OpenRaiser/Intern-Atlas huggingface dataset:
Interpretable VAEs for nonlinear group factor analysis
Venue: arXiv cs.LG 2018
Year: 2018

Deep generative models have recently yielded encouraging results in producing subjectively realistic samples of complex data. Far less attention has been paid to making these generative models interpretable. In many scenarios, ranging from scientific applications to finance, the observed variables have a natural grouping. It is often of interest to understand systems of interaction amongst these groups, and latent factor models (LFMs) are an attractive approach. However, traditional LFMs are limited by assuming a linear correlation structure. We present an output interpretable VAE (oi-VAE) for grouped data that models complex, nonlinear latent-to-observed relationships. We combine a structured VAE comprised of group-specific generators with a sparsity-inducing prior. We demonstrate that oi-VAE yields meaningful notions of interpretability in the analysis of motion capture and MEG data. We further show that in these situations, the regularization inherent to oi-VAE can actually lead to improved generalization and learned generative processes.
Seasonal Web Search Query Selection for Influenza-Like Illness (ILI) Estimation
Venue: arXiv cs.IR 2018
Year: 2018

Influenza-like illness (ILI) estimation from web search data is an important web analytics task. The basic idea is to use the frequencies of queries in web search logs that are correlated with past ILI activity as features when estimating current ILI activity. It has been noted that since influenza is seasonal, this approach can lead to spurious correlations with features/queries that also exhibit seasonality, but have no relationship with ILI. Spurious correlations can, in turn, degrade performance. To address this issue, we propose modeling the seasonal variation in ILI activity and selecting queries that are correlated with the residual of the seasonal model and the observed ILI signal. Experimental results show that re-ranking queries obtained by Google Correlate based on their correlation with the residual strongly favours ILI-related queries.
Simplicial Closure and higher-order link prediction
Venue: arXiv cs.SI 2018
Year: 2018

Networks provide a powerful formalism for modeling complex systems by using a model of pairwise interactions. But much of the structure within these systems involves interactions that take place among more than two nodes at once; for example, communication within a group rather than person-to person, collaboration among a team rather than a pair of coauthors, or biological interaction between a set of molecules rather than just two. Such higher-order interactions are ubiquitous, but their empirical study has received limited attention, and little is known about possible organizational principles of such structures. Here we study the temporal evolution of 19 datasets with explicit accounting for higher-order interactions. We show that there is a rich variety of structure in our datasets but datasets from the same system types have consistent patterns of higher-order structure. Furthermore, we find that tie strength and edge density are competing positive indicators of higher-order organization, and these trends are consistent across interactions involving differing numbers of nodes. To systematically further the study of theories for such higher-order structures, we propose higher-order link prediction as a benchmark problem to assess models and algorithms that predict higher-order structure. We find a fundamental differences from traditional pairwise link prediction, with a greater role for local rather than long-range information in predicting the appearance of new interactions.
Distilling Knowledge Using Parallel Data for Far-field Speech Recognition
Venue: arXiv cs.CL 2018
Year: 2018

In order to improve the performance for far-field speech recognition, this paper proposes to distill knowledge from the close-talking model to the far-field model using parallel data. The close-talking model is called the teacher model. The far-field model is called the student model. The student model is trained to imitate the output distributions of the teacher model. This constraint can be realized by minimizing the Kullback-Leibler (KL) divergence between the output distribution of the student model and the teacher model. Experimental results on AMI corpus show that the best student model achieves up to 4.7% absolute word error rate (WER) reduction when compared with the conventionally-trained baseline models.
Comparison Based Learning from Weak Oracles
Venue: arXiv cs.LG 2018
Year: 2018

There is increasing interest in learning algorithms that involve interaction between human and machine. Comparison-based queries are among the most natural ways to get feedback from humans. A challenge in designing comparison-based interactive learning algorithms is coping with noisy answers. The most common fix is to submit a query several times, but this is not applicable in many situations due to its prohibitive cost and due to the unrealistic assumption of independent noise in different repetitions of the same query. In this paper, we introduce a new weak oracle model, where a non-malicious user responds to a pairwise comparison query only when she is quite sure about the answer. This model is able to mimic the behavior of a human in noise-prone regions. We also consider the application of this weak oracle model to the problem of content search (a variant of the nearest neighbor search problem) through comparisons. More specifically, we aim at devising efficient algorithms to locate a target object in a database equipped with a dissimilarity metric via invocation of the weak comparison oracle. We propose two algorithms termed WORCS-I and WORCS-II (Weak-Oracle Comparison-based Search), which provably locate the target object in a number of comparisons close to the entropy of the target distribution. While WORCS-I provides better theoretical guarantees, WORCS-II is applicable to more technically challenging scenarios where the algorithm has limited access to the ranking dissimilarity between objects. A series of experiments validate the performance of our proposed algorithms.
BAN-GZKP: Optimal Zero Knowledge Proof based Scheme for Wireless Body Area Networks
Venue: arXiv cs.NI 2018
Year: 2018

BANZKP is the best to date Zero Knowledge Proof (ZKP) based secure lightweight and energy efficient authentication scheme designed for Wireless Area Network (WBAN). It is vulnerable to several security attacks such as the replay attack, Distributed Denial-of-Service (DDoS) attacks at sink and redundancy information crack. However, BANZKP needs an end-to-end authentication which is not compliant with the human body postural mobility. We propose a new scheme BAN-GZKP. Our scheme improves both the security and postural mobility resilience of BANZKP. Moreover, BAN-GZKP uses only a three-phase authentication which is optimal in the class of ZKP protocols. To fix the security vulnerabilities of BANZKP, BAN-GZKP uses a novel random key allocation and a Hop-by-Hop authentication definition. We further prove the reliability of our scheme to various attacks including those to which BANZKP is vulnerable. Furthermore, via extensive simulations we prove that our scheme, BAN-GZKP, outperforms BANZKP in terms of reliability to human body postural mobility for various network parameters (end-to-end delay, number of packets exchanged in the network, number of transmissions). We compared both schemes using representative convergecast strategies with various transmission rates and human postural mobility. Finally, it is important to mention that BAN-GZKP has no additional cost compared to BANZKP in terms memory, computational complexity or energy consumption.
Novel View Synthesis for Large-scale Scene using Adversarial Loss
Venue: arXiv cs.CV 2018
Year: 2018

Novel view synthesis aims to synthesize new images from different viewpoints of given images. Most of previous works focus on generating novel views of certain objects with a fixed background. However, for some applications, such as virtual reality or robotic manipulations, large changes in background may occur due to the egomotion of the camera. Generated images of a large-scale environment from novel views may be distorted if the structure of the environment is not considered. In this work, we propose a novel fully convolutional network, that can take advantage of the structural information explicitly by incorporating the inverse depth features. The inverse depth features are obtained from CNNs trained with sparse labeled depth values. This framework can easily fuse multiple images from different viewpoints. To fill the missing textures in the generated image, adversarial loss is applied, which can also improve the overall image quality. Our method is evaluated on the KITTI dataset. The results show that our method can generate novel views of large-scale scene without distortion. The effectiveness of our approach is demonstrated through qualitative and quantitative evaluation.
Bias Compensation in Iterative Soft-Feedback Algorithms with Application to (Discrete) Compressed Sensing
Venue: arXiv cs.IT 2018
Year: 2018

In all applications in digital communications, it is crucial for an estimator to be unbiased. Although so-called soft feedback is widely employed in many different fields of engineering, typically the biased estimate is used. In this paper, we contrast the fundamental unbiasing principles, which can be directly applied whenever soft feedback is required. To this end, the problem is treated from a signal-based perspective, as well as from the approach of estimating the signal based on an estimate of the noise. Numerical results show that when employed in iterative reconstruction algorithms for Compressed Sensing, a gain of 1.2 dB due to proper unbiasing is possible.
Transport-Based Pattern Theory: A Signal Transformation Approach
Venue: arXiv cs.CV 2018
Year: 2018

In many scientific fields imaging is used to relate a certain physical quantity to other dependent variables. Therefore, images can be considered as a map from a real-world coordinate system to the non-negative measurements being acquired. In this work we describe an approach for simultaneous modeling and inference of such data, using the mathematics of optimal transport. To achieve this, we describe a numerical implementation of the linear optimal transport transform, based on the solution of the Monge-Ampere equation, which uses Brenier's theorem to characterize the solution of the Monge functional as the derivative of a convex potential function. We use our implementation of the transform to compute a curl-free mapping between two images, and show that it is able to match images with lower error that existing methods. Moreover, we provide theoretical justification for properties of the linear optimal transport framework observed in the literature, including a theorem for the linear separation of data classes. Finally, we use our optimal transport method to empirically demonstrate that the linear separability theorem holds, by rendering non-linearly separable data as linearly separable following transform to transport space.
007: Democratically Finding The Cause of Packet Drops
Venue: arXiv cs.NI 2018
Year: 2018

Network failures continue to plague datacenter operators as their symptoms may not have direct correlation with where or why they occur. We introduce 007, a lightweight, always-on diagnosis application that can find problematic links and also pinpoint problems for each TCP connection. 007 is completely contained within the end host. During its two month deployment in a tier-1 datacenter, it detected every problem found by previously deployed monitoring tools while also finding the sources of other problems previously undetected.
A fuzzy logic feedback filter design tuned with PSO for L1 adaptive controller
Venue: arXiv cs.SY 2018
Year: 2018

L1 adaptive controller has been recognized for having a structure that allows decoupling between robustness and adaption owing to the introduction of a low pass filter with adjustable gain in the feedback loop. The trade-off between performance, fast adaptation and robustness, is the main criteria when selecting the structure or the coefficients of the filter. Several off-line methods with varying levels of complexity exist to help finding bounds or initial values for these coefficients. Such values may require further refinement using trial-and-error procedures upon implementation. Subsequently, these approaches suggest that once implemented these values are kept fixed leading to sub-optimal performance in both speed of adaptation and robustness. In this paper, a new practical approach based on fuzzy rules for online continuous tuning of these coefficients is proposed. The fuzzy controller is optimally tuned using Particle Swarm Optimization (PSO) taking into accounts both the tracking error and the controller output signal range. The simulation of several examples of systems with moderate to severe nonlinearities demonstrate that the proposed approach offers improved control performance. Keywords: Fuzzy logic control, single-objective, multi-objective particle swarm optimization, L1 Adaptive control, fuzzy L1 adaptive controller, L1 fuzzy adaptive control, L1 fuzzy adaptive controller, fuzzy L1 adaptive control, Filter tuning, Fuzzy membership function tuning, optimal, optimal tuning, Fuzzy membership function optimization, Robustness, Adaptation, multi-input multi-output, single-input single-output, estimate, PSO, FLC, nonlinear, adaptive, online, off-line, Fuzzy adaptive controller, Fuzzy adaptive control, single input single output, multi input multi output, SISO, MIMO, robust, uncertain, uncertain nonlinear system, disturbance, unknown, Adaptive Fuzzy Control Design, stable.
Simulating the Ridesharing Economy: The Individual Agent Metro-Washington Area Ridesharing Model
Venue: arXiv cs.MA 2018
Year: 2018

The ridesharing economy is experiencing rapid growth and innovation. Companies such as Uber and Lyft are continuing to grow at a considerable pace while providing their platform as an organizing medium for ridesharing services, increasing consumer utility as well as employing thousands in part-time positions. However, many challenges remain in the modeling of ridesharing services, many of which are not currently under wide consideration. In this paper, an agent-based model is developed to simulate a ridesharing service in the Washington D.C. metropolitan region. The model is used to examine levels of utility gained for both riders (customers) and drivers (service providers) of a generic ridesharing service. A description of the Individual Agent Metro-Washington Area Ridesharing Model (IAMWARM) is provided, as well as a description of a typical simulation run. We investigate the financial gains of drivers for a 24-hour period under two scenarios and two spatial movement behaviors. The two spatial behaviors were random movement and Voronoi movement, which we describe. Both movement behaviors were tested under a stationary run conditions scenario and a variable run conditions scenario. We find that Voronoi movement increased drivers' utility gained but that emergence of this system property was only viable under variable scenario conditions. This result provides two important insights: The first is that driver movement decisions prior to passenger pickup can impact financial gain for the service and drivers, and consequently, rate of successful pickup for riders. The second is that this phenomenon is only evident under experimentation conditions where variability in passenger and driver arrival rates are administered.
Periodicity in Data Streams with Wildcards
Venue: arXiv cs.DS 2018
Year: 2018

We investigate the problem of detecting periodic trends within a string $S$ of length $n$, arriving in the streaming model, containing at most $k$ wildcard characters, where $k=o(n)$. A wildcard character is a special character that can be assigned any other character. We say $S$ has wildcard-period $p$ if there exists an assignment to each of the wildcard characters so that in the resulting stream the length $n-p$ prefix equals the length $n-p$ suffix. We present a two-pass streaming algorithm that computes wildcard-periods of $S$ using $\mathcal{O}(k^3\,\mathsf{polylog}\,n)$ bits of space, while we also show that this problem cannot be solved in sublinear space in one pass. We then give a one-pass randomized streaming algorithm that computes all wildcard-periods $p$ of $S$ with $p<\frac{n}{2}$ and no wildcard characters appearing in the last $p$ symbols of $S$, using $\mathcal{O}(k^3\mathsf{polylog}\, n)$ space.
Max-size popular matchings and extensions
Venue: arXiv cs.DS 2018
Year: 2018

We consider the max-size popular matching problem in a roommates instance G = (V,E) with strict preference lists. A matching M is popular if there is no matching M' in G such that the vertices that prefer M' to M outnumber those that prefer M to M'. We show it is NP-hard to compute a max-size popular matching in G. This is in contrast to the tractability of this problem in bipartite graphs where a max-size popular matching can be computed in linear time. We define a subclass of max-size popular matchings called strongly dominant matchings and show a linear time algorithm to solve the strongly dominant matching problem in a roommates instance. We consider a generalization of the max-size popular matching problem in bipartite graphs: this is the max-weight popular matching problem where there is also an edge weight function w and we seek a popular matching of largest weight. We show this is an NP-hard problem and this is so even when w(e) is either 1 or 2 for every edge e. We also show an algorithm with running time O*(2^{n/4}) to find a max-weight popular matching matching in G = (A U B,E)$ on n vertices.
Load Balanced GANs for Multi-view Face Image Synthesis
Venue: arXiv cs.CV 2018
Year: 2018

Multi-view face synthesis from a single image is an ill-posed problem and often suffers from serious appearance distortion. Producing photo-realistic and identity preserving multi-view results is still a not well defined synthesis problem. This paper proposes Load Balanced Generative Adversarial Networks (LB-GAN) to precisely rotate the yaw angle of an input face image to any specified angle. LB-GAN decomposes the challenging synthesis problem into two well constrained subtasks that correspond to a face normalizer and a face editor respectively. The normalizer first frontalizes an input image, and then the editor rotates the frontalized image to a desired pose guided by a remote code. In order to generate photo-realistic local details, the normalizer and the editor are trained in a two-stage manner and regulated by a conditional self-cycle loss and an attention based L2 loss. Exhaustive experiments on controlled and uncontrolled environments demonstrate that the proposed method not only improves the visual realism of multi-view synthetic images, but also preserves identity information well.
Empirical evaluation of predictive channel-aware transmission for resource efficient car-to-cloud communication
Venue: arXiv cs.NI 2018
Year: 2018

Nowadays vehicles are by default equipped with communication hardware. This enables new possibilities of connected services, like vehicles serving as highly mobile sensor platforms in the Internet of Things (IoT) context. Hereby, cars need to upload and transfer their data via a mobile communication network into the cloud for further evaluation. As wireless resources are limited and shared by all users, data transfers need to be conducted efficiently. Within the scope of this work three car-to-cloud data transmission algorithms Channel-Aware Transmission (CAT), predictive CAT (pCAT) and a periodic scheme are evaluated in an empirical setup. CAT leverages channel quality measurements to start data uploads preferably when the channel quality is good. CAT's extension pCAT uses past measurements in addition to estimate future channel conditions. For the empirical evaluation, a research vehicle was equipped with a measurement platform. On test drives along a reference route vehicle sensor data was collected and subsequently uploaded to a cloud server via a Long Term Evolution (LTE) network.
Skueue: A Scalable and Sequentially Consistent Distributed Queue
Venue: arXiv cs.DC 2018
Year: 2018

We propose a distributed protocol for a queue, called \textsc{Skueue}, which spreads its data fairly onto multiple processes, avoiding bottlenecks in high throughput scenarios. \textsc{Skueue} can be used in highly dynamic environments, through the addition of join and leave requests to the standard queue operations enqueue and dequeue. Furthermore \textsc{Skueue} satisfies sequential consistency in the asynchronous message passing model. Scalability is achieved by aggregating multiple requests to a batch, which can then be processed in a distributed fashion without hurting the queue semantics. Operations in \textsc{Skueue} need a logarithmic number of rounds w.h.p. until they are processed, even under a high rate of incoming requests.
Information Theoretic Co-Training
Venue: arXiv cs.LG 2018
Year: 2018

This paper introduces an information theoretic co-training objective for unsupervised learning. We consider the problem of predicting the future. Rather than predict future sensations (image pixels or sound waves) we predict "hypotheses" to be confirmed by future sensations. More formally, we assume a population distribution on pairs $(x,y)$ where we can think of $x$ as a past sensation and $y$ as a future sensation. We train both a predictor model $P_Φ(z|x)$ and a confirmation model $P_Ψ(z|y)$ where we view $z$ as hypotheses (when predicted) or facts (when confirmed). For a population distribution on pairs $(x,y)$ we focus on the problem of measuring the mutual information between $x$ and $y$. By the data processing inequality this mutual information is at least as large as the mutual information between $x$ and $z$ under the distribution on triples $(x,z,y)$ defined by the confirmation model $P_Ψ(z|y)$. The information theoretic training objective for $P_Φ(z|x)$ and $P_Ψ(z|y)$ can be viewed as a form of co-training where we want the prediction from $x$ to match the confirmation from $y$.
Improving Recommender Systems Beyond the Algorithm
Venue: arXiv cs.HC 2018
Year: 2018

Recommender systems rely heavily on the predictive accuracy of the learning algorithm. Most work on improving accuracy has focused on the learning algorithm itself. We argue that this algorithmic focus is myopic. In particular, since learning algorithms generally improve with more and better data, we propose shaping the feedback generation process as an alternate and complementary route to improving accuracy. To this effect, we explore how changes to the user interface can impact the quality and quantity of feedback data -- and therefore the learning accuracy. Motivated by information foraging theory, we study how feedback quality and quantity are influenced by interface design choices along two axes: information scent and information access cost. We present a user study of these interface factors for the common task of picking a movie to watch, showing that these factors can effectively shape and improve the implicit feedback data that is generated while maintaining the user experience.