Query: Global outer-urban navigation with OpenStreetMap

Relevant passages:
HD Maps: Fine-Grained Road Segmentation by Parsing Ground and Aerial Images <=SEP=> In this paper we present an approach to enhance existing maps with fine grained segmentation categories such as parking spots and sidewalk, as well as the number and location of road lanes. Towards this goal, we propose an efficient approach that is able to estimate these fine grained categories by doing joint inference over both, monocular aerial imagery, as well as ground images taken from a stereo camera pair mounted on top of a car. Important to this is reasoning about the alignment between the two types of imagery, as even when the measurements are taken with sophisticated GPS+IMU systems, this alignment is not sufficiently accurate. We demonstrate the effectiveness of our approach on a new dataset which enhances KITTI [8] with aerial images taken with a camera mounted on an airplane and flying around the city of Karlsruhe, Germany.

OpenStreetMap: User-Generated Street Maps <=SEP=> The OpenStreetMap project is a knowledge collective that provides user-generated street maps. OSM follows the peer production model that created Wikipedia; its aim is to create a set of map data that's free to use, editable, and licensed under new copyright schemes. A considerable number of contributors edit the world map collaboratively using the OSM technical infrastructure, and a core group, estimated at approximately 40 volunteers, dedicate their time to creating and improving OSM's infrastructure, including maintaining the server, writing the core software that handles the transactions with the server, and creating cartographical outputs. There's also a growing community of software developers who develop software tools to make OSM data available for further use across different application domains, software platforms, and hardware devices. The OSM project's hub is the main OSM Web site.

Metric localization using Google Street View <=SEP=> Accurate metrical localization is one of the central challenges in mobile robotics. Many existing methods aim at localizing after building a map with the robot. In this paper, we present a novel approach that instead uses geo-tagged panoramas from the Google Street View as a source of global positioning. We model the problem of localization as a non-linear least squares estimation in two phases. The first estimates the 3D position of tracked feature points from short monocular camera sequences. The second computes the rigid body transformation between the Street View panoramas and the estimated points. The only input of this approach is a stream of monocular camera images and odometry estimates. We quantified the accuracy of the method by running the approach on a robotic platform in a parking lot by using visual fiducials as ground truth. Additionally, we applied the approach in the context of personal localization in a real urban scenario by using data from a Google Tango tablet.

Autonomous Robot Navigation in Highly Populated Pedestrian Zones <=SEP=> In the past, there has been a tremendous progress in the area of autonomous robot navigation and a large variety of robots have been developed who demonstrated robust navigation capabilities indoors, in non-urban outdoor environments, or on roads and relatively few approaches focus on navigation in urban environments such as city centers. Urban areas, however, introduce numerous challenges for autonomous robots as they are rather unstructured and dynamic. In this paper, we present a navigation system for mobile robots designed to operate in crowded city environments and pedestrian zones. We describe the different components of this system including a SLAM module for dealing with huge maps of city centers, a planning component for inferring feasible paths taking also into account the traversability and type of terrain, a module for accurate localization in dynamic environments, and means for calibrating and monitoring the platform. Our navigation system has been implemented and tested in several large-scale field tests, in which a real robot autonomously navigated over several kilometers in a complex urban environment. This also included a public demonstration, during which the robot autonomously traveled along a more than three kilometer long route through the city center of Freiburg, Germany.

Distance Transforms of Sampled Functions <=SEP=> We describe linear-time algorithms for solving a class of problems that involve transforming a cost function on a grid using spatial information. These problems can be viewed as a generalization of classical distance transforms of binary images, where the binary image is replaced by an arbitrary function on a grid. Alternatively they can be viewed in terms of the minimum convolution of two functions, which is an important operation in grayscale morphology. A consequence of our techniques is a simple and fast method for computing the Euclidean distance transform of a binary image. Our algorithms are also applicable to Viterbi decoding, belief propagation, and optimal control. ACM Classification: F.2.1, I.4 AMS Classification: 68T45, 68W40

The ODD protocol : a review and first update <=SEP=> The ‘ODD’ (Overview, Design concepts, and Details) protocol was published in 2006 to standardize the published descriptions of individual-based and agent-based models (ABMs). The primary objectives of ODD are to make model descriptions more understandable and complete, thereby making ABMs less subject to criticism for being irreproducible. We have systematically evaluated existing uses of the ODD protocol and identified, as expected, parts of ODD needing improvement and clarification. Accordingly, we revise the definition of ODD to clarify aspects of the original version and thereby facilitate future standardization of ABM descriptions. We discuss frequently raised critiques in ODD but also two emerging, and unanticipated, benefits: ODD improves the rigorous formulation of models and helps make the theoretical foundations of large models more visible. Although the protocol was designed for ABMs, it can help with documenting any large, complex model, alleviating some general objections against such models.

Predicting inter-thread cache contention on a chip multi-processor architecture <=SEP=> This paper studies the impact of L2 cache sharing on threads that simultaneously share the cache, on a chip multi-processor (CMP) architecture. Cache sharing impacts threads nonuniformly, where some threads may be slowed down significantly, while others are not. This may cause severe performance problems such as sub-optimal throughput, cache thrashing, and thread starvation for threads that fail to occupy sufficient cache space to make good progress. Unfortunately, there is no existing model that allows extensive investigation of the impact of cache sharing. To allow such a study, we propose three performance models that predict the impact of cache sharing on co-scheduled threads. The input to our models is the isolated L2 cache stack distance or circular sequence profile of each thread, which can be easily obtained on-line or off-line. The output of the models is the number of extra L2 cache misses for each thread due to cache sharing. The models differ by their complexity and prediction accuracy. We validate the models against a cycle-accurate simulation that implements a dual-core CMP architecture, on fourteen pairs of mostly SPEC benchmarks. The most accurate model, the inductive probability model, achieves an average error of only 3.9%. Finally, to demonstrate the usefulness and practicality of the model, a case study that details the relationship between an application's temporal reuse behavior and its cache sharing impact is presented.

Accounting Information System Risk Assessment Algorithm Based on Analytic Hierarchy Process <=SEP=> So far, there is little research on accounting information system risk assessment in our country. In order to provide the basis to meet the security needs of the accounting information system and reduce the risk of accounting information system security, reduce financial loses and improve the work efficiency, a model of enterprise accounting information system risk assessment method based on Analytic Hierarchy Process is proposed. The analytic hierarchy process model is applied to one corporate accounting information system for risk assessment. It can be concluded that the proposed method get better result of risk assessment, and have strong operability and effectiveness of risk assessment in the accounting information system for enterprise.

Scalable pseudo-random noise radar <=SEP=> This paper describes the implementation of a pseudo-random noise radar system on a scalable sensor platform that consists of several multi-purpose 61- and 122-GHz transceivers in a Silicon-Germanium BiCMOS technology. The multi-band transceivers are equipped with a frequency multiplier to generate the 61- and 122-GHz carrier signals from a single 30.5-GHz LO signal and can be cascaded to build a MIMO radar system with 2 different frequency bands. The implemented scalable sensor system is suitable for various radar and communication applications by using the same transceiver chips with integrated binary phase-shift keying modulators and I/Q receiver. A suitable pseudo-random binary sequence was used in a radar measurement to demonstrate the applicability of the developed chips.

Variational shape approximation <=SEP=> A method for concise, faithful approximation of complex 3D datasets is key to reducing the computational cost of graphics applications. Despite numerous applications ranging from geometry compression to reverse engineering, efficiently capturing the geometry of a surface remains a tedious task. In this paper, we present both theoretical and practical contributions that result in a novel and versatile framework for geometric approximation of surfaces. We depart from the usual strategy by casting shape approximation as a variational geometric partitioning problem. Using the concept of geometric proxies, we drive the distortion error down through repeated clustering of faces into best-fitting regions. Our approach is entirely discrete and error-driven, and does not require parameterization or local estimations of differential quantities. We also introduce a new metric based on normal deviation, and demonstrate its superior behavior at capturing anisotropy.

The Gut Microbiota and Autism Spectrum Disorders <=SEP=> Gastrointestinal (GI) symptoms are a common comorbidity in patients with autism spectrum disorder (ASD), but the underlying mechanisms are unknown. Many studies have shown alterations in the composition of the fecal flora and metabolic products of the gut microbiome in patients with ASD. The gut microbiota influences brain development and behaviors through the neuroendocrine, neuroimmune and autonomic nervous systems. In addition, an abnormal gut microbiota is associated with several diseases, such as inflammatory bowel disease (IBD), ASD and mood disorders. Here, we review the bidirectional interactions between the central nervous system and the gastrointestinal tract (brain-gut axis) and the role of the gut microbiota in the central nervous system (CNS) and ASD. Microbiome-mediated therapies might be a safe and effective treatment for ASD.

Efficient Private ERM for Smooth Objectives <=SEP=> In this paper, we consider efficient differentially private empirical risk minimization from the viewpoint of optimization algorithms. For strongly convex and smooth objectives, we prove that gradient descent with output perturbation not only achieves nearly optimal utility, but also significantly improves the running time of previous state-of-the-art private optimization algorithms, for both ǫ-DP and (ǫ, δ)-DP. For non-convex but smooth objectives, we propose an RRPSGD (Random Round Private Stochastic Gradient Descent) algorithm, which provably converges to a stationary point with privacy guarantee. Besides the expected utility bounds, we also provide guarantees in high probability form. Experiments demonstrate that our algorithm consistently outperforms existing method in both utility and running time.

DENSER Cities: A System for Dense Efficient Reconstructions of Cities <=SEP=> This paper is about the efficient generation of dense, colored models of city-scale environments from range data and in particular, stereo cameras. Better maps make for better understanding; better understanding leads to better robots, but this comes at a cost. The computational and memory requirements of large dense models can be prohibitive. We provide the theory and the system needed to create cityscale dense reconstructions. To do so, we apply a regularizer over a compressed 3D data structure while dealing with the complex boundary conditions this induces during the data-fusion stage. We show that only with these considerations can we swiftly create neat, large, “well behaved” reconstructions. We evaluate our system using the KITTI dataset and provide statistics for the metric errors in all surfaces created compared to those measured with 3D laser. Our regularizer reduces the median error by 40% in 3.4 km of dense reconstructions with a median accuracy of 6 cm. For subjective analysis, we provide a qualitative review of 6.1 km of our dense reconstructions in an attached video. These are the largest dense reconstructions from a single passive camera we are aware of in the literature. Video: https://youtu.be/FRmF7mH86EQ

Running on four legs as though they were one <=SEP=> that run on one leg. The generalization uf these one-leg algorithms for control of machines with several legs is explored. The generalization is quite simple when multilegged systems run with gaits that use the support legs one at a time. For these gaits the ~ n e-l e g algorithms can be used b o control multilegged running. The cortcept of a virtual leg is introduced to further extend the approach to gaits that use the legs in pairs, such as the trot, the pace, and the bound. These quadruped running gaits map into gaits that use one virtual leg for support at a rime, for which the one-leg algorithms can provide control. This approach was used in laboratory experiments to control a quadruped machine that runs with a trotting gait. 1. ~N1RUI)IICTION UNNING is a form of legged locomotion characterized by travel at high speed and by periods of ballistic flight during which all feet leave the ground. The basic control task is to establish a pattern of leg and body motions that stabilizes the attitude and altitude of the body while propelling the body in the desired direction at the desired speed 121. The characteristics of high speed and ballistic flight suggest that dynamics and active stability are important to accomplishing this control task, and to getting a better general understanding of running. In recent work the aulhors used dynamic techniques to control an experimental one-legged machine that balances itself as it runs by hopping [ 3 , 41. The goal of these experiments was to focus on the dynamic aspects of locomo-tion, while avoiding the difficult task of coordinating the behavior of several legs. This paper addresses the task of leg coordination by starting with the algorithms that were used to control the one-legged hopping machine, and extending them to control a quadruped running machine. The general goal is to develop a set of locomotion principles that applies to the dynamic behavior of diverse legged systems-machines and animals alike-independent of the number of legs.

Fusion of IMU and Vision for Absolute Scale Estimation in Monocular SLAM <=SEP=> The fusion of inertial and visual data is widely used to improve an object’s pose estimation. However, this type of fusion is rarely used to estimate further unknowns in the visual framework. In this paper we present and compare two different approaches to estimate the unknown scale parameter in a monocular SLAM framework. Directly linked to the scale is the estimation of the object’s absolute velocity and position in 3D. The first approach is a spline fitting task adapted from Jung and Taylor and the second is an extended Kalman filter. Both methods have been simulated offline on arbitrary camera paths to analyze their behavior and the quality of the resulting scale estimation. We then embedded an online multi rate extended Kalman filter in the Parallel Tracking and Mapping (PTAM) algorithm of Klein and Murray together with an inertial sensor. In this inertial / monocular SLAM framework, we show a real time, robust and fast converging scale estimation. Our approach does not depend on known patterns in the vision part nor a complex temporal synchronization between the visual and inertial sensor. The research leading to these results has received funding from the European Community’s Seventh Framework Programme (FP7/2007-2013) under grant agreement n. 231855 (sFly). Gabriel Nützi is currently a Master student at the ETH Zurich. Stephan Weiss is currently PhD student at the ETH Zurich. Davide Scaramuzza is currently senior researcher and team leader at the ETH Zurich. Roland Siegwart is full professor at the ETH Zurich and head of the Autonomous Systems Lab. G. Nützi · S. Weiss · D. Scaramuzza · R. Siegwart ETH Autonomous Systems Laboratory, 8092, Zurich, Switzerland, www.asl.ethz.ch E-mail: nuetzi@student.ethz.ch, stephan.weiss@mavt.ethz.ch, davide.scaramuzza@ieee.org, rsiegwart@ethz.ch 2

Arm pose copying for humanoid robots <=SEP=> Learning by imitation is becoming increasingly important for teaching humanoid robots new skills. The simplest form of imitation is behavior copying in which the robot is minimizing the difference between its perceived motion and that of the imitated agent. One problem that must be solved even in this simplest of all imitation tasks is calculating the learner's pose corresponding to the perceived pose of the agent it is imitating. This paper presents a general framework for solving this problem in closed form for the arms of a generalized humanoid robot of which most available humanoids are special cases. The paper also reports the evaluation of the proposed system for real and simulated robots.

On Realistically Attacking Tor with Website Fingerprinting <=SEP=> Website fingerprinting allows a local, passive observer monitoring a web-browsing client’s encrypted channel to determine her web activity. Previous attacks have shown that website fingerprinting could be a threat to anonymity networks such as Tor under laboratory conditions. However, there are significant differences between laboratory conditions and realistic conditions. First, the training data set is very similar to the testing data set under laboratory conditions, but the attacker may not be able to guarantee similarity realistically. Second, laboratory packet sequences correspond to a single page each, but for realistic packet sequences the split between pages is not obvious. Third, packet sequences may include noise, which may adversely affect website fingerprinting, but this effect has not been studied. In this paper, we tackle these three problems to bridge the gap between laboratory and realistic conditions for website fingerprinting. We show that we can maintain a fresh training set with minimal resources. We demonstrate several classification-based techniques that allow us to split full packet sequences effectively into sequences corresponding to a single page each. Although we were not able to remove noise effectively, we will show that it is difficult for users to generate sufficient background noise to disrupt website fingerprinting on Tor. With our techniques, we are able to build the first website fingerprinting system that can operate on packet sequences collected in the wild.

Low Precision Policy Distillation with Application to Low-Power, Real-time Sensation-Cognition-Action Loop with Neuromorphic Computing <=SEP=> Low precision networks in the reinforcement learning (RL) setting are relatively unexplored because of the limitations of binary activations for function approximation. Here, in the discrete action ATARI domain, we demonstrate, for the first time, that low precision policy distillation from a high precision network provides a principled, practical way to train an RL agent. As an application, on 10 different ATARI games, we demonstrate real-time end-to-end game playing on lowpower neuromorphic hardware by converting a sequence of game frames into discrete actions.

Embodied Cognition is Not What you Think it is <=SEP=> The most exciting hypothesis in cognitive science right now is the theory that cognition is embodied. Like all good ideas in cognitive science, however, embodiment immediately came to mean six different things. The most common definitions involve the straight-forward claim that "states of the body modify states of the mind." However, the implications of embodiment are actually much more radical than this. If cognition can span the brain, body, and the environment, then the "states of mind" of disembodied cognitive science won't exist to be modified. Cognition will instead be an extended system assembled from a broad array of resources. Taking embodiment seriously therefore requires both new methods and theory. Here we outline four key steps that research programs should follow in order to fully engage with the implications of embodiment. The first step is to conduct a task analysis, which characterizes from a first person perspective the specific task that a perceiving-acting cognitive agent is faced with. The second step is to identify the task-relevant resources the agent has access to in order to solve the task. These resources can span brain, body, and environment. The third step is to identify how the agent can assemble these resources into a system capable of solving the problem at hand. The last step is to test the agent's performance to confirm that agent is actually using the solution identified in step 3. We explore these steps in more detail with reference to two useful examples (the outfielder problem and the A-not-B error), and introduce how to apply this analysis to the thorny question of language use. Embodied cognition is more than we think it is, and we have the tools we need to realize its full potential.

Approximation Theory of the Mlp Model in Neural Networks <=SEP=> In this survey we discuss various approximation-theoretic problems that arise in the multilayer feedforward perceptron (MLP) model in neural networks. The MLP model is one of the more popular and practical of the many neural network models. Mathematically it is also one of the simpler models. Nonetheless the mathematics of this model is not well understood, and many of these problems are approximation-theoretic in character. Most of the research we will discuss is of very recent vintage. We will report on what has been done and on various unanswered questions. We will not be presenting practical (algorithmic) methods. We will, however, be exploring the capabilities and limitations of this model. In the rst two sections we present a brief introduction and overview of neural networks and the multilayer feedforward perceptron model. In Section 3 we discuss in great detail the question of density. When does this model have the theoretical ability to approximate any reasonable function arbritrarily well? In Section 4 we present conditions for simultaneously approximating a function and its derivatives. Section 5 considers the interpolation capability of this model. In Section 6 we study upper and lower bounds on the order of approximation of this model. The material presented in Sections 3{6 treats the single hidden layer MLP model. In Section 7 we discuss some of the diierences that arise when considering more than one hidden layer. The lengthy list of references includes many papers not cited in the text, but relevant to the subject matter of this survey.

aBCD18 - An advanced 0.18um BCD technology for PMIC application <=SEP=> We present a new advanced 0.18um BCD(Bipolar-CMOS-DMOS) technology with the key features being a 40V HV-MOS and an SSTC(Sidewall Selective Transistor Cell) type EEPROM as well as complimentary available analog devices such as a high gain BJT, 4fF/um2 MIM capacitor, and 10k Ω/sq. poly resistor. To reduce device area and enhance latch up immunity, a 15um depth deep trench isolation process has been developed, which will help to significantly reduce the chip size.

Comparison of Sparse Representation and Fourier Discriminant Methods : Damage Location Classification in Indirect Lab-scale Bridge Structural Health Monitoring <=SEP=> This paper presents a novel method for interpreting data to improve the indirect structural health monitoring (SHM) of bridges. The research presented in the study is part of an ongoing study aimed at developing a novel SHM paradigm for the health assessment of bridges. In this paradigm, we envision the use of an instrumented vehicle that assesses a bridge’s dynamic characteristics while traveling across the bridge. These characteristics are then correlated to the health of the structure by means of advanced signal processing and pattern recognition approaches. In this paper, we present and compare two classification algorithms that locate the presence of damages at well-defined locations on the structure: sparse representation and the Fourier discriminant methods, and find that the sparse representation method provides superior classification accuracy.

Digital Literacy: A Conceptual Framework for Survival Skills in the Digital Era <=SEP=> Digital literacy involves more than the mere ability to use software or operate a digital device; it includes a large variety of complex cognitive, motor, sociological, and emotional skills, which users need in order to function effectively in digital environments. The tasks required in this context include, for example, “reading” instructions from graphical displays in user interfaces; using digital reproduction to create new, meaningful materials from existing ones; constructing knowledge from a nonlinear, hypertextual navigation; evaluating the quality and validity of information; and have a mature and realistic understanding of the “rules” that prevail in the cyberspace. This newly emerging concept of digital literacy may be used as a measure of the quality of learners’ work in digital environments, and provide scholars and developers with a more effective means of communication in designing better user-oriented environments. This article proposes a holistic, refined conceptual framework for digital literacy, which includes photo-visual literacy; reproduction literacy; branching literacy; information literacy; and socioemotional literacy.

Three control flow obfuscation methods for Java software <=SEP=> Three novel control computation (control flow) obfuscation methods are described for protecting Java class files. They are basic block fission obfuscation, intersecting loop obfuscation and replacing goto obfuscation. The basic block fission obfuscation splits some chosen basic block(s) into more basic blocks, in which opaque predicates and goto instructions are inserted to make decompiling unsuccessful. The intersecting loop obfuscation intersects two loops and then adds this pattern into programs. The intersecting loop structure should not appear in a Java source program or a class file. The replacing goto obfuscation replaces goto instructions with conditional branch instructions. The newmethods were tested against 16 decompilers. The study also implemented multi-level exit obfuscation and single-level exit obfuscation for comparison. Both the intersecting loop obfuscation and the replacing goto obfuscation successfully defeated all the decompilers.

Increasing Brand Attractiveness and Sales through Social Media Comments on Public Displays - Evidence from a Field Experiment in the Retail Industry <=SEP=> Retailers and brands are just starting to utilize online social media to support their businesses. Simultaneously, public displays are becoming ubiquitous in public places, raising the question about how these two technologies could be used together to attract new and existing customers as well as strengthen the relationship toward a focal brand. Accordingly, in a field experiment we displayed brandand product-related comments from the social network Facebook as pervasive advertising in small-space retail stores, known as kiosks. From interviews conducted with real customers during the experiment and the corresponding analysis of sales data we could conclude three findings. Showing social media comments resulted in (1) customers perceiving brands as more innovative and attractive, (2) a measurable, positive effect on sales on both the brand and the product in question and (3) customers wanting to see the comments of others, but not their own, creating a give-andtake paradox for using public displays to show social media comments.

Proposal Manipulation of 3 D Objects in Immersive Virtual Environments <=SEP=> Interactions within virtual environments (VE) often require manipulation of 3D virtual objects. For this purpose, several research have been carried out focusing on mouse, touch and gestural input. While existing mid-air gestures in immersive VE (IVE) can offer natural manipulations, mimicking interactions with physical objects, they cannot achieve the same levels of precision attained with mouse-based applications for computer-aided design (CAD). Following prevailing trends in previous research, such as degrees-offreedom (DOF) separation, virtual widgets and scaled user motion, we intend to explore techniques for IVEs that combine the naturalness of mid-air gestures and the precision found in traditional CAD systems. In this document we survey and discuss the state-of-the-art in 3D object manipulation. With challenges identified, we present a research proposal and corresponding work plan.

Invariant Visual Object and Face Recognition: Neural and Computational Bases, and a Model, VisNet <=SEP=> Neurophysiological evidence for invariant representations of objects and faces in the primate inferior temporal visual cortex is described. Then a computational approach to how invariant representations are formed in the brain is described that builds on the neurophysiology. A feature hierarchy model in which invariant representations can be built by self-organizing learning based on the temporal and spatial statistics of the visual input produced by objects as they transform in the world is described. VisNet can use temporal continuity in an associative synaptic learning rule with a short-term memory trace, and/or it can use spatial continuity in continuous spatial transformation learning which does not require a temporal trace. The model of visual processing in the ventral cortical stream can build representations of objects that are invariant with respect to translation, view, size, and also lighting. The model has been extended to provide an account of invariant representations in the dorsal visual system of the global motion produced by objects such as looming, rotation, and object-based movement. The model has been extended to incorporate top-down feedback connections to model the control of attention by biased competition in, for example, spatial and object search tasks. The approach has also been extended to account for how the visual system can select single objects in complex visual scenes, and how multiple objects can be represented in a scene. The approach has also been extended to provide, with an additional layer, for the development of representations of spatial scenes of the type found in the hippocampus.

Nerve entrapment syndromes as a cause of pain in the hip, groin and buttock. <=SEP=> In sports medicine, chronic hip, groin and buttock pain is a common diagnostic problem. Because of the complex anatomy of this region and the many potential neurological causes for pain, few sports clinicians have a detailed understanding of this problem. This paper discusses the clinical aspects of nerve entrapment syndromes related to sport and takes a regional approach in order to provide a diagnostic framework for the general sports physician. The various neurological syndromes are discussed and the surgical management elaborated in detail. For some specific conditions, such as the so-called 'piriformis syndrome', the pathophysiological understanding has changed since the early descriptions and now this particular diagnosis is often ascribed to almost any cause of buttock and/or hamstring symptoms. We discuss the nature of the origin of local symptoms and note that the often described symptoms are more likely due to compression of structures other then the sciatic nerve. Furthermore, the role of piriformis hypertrophy or anatomical nerve variations in the genesis of this syndrome must be questioned. We suggest renaming this the 'deep gluteal syndrome' to account for all of the observed phenomena. As sports medicine continues to develop a scientific basis, the role of nerve entrapments as the basis for chronic symptomatology is undergoing a new understanding and clinicians need to be aware of the diagnostic possibilities and be able to advise patients accordingly on the basis of scientific fact not anecdotal fiction.

On the use of words and n-grams for Chinese information retrieval <=SEP=> In the processing of Chinese documents and queries in information retrieval (IR), one has to identify the units that are used as indexes. Words and n-grams have been used as indexes in several previous studies, which showed that both kinds of indexes lead to comparable IR performances. In this study, we carry out more experiments on different ways to segment documents and queries, and to combine words with n-grams. Our experiments show that a combination of the longest-matching algorithm with single characters is the best choice.

Detecting topic evolution in scientific literature: how can citations help? <=SEP=> Understanding how topics in scientific literature evolve is an interesting and important problem. Previous work simply models each paper as a bag of words and also considers the impact of authors. However, the impact of one document on another as captured by citations, one important inherent element in scientific literature, has not been considered. In this paper, we address the problem of understanding topic evolution by leveraging citations, and develop citation-aware approaches. We propose an iterative topic evolution learning framework by adapting the Latent Dirichlet Allocation model to the citation network and develop a novel inheritance topic model. We evaluate the effectiveness and efficiency of our approaches and compare with the state of the art approaches on a large collection of more than 650,000 research papers in the last 16 years and the citation network enabled by CiteSeerX. The results clearly show that citations can help to understand topic evolution better.