Query: Parking space detection from a radar based target list

Relevant passages:
Multi-sensor multi-object tracking of vehicles using high-resolution radars <=SEP=> Recent advances in automotive radar technology have led to increasing sensor resolution and hence a more detailed image of the environment with multiple measurements per object. This poses several challenges for tracking systems: new algorithms are necessary to fully exploit the additional information and algorithms need to resolve measurement-to-object association ambiguities in cluttered multi-object scenarios. Also, the information has to be fused if multi-sensor setups are used to obtain redundancy and increased fields of view. In this paper, a Labeled Multi-Bernoulli filter for tracking multiple vehicles using multiple high-resolution radars is presented. This finite-set-statistics-based filter tackles all three challenges in a fully probabilistic fashion and is the first Monte Carlo implementation of its kind. The filter performance is evaluated using radar data from an experimental vehicle.

Efficient occupancy grid computation on the GPU with lidar and radar for road boundary detection <=SEP=> Accurate maps of the static environment are essential for many advanced driver-assistance systems. A new method for the fast computation of occupancy grid maps with laser range-finders and radar sensors is proposed. The approach utilizes the Graphics Processing Unit to overcome the limitations of classical occupancy grid computation in automotive environments. It is possible to generate highly accurate grid maps in just a few milliseconds without the loss of sensor precision. Moreover, in the case of a lower resolution radar sensor it is shown that it is suitable to apply super-resolution algorithms to achieve the accuracy of a higher resolution laser-scanner. Finally, a novel histogram based approach for road boundary detection with lidar and radar sensors is presented.

High resolution maps from wide angle sonar <=SEP=> 

Detection of parked vehicles from a radar based occupancy grid <=SEP=> For autonomous parking applications to become possible, knowledge about the parking environment is required. Therefore, a real-time algorithm for detecting parked vehicles from radar data is presented. These data are first accumulated in an occupancy grid from which objects are detected by applying techniques borrowed from the computer vision field. Two random forest classifiers are trained to recognize two categories of objects: parallel-parked vehicles and cross-parked vehicles. Performances of the classifiers are evaluated as well as the capacity of the complete system to detect parked vehicles in real world scenarios.

Instantaneous lateral velocity estimation of a vehicle using Doppler radar <=SEP=> High-resolution image radars open new opportunities for estimating velocity and direction of movement of extended objects from a single observation. Since radar sensors only measure the radial velocity, a tracking system is normally used to determine the velocity vector of the object. A stable velocity is estimated after several frames at the earliest, resulting in a significant loss of time for reacting to certain situations such as cross-traffic. The following paper presents a robust and model-free approach to determine the velocity vector of an extended target. In contrast to the Kalman filter, it does not require data association in time and space. An instant (~ 50 ms) and bias free estimation of its velocity vector is possible. Our approach can handle noise and systematic variations (e.g., micro-Doppler of wheels) in the signal. It is optimized to deal with measurement errors of the radar sensor not only in the radial velocity, but in the azimuth position as well. The accuracy of this method is increased by the fusion of multiple radar sensors.

Food, livestock production, energy, climate change, and health <=SEP=> Food provides energy and nutrients, but its acquisition requires energy expenditure. In post-hunter-gatherer societies, extra-somatic energy has greatly expanded and intensified the catching, gathering, and production of food. Modern relations between energy, food, and health are very complex, raising serious, high-level policy challenges. Together with persistent widespread under-nutrition, over-nutrition (and sedentarism) is causing obesity and associated serious health consequences. Worldwide, agricultural activity, especially livestock production, accounts for about a fifth of total greenhouse-gas emissions, thus contributing to climate change and its adverse health consequences, including the threat to food yields in many regions. Particular policy attention should be paid to the health risks posed by the rapid worldwide growth in meat consumption, both by exacerbating climate change and by directly contributing to certain diseases. To prevent increased greenhouse-gas emissions from this production sector, both the average worldwide consumption level of animal products and the intensity of emissions from livestock production must be reduced. An international contraction and convergence strategy offers a feasible route to such a goal. The current global average meat consumption is 100 g per person per day, with about a ten-fold variation between high-consuming and low-consuming populations. 90 g per day is proposed as a working global target, shared more evenly, with not more than 50 g per day coming from red meat from ruminants (ie, cattle, sheep, goats, and other digastric grazers).

Message in a Sealed Bottle: Privacy Preserving Friending in Social Networks <=SEP=> Many proximity-based mobile social networks are developed to facilitate connections between any two people, or to help a user to find people with a matched profile within a certain distance. A challenging task in these applications is to protect the privacy of the participants' profiles and personal interests. In this paper, we design novel mechanisms, when given a preference-profile submitted by a user, that search persons with matching-profile in decentralized multi-hop mobile social networks. Our mechanisms also establish a secure communication channel between the initiator and matching users at the time when the matching user is found. Our rigorous analysis shows that our mechanism is privacy-preserving (no participants' profile and the submitted preference-profile are exposed), verifiable (both the initiator and the unmatched user cannot cheat each other to pretend to be matched), and efficient in both communication and computation. Extensive evaluations using real social network data, and actual system implementation on smart phones show that our mechanisms are significantly more efficient than existing solutions.

A direct scattering model for tracking vehicles with high-resolution radars <=SEP=> In advanced driver assistance systems and autonomous driving, reliable environment perception and object tracking based on radar is fundamental. High-resolution radar sensors often provide multiple measurements per object. Since in this case traditional point tracking algorithms are not applicable any more, novel approaches for extended object tracking emerged in the last few years. However, they are primarily designed for lidar applications or omit the additional Doppler information of radars. Classical radar based tracking methods using the Doppler information are mostly designed for point tracking of parallel traffic. The measurement model presented in this paper is developed to track vehicles of approximately rectangular shape in arbitrary traffic scenarios including parallel and cross traffic. In addition to the kinematic state, it allows to determine and track the geometric state of the object. Using the Doppler information is an important component in the model. Furthermore, it neither requires measurement preprocessing, data clustering, nor explicit data association. For object tracking, a Rao-Blackwellized particle filter (RBPF) adapted to the measurement model is presented.

LIBOL: a library for online learning algorithms <=SEP=> LIBOL is an open-source library for large-scale online learning, which consists of a large family of efficient and scalable state-of-the-art online learning algorithms for large-scale online classification tasks. We have offered easy-to-use command-line tools and examples for users and developers, and also have made comprehensive documents available for both beginners and advanced users. LIBOL is not only a machine learning toolbox, but also a comprehensive experimental platform for conducting online learning research.

Basic Concepts of Lexical Resource Semantics <=SEP=> Semanticists use a range of highly expressive logical langu ges to characterize the meaning of natural language expressions. The logical languages are usually taken from an i nventory of standard mathematical systems, with which generative linguists are familiar. They are, thus, easily a ccessible beyond the borders of a given framework such as Categorial Grammar, Lexical Functional Grammar, or Govern me t and Binding Theory. Linguists working in the HPSG framework, on the other hand, often use rather idiosync ratic and specialized semantic representations. Their choice is sometimes motivated by computational applicatio ns n parsing, generation, or machine translation. Natural ly, the intended areas of application influence the design of sem antic representations. A typical property of semantic representations in HPSG that is concerned with computational a pplications is underspecification, and other properties come from the particular unification or constraint solving a l orithms that are used for processing grammars. While the resulting semantic representations have properties th at are motivated by, and are adequate for, certain practical applications, their relationship to standard languages is sometimes left on an intuitive level. In addition, the theor etical and ontological status of the semantic representations is o ften neglected. This vagueness tends to be unsatisfying to many semanticists, and the idiosyncratic shape of the seman tic representations confines their usage to HPSG. Since their entire architecture is highly dependent on HPSG, hard ly anyone working outside of that framework is interested in studying them. With our work on Lexical Resource Semantics (LRS), we want to contribute to the investigation of a number of important theoretical issues surrounding semantic repres ntations and possible ways of underspecification. While LR S is formulated in a constraint-based grammar environment an d t kes advantage of the tight connection between syntax proper and logical representations that can easily be achie ved in HPSG, the architecture of LRS remains independent from that framework, and combines attractive properties of various semantic systems. We will explore the types of semantic frameworks which can be specified in Relational Spe ciate Re-entrant Language (RSRL), the formalism that we choose to express our grammar principles, and we evaluate the semantic frameworks with respect to their potential for providing empirically satisfactory analyses of typica l problems in the semantics of natural languages. In LRS, we want to synthesize a flexible meta-theory that can be applied to different interesting semantic representation languag es and make computing with them feasible. We will start our investigation with a standard semantic rep resentation language from natural language semantics, Ty2 (Gallin, 1975). We are well aware of the debate about the a ppropriateness of Montagovian-style intensionality for the analysis of natural language semantics, but we belie ve that it is best to start with a semantic representation tha t most generative linguists are familiar with. As will become cl ar in the course of our discussion, the LRS framework is a meta-theory of semantic representations, and we believ e that it is suitable for various representation languages, This paper can be regarded as a snapshot of our work on LRS. It w as written as material for the authors’ course Constraint-based Combinatorial Semanticsat the 15th European Summer School in Logic, Language and Inf ormation in Vienna in August 2003. It is meant as background r eading and as a basis of discussion for our class. Its air of a work in progressis deliberate. As we see continued development in LRS, its ap plication to a wider range of languages and empirical phenomena, and espec ially the implementation of an LRS module as a component of th e TRALE grammar development environment; we expect further modifications a d refinements to the theory. The implementation of LRS is rea liz d in collaboration with Gerald Penn of the University of Toronto. We would like t o thank Carmella Payne for proofreading various versions of this paper.

A Framework for Dynamic Image Sampling Based on Supervised Learning <=SEP=> Sparse sampling schemes can broadly be classified into two main categories: static sampling where the sampling pattern is predetermined, and dynamic sampling where each new measurement location is selected based on information obtained from previous measurements. Dynamic sampling methods are particularly appropriate for pointwise imaging methods, in which pixels are measured sequentially in arbitrary order. Examples of pointwise imaging schemes include certain implementations of atomic force microscopy, electron back scatter diffraction, and synchrotron X-ray imaging. In these pointwise imaging applications, dynamic sparse sampling methods have the potential to dramatically reduce the number of measurements required to achieve a desired level of fidelity. However, the existing dynamic sampling methods tend to be computationally expensive and are, therefore, too slow for many practical applications. In this paper, we present a framework for dynamic sampling based on machine learning techniques, which we call a supervised learning approach for dynamic sampling (SLADS). In each step of SLADS, the objective is to find the pixel that maximizes the expected reduction in distortion (ERD) given previous measurements. SLADS is fast because we use a simple regression function to compute the ERD, and it is accurate because the regression function is trained using datasets that are representative of the specific application. In addition, we introduce an approximate method to terminate dynamic sampling at a desired level of distortion. We then extend our algorithm to incorporate multiple measurements at each step, which we call groupwise SLADS. Finally, we present results on computationally generated synthetic data and experimentally collected data to demonstrate a dramatic improvement over state-of-the-art static sampling methods

Design of a scalable InfiniBand topology service to enable network-topology-aware placement of processes <=SEP=> Over the last decade, InfiniBand has become an increasingly popular interconnect for deploying modern super-computing systems. However, there exists no detection service that can discover the underlying network topology in a scalable manner and expose this information to runtime libraries and users of the high performance computing systems in a convenient way. In this paper, we design a novel and scalable method to detect the InfiniBand network topology by using Neighbor-Joining techniques (NJ). To the best of our knowledge, this is the first instance where the neighbor joining algorithm has been applied to solve the problem of detecting InfiniBand network topology. We also design a network-topology-aware MPI library that takes advantage of the network topology service. The library places processes taking part in the MPI job in a network-topology-aware manner with the dual aim of increasing intra-node communication and reducing the long distance inter-node communication across the InfiniBand fabric.

Techniques to Detect Spammers in Twitter- A Survey <=SEP=> With the rapid growth of social networking sites for communicating, sharing, storing and managing significant information, it is attracting cybercriminals who misuse the Web to exploit vulnerabilities for their illicit benefits. Forged online accounts crack up every day. Impersonators, phishers, scammers and spammers crop up all the time in Online Social Networks (OSNs), and are harder to identify. Spammers are the users who send unsolicited messages to a large audience with the intention of advertising some product or to lure victims to click on malicious links or infecting user&apos;s system just for the purpose of making money. A lot of research has been done to detect spam profiles in OSNs. In this paper we have reviewed the existing techniques for detecting spam users in Twitter social network. Features for the detection of spammers could be user based or content based or both. Current study provides an overview of the methods, features used, detection rate and their limitations (if any) for detecting spam profiles mainly in Twitter.

Which Hotel attributes Matter ? A review of previous and a framework for future research <=SEP=> A lot of effort has been made in the last decades to reveal, which hotel attributes guest care about. Due to the high costs that are typically involved with investments in the hotel industry, it makes a lot of sense to study, which product components the travellers appreciate. This study reveals that hotel attribute research turns out to be a wide and extremely heterogeneous field of research. The authors review empirical studies investigating the importance of hotel attributes, provide attribute rankings and suggest a framework for past and future research projects in the field, based on the dimensions “focus of research”, ”risk versus utility” and “trade-off versus no trade-off questioning situation”.

A New Similarity Measure to Understand Visitor Behavior in a Web Site <=SEP=> The behavior of visitors browsing in a web site offers a lot of information about their requirements and the way they use the respective site. Analyzing such behavior can provide the necessary information in order to improve the web site’s structure. The literature contains already several suggestions on how to characterize web site usage and to identify the respective visitor requirements based on clustering of visitor sessions. Here we propose to combine visitor behavior with the content of the respective web pages and the similarity between different page sequences in order to define a similarity measure between different visits. This similarity serves as input for clustering of visitor sessions. The application of our approach to a bank’s web site and its visitor sessions shows its potential for internet-based businesses. key words: web mining, browsing behavior, similarity measure, clustering.

Variational Inference with Normalizing Flows <=SEP=> The choice of approximate posterior distribution is one of the core problems in variational inference. Most applications of variational inference employ simple families of posterior approximations in order to allow for efficient inference, focusing on mean-field or other simple structured approximations. This restriction has a significant impact on the quality of inferences made using variational methods. We introduce a new approach for specifying flexible, arbitrarily complex and scalable approximate posterior distributions. Our approximations are distributions constructed through a normalizing flow, whereby a simple initial density is transformed into a more complex one by applying a sequence of invertible transformations until a desired level of complexity is attained. We use this view of normalizing flows to develop categories of finite and infinitesimal flows and provide a unified view of approaches for constructing rich posterior approximations. We demonstrate that the theoretical advantages of having posteriors that better match the true posterior, combined with the scalability of amortized variational approaches, provides a clear improvement in performance and applicability of variational inference.

Testing Django Configurations Using Combinatorial Interaction Testing <=SEP=> Combinatorial Interaction Testing (CIT) is important because it tests the interactions between the many parameters that make up the configuration space of software systems. We apply this testing paradigm to a Python-based framework for rapid development of webbased applications called Django. In particular, we automatically create a CIT model for Django website configurations and run a state-of-the-art tool for CIT test suite generation to obtain sets of test configurations. Our automatic CIT-based approach is able to efficiently detect invalid configurations.

A Top-Down Compiler for Sentential Decision Diagrams <=SEP=> The sentential decision diagram (SDD) has been recently proposed as a new tractable representation of Boolean functions that generalizes the influential ordered binary decision diagram (OBDD). Empirically, compiling CNFs into SDDs has yielded significant improvements in both time and space over compiling them into OBDDs, using a bottomup compilation approach. In this work, we present a top-down CNF to SDD compiler that is based on techniques from the SAT literature. We compare the presented compiler empirically to the state-ofthe-art, bottom-up SDD compiler, showing ordersof-magnitude improvements in compilation time.

Collaboration- and Fairness-Aware Big Data Management in Distributed Clouds <=SEP=> With the advancement of information and communication technology, data are being generated at an exponential rate via various instruments and collected at an unprecedented scale. Such large volume of data generated is referred to as big data, which now are revolutionizing all aspects of our life ranging from enterprises to individuals, from science communities to governments, as they exhibit great potentials to improve efficiency of enterprises and the quality of life. To obtain nontrivial patterns and derive valuable information from big data, a fundamental problem is how to properly place the collected data by different users to distributed clouds and to efficiently analyze the collected data to save user costs in data storage and processing, particularly the cost savings of users who share data. By doing so, it needs the close collaborations among the users, by sharing and utilizing the big data in distributed clouds due to the complexity and volume of big data. Since computing, storage and bandwidth resources in a distributed cloud usually are limited, and such resource provisioning typically is expensive, the collaborative users require to make use of the resources fairly. In this paper, we study a novel collaboration- and fairness-aware big data management problem in distributed cloud environments that aims to maximize the system throughout, while minimizing the operational cost of service providers to achieve the system throughput, subject to resource capacity and user fairness constraints. We first propose a novel optimization framework for the problem. We then devise a fast yet scalable approximation algorithm based on the built optimization framework. We also analyze the time complexity and approximation ratio of the proposed algorithm. We finally conduct experiments by simulations to evaluate the performance of the proposed algorithm. Experimental results demonstrate that the proposed algorithm is promising, and outperforms other heuristics.

High-Q 3D RF solenoid inductors in glass <=SEP=> In this paper, we demonstrate the fabrication and characterization of various 3D solenoid inductors using a glass core substrate. Solenoid inductors were fabricated in glass by drilling through holes in glass and semi-additive copper plating for metallization. This topology is compared to similar solenoid structures in terms of Q-factor performance and inductance density. Inductances of 1.8-4.5nH with Q ~ 60 at 1GHz were demonstrated.

Amortized Inference in Probabilistic Reasoning <=SEP=> Recent studies of probabilistic reasoning have postulated general-purpose inference algorithms that can be used to answer arbitrary queries. These algorithms are memoryless, in the sense that each query is processed independently, without reuse of earlier computation. We argue that the brain operates in the setting of amortized inference, where numerous related queries must be answered (e.g., recognizing a scene from multiple viewpoints); in this setting, memoryless algorithms can be computationally wasteful. We propose a simple form of flexible reuse, according to which shared inferences are cached and composed together to answer new queries. We present experimental evidence that humans exploit this form of reuse: the answer to a complex query can be systematically predicted from a person’s response to a simpler query if the simpler query was presented first and entails a sub-inference (i.e., a sub-component of the more complex query). People are also faster at answering a complex query when it is preceded by a sub-inference. Our results suggest that the astonishing efficiency of human probabilistic reasoning may be supported by interactions between inference and memory.

Efficient Phrase-Based Document Similarity for Clustering <=SEP=> In this paper, we propose a phrase-based document similarity to compute the pair-wise similarities of documents based on the suffix tree document (STD) model. By mapping each node in the suffix tree of STD model into a unique feature term in the vector space document (VSD) model, the phrase-based document similarity naturally inherits the term tf-idf weighting scheme in computing the document similarity with phrases. We apply the phrase-based document similarity to the group-average Hierarchical Agglomerative Clustering (HAC) algorithm and develop a new document clustering approach. Our evaluation experiments indicate that, the new clustering approach is very effective on clustering the documents of two standard document benchmark corpora OHSUMED and RCV1. The quality of the clustering results significantly surpass the results of traditional single-word \textit{tf-idf} similarity measure in the same HAC algorithm, especially in large document data sets. Furthermore, by studying the property of STD model, we conclude that the feature vector of phrase terms in the STD model can be considered as an expanded feature vector of the traditional single-word terms in the VSD model. This conclusion sufficiently explains why the phrase-based document similarity works much better than the single-word tf-idf similarity measure.

An Edge Detection Algorithm for Online Image Analysis <=SEP=> Online image analysis is used in a wide variety of applications. Edge detection is a fundamental tool used to obtain features of objects as a prerequisite step to object segmentation. This paper presents a simple and relatively fast online edge detection algorithm based on second derivative. The proposed edge detector is less sensitive to noise and may be applied on color, gray and binary images without preprocessing requirements. The merits of the algorithm are demonstrated by comparison with Canny’s and Sobel’s edge detectors.

HermitCore: A Unikernel for Extreme Scale Computing <=SEP=> We expect that the size and the complexity of future supercomputers will increase on their path to exascale systems and beyond. Therefore, system software has to adapt to the complexity of these systems for a simplification of the development of scalable applications. In this paper, we present a unikernel operating system design for HPC. It extends the multi-kernel approach while providing better programmability and scalability for hierarchical systems, such as HLRS' Hazel Hen, which base on multiple cluster-on-a-chip processors. We prove the scalability of the design via micro benchmarks by taking the example of HermitCore---our prototype implementation of the new design.

Sugar: Secure GPU Acceleration in Web Browsers <=SEP=> Modern personal computers have embraced increasingly powerful Graphics Processing Units (GPUs). Recently, GPU-based graphics acceleration in web apps (i.e., applications running inside a web browser) has become popular. WebGL is the main effort to provide OpenGL-like graphics for web apps and it is currently used in 53% of the top-100 websites. Unfortunately, WebGL has posed serious security concerns as several attack vectors have been demonstrated through WebGL. Web browsers» solutions to these attacks have been reactive: discovered vulnerabilities have been patched and new runtime security checks have been added. Unfortunately, this approach leaves the system vulnerable to zero-day vulnerability exploits, especially given the large size of the Trusted Computing Base of the graphics plane. We present Sugar, a novel operating system solution that enhances the security of GPU acceleration for web apps by design. The key idea behind Sugar is using a dedicated virtual graphics plane for a web app by leveraging modern GPU virtualization solutions. A virtual graphics plane consists of a dedicated virtual GPU (or vGPU) as well as all the software graphics stack (including the device driver). Sugar enhances the system security since a virtual graphics plane is fully isolated from the rest of the system. Despite GPU virtualization overhead, we show that Sugar achieves high performance. Moreover, unlike current systems, Sugar is able to use two underlying physical GPUs, when available, to co-render the User Interface (UI): one GPU is used to provide virtual graphics planes for web apps and the other to provide the primary graphics plane for the rest of the system. Such a design not only provides strong security guarantees, it also provides enhanced performance isolation.

3D-stackable crossbar resistive memory based on Field Assisted Superlinear Threshold (FAST) selector <=SEP=> We report the integration of 3D-stackable 1S1R passive crossbar RRAM arrays utilizing a Field Assisted Superlinear Threshold (FAST) selector. The sneak path issue in crossbar memory integration has been solved using the highest reported selectivity of 1010. Excellent selector performance is presented such as extremely sharp switching slope of <; 5mV/dec., selectivity of 1010, sub-50ns operations, > 100M endurance and processing temperature less than 300°C. Measurements on the 4Mb 1S1R crossbar array show that the sneak current is suppressed below 0.1nA, while maintaining 102 memory on/off ratio and > 106 selectivity during cycling, enabling high density memory applications.

Towards detecting fake user accounts in facebook <=SEP=> People are highly dependent on online social networks (OSNs) which have attracted the interest of cyber criminals for carrying out a number of malicious activities. An entire industry of black-market services has emerged which offers fake accounts based services for sale. We, therefore, in our work, focus on detecting fake accounts on a very popular (and difficult for data collection) online social network, Facebook. Key contributions of our work are as follows. The first contribution has been collection of data related to real and fake accounts on Facebook. Due to strict privacy settings and ever evolving API of Facebook with each version adding more restrictions, collecting user accounts data became a major challenge. Our second contribution is the use of user-feed information on Facebook to understand user profile activity and identifying an extensive set of 17 features which play a key role in discriminating fake users on Facebook with real users. Third contribution is the use these features and identifying the key machine learning based classifiers who perform well in detection task out of a total of 12 classifiers employed. Fourth contribution is the identifying which type of activities (like, comment, tagging, sharing, etc) contribute the most in fake user detection. Results exhibit classification accuracy of 79% among the best performing classifiers. In terms of activities, likes and comments contribute well towards detection task. Although the accuracy is not very high, however, our work forms a baseline for further improvement. Our results indicate that many fake users are classified as real suggesting clearly that fake accounts are mimicking real user behavior to evade detection mechanisms. Our work concludes by enlisting a number of future course of actions that can be undertaken.

Telemanipulation with force-based display of proximity fields <=SEP=> In this paper we show and evaluate the design of a novel telemanipulation system that maps proximity values, acquired inside of a gripper, to forces a user can feel through a haptic input device. The command console is complemented by input-devices that give the user an intuitive control over parameters relevant to the system. Furthermore, proximity sensors enable the autonomous alignment/centering of the gripper to objects in user-selected DoFs with the potential of aiding the user and lowering the workload. We evaluate our approach in a user study that shows that the telemanipulation system benefits from the supplementary proximity information and that the workload can indeed be reduced when the system operates with partial autonomy.

Learning to Decode Cognitive States from Brain Images <=SEP=> Over the past decade, functional Magnetic Resonance Imaging (fMRI) has emerged as a powerful new instrument to collect vast quantities of data about activity in the human brain. A typical fMRI experiment can produce a three-dimensional image related to the human subject's brain activity every half second, at a spatial resolution of a few millimeters. As in other modern empirical sciences, this new instrumentation has led to a flood of new data, and a corresponding need for new data analysis methods. We describe recent research applying machine learning methods to the problem of classifying the cognitive state of a human subject based on fRMI data observed over a single time interval. In particular, we present case studies in which we have successfully trained classifiers to distinguish cognitive states such as (1) whether the human subject is looking at a picture or a sentence, (2) whether the subject is reading an ambiguous or non-ambiguous sentence, and (3) whether the word the subject is viewing is a word describing food, people, buildings, etc. This learning problem provides an interesting case study of classifier learning from extremely high dimensional (105 features), extremely sparse (tens of training examples), noisy data. This paper summarizes the results obtained in these three case studies, as well as lessons learned about how to successfully apply machine learning methods to train classifiers in such settings.

Situation models in language comprehension and memory. <=SEP=> This article reviews research on the use of situation models in language comprehension and memory retrieval over the past 15 years. Situation models are integrated mental representations of a described state of affairs. Significant progress has been made in the scientific understanding of how situation models are involved in language comprehension and memory retrieval. Much of this research focuses on establishing the existence of situation models, often by using tasks that assess one dimension of a situation model. However, the authors argue that the time has now come for researchers to begin to take the multidimensionality of situation models seriously. The authors offer a theoretical framework and some methodological observations that may help researchers to tackle this issue.