OpenRaiser/Intern-Atlas huggingface dataset:
Information and Power Transfer by Energy Harvesting Transmitters over a Fading Multiple Access Channel with Minimum Rate Constraints
Venue: arXiv cs.IT 2016
Year: 2016

We consider the problem of Simultaneous Wireless Information and Power Transfer (SWIPT) over a fading multiple access channel with additive Gaussian noise. The transmitters as well as the receiver harvest energy from ambient sources. We assume that the transmitters have two classes of data to send, viz. delay sensitive and delay tolerant data. Each transmitter sends the delay sensitive data at a certain minimum rate irrespective of the channel conditions (fading states). In addition, if the channel conditions are good, the delay tolerant data is sent. {Along with data, the transmitters also transfer power to aid the receiver in meeting its energy requirements.} In this setting, we characterize the \textit{minimum-rate capacity region} which provides the fundamental limit of transferring information and power simultaneously with minimum rate guarantees. Owing to the limitations of current technology, these limits might not be achievable in practice. Among the practical receiver structures proposed for SWIPT in literature, two popular architectures are the \textit{time switching} and \textit{power splitting} receivers. For each of these architectures, we derive the minimum-rate capacity regions. We show that power splitting receivers although more complex, provide a larger capacity region.
Performance Analysis of License Assisted Access LTE with Asymmetric Hidden Terminals
Venue: arXiv cs.NI 2016
Year: 2016

License Assisted Access (LAA) LTE (LTE-LAA) is a new type of LTE that aggregates the licensed LTE bands with the unlicensed bands via carrier aggregation. To operate in unlicensed bands, LTE-LAA adopts the listen-before-talk policy and designs its channel access mechanism similar to WLAN's DCF. In this paper, we consider an LAA network consisting of an LTE-LAA eNB coexisting with a Wi-Fi STA, and capture the {\em asymmetric hidden terminal problem} where the eNB recognizes the STA while the opposite is not true, which is caused by the asymmetric CCA thresholds between them. We model the network as a joint Markov chain (MC) consisting of two individual MCs, and derive its steady-state probabilities, throughput, and channel access delay along with other key metrics like transmit, busy, collision, and doubling probabilities. Through extensive evaluation, we confirm that the proposed model well predicts the dynamics of the LAA network, and identify important design guidelines for fair coexistence between LTE-LAA and WLAN as follows. First, LTE-LAA should design its contention window (CW) doubling policy by considering Wi-Fi's packet duration and subframe-dependent collision probabilities. Second, there exists a tradeoff between throughput and channel access delay, according to which the CW doubling policy should be adapted.
Stabilization and Trajectory Control of a Quadrotor with Uncertain Suspended Load
Venue: arXiv cs.RO 2016
Year: 2016

Stabilization and trajectory control of a quadrotor carrying a suspended load with a fixed known mass has been extensively studied in recent years. However, the load mass is not always known beforehand or may vary during the practical transportations. This mass uncertainty brings uncertain disturbances to the quadrotor system, causing existing controllers to have worse stability and trajectory tracking performance. To improve the quadrotor stability and trajectory tracking capability in this situation, we fully investigate the impacts of the uncertain load mass on the quadrotor. By comparing the performances of three different controllers -- the proportional-derivative (PD) controller, the sliding mode controller (SMC), and the model predictive controller (MPC) -- stabilization rather than trajectory tracking error is proved to be the main influence in the load mass uncertainty. A critical motion mass exists for the quadrotor to maintain a desired transportation performance. Moreover, simulation results verify that a controller with strong robustness against disturbances is a good choice for practical applications.
A High-Capacity Separable Reversible Method for Hiding Multiple Messages in Encrypted Images
Venue: arXiv cs.CR 2016
Year: 2016

This work proposes a high-capacity scheme for separable reversible data hiding in encrypted images. At the sender side, the original uncompressed image is encrypted using an encryption key. One or several data hiders use the MSB of some image pixels to hide additional data. Given the encrypted image containing this additional data, with only one of those data hiding keys, the receiver can extract the corresponding embedded data, although the image content will remain inaccessible. With all of the embedding keys, the receiver can extract all of the embedded data. Finally, with the encryption key, the receiver can decrypt the received data and reconstruct the original image perfectly\ignore{ without the data embedding key(s) }by exploiting the spatial correlation of natural images. Based on the proposed method a receiver could recover the original image perfectly even when it does not have the data embedding key(s) and the embedding rate is high.
A Logical Approach to Generating Test Plans
Venue: arXiv cs.SE 2016
Year: 2016

During the execution of a test plan, a test manager may decide to drop a test case if its result can be inferred from already executed test cases. We show that it is possible to automatically generate a test plan to exploit the potential to justifiably drop a test case and thus reduce the number of test cases. Our approach uses Boolean formulas to model the mutual dependencies between test results. The algorithm to generate a test plan comes with the formal guarantee of optimality with regards to the inference of the result of a test case from already executed test cases.
Identification of Cancer Patient Subgroups via Smoothed Shortest Path Graph Kernel
Venue: arXiv cs.CE 2016
Year: 2016

Characterizing patient somatic mutations through next-generation sequencing technologies opens up possibilities for refining cancer subtypes. However, catalogues of mutations reveal that only a small fraction of the genes are altered frequently in patients. On the other hand different genomic alterations may perturb the same pathways. We propose a novel clustering procedure that quantifies the similarities of patients from their mutational profile on pathways via a novel graph kernel. We represent each KEGG pathway as an undirected graph. For each patient the vertex labels are assigned based on her altered genes. Smoothed shortest path graph kernel (smSPK) evaluates each pair of patients by comparing their vertex labeled pathway graphs. Our clustering procedure involves two steps: the smSPK kernel matrix derived for each pathway are input to kernel k-means algorithm and each pathway is evaluated individually. In the next step, only those pathways that are successful are combined in to a single kernel input to kernel k-means to stratify patients. Evaluating the procedure on simulated data showed that smSPK clusters patients up to 88\% accuracy. Finally to identify ovarian cancer patient subgroups, we apply our methodology to the cancer genome atlas ovarian data that involves 481 patients. The identified subgroups are evaluated through survival analysis. Grouping patients into four clusters results with patients groups that are significantly different in their survival times ($p$-value $\le 0.005$).
Sparse Factorization Layers for Neural Networks with Limited Supervision
Venue: arXiv cs.CV 2016
Year: 2016

Whereas CNNs have demonstrated immense progress in many vision problems, they suffer from a dependence on monumental amounts of labeled training data. On the other hand, dictionary learning does not scale to the size of problems that CNNs can handle, despite being very effective at low-level vision tasks such as denoising and inpainting. Recently, interest has grown in adapting dictionary learning methods for supervised tasks such as classification and inverse problems. We propose two new network layers that are based on dictionary learning: a sparse factorization layer and a convolutional sparse factorization layer, analogous to fully-connected and convolutional layers, respectively. Using our derivations, these layers can be dropped in to existing CNNs, trained together in an end-to-end fashion with back-propagation, and leverage semisupervision in ways classical CNNs cannot. We experimentally compare networks with these two new layers against a baseline CNN. Our results demonstrate that networks with either of the sparse factorization layers are able to outperform classical CNNs when supervised data are few. They also show performance improvements in certain tasks when compared to the CNN with no sparse factorization layers with the same exact number of parameters.
Grammatical Constraints on Intra-sentential Code-Switching: From Theories to Working Models
Venue: arXiv cs.CL 2016
Year: 2016

We make one of the first attempts to build working models for intra-sentential code-switching based on the Equivalence-Constraint (Poplack 1980) and Matrix-Language (Myers-Scotton 1993) theories. We conduct a detailed theoretical analysis, and a small-scale empirical study of the two models for Hindi-English CS. Our analyses show that the models are neither sound nor complete. Taking insights from the errors made by the models, we propose a new model that combines features of both the theories.
Wiener system identification with generalized orthonormal basis functions
Venue: arXiv cs.SY 2016
Year: 2016

Many nonlinear systems can be described by a Wiener-Schetzen model. In this model, the linear dynamics are formulated in terms of orthonormal basis functions (OBFs). The nonlinearity is modeled by a multivariate polynomial. In general, an infinite number of OBFs is needed for an exact representation of the system. This paper considers the approximation of a Wiener system with finite-order infinite impulse response dynamics and a polynomial nonlinearity. We propose to use a limited number of generalized OBFs (GOBFs). The pole locations, needed to construct the GOBFs, are estimated via the best linear approximation of the system. The coefficients of the multivariate polynomial are determined with a linear regression. This paper provides a convergence analysis for the proposed identification scheme. It is shown that the estimated output converges in probability to the exact output. Fast convergence rates, in the order $O_p({N_F}^{-n_{rep}/2})$, can be achieved, with $N_F$ the number of excited frequencies and $n_{rep}$ the number of repetitions of the GOBFs.
Initial estimates for Wiener-Hammerstein models using phase-coupled multisines
Venue: arXiv cs.SY 2016
Year: 2016

Block-oriented models are often used to model nonlinear systems. These models consist of linear dynamic (L) and nonlinear static (N) sub-blocks. This paper addresses the generation of initial estimates for a Wiener-Hammerstein model (LNL cascade). While it is easy to measure the product of the two linear blocks using a Gaussian excitation and linear identification methods, it is difficult to split the global dynamics over the individual blocks. This paper first proposes a well-designed multisine excitation with pairwise coupled random phases. Next, a modified best linear approximation is estimated on a shifted frequency grid. It is shown that this procedure creates a shift of the input dynamics with a known frequency offset, while the output dynamics do not shift. The resulting transfer function, which has complex coefficients due to the frequency shift, is estimated with a modified frequency domain estimation method. The identified poles and zeros can be assigned to either the input or output dynamics. Once this is done, it is shown in the literature that the remaining initialization problem can be solved much easier than the original one. The method is illustrated on experimental data obtained from the Wiener-Hammerstein benchmark system.
Socioeconomic correlations and stratification in social-communication networks
Venue: arXiv cs.SI 2016
Year: 2016

The uneven distribution of wealth and individual economic capacities are among the main forces which shape modern societies and arguably bias the emerging social structures. However, the study of correlations between the social network and economic status of individuals is difficult due to the lack of large-scale multimodal data disclosing both the social ties and economic indicators of the same population. Here, we close this gap through the analysis of coupled datasets recording the mobile phone communications and bank transaction history of one million anonymised individuals living in a Latin American country. We show that wealth and debt are unevenly distributed among people in agreement with the Pareto principle; the observed social structure is strongly stratified, with people being better connected to others of their own socioeconomic class rather than to others of different classes; the social network appears with assortative socioeconomic correlations and tightly connected "rich clubs"; and that egos from the same class live closer to each other but commute further if they are wealthier. These results are based on a representative, society-large population, and empirically demonstrate some long-lasting hypotheses on socioeconomic correlations which potentially lay behind social segregation, and induce differences in human mobility.
Neural Emoji Recommendation in Dialogue Systems
Venue: arXiv cs.CL 2016
Year: 2016

Emoji is an essential component in dialogues which has been broadly utilized on almost all social platforms. It could express more delicate feelings beyond plain texts and thus smooth the communications between users, making dialogue systems more anthropomorphic and vivid. In this paper, we focus on automatically recommending appropriate emojis given the contextual information in multi-turn dialogue systems, where the challenges locate in understanding the whole conversations. More specifically, we propose the hierarchical long short-term memory model (H-LSTM) to construct dialogue representations, followed by a softmax classifier for emoji classification. We evaluate our models on the task of emoji classification in a real-world dataset, with some further explorations on parameter sensitivity and case study. Experimental results demonstrate that our method achieves the best performances on all evaluation metrics. It indicates that our method could well capture the contextual information and emotion flow in dialogues, which is significant for emoji recommendation.
Blind Measurement Selection: A Random Matrix Theory Approach
Venue: arXiv cs.IT 2016
Year: 2016

This paper considers the problem of selecting a set of $k$ measurements from $n$ available sensor observations. The selected measurements should minimize a certain error function assessing the error in estimating a certain $m$ dimensional parameter vector. The exhaustive search inspecting each of the $n\choose k$ possible choices would require a very high computational complexity and as such is not practical for large $n$ and $k$. Alternative methods with low complexity have recently been investigated but their main drawbacks are that 1) they require perfect knowledge of the measurement matrix and 2) they need to be applied at the pace of change of the measurement matrix. To overcome these issues, we consider the asymptotic regime in which $k$, $n$ and $m$ grow large at the same pace. Tools from random matrix theory are then used to approximate in closed-form the most important error measures that are commonly used. The asymptotic approximations are then leveraged to select properly $k$ measurements exhibiting low values for the asymptotic error measures. Two heuristic algorithms are proposed: the first one merely consists in applying the convex optimization artifice to the asymptotic error measure. The second algorithm is a low-complexity greedy algorithm that attempts to look for a sufficiently good solution for the original minimization problem. The greedy algorithm can be applied to both the exact and the asymptotic error measures and can be thus implemented in blind and channel-aware fashions. We present two potential applications where the proposed algorithms can be used, namely antenna selection for uplink transmissions in large scale multi-user systems and sensor selection for wireless sensor networks. Numerical results are also presented and sustain the efficiency of the proposed blind methods in reaching the performances of channel-aware algorithms.
An Integer Interior Point Method for Min-Cost Flow Using Arc Contractions and Deletions
Venue: arXiv cs.DS 2016
Year: 2016

We present an interior point method for the min-cost flow problem that uses arc contractions and deletions to steer clear from the boundary of the polytope when path-following methods come too close. We obtain a randomized algorithm running in expected $\tilde O( m^{3/2} )$ time that only visits integer lattice points in the vicinity of the central path of the polytope. This enables us to use integer arithmetic like classical combinatorial algorithms typically do. We provide explicit bounds on the size of the numbers that appear during all computations. By presenting an integer arithmetic interior point algorithm we avoid the tediousness of floating point error analysis and achieve a method that is guaranteed to be free of any numerical issues. We thereby eliminate one of the drawbacks of numerical methods in contrast to combinatorial min-cost flow algorithms that still yield the most efficient implementations in practice, despite their inferior worst-case time complexity.
Financial Studio: Android Based Application for Computing Tax, Pension, Zakat and Loan
Venue: arXiv cs.CY 2016
Year: 2016

This work deals with the development of android-based financial studio, an integrated application for calculating tax, pension, zakat, and loan. Financial studio can facilitate employers of any department and other individuals. The application is developed using MIT app inventor-based android platform. The financial studio has four computational modules, namely: (i) tax, (ii) pension, (iii) zakat, and (iv) loan. The system provides an integrated environment for performing aforementioned distinct calculations by integrating different financial modules into a single application in a user-friendly way. The statistical analysis shows that the application is effective to deal with different financial calculations
Registering large volume serial-section electron microscopy image sets for neural circuit reconstruction using FFT signal whitening
Venue: arXiv cs.CV 2016
Year: 2016

The detailed reconstruction of neural anatomy for connectomics studies requires a combination of resolution and large three-dimensional data capture provided by serial section electron microscopy (ssEM). The convergence of high throughput ssEM imaging and improved tissue preparation methods now allows ssEM capture of complete specimen volumes up to cubic millimeter scale. The resulting multi-terabyte image sets span thousands of serial sections and must be precisely registered into coherent volumetric forms in which neural circuits can be traced and segmented. This paper introduces a Signal Whitening Fourier Transform Image Registration approach (SWiFT-IR) under development at the Pittsburgh Supercomputing Center and its use to align mouse and zebrafish brain datasets acquired using the wafer mapper ssEM imaging technology recently developed at Harvard University. Unlike other methods now used for ssEM registration, SWiFT-IR modifies its spatial frequency response during image matching to maximize a signal-to-noise measure used as its primary indicator of alignment quality. This alignment signal is more robust to rapid variations in biological content and unavoidable data distortions than either phase-only or standard Pearson correlation, thus allowing more precise alignment and statistical confidence. These improvements in turn enable an iterative registration procedure based on projections through multiple sections rather than more typical adjacent-pair matching methods. This projection approach, when coupled with known anatomical constraints and iteratively applied in a multi-resolution pyramid fashion, drives the alignment into a smooth form that properly represents complex and widely varying anatomical content such as the full cross-section zebrafish data.
Scalable Computation of Optimized Queries for Sequential Diagnosis
Venue: arXiv cs.AI 2016
Year: 2016

In many model-based diagnosis applications it is impossible to provide such a set of observations and/or measurements that allow to identify the real cause of a fault. Therefore, diagnosis systems often return many possible candidates, leaving the burden of selecting the correct diagnosis to a user. Sequential diagnosis techniques solve this problem by automatically generating a sequence of queries to some oracle. The answers to these queries provide additional information necessary to gradually restrict the search space by removing diagnosis candidates inconsistent with the answers. During query computation, existing sequential diagnosis methods often require the generation of many unnecessary query candidates and strongly rely on expensive logical reasoners. We tackle this issue by devising efficient heuristic query search methods. The proposed methods enable for the first time a completely reasoner-free query generation while at the same time guaranteeing optimality conditions, e.g. minimal cardinality or best understandability, of the returned query that existing methods cannot realize. Hence, the performance of this approach is independent of the (complexity of the) diagnosed system. Experiments conducted using real-world problems show that the new approach is highly scalable and outperforms existing methods by orders of magnitude.
Robust Multigrid for Cartesian Interior Penalty DG Formulations of the Poisson Equation in 3D
Venue: arXiv cs.NA 2016
Year: 2016

We present a polynomial multigrid method for the nodal interior penalty formulation of the Poisson equation on three-dimensional Cartesian grids. Its key ingredient is a weighted overlapping Schwarz smoother operating on element-centered subdomains. The MG method reaches superior convergence rates corresponding to residual reductions of about two orders of magnitude within a single V(1,1) cycle. It is robust with respect to the mesh size and the ansatz order, at least up to ${P=32}$. Rigorous exploitation of tensor-product factorization yields a computational complexity of $O(PN)$ for $N$ unknowns, whereas numerical experiments indicate even linear runtime scaling. Moreover, by allowing adjustable subdomain overlaps and adding Krylov acceleration, the method proved feasible for anisotropic grids with element aspect ratios up to 48.