OpenRaiser/Intern-Atlas huggingface dataset:
Efficient human-like semantic representations via the Information Bottleneck principle
Venue: arXiv cs.CL 2018
Year: 2018

Maintaining efficient semantic representations of the environment is a major challenge both for humans and for machines. While human languages represent useful solutions to this problem, it is not yet clear what computational principle could give rise to similar solutions in machines. In this work we propose an answer to this open question. We suggest that languages compress percepts into words by optimizing the Information Bottleneck (IB) tradeoff between the complexity and accuracy of their lexicons. We present empirical evidence that this principle may give rise to human-like semantic representations, by exploring how human languages categorize colors. We show that color naming systems across languages are near-optimal in the IB sense, and that these natural systems are similar to artificial IB color naming systems with a single tradeoff parameter controlling the cross-language variability. In addition, the IB systems evolve through a sequence of structural phase transitions, demonstrating a possible adaptation process. This work thus identifies a computational principle that characterizes human semantic systems, and that could usefully inform semantic representations in machines.
Proof Nets and the Linear Substitution Calculus
Venue: arXiv cs.LO 2018
Year: 2018

Since the very beginning of the theory of linear logic it is known how to represent the $λ$-calculus as linear logic proof nets. The two systems however have different granularities, in particular proof nets have an explicit notion of sharing---the exponentials---and a micro-step operational semantics, while the $λ$-calculus has no sharing and a small-step operational semantics. Here we show that the \emph{linear substitution calculus}, a simple refinement of the $λ$-calculus with sharing, is isomorphic to proof nets at the operational level. Nonetheless, two different terms with sharing can still have the same proof nets representation---a further result is the characterisation of the equality induced by proof nets over terms with sharing. Finally, such a detailed analysis of the relationship between terms and proof nets, suggests a new, abstract notion of proof net, based on rewriting considerations and not necessarily of a graphical nature.
Efficient Measurement on Programmable Switches Using Probabilistic Recirculation
Venue: arXiv cs.NI 2018
Year: 2018

Programmable network switches promise flexibility and high throughput, enabling applications such as load balancing and traffic engineering. Network measurement is a fundamental building block for such applications, including tasks such as the identification of heavy hitters (largest flows) or the detection of traffic changes. However, high-throughput packet processing architectures place certain limitations on the programming model, such as restricted branching, limited capability for memory access, and a limited number of processing stages. These limitations restrict the types of measurement algorithms that can run on programmable switches. In this paper, we focus on the RMT programmable high-throughput switch architecture, and carefully examine its constraints on designing measurement algorithms. We demonstrate our findings while solving the heavy hitter problem. We introduce PRECISION, an algorithm that uses \emph{Probabilistic Recirculation} to find top flows on a programmable switch. By recirculating a small fraction of packets, PRECISION simplifies the access to stateful memory to conform with RMT limitations and achieves higher accuracy than previous heavy hitter detection algorithms that avoid recirculation. We also analyze the effect of each architectural constraint on the measurement accuracy and provide insights for measurement algorithm designers.
Accelerated Methods for the SOCP-relaxed Component-based Distributed Optimal Power Flow
Venue: arXiv cs.CE 2018
Year: 2018

In light of the increased focus on distributed methods, this paper proposes two accelerated subgradient methods and an adaptive penalty parameter scheme to speed-up the convergence of ADMM on the component-based dual decomposition of the second-order cone programming (SOCP) relaxation of the OPF. This work is the first to apply an adaptive penalty parameter method along with an accelerated subgradient method together in one scheme for distributed OPF. This accelerated scheme is demonstrated to reach substantial speed-ups, as high as 87%, on real-world test systems with more than 9000 buses, as well as on other difficult test cases.
Maximum Weight Online Matching with Deadlines
Venue: arXiv cs.DS 2018
Year: 2018

We study the problem of matching agents who arrive at a marketplace over time and leave after d time periods. Agents can only be matched while they are present in the marketplace. Each pair of agents can yield a different match value, and the planner's goal is to maximize the total value over a finite time horizon. First we study the case in which vertices arrive in an adversarial order. We provide a randomized 0.25-competitive algorithm building on a result by Feldman et al. (2009) and Lehman et al. (2006). We extend the model to the case in which departure times are drawn independently from a distribution with non-decreasing hazard rate, for which we establish a 1/8-competitive algorithm. When the arrival order is chosen uniformly at random, we show that a batching algorithm, which computes a maximum-weighted matching every (d+1) periods, is 0.279-competitive.
Atmospheric turbulence mitigation for sequences with moving objects using recursive image fusion
Venue: arXiv cs.CV 2018
Year: 2018

This paper describes a new method for mitigating the effects of atmospheric distortion on observed sequences that include large moving objects. In order to provide accurate detail from objects behind the distorting layer, we solve the space-variant distortion problem using recursive image fusion based on the Dual Tree Complex Wavelet Transform (DT-CWT). The moving objects are detected and tracked using the improved Gaussian mixture models (GMM) and Kalman filtering. New fusion rules are introduced which work on the magnitudes and angles of the DT-CWT coefficients independently to achieve a sharp image and to reduce atmospheric distortion, respectively. The subjective results show that the proposed method achieves better video quality than other existing methods with competitive speed.
Finding a Small Number of Colourful Components
Venue: arXiv cs.DS 2018
Year: 2018

A partition $(V_1,\ldots,V_k)$ of the vertex set of a graph $G$ with a (not necessarily proper) colouring $c$ is colourful if no two vertices in any $V_i$ have the same colour and every set $V_i$ induces a connected graph. The COLOURFUL PARTITION problem is to decide whether a coloured graph $(G,c)$ has a colourful partition of size at most $k$. This problem is closely related to the COLOURFUL COMPONENTS problem, which is to decide whether a graph can be modified into a graph whose connected components form a colourful partition by deleting at most $p$ edges. Nevertheless we show that COLOURFUL PARTITION and COLOURFUL COMPONENTS may have different complexities for restricted instances. We tighten known NP-hardness results for both problems and in addition we prove new hardness and tractability results for COLOURFUL PARTITION. Using these results we complete our paper with a thorough parameterized study of COLOURFUL PARTITION.
Choosing the optimal multi-point iterative method for the Colebrook flow friction equation -- Numerical validation
Venue: arXiv cs.NA 2018
Year: 2018

The Colebrook equation $ζ$ is implicitly given in respect to the unknown flow friction factor $λ$; $λ=ζ(Re,ε^*,λ)$ which cannot be expressed explicitly in exact way without simplifications and use of approximate calculus. Common approach to solve it is through the Newton-Raphson iterative procedure or through the fixed-point iterative procedure. Both requires in some case even eight iterations. On the other hand numerous more powerful iterative methods such as three-or two-point methods, etc. are available. The purpose is to choose optimal iterative method in order to solve the implicit Colebrook equation for flow friction accurately using the least possible number of iterations. The methods are thoroughly tested and those which require the least possible number of iterations to reach the accurate solution are identified. The most powerful three-point methods require in worst case only two iterations to reach final solution. The recommended representatives are Sharma-Guha-Gupta, Sharma-Sharma, Sharma-Arora, Džunić-Petković-Petković; Bi-Ren-Wu, Chun-Neta based on Kung-Traub, Neta, and Jain method based on Steffensen scheme. The recommended iterative methods can reach the final accurate solution with the least possible number of iterations. The approach is hybrid between iterative procedure and one-step explicit approximations and can be used in engineering design for initial rough, but also for final fine calculations.
Modeling Meaning Associated with Documental Entities: Introducing the Brussels Quantum Approach
Venue: arXiv cs.IR 2018
Year: 2018

We show that the Brussels operational-realistic approach to quantum physics and quantum cognition offers a fundamental strategy for modeling the meaning associated with collections of documental entities. To do so, we take the World Wide Web as a paradigmatic example and emphasize the importance of distinguishing the Web, made of printed documents, from a more abstract meaning entity, which we call the Quantum Web, or QWeb, where the former is considered to be the collection of traces that can be left by the latter, in specific measurements, similarly to how a non-spatial quantum entity, like an electron, can leave localized traces of impact on a detection screen. The double-slit experiment is extensively used to illustrate the rationale of the modeling, which is guided by how physicists constructed quantum theory to describe the behavior of the microscopic entities. We also emphasize that the superposition principle and the associated interference effects are not sufficient to model all experimental probabilistic data, like those obtained by counting the relative number of documents containing certain words and co-occurrences of words. For this, additional effects, like context effects, must also be taken into consideration.
An Integrated Design and Simulation Environment for Rapid Prototyping of Laminate Robotic Mechanisms
Venue: arXiv cs.RO 2018
Year: 2018

Laminate mechanisms are a reliable concept in producing lowcost robots for educational and commercial purposes. These mechanisms are produced using low-cost manufacturing techniques which have improved significantly during recent years and are more accessible to novices and hobbyists. However, iterating through the design space to come up with the best design for a robot is still a time consuming and rather expensive task and therefore, there is still a need for model-based analysis before manufacturing. Until now, there has been no integrated design and analysis software for laminate robots. This paper addresses some of the issues surrounding laminate analysis by introducing a companion to an existing laminate design tool that automates the generation of dynamic equations and produces simulation results via rendered plots and videos. We have validated the accuracy of the software by comparing the position, velocity and acceleration of the simulated mechanisms with the measurements taken from physical laminate prototypes using a motion capture system.
Grey-box Process Control Mining for Anomaly Monitoring and Deconstruction
Venue: arXiv cs.SY 2018
Year: 2018

We present a new "grey-box" approach to anomaly detection in smart manufacturing. The approach is designed for tools run by control systems which execute recipe steps to produce semiconductor wafers. Multiple streaming sensors capture trace data to guide the control systems and for quality control. These control systems are typically PI controllers which can be modeled as an ordinary differential equation (ODE) coupled with a control equation, capturing the physics of the process. The ODE "white-box" models capture physical causal relationships that can be used in simulations to determine how the process will react to changes in control parameters, but they have limited utility for anomaly detection. Many "black-box" approaches exist for anomaly detection in manufacturing, but they typically do not exploit the underlying process control. The proposed "grey-box" approach uses the process-control ODE model to derive a parametric function of sensor data. Bayesian regression is used to fit the parameters of these functions to form characteristic "shape signatures". The probabilistic model provides a natural anomaly score for each wafer, which captures poor control and strange shape signatures. The anomaly score can be deconstructed into its constituent parts in order to identify which parameters are contributing to anomalies. We demonstrate how the anomaly score can be used to monitor complex multi-step manufacturing processes to detect anomalies and changes and show how the shape signatures can provide insight into the underlying sources of process variation that are not readily apparent in the sensor data.
Dropout during inference as a model for neurological degeneration in an image captioning network
Venue: arXiv cs.CL 2018
Year: 2018

We replicate a variation of the image captioning architecture by Vinyals et al. (2015), then introduce dropout during inference mode to simulate the effects of neurodegenerative diseases like Alzheimer's disease (AD) and Wernicke's aphasia (WA). We evaluate the effects of dropout on language production by measuring the KL-divergence of word frequency distributions and other linguistic metrics as dropout is added. We find that the generated sentences most closely approximate the word frequency distribution of the training corpus when using a moderate dropout of 0.4 during inference.
The Boyer-Moore Waterfall Model Revisited
Venue: arXiv cs.LO 2018
Year: 2018

In this paper, we investigate the potential of the Boyer-Moore waterfall model for the automation of inductive proofs within a modern proof assistant. We analyze the basic concepts and methodology underlying this 30-year-old model and implement a new, fully integrated tool in the theorem prover HOL Light that can be invoked as a tactic. We also describe several extensions and enhancements to the model. These include the integration of existing HOL Light proof procedures and the addition of state-of-the-art generalization techniques into the waterfall. Various features, such as proof feedback and heuristics dealing with non-termination, that are needed to make this automated tool useful within our interactive setting are also discussed. Finally, we present a thorough evaluation of the approach using a set of 150 theorems, and discuss the effectiveness of our additions and relevance of the model in light of our results.
Learning Discriminative 3D Shape Representations by View Discerning Networks
Venue: arXiv cs.CV 2018
Year: 2018

In view-based 3D shape recognition, extracting discriminative visual representation of 3D shapes from projected images is considered the core problem. Projections with low discriminative ability can adversely influence the final 3D shape representation. Especially under the real situations with background clutter and object occlusion, the adverse effect is even more severe. To resolve this problem, we propose a novel deep neural network, View Discerning Network, which learns to judge the quality of views and adjust their contributions to the representation of shapes. In this network, a Score Generation Unit is devised to evaluate the quality of each projected image with score vectors. These score vectors are used to weight the image features and the weighted features perform much better than original features in 3D shape recognition task. In particular, we introduce two structures of Score Generation Unit, Channel-wise Score Unit and Part-wise Score Unit, to assess the quality of feature maps from different perspectives. Our network aggregates features and scores in an end-to-end framework, so that final shape descriptors are directly obtained from its output. Our experiments on ModelNet and ShapeNet Core55 show that View Discerning Network outperforms the state-of-the-arts in terms of the retrieval task, with excellent robustness against background clutter and object occlusion.
Protecting the Grid against IoT Botnets of High-Wattage Devices
Venue: arXiv cs.SY 2018
Year: 2018

We provide methods to prevent line failures in the power grid caused by a newly revealed MAnipulation of Demand (MAD) attacks via an IoT botnet of high-wattage devices. In particular, we develop two algorithms named Securing Additional margin For generators in Economic dispatch (SAFE) Algorithm and Iteratively MiniMize and boUNd Economic dispatch (IMMUNE) Algorithm for finding robust operating points for generators during the economic dispatch such that no lines are overloaded after automatic primary control response to any MAD attacks. In situations that the operating cost of the grid in a robust state is costly (or no robust operating points exist), we provide efficient methods to verify--in advance--if possible line overloads can be cleared during the secondary control after any MAD attacks. We then define the $αD$-robustness notion for the grids indicating that any line failures can be cleared during the secondary control if an adversary can increase/decrease the demands by $α$ fraction. We demonstrate that practical upper and lower bounds on the maximum $α$ for which the grid is $αD$-robust can be found efficiently in polynomial time. Finally, we evaluate the performance of the developed algorithms and methods on realistic power grid test cases. Our work provides the first methods for protecting the grid against potential line failures caused by MAD attacks.
Compound Poisson Noise Sources in Diffusion-based Molecular Communication
Venue: arXiv cs.IT 2018
Year: 2018

Diffusion-based molecular communication (DMC) is one of the most promising approaches for realizing nano-scale communications for healthcare applications. The DMC systems in in-vivo environments may encounter biological entities that release molecules identical to the molecules used for signaling as part of their functionality. Such entities in the environment act as external noise sources from the DMC system's perspective. In this paper, the release of molecules by biological external noise sources is particularly modeled as a compound Poisson process. The impact of compound Poisson noise sources (CPNSs) on the performance of a point-to-point DMC system is investigated. To this end, the noise from the CPNS observed at the receiver is characterized. Considering a simple on-off keying (OOK) modulation and formulating symbol-by-symbol maximum likelihood (ML) detector, the performance of DMC system in the presence of the CPNS is analyzed. For special case of CPNS in high-rate regime, the noise received from the CPNS is approximated as a Poisson process whose rate is normally distributed. In this case, it is proved that a simple single-threshold detector (STD) is an optimal ML detector. Our results reveal that in general, adopting the conventional simple homogeneous Poisson noise model may lead to overly optimistic performance predictions, if a CPNS is present.
Stability Analysis of Device-to-Device Relay-Assisted Cellular Networks
Venue: arXiv cs.NI 2018
Year: 2018

Motivated by increasing popularity of delay sensitive applications, we investigate the queue stability in device-to-device (D2D) relay-assisted cellular networks. In contrast to prior works on D2D relay-assisted cellular networks, we incorporate practical properties of these networks such as bursty packet arrivals, user mobility and relays generating their own traffic. Assuming network topology evolving in IID fashion, we first evaluate the system stability region to quantify its delay performance. Subsequently, we formulate a policy for joint resource allocation and power control under a more realistic mobility scenario of generalized reflected random walk. Also, the throughput optimality of this policy is investigated. Simulation results are provided to give better understanding of queue stability in the network.
Configuration Space Metrics
Venue: arXiv cs.RO 2018
Year: 2018

When robot manipulators decide how to reach for an object, hand it over, or obey some task constraint, they implicitly assume a Euclidean distance metric in their configuration space. Their notion of what makes a configuration closer or further is dictated by this assumption. But different distance metrics will lead to different solutions. What is efficient under a Euclidean metric might not necessarily look the most efficient or natural to a person observing the robot. In this paper, we analyze the effect of the metric on robot behavior, examining both Euclidean, as well as non-Euclidean metrics -- metrics that make certain joints cheaper, or that correlate different joints. Our user data suggests that tasks on a 3DOF arm and the Jaco 7DOF arm can typically be grouped into ones where a Euclidean metric works well, and tasks where that is no longer the case: there, surprisingly, penalizing elbow motion (and sometimes correlating the shoulder and wrist) leads to solutions that are more aligned with what users prefer.
Seamless roaming and guaranteed communication using a synchronized single-hop multi-gateway 802.15.4e TSCH network
Venue: arXiv cs.NI 2018
Year: 2018

Industrial wireless sensor networks (WSNs) are being used to improve the efficiency, productivity and safety of industrial processes. An open standard that is commonly used in such cases is IEEE 802.15.4e. Its TSCH mode employs a time synchronized based MAC scheme together with channel hopping to alleviate the impact of channel fading. Until now, most of the industrial WSNs have been designed to only support static nodes and are not able to deal with mobility. In this paper, we show how a single-hop, multi-gateway IEEE 802.15.4e TSCH network architecture can tackle the mobility problem. We introduce the Virtual Grand Master (VGM) concept that moves the synchronization point from separated Backbone Border Routers (BBRs) towards the backbone network. With time synchronization of all BBRs, mobile nodes can roam from one BBR to another without time desynchronization. In addition to time synchronization, we introduce a mechanism to synchronize the schedules between BBRs to support fast handover of mobile nodes.
Unsupervised learning for cross-domain medical image synthesis using deformation invariant cycle consistency networks
Venue: arXiv cs.CV 2018
Year: 2018

Recently, the cycle-consistent generative adversarial networks (CycleGAN) has been widely used for synthesis of multi-domain medical images. The domain-specific nonlinear deformations captured by CycleGAN make the synthesized images difficult to be used for some applications, for example, generating pseudo-CT for PET-MR attenuation correction. This paper presents a deformation-invariant CycleGAN (DicycleGAN) method using deformable convolutional layers and new cycle-consistency losses. Its robustness dealing with data that suffer from domain-specific nonlinear deformations has been evaluated through comparison experiments performed on a multi-sequence brain MR dataset and a multi-modality abdominal dataset. Our method has displayed its ability to generate synthesized data that is aligned with the source while maintaining a proper quality of signal compared to CycleGAN-generated data. The proposed model also obtained comparable performance with CycleGAN when data from the source and target domains are alignable through simple affine transformations.