.

discrete-state continuous-time Markov process models unambiguously describe the changes of system components across all the system states (i.e., not only at equilibrium) in term of hazard functions [11,28].


We model the system as a continuous-time discrete-state Markov process M with hazard rates functions in Table 1.

create "observation" and "intervention" models (Algorithm 2 lines 3-4) In a probabilistic programming language, the deterministic functions in Eq. (19) are specified with a Dirac Delta distribution.


The evaluation stems from the insight that noise at the equilibrium captures the stochasticity in the Markov process trajectories.

we repeatedly simulate pairs of the trajectories with and without the counterfactual intervention, with a same random seed in a pair, such that each pair has an identical stochastic component.


We propose a novel deep segmentation method that learns to segment with correct topology.

we propose a topological loss that enforces the segmentation results to have the same topology as the ground truth, i.e., having the same Betti number (number of connected components and handles).


This is understandable; while cross entropy loss gradient is applied to all pixels, topological gradient is only applied to a sparse set of critical pixels.

the weight needs to be much smaller to avoid overfitting with these critical pixels.


To have meaningful topological measure on these small patches, we apply relative persistent homology as a more localized approach for the computation of topological structures.

for each patch, we consider the topological structures relative to the boundary.


Note we adopt a cubical complex discretization, which is more suitable for images.

as shown in the figure on the right, with the additional frame, a Y -shaped branching structure cropped within the patch will create two handles and be captured by persistent homology.


We propose a novel method that learns to segment with correct topology.

we design a continuous-valued loss function that enforces a segmentation to have the same topology as the ground truth, i.e., having the same Betti number.


there is one single likelihood function f , whose value ranges between 0 and 1.


It then forces the network to learn patterns near these locations, at the expense of overfitting and consequently slightly compromised per-pixel accuracy.

on this version of the Rand index we exclude the zero component of the original labels (background pixels of the ground truth).


they are both moved closer to the matched persistent dot in Dgm(g).


In Fig. 7, for most membrane pixels, the network learns to make correct prediction quickly.

for a small amount of difficult locations (blurred regions), it is much harder to learn to predict correctly.


These overlaps between patches propagate correct topology everywhere.

correct topology within a patch means the segmentation can be a deformation of the ground truth.


Unlike the classical MABs, the arms have non-stationary reward distributions.

we will focus on the class of restless bandits whose arms change their states based on Markov chains.


Below we discuss two popular methods for large scale score matching.

there are two main challenges with this approach.


We then produce samples using Langevin dynamics, which approximately works by gradually moving a random initial sample to high density regions along the (estimated) vector field of scores.

our sampling strategy is inspired by simulated annealing [30,37] which heuristically improves optimization for multimodal landscapes.


First, we do not need to sample from a Markov chain during training.

the walkback procedure of GSNs needs multiple runs of the chain to generate "negative samples".


Nonequilibrium Thermodynamics (NET [51]) used a prescribed diffusion process to slowly transform data into random noise, and then learned to reverse this procedure by training an inverse diffusion.

nET is not very scalable because it requires the diffusion process to have very small steps, and needs to simulate chains with thousands of steps at training time.


With a lower memory footprint, our method allows training with larger batches in each iteration and better utilization of available GPU resources, making it more practical and economical to deploy and train very deep architectures in production environments.

training with fewer features per layer leads to much lower accuracy, while the naive approximation baseline simply fails, highlighting the importance of preventing accumulation of errors across layers using our approach.


To examine the reason behind the robustness of our method, Fig. 4 visualizes the error in the final parameter gradients used to update the model.

we take two models for CIFAR-100-at the start and end of training-and then compute gradients for a 100 batches with respect to the convolution kernels of all layers exactly, and using our approximate strategy.


In our experiments, we use quantization to K-bit fixed point representations as a simple and computationally-inexpensive approximation strategy to validate our method.

experiments on CIFAR-10, CIFAR-100, and ImageNet show that our method yields performance close to exact training, while storing activations compactly with as low as 4-bit precision.


However, we need to store all intermediate activations during training because they are needed to compute gradients during back-propagation: ( 5)-( 8) involve not just the values of the incoming gradient, but also the values of the activations themselves.

training requires enough available memory to hold the activations of all layers in the network.


A number of works focus on reducing the memory footprint of a model during inference, e.g., by compression [7] and quantization [11], to ensure that it can be deployed on resource-limited mobile devices, while still delivering reasonable accuracy.

these methods do not address the significant amount of memory required to train the networks themselves (note [7,11] require storing full-precision activations during training), which is significantly larger than what is needed for inference.


A major obstacle to using approximate activations for training is that, with the standard implementation of back-propagation (backprop), approximation errors compound across the forward and then backward pass through all layers.

we need to store all intermediate activations during training because they are needed to compute gradients during back-propagation: ( 5)-( 8) involve not just the values of the incoming gradient, but also the values of the activations themselves.


For max-pooling, exact back-propagation through the pooling operation would require storing the arg-max indices (the number of bits required to store these would depend on the max-pool receptive field size).

since max-pool layers are used less often in recent architectures in favor of learned downsampling (ResNet architectures for image classification use max-pooling only in one layer), we instead choose not to approximate layers with max-pooling for simplicity.


We visualize the evolution of training losses in Fig.  3, and report test set errors of the final model in Table 1.

since this is typically at the final layer for most convolutional networks, and computing these activations is immediately followed by back-propagating through that layer, approximating these activations offers no savings in memory.


When the goal is to just compute the final output of the network, the activations of an intermediate layer can be discarded during the forward pass as soon as we finish processing the subsequent layer or layers that use it as input.


We plot the average squared error between these gradients.

these methods likely represent the best possible solutions if the goal is restricted to computing exact gradients.


This in-turn can lead to the computation being inefficient and "memory-bound": it forces the use of smaller training batches for each GPU leading to under-utilization of available GPU cores (smaller batches also complicate the use of batch-normalization [12] with batch statistics computed over fewer samples).

practitioners are forced to either use a larger number of GPUs for parallelism, or contend with slower training.


While self-training has shown some gains in standard accuracy [25], the consistency-based approaches perform significantly better on popular semisupervised learning benchmarks [33].

our paper considers the very different regime of adversarial robustness, and we observe that robust self-training offers significant gains in robustness over fully-supervised methods.


The unique challenge of point cloud data lies in its abundant spatial geometric information, and the semantics of the whole object is contributed by including regional geometric structures.

most general-purpose DA methods that struggle for global feature alignment and ignore local geometric information are not suitable for 3D domain alignment.


Domain adaptation (DA) solves this problem by building a model utilizing the knowledge of label-rich dataset, i.e., source domain, which generalizes well on the label-scarce dataset, i.e., target domain.

it means that inference of pseudo labels is easily influenced by imbalance distribution of samples in different classes where certain classes would dominate the process of self-training and cause errors accumulation.


First, feedback is only binary: either the customer buys the item, or she does not.

the platform must learn from censored feedback.


The subset of arms for which the reward is revealed at time t depends on the covariate x t , and the exact price p t from the above subset.

the assumption of a pre-defined graph structure is not satisfied.


DEEP-C with Rounds: Theoretical analysis of regret for arm elimination algorithms typically involves tracking the number of times each sub-optimal arm is pulled before being eliminated.

this is challenging in our setting, since the set of arms which get "pulled" at an offered price depends on the covariate vector at that time.


For our setup with d = 100, while both Decoupled DEEP-C and Sparse DEEP-C perform similar in average regret, we find that Sparse DEEP-C significantly outperforms Decoupled DEEP-C in standard deviation and in 95th-percentile.

95th-percentile of Sparse DEEP-C is 33% lower than that under Decoupled DEEP-C.


However, there are some crucial differences.

these works assume (a) a discrete set of arms, (b) the existence of a sequence of graphs indexed by time (possibly fixed) with the arms Contextual Non-parametric residuals Binary feedback Kleinberg and Leighton (2003) X X as its nodes, (c) the action involves pulling an arm, and at each time the reward at each neighbor of the pulled arm is revealed.


Motivated by the application of real-time pricing in e-commerce platforms, we consider the problem of revenue-maximization in a setting where the seller can leverage contextual information describing the customer's history and the product's type to predict her valuation of the product.

her true valuation is unobservable to the seller, only binary outcome in the form of success-failure of a transaction is observed.


In our work, we assume a parametric model for this relationship.

we presume that the expected value of the logarithm of the valuation is linear in the covariates.


The MNL contextual bandit is a multinomial generalization of generalized linear contextual bandits [23,30], particularly logistic bandits, that reduces to generalized linear bandits when the assortment contains a single item.

this extension is non-trivial since the MNL model cannot be expressed in the form of a generalized linear model [15]; hence, the results of generalized linear bandits do not directly apply.


Also, in contrast to the standard contextual bandit problems, in the MNL contextual bandit, the item choice (feedback) is a function of the entire offered assortment.


The proof of Theorem 2 utilizes the anti-concentration property of the maximum of Gaussian random variables for ensuring frequent optimism.

we show in the following lemma that the proposed optimistic sampling can ensure a constant probability of optimism.


They suggest that further supervised pretraining of models can provide significant benefits.

they propose supervised pretraining on contact prediction and remote homology detection, and show it increases performance on secondary structure prediction.


From a theoretical point of view, among all these works, some algorithms are backed up with convergence results [4] or have quality of approximation guarantees materialized by a recovery bound [1].

there is still a lack of convergence rate analysis for the scalable Tucker problem.


As shown in Figure 6(a), our initial experiments showed that it is hard to capture shape details such as holes and thin structures when only global image features are used.

we introduce a local feature extraction method to focus on reconstructing fine-grained details, such as the back poles of a chair (Figure 6).


To represent 3D shapes, many of these methods utilize either voxels [2][3][4][5][6][7][8][9] or point clouds [10] due to ease of encoding them in a neural network.

such representations are often limited in terms of resolution.


An SDF simply encodes the signed distance of each point sample in 3D from the boundary of the underlying shape.

given a set of signed distance values, the shape can be extracted by identifying the iso-surface using methods such as Marching Cubes [17].


To overcome this issue, Insafutdinov and Dosovitskiy [28] introduce a distilled ensemble approach to regress camera pose by combining several pose candidates.

dISN first estimates the camera parameters that map an object in world coordinates to the image plane.


To address this problem, we introduce a local feature extraction module.

we estimate the viewpoint parameters of the input image.


We define precision as the percentage of the generated points whose distance to the closest ground truth point is less than a threshold.

we also report the results of DISN using estimated camera poses and ground truth poses, denoted by 'Ours cam ' and 'Ours' respectively.


The classical analyses for non-private SCO depend crucially on making only one pass over the dataset.

a single pass noisy SGD is not sufficiently accurate as we need a non-trivial amount of noise in each step to carry out the privacy analysis.


We argue that the approximate proximal operation will essentially have no impact on the guarantees of A ProxGD as compared to those of A NSGD .

in terms of privacy, the sensitivity of the approximate gradients (evaluated via the approximate prox operator) will basically remain the same as that of the exact gradients.


Aside from extending the analysis of this approach to population loss, we show that it can lead to algorithms for private SCO that use only near-linear number of gradient evaluations (whenever these assumptions hold).

we give a variant of objective perturbation in conjunction with the stochastic variance reduced gradient descent (SVRG) with only O(n log n) gradient evaluations.


For example, for convex Lipschitz losses in d-dimensional Euclidean space, the best bound on the population loss achievable via uniform convergence is â„¦( d/n) [Fel16].

sGD is known to achieve excess loss of O( 1/n) which is independent of the dimension.


In general, Longer input sequences yield better recognition results.

one problem arising for a model requesting more input frames is that the GPU resources required for training and inference also significantly increase in both memory and time.


The proposed architecture is based on a combination of a deep subnet operating on low-resolution frames with a compact subnet operating on high-resolution frames, allowing for high efficiency and accuracy at the same time.

we shall demonstrate the benefit of leveraging more frames for temporal modeling in Section 4.


Several of these methods are based on the observation that if all the candidate subsequences in the Viterbi algorithm converge at some point, then all subsequent states will share a common subsequence up to that point [32,33].

these methods do not guarantee reduction in latency since, in the worst case, they still need to process all the rewards before producing any output.


We evaluate the performance of any online algorithm in terms of its competitive ratio Ï, which is defined as the ratio OP T /ON .

the algorithm then leverages the subsequence w [i,i+L] to decide its next state Å·i .


Moreover, similar results were obtained when we used 3 beams to approximate the optimal Î³-discounted reward within each (L + 1)-long peek window.

we can potentially design fast yet accurate heuristics for some low latency settings.


Unlike Peek Search, this method does not discount the rewards on paths.

the algorithm first selects a reset point uniformly at random from {1, 2, . . . , L + 1}.


Our algorithms are efficient dynamic programs that may not only be deployed in settings where Viterbi algorithm is typically used but also, as we mentioned earlier, several others where it is impractical.

our work would potentially widen the scope of and expedite scientific discovery in several fields that rely critically on efficient online Markov decoding.


The corpus is not divided into separate train and test sets.

we formed 5 random partitions each having 80% train and 20% test sentences.


The proofs revolve around our novel âˆ†-dimensional prismatic polytope constructions, where each vertex corresponds to a state.

(Sketch) The algorithm gives up reward on at most âˆ† steps every L + 1 steps, however these steps are cleverly selected.


In particular, OSA agreed with the optimal algorithm (Viterbi) on only 58.8% of predictions under both entropy and expected classification error measures suggested in [24].

just with L = 1, Peek Search matched with Viterbi on 77.4% predictions thereby outperforming OSA by an overwhelming amount (over 30%).


We leverage insights from Randomized Peek Search to translate its almost optimal expected performance to the deterministic setting.

we introduce the Peek Reset algorithm that may be loosely viewed as a derandomization of Randomized Peek Search.


Note that we do not make any distributional assumptions on the rewards for any (L + 1)-long peek window.

our algorithms accommodate various settings, including those where the rewards may be revealed in an adaptive (e.g.  non-stochastic, possibly adversarial) manne


(Right) If the graph is not fully connected, some of the transitions may not be available (e.g. state 4 to state 1 in our case).

the online algorithm might not be able to join the optimal path in one step, and thus may have to forgo additional rewards.


We also provide the first results on the limits of online Markov decoding under latency constraints.

we craft lower bounds for the online approximation of Viterbi algorithm in both deterministic and randomized ergodic chain settings.


We resolve the fundamental problem of online decoding with general n th order ergodic Markov chain models.

we provide deterministic and randomized algorithms whose performance is close to that of the optimal offline algorithm even when latency is small.


On the other hand, we found OSA to be significantly suboptimal in terms of both log-probability and label agreement.

oSA agreed with the optimal algorithm (Viterbi) on only 58.8% of predictions under both entropy and expected classification error measures suggested in [24].


Moreover, we establish that no online algorithm can perform significantly better than our algorithms.

our algorithms provide strong guarantees even for low latency, and nearly match the lower bounds for sufficiently large latency.


The facility costs for a and b are respectively f and 0.

we can assume the facility b is always open.


The expected connection cost of the solution S 1 is O(1) times that of S 0 .

combining the two lemmas, we have that the expected cost of the solution S 1 is at most O(1/ )opt, finishing the proof of Theorem 6.


Intuitively, we can imagine that the central curator should treat all copies independently: the input data for one copy should not affect the decisions we make for the other copies.


Almost all existing methods assume that the labelled nodes are randomly selected.

the probability of missingness may depend on the unobserved data after conditioning on the observed data in the real world.


On the other hand, the normality assumption of the error term can be restrictive for GNM consisting of (1) and (3).

in the real data part, GNM is also compared with the model with a misspecified ignorable missing mechanism, and some other state-of-art 'de-biasing' methods.


This is the first model selection result for contextual bandits with non-vacuous regret for all values of d m , and to the best of our knowledge is the first positive result of this type for any online learning setting with partial information.

our problem of estimating E m,i clearly falls into this general setup if we uniformly explore the actions for n rounds, then set {x s } n s=1 to be the features obtained through the feature map i and {y s } n s=1 to be the observed losses.


In any case this function is run only once on each node (line 33): all pending messages are canceled (lines 36-37) and the decision is published (line 40) with a unique message identifier that is produced using grpId as a seed (line 39).

if several nodes make a decision in the same session, all messages carrying this decision will have the same identifier and will thus be considered as duplicates of the same message by the communication layer.


Once an interim topology is formed, the process with the lowest id becomes the leader, and only that process takes up the responsibility of rebuilding the power-law topology.

in the domain of man-made systems, if the topology of a power-law netÂwork gets altered due to failures or adversarial attacks, then remedial actions to restore the power-law property are very important.


All such slots are summed and are assigned as the credit of link ej denoted as creditj .

some of these slots may have been already allotted to the links preceding ej in this group Gk.


In [5], the authors propose a heuristic for determining the transmission order by ranking links along a path.

their ranking does not allow slot reuse along a path and is for minimizing the round-trip delay -not the end-to-end delay.


The scheme results in end-to-end delay spanning multiple frames.

because the TDMA frame length is minimized, the end-to-end delay is minimized while increasing the throughput.


we recycle the discharging current of the LCD to reduce energy consumption.


We assume that at any time interval, a node is in contact with at most (N - 1) nodes.

snw and EBR routing defines specific number for message copies.


These protocols replicate a fixed number of messages to the encountered nodes.

in Epidemic [20] and Prophet [13], unlimited copies of message is replicated in the network for increasing the chances of delivery.


It is important to note that our system is designed to be transparent to already-existing streaming infrastructure in order to ensure both vendor neutrality as well as simplified deployment.

we do not operate our own stream source, but instead we assume that there is an already-published stream, for instance from a Content Distribution Network (CDN), that can be played directly from its source and our customers would transparently use our system to minimize the load on that source.


This can be direct interactions between peers, such as the communication over a chat, but also less immediate elements, such as the higher probability of porting to a friend’s location who can then act as initial seeder for that region.

physical proximity (e.g., when inducing low latencies between peers) can also influence the overlay topology as well as the function of specific peers.


According to our measurement, the content popularity decays quite fast with time since content consumers attention mainly focuses on the newly created content.

the out-of-date content will lose user interests very soon.


They show that EFT (Earliest Finish Time first) scheduler outperforms in most cases.

it also requires a performance model and parameters for given tasks before scheduling.


In addition,the appearance of the light (e.g., repeated lamp pattern) might also interfere the stripe pattern in the captured image.

we first apply a noise subtraction technique to reduce the noise.


It however has a limit because the allowed strip width is eventually constrained by the resolution of an image.

as a result, it is very hard for a light to dynamically adapt the level of redundancy required by a specific receiver.


To understand the causes of these problems, we classify the unsynchronized scenarios into two cases.

in practice, a receiver can only measure the number of rows occupied by a strip W as an integer estimate of Wreal ,and demodulate the symbol by 1f′ = 1 2W Tr.


However, the exposure duration of the tested smartphones cannot be configured manually.

we reduce the distance of the light-to-camera link so that the camera can automatically pick a low exposure duration.


Existing systems that either have low data rate or support only repetitive transmission can ignore this problem since small error in strip width estimation would not affect the bit error rate.

to boost the throughput, a high order frequency modulation is needed, in which case small error in strip width estimation would result in a high bit error rate.


Signal losses in a NLOS link hence only occur during the aforementioned idle time gap.

aNLOS link experiences relatively constant losses, while a LOS link might suffer from dynamic losses, depending on not only the idle time gap but also the size and the location of the captured light in the image.


In contrast, when the original SNR was low (12 dB with minimum Data PHY rate of 385 Mbps), no link connectivity is possible irrespective of the blockage position.

under human blockage, beam dilation can help maintain link connectivity, but only if the original link SNR is high, and when blockage is sufficiently far away from the transmitter/receiver, such that only part of the beam is blocked.


The measurement reveals new challenges pertinent to MAC protocols involving ultra-directional, flexible 60 GHz beams.

60 GHz links become highly sensitive to human blockage and device motion, posing non-trivial tradeoffs between link quality and responsiveness, especially w.r.t. the beam searching and interference-aware scheduling.


beam to quasi-omni leads to 3Ã— higher throughput, despite its lower bit-rate (when steering overhead is not considered).

beam steering overhead is much smaller compared with the device motion case, and SNR-maximizing beam-steering can still be effective


The theory of optimal experiment design [2] has established a set of such objective functions.

maximizing the determinant of FIM (aka D-optimality )leads to a design that minimizes (a bound on) the volume of the error ellipsoid, and minimizing the trace of the inverse FIM (aka A-optimality) leads to a design that minimizes (a bound on) the average mean squared error (MSE).


Similarly, node 5 takes the place of node 1, while node 1 becomes part of the corresponding degree 1 chain.

whenever a peer departs, the child of this peer in any Gi can connect to his parent in that Gi.


In this scheme, BSs are also deployed in the sensing field and are supplied with limited available energy.

keeping all deployed BSs always active is not energy effcient and we have to optimize both the energy effciency of BSs and that of sensor nodes.


We can bound the number of consecutive iterations such that there is no improvement in the max-flow.

every such iteration will add at least one node to the source set.


depends on the topology of G, our proposed policy LFBP can adapt to the topology changes and efficiently track the optimal solution.Additionally, the loop free structure of a DAG is preserved under link removals.

can be chosen such that it is greater than the largest difference between the rescaled states.


An edge E i. bidders i and j conflict with each other when they use a same channel simultaneously.

a channel can be allocated to multiple bidders as long as no edge exists between any pair of these bidders, which is referred to as spatial reusability.


Finally, list B is obtained by walking through the tree layer by layer from the root node and from left to right on each layer.

whenever we visit a new node, we append it at the tail of a FIFO list.


Existing auction mechanisms are mainly proposed to be strategy-proof to stimulate bidders to reveal their valuations of spectrum truthfully.

(a) It can effectively improve bidder utility, giving bidders incentive to cheat.


With network diversity, the cooperation could lead to higher robustness to fading and mobility due to a diversity gain.

there could be static clients that experience low coherence times due to the dynamics of the environment they are in (e.g., human motion, closing/opening of doors).


The main objective here is to observe the influence of performance factors on the systems and test the workability of our method.

in section III, we present our service quality-based reimbursement model.


In [8], we showed that GameOn can network players with reliable p2p connections.

a question we did not answer is how to improve the second dimension of reliability, i.e., forming a group with sufficient long collocation time.


GameOn uses p2p network techniques such as Wi-Fi Direct and Bluetooth to avoid long latencies of the cellular network.

gameOn has to take the peer connectivity into consideration so that peers can reach out to each other directly once a game session gets started.


Although physical systems can often be modeled and described using first principles from physics and conservation laws [1], it is fundamental to ensure that the values of the parameters in the model describing the plant be inferred from input/output data.

it is required to ensure identifiability of the corresponding dynamical systems [2]; in particular, due to the unknown nature of the parameters that we aim to determine (i.e., to identify) it is common to consider structural identifiability – two models are the same if and only if the parameters describing this model are the same [3].


In any case, the application of a hard decision mechanism on an analog signal (i.e., single thresholding 0 or 1 , all-or-none) requires careful control of its features to operate without additional calibration mechanisms.

moreover, a two bit additional selection input is considered to enable a user to choose the possible 4 frame_size.


By statistics analysis, we find that the traffic density of the aggregate traffic load of different days do not change obviously.

the traffic density of different hours varies significantly.


Researchers found out that the inhomogeneity in the spatio-temporal distribution of the data traffic leads to extremely insufficient utilization of network resources.

it is important to fundamentally understand this distribution to help us make better resource planning or introduce new management tools such as time-dependent pricing to reduce the congestion.


The Root-MST algorithm is suitable for real-time application.

the MUSIC-MST is suitalbe for the high accuracy DOA estimation when the real-time is not necessary.


the computational complexity is very high.


Then the teleoperator can have a understanding of the detected environment and operate the telerobot precisely and efficiently.

in the link, the idea of cognitive radio is utilized and an adaptive channel switching mechanism is employed to avoid unpredictable interferences.


The internal jammer plays a twofold role: we assume ADV has previously compromised the secret keys of a set c = {0, 8, 16, 24} of nodes of the network (with N = 32) , while retaining further jamming capabilities.

aDV jams the transmissions with probability  pA ∈ [0.1, 0.9]: the adversary firstly jams the frequencies related to the keys it is in possession of, than if it has enough jamming power, it random chooses other frequencies.


Pairs in TDBS are pre-computed during the off-line phase.

the protocol is “static” —with respect to the network pairs— and cannot deal with the join of a new node: a new node joining the network involves a new computation of all the 1-factorizations for all the network pairs, and therefore, all the pseudo-random frequency sequences must be invalidated and re-computed.


Note that a hardness result for execution time follows also from the hardness of the communication cost when using a single object for all transactions, since the optimal TSP tour for an object provides the best time bound for scheduling the object in its transactions.

this reduction does not prove that the execution time is hard to approximate.


Therefore, an execution schedule in G has duration χ time steps if and only if H has a valid coloring with χ colors.

fig. 1 highlights the sets of transactions to use q1,2 in block B1


It also presented mutated binary search which takes advantage of the prefix length distribution for each specific prefix.

the algorithm makes branching decisions based on the characteristics of the sufixes of the visited prefix entry, reducing the average number of memory references significantly.


These URL datasets may not fully characterize the NDN forwarding rules in the future.

as namespace design and name assignment principles are still being studied, the characteristics of the NDN FIB tables have not been determined, and it is even possible that multiple namespaces with distinct characteristics may co-exist.


The translation lookaside buffer (TLB) is employed to accelerate the address translation procedure.

for applications require large memory space and expose scarce memory access locality, the page-based virtual memory system consumes a considerable amount of CPU cycles due to TLB misses [28].


The prefetching performance with CityA was better than SipE, but the benefits of prefetching all buckets still diminished due to excessive prefetching.

more efficient name parsing and hash implementation as well as prefetching strategies are needed.


It is the first Interest-based forwarding strategy shown to be correct in the presence of Interest loops, Interest aggregation, faults, and the forwarding of Interests over multiple paths.

unlike ndnSIM in which the next hop selection for requested prefixes is based on hop count, in SIFAH next hops are sorted based on rank of each FIB entry.


To satisfy the requirement described in Subsection 2.1, we consider content name elements as follows.

these results show that the system can access the newest content and can change the retrieval stream dynamically by changing only the requested content name.


Experimentation in real testbed using GeoNetworking implementation can provide very precise evaluation result in the deployment phase of the implementation, however it requires heavy cost (i.e. vehicles, equipment, drivers, time, etc.).

the simulation can provide the evaluation result in flexible networks with various scenarios in a low cost, however the result is based on the preconfigured model in the simulation and often diverse from the experimental evaluation in the real testbed.


Then, we can know that when an accident occurs on road (1), vehicles on roads (2), (3) and (4) are expected to go to road (1) with probabilities 3/8, 3/8 and 1/3 respectively.

with those probabilities, we can represent the roads using the spanning tree in figure 3.


The STRIDE Threat Model does not rate the extent of a threat, it only reveals if the system is vulnerable to it.


Again, as the wireless links are direct point to point and are not shared with any wired paths, they do not contribute to any cyclic dependencies involving either wired or wireless links.

the dynamic MAC operates as a token passing MAC when the wireless utilization is higher than a pre-set threshold.


In line 23, a generic monitoring socket (msock) is created.

a new event can be defined by a built-in event and a custom function that further determines the condition for the event.


When SRVCC is integrated into the network, existing threshold values should be adjusted to guarantee that inter-RAT handover never occurs before SRVCC.

even after verifying the proper threshold, call drops due to SRVCC failures are still captured, which indicates that incorrectly ordered delivery of the signaling messages still exists.


To address these issues, recently several breakthrough systems [10 15] are developed for seeing through the walls with WiFi.

these past systems still need either customized devices


These virtual antennas are wirelessly connected to the reader and carry the reflections through the backscatter signals.

an additional step is required to separate the reflections off the moving object from the backscatter signals reflected by the tags.


Specifically, the tag right below the phone has the maximum value.

our problem is to recovery the trajectory of the moving object behind walls and closed doors given a sequence of read frames.


In the case of absence (or presence) of moving object, the tags will suggest a signal difference lower (or higher) than a threshold.

if the tags suggest the signal difference is lower (or higher) than a threshold, it indicates the absence (or presence) of moving object.


Deploying ECNs together with cameras can inevitably increase the infrastructure cost.

when wireless capacity is limited, the saved bandwidth by Vigil can be used to forward users' traffic, thereby recouping the cost of ECNs.


There is no one fixed choice of tperiod; it depends on the application scenario.

we achieve VLC in the dark by reducing the duty cycle of the light source to an extremely low level such that it becomes imperceptible (Figure 1) to human eyes.


This new primitive enables VLC links to maintain always-on communication, greatly broadening its applicable scenarios and lowering the energy consumption of LED lights.


In addition, we used base station user currently connect as user's current location.

if a user switches frequently among very close base stations due to cell oscillation, methods of removing data noise is expected.


Handover incentives for self-interested wlans with overlapping coverage.

a low rate transmitter influences the throughput of all Wi-Fi transmitters and all transmitters achieve the same throughput.


When the Wi-Fi direct transmission rate is higher than the Wi-Fi hotspot rate, then the throughput to mobile A in Figure 1(a) is maximized if only the Wi-Fi direct link is used.

if the Wi-Fi hotspot rate is higher, then the selection that maximizes the throughput depends on whether the hotspot backhaul is a bottleneck.


When performing an assigned task, nodes will constantly report their findings while they might only communicate occasionally when returning from or moving to the task location, or while they rest.

the report messages sent during the actual task performance contain rather novel information with respect to overall situational awareness at the central command.


Currently, rescue organizations mainly use interactive voice communication during their missions [4].

the communication is very sensitive to delays.


The random traffic model creates rather static traffic due to constant time intervals and message sizes.

changes of the traffic intensity are not covered.


Classic forwarding schemes do not take into consideration the peculiarities of the THz communications which may limit the network performance.

a novel channel-aware forwarding scheme is proposed to overcome the frequency selective feature of the THz band via judicious selection of the next-hop node.


The longest forwarding scheme only experiences two hops to reach the sink hence the end-toend delay is low.

the long transmission distance of the first hop penalizes the capacity.


As a result, we find that the uniform throughput of a nanonetwork powered by energy harvesting is upper bounded by the power constrained bound [12].

the operation of an energy harvesting enabled nanonetwork operates as a power constrained network if the energy conversion is perfectly performed.


Sections 3 and 4 provide an upper and a lower bound for the throughput capacity.

implementing nanosensors will set very strict constrains in the area and maximum capacity of the energy bu↵ers.


Several types of raindrop-size distributions are known and a specific attenuation model recommended by the ITU-R is usually being used for attenuation calculation.

they are not fully suitable for describing our artificial rain conditions which obey a different raindrop size distribution.


A bigger bag is the more accurate because it models more interactions between nodes and octree areas.

the figure 3 indicates that increasing the bag volume over a liter consume more and more CPU.


Context discovery can also be achieved without performing ambience fingerprinting, where crowdsourcing is used for the extraction of semantic location from sensor data (e.g., audio, image), without any need for fingerprinting [8].

this type of approach relies on heavy computer vision techniques (e.g., scene classification, optical-character-recognition, object recognition), as well as heavy natural language processing (e.g., speech recognition, sound classification).


This can also be achieved in the context of IoT, by associating access to beacon-advertised websites (e.g., by UriBeacons [14]) to the user's MAC address.

whenever a user clicks on a link that was advertised by a specific beacon, it creates an opportunity for the server to associate the user's MAC address to the IP address from which the website was accessed.


In the grocery store scenario, besides having the familiar functionality to search, see product recommendations and browse for products, IoT apps supported by Incognito will be able to provide richer, interactive shopping experiences.

ioT apps will enable more natural text query scenarios: What is popular with women?


The specific requirements will depend on the design of the ecosystem.

additionally, we assume that the user has access to a service that maps the location to an authoritative server for that location/environment.


By exposing a little more information about their identity and shopping history, the user may be given new suggestions for what to buy or even access to special sales.

a user may not want to expose all of their personal information in a given context.


The stricter insertion rule of OPC, which only inserts chunks in sequence, reduces the DRAM accesses for insertions/evictions by roughly 40%, leading to better memory performance.

nevertheless, ICN caching has not yet met these expectations, receiving criticism for its effciency [8, 9], based on the debatable performance superiority of distributed in-network caching over independent caches at the network edge, as well as on the questionable support for packet-level caching by today's hardware.


By individually naming each packet, ICN allows routers to turn their queueing buffers into packet caches, thus exploiting the network s existing storage resources.

the performance of packet caching at commodity routers is restricted by the small capacity of their SRAM, which holds the index for the packets stored at the, slower, DRAM.


(b) The spatial distribution of the waypoints visited by humans can be modeled by fractal points.

people are more attracted to certain areas than to others, thus creating a clustering phenomenon around popular places that can be observed across several spatial scales [10].


We did not use the speed model suggested by the authors of SLAW because it is used to represent both pedestrians and people moving by other means.

we are only considering walkers in a campus.


Results corresponding to this metric are plotted in Figure 3.

we found that the MANET topology under SLAW shows a high level of connectivity, whereas random waypoint based models exhibit a high isolation ratio.


It is important to take into account that different mobility models lead to differences in the spatial node distribution.

the first step in our evaluation methodology is to determine the connectivity properties resulting from the use of different mobility models.


However, for a route to be useful, it is needed that it remains valid at least until the reply for a query returns to the sender node; this is not always the case.

a flexible routing mechanism able to find alternate routes is necessary in order to maximize the benefits of the services provided by the MANET.


Besides, Figure 2(a) shows that the probability of having 2-hop neighbors with either RWP or HRWP is nearly zero.

this probability is about 0.3 considering the SLAW mobility pattern.


It is directly affected by node mobility.

it is useful to determine the effects that node mobility causes on a routing protocol.


All name mappings overheard by any (intermediate) node are stored for future use.

we realize a (partial) knowledge distribution in order to answer future requests more quickly.


If a node needs the corresponding IP address to a given name, it sends a request to the multicast group and the corresponding node answers the request again with a multicast group message.

the highest delay is observed in the DNS mode, because of the combination of the three time delays to find a route to the server, communicate with the server, and finally find a route to the node.


In mDNS, the request and the response are both sent multicast to the whole group – or in our system broadcast to the whole network.

the necessary information, e.g., finding an IP and a route to its host, can be gained in parallel rather than sequential.


Due to the appearance of the licensed users, the unlicensed users cannot access all of n channels, and we denote the channels not occupied by the licensed users as available.

both users can find out the set of available channels after taking a short sensing stage.


In the long term, addressing the problem of increasingly poor quality information obtained through channel sensing will require new approaches to medium access control in wireless networks.

given the large existing userbase of devices operating on CSMA-based protocols, in particular the 802.11 protocol family, it is worth considering what we can do to mitigate the problem without drastically changing the current protocols, as an interim solution while new protocols are developed and standardised.


This type of collision can be a large source of overhead if many nodes are waiting to transmit and we are using a small p value (or small contention window size in 802.11).

the rate of these collisions does not depend on a; it is instead a function of p (or contention window size).


Conversely, at low G, we see very high optimal p values, as there is so little traffic that the probability of collision is very low, and the best strategy is for nodes to simply transmit as soon as they have data available.

if we wish to replicate this behaviour using the contention window in the 802.11 DCF, both very high and very low values of p will be problematic.


This makes certain simplifications possible.

there is no notion of packet transmission time, since the packet arrival rate does not need to be considered.


The goal of CSMA is to use channel sensing to gather more information about the channel state before transmitting.

the information gathered is not perfect — it does not exactly match the true channel state at any given time.


Thus, considering only the service rates of the cells to account for their heterogeneity, as in JSQ-µ, is not sufficient and could even turn out to be counterproductive.

jSQ-I, that uses knowledge of femto channel state is systematically performing better than pure JSQ.


We study various load balancing strategies that aid the machines to select a proper cell in such a multi-RAT heterogeneous network deployment scenario so that the access delay for the M2M traffic is minimized.

we derive the optimal static policy of choosing between the WLAN femtocell and the LTE macrocell.


The reason becomes obvious if we look at the optimal static assignment probabilities at the femto (Figure 4, lower panel), which indicates that for these values of arrival rates, it is always optimal to stay in the femto as macro already has enough users.

at higher values of the arrival rate, FPI performs much better than the optimal static policy, and even the JSQ-based policies outperform the optimal static policy by a good margin.


In the proposed scheme, nodes periodically exchange constant-size hello messages with their one-hop neighbors.

the protocol's network complexity is constant in terms of the network size.


Consumer mobility is handled as if there is a failure in the network where unsatisfied Interests will be retransmitted.

during this process the Consumer will experience a long delay to retrieve the data which may affect the QoS of real-time applications [3].


3.1 Mobility Anchor Approach Schemes in this class are based on the Mobile IP protocol [6], which is a proposed standard in the Internet Engineering Task Force (IETF) to support host s mobility in IP networks.

mobile IP uses fixed nodes called Home Agents that keep track of the location of all mobile hosts originally registered in the home network.


The Hybrid scheme takes advantage of both techniques.

regular Data will always be sent using the shortest path, while Data during handover will be tunneled and will not be dropped.


This process needs three Data structures: Forwarding Information Base (FIB), Pending Interest Table (PIT) and Content Store (CS) [23].

fIB is used to forward Interest s to the nearest content location.


The existing ndnSIM (and ns-3) does not provide Wi-Fi handover between access points, nor a mobility management scheme.

two main updates are done to the current ndnSIM: AP handover handler and a base for mobility management schemes.


The main functionalities of NDN are assumed to have the default values in ndnSIM.

ndnSIM uses an opportunistic scheme named as Cache Everything Everywhere for object caching [7].


Name-based information dissemination for fragmented networks in disasters.

rP chooses data mule b as the next hop because data mule b is likely to reach Shelter#4 faster compared with data mule c. Similarly, Shelter#4 selects data mule c as the next hop for a message to RP.


The sums of weights over the routes are 67 (57 + 10) and 74 (42 + 32), respectively.

a rendezvous point-based publish/subscribe mechanism to support information dissemination in fragmented networks has been proposed in [6].


The events can take place at dedicated venues, for example, arenas, or take place ad-hoc on the streets of a city or in the countryside.

the system functionality is implemented on a set of fixed NetInf routers together with a mobile streaming application developed for video recording and viewing on Android phones and tablets.


The manifest is an object (say, in xml) describing the properties of the content object that are relevant at the network layer.

the manifest includes the location of the object, some security properties and some information regarding the transport of the object, including for instance its size.


There is a tension in making the manifest expressive, but at the same time, in keeping it simple.

the DNS++ request also includes the source address, as the network needs to know which client is making the request for path optimization.


We collect the link delays to show the network performance is comparable to the NDN Testbed.

developers could evaluate end-to-end application performance before evaluating it in the NDN Testbed.


With respect to the first strategy, if a rational gateway manages to convince clients of a smaller level of deduplication, the gateway can charge higher prices.

with respect to the second strategy, the gateway may try to derive information about the contents of the uploaded files3; notice that acquiring information about users data can be economically beneficial for the gateway since the stored data can be misused, e.g.


This is mainly due to the fact that BLS signatures are considerably faster to compute by the gateway when compared to RSA signatures.

as shown in Figure 4(b), BLS signatures are more expensive to verify by the clients than the RSA-variant employed in DupLESS.


Namely, Bitcoin relies on blocks, a hash-based Proof of Work concept, to ensure the security of transactions.

bitcoin peers need to find a nonce, which when hashed with the last block hash and the root of the Merkle tree accumulating recent transactions, the overall hash is smaller than a 256-bit threshold.


Moreover, we assume that all parties are synchronized.

at each point in time, all parties share the same view on the current epoch (e.g., its index number if epochs are represented by an increasing counter).


Similar to [10], we instantiate GetRandomness by leveraging functionality from Bitcoin, since the latter offers a convenient means (e.g., by means of API) to acquire time-dependent randomness.


This condition minimizes the number of open nodes in the tree, and hence the effort in proving/verifying membership and cardinality.

by doing so, ClearBox enables cloud users to verify the effective storage space that their data is occupying in the cloud, and consequently to check whether they qualify for benefits such as price reductions, etc.


According to [21], from context information, the services may be adapted, adjusting to characteristics of the current environment and from the user profile.

it is possible to provide more optimized and customized services, in order to offer a greater quality of experience to users.


In the third and fourth experiments, it was observed that the time to restore the QoE after the degradation detection is 5 seconds.

the time of restoration of the QoE can range between 9 seconds, once the semantic engine is programmed to perform the inference process every 5 seconds.


The first group requires from 20% up to 40% more time for both elapsed and CPU time.

note that CPU time represents both the kernel-land and user-land CPU time.


Note that CBC has 128 bit block size which adds overhead over CTR.


Using IPsec, the encrypted data are independent of the MTU as IPsec fragmentation occurs over the encrypted payload.

reducing the MTU size increases the transmitted IP headers as well as networking packet processing, whereas encrypted data remain the same.


Intelligent Transportation Systems (ITS) include techniques and communication technologies between vehicles (V2V) and between vehicles and infrastructure (V2I).

iTS are not limited to road transport but include the use of information and communication technologies (ICT) for rail, water and air transports.


This can be explained by the fact that in the case of a low number of nodes, multiple forwarders retransmit the received messages since they do not hear other retransmissions (low number of nodes).

as long as the number of nodes increases, several nodes close to forwarders mute themselves since the schedules of other nodes expire first.


While this momentum is encouraging, a closer look under the hood reveals a less rosy picture: NFV products and prototypes tend to be merely virtualized software implementations of products that were previously offered as dedicated hardware appliances.

this native API provides support for: zero-copy packet transfer over vports for high throughput communication between E2D and NFs, and rich message abstractions which allow NFs to go beyond traditional packetbased communication.


nFV is currently replacing, on a one-to-one basis, monolithic hardware with monolithic software.


We selected it for consideration because it contains multiple 2@/112-dense prefixes, identified in the forthcoming results in Section 6.2.2.

the structure shown is markedly different from other plots.


First, we monitor the landscape of devices that listen to DNS requests and reply with valid DNS responses.

we perform weekly Internet-wide scans in IPv4 for more than one year and enumerate the responsive DNS resolvers.


We begin with a general overview of DNS resolvers in the IPv4 address space.

concurrent to our work, Scott et al. [32] probed the IPv4 address space for open resolvers to analyze DNS resolutions.


Our clustering mechanism helped to group similar HTTP responses and served as data exploration step to further categorize the results.

we assigned labels to each individual cluster and mapped these labels to website categories that classify the type of served HTTP content.


In the second phase of our study, we take the viewpoint of a client system to analyze the integrity of the DNS resolutions provided by open recursive DNS resolvers.

we aim to understand whether the resolvers actually return legitimate answers or perform bogus resolutions to provide clients with false IP address information.


To verify that our results are not biased due to Internet-wide scans performed by other organizations or research projects, we monitored incoming DNS requests to our network and identified a small set of hosts that performed DNS scanning activities.

we observe a single daily DNS request initiated by the Shadowserver Foundation and single DNS requests from Team Cymru and the Open Resolver Project within our monitoring period of 10 days.


To aggregate HTTP data, we use the IPs returned by the resolvers upon resolving the domains and request HTTP information as if they belong to the original website that we tried to contact.


We found these executables to be linked to malicious software.

we analyzed the samples dynamically and observed them to attempt downloading further (potentially malicious) executables.


Prior to learning, we bin data as described in Section 0 5.1.1.

we use only 5 bins for each management practice (instead of 10), because the amount of data we have is insuf.cient to accurately learn .ne-grained models.


By comparing cases that have the same propensity scores i.e., an equally likely probability of being treated based on the observed confounding practices we can be confident that the actual presence or absence of treatment is not determined by the confounding practices.

a treated case and an untreated case with the same propensity score have the same probability of having a given value for a confounding practice (e.g., number of roles); thus propensity score matching mimics a randomized experiment.


The main issue is that the components output by PCA are linear combinations of a subset of management practices.

this approach also makes the implicit assumption that linear combinations of practice metrics can explain network health.


We find that basic learning algorithms (e.g., C4.5 [27]) produce mediocre models because of the skewed nature of management practices and health outcomes.

they over-fit for the majority healthy network case.


Given that ticket logs capture a wide-range of network issues, operators view tickets as a valuable measure of network health.

operators from the OSP indicated that number of tickets is a useful metric.


To model control plane structure, we leverage prior work on configuration models [5].

we extract routing instances from device configurations, where each instance is a collection of routing processes of the same type (e.g., OSPF processes) on different devices that are in the transitive closure of the adjacent-to relationship.


Furthermore, not every device is changed every month in 77% of networks less than half of a network's devices are changed in a given month but most devices are changed at least once per year in 80% of networks more than three-quarters of the devices are changed in a year (Figure 12(b)).

changes occur frequently, and to different sets of devices in different months.


Many of the certificates that we find in the scans are invalid (e.g., self-signed certificates on WiFi routers).

we pre-process the dataset by verifying all observed certificates.


In addition to being the first approach to reliably detect differentiation from mobile devices without special privileges, our study also identifies several instances of middleboxes violating end-to-end principles for a variety of popular apps.

we find evidence of video transcoding, HTTP header manipulation in requests and responses, and proxies that modify TCP behavior.


However, using our testbed containing commercial traffic shapers (Section 4.2), we found that some shapers will by default label random traffic with high port numbers as peer-to-peer traffic, a common target of differentiation.

this approach is unreliable to generate control trials because one can reasonably expect it to be shaped.


In particular, we can attribute the increased number of sources in recent years to bugs and misconfigurations in P2P networks (Qihoo 360 Safe and BitTorrent).

sources sending P2P traffic generally produce few connection attempts at irregular intervals.


The Bro Scanner category is based on Bro's definition of a scanner: contacting at least 25 unique destinations on the same port within 5 minutes [46].

the mix of popular applications changes regularly [41], which reduces the predictability of IBR.


We cannot validate all path changes from the hosts sending IBR, as we do not know when these hosts start sharing links to the darknet.

aS-level path changes should be observable in both Ark data and IBR.


Like PlanetSeer and Hubble, passive traffic measurements such as IBR can help inform when and where active measurements would be most useful [31, 51].

for some IP addresses the inter-observation time is still short (27% of IP addresses have a median inter-observation time of less then 1 hour).


Although IBR originates from many sources, we lack control over who sends it and when.


Kumar et al. examined IBR from the Witty worm to extract host uptimes.

since Witty targeted a buffer overflow in network security products, the number of networks they could analyze was limited (inferring uptime for only about 800 machines) and not diverse (about a quarter of the machines were from only two institutions) [32].


For these devices, the paradigm for user-interaction has shifted away from on-device interfaces to richer, downloadable soft user interfaces (UIs) on remote devices like smartphones.

requiring the involved process of app installation for every new device, as is currently prescribed in industry, is unrealistic and does not scale well.


when a user selects an item from the list, the best possible interface for a given device is opened within the app, presenting a user interface that may interact directly with the device.


The emerging user interface paradigm for many such things eschews physical buttons, knobs, and displays in favor of virtual interfaces that are downloaded from the web and rendered on remote platforms like smartphones.

such smartphone app-based interfaces often require tedious discovery and installation, as well as device discovery, pairing, and configuration before a user can interact with a nearby device.


As the flying foxes dataset is relatively sparse in recorded contacts between animals, we model behavior and motion patterns of the animals based on empirical traces.

we classify the animal behavior into one of three states: resting, foraging, and travelling between foraging sites.


PROPHET [14] attempts to improve delivery probability for a limited number of copied messages by sending copies to the best nodes it meets.

conceptually, there is need for balancing the energy spent on sensing, data mulling, and delivery of direct packets to destination.


To save energy, the static nodes work in a (q, p) duty-cycle schedule, which impacts the packet receiving for the static node.

the probability for the static nodes to be successfully synchronized and localized is analyzed.


In the proposed scheme, if the static node successfully receives 4 or more packets, it could be synchronized and localized.

due to the mobility of the mobile node, the limited communication range of the nodes, and the (q, p) duty-cycle scheme, the probability of successfully receiving at least 4 packets is not always 1.


To detect or enforce neutrality through technical means, we need a concrete technical definition.

the way our community has defined neutrality, non-neutral networks may be preferable to neutral ones.


Presto argues that the main culprit of inef.ciencies in schemes like ECMP is the coarse granularity: each flow, even a large one, hashes all its packets onto one path.

presto partitions flows into equal size chunks of 64KB, called flowcells, and sprays them in a round-robin fashion among available paths.


The well-connected nature of PlanetLab nodes means that our results are more representative of enterprise users rather than wireless or home users.

some of our insights (e.g., loss correlation on wide area paths) have broader applicability.


In contrast, OVS3 had already finished FlowMod processing when it received the packet.

oVS3 received it with the new routing information, i.e., clockwise type.


Network routing information can be dynamically modified with ease using software-defined networking (SDN).

it is not possible to simultaneously change the routing information in all the switches in a network.