OpenRaiser/Intern-Atlas huggingface dataset:
Register automata with linear arithmetic
Venue: arXiv cs.FL 2017
Year: 2017

We propose a novel automata model over the alphabet of rational numbers, which we call register automata over the rationals (RA-Q). It reads a sequence of rational numbers and outputs another rational number. RA-Q is an extension of the well-known register automata (RA) over infinite alphabets, which are finite automata equipped with a finite number of registers/variables for storing values. Like in the standard RA, the RA-Q model allows both equality and ordering tests between values. It, moreover, allows to perform linear arithmetic between certain variables. The model is quite expressive: in addition to the standard RA, it also generalizes other well-known models such as affine programs and arithmetic circuits. The main feature of RA-Q is that despite the use of linear arithmetic, the so-called invariant problem---a generalization of the standard non-emptiness problem---is decidable. We also investigate other natural decision problems, namely, commutativity, equivalence, and reachability. For deterministic RA-Q, commutativity and equivalence are polynomial-time inter-reducible with the invariant problem.
Architectural Techniques to Enable Reliable and Scalable Memory Systems
Venue: arXiv cs.AR 2017
Year: 2017

High capacity and scalable memory systems play a vital role in enabling our desktops, smartphones, and pervasive technologies like Internet of Things (IoT). Unfortunately, memory systems are becoming increasingly prone to faults. This is because we rely on technology scaling to improve memory density, and at small feature sizes, memory cells tend to break easily. Today, memory reliability is seen as the key impediment towards using high-density devices, adopting new technologies, and even building the next Exascale supercomputer. To ensure even a bare-minimum level of reliability, present-day solutions tend to have high performance, power and area overheads. Ideally, we would like memory systems to remain robust, scalable, and implementable while keeping the overheads to a minimum. This dissertation describes how simple cross-layer architectural techniques can provide orders of magnitude higher reliability and enable seamless scalability for memory systems while incurring negligible overheads.
Land Cover Classification via Multi-temporal Spatial Data by Recurrent Neural Networks
Venue: arXiv cs.CV 2017
Year: 2017

Nowadays, modern earth observation programs produce huge volumes of satellite images time series (SITS) that can be useful to monitor geographical areas through time. How to efficiently analyze such kind of information is still an open question in the remote sensing field. Recently, deep learning methods proved suitable to deal with remote sensing data mainly for scene classification (i.e. Convolutional Neural Networks - CNNs - on single images) while only very few studies exist involving temporal deep learning approaches (i.e Recurrent Neural Networks - RNNs) to deal with remote sensing time series. In this letter we evaluate the ability of Recurrent Neural Networks, in particular the Long-Short Term Memory (LSTM) model, to perform land cover classification considering multi-temporal spatial data derived from a time series of satellite images. We carried out experiments on two different datasets considering both pixel-based and object-based classification. The obtained results show that Recurrent Neural Networks are competitive compared to state-of-the-art classifiers, and may outperform classical approaches in presence of low represented and/or highly mixed classes. We also show that using the alternative feature representation generated by LSTM can improve the performances of standard classifiers.
Learning to Estimate Pose by Watching Videos
Venue: arXiv cs.CV 2017
Year: 2017

In this paper we propose a technique for obtaining coarse pose estimation of humans in an image that does not require any manual supervision. While a general unsupervised technique would fail to estimate human pose, we suggest that sufficient information about coarse pose can be obtained by observing human motion in multiple frames. Specifically, we consider obtaining surrogate supervision through videos as a means for obtaining motion based grouping cues. We supplement the method using a basic object detector that detects persons. With just these components we obtain a rough estimate of the human pose. With these samples for training, we train a fully convolutional neural network (FCNN)[20] to obtain accurate dense blob based pose estimation. We show that the results obtained are close to the ground-truth and to the results obtained using a fully supervised convolutional pose estimation method [31] as evaluated on a challenging dataset [15]. This is further validated by evaluating the obtained poses using a pose based action recognition method [5]. In this setting we outperform the results as obtained using the baseline method that uses a fully supervised pose estimation algorithm and is competitive with a new baseline created using convolutional pose estimation with full supervision.
Recognizing Activities of Daily Living from Egocentric Images
Venue: arXiv cs.CV 2017
Year: 2017

Recognizing Activities of Daily Living (ADLs) has a large number of health applications, such as characterize lifestyle for habit improvement, nursing and rehabilitation services. Wearable cameras can daily gather large amounts of image data that provide rich visual information about ADLs than using other wearable sensors. In this paper, we explore the classification of ADLs from images captured by low temporal resolution wearable camera (2fpm) by using a Convolutional Neural Networks (CNN) approach. We show that the classification accuracy of a CNN largely improves when its output is combined, through a random decision forest, with contextual information from a fully connected layer. The proposed method was tested on a subset of the NTCIR-12 egocentric dataset, consisting of 18,674 images and achieved an overall accuracy of 86% activity recognition on 21 classes.
A Procedural Texture Generation Framework Based on Semantic Descriptions
Venue: arXiv cs.CV 2017
Year: 2017

Procedural textures are normally generated from mathematical models with parameters carefully selected by experienced users. However, for naive users, the intuitive way to obtain a desired texture is to provide semantic descriptions such as "regular," "lacelike," and "repetitive" and then a procedural model with proper parameters will be automatically suggested to generate the corresponding textures. By contrast, it is less practical for users to learn mathematical models and tune parameters based on multiple examinations of large numbers of generated textures. In this study, we propose a novel framework that generates procedural textures according to user-defined semantic descriptions, and we establish a mapping between procedural models and semantic texture descriptions. First, based on a vocabulary of semantic attributes collected from psychophysical experiments, a multi-label learning method is employed to annotate a large number of textures with semantic attributes to form a semantic procedural texture dataset. Then, we derive a low dimensional semantic space in which the semantic descriptions can be separated from one other. Finally, given a set of semantic descriptions, the diverse properties of the samples in the semantic space can lead the framework to find an appropriate generation model that uses appropriate parameters to produce a desired texture. The experimental results show that the proposed framework is effective and that the generated textures closely correlate with the input semantic descriptions.
Accurate and reduced SISO Sequence Impedance Models of Grid-tied Voltage Source Converter for Small Signal Stability Analysis
Venue: arXiv cs.SY 2017
Year: 2017

Impedance models are widely used in assessing small signal stability of grid-tied voltage source converter (VSC) systems. Recent research has proven that impedance models of grid-tied VSC in both dq and sequence domains are generally Multi-Input Multi-Output (MIMO) systems, and the generalized Nyquist criterion has to be applied for stability analysis to these MIMO systems. However, finding Single-Input and Single-Output (SISO) equivalents for this system is always appealing because of the simplicity and the convenience for physical interpretation when assessing the stability, compared to MIMO systems. This paper presents two types of SISO impedance models of grid-tied VSC system, one is derived from the strong grid assumption, and the other is from the closed-loop equivalence. The accuracy of these models is assessed with respect to the measured impedances in PSCAD/EMTDC, and their effects on the stability assessment were analyzed as well. It is proven that the accurate SISO model gives identical result as the MIMO (matrix-based) impedance model with respect to stability analysis. However, the reduced SISO model may lead to wrong results if the bandwidth of phase locked loop is large.
I-MMSE relations in random linear estimation and a sub-extensive interpolation method
Venue: arXiv cs.IT 2017
Year: 2017

Consider random linear estimation with Gaussian measurement matrices and noise. One can compute infinitesimal variations of the mutual information under infinitesimal variations of the signal-to-noise ratio or of the measurement rate. We discuss how each variation is related to the minimum mean-square error and deduce that the two variations are directly connected through a very simple identity. The main technical ingredient is a new interpolation method called "sub-extensive interpolation method". We use it to provide a new proof of an I-MMSE relation recently found by Reeves and Pfister [1] when the measurement rate is varied. Our proof makes it clear that this relation is intimately related to another I-MMSE relation also recently proved in [2]. One can directly verify that the identity relating the two types of variation of mutual information is indeed consistent with the one letter replica symmetric formula for the mutual information, first derived by Tanaka [3] for binary signals, and recently proved in more generality in [1,2,4,5] (by independent methods). However our proof is independent of any knowledge of Tanaka's formula.
Does Bidirectional Traffic Do More Harm Than Good in LoRaWAN Based LPWA Networks?
Venue: arXiv cs.NI 2017
Year: 2017

The need for low power, long range and low cost connectivity to meet the requirements of IoT applications has led to the emergence of Low Power Wide Area (LPWA) networking technologies. The promise of these technologies to wirelessly connect massive numbers of geographically dispersed devices at a low cost continues to attract a great deal of attention in the academic and commercial communities. Several rollouts are already underway even though the performance of these technologies is yet to be fully understood. In light of these developments, tools to carry out `what-if analyses' and pre-deployment studies are needed to understand the implications of choices that are made at design time. While there are several promising technologies in the LPWA space, this paper specifically focuses on the LoRa/LoRaWAN technology. In particular, we present LoRaWANSim, a simulator which extends the LoRaSim tool to add support for the LoRaWAN MAC protocol, which employs bidirectional communication. This is a salient feature not available in any other LoRa simulator. Subsequently, we provide vital insights into the performance of LoRaWAN based networks through extensive simulations. In particular, we show that the achievable network capacity reported in earlier studies is quite optimistic. The introduction of downlink traffic can have a significant impact on the uplink throughput. The number of transmit attempts recommended in the LoRaWAN specification may not always be the best choice. We also highlight the energy consumption versus reliability trade-offs associated with the choice of number of retransmission attempts.
Seamless Requirements
Venue: arXiv cs.SE 2017
Year: 2017

Popular notations for functional requirements specifications frequently ignore developers' needs, target specific development models, or require translation of requirements into tests for verification; the results can give out-of-sync or downright incompatible artifacts. Seamless Requirements, a new approach to specifying functional requirements, contributes to developers' understanding of requirements and to software quality regardless of the process, while the process itself becomes lighter due to the absence of tests in the presence of formal verification. A development case illustrates these benefits, and a discussion compares seamless requirements to other approaches.
A Contractive Approach to Separable Lyapunov Functions for Monotone Systems
Venue: arXiv cs.SY 2017
Year: 2017

Monotone systems preserve a partial ordering of states along system trajectories and are often amenable to separable Lyapunov functions that are either the sum or the maximum of a collection of functions of a scalar argument. In this paper, we consider constructing separable Lyapunov functions for monotone systems that are also contractive, that is, the distance between any pair of trajectories exponentially decreases. The distance is defined in terms of a possibly state-dependent norm. When this norm is a weighted one-norm, we obtain conditions which lead to sum-separable Lyapunov functions, and when this norm is a weighted infinity-norm, symmetric conditions lead to max-separable Lyapunov functions. In addition, we consider two classes of Lyapunov functions: the first class is separable along the system's state, and the second class is separable along components of the system's vector field. The latter case is advantageous for many practically motivated systems for which it is difficult to measure the system's state but easier to measure the system's velocity or rate of change. In addition, we present an algorithm based on sum-of-squares programming to compute such separable Lyapunov functions. We provide several examples to demonstrate our results.
On LoRaWAN Scalability: Empirical Evaluation of Susceptibility to Inter-Network Interference
Venue: arXiv cs.NI 2017
Year: 2017

Appearing on the stage quite recently, the Low Power Wide Area Networks (LPWANs) are currently getting much of attention. In the current paper we study the susceptibility of one LPWAN technology, namely LoRaWAN, to the inter-network interferences. By means of excessive empirical measurements employing the certified commercial transceivers, we characterize the effect of modulation coding schemes (known for LoRaWAN as data rates (DRs)) of a transmitter and an interferer on probability of successful packet delivery while operating in EU 868 MHz band. We show that in reality the transmissions with different DRs in the same frequency channel can negatively affect each other and that the high DRs are influenced by interferences more severely than the low ones. Also, we show that the LoRa-modulated DRs are affected by the interferences much less than the FSK-modulated one. Importantly, the presented results provide insight into the network-level operation of the LoRa LPWAN technology in general, and its scalability potential in particular. The results can also be used as a reference for simulations and analyses or for defining the communication parameters for real-life applications.
Environment-Independent Task Specifications via GLTL
Venue: arXiv cs.AI 2017
Year: 2017

We propose a new task-specification language for Markov decision processes that is designed to be an improvement over reward functions by being environment independent. The language is a variant of Linear Temporal Logic (LTL) that is extended to probabilistic specifications in a way that permits approximations to be learned in finite time. We provide several small environments that demonstrate the advantages of our geometric LTL (GLTL) language and illustrate how it can be used to specify standard reinforcement-learning tasks straightforwardly.
Impulse-Based Hybrid Motion Control
Venue: arXiv cs.SY 2017
Year: 2017

The impulse-based discrete feedback control has been proposed in previous work for the second-order motion systems with damping uncertainties. The sate-dependent discrete impulse action takes place at zero crossing of one of both states, either relative position or velocity. In this paper, the proposed control method is extended to a general hybrid motion control form. We are using the paradigm of hybrid system modeling while explicitly specifying the state trajectories each time the continuous system state hits the guards that triggers impulsive control actions. The conditions for a stable convergence to zero equilibrium are derived in relation to the control parameters, while requiring only the upper bound of damping uncertainties to be known. Numerical examples are shown for an underdamped closed-loop dynamics with oscillating transients, an upper bounded time-varying positive system damping, and system with an additional Coulomb friction damping.
HPTT: A High-Performance Tensor Transposition C++ Library
Venue: arXiv cs.MS 2017
Year: 2017

Recently we presented TTC, a domain-specific compiler for tensor transpositions. Despite the fact that the performance of the generated code is nearly optimal, due to its offline nature, TTC cannot be utilized in all the application codes in which the tensor sizes and the necessary tensor permutations are determined at runtime. To overcome this limitation, we introduce the open-source C++ library High-Performance Tensor Transposition (HPTT). Similar to TTC, HPTT incorporates optimizations such as blocking, multi-threading, and explicit vectorization; furthermore it decomposes any transposition into multiple loops around a so called micro-kernel. This modular design---inspired by BLIS---makes HPTT easy to port to different architectures, by only replacing the hand-vectorized micro-kernel (e.g., a 4x4 transpose). HPTT also offers an optional autotuning framework---guided by a performance model---that explores a vast search space of implementations at runtime (similar to FFTW). Across a wide range of different tensor transpositions and architectures (e.g., Intel Ivy Bridge, Intel Knights Landing, ARMv7, IBM Power7), HPTT attains a bandwidth comparable to that of SAXPY, and yields remarkable speedups over Eigen's tensor transposition implementation. Most importantly, the integration of HPTT into the Cyclops Tensor Framework (CTF) improves the overall performance of tensor contractions by up to 3.1x.
3D seismic data denoising using two-dimensional sparse coding scheme
Venue: arXiv cs.CV 2017
Year: 2017

Seismic data denoising is vital to geophysical applications and the transform-based function method is one of the most widely used techniques. However, it is challenging to design a suit- able sparse representation to express a transform-based func- tion group due to the complexity of seismic data. In this paper, we apply a seismic data denoising method based on learning- type overcomplete dictionaries which uses two-dimensional sparse coding (2DSC). First, we model the input seismic data and dictionaries as third-order tensors and introduce tensor- linear combinations for data approximation. Second, we ap- ply learning-type overcomplete dictionary, i.e., optimal sparse data representation is achieved through learning and training. Third, we exploit the alternating minimization algorithm to solve the optimization problem of seismic denoising. Finally we evaluate its denoising performance on synthetic seismic data and land data survey. Experiment results show that the two-dimensional sparse coding scheme reduces computational costs and enhances the signal-to-noise ratio.
Qumin, a minimalist quantum programming language
Venue: arXiv cs.PL 2017
Year: 2017

In this work we introduce Qumin, a novel quantum programming language with a focus on providing an easy to use, minimalist, high-level, and easily extensible platform for quantum programming. Qumin's design concentrates on encompassing the various interactions between classical and quantum computation via the use of two sublanguages: an untyped one that handles classical preparation and control, and one linearly typed that explicitly handles quantum routines. This allows the classical part of the language to be freely used for general programming while placing restrictions on the quantum part that enforce rules of quantum computing like the no-cloning of qubits. We describe both the language's theoretical foundations in terms of lambda calculi and linear type systems, and more practical matters such as implementations of algorithms and useful programming tools like matrix and oracle generators that streamline the interaction of the classical and quantum fragments of a program. Finally, we provide an experimental open-source implementation of an interpreter, typechecker and related tools for the language (which can be found in \url{https://github.com/wintershammer/QImp}).
ShapeWorld - A new test methodology for multimodal language understanding
Venue: arXiv cs.CL 2017
Year: 2017

We introduce a novel framework for evaluating multimodal deep learning models with respect to their language understanding and generalization abilities. In this approach, artificial data is automatically generated according to the experimenter's specifications. The content of the data, both during training and evaluation, can be controlled in detail, which enables tasks to be created that require true generalization abilities, in particular the combination of previously introduced concepts in novel ways. We demonstrate the potential of our methodology by evaluating various visual question answering models on four different tasks, and show how our framework gives us detailed insights into their capabilities and limitations. By open-sourcing our framework, we hope to stimulate progress in the field of multimodal language understanding.
On the Gap Between Strict-Saddles and True Convexity: An Omega(log d) Lower Bound for Eigenvector Approximation
Venue: arXiv cs.LG 2017
Year: 2017

We prove a \emph{query complexity} lower bound on rank-one principal component analysis (PCA). We consider an oracle model where, given a symmetric matrix $M \in \mathbb{R}^{d \times d}$, an algorithm is allowed to make $T$ \emph{exact} queries of the form $w^{(i)} = Mv^{(i)}$ for $i \in \{1,\dots,T\}$, where $v^{(i)}$ is drawn from a distribution which depends arbitrarily on the past queries and measurements $\{v^{(j)},w^{(j)}\}_{1 \le j \le i-1}$. We show that for a small constant $ε$, any adaptive, randomized algorithm which can find a unit vector $\widehat{v}$ for which $\widehat{v}^{\top}M\widehat{v} \ge (1-ε)\|M\|$, with even small probability, must make $T = Ω(\log d)$ queries. In addition to settling a widely-held folk conjecture, this bound demonstrates a fundamental gap between convex optimization and "strict-saddle" non-convex optimization of which PCA is a canonical example: in the former, first-order methods can have dimension-free iteration complexity, whereas in PCA, the iteration complexity of gradient-based methods must necessarily grow with the dimension. Our argument proceeds via a reduction to estimating the rank-one spike in a deformed Wigner model. We establish lower bounds for this model by developing a "truncated" analogue of the $χ^2$ Bayes-risk lower bound of Chen et al.
Distributional Modeling on a Diet: One-shot Word Learning from Text Only
Venue: arXiv cs.CL 2017
Year: 2017

We test whether distributional models can do one-shot learning of definitional properties from text only. Using Bayesian models, we find that first learning overarching structure in the known data, regularities in textual contexts and in properties, helps one-shot learning, and that individual context items can be highly informative. Our experiments show that our model can learn properties from a single exposure when given an informative utterance.