OpenRaiser/Intern-Atlas huggingface dataset:
On Universal Approximation by Neural Networks with Uniform Guarantees on Approximation of Infinite Dimensional Maps
Venue: arXiv cs.LG 2019
Year: 2019

The study of universal approximation of arbitrary functions $f: \mathcal{X} \to \mathcal{Y}$ by neural networks has a rich and thorough history dating back to Kolmogorov (1957). In the case of learning finite dimensional maps, many authors have shown various forms of the universality of both fixed depth and fixed width neural networks. However, in many cases, these classical results fail to extend to the recent use of approximations of neural networks with infinitely many units for functional data analysis, dynamical systems identification, and other applications where either $\mathcal{X}$ or $\mathcal{Y}$ become infinite dimensional. Two questions naturally arise: which infinite dimensional analogues of neural networks are sufficient to approximate any map $f: \mathcal{X} \to \mathcal{Y}$, and when do the finite approximations to these analogues used in practice approximate $f$ uniformly over its infinite dimensional domain $\mathcal{X}$? In this paper, we answer the open question of universal approximation of nonlinear operators when $\mathcal{X}$ and $\mathcal{Y}$ are both infinite dimensional. We show that for a large class of different infinite analogues of neural networks, any continuous map can be approximated arbitrarily closely with some mild topological conditions on $\mathcal{X}$. Additionally, we provide the first lower-bound on the minimal number of input and output units required by a finite approximation to an infinite neural network to guarantee that it can uniformly approximate any nonlinear operator using samples from its inputs and outputs.
On partisan bias in redistricting: computational complexity meets the science of gerrymandering
Venue: arXiv cs.CC 2019
Year: 2019

The topic of this paper is "gerrymandering", namely the curse of deliberate creations of district maps with highly asymmetric electoral outcomes to disenfranchise voters, and it has a long legal history. Measuring and eliminating gerrymandering has enormous implications to sustain the backbone of democratic principles of a society. Although there is no dearth of legal briefs involving gerrymandering over many years, it is only more recently that mathematicians and applied computational researchers have started to investigate this topic. However, it has received relatively little attention so far from the computational complexity researchers dealing with theoretical analysis of computational complexity issues, such as computational hardness, approximability issues, etc. There could be many reasons for this, such as descriptions of these problem non-CS non-math (often legal or political) journals that theoretical CS (TCS) people usually do not follow, or the lack of coverage of these topics in TCS publication venues. One of our modest goals in writing this article is to improve upon this situation by stimulating further interactions between the gerrymandering and TCS researchers. To this effect, our main contributions are twofold: (1) we provide formalization of several models, related concepts, and corresponding problem statements using TCS frameworks from the descriptions of these problems as available in existing non-TCS (perhaps legal) venues, and (2) we also provide computational complexity analysis of some versions of these problems, leaving other versions for future research. The goal of writing this article is not to have the final word on gerrymandering, but to introduce a series of concepts, models and problems to the TCS community and to show that science of gerrymandering involves an intriguing set of partitioning problems involving geometric and combinatorial optimization.
Prediction of GNSS Phase Scintillations: A Machine Learning Approach
Venue: arXiv cs.LG 2019
Year: 2019

A Global Navigation Satellite System (GNSS) uses a constellation of satellites around the earth for accurate navigation, timing, and positioning. Natural phenomena like space weather introduce irregularities in the Earth's ionosphere, disrupting the propagation of the radio signals that GNSS relies upon. Such disruptions affect both the amplitude and the phase of the propagated waves. No physics-based model currently exists to predict the time and location of these disruptions with sufficient accuracy and at relevant scales. In this paper, we focus on predicting the phase fluctuations of GNSS radio waves, known as phase scintillations. We propose a novel architecture and loss function to predict 1 hour in advance the magnitude of phase scintillations within a time window of plus-minus 5 minutes with state-of-the-art performance.
Location Order Recovery in Trails with Low Temporal Resolution
Venue: arXiv cs.SI 2019
Year: 2019

Researchers who study object movement problems related to topics like traffic flow analysis, patient monitoring, and software operation, need to know the correct order in which objects move. Here, we use the term trail to refer to a series of movements by an object. This paper introduces a new missing data problem that occurs when analyzing trails where there is inadequate temporal resolution on the events. The temporal resolution is inadequate when an object, which can only be in one place at one time, appears in the data to be in two or more locations at once. We refer to this lack of resolution as a broken point. Broken points prevent us from knowing the correct order of movement. We propose a three-phase framework for recovering the location order. Based on the Markov transition network, we are able to find the route with the highest probability. Our results show that this framework can efficiently find the correct location order in trails with low temporal resolution. We also demonstrate that by correcting the location order, the criticality of locations can change significantly.
Authentication Modeling with Five Generic Processes
Venue: arXiv cs.OH 2019
Year: 2019

Conceptual modeling is an essential tool in many fields of study, including security specification in information technology systems. As a model, it restricts access to resources and identifies possible threats to the system. We claim that current modeling languages (e.g., Unified Modeling Language, Business Process Model and Notation) lack the notion of genericity, which refers to a limited set of elementary processes. This paper proposes five generic processes for modeling the structural behavior of a system: creating, releasing, transferring, receiving, and processing. The paper demonstrates these processes within the context of public key infrastructure, biometric, and multifactor authentication. The results indicate that the proposed generic processes are sufficient to represent these authentication schemes.
Information based Deep Clustering: An experimental study
Venue: arXiv cs.CV 2019
Year: 2019

Recently, two methods have shown outstanding performance for clustering images and jointly learning the feature representation. The first, called Information Maximiz-ing Self-Augmented Training (IMSAT), maximizes the mutual information between input and clusters while using a regularization term based on virtual adversarial examples. The second, named Invariant Information Clustering (IIC), maximizes the mutual information between the clustering of a sample and its geometrically transformed version. These methods use mutual information in distinct ways and leverage different kinds of transformations. This work proposes a comprehensive analysis of transformation and losses for deep clustering, where we compare numerous combinations of these two components and evaluate how they interact with one another. Results suggest that mutual information between a sample and its transformed representation leads to state-of-the-art performance for deep clustering, especially when used jointly with geometrical and adversarial transformations.
Critical Requirements Engineering in Practice
Venue: arXiv cs.SE 2019
Year: 2019

The design of software systems inevitably enacts normative boundaries around the site of intervention. These boundaries are, in part, a reflection of the values, ethics, power, and politics of the situation and the process of design itself. This paper argues that Requirements Engineering (RE) require more robust frameworks and techniques to navigate the values implicit in systems design work. To this end, we present the findings from a case of action research where we employed Critical Systems Heuristics (CSH), a framework from Critical Systems Thinking (CST) during requirements gathering for Homesound, a system to safeguard elderly people living alone while protecting their autonomy. We use categories from CSH to inform expert interviews and reflection, showing how CSH can be simply combined with RE techniques (such as the Volere template) to explore and reveal the value-judgements underlying requirements.
1-point RANSAC for Circular Motion Estimation in Computed Tomography (CT)
Venue: arXiv cs.CV 2019
Year: 2019

This paper proposes a RANSAC-based algorithm for determining the axial rotation angle of an object from a pair of its tomographic projections. An equation is derived for calculating the rotation angle using one correct keypoints correspondence of two tomographic projections. The proposed algorithm consists of the following steps: keypoints detection and matching, rotation angle estimation for each correspondence, outliers filtering with the RANSAC algorithm, finally, calculation of the desired angle by minimizing the re-projection error from the remaining correspondences. To validate the proposed method an experimental comparison against methods based on analysis of the distribution of the angles computed from all correspondences is conducted.
Low-cost LIDAR based Vehicle Pose Estimation and Tracking
Venue: arXiv cs.RO 2019
Year: 2019

Detecting surrounding vehicles by low-cost LIDAR has been drawing enormous attention. In low-cost LIDAR, vehicles present a multi-layer L-Shape. Based on our previous optimization/criteria-based L-Shape fitting algorithm, we here propose a data-driven and model-based method for robust vehicle segmentation and tracking. The new method uses T-linkage RANSAC to take a limited amount of noisy data and performs a robust segmentation for a moving car against noise. Compared with our previous method, T-Linkage RANSAC is more tolerant of observation uncertainties, i.e., the number of sides of the target being observed, and gets rid of the L-Shape assumption. In addition, a vehicle tracking system with Multi-Model Association (MMA) is built upon the segmentation result, which provides smooth trajectories of tracked objects. A manually labeled dataset from low-cost multi-layer LIDARs for validation will also be released with the paper. Experiments on the dataset show that the new approach outperforms previous ones based on multiple criteria. The new algorithm can also run in real-time.
Is Fast Adaptation All You Need?
Venue: arXiv cs.LG 2019
Year: 2019

Gradient-based meta-learning has proven to be highly effective at learning model initializations, representations, and update rules that allow fast adaptation from a few samples. The core idea behind these approaches is to use fast adaptation and generalization -- two second-order metrics -- as training signals on a meta-training dataset. However, little attention has been given to other possible second-order metrics. In this paper, we investigate a different training signal -- robustness to catastrophic interference -- and demonstrate that representations learned by directing minimizing interference are more conducive to incremental learning than those learned by just maximizing fast adaptation.
An Efficient Framework for Computing Structure- And Semantics-Preserving Community in a Heterogeneous Multilayer Network
Venue: arXiv cs.SI 2019
Year: 2019

Multilayer networks or MLNs (also called multiplexes or network of networks) are being used extensively for modeling and analysis of data sets with multiple entity and feature types and associated relationships. Although the concept of community is widely-used for aggregate analysis, a structure- and semantics preserving definition for it is lacking for MLNs. Retention of original MLN structure and entity relationships is important for detailed drill-down analysis. In addition, efficient computation is also critical for large number of analysis. In this paper, we introduce a structure-preserving community definition for MLNs as well as a framework for its efficient computation using the decoupling approach. The proposed decoupling approach combines communities from individual layers to form a serial k-community for connected k layers in a MLN. We propose a new algorithm for pairing communities across layers and introduce several weight metrics for composing communities from two layers using participating community characteristics. In addition to the definition, our proposed approach has a number of desired characteristics. It: i) leverages extant single graph community detection algorithms, ii) introduces several weight metrics that are customized for the community concept, iii) is a new algorithm for pairing communities using bipartite graphs, and iv) experimentally validates the community computation and its efficiency on widely-used IMDb and DBLP data sets.
Causal Induction from Visual Observations for Goal Directed Tasks
Venue: arXiv cs.LG 2019
Year: 2019

Causal reasoning has been an indispensable capability for humans and other intelligent animals to interact with the physical world. In this work, we propose to endow an artificial agent with the capability of causal reasoning for completing goal-directed tasks. We develop learning-based approaches to inducing causal knowledge in the form of directed acyclic graphs, which can be used to contextualize a learned goal-conditional policy to perform tasks in novel environments with latent causal structures. We leverage attention mechanisms in our causal induction model and goal-conditional policy, enabling us to incrementally generate the causal graph from the agent's visual observations and to selectively use the induced graph for determining actions. Our experiments show that our method effectively generalizes towards completing new tasks in novel environments with previously unseen causal structures.
Width Parameterizations for Knot-free Vertex Deletion on Digraphs
Venue: arXiv cs.DS 2019
Year: 2019

A knot in a directed graph $G$ is a strongly connected subgraph $Q$ of $G$ with at least two vertices, such that no vertex in $V(Q)$ is an in-neighbor of a vertex in $V(G)\setminus V(Q)$. Knots are important graph structures, because they characterize the existence of deadlocks in a classical distributed computation model, the so-called OR-model. Deadlock detection is correlated with the recognition of knot-free graphs as well as deadlock resolution is closely related to the {\sc Knot-Free Vertex Deletion (KFVD)} problem, which consists of determining whether an input graph $G$ has a subset $S \subseteq V(G)$ of size at most $k$ such that $G[V\setminus S]$ contains no knot. In this paper we focus on graph width measure parameterizations for {\sc KFVD}. First, we show that: (i) {\sc KFVD} parameterized by the size of the solution $k$ is W[1]-hard even when $p$, the length of a longest directed path of the input graph, as well as $κ$, its Kenny-width, are bounded by constants, and we remark that {\sc KFVD} is para-NP-hard even considering many directed width measures as parameters, but in FPT when parameterized by clique-width; (ii) {\sc KFVD} can be solved in time $2^{O(tw)}\times n$, but assuming ETH it cannot be solved in $2^{o(tw)}\times n^{O(1)}$, where $tw$ is the treewidth of the underlying undirected graph. Finally, since the size of a minimum directed feedback vertex set ($dfv$) is an upper bound for the size of a minimum knot-free vertex deletion set, we investigate parameterization by $dfv$ and we show that (iii) {\sc KFVD} can be solved in FPT-time parameterized by either $dfv+κ$ or $dfv+p$; and it admits a Turing kernel by the distance to a DAG having an Hamiltonian path.
Unsupervised Representation for EHR Signals and Codes as Patient Status Vector
Venue: arXiv cs.LG 2019
Year: 2019

Effective modeling of electronic health records presents many challenges as they contain large amounts of irregularity most of which are due to the varying procedures and diagnosis a patient may have. Despite the recent progress in machine learning, unsupervised learning remains largely at open, especially in the healthcare domain. In this work, we present a two-step unsupervised representation learning scheme to summarize the multi-modal clinical time series consisting of signals and medical codes into a patient status vector. First, an auto-encoder step is used to reduce sparse medical codes and clinical time series into a distributed representation. Subsequently, the concatenation of the distributed representations is further fine-tuned using a forecasting task. We evaluate the usefulness of the representation on two downstream tasks: mortality and readmission. Our proposed method shows improved generalization performance for both short duration ICU visits and long duration ICU visits.
System Stability Under Adversarial Injection of Dependent Tasks
Venue: arXiv cs.NI 2019
Year: 2019

In this work, we consider a computational model of a distributed system formed by a set of servers in which jobs, that are continuously arriving, have to be executed. Every job is formed by a set of dependent tasks (i.~e., each task may have to wait for others to be completed before it can be started), each of which has to be executed in one of the servers. The arrival of jobs and their properties is assumed to be controlled by a bounded adversary, whose only restriction is that it cannot overload any server. This model is a non-trivial generalization of the Adversarial Queuing Theory model of Borodin et al., and, like that model, focuses on the stability of the system: whether the number of jobs pending to be completed is bounded at all times. We show multiple results of stability and instability for this adversarial model under different combinations of the scheduling policy used at the servers, the arrival rate, and the dependence between tasks in the jobs.
On the Cost-Optimality Trade-off for Service Function Chain Reconfiguration
Venue: arXiv cs.NI 2019
Year: 2019

Optimal placement of Virtual Network Functions (VNFs) in virtualized data centers enhances the overall performance of Service Function Chains (SFCs) and decreases the operational costs for mobile network operators. Maintaining an optimal placement of VNFs under changing load requires a dynamic reconfiguration that includes adding or removing VNF instances, changing the resource allocation of VNFs, and re-routing corresponding service flows. However, such reconfiguration may lead to notable service disruptions and impose additional overhead on the VNF infrastructure, especially when reconfiguration entails state or VNF migration. On the other hand, not changing the existing placement may lead to high operational costs. In this paper, we investigate the trade-off between the reconfiguration of SFCs and the optimality of the resulting placement and service flow (re)routing. We model different reconfiguration costs related to the migration of stateful VNFs and solve a joint optimization problem that aims to minimize both the total cost of the VNF placement and the reconfiguration cost necessary for repairing a suboptimal placement. Numerical results show that a small number of reconfiguration operations can significantly reduce the operational cost of the VNF infrastructure; however, too much reconfiguration may not pay off should heavy costs be involved.
Secret key agreement for hypergraphical sources with limited total discussion
Venue: arXiv cs.IT 2019
Year: 2019

This work considers the problem of multiterminal secret key agreement by limited total public discussion under the hypergraphical source model. The secrecy capacity as a function of the total discussion rate is completely characterized by a polynomial-time computable linear program. Compared to the existing solution for a particular hypergraphical source model called the pairwise independent network (PIN) model, the current result is a non-trivial extension as it applies to a strictly larger class of sources and a more general scenario involving helpers and wiretapper's side information. In particular, while the existing solution by tree-packing can be strictly suboptimal for the PIN model with helpers and the hypergraphical source model in general, we can show that decremental secret key agreement and linear network coding is optimal, resolving a previous conjecture in the affirmative. The converse is established by a single-letter upper bound on the secrecy capacity for discrete memoryless multiple sources and individual discussion rate constraints. The minimax optimization involved in the bound can be relaxed to give the best existing upper bounds on secrecy capacities such as the lamination bounds for hypergraphical sources, helper-set bound for general sources, the bound at asymptotically zero discussion rate via the multivariate Gác--Körner common information, and the lower bound on communication complexity via a multivariate extension of the Wyner common information. These reductions unify existing bounding techniques and reveal surprising connections between seemingly different information-theoretic notions. Further challenges are posed in this work along with a simple example of finite linear source where the current converse techniques fail even though the proposed achieving scheme remains optimal.
Physical Layer Security in Frequency-Domain Time-Reversal SISO OFDM Communication
Venue: arXiv cs.IT 2019
Year: 2019

A frequency domain (FD) time-reversal (TR) precoder is proposed to perform physical layer security (PLS) in single-input single-output (SISO) system using orthogonal frequency-division multiplexing (OFDM). To maximize the secrecy of the communication, the design of an artificial noise (AN) signal well-suited to the proposed FD TR-based OFDM SISO system is derived. This new scheme guarantees the secrecy of a communication toward a legitimate user when the channel state information (CSI) of a potential eavesdropper is not known. In particular, we derive an AN signal that does not corrupt the data transmission to the legitimate receiver but degrades the decoding performance of the eavesdropper. A closed-form approximation of the AN energy to inject is defined in order to maximize the secrecy rate (SR) of the communication. Simulation results are presented to demonstrate the security performance of the proposed secure FD TR SISO OFDM system.