OpenRaiser/Intern-Atlas huggingface dataset:
Streaming PCA: Matching Matrix Bernstein and Near-Optimal Finite Sample Guarantees for Oja's Algorithm
Venue: arXiv cs.LG 2016
Year: 2016

This work provides improved guarantees for streaming principle component analysis (PCA). Given $A_1, \ldots, A_n\in \mathbb{R}^{d\times d}$ sampled independently from distributions satisfying $\mathbb{E}[A_i] = Σ$ for $Σ\succeq \mathbf{0}$, this work provides an $O(d)$-space linear-time single-pass streaming algorithm for estimating the top eigenvector of $Σ$. The algorithm nearly matches (and in certain cases improves upon) the accuracy obtained by the standard batch method that computes top eigenvector of the empirical covariance $\frac{1}{n} \sum_{i \in [n]} A_i$ as analyzed by the matrix Bernstein inequality. Moreover, to achieve constant accuracy, our algorithm improves upon the best previous known sample complexities of streaming algorithms by either a multiplicative factor of $O(d)$ or $1/\mathrm{gap}$ where $\mathrm{gap}$ is the relative distance between the top two eigenvalues of $Σ$. These results are achieved through a novel analysis of the classic Oja's algorithm, one of the oldest and most popular algorithms for streaming PCA. In particular, this work shows that simply picking a random initial point $w_0$ and applying the update rule $w_{i + 1} = w_i + η_i A_i w_i$ suffices to accurately estimate the top eigenvector, with a suitable choice of $η_i$. We believe our result sheds light on how to efficiently perform streaming PCA both in theory and in practice and we hope that our analysis may serve as the basis for analyzing many variants and extensions of streaming PCA.
Data-Driven Real-Time Power Dispatch for Maximizing Variable Renewable Generation
Venue: arXiv cs.SY 2016
Year: 2016

Traditional power dispatch methods have difficulties in accommodating large-scale variable renewable generation (VRG) and have resulted in unnecessary VRG spillage in the practical industry. The recent dispatchable-interval-based methods have the potential to reduce VRG curtailment, but the dispatchable intervals are not allocated effectively due to the lack of exploiting historical dispatch records of VRG units. To bridge this gap, this paper proposes a novel data-driven real-time dispatch approach to maximize VRG utili-zation by using do-not-exceed (DNE) limits. This approach defines the maximum generation output ranges that the system can ac-commodate without compromising reliability. The DNE limits of VRG units and operating base points of conventional units are co-optimized by hybrid stochastic and robust optimization, and the decision models are formulated as mixed-integer linear programs by the sample average approximation technique exploiting historical VRG data. A strategy for selecting historical data samples is also proposed to capture the VRG uncertainty more accurately under variant prediction output levels. Computational experiments show the effectiveness of the proposed methods.
A survey of sparse representation: algorithms and applications
Venue: arXiv cs.CV 2016
Year: 2016

Sparse representation has attracted much attention from researchers in fields of signal processing, image processing, computer vision and pattern recognition. Sparse representation also has a good reputation in both theoretical research and practical applications. Many different algorithms have been proposed for sparse representation. The main purpose of this article is to provide a comprehensive study and an updated review on sparse representation and to supply a guidance for researchers. The taxonomy of sparse representation methods can be studied from various viewpoints. For example, in terms of different norm minimizations used in sparsity constraints, the methods can be roughly categorized into five groups: sparse representation with $l_0$-norm minimization, sparse representation with $l_p$-norm (0$<$p$<$1) minimization, sparse representation with $l_1$-norm minimization and sparse representation with $l_{2,1}$-norm minimization. In this paper, a comprehensive overview of sparse representation is provided. The available sparse representation algorithms can also be empirically categorized into four groups: greedy strategy approximation, constrained optimization, proximity algorithm-based optimization, and homotopy algorithm-based sparse representation. The rationales of different algorithms in each category are analyzed and a wide range of sparse representation applications are summarized, which could sufficiently reveal the potential nature of the sparse representation theory. Specifically, an experimentally comparative study of these sparse representation algorithms was presented. The Matlab code used in this paper can be available at: http://www.yongxu.org/lunwen.html.
SIFT: An Algorithm for Extracting Structural Information From Taxonomies
Venue: arXiv cs.DB 2016
Year: 2016

In this work we present SIFT, a 3-step algorithm for the analysis of the structural information represented by means of a taxonomy. The major advantage of this algorithm is the capability to leverage the information inherent to the hierarchical structures of taxonomies to infer correspondences which can allow to merge them in a later step. This method is particular relevant in scenarios where taxonomy alignment techniques exploiting textual information from taxonomy nodes cannot operate successfully.
ZeroDB white paper
Venue: arXiv cs.CR 2016
Year: 2016

ZeroDB is an end-to-end encrypted database that enables clients to operate on (search, sort, query, and share) encrypted data without exposing encryption keys or cleartext data to the database server. The familiar client-server architecture is unchanged, but query logic and encryption keys are pushed client-side. Since the server has no insight into the nature of the data, the risk of data being exposed via a server-side data breach is eliminated. Even if the server is successfully infiltrated, adversaries would not have access to the cleartext data and cannot derive anything useful out of disk or RAM snapshots. ZeroDB provides end-to-end encryption while maintaining much of the functionality expected of a modern database, such as full-text search, sort, and range queries. Additionally, ZeroDB uses proxy re-encryption and/or delta key technology to enable secure, granular sharing of encrypted data without exposing keys to the server and without sharing the same encryption key between users of the database.
Crowdsourcing Health Labels: Inferring Body Weight from Profile Pictures
Venue: arXiv cs.HC 2016
Year: 2016

To use social media for health-related analysis, one key step is the detection of health-related labels for users. But unlike transient conditions like flu, social media users are less vocal about chronic conditions such as obesity, as users might not tweet "I'm still overweight". As, however, obesity-related conditions such as diabetes, heart disease, osteoarthritis, and even cancer are on the rise, this obese-or-not label could be one of the most useful for studies in public health. In this paper we investigate the feasibility of using profile pictures to infer if a user is overweight or not. We show that this is indeed possible and further show that the fraction of labeled-as-overweight users is higher in U.S. counties with higher obesity rates. Going from public to individual health analysis, we then find differences both in behavior and social networks, for example finding users labeled as overweight to have fewer followers.
Exploring the Neural Algorithm of Artistic Style
Venue: arXiv cs.CV 2016
Year: 2016

We explore the method of style transfer presented in the article "A Neural Algorithm of Artistic Style" by Leon A. Gatys, Alexander S. Ecker and Matthias Bethge (arXiv:1508.06576). We first demonstrate the power of the suggested style space on a few examples. We then vary different hyper-parameters and program properties that were not discussed in the original paper, among which are the recognition network used, starting point of the gradient descent and different ways to partition style and content layers. We also give a brief comparison of some of the existing algorithm implementations and deep learning frameworks used. To study the style space further we attempt to generate synthetic images by maximizing a single entry in one of the Gram matrices $\mathcal{G}_l$ and some interesting results are observed. Next, we try to mimic the sparsity and intensity distribution of Gram matrices obtained from a real painting and generate more complex textures. Finally, we propose two new style representations built on top of network's features and discuss how one could be used to achieve local and potentially content-aware style transfer.
Strictly Positive and Continuous Random Fibonacci Sequences and Network Theory Applications
Venue: arXiv cs.IT 2016
Year: 2016

We motivate the study of a certain class of random Fibonacci sequences - which we call continuous random Fibonacci sequences - by demonstrating that their exponential growth rate can be used to establish capacity and power scaling laws for multihop cooperative amplify-and-forward (AF) relay networks. With these laws, we show that it is possible to construct multihop cooperative AF networks that simultaneously avoid 1) exponential capacity decay and 2) exponential transmit power growth across the network. This is achieved by ensuring the network's Lyapunov exponent is zero.
Petrarch 2 : Petrarcher
Venue: arXiv cs.CL 2016
Year: 2016

PETRARCH 2 is the fourth generation of a series of Event-Data coders stemming from research by Phillip Schrodt. Each iteration has brought new functionality and usability, and this is no exception.Petrarch 2 takes much of the power of the original Petrarch's dictionaries and redirects it into a faster and smarter core logic. Earlier iterations handled sentences largely as a list of words, incorporating some syntactic information here and there. Petrarch 2 now views the sentence entirely on the syntactic level. It receives the syntactic parse of a sentence from the Stanford CoreNLP software, and stores this data as a tree structure of linked nodes, where each node is a Phrase object. Prepositional, noun, and verb phrases each have their own version of this Phrase class, which deals with the logic particular to those kinds of phrases. Since this is an event coder, the core of the logic focuses around the verbs: who is acting, who is being acted on, and what is happening. The theory behind this new structure and its logic is founded in Generative Grammar, Information Theory, and Lambda-Calculus Semantics.
Philosophical Fictionalism and Problem of Artificial Intelligence
Venue: arXiv cs.OH 2016
Year: 2016

The artificial intelligence received broad interpretation as a literary image. This approach did not have unambiguous refering to the scopes of logical studies and mathematical investigations. An author applied methods peculiar to the semiotic approach, offered by Boris Uspensky and Yury Lotman. In addition, the article presented the criticism of modern versions of educational technologies, which led to the unconditional expectations for possibilities of information and telecommunication technologies. Methodological culture's growth, which was described on the base of semiotics and functional approach to word formation of new meanings for the description of the studied subjects, provided the development of pupils' thought. As a result, the research opened new prospects on understanding of artificial intelligence within educational practice.
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
Venue: arXiv cs.CV 2016
Year: 2016

Very deep convolutional networks have been central to the largest advances in image recognition performance in recent years. One example is the Inception architecture that has been shown to achieve very good performance at relatively low computational cost. Recently, the introduction of residual connections in conjunction with a more traditional architecture has yielded state-of-the-art performance in the 2015 ILSVRC challenge; its performance was similar to the latest generation Inception-v3 network. This raises the question of whether there are any benefit in combining the Inception architecture with residual connections. Here we give clear empirical evidence that training with residual connections accelerates the training of Inception networks significantly. There is also some evidence of residual Inception networks outperforming similarly expensive Inception networks without residual connections by a thin margin. We also present several new streamlined architectures for both residual and non-residual Inception networks. These variations improve the single-frame recognition performance on the ILSVRC 2012 classification task significantly. We further demonstrate how proper activation scaling stabilizes the training of very wide residual Inception networks. With an ensemble of three residual and one Inception-v4, we achieve 3.08 percent top-5 error on the test set of the ImageNet classification (CLS) challenge
Uplink Performance of Wideband Massive MIMO with One-Bit ADCs
Venue: arXiv cs.IT 2016
Year: 2016

Analog-to-digital converters (ADCs) stand for a significant part of the total power consumption in a massive MIMO base station. One-bit ADCs are one way to reduce power consumption. This paper presents an analysis of the spectral efficiency of single-carrier and OFDM transmission in massive MIMO systems that use one-bit ADCs. A closed-form achievable rate, i.e., a lower bound on capacity, is derived for a wideband system with a large number of channel taps that employs low-complexity linear channel estimation and symbol detection. Quantization results in two types of error in the symbol detection. The circularly symmetric error becomes Gaussian in massive MIMO and vanishes as the number of antennas grows. The amplitude distortion, which severely degrades the performance of OFDM, is caused by variations between symbol durations in received interference energy. As the number of channel taps grows, the amplitude distortion vanishes and OFDM has the same performance as single-carrier transmission. A main conclusion of this paper is that wideband massive MIMO systems work well with one-bit ADCs.
Improved Accent Classification Combining Phonetic Vowels with Acoustic Features
Venue: arXiv cs.SD 2016
Year: 2016

Researches have shown accent classification can be improved by integrating semantic information into pure acoustic approach. In this work, we combine phonetic knowledge, such as vowels, with enhanced acoustic features to build an improved accent classification system. The classifier is based on Gaussian Mixture Model-Universal Background Model (GMM-UBM), with normalized Perceptual Linear Predictive (PLP) features. The features are further optimized by Principle Component Analysis (PCA) and Hetroscedastic Linear Discriminant Analysis (HLDA). Using 7 major types of accented speech from the Foreign Accented English (FAE) corpus, the system achieves classification accuracy 54% with input test data as short as 20 seconds, which is competitive to the state of the art in this field.
Refining Wi-Fi Based Indoor Localization with Li-Fi Assisted Model Calibration in Smart Buildings
Venue: arXiv cs.NI 2016
Year: 2016

In recent years, there has been an increasing number of information technologies utilized in buildings to advance the idea of "smart buildings". Among various potential techniques, the use of Wi-Fi based indoor positioning allows to locate and track smartphone users inside a building, therefore, location-aware intelligent solutions can be applied to control and of building operations. These location-aware indoor services (e.g., path finding, internet of things, location based advertising) demand real-time accurate indoor localization, which is a key issue to guarantee high quality of service in smart buildings. This paper presents a new Wi-Fi based indoor localization technique that achieves significantly improvement of indoor positioning accuracy with the help of Li-Fi assisted coefficient calibration. The proposed technique leverages indoor existing Li-Fi lighting and Wi-Fi infrastructure, and results in a cost-effective and user-convenient indoor accurate localization framework. In this work, experimental study and measurements are conducted to verify the performance of the proposed idea. The results substantiate the concept of refining Wi-Fi based indoor localization with Li-Fi assisted computation calibration.
Hamiltonian Paths in C-shaped Grid Graphs
Venue: arXiv cs.CC 2016
Year: 2016

We study the Hamiltonian path problem in C-shaped grid graphs, and present the necessary and sufficient conditions for the existence of a Hamiltonian path between two given vertices in these graphs. We also give a linear-time algorithm for finding a Hamiltonian path between two given vertices of a C-shaped grid graph, if it exists.
Unidirectional Secure Information Transfer via RabbitMQ
Venue: arXiv cs.NI 2016
Year: 2016

Protecting computer systems handling possible sensitive information is of the utmost importance. Those systems are typically air-gapped with data diodes to assure that no information can physically flow back. Traditional computer protocols like HTTP or SOAP which are normally used to transport information between computers are typical bi-directional communication protocols and are thus unsuitable to be used over a data diode. Currently the only commercially available protocols over a data diode sold by vendors are file-based protocols. Other protocols can be custom made but are expensive and proprietary. There are currently no open source solutions to stream data in a generic way over a data diode other than those file-based solutions. Purpose of the dissertation is to research if open source technology can be used to mirror the contents of a messagebus over a data diode to get a cost effective security-proof and almost maintenance-free solution. and to further research if this technology can be used to transfer not only plain text data but also data sensitive by nature by using end-to-end encryption so that this information could even be admitted as evidence. Method used to validate the research is a practical case study that shows how a sensor stream can send unencrypted and encrypted events over a data diode of arbitrary size via a message bus which are transparently and securely transferred and re-emitted internally without any kind of configuration management. Results show that it is indeed possible to successfully mirror data from a Message Bus over a data diode and it is thus worthwhile to further invest in this technology.
Primitive sets of nonnegative matrices and synchronizing automata
Venue: arXiv cs.FL 2016
Year: 2016

A set of nonnegative matrices $\mathcal{M}=\{M_1, M_2, \ldots, M_k\}$ is called primitive if there exist indices $i_1, i_2, \ldots, i_m$ such that $M_{i_1} M_{i_2} \ldots M_{i_m}$ is positive (i.e. has all its entries $>0$). The length of the shortest such product is called the exponent of $\mathcal{M}$. The concept of primitive sets of matrices comes up in a number of problems within control theory, non-homogeneous Markov chains, automata theory etc. Recently, connections between synchronizing automata and primitive sets of matrices were established. In the present paper, we significantly strengthen these links by providing equivalence results, both in terms of combinatorial characterization, and computational aspects. We study the maximal exponent among all primitive sets of $n \times n$ matrices, which we denote by $\exp(n)$. We prove that $\lim_{n\rightarrow\infty} \tfrac{\log \exp(n)}{n} = \tfrac{\log 3}{3}$, and moreover, we establish that this bound leads to a resolution of the Černý problem for carefully synchronizing automata. We also study the set of matrices with no zero rows and columns, denoted by $\mathcal{NZ}$, due to its intriguing connections to the Černý conjecture and the recent generalization of Perron-Frobenius theory for this class. We characterize computational complexity of different problems related to the exponent of $\mathcal{NZ}$ matrix sets, and present a quadratic bound on the exponents of sets belonging to a special subclass. Namely, we show that the exponent of a set of matrices having total support is bounded by $2n^2 -5n +5$.
Time and Activity Sequence Prediction of Business Process Instances
Venue: arXiv cs.AI 2016
Year: 2016

The ability to know in advance the trend of running process instances, with respect to different features, such as the expected completion time, would allow business managers to timely counteract to undesired situations, in order to prevent losses. Therefore, the ability to accurately predict future features of running business process instances would be a very helpful aid when managing processes, especially under service level agreement constraints. However, making such accurate forecasts is not easy: many factors may influence the predicted features. Many approaches have been proposed to cope with this problem but all of them assume that the underling process is stationary. However, in real cases this assumption is not always true. In this work we present new methods for predicting the remaining time of running cases. In particular we propose a method, assuming process stationarity, which outperforms the state-of-the-art and two other methods which are able to make predictions even with non-stationary processes. We also describe an approach able to predict the full sequence of activities that a running case is going to take. All these methods are extensively evaluated on two real case studies.
Linear Modeling of a Flexible Substructure Actuated through Piezoelectric Components for Use in Integrated Control/Structure Design
Venue: arXiv cs.SY 2016
Year: 2016

This study presents a generic TITOP (Two-Input Two-Output Port) model of a substructure actuated with embedded piezoelectric materials as actuators (PEAs), previously modeled with the FE technique. This allows intuitive assembly of actuated flexible substructures in large flexible multi-body systems. The modeling technique is applied to an illustrative example of a flexible beam with bonded piezoelectric strip and vibration attenuation of a chain of flexible beams.
A Survey on Domain-Specific Languages for Machine Learning in Big Data
Venue: arXiv cs.SE 2016
Year: 2016

The amount of data generated in the modern society is increasing rapidly. New problems and novel approaches of data capture, storage, analysis and visualization are responsible for the emergence of the Big Data research field. Machine Learning algorithms can be used in Big Data to make better and more accurate inferences. However, because of the challenges Big Data imposes, these algorithms need to be adapted and optimized to specific applications. One important decision made by software engineers is the choice of the language that is used in the implementation of these algorithms. Therefore, this literature survey identifies and describes domain-specific languages and frameworks used for Machine Learning in Big Data. By doing this, software engineers can then make more informed choices and beginners have an overview of the main languages used in this domain.