OpenRaiser/Intern-Atlas huggingface dataset:
Refining Adverse Drug Reactions using Association Rule Mining for Electronic Healthcare Data
Venue: arXiv cs.DB 2015
Year: 2015

Side effects of prescribed medications are a common occurrence. Electronic healthcare databases present the opportunity to identify new side effects efficiently but currently the methods are limited due to confounding (i.e. when an association between two variables is identified due to them both being associated to a third variable). In this paper we propose a proof of concept method that learns common associations and uses this knowledge to automatically refine side effect signals (i.e. exposure-outcome associations) by removing instances of the exposure-outcome associations that are caused by confounding. This leaves the signal instances that are most likely to correspond to true side effect occurrences. We then calculate a novel measure termed the confounding-adjusted risk value, a more accurate absolute risk value of a patient experiencing the outcome within 60 days of the exposure. Tentative results suggest that the method works. For the four signals (i.e. exposure-outcome associations) investigated we are able to correctly filter the majority of exposure-outcome instances that were unlikely to correspond to true side effects. The method is likely to improve when tuning the association rule mining parameters for specific health outcomes. This paper shows that it may be possible to filter signals at a patient level based on association rules learned from considering patients' medical histories. However, additional work is required to develop a way to automate the tuning of the method's parameters.
DCAFE: Dynamic load-balanced loop Chunking & Aggressive Finish Elimination for Recursive Task Parallel Programs
Venue: arXiv cs.DC 2015
Year: 2015

In this paper, we present two symbiotic optimizations to optimize recursive task parallel (RTP) programs by reducing the task creation and termination overheads. Our first optimization Aggressive Finish-Elimination (AFE) helps reduce the redundant join operations to a large extent. The second optimization Dynamic Load-Balanced loop Chunking (DLBC) extends the prior work on loop chunking to decide on the number of parallel tasks based on the number of available worker threads, at runtime. Further, we discuss the impact of exceptions on our optimizations and extend them to handle RTP programs that may throw exceptions. We implemented DCAFE (= DLBC+AFE) in the X10v2.3 compiler and tested it over a set of benchmark kernels on two different hardwares (a 16-core Intel system and a 64-core AMD system). With respect to the base X10 compiler extended with loop-chunking of Nandivada et al [Nandivada et al.(2013)Nandivada, Shirako, Zhao, and Sarkar](LC), DCAFE achieved a geometric mean speed up of 5.75x and 4.16x on the Intel and AMD system, respectively. We also present an evaluation with respect to the energy consumption on the Intel system and show that on average, compared to the LC versions, the DCAFE versions consume 71.2% less energy.
Compressive sensing based velocity estimation in video data
Venue: arXiv cs.MM 2015
Year: 2015

This paper considers the use of compressive sensing based algorithms for velocity estimation of moving vehicles. The procedure is based on sparse reconstruction algorithms combined with time-frequency analysis applied to video data. This algorithm provides an accurate estimation of object's velocity even in the case of a very reduced number of available video frames. The influence of crucial parameters is analysed for different types of moving vehicles.
Minimal Switch Step Tracking Control of Switched Systems with Application to Induction Motor Control
Venue: arXiv cs.SY 2015
Year: 2015

The problem of step tracking control with a switching input and without any continuous-valued inputs is considered. The control objective is to reduce the number of switchings to a minimal value. This approach finds interesting applications when switching comprises costs and should be avoided. To solve the problem, a state dependent switching strategy should be designed and the resulting closed loop is indeed a hybrid system. Therefore, first we investigate the conditions on a hybrid system for being the desired solution. Then, we propose a method for designing the switching strategy such that the closed loop as a hybrid system solves the problem. The proposed method is applied to the induction motor control problem which results in relatively simple and efficient control algorithm. Comparison with the direct torque control for induction motors show that our method has a superior performance in reducing the number of mode switches.
Universal Memory Architectures for Autonomous Machines
Venue: arXiv cs.AI 2015
Year: 2015

We propose a self-organizing memory architecture for perceptual experience, capable of supporting autonomous learning and goal-directed problem solving in the absence of any prior information about the agent's environment. The architecture is simple enough to ensure (1) a quadratic bound (in the number of available sensors) on space requirements, and (2) a quadratic bound on the time-complexity of the update-execute cycle. At the same time, it is sufficiently complex to provide the agent with an internal representation which is (3) minimal among all representations of its class which account for every sensory equivalence class subject to the agent's belief state; (4) capable, in principle, of recovering the homotopy type of the system's state space; (5) learnable with arbitrary precision through a random application of the available actions. The provable properties of an effectively trained memory structure exploit a duality between weak poc sets -- a symbolic (discrete) representation of subset nesting relations -- and non-positively curved cubical complexes, whose rich convexity theory underlies the planning cycle of the proposed architecture.
On mesh restrictions to satisfy comparison principles, maximum principles, and the non-negative constraint: Recent developments and new results
Venue: arXiv cs.NA 2015
Year: 2015

This paper concerns with mesh restrictions that are needed to satisfy several important mathematical properties -- maximum principles, comparison principles, and the non-negative constraint -- for a general linear second-order elliptic partial differential equation. We critically review some recent developments in the field of discrete maximum principles, derive new results, and discuss some possible future research directions in this area. In particular, we derive restrictions for a three-node triangular (T3) element and a four-node quadrilateral (Q4) element to satisfy comparison principles, maximum principles, and the non-negative constraint under the standard single-field Galerkin formulation. Analysis is restricted to uniformly elliptic linear differential operators in divergence form with Dirichlet boundary conditions specified on the entire boundary of the domain. Various versions of maximum principles and comparison principles are discussed in both continuous and discrete settings. In the literature, it is well-known that an acute-angled triangle is sufficient to satisfy the discrete weak maximum principle for pure isotropic diffusion. An iterative algorithm is developed to construct simplicial meshes that preserves discrete maximum principles using existing open source mesh generators. Various numerical examples based on different types of triangulations are presented to show the pros and cons of placing restrictions on a computational mesh. We also quantify local and global mass conservation errors using representative numerical examples, and illustrate the performance of metric-based meshes with respect to mass conservation.
Algorithm for the k-Position Tree Automaton Construction
Venue: arXiv cs.FL 2015
Year: 2015

The word position automaton was introduced by Glushkov and McNaughton in the early 1960. This automaton is homogeneous and has (||\E||+1) states for a word expression of alphabetic width ||\E||. This kind of automata is extended to regular tree expressions. In this paper, we give an efficient algorithm that computes the \Follow sets, which are used in different algorithms of conversion of a regular expression into tree automata. In the following, we consider the k-position tree automaton construction. We prove that for a regular expression \E of a size |\E| and alphabetic width ||\E||, the \Follow sets can be computed in O(||\E||\cdot |\E|) time complexity.
On Probabilistic Certification of Combined Cancer Therapies Using Strongly Uncertain Models
Venue: arXiv cs.SY 2015
Year: 2015

This paper proposes a general framework for probabilistic certification of cancer therapies. The certification is defined in terms of two key issues which are the tumor contraction and the lower admissible bound on the circulating lymphocytes which is viewed as indicator of the patient health. The certification is viewed as the ability to guarantee with a predefined high probability the success of the therapy over a finite horizon despite of the unavoidable high uncertainties affecting the dynamic model that is used to compute the optimal scheduling of drugs injection. The certification paradigm can be viewed as a tool for tuning the treatment parameters and protocols as well as for getting a rational use of limited or expensive drugs. The proposed framework is illustrated using the specific problem of combined immunotherapy/chemotherapy of cancer.
Hardware Architecture for Single Iteration Reconstruction Algorithm
Venue: arXiv cs.IT 2015
Year: 2015

A hardware architecture for the single iteration algorithm is proposed in this paper. Single iteration algorithm enables reconstruction of the full signal when small number of signal samples is available. The algorithm is based on the threshold calculation, and allows distinguishing between signal components and noise that appears as a consequence of missing samples. The proposed system for hardware realization is divided into three parts, each part with different functionality. The system is suitable for the FPGA realization. Realization of the blocks for which there are no standard components in FPGA, is discussed as well.
QoS Provisioning Using Optimal Call Admission Control for Wireless Cellular Networks
Venue: arXiv cs.NI 2015
Year: 2015

The increasing demand for advanced services in wireless networks raises the problem for quality of service (QoS) provisioning with proper resource management. In this research, such a provisioning technique for wireless networks is performed by Call Admission Control (CAC). A new approach in CAC named by Uniform Fractional Band (UFB) is proposed in this work for the wireless networks for providing proper priority between new calls and handover calls. This UFB scheme is basically a new style of handover priority scheme. Handover priority is provided by two stages in this scheme which help the network to utilize more resources. In addition, the handover call rate estimation and its impact on QoS provisioning is discussed widely to attain the optimum QoS in proposed handover priority scheme. In multiple services providing wireless network, excessive call blocking of lower priority traffic is very often event at very high traffic rate which is a concerning issue for QoS provisioning. To attain such QoS provisioning for multiple services, another CAC scheme is proposed in this research work. This scheme is recognized by Uniform Band Thinning (UBT) scheme which is based on uniform thinning technique (UTT) and this is quite similar idea as UFB scheme. In this scheme, a set of channels experiences the fractionizing policy. This scheme reduces the call blocking probabilities (CBP) of lower priority traffic classes without notably increasing the CBP of the higher priority traffic classes. The analytical functions of this scheme are deduced in general form which is useful to deduce for any number of traffic classes. In addition, numerical analysis of the proposed UBT scheme shows that the performances in terms of call blocking probability, overall call blocking probability, and channel utilization are improved and optimized compared to the conventional fixed guard channel scheme.
Convolutional Patch Networks with Spatial Prior for Road Detection and Urban Scene Understanding
Venue: arXiv cs.CV 2015
Year: 2015

Classifying single image patches is important in many different applications, such as road detection or scene understanding. In this paper, we present convolutional patch networks, which are convolutional networks learned to distinguish different image patches and which can be used for pixel-wise labeling. We also show how to incorporate spatial information of the patch as an input to the network, which allows for learning spatial priors for certain categories jointly with an appearance model. In particular, we focus on road detection and urban scene understanding, two application areas where we are able to achieve state-of-the-art results on the KITTI as well as on the LabelMeFacade dataset. Furthermore, our paper offers a guideline for people working in the area and desperately wandering through all the painstaking details that render training CNs on image patches extremely difficult.
Contextual Dueling Bandits
Venue: arXiv cs.LG 2015
Year: 2015

We consider the problem of learning to choose actions using contextual information when provided with limited feedback in the form of relative pairwise comparisons. We study this problem in the dueling-bandits framework of Yue et al. (2009), which we extend to incorporate context. Roughly, the learner's goal is to find the best policy, or way of behaving, in some space of policies, although "best" is not always so clearly defined. Here, we propose a new and natural solution concept, rooted in game theory, called a von Neumann winner, a randomized policy that beats or ties every other policy. We show that this notion overcomes important limitations of existing solutions, particularly the Condorcet winner which has typically been used in the past, but which requires strong and often unrealistic assumptions. We then present three efficient algorithms for online learning in our setting, and for approximating a von Neumann winner from batch-like data. The first of these algorithms achieves particularly low regret, even when data is adversarial, although its time and space requirements are linear in the size of the policy space. The other two algorithms require time and space only logarithmic in the size of the policy space when provided access to an oracle for solving classification problems on the space.
A framework for space-efficient string kernels
Venue: arXiv cs.DS 2015
Year: 2015

String kernels are typically used to compare genome-scale sequences whose length makes alignment impractical, yet their computation is based on data structures that are either space-inefficient, or incur large slowdowns. We show that a number of exact string kernels, like the $k$-mer kernel, the substrings kernels, a number of length-weighted kernels, the minimal absent words kernel, and kernels with Markovian corrections, can all be computed in $O(nd)$ time and in $o(n)$ bits of space in addition to the input, using just a $\mathtt{rangeDistinct}$ data structure on the Burrows-Wheeler transform of the input strings, which takes $O(d)$ time per element in its output. The same bounds hold for a number of measures of compositional complexity based on multiple value of $k$, like the $k$-mer profile and the $k$-th order empirical entropy, and for calibrating the value of $k$ using the data.
Shannon, Tsallis and Kaniadakis entropies in bi-level image thresholding
Venue: arXiv cs.CV 2015
Year: 2015

The maximum entropy principle is often used for bi-level or multi-level thresholding of images. For this purpose, some methods are available based on Shannon and Tsallis entropies. In this paper, we discuss them and propose a method based on Kaniadakis entropy.
System Interactive Cyber Presence for E learning to Break Down Learner Isolation
Venue: arXiv cs.CY 2015
Year: 2015

The development of technologies of multimedia, linked to that of Internet and democratization of high speed, has made henceforth E-learning possible for learners being in virtual classes and geographically distributed. One benefit to taking course online is that the online course structure is typically more student focused than teacher centered and encouraging more active participation by students in collaborative learning activities. The quality and quantity of asynchronous and synchronous communications are the key elements for E-learning success. A potential problem that has received little exploration is student's feeling of isolation. It is important to have a propitious supervision to breaking down learner feeling isolation in E learning environment. This feeling of isolation is among the main causes of loss and high rates of dropout in E-learning. It impacts on their levels of participation, satisfaction and learning. To overcome this feeling of isolation, we aim, by this research, to provide the trainer and each learner with an environment allowing them to behave as if being face to face; in other words, to approach the pedagogy of classroom teaching. Our contribution to reduce the feeling of isolation is to ensure the presence of the teacher in the educational tools. These tools aim to establish a real dialogue with the learner, forcing him to take an active part in their learning. Among the tools we offer, video conference Openmeeting integrated in Moodle providing the possibility of using the notion of class and whiteboard, the indicator of motivation quantification tool based hand gesture that we developed and finally social networks web 2. 0 like Facebook, youtube, twitter to promote collaboration, sharing and communication of the learner with his peers.
Recognizing Fine-Grained and Composite Activities using Hand-Centric Features and Script Data
Venue: arXiv cs.CV 2015
Year: 2015

Activity recognition has shown impressive progress in recent years. However, the challenges of detecting fine-grained activities and understanding how they are combined into composite activities have been largely overlooked. In this work we approach both tasks and present a dataset which provides detailed annotations to address them. The first challenge is to detect fine-grained activities, which are defined by low inter-class variability and are typically characterized by fine-grained body motions. We explore how human pose and hands can help to approach this challenge by comparing two pose-based and two hand-centric features with state-of-the-art holistic features. To attack the second challenge, recognizing composite activities, we leverage the fact that these activities are compositional and that the essential components of the activities can be obtained from textual descriptions or scripts. We show the benefits of our hand-centric approach for fine-grained activity classification and detection. For composite activity recognition we find that decomposition into attributes allows sharing information across composites and is essential to attack this hard task. Using script data we can recognize novel composites without having training data for them.
Reified Context Models
Venue: arXiv cs.LG 2015
Year: 2015

A classic tension exists between exact inference in a simple model and approximate inference in a complex model. The latter offers expressivity and thus accuracy, but the former provides coverage of the space, an important property for confidence estimation and learning with indirect supervision. In this work, we introduce a new approach, reified context models, to reconcile this tension. Specifically, we let the amount of context (the arity of the factors in a graphical model) be chosen "at run-time" by reifying it---that is, letting this choice itself be a random variable inside the model. Empirically, we show that our approach obtains expressivity and coverage on three natural language tasks.
Data-Driven Shape Analysis and Processing
Venue: arXiv cs.GR 2015
Year: 2015

Data-driven methods play an increasingly important role in discovering geometric, structural, and semantic relationships between 3D shapes in collections, and applying this analysis to support intelligent modeling, editing, and visualization of geometric data. In contrast to traditional approaches, a key feature of data-driven approaches is that they aggregate information from a collection of shapes to improve the analysis and processing of individual shapes. In addition, they are able to learn models that reason about properties and relationships of shapes without relying on hard-coded rules or explicitly programmed instructions. We provide an overview of the main concepts and components of these techniques, and discuss their application to shape classification, segmentation, matching, reconstruction, modeling and exploration, as well as scene analysis and synthesis, through reviewing the literature and relating the existing works with both qualitative and numerical comparisons. We conclude our report with ideas that can inspire future research in data-driven shape analysis and processing.
Minimizing Energy Consumption of MPI Programs in Realistic Environment
Venue: arXiv cs.DC 2015
Year: 2015

Dynamic voltage and frequency scaling proves to be an efficient way of reducing energy consumption of servers. Energy savings are typically achieved by setting a well-chosen frequency during some program phases. However, determining suitable program phases and their associated optimal frequencies is a complex problem. Moreover, hardware is constrained by non negligible frequency transition latencies. Thus, various heuristics were proposed to determine and apply frequencies, but evaluating their efficiency remains an issue. In this paper, we translate the energy minimization problem into a mixed integer program that specifically models most current hardware limitations. The problem solution then estimates the minimal energy consumption and the associated frequency schedule. The paper provides two different formulations and a discussion on the feasibility of each of them on realistic applications.
Half a billion simulations: evolutionary algorithms and distributed computing for calibrating the SimpopLocal geographical model
Venue: arXiv cs.CY 2015
Year: 2015

Multi-agent geographical models integrate very large numbers of spatial interactions. In order to validate those models large amount of computing is necessary for their simulation and calibration. Here a new data processing chain including an automated calibration procedure is experimented on a computational grid using evolutionary algorithms. This is applied for the first time to a geographical model designed to simulate the evolution of an early urban settlement system. The method enables us to reduce the computing time and provides robust results. Using this method, we identify several parameter settings that minimise three objective functions that quantify how closely the model results match a reference pattern. As the values of each parameter in different settings are very close, this estimation considerably reduces the initial possible domain of variation of the parameters. The model is thus a useful tool for further multiple applications on empirical historical situations.
New extremal binary self-dual codes of lengths 66 and 68 from codes over r_k,m
Venue: arXiv cs.IT 2015
Year: 2015

In this work, four circulant and quadratic double circulant (QDC) constructions are applied to the family of the rings R_k,m. Self-dual binary codes are obtained as the Gray images of self-dual QDC codes over R_k,m. Extremal binary self-dual codes of length 64 are obtained as Gray images of ?-four circulant codes over R_2,1 and R_2,2. Extremal binary self-dual codes of lengths 66 and 68 are constructed by applying extension theorems to the F_2 and R_2,1 images of these codes. More precisely, 11 new codes of length 66 and 39 new codes of length 68 are discovered. The codes with these weight enumerators are constructed for the first time in literature. The results are tabulated.