OpenRaiser/Intern-Atlas huggingface dataset:
Old Techniques for New Join Algorithms: A Case Study in RDF Processing
Venue: arXiv cs.DB 2016
Year: 2016

Recently there has been significant interest around designing specialized RDF engines, as traditional query processing mechanisms incur orders of magnitude performance gaps on many RDF workloads. At the same time researchers have released new worst-case optimal join algorithms which can be asymptotically better than the join algorithms in traditional engines. In this paper we apply worst-case optimal join algorithms to a standard RDF workload, the LUBM benchmark, for the first time. We do so using two worst-case optimal engines: (1) LogicBlox, a commercial database engine, and (2) EmptyHeaded, our prototype research engine with enhanced worst-case optimal join algorithms. We show that without any added optimizations both LogicBlox and EmptyHeaded outperform two state-of-the-art specialized RDF engines, RDF-3X and TripleBit, by up to 6x on cyclic join queries-the queries where traditional optimizers are suboptimal. On the remaining, less complex queries in the LUBM benchmark, we show that three classic query optimization techniques enable EmptyHeaded to compete with RDF engines, even when there is no asymptotic advantage to the worst-case optimal approach. We validate that our design has merit as EmptyHeaded outperforms MonetDB by three orders of magnitude and LogicBlox by two orders of magnitude, while remaining within an order of magnitude of RDF-3X and TripleBit.
Broadcast and aggregation in BBC
Venue: arXiv cs.LO 2016
Year: 2016

In distributed systems, where multi-party communication is essential, two communication paradigms are ever present: (1) one-to-many, commonly denoted as broadcast; and (2) many-to-one denoted as aggregation or collection. In this paper we present the BBC process calculus, which inherently models the broadcast and aggregation communication modes. We then apply this process calculus to reason on hierarchical network structure and provide examples on its expressive power.
A Typed Model for Dynamic Authorizations
Venue: arXiv cs.LO 2016
Year: 2016

Security requirements in distributed software systems are inherently dynamic. In the case of authorization policies, resources are meant to be accessed only by authorized parties, but the authorization to access a resource may be dynamically granted/yielded. We describe ongoing work on a model for specifying communication and dynamic authorization handling. We build upon the pi-calculus so as to enrich communication-based systems with authorization specification and delegation; here authorizations regard channel usage and delegation refers to the act of yielding an authorization to another party. Our model includes: (i) a novel scoping construct for authorization, which allows to specify authorization boundaries, and (ii) communication primitives for authorizations, which allow to pass around authorizations to act on a given channel. An authorization error may consist in, e.g., performing an action along a name which is not under an appropriate authorization scope. We introduce a typing discipline that ensures that processes never reduce to authorization errors, even when authorizations are dynamically delegated.
Multifaceted Feature Visualization: Uncovering the Different Types of Features Learned By Each Neuron in Deep Neural Networks
Venue: arXiv cs.NE 2016
Year: 2016

We can better understand deep neural networks by identifying which features each of their neurons have learned to detect. To do so, researchers have created Deep Visualization techniques including activation maximization, which synthetically generates inputs (e.g. images) that maximally activate each neuron. A limitation of current techniques is that they assume each neuron detects only one type of feature, but we know that neurons can be multifaceted, in that they fire in response to many different types of features: for example, a grocery store class neuron must activate either for rows of produce or for a storefront. Previous activation maximization techniques constructed images without regard for the multiple different facets of a neuron, creating inappropriate mixes of colors, parts of objects, scales, orientations, etc. Here, we introduce an algorithm that explicitly uncovers the multiple facets of each neuron by producing a synthetic visualization of each of the types of images that activate a neuron. We also introduce regularization methods that produce state-of-the-art results in terms of the interpretability of images obtained by activation maximization. By separately synthesizing each type of image a neuron fires in response to, the visualizations have more appropriate colors and coherent global structure. Multifaceted feature visualization thus provides a clearer and more comprehensive description of the role of each neuron.
Joint Beamforming and Broadcasting in Massive MIMO
Venue: arXiv cs.IT 2016
Year: 2016

The downlink of a massive MIMO system is considered for the case in which the base station must concurrently serve two categories of terminals: one group to which imperfect instantaneous channel state information (CSI) is available, and one group to which no CSI is available. Motivating applications include broadcasting of public channels and control information in wireless networks. A new technique is developed and analyzed: joint beamforming and broadcasting (JBB), by which the base station beamforms to the group of terminals to which CSI is available, and broadcasts to the other group of terminals, to which no CSI is available. The broadcast information does not interfere with the beamforming as it is placed in the nullspace of the channel matrix collectively seen by the terminals targeted by the beamforming. JBB is compared to orthogonal access (OA), by which the base station partitions the time-frequency resources into two disjunct parts, one for each group of terminals. It is shown that JBB can substantially outperform OA in terms of required total radiated power for given rate targets.
Adaptive TXOP Assignment for QoS Support of Video Traffic in IEEE 802.11e Networks
Venue: arXiv cs.NI 2016
Year: 2016

Quality of Service (QoS) is provided in IEEE 802.11e protocol by means of HCF Controlled Channel Access (HCCA) scheduler which is efficient for supporting Constant Bit Rate (CBR) applications. Numerous researches have been carried out to enhance the HCCA scheduler attempting to accommodate the needs of Variable Bit Rate (VBR) video traffics which probably demonstrates a non-deterministic profile during the time. This paper presents an adaptive TXOP assignment mechanism for supporting the transmission of the prerecorded video traffics over IEEE 802.11e wireless networks. The proposed mechanism uses a feedback about the size of the subsequent video frames of the uplink traffic to assist the Hybrid Coordinator (HC) accurately assign TXOP according to the fast changes in the VBR profile. The simulation results show that our mechanism reduces the delay experienced by VBR traffic streams comparable to HCCA scheduler due to the accurate assignment of the TXOP which preserve the channel time for data transmission.
On Repetitive Scenario Design
Venue: arXiv cs.SY 2016
Year: 2016

Repetitive Scenario Design (RSD) is a randomized approach to robust design based on iterating two phases: a standard scenario design phase that uses $N$ scenarios (design samples), followed by randomized feasibility phase that uses $N_o$ test samples on the scenario solution. We give a full and exact probabilistic characterization of the number of iterations required by the RSD approach for returning a solution, as a function of $N$, $N_o$, and of the desired levels of probabilistic robustness in the solution. This novel approach broadens the applicability of the scenario technology, since the user is now presented with a clear tradeoff between the number $N$ of design samples and the ensuing expected number of repetitions required by the RSD algorithm. The plain (one-shot) scenario design becomes just one of the possibilities, sitting at one extreme of the tradeoff curve, in which one insists in finding a solution in a single repetition: this comes at the cost of possibly high $N$. Other possibilities along the tradeoff curve use lower $N$ values, but possibly require more than one repetition.
Providing Dynamic TXOP for QoS Support of Video Transmission in IEEE 802.11e WLANs
Venue: arXiv cs.NI 2016
Year: 2016

The IEEE 802.11e standard introduced by IEEE 802.11 Task Group E (TGe) enhances the Quality of Service (QoS) by means of HCF Controlled Channel Access (HCCA). The scheduler of HCCA allocates Transmission Opportunities (TXOPs) to QoS-enabled Station (QSTA) based on their TS Specifications (TSPECs) negotiated at the traffic setup time so that it is only efficient for Constant Bit Rate (CBR) applications. However, Variable Bit Rate (VBR) traffics are not efficiently supported as they exhibit nondeterministic profile during the time. In this paper, we present a dynamic TXOP assignment Scheduling Algorithm for supporting the video traffics transmission over IEEE 802.11e wireless networks. This algorithm uses a piggybacked information about the size of the subsequent video frames of the uplink traffic to assist the Hybrid Coordinator accurately assign the TXOP according to the fast changes in the VBR profile. The proposed scheduling algorithm has been evaluated using simulation with different variability level video streams. The simulation results show that the proposed algorithm reduces the delay experienced by VBR traffic streams comparable to HCCA scheduler due to the accurate assignment of the TXOP which preserve the channel time for transmission.
Wavelet-Based Semantic Features for Hyperspectral Signature Discrimination
Venue: arXiv cs.CV 2016
Year: 2016

Hyperspectral signature classification is a quantitative analysis approach for hyperspectral imagery which performs detection and classification of the constituent materials at the pixel level in the scene. The classification procedure can be operated directly on hyperspectral data or performed by using some features extracted from the corresponding hyperspectral signatures containing information like the signature's energy or shape. In this paper, we describe a technique that applies non-homogeneous hidden Markov chain (NHMC) models to hyperspectral signature classification. The basic idea is to use statistical models (such as NHMC) to characterize wavelet coefficients which capture the spectrum semantics (i.e., structural information) at multiple levels. Experimental results show that the approach based on NHMC models can outperform existing approaches relevant in classification tasks.
Bouncing Towers move faster than Hanoi Towers, but still require exponential time
Venue: arXiv cs.GL 2016
Year: 2016

The problem of the Hanoi Tower is a classic exercise in recursive programming: the solution has a simple recursive definition, and its complexity and the matching lower bound are the solution of a simple recursive function (the solution is so easy that most students memorize it and regurgitate it at exams without truly understanding it). We describe how some very minor changes in the rules of the Hanoi Tower yield various increases of complexity in the solution, so that they require a deeper analysis than the classical Hanoi Tower problem while still yielding exponential solutions. In particular, we analyze the problem fo the Bouncing Tower, where just changing the insertion and extraction position from the top to the middle of the tower results in a surprising increase of complexity in the solution: such a tower of $n$ disks can be optimally moved in $\sqrt{3}^n$ moves for $n$ even (i.e. less than a Hanoi Tower of same height), via $5$ recursive functions (or, equivalently, one recursion function with $5$ states).
Efficient Call Path Detection for Android-OS Size of Huge Source Code
Venue: arXiv cs.DS 2016
Year: 2016

Today most developers utilize source code written by other parties. Because the code is modified frequently, the developers need to grasp the impact of the modification repeatedly. A call graph and especially its special type, a call path, help the developers comprehend the modification. Source code written by other parties, however, becomes too huge to be held in memory in the form of parsed data for a call graph or path. This paper offers a bidirectional search algorithm for a call graph of too huge amount of source code to store all parse results of the code in memory. It refers to a method definition in source code corresponding to the visited node in the call graph. The significant feature of the algorithm is the referenced information is used not in order to select a prioritized node to visit next but in order to select a node to postpone visiting. It reduces path extraction time by 8% for a case in which ordinary path search algorithms do not reduce the time.
Detection of Cooperative Interactions in Logistic Regression Models
Venue: arXiv cs.AI 2016
Year: 2016

An important problem in the field of bioinformatics is to identify interactive effects among profiled variables for outcome prediction. In this paper, a logistic regression model with pairwise interactions among a set of binary covariates is considered. Modeling the structure of the interactions by a graph, our goal is to recover the interaction graph from independently identically distributed (i.i.d.) samples of the covariates and the outcome. When viewed as a feature selection problem, a simple quantity called influence is proposed as a measure of the marginal effects of the interaction terms on the outcome. For the case when the underlying interaction graph is known to be acyclic, it is shown that a simple algorithm that is based on a maximum-weight spanning tree with respect to the plug-in estimates of the influences not only has strong theoretical performance guarantees, but can also outperform generic feature selection algorithms for recovering the interaction graph from i.i.d. samples of the covariates and the outcome. Our results can also be extended to the model that includes both individual effects and pairwise interactions via the help of an auxiliary covariate.
Energetics of the brain and AI
Venue: arXiv cs.AI 2016
Year: 2016

Does the energy requirements for the human brain give energy constraints that give reason to doubt the feasibility of artificial intelligence? This report will review some relevant estimates of brain bioenergetics and analyze some of the methods of estimating brain emulation energy requirements. Turning to AI, there are reasons to believe the energy requirements for de novo AI to have little correlation with brain (emulation) energy requirements since cost could depend merely of the cost of processing higher-level representations rather than billions of neural firings. Unless one thinks the human way of thinking is the most optimal or most easily implementable way of achieving software intelligence, we should expect de novo AI to make use of different, potentially very compressed and fast, processes.
Image Restoration and Reconstruction using Variable Splitting and Class-adapted Image Priors
Venue: arXiv cs.CV 2016
Year: 2016

This paper proposes using a Gaussian mixture model as a prior, for solving two image inverse problems, namely image deblurring and compressive imaging. We capitalize on the fact that variable splitting algorithms, like ADMM, are able to decouple the handling of the observation operator from that of the regularizer, and plug a state-of-the-art algorithm into the pure denoising step. Furthermore, we show that, when applied to a specific type of image, a Gaussian mixture model trained from an database of images of the same type is able to outperform current state-of-the-art methods.
Row-constrained effective sets of colourings in the $2$-fold horocyclic tessellations of $\mathbb{H}^2$ are sofic
Venue: arXiv cs.DM 2016
Year: 2016

In this article we prove that, restricted to the row-constrained case, effective sets of colourings in the $2$-fold horocyclic tessellations of the hyperbolic plane $\mathbb{H}^2$ are sofic.
Evaluation of Protein Structural Models Using Random Forests
Venue: arXiv cs.LG 2016
Year: 2016

Protein structure prediction has been a grand challenge problem in the structure biology over the last few decades. Protein quality assessment plays a very important role in protein structure prediction. In the paper, we propose a new protein quality assessment method which can predict both local and global quality of the protein 3D structural models. Our method uses both multi and single model quality assessment method for global quality assessment, and uses chemical, physical, geo-metrical features, and global quality score for local quality assessment. CASP9 targets are used to generate the features for local quality assessment. We evaluate the performance of our local quality assessment method on CASP10, which is comparable with two stage-of-art QA methods based on the average absolute distance between the real and predicted distance. In addition, we blindly tested our method on CASP11, and the good performance shows that combining single and multiple model quality assessment method could be a good way to improve the accuracy of model quality assessment, and the random forest technique could be used to train a good local quality assessment model.
Urban sidewalks: visualization and routing for individuals with limited mobility
Venue: arXiv cs.CY 2016
Year: 2016

People with limited mobility in the U.S. (defined as having difficulty or inability to walk a quarter of a mile without help and without the use of special equipment) face a growing informational gap: while pedestrian routing algorithms are getting faster and more informative, planning a route with a wheeled device in urban centers is very difficult due to lack of integrated pertinent information regarding accessibility along the route. Moreover, reducing access to street-spaces translates to reduced access to other public information and services that are increasingly made available to the public along urban streets. To adequately plan a commute, a traveler with limited or wheeled mobility must know whether her path may be blocked by construction, whether the sidewalk would be too steep or rendered unusable due to poor conditions, whether the street can be crossed or a highway is blocking the way, or whether there is a sidewalk at all. These details populate different datasets in many modern municipalities, but they are not immediately available in a convenient, integrated format to be useful to people with limited mobility. Our project, AccessMap, in its first phase (v.1) overlayed the information that is most relevant to people with limited mobility on a map, enabling self-planning of routes. Here, we describe the next phase of the project: synthesizing commonly available open data (including streets, sidewalks, curb ramps, elevation data, and construction permit information) to generate a graph of paths to enable variable cost-function accessible routing.
Balanced Truncation of Linear Time-Invariant Systems over Finite-frequency Ranges
Venue: arXiv cs.SY 2016
Year: 2016

This paper discusses model order reduction of LTI systems over limited frequency intervals within the framework of balanced truncation. Two new \emph{frequency-dependent balanced truncation} methods were developed, one is \emph{SF-type frequency-dependent balanced truncation} to copy with the cases that only a single dominating point of the operating frequency interval is pre-known, the other is \emph{interval-type frequency-dependent balanced truncation} to deal with the cases that both of the upper and lower bound of frequency interval are known \emph{a priori}. SF-type error bound and interval-type error bound are derived for the first time to estimate the desired approximation error over pre-specified frequency interval. We show that the new methods generally lead to good in-band approximation performance, at the same time, provide accurate error bounds under certain conditions. Examples are included for illustration.
Random Forest Based Approach for Concept Drift Handling
Venue: arXiv cs.AI 2016
Year: 2016

Concept drift has potential in smart grid analysis because the socio-economic behaviour of consumers is not governed by the laws of physics. Likewise there are also applications in wind power forecasting. In this paper we present decision tree ensemble classification method based on the Random Forest algorithm for concept drift. The weighted majority voting ensemble aggregation rule is employed based on the ideas of Accuracy Weighted Ensemble (AWE) method. Base learner weight in our case is computed for each sample evaluation using base learners accuracy and intrinsic proximity measure of Random Forest. Our algorithm exploits both temporal weighting of samples and ensemble pruning as a forgetting strategy. We present results of empirical comparison of our method with original random forest with incorporated "replace-the-looser" forgetting andother state-of-the-art concept-drfit classifiers like AWE2.
Recursive Recovery of Sparse Signal Sequences from Compressive Measurements: A Review
Venue: arXiv cs.IT 2016
Year: 2016

In this article, we review the literature on design and analysis of recursive algorithms for reconstructing a time sequence of sparse signals from compressive measurements. The signals are assumed to be sparse in some transform domain or in some dictionary. Their sparsity patterns can change with time, although, in many practical applications, the changes are gradual. An important class of applications where this problem occurs is dynamic projection imaging, e.g., dynamic magnetic resonance imaging (MRI) for real-time medical applications such as interventional radiology, or dynamic computed tomography.
Adversarial Top-$K$ Ranking
Venue: arXiv cs.IR 2016
Year: 2016

We study the top-$K$ ranking problem where the goal is to recover the set of top-$K$ ranked items out of a large collection of items based on partially revealed preferences. We consider an adversarial crowdsourced setting where there are two population sets, and pairwise comparison samples drawn from one of the populations follow the standard Bradley-Terry-Luce model (i.e., the chance of item $i$ beating item $j$ is proportional to the relative score of item $i$ to item $j$), while in the other population, the corresponding chance is inversely proportional to the relative score. When the relative size of the two populations is known, we characterize the minimax limit on the sample size required (up to a constant) for reliably identifying the top-$K$ items, and demonstrate how it scales with the relative size. Moreover, by leveraging a tensor decomposition method for disambiguating mixture distributions, we extend our result to the more realistic scenario in which the relative population size is unknown, thus establishing an upper bound on the fundamental limit of the sample size for recovering the top-$K$ set.