OpenRaiser/Intern-Atlas huggingface dataset:
SurfCuit: Surface Mounted Circuits on 3D Prints
Venue: arXiv cs.GR 2016
Year: 2016

We present, SurfCuit, a novel approach to design and construction of electric circuits on the surface of 3D prints. Our surface mounting technique allows durable construction of circuits on the surface of 3D prints. SurfCuit does not require tedious circuit casing design or expensive set-ups, thus we can expedite the process of circuit construction for 3D models. Our technique allows the user to construct complex circuits for consumer-level desktop fused decomposition modeling (FDM) 3D printers. The key idea behind our technique is that FDM plastic forms a strong bond with metal when it is melted. This observation enables construction of a robust circuit traces using copper tape and soldering. We also present an interactive tool to design such circuits on arbitrary 3D geometry. We demonstrate the effectiveness of our approach through various actual construction examples.
Neural Network-based Word Alignment through Score Aggregation
Venue: arXiv cs.CL 2016
Year: 2016

We present a simple neural network for word alignment that builds source and target word window representations to compute alignment scores for sentence pairs. To enable unsupervised training, we use an aggregation operation that summarizes the alignment scores for a given target word. A soft-margin objective increases scores for true target words while decreasing scores for target words that are not present. Compared to the popular Fast Align model, our approach improves alignment accuracy by 7 AER on English-Czech, by 6 AER on Romanian-English and by 1.7 AER on English-French alignment.
Lifted Region-Based Belief Propagation
Venue: arXiv cs.AI 2016
Year: 2016

Due to the intractable nature of exact lifted inference, research has recently focused on the discovery of accurate and efficient approximate inference algorithms in Statistical Relational Models (SRMs), such as Lifted First-Order Belief Propagation. FOBP simulates propositional factor graph belief propagation without constructing the ground factor graph by identifying and lifting over redundant message computations. In this work, we propose a generalization of FOBP called Lifted Generalized Belief Propagation, in which both the region structure and the message structure can be lifted. This approach allows more of the inference to be performed intra-region (in the exact inference step of BP), thereby allowing simulation of propagation on a graph structure with larger region scopes and fewer edges, while still maintaining tractability. We demonstrate that the resulting algorithm converges in fewer iterations to more accurate results on a variety of SRMs.
Tensor Network alternating linear scheme for MIMO Volterra system identification
Venue: arXiv cs.NA 2016
Year: 2016

This article introduces two Tensor Network-based iterative algorithms for the identification of high-order discrete-time nonlinear multiple-input multiple-output (MIMO) Volterra systems. The system identification problem is rewritten in terms of a Volterra tensor, which is never explicitly constructed, thus avoiding the curse of dimensionality. It is shown how each iteration of the two identification algorithms involves solving a linear system of low computational complexity. The proposed algorithms are guaranteed to monotonically converge and numerical stability is ensured through the use of orthogonal matrix factorizations. The performance and accuracy of the two identification algorithms are illustrated by numerical experiments, where accurate degree-10 MIMO Volterra models are identified in about 1 second in Matlab on a standard desktop pc.
Missing Data Estimation in High-Dimensional Datasets: A Swarm Intelligence-Deep Neural Network Approach
Venue: arXiv cs.AI 2016
Year: 2016

In this paper, we examine the problem of missing data in high-dimensional datasets by taking into consideration the Missing Completely at Random and Missing at Random mechanisms, as well as theArbitrary missing pattern. Additionally, this paper employs a methodology based on Deep Learning and Swarm Intelligence algorithms in order to provide reliable estimates for missing data. The deep learning technique is used to extract features from the input data via an unsupervised learning approach by modeling the data distribution based on the input. This deep learning technique is then used as part of the objective function for the swarm intelligence technique in order to estimate the missing data after a supervised fine-tuning phase by minimizing an error function based on the interrelationship and correlation between features in the dataset. The investigated methodology in this paper therefore has longer running times, however, the promising potential outcomes justify the trade-off. Also, basic knowledge of statistics is presumed.
Representing Pattern Matching Algorithms by Polynomial-Size Automata
Venue: arXiv cs.DS 2016
Year: 2016

Pattern matching algorithms to find exact occurrences of a pattern $S\inΣ^m$ in a text $T\inΣ^n$ have been analyzed extensively with respect to asymptotic best, worst, and average case runtime. For more detailed analyses, the number of text character accesses $X^{\mathcal{A},S}_n$ performed by an algorithm $\mathcal{A}$ when searching a random text of length $n$ for a fixed pattern $S$ has been considered. Constructing a state space and corresponding transition rules (e.g. in a Markov chain) that reflect the behavior of a pattern matching algorithm is a key step in existing analyses of $X^{\mathcal{A},S}_n$ in both the asymptotic ($n\to\infty$) and the non-asymptotic regime. The size of this state space is hence a crucial parameter for such analyses. In this paper, we introduce a general methodology to construct corresponding state spaces and demonstrate that it applies to a wide range of algorithms, including Boyer-Moore (BM), Boyer-Moore-Horspool (BMH), Backward Oracle Matching (BOM), and Backward (Non-Deterministic) DAWG Matching (B(N)DM). In all cases except BOM, our method leads to state spaces of size $O(m^3)$ for pattern length $m$, a result that has previously only been obtained for BMH. In all other cases, only state spaces with size exponential in $m$ had been reported. Our results immediately imply an algorithm to compute the distribution of $X^{\mathcal{A},S}_n$ for fixed $S$, fixed $n$, and $\mathcal{A}\in\{\text{BM},\text{BMH},\text{B(N)DM}\}$ in polynomial time for a very general class of random text models.
A Fully Abstract Semantics for Value-passing CCS for Trees
Venue: arXiv cs.LO 2016
Year: 2016

This paper provides a fully abstract semantics for value-passing CCS for trees (VCCTS). The operational semantics is given both in terms of a reduction semantics and in terms of a labelled transition semantics. The labelled transition semantics is non-sequential, allowing more than one action occurring simultaneously. We develop the theory of behavioral equivalence by introducing both weak barbed congruence and weak bisimilarity. In particular, we show that weak barbed congruence coincides with weak bisimilarity on image-finite processes. This is the first such result for a concurrent model with tree structures. Distributed systems can be naturally modeled by means of this graph-based system, and some examples are given to illustrate this.
Neutrosophic Overset, Neutrosophic Underset, and Neutrosophic Offset. Similarly for Neutrosophic Over-/Under-/Off- Logic, Probability, and Statistics
Venue: arXiv cs.AI 2016
Year: 2016

Neutrosophic Over-/Under-/Off-Set and -Logic were defined by the author in 1995 and published for the first time in 2007. We extended the neutrosophic set respectively to Neutrosophic Overset {when some neutrosophic component is over 1}, Neutrosophic Underset {when some neutrosophic component is below 0}, and to Neutrosophic Offset {when some neutrosophic components are off the interval [0, 1], i.e. some neutrosophic component over 1 and other neutrosophic component below 0}. This is no surprise with respect to the classical fuzzy set/logic, intuitionistic fuzzy set/logic, or classical/imprecise probability, where the values are not allowed outside the interval [0, 1], since our real-world has numerous examples and applications of over-/under-/off-neutrosophic components. For example, person working overtime deserves a membership degree over 1, while a person producing more damage than benefit to a company deserves a membership below 0. Then, similarly, the Neutrosophic Logic/Measure/Probability/Statistics etc. were extended to respectively Neutrosophic Over-/Under-/Off-Logic, -Measure, -Probability, -Statistics etc. [Smarandache, 2007].
Lower Bounds for Alternating Online State Complexity
Venue: arXiv cs.CC 2016
Year: 2016

The notion of Online State Complexity, introduced by Karp in 1967, quantifies the amount of states required to solve a given problem using an online algorithm, which is represented by a deterministic machine scanning the input from left to right in one pass. In this paper, we extend the setting to alternating machines as introduced by Chandra, Kozen and Stockmeyer in 1976: such machines run independent passes scanning the input from left to right and gather their answers through boolean combinations. We devise a lower bound technique relying on boundedly generated lattices of languages, and give two applications of this technique. The first is a hierarchy theorem , stating that the polynomial hierarchy of alternating online state complexity is infinite, and the second is a linear lower bound on the alternating online state complexity of the prime numbers written in binary. This second result strengthens a result of Hartmanis and Shank from 1968, which implies an exponentially worse lower bound for the same model.
Design of Robust, Protograph Based LDPC Codes for Rate-Adaptation via Probabilistic Shaping
Venue: arXiv cs.IT 2016
Year: 2016

In this work, the design of robust, protograph-based low-density parity-check (LDPC) codes for rate-adaptive communication via probabilistic shaping is considered. Recently, probabilistic amplitude shaping (PAS) by Böcherer et al. has been introduced for capacity approaching and rate-adaptive communication with a bitwise-demapper and binary decoder. Previous work by the authors considered the optimization of protograph based LDPC codes for PAS and specific spectral efficiencies (SEs) to jointly optimize the LDPC code node degrees and the mapping of the coded bits to the bit-interleaved coded modulation (BICM) bit-channels. We show that these codes tend to perform poor when operated at other rates and propose the design of robust LDPC codes by employing a min-max approach in the search for good protograph ensembles via differential evolution. The considered design uses a single 16 amplitude-shift-keying (ASK) constellation and a robust 13/16 rate LDPC code to operate between 0.7 to 2.7 bits per channel use. For a blocklength of 16224 bits and a target frame error rate of 1e-3 the proposed code operates within 1.32 dB of continuous AWGN capacity for 0.7 to 1.3 bpcu and within 1.05 dB for 1.3 bpcu to 2.7 bpcu.
Learning Relational Dependency Networks for Relation Extraction
Venue: arXiv cs.AI 2016
Year: 2016

We consider the task of KBP slot filling -- extracting relation information from newswire documents for knowledge base construction. We present our pipeline, which employs Relational Dependency Networks (RDNs) to learn linguistic patterns for relation extraction. Additionally, we demonstrate how several components such as weak supervision, word2vec features, joint learning and the use of human advice, can be incorporated in this relational framework. We evaluate the different components in the benchmark KBP 2015 task and show that RDNs effectively model a diverse set of features and perform competitively with current state-of-the-art relation extraction.
Situated Structure Learning of a Bayesian Logic Network for Commonsense Reasoning
Venue: arXiv cs.AI 2016
Year: 2016

This paper details the implementation of an algorithm for automatically generating a high-level knowledge network to perform commonsense reasoning, specifically with the application of robotic task repair. The network is represented using a Bayesian Logic Network (BLN) (Jain, Waldherr, and Beetz 2009), which combines a set of directed relations between abstract concepts, including IsA, AtLocation, HasProperty, and UsedFor, with a corresponding probability distribution that models the uncertainty inherent in these relations. Inference over this network enables reasoning over the abstract concepts in order to perform appropriate object substitution or to locate missing objects in the robot's environment. The structure of the network is generated by combining information from two existing knowledge sources: ConceptNet (Speer and Havasi 2012), and WordNet (Miller 1995). This is done in a "situated" manner by only including information relevant a given context. Results show that the generated network is able to accurately predict object categories, locations, properties, and affordances in three different household scenarios.
Algebraic Proof Complexity: Progress, Frontiers and Challenges
Venue: arXiv cs.CC 2016
Year: 2016

We survey recent progress in the proof complexity of strong proof systems and its connection to algebraic circuit complexity, showing how the synergy between the two gives rise to new approaches to fundamental open questions, solutions to old problems, and new directions of research. In particular, we focus on tight connections between proof complexity lower bounds (namely, lower bounds on the size of proofs of certain tautologies), algebraic circuit lower bounds, and the Polynomial Identity Testing problem from derandomization theory.
A Greedy Approach to Adapting the Trace Parameter for Temporal Difference Learning
Venue: arXiv cs.AI 2016
Year: 2016

One of the main obstacles to broad application of reinforcement learning methods is the parameter sensitivity of our core learning algorithms. In many large-scale applications, online computation and function approximation represent key strategies in scaling up reinforcement learning algorithms. In this setting, we have effective and reasonably well understood algorithms for adapting the learning-rate parameter, online during learning. Such meta-learning approaches can improve robustness of learning and enable specialization to current task, improving learning speed. For temporal-difference learning algorithms which we study here, there is yet another parameter, $λ$, that similarly impacts learning speed and stability in practice. Unfortunately, unlike the learning-rate parameter, $λ$ parametrizes the objective function that temporal-difference methods optimize. Different choices of $λ$ produce different fixed-point solutions, and thus adapting $λ$ online and characterizing the optimization is substantially more complex than adapting the learning-rate parameter. There are no meta-learning method for $λ$ that can achieve (1) incremental updating, (2) compatibility with function approximation, and (3) maintain stability of learning under both on and off-policy sampling. In this paper we contribute a novel objective function for optimizing $λ$ as a function of state rather than time. We derive a new incremental, linear complexity $λ$-adaption algorithm that does not require offline batch updating or access to a model of the world, and present a suite of experiments illustrating the practicality of our new algorithm in three different settings. Taken together, our contributions represent a concrete step towards black-box application of temporal-difference learning methods in real world problems.
Alzheimer's Disease Diagnostics by Adaptation of 3D Convolutional Network
Venue: arXiv cs.LG 2016
Year: 2016

Early diagnosis, playing an important role in preventing progress and treating the Alzheimer\{'}s disease (AD), is based on classification of features extracted from brain images. The features have to accurately capture main AD-related variations of anatomical brain structures, such as, e.g., ventricles size, hippocampus shape, cortical thickness, and brain volume. This paper proposed to predict the AD with a deep 3D convolutional neural network (3D-CNN), which can learn generic features capturing AD biomarkers and adapt to different domain datasets. The 3D-CNN is built upon a 3D convolutional autoencoder, which is pre-trained to capture anatomical shape variations in structural brain MRI scans. Fully connected upper layers of the 3D-CNN are then fine-tuned for each task-specific AD classification. Experiments on the CADDementia MRI dataset with no skull-stripping preprocessing have shown our 3D-CNN outperforms several conventional classifiers by accuracy. Abilities of the 3D-CNN to generalize the features learnt and adapt to other domains have been validated on the ADNI dataset.
On The Security of AoA Estimation
Venue: arXiv cs.IT 2016
Year: 2016

Angle of Arrival (AoA) estimation has found its way to a wide range of applications. Much attention have been paid to study different techniques for AoA estimation and its applications for jamming suppression, however, security vulnerability issues of AoA estimation itself under hostile activity have not been paid the same attention. In this paper, the problem of AoA estimation in Rician flat fading channel under jamming condition is investigated. We consider the scenario in which a receiver with multiple antenna is trying to estimate the AoA of the specular line of sight (LOS) component of signal received from a given single antenna transmitter using a predefined training sequence. A jammer equipped with multiple antennas is trying to interrupt the AoA estimation phase by sending an arbitrary signal. We derive the optimal jammer and receiver strategies in various scenarios based on the knowledge of the opponent strategies and the available information about the communication channel. In all scenarios, we derive the optimal jammer signal design as well as its optimal power allocation policy. The results show the optimality of the training based Maximum Likelihood (ML) AoA estimator in case of randomly generated jamming signal. We also show that, the optimal jammer strategy is to emit a signal identical to the predefined training sequence turning the estimation process into a highest power competition scenario in which the detected AoA is the one for the transmitting entity of higher power. The obtained results are supported by the provided computer simulation.
Big IoT and social networking data for smart cities: Algorithmic improvements on Big Data Analysis in the context of RADICAL city applications
Venue: arXiv cs.CY 2016
Year: 2016

In this paper we present a SOA (Service Oriented Architecture)-based platform, enabling the retrieval and analysis of big datasets stemming from social networking (SN) sites and Internet of Things (IoT) devices, collected by smart city applications and socially-aware data aggregation services. A large set of city applications in the areas of Participating Urbanism, Augmented Reality and Sound-Mapping throughout participating cities is being applied, resulting into produced sets of millions of user-generated events and online SN reports fed into the RADICAL platform. Moreover, we study the application of data analytics such as sentiment analysis to the combined IoT and SN data saved into an SQL database, further investigating algorithmic and configurations to minimize delays in dataset processing and results retrieval.
On a Hypergraph Approach to Multistage Group Testing Problems
Venue: arXiv cs.IT 2016
Year: 2016

Group testing is a well known search problem that consists in detecting up to $s$ defective elements of the set $[t]=\{1,\ldots,t\}$ by carrying out tests on properly chosen subsets of $[t]$. In classical group testing the goal is to find all defective elements by using the minimal possible number of tests. In this paper we consider multistage group testing. We propose a general idea how to use a hypergraph approach to searching defects. For the case $s=2$, we design an explicit construction, which makes use of $2\log_2t(1+o(1))$ tests in the worst case and consists of $4$ stages.
Alzheimer's Disease Diagnostics by a Deeply Supervised Adaptable 3D Convolutional Network
Venue: arXiv cs.LG 2016
Year: 2016

Early diagnosis, playing an important role in preventing progress and treating the Alzheimer's disease (AD), is based on classification of features extracted from brain images. The features have to accurately capture main AD-related variations of anatomical brain structures, such as, e.g., ventricles size, hippocampus shape, cortical thickness, and brain volume. This paper proposes to predict the AD with a deep 3D convolutional neural network (3D-CNN), which can learn generic features capturing AD biomarkers and adapt to different domain datasets. The 3D-CNN is built upon a 3D convolutional autoencoder, which is pre-trained to capture anatomical shape variations in structural brain MRI scans. Fully connected upper layers of the 3D-CNN are then fine-tuned for each task-specific AD classification. Experiments on the \emph{ADNI} MRI dataset with no skull-stripping preprocessing have shown our 3D-CNN outperforms several conventional classifiers by accuracy and robustness. Abilities of the 3D-CNN to generalize the features learnt and adapt to other domains have been validated on the \emph{CADDementia} dataset.
A Hierarchical Distributed Processing Framework for Big Image Data
Venue: arXiv cs.CV 2016
Year: 2016

This paper introduces an effective processing framework nominated ICP (Image Cloud Processing) to powerfully cope with the data explosion in image processing field. While most previous researches focus on optimizing the image processing algorithms to gain higher efficiency, our work dedicates to providing a general framework for those image processing algorithms, which can be implemented in parallel so as to achieve a boost in time efficiency without compromising the results performance along with the increasing image scale. The proposed ICP framework consists of two mechanisms, i.e. SICP (Static ICP) and DICP (Dynamic ICP). Specifically, SICP is aimed at processing the big image data pre-stored in the distributed system, while DICP is proposed for dynamic input. To accomplish SICP, two novel data representations named P-Image and Big-Image are designed to cooperate with MapReduce to achieve more optimized configuration and higher efficiency. DICP is implemented through a parallel processing procedure working with the traditional processing mechanism of the distributed system. Representative results of comprehensive experiments on the challenging ImageNet dataset are selected to validate the capacity of our proposed ICP framework over the traditional state-of-the-art methods, both in time efficiency and quality of results.
Context-Dependent Word Representation for Neural Machine Translation
Venue: arXiv cs.CL 2016
Year: 2016

We first observe a potential weakness of continuous vector representations of symbols in neural machine translation. That is, the continuous vector representation, or a word embedding vector, of a symbol encodes multiple dimensions of similarity, equivalent to encoding more than one meaning of the word. This has the consequence that the encoder and decoder recurrent networks in neural machine translation need to spend substantial amount of their capacity in disambiguating source and target words based on the context which is defined by a source sentence. Based on this observation, in this paper we propose to contextualize the word embedding vectors using a nonlinear bag-of-words representation of the source sentence. Additionally, we propose to represent special tokens (such as numbers, proper nouns and acronyms) with typed symbols to facilitate translating those words that are not well-suited to be translated via continuous vectors. The experiments on En-Fr and En-De reveal that the proposed approaches of contextualization and symbolization improves the translation quality of neural machine translation systems significantly.