OpenRaiser/Intern-Atlas huggingface dataset:
On the Complexity of Semantic Integration of OWL Ontologies
Venue: arXiv cs.AI 2017
Year: 2017

We propose a new mechanism for integration of OWL ontologies using semantic import relations. In contrast to the standard OWL importing, we do not require all axioms of the imported ontologies to be taken into account for reasoning tasks, but only their logical implications over a chosen signature. This property comes natural in many ontology integration scenarios, especially when the number of ontologies is large. In this paper, we study the complexity of reasoning over ontologies with semantic import relations and establish a range of tight complexity bounds for various fragments of OWL.
High-Precision Trajectory Tracking in Changing Environments Through $\mathcal{L}_1$ Adaptive Feedback and Iterative Learning
Venue: arXiv cs.RO 2017
Year: 2017

As robots and other automated systems are introduced to unknown and dynamic environments, robust and adaptive control strategies are required to cope with disturbances, unmodeled dynamics and parametric uncertainties. In this paper, we propose and provide theoretical proofs of a combined $\mathcal{L}_1$ adaptive feedback and iterative learning control (ILC) framework to improve trajectory tracking of a system subject to unknown and changing disturbances. The $\mathcal{L}_1$ adaptive controller forces the system to behave in a repeatable, predefined way, even in the presence of unknown and changing disturbances; however, this does not imply that perfect trajectory tracking is achieved. ILC improves the tracking performance based on experience from previous executions. The performance of ILC is limited by the robustness and repeatability of the underlying system, which, in this approach, is handled by the $\mathcal{L}_1$ adaptive controller. In particular, we are able to generalize learned trajectories across different system configurations because the $\mathcal{L}_1$ adaptive controller handles the underlying changes in the system. We demonstrate the improved trajectory tracking performance and generalization capabilities of the combined method compared to pure ILC in experiments with a quadrotor subject to unknown, dynamic disturbances. This is the first work to show $\mathcal{L}_1$ adaptive control combined with ILC in experiment.
Advancing Consumer Adoption of Blockchain Applications
Venue: arXiv cs.CY 2017
Year: 2017

Blockchain technology as a whole is experiencing a dramatic rise in adoption, in no small part due to the developer-friendly Ethereum network. While the number of smart-contract powered distributed applications (Dapps) continues to rise, they face many of the same challenges all new technologies face as they are introduced to a market. By modeling the consumer adoption of blockchain technology and analyzing scholarly literature on supply-side factors affecting the diffusion of technology, we seek to prove the growth of a Dapp can be accelerated using abstraction, whole product planning, and complementaries.
Cache-oblivious Matrix Multiplication for Exact Factorisation
Venue: arXiv cs.NA 2017
Year: 2017

We present a cache-oblivious adaptation of matrix multiplication to be incorporated in the parallel TU decomposition for rectangular matrices over finite fields, based on the Morton-hybrid space-filling curve representation. To realise this, we introduce the concepts of alignment and containment of sub-matrices under the Morton-hybrid layout. We redesign the decompositions within the recursive matrix multiplication to force the base case to avoid all jumps in address space, at the expense of extra recursive matrix multiplication (MM) calls. We show that the resulting cache oblivious adaptation has low span, and our experiments demonstrate that its sequential evaluation order demonstrates orders of magnitude improvement in run-time, despite the recursion overhead.
Adaptive Modularity Maximization via Edge Weighting Scheme
Venue: arXiv cs.SI 2017
Year: 2017

Modularity maximization is one of the state-of-the-art methods for community detection that has gained popularity in the last decade. Yet it suffers from the resolution limit problem by preferring under certain conditions large communities over small ones. To solve this problem, we propose to expand the meaning of the edges that are currently used to indicate propensity of nodes for sharing the same community. In our approach this is the role of edges with positive weights while edges with negative weights indicate aversion for putting their end-nodes into one community. We also present a novel regression model which assigns weights to the edges of a graph according to their local topological features to enhance the accuracy of modularity maximization algorithms. We construct artificial graphs based on the parameters sampled from a given unweighted network and train the regression model on ground truth communities of these artificial graphs in a supervised fashion. The extraction of local topological edge features can be done in linear time, making this process efficient. Experimental results on real and synthetic networks show that the state-of-the-art community detection algorithms improve their performance significantly by finding communities in the weighted graphs produced by our model.
Full-Duplex Massive MIMO Relaying Systems with Low-Resolution ADCs
Venue: arXiv cs.IT 2017
Year: 2017

This paper considers a multipair amplify-and-forward massive MIMO relaying system with low-resolution ADCs at both the relay and destinations. The channel state information (CSI) at the relay is obtained via pilot training, which is then utilized to perform simple maximum-ratio combining/maximum-ratio transmission processing by the relay. Also, it is assumed that the destinations use statistical CSI to decode the transmitted signals. Exact and approximated closed-form expressions for the achievable sum rate are presented, which enable the efficient evaluation of the impact of key system parameters on the system performance. In addition, optimal relay power allocation scheme is studied, and power scaling law is characterized. It is found that, with only low-resolution ADCs at the relay, increasing the number of relay antennas is an effective method to compensate for the rate loss caused by coarse quantization. However, it becomes ineffective to handle the detrimental effect of low-resolution ADCs at the destination. Moreover, it is shown that deploying massive relay antenna arrays can still bring significant power savings, i.e., the transmit power of each source can be cut down proportional to $1/M$ to maintain a constant rate, where $M$ is the number of relay antennas.
Discovery and visualization of structural biomarkers from MRI using transport-based morphometry
Venue: arXiv cs.CV 2017
Year: 2017

Disease in the brain is often associated with subtle, spatially diffuse, or complex tissue changes that may lie beneath the level of gross visual inspection, even on magnetic resonance imaging (MRI). Unfortunately, current computer-assisted approaches that examine pre-specified features, whether anatomically-defined (i.e. thalamic volume, cortical thickness) or based on pixelwise comparison (i.e. deformation-based methods), are prone to missing a vast array of physical changes that are not well-encapsulated by these metrics. In this paper, we have developed a technique for automated pattern analysis that can fully determine the relationship between brain structure and observable phenotype without requiring any a priori features. Our technique, called transport-based morphometry (TBM), is an image transformation that maps brain images losslessly to a domain where they become much more separable. The new approach is validated on structural brain images of healthy older adult subjects where even linear models for discrimination, regression, and blind source separation enable TBM to independently discover the characteristic changes of aging and highlight potential mechanisms by which aerobic fitness may mediate brain health later in life. TBM is a generative approach that can provide visualization of physically meaningful shifts in tissue distribution through inverse transformation. The proposed framework is a powerful technique that can potentially elucidate genotype-structural-behavioral associations in myriad diseases.
GeneGAN: Learning Object Transfiguration and Attribute Subspace from Unpaired Data
Venue: arXiv cs.CV 2017
Year: 2017

Object Transfiguration replaces an object in an image with another object from a second image. For example it can perform tasks like "putting exactly those eyeglasses from image A on the nose of the person in image B". Usage of exemplar images allows more precise specification of desired modifications and improves the diversity of conditional image generation. However, previous methods that rely on feature space operations, require paired data and/or appearance models for training or disentangling objects from background. In this work, we propose a model that can learn object transfiguration from two unpaired sets of images: one set containing images that "have" that kind of object, and the other set being the opposite, with the mild constraint that the objects be located approximately at the same place. For example, the training data can be one set of reference face images that have eyeglasses, and another set of images that have not, both of which spatially aligned by face landmarks. Despite the weak 0/1 labels, our model can learn an "eyeglasses" subspace that contain multiple representatives of different types of glasses. Consequently, we can perform fine-grained control of generated images, like swapping the glasses in two images by swapping the projected components in the "eyeglasses" subspace, to create novel images of people wearing eyeglasses. Overall, our deterministic generative model learns disentangled attribute subspaces from weakly labeled data by adversarial training. Experiments on CelebA and Multi-PIE datasets validate the effectiveness of the proposed model on real world data, in generating images with specified eyeglasses, smiling, hair styles, and lighting conditions etc. The code is available online.
Analog Beamsteering for Flexible Hybrid Beamforming Design in Mmwave Communications
Venue: arXiv cs.IT 2017
Year: 2017

In this paper, we propose an analog beamsteering approach for enabling flexible hybrid beamforming design that can achieve performance close to singular value decomposition (SVD) based digital beamforming with single user case. As a starting point, assuming the use of a codebook with infinite precision, we apply transposes of antenna array response matrices as analog beam-formers at the BS and the UE sides. The resulting effective chan-nel matrix including both propagation channel and analog beam-formers is shown to be able to provide a maximal achievable rate very close to SVD based digital beamforming at low to medium SNR. Next, the impact of a finite size codebook is analyzed for practical analog beamformer design. A closed-form derivation is obtained for mapping rate loss to the number of antennas, code-book sizes and the received SNR. The analysis shows that in or-der to achieve performance comparable to analog beamforming with infinite precision, the codebook size should be at least larger than the number of implemented antennas. Therefore, the pro-posed analog beamforming approach can be designed inde-pendently without taking digital beamforming into account. Such an approach can lead to the development of flexible hybrid beam-forming architectures that could adapt to a changing propagation environment via agile analog beam selection and maximize spatial multiplexing gain via proper digital beamformer design.
Machine learning methods for multimedia information retrieval
Venue: arXiv cs.CV 2017
Year: 2017

In this thesis we examined several multimodal feature extraction and learning methods for retrieval and classification purposes. We reread briefly some theoretical results of learning in Section 2 and reviewed several generative and discriminative models in Section 3 while we described the similarity kernel in Section 4. We examined different aspects of the multimodal image retrieval and classification in Section 5 and suggested methods for identifying quality assessments of Web documents in Section 6. In our last problem we proposed similarity kernel for time-series based classification. The experiments were carried over publicly available datasets and source codes for the most essential parts are either open source or released. Since the used similarity graphs (Section 4.2) are greatly constrained for computational purposes, we would like to continue work with more complex, evolving and capable graphs and apply for different problems such as capturing the rapid change in the distribution (e.g. session based recommendation) or complex graphs of the literature work. The similarity kernel with the proper metrics reaches and in many cases improves over the state-of-the-art. Hence we may conclude generative models based on instance similarities with multiple modes is a generally applicable model for classification and regression tasks ranging over various domains, including but not limited to the ones presented in this thesis. More generally, the Fisher kernel is not only unique in many ways but one of the most powerful kernel functions. Therefore we may exploit the Fisher kernel in the future over widely used generative models, such as Boltzmann Machines [Hinton et al., 1984], a particular subset, the Restricted Boltzmann Machines and Deep Belief Networks [Hinton et al., 2006]), Latent Dirichlet Allocation [Blei et al., 2003] or Hidden Markov Models [Baum and Petrie, 1966] to name a few.
On Hierarchical Statistical Static Timing Analysis
Venue: arXiv cs.AR 2017
Year: 2017

Statistical static timing analysis deals with the increasing variations in manufacturing processes to reduce the pessimism in the worst case timing analysis. Because of the correlation between delays of circuit components, timing model generation and hierarchical timing analysis face more challenges than in static timing analysis. In this paper, a novel method to generate timing models for combinational circuits considering variations is proposed. The resulting timing models have accurate input-output delays and are about 80% smaller than the original circuits. Additionally, an accurate hierarchical timing analysis method at design level using pre-characterized timing models is proposed. This method incorporates the correlation between modules by replacing independent random variables to improve timing accuracy. Experimental results show that the correlation between modules strongly affects the delay distribution of the hierarchical design and the proposed method has good accuracy compared with Monte Carlo simulation, but is faster by three orders of magnitude.
Novel CMOS RFIC Layout Generation with Concurrent Device Placement and Fixed-Length Microstrip Routing
Venue: arXiv cs.ET 2017
Year: 2017

With advancing process technologies and booming IoT markets, millimeter-wave CMOS RFICs have been widely developed in re- cent years. Since the performance of CMOS RFICs is very sensi- tive to the precision of the layout, precise placement of devices and precisely matched microstrip lengths to given values have been a labor-intensive and time-consuming task, and thus become a major bottleneck for time to market. This paper introduces a progressive integer-linear-programming-based method to gener- ate high-quality RFIC layouts satisfying very stringent routing requirements of microstrip lines, including spacing/non-crossing rules, precise length, and bend number minimization, within a given layout area. The resulting RFIC layouts excel in both per- formance and area with much fewer bends compared with the simulation-tuning based manual layout, while the layout gener- ation time is significantly reduced from weeks to half an hour.
Design-Phase Buffer Allocation for Post-Silicon Clock Binning by Iterative Learning
Venue: arXiv cs.AR 2017
Year: 2017

At submicron manufacturing technology nodes, pro- cess variations affect circuit performance significantly. To counter these variations, engineers are reserving more timing margin to maintain yield, leading to an unaffordable overdesign. Most of these margins, however, are wasted after manufacturing, because process variations cause only some chips to be really slow, while other chips can easily meet given timing specifications. To reduce this pessimism, we can reserve less timing margin and tune failed chips after manufacturing with clock buffers to make them meet timing specifications. With this post-silicon clock tuning, critical paths can be balanced with neighboring paths in each chip specifically to counter the effect of process variations. Consequently, chips with timing failures can be rescued and the yield can thus be improved. This is specially useful in high- performance designs, e.g., high-end CPUs, where clock binning makes chips with higher performance much more profitable. In this paper, we propose a method to determine where to insert post-silicon tuning buffers during the design phase to improve the overall profit with clock binning. This method learns the buffer locations with a Sobol sequence iteratively and reduces the buffer ranges afterwards with tuning concentration and buffer grouping. Experimental results demonstrate that the proposed method can achieve a profit improvement of about 14% on average and up to 26%, with only a small number of tuning buffers inserted into the circuit.
Statistical Physics and Information Theory Perspectives on Linear Inverse Problems
Venue: arXiv cs.IT 2017
Year: 2017

Many real-world problems in machine learning, signal processing, and communications assume that an unknown vector $x$ is measured by a matrix A, resulting in a vector $y=Ax+z$, where $z$ denotes the noise; we call this a single measurement vector (SMV) problem. Sometimes, multiple dependent vectors $x^{(j)}, j\in \{1,...,J\}$, are measured at the same time, forming the so-called multi-measurement vector (MMV) problem. Both SMV and MMV are linear models (LM's), and the process of estimating the underlying vector(s) $x$ from an LM given the matrices, noisy measurements, and knowledge of the noise statistics, is called a linear inverse problem. In some scenarios, the matrix A is stored in a single processor and this processor also records its measurements $y$; this is called centralized LM. In other scenarios, multiple sites are measuring the same underlying unknown vector $x$, where each site only possesses part of the matrix A; we call this multi-processor LM. Recently, due to an ever-increasing amount of data and ever-growing dimensions in LM's, it has become more important to study large-scale linear inverse problems. In this dissertation, we take advantage of tools in statistical physics and information theory to advance the understanding of large-scale linear inverse problems. The intuition of the application of statistical physics to our problem is that statistical physics deals with large-scale problems, and we can make an analogy between an LM and a thermodynamic system. In terms of information theory, although it was originally developed to characterize the theoretic limits of digital communication systems, information theory was later found to be rather useful in analyzing and understanding other inference problems. (The full abstract cannot fit in due to the space limit. Please refer to the PDF.)
A Perceptually Weighted Rank Correlation Indicator for Objective Image Quality Assessment
Venue: arXiv cs.CV 2017
Year: 2017

In the field of objective image quality assessment (IQA), the Spearman's $ρ$ and Kendall's $τ$ are two most popular rank correlation indicators, which straightforwardly assign uniform weight to all quality levels and assume each pair of images are sortable. They are successful for measuring the average accuracy of an IQA metric in ranking multiple processed images. However, two important perceptual properties are ignored by them as well. Firstly, the sorting accuracy (SA) of high quality images are usually more important than the poor quality ones in many real world applications, where only the top-ranked images would be pushed to the users. Secondly, due to the subjective uncertainty in making judgement, two perceptually similar images are usually hardly sortable, whose ranks do not contribute to the evaluation of an IQA metric. To more accurately compare different IQA algorithms, we explore a perceptually weighted rank correlation indicator in this paper, which rewards the capability of correctly ranking high quality images, and suppresses the attention towards insensitive rank mistakes. More specifically, we focus on activating `valid' pairwise comparison towards image quality, whose difference exceeds a given sensory threshold (ST). Meanwhile, each image pair is assigned an unique weight, which is determined by both the quality level and rank deviation. By modifying the perception threshold, we can illustrate the sorting accuracy with a more sophisticated SA-ST curve, rather than a single rank correlation coefficient. The proposed indicator offers a new insight for interpreting visual perception behaviors. Furthermore, the applicability of our indicator is validated in recommending robust IQA metrics for both the degraded and enhanced image data.
On the Maximum Crossing Number
Venue: arXiv cs.CG 2017
Year: 2017

Research about crossings is typically about minimization. In this paper, we consider \emph{maximizing} the number of crossings over all possible ways to draw a given graph in the plane. Alpert et al. [Electron. J. Combin., 2009] conjectured that any graph has a \emph{convex} straight-line drawing, e.g., a drawing with vertices in convex position, that maximizes the number of edge crossings. We disprove this conjecture by constructing a planar graph on twelve vertices that allows a non-convex drawing with more crossings than any convex one. Bald et al. [Proc. COCOON, 2016] showed that it is NP-hard to compute the maximum number of crossings of a geometric graph and that the weighted geometric case is NP-hard to approximate. We strengthen these results by showing hardness of approximation even for the unweighted geometric case and prove that the unweighted topological case is NP-hard.
Representation learning of drug and disease terms for drug repositioning
Venue: arXiv cs.CL 2017
Year: 2017

Drug repositioning (DR) refers to identification of novel indications for the approved drugs. The requirement of huge investment of time as well as money and risk of failure in clinical trials have led to surge in interest in drug repositioning. DR exploits two major aspects associated with drugs and diseases: existence of similarity among drugs and among diseases due to their shared involved genes or pathways or common biological effects. Existing methods of identifying drug-disease association majorly rely on the information available in the structured databases only. On the other hand, abundant information available in form of free texts in biomedical research articles are not being fully exploited. Word-embedding or obtaining vector representation of words from a large corpora of free texts using neural network methods have been shown to give significant performance for several natural language processing tasks. In this work we propose a novel way of representation learning to obtain features of drugs and diseases by combining complementary information available in unstructured texts and structured datasets. Next we use matrix completion approach on these feature vectors to learn projection matrix between drug and disease vector spaces. The proposed method has shown competitive performance with state-of-the-art methods. Further, the case studies on Alzheimer's and Hypertension diseases have shown that the predicted associations are matching with the existing knowledge.