OpenRaiser/Intern-Atlas huggingface dataset:
Development of Graphical User Interface For Microwave Filter Design
Venue: arXiv cs.HC 2016
Year: 2016

This research project aims at developing a low-cost, time-effective and a stand-alone graphical user interface (GUI) that will be used to design microwave filters. Throughout the projects, the main theory behind the technology of microwave filters, their generalized mathematical equations and the analysis of their different circuit topologies have been reviewed. This review helps to extract the necessary information needed for the design of microwave filters. Besides, the guiding principles and the underlying engineering factors for a successful and information-oriented GUI were also highlighted. To carry out the project, the High-Level GUI Development Environment (GUIDE) together with a structured programming approach have been used to design the GUI, and to program its related functionalities. The frequency responses are generated by using the generalized equation of each filter class and type; and by using their different circuit topologies (Shunt or Series topology). The GUI can also provide reactive element values from given specification. Moreover, the features for the design of ultra-wideband (UWB) band-pass filter, capacitively coupled and combline filter are also incorporated into the stand-alone application. The finalized prototype will serve both industries and educational institutions.
Concatenated image completion via tensor augmentation and completion
Venue: arXiv cs.LG 2016
Year: 2016

This paper proposes a novel framework called concatenated image completion via tensor augmentation and completion (ICTAC), which recovers missing entries of color images with high accuracy. Typical images are second- or third-order tensors (2D/3D) depending if they are grayscale or color, hence tensor completion algorithms are ideal for their recovery. The proposed framework performs image completion by concatenating copies of a single image that has missing entries into a third-order tensor, applying a dimensionality augmentation technique to the tensor, utilizing a tensor completion algorithm for recovering its missing entries, and finally extracting the recovered image from the tensor. The solution relies on two key components that have been recently proposed to take advantage of the tensor train (TT) rank: A tensor augmentation tool called ket augmentation (KA) that represents a low-order tensor by a higher-order tensor, and the algorithm tensor completion by parallel matrix factorization via tensor train (TMac-TT), which has been demonstrated to outperform state-of-the-art tensor completion algorithms. Simulation results for color image recovery show the clear advantage of our framework against current state-of-the-art tensor completion algorithms.
Vision-based Traffic Flow Prediction using Dynamic Texture Model and Gaussian Process
Venue: arXiv cs.CV 2016
Year: 2016

In this paper, we describe work in progress towards a real-time vision-based traffic flow prediction (TFP) system. The proposed method consists of three elemental operators, that are dynamic texture model based motion segmentation, feature extraction and Gaussian process (GP) regression. The objective of motion segmentation is to recognize the target regions covering the moving vehicles in the sequence of visual processes. The feature extraction operator aims to extract useful features from the target regions. The extracted features are then mapped to the number of vehicles through the operator of GP regression. A training stage using historical visual data is required for determining the parameter values of the GP. Using a low-resolution visual data set, we performed preliminary evaluations on the performance of the proposed method. The results show that our method beats a benchmark solution based on Gaussian mixture model, and has the potential to be developed into qualified and practical solutions to real-time TFP.
Two Counterexamples Concerning the Scott Topology on a Partial Order
Venue: arXiv cs.LO 2016
Year: 2016

We construct a complete lattice $Z$ such that the binary supremum function $\sup:Z\times Z\to Z$ is discontinuous with respect to the product topology on $Z\times Z$ of the Scott topologies on each copy of $Z$. In addition, we show that bounded completeness of a complete lattice $Z$ is in general not inherited by the dcpo $C(X,Z)$ of continuous functions from $X$ to $Z$ where $X$ may be any topological space and where on $Z$ the Scott topology is considered.
Towards Persistent Storage and Retrieval of Domain Models using Graph Database Technology
Venue: arXiv cs.RO 2016
Year: 2016

We employ graph database technology to persistently store and retrieve robot domain models.
Sampling Rate Distortion
Venue: arXiv cs.IT 2016
Year: 2016

Consider a discrete memoryless multiple source with $m$ components of which $k \leq m$ possibly different sources are sampled at each time instant and jointly compressed in order to reconstruct all the $m$ sources under a given distortion criterion. A new notion of sampling rate distortion function is introduced, and is characterized first for the case of fixed-set sampling. Next, for independent random sampling performed without knowledge of the source outputs, it is shown that the sampling rate distortion function is the same regardless of whether or not the decoder is informed of the sequence of sampled sets. Furthermore, memoryless random sampling is considered with the sampler depending on the source outputs and with an informed decoder. It is shown that deterministic sampling, characterized by a conditional point-mass, is optimal and suffices to achieve the sampling rate distortion function. For memoryless random sampling with an uninformed decoder, an upper bound for the sampling rate distortion function is seen to possess a similar property of conditional point-mass optimality. It is shown by example that memoryless sampling with an informed decoder can outperform strictly any independent random sampler, and that memoryless sampling can do strictly better with an informed decoder than without.
Multi-modal dictionary learning for image separation with application in art investigation
Venue: arXiv cs.CV 2016
Year: 2016

In support of art investigation, we propose a new source separation method that unmixes a single X-ray scan acquired from double-sided paintings. In this problem, the X-ray signals to be separated have similar morphological characteristics, which brings previous source separation methods to their limits. Our solution is to use photographs taken from the front and back-side of the panel to drive the separation process. The crux of our approach relies on the coupling of the two imaging modalities (photographs and X-rays) using a novel coupled dictionary learning framework able to capture both common and disparate features across the modalities using parsimonious representations; the common component models features shared by the multi-modal images, whereas the innovation component captures modality-specific information. As such, our model enables the formulation of appropriately regularized convex optimization procedures that lead to the accurate separation of the X-rays. Our dictionary learning framework can be tailored both to a single- and a multi-scale framework, with the latter leading to a significant performance improvement. Moreover, to improve further on the visual quality of the separated images, we propose to train coupled dictionaries that ignore certain parts of the painting corresponding to craquelure. Experimentation on synthetic and real data - taken from digital acquisition of the Ghent Altarpiece (1432) - confirms the superiority of our method against the state-of-the-art morphological component analysis technique that uses either fixed or trained dictionaries to perform image separation.
Finite Element Integration with Quadrature on the GPU
Venue: arXiv cs.MS 2016
Year: 2016

We present a novel, quadrature-based finite element integration method for low-order elements on GPUs, using a pattern we call \textit{thread transposition} to avoid reductions while vectorizing aggressively. On the NVIDIA GTX580, which has a nominal single precision peak flop rate of 1.5 TF/s and a memory bandwidth of 192 GB/s, we achieve close to 300 GF/s for element integration on first-order discretization of the Laplacian operator with variable coefficients in two dimensions, and over 400 GF/s in three dimensions. From our performance model we find that this corresponds to 90\% of our measured achievable bandwidth peak of 310 GF/s. Further experimental results also match the predicted performance when used with double precision (120 GF/s in two dimensions, 150 GF/s in three dimensions). Results obtained for the linear elasticity equations (220 GF/s and 70 GF/s in two dimensions, 180 GF/s and 60 GF/s in three dimensions) also demonstrate the applicability of our method to vector-valued partial differential equations.
Optimal Placement of Cores, Caches and Memory Controllers in Network On-Chip
Venue: arXiv cs.PF 2016
Year: 2016

Parallel programming is emerging fast and intensive applications need more resources, so there is a huge demand for on-chip multiprocessors. Accessing L1 caches beside the cores are the fastest after registers but the size of private caches cannot increase because of design, cost and technology limits. Then split I-cache and D-cache are used with shared LLC (last level cache). For a unified shared LLC, bus interface is not scalable, and it seems that distributed shared LLC (DSLLC) is a better choice. Most of papers assume a distributed shared LLC beside each core in on-chip network. Many works assume that DSLLCs are placed in all cores; however, we will show that this design ignores the effect of traffic congestion in on-chip network. In fact, our work focuses on optimal placement of cores, DSLLCs and even memory controllers to minimize the expected latency based on traffic load in a mesh on-chip network with fixed number of cores and total cache capacity. We try to do some analytical modeling deriving intended cost function and then optimize the mean delay of the on-chip network communication. This work is supposed to be verified using some traffic patterns that are run on CSIM simulator.
Decidability of Non-Interactive Simulation of Joint Distributions
Venue: arXiv cs.IT 2016
Year: 2016

We present decidability results for a sub-class of "non-interactive" simulation problems, a well-studied class of problems in information theory. A non-interactive simulation problem is specified by two distributions $P(x,y)$ and $Q(u,v)$: The goal is to determine if two players, Alice and Bob, that observe sequences $X^n$ and $Y^n$ respectively where $\{(X_i, Y_i)\}_{i=1}^n$ are drawn i.i.d. from $P(x,y)$ can generate pairs $U$ and $V$ respectively (without communicating with each other) with a joint distribution that is arbitrarily close in total variation to $Q(u,v)$. Even when $P$ and $Q$ are extremely simple: e.g., $P$ is uniform on the triples $\{(0,0), (0,1), (1,0)\}$ and $Q$ is a "doubly symmetric binary source", i.e., $U$ and $V$ are uniform $\pm 1$ variables with correlation say $0.49$, it is open if $P$ can simulate $Q$. In this work, we show that whenever $P$ is a distribution on a finite domain and $Q$ is a $2 \times 2$ distribution, then the non-interactive simulation problem is decidable: specifically, given $δ> 0$ the algorithm runs in time bounded by some function of $P$ and $δ$ and either gives a non-interactive simulation protocol that is $δ$-close to $Q$ or asserts that no protocol gets $O(δ)$-close to $Q$. The main challenge to such a result is determining explicit (computable) convergence bounds on the number $n$ of samples that need to be drawn from $P(x,y)$ to get $δ$-close to $Q$. We invoke contemporary results from the analysis of Boolean functions such as the invariance principle and a regularity lemma to obtain such explicit bounds.
Space-Efficient Construction of Compressed Indexes in Deterministic Linear Time
Venue: arXiv cs.DS 2016
Year: 2016

We show that the compressed suffix array and the compressed suffix tree of a string $T$ can be built in $O(n)$ deterministic time using $O(n\logσ)$ bits of space, where $n$ is the string length and $σ$ is the alphabet size. Previously described deterministic algorithms either run in time that depends on the alphabet size or need $ω(n\log σ)$ bits of working space. Our result has immediate applications to other problems, such as yielding the first linear-time LZ77 and LZ78 parsing algorithms that use $O(n \logσ)$ bits.
On the Stability of a Full-Duplex Aloha Network
Venue: arXiv cs.NI 2016
Year: 2016

This letter offers the first characterisation of the stability for full-duplex large size networks. Through stochastic geometry tools, key performance metrics like delay and maximum stable arrival rate are derived for the non saturated system and compared to a half-duplex counterpart, also accounting for imperfect self-interference cancellation. This analysis better identifies that the full-duplex advantage is prominent for sparse networks, whereas in dense topologies residual self-interference may hinder achievable gains.
Removing Unnecessary Variables from Horn Clause Verification Conditions
Venue: arXiv cs.LO 2016
Year: 2016

Verification conditions (VCs) are logical formulas whose satisfiability guarantees program correctness. We consider VCs in the form of constrained Horn clauses (CHC) which are automatically generated from the encoding of (an interpreter of) the operational semantics of the programming language. VCs are derived through program specialization based on the unfold/fold transformation rules and, as it often happens when specializing interpreters, they contain unnecessary variables, that is, variables which are not required for the correctness proofs of the programs under verification. In this paper we adapt to the CHC setting some of the techniques that were developed for removing unnecessary variables from logic programs, and we show that, in some cases, the application of these techniques increases the effectiveness of Horn clause solvers when proving program correctness.
Secure Analog Network Coding in Layered Networks
Venue: arXiv cs.IT 2016
Year: 2016

We consider a class of Gaussian layered networks where a source communicates with a destination through $L$ intermediate relay layers with $N$ nodes in each layer in the presence of a single eavesdropper which can overhear the transmissions of the nodes in any one layer. The problem of maximum secrecy rate achievable with analog network coding for a unicast communication over such layered wireless relay networks with directed links is considered. A relay node performing analog network coding scales and forwards the signals received at its input. The key contribution of this work is a lemma that provides the globally optimal set of scaling factors for the nodes that maximizes the end-to-end secrecy rate for a class of layered networks. We also show that in the high-SNR regime, ANC achieves secrecy rates within a constant gap of the cutset upper bound on the secrecy capacity. To the best of our knowledge, this work offers the first characterization of the performance of secure ANC in multi-layered networks in the presence of an eavesdropper.
Maximizing a Monotone Submodular Function with a Bounded Curvature under a Knapsack Constraint
Venue: arXiv cs.DS 2016
Year: 2016

We consider the problem of maximizing a monotone submodular function under a knapsack constraint. We show that, for any fixed $ε> 0$, there exists a polynomial-time algorithm with an approximation ratio $1-c/e-ε$, where $c \in [0,1]$ is the (total) curvature of the input function. This approximation ratio is tight up to $ε$ for any $c \in [0,1]$. To the best of our knowledge, this is the first result for a knapsack constraint that incorporates the curvature to obtain an approximation ratio better than $1-1/e$, which is tight for general submodular functions. As an application of our result, we present a polynomial-time algorithm for the budget allocation problem with an improved approximation ratio.
Neural Discourse Modeling of Conversations
Venue: arXiv cs.CL 2016
Year: 2016

Deep neural networks have shown recent promise in many language-related tasks such as the modeling of conversations. We extend RNN-based sequence to sequence models to capture the long range discourse across many turns of conversation. We perform a sensitivity analysis on how much additional context affects performance, and provide quantitative and qualitative evidence that these models are able to capture discourse relationships across multiple utterances. Our results quantifies how adding an additional RNN layer for modeling discourse improves the quality of output utterances and providing more of the previous conversation as input also improves performance. By searching the generated outputs for specific discourse markers we show how neural discourse models can exhibit increased coherence and cohesion in conversations.
Person Re-identification with Hyperspectral Multi-Camera Systems --- A Pilot Study
Venue: arXiv cs.CV 2016
Year: 2016

Person re-identification in a multi-camera environment is an important part of modern surveillance systems. Person re-identification from color images has been the focus of much active research, due to the numerous challenges posed with such analysis tasks, such as variations in illumination, pose and viewpoints. In this paper, we suggest that hyperspectral imagery has the potential to provide unique information that is expected to be beneficial for the re-identification task. Specifically, we assert that by accurately characterizing the unique spectral signature for each person's skin, hyperspectral imagery can provide very useful descriptors (e.g. spectral signatures from skin pixels) for re-identification. Towards this end, we acquired proof-of-concept hyperspectral re-identification data under challenging (practical) conditions from 15 people. Our results indicate that hyperspectral data result in a substantially enhanced re-identification performance compared to color (RGB) images, when using spectral signatures over skin as the feature descriptor.
Guided Policy Search as Approximate Mirror Descent
Venue: arXiv cs.LG 2016
Year: 2016

Guided policy search algorithms can be used to optimize complex nonlinear policies, such as deep neural networks, without directly computing policy gradients in the high-dimensional parameter space. Instead, these methods use supervised learning to train the policy to mimic a "teacher" algorithm, such as a trajectory optimizer or a trajectory-centric reinforcement learning method. Guided policy search methods provide asymptotic local convergence guarantees by construction, but it is not clear how much the policy improves within a small, finite number of iterations. We show that guided policy search algorithms can be interpreted as an approximate variant of mirror descent, where the projection onto the constraint manifold is not exact. We derive a new guided policy search algorithm that is simpler and provides appealing improvement and convergence guarantees in simplified convex and linear settings, and show that in the more general nonlinear setting, the error in the projection step can be bounded. We provide empirical results on several simulated robotic navigation and manipulation tasks that show that our method is stable and achieves similar or better performance when compared to prior guided policy search methods, with a simpler formulation and fewer hyperparameters.
How Much Do Downlink Pilots Improve Cell-Free Massive MIMO?
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, we analyze the benefits of including downlink pilots in a cell-free massive MIMO system. We derive an approximate per-user achievable downlink rate for conjugate beamforming processing, which takes into account both uplink and downlink channel estimation errors, and power control. A performance comparison is carried out, in terms of per-user net throughput, considering cell-free massive MIMO operation with and without downlink training, for different network densities. We take also into account the performance improvement provided by max-min fairness power control in the downlink. Numerical results show that, exploiting downlink pilots, the performance can be considerably improved in low density networks over the conventional scheme where the users rely on statistical channel knowledge only. In high density networks, performance improvements are moderate.
Design and implementation of computational platform for social-humanoid robot Lumen as an exhibition guide in Electrical Engineering Days 2015
Venue: arXiv cs.RO 2016
Year: 2016

Social Robot Lumen is an Artificial Intelligence development project that aims to create an Artificial Intelligence (AI) which allows a humanoid robot to communicate with human being naturally. In this study, Lumen will be developed to be a tour guide in Electrical Engineering Days 2015 exhibition. In developing an AI, there are a lot of modules that need to be developed separately. To make the development easier, we need a computational platform which becomes basis for all developers to give easiness in developing the modules in parallel way. That computational platform that developed by the writer is called Lumen Server. Lumen Server has two main function, which are to be a bridge between all Lumen intelligence modules with NAO robot, and to be the communication bridge between those Lumen intelligence modules. For the second function, Lumen Server implements the AMQP protocol using RabbitMQ. Besides that, writer also developed a control system for robot movement called Lumen Motion. Lumen motion is implemented by modelling the movement of NAO robot and also by creating a control system using fuzzy logic controller. Writer also developed a program that connects all Lumen intelligence modules so that Lumen can act like a tour guide. The implementation of this program uses FSM and event-driven program. From implementation result, all the features which were designed are successfully implemented. By the developing of this computational platform, it can ease the development of Lumen in the future. For next development, it must be focused on creating integration system so that Lumen can be more responsive to the environment. ----- Sosial Robot Lumen adalah proyek pengembangan kecerdasan buatan yang bertujuan untuk menciptakan kecerdasan buatan atau artificial intelligence (AI) yang memungkinkan robot untuk dapat berkomunikasi dengan manusia secara alami.