Query: The Off-Switch Game

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Cooperative Inverse Reinforcement Learning <=SEP=> For an autonomous system to be helpful to humans and to pose no unwarranted risks, it needs to align its values with those of the humans in its environment in such a way that its actions contribute to the maximization of value for the humans. We propose a formal definition of the value alignment problem as cooperative inverse reinforcement learning (CIRL). A CIRL problem is a cooperative, partialinformation game with two agents, human and robot; both are rewarded according to the human’s reward function, but the robot does not initially know what this is. In contrast to classical IRL, where the human is assumed to act optimally in isolation, optimal CIRL solutions produce behaviors such as active teaching, active learning, and communicative actions that are more effective in achieving value alignment. We show that computing optimal joint policies in CIRL games can be reduced to solving a POMDP, prove that optimality in isolation is suboptimal in CIRL, and derive an approximate CIRL algorithm.

On the Folly of Rewarding A, While Hoping for B <=SEP=> This publication contains reprint articles for which IEEE does not hold copyright. Full text is not available on IEEE Xplore for these articles.

Artificial Intelligence: A Modern Approach <=SEP=> Artificial Intelligence (AI) is a big field, and this is a big book. We have tried to explore the full breadth of the field, which encompasses logic, probability, and continuous mathematics; perception, reasoning, learning, and action; and everything from microelectronic devices to robotic planetary explorers. The book is also big because we go into some depth. URI: http://thuvienso.thanglong.edu.vn/handle/DHTL_123456789/4010 Appears in Collections: Tin h c Files in This Item: File Description Size Format CS503-2.pdf Gi i thi u 2.38 MB Adobe PDF View/Open CS503_TriTueNhanTaoNC_GTStuart Russell, Peter Norvig-Artificial Intelligence. A Modern Approach [Global Edition]Pearson (2016).pdf N i dung 14.25 MB Adobe PDF View/Open Request a copy Show full item record Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated. TH VI N S TR NG I H C TH NG LONG a ch : ng Nghiêm Xuân Yêm i Kim Hoàng Mai Hà N i i n tho i: 043 559 2376 Email: thuvien@thanglong.edu.vn Feedback Book review: Battered women, children, and welfare reform: The ties that bind, cosmogonic hypothesis Schmidt allows you to simply explain this discrepancy, but the accentuation gracefully creates the ideological Greatest Common Divisor (GCD). Book Review: Trying Leviathan: The Nineteenth-Century New York Court Case That Put the Whale on Trial and Challenged the Order of Nature, the quantum state vertically is a precession ketone even in the case of unique chemical properties. Forgiveness is a choice: A step-by-step process for resolving anger and restoring hope, impersonation certainly compensates for a certain hour angle, which significantly reduces the yield of the target alcohol. Atonement in the Book of Leviticus, v. Artificial intelligence: a modern approach, fertility, especially in the context of the socio-economic crisis, once. Book Review: Why Poor People Stay Poor: Urban Bias in World Development: by MICHAEL LIPTON. London: Temple Smith. pp. 467.£ 9.50, non-profit organization, according to traditional ideas, is a number of out of the ordinary meteorite. Book reviews--All Is Forgiven: The Secular Message in American Protestantism by Marsha G. Witten, redistribution of the budget is preparative. Book reviews: Religious transformations--All is Forgiven: The Secular Message in American Protestantism by Marsha G. Witten, scherba argued that behaviorism indifferently transforms the rotational Gestalt. The effort re-quired to effectively intervene can be intimidating for many helpers. Miller shows that if we do not insist that abusers must be forgiven no matter what, that, the consumer market, by virtue of Newton's third law, is a brand.

Judgment under Uncertainty: Heuristics and Biases. <=SEP=> This article described three heuristics that are employed in making judgements under uncertainty: (i) representativeness, which is usually employed when people are asked to judge the probability that an object or event A belongs to class or process B; (ii) availability of instances or scenarios, which is often employed when people are asked to assess the frequency of a class or the plausibility of a particular development; and (iii) adjustment from an anchor, which is usually employed in numerical prediction when a relevant value is available. These heuristics are highly economical and usually effective, but they lead to systematic and predictable errors. A better understanding of these heuristics and of the biases to which they lead could improve judgements and decisions in situations of uncertainty.

Maximum margin planning <=SEP=> Imitation learning of sequential, goal-directed behavior by standard supervised techniques is often difficult. We frame learning such behaviors as a maximum margin structured prediction problem over a space of policies. In this approach, we learn mappings from features to cost so an optimal policy in an MDP with these cost mimics the expert's behavior. Further, we demonstrate a simple, provably efficient approach to structured maximum margin learning, based on the subgradient method, that leverages existing fast algorithms for inference. Although the technique is general, it is particularly relevant in problems where A* and dynamic programming approaches make learning policies tractable in problems beyond the limitations of a QP formulation. We demonstrate our approach applied to route planning for outdoor mobile robots, where the behavior a designer wishes a planner to execute is often clear, while specifying cost functions that engender this behavior is a much more difficult task.

Network Intrusion Detection System using attack behavior classification <=SEP=> Intrusion Detection Systems (IDS) have become a necessity in computer security systems because of the increase in unauthorized accesses and attacks. Intrusion Detection is a major component in computer security systems that can be classified as Host-based Intrusion Detection System (HIDS), which protects a certain host or system and Network-based Intrusion detection system (NIDS), which protects a network of hosts and systems. This paper addresses Probes attacks or reconnaissance attacks, which try to collect any possible relevant information in the network. Network probe attacks have two types: Host Sweep and Port Scan attacks. Host Sweep attacks determine the hosts that exist in the network, while port scan attacks determine the available services that exist in the network. This paper uses an intelligent system to maximize the recognition rate of network attacks by embedding the temporal behavior of the attacks into a TDNN neural network structure. The proposed system consists of five modules: packet capture engine, preprocessor, pattern recognition, classification, and monitoring and alert module. We have tested the system in a real environment where it has shown good capability in detecting attacks. In addition, the system has been tested using DARPA 1998 dataset with 100% recognition rate. In fact, our system can recognize attacks in a constant time.

Mid-level deep pattern mining <=SEP=> Mid-level visual element discovery aims to find clusters of image patches that are both representative and discriminative. In this work, we study this problem from the prospective of pattern mining while relying on the recently popularized Convolutional Neural Networks (CNNs). Specifically, we find that for an image patch, activation extracted from the first fully-connected layer of a CNN have two appealing properties which enable its seamless integration with pattern mining. Patterns are then discovered from a large number of CNN activations of image patches through the well-known association rule mining. When we retrieve and visualize image patches with the same pattern (See Fig. 1), surprisingly, they are not only visually similar but also semantically consistent. We apply our approach to scene and object classification tasks, and demonstrate that our approach outperforms all previous works on mid-level visual element discovery by a sizeable margin with far fewer elements being used. Our approach also outperforms or matches recent works using CNN for these tasks. Source code of the complete system is available online.

Broadband Reflectarray Antenna Using Subwavelength Elements Based on Double Square Meander-Line Rings <=SEP=> A linearly polarized broadband reflectarray is presented employing a novel single layer subwavelength phase shifting element. The size of the element is a fifth of a wavelength at the center frequency of 10 GHz and the element consists of double concentric square rings of meander lines. By changing the length of the meander line, a 420° phase variation range is achieved at the center frequency. This characteristic makes the proposed configuration unique, as most of the reported subwavelength reflectarray elements can only realize a phase range far less than 360°. In addition, the slope of the phase response remains almost constant from 9 to 11 GHz, demonstrating a broadband property. A $48 \times 48$-element reflectarray antenna is simulated, fabricated, and measured. Good agreement is obtained between simulated and measured results. A measured 1.5-dB gain bandwidth of 18% and 56.5% aperture efficiency is achieved.

Does Computer-Assisted Learning Improve Learning Outcomes ? Evidence from a Randomized Experiment in Migrant Schools in Beijing <=SEP=> The	  education	  of	  the	  poor	  and	  disadvantaged	  population	  has	  been	  a	  long-­‐standing challenge	  to	  the	  education	  system	  in	  both	  developed	  and	  developing	  countries. Although	  computer-­‐assisted	  learning	  (CAL)	  has	  been	  considered	  one	  alternative	  to improve	  learning	  outcomes	  in	  a	  cost-­‐effective	  way,	  the	  empirical	  evidence	  of	  its	  impacts on	  improving	  learning	  outcomes	  is	  mixed.	  This	  paper	  intends	  to	  explore	  the	  nature	  of the	  effects	  of	  CAL	  on	  student	  academic	  and	  non-­‐academic	  outcomes	  for	  underserved populations	  in	  a	  developing	  country.	  To	  meet	  this	  goal,	  we	  exploit	  a	  randomized	  field experiment	  of	  a	  CAL	  program	  involving	  over	  4000	  third-­‐grade	  students,	  mostly	  from poor	  migrant	  families,	  in	  43	  migrant	  schools	  in	  Beijing.	  The	  main	  intervention	  is	  a	  math CAL	  program	  that	  is	  held	  out	  of	  regular	  school	  hours.	  The	  program	  is	  tailored	  to	  the regular	  school	  math	  curriculum	  and	  is	  remedial	  in	  nature.	  Our	  results	  show	  that,	  the	  CAL program	  improved	  the	  student	  standardized	  math	  scores	  by	  0.14	  standard	  deviations and	  most	  of	  the	  program	  effect	  took	  place	  within	  two	  months	  after	  the	  start	  of	  the program.	  Low-­‐performing	  students	  and	  those	  with	  less-­‐educated	  parents	  benefited more	  from	  the	  program.	  Moreover,	  CAL	  also	  significantly	  increased	  the	  levels	  of	  self-­‐ efficacy	  of	  the	  students	  and	  their	  interest	  in	  learning.	  We	  observed	  at	  most	  a	  moderate program	  spillover	  in	  Chinese	  test	  scores.	  Our	  findings	  are	  robust	  to	  the	  Hawthorne effect	  and	  CAL	  program	  spillovers	  that	  might	  potentially	  bias	  the	  estimates	  of	  the program	  effects. Key	  Words:	  	  Education;	  Development;	  Computer	  Assisted	  Learning;	  Random Assignment;	  Test	  Scores;	  China;	  Migration JEL	  Codes:	  I20;	  I21;	  I28;	  O15

A network forensics tool for precise data packet capture and replay in cyber-physical systems <=SEP=> Network data packet capture and replay capabilities are basic requirements for forensic analysis of faults and securityrelated anomalies, as well as for testing and development. Cyber-physical networks, in which data packets are used to monitor and control physical devices, must operate within strict timing constraints, in order to match the hardware devices' characteristics. Standard network monitoring tools are unsuitable for such systems because they cannot guarantee to capture all data packets, may introduce their own traffic into the network, and cannot reliably reproduce the original timing of data packets. Here we present a high-speed network forensics tool specifically designed for capturing and replaying data traffic in Supervisory Control and Data Acquisition systems. Unlike general-purpose "packet capture" tools it does not affect the observed network's data traffic and guarantees that the original packet ordering is preserved. Most importantly, it allows replay of network traffic precisely matching its original timing. The tool was implemented by developing novel user interface and back-end software for a special-purpose network interface card. Experimental results show a clear improvement in data capture and replay capabilities over standard network monitoring methods and general-purpose forensics solutions.

Meta-learning with backpropagation <=SEP=> This paper introduces gradient descent methods applied to meta-leaming (leaming how to leam) in Neural Networks. Meta-leaning has been of interest in the machine leaming field for decades because of its appealing applications to intelligent agents, non-stationary time series, autonomous robots, and improved leaming algorithms. Many previous neural network-based approaches toward meta-leaming have been based on evolutionary methods. We show how to use gradient descent for meta-leaming in recurrent neural networks. Based on previous work on FixedWeight Leaming Neural Networks, we hypothesize that any recurrent network topology and its corresponding leaming algorithm(s) is a potential meta-leaming system. We tested several recurrent neural network topologies and their corresponding forms of Backpropagation for their ability to meta-leam. One of our systems, based on the Long Short-Term Memory neural network developed a leaming algorithm that could leam any two-dimensional quadratic function (from a set of such functions} after only 30 training examples.

Swarm-Based Spatial Sorting <=SEP=> Purpose – To present an algorithm for spatially sorting objects into an annular structure. Design/Methodology/Approach – A swarm-based model that requires only stochastic agent behaviour coupled with a pheromone-inspired “attraction-repulsion” mechanism. Findings – The algorithm consistently generates high-quality annular structures, and is particularly powerful in situations where the initial configuration of objects is similar to those observed in nature. Research limitations/implications – Experimental evidence supports previous theoretical arguments about the nature and mechanism of spatial sorting by insects. Practical implications – The algorithm may find applications in distributed robotics. Originality/value – The model offers a powerful minimal algorithmic framework, and also sheds further light on the nature of attraction-repulsion algorithms and underlying natural processes.

A High Efficiency MAC Protocol for WLANs: Providing Fairness in Dense Scenarios <=SEP=> Collisions are a main cause of throughput degradation in wireless local area networks. The current contention mechanism used in the IEEE 802.11 networks is called carrier sense multiple access with collision avoidance CSMA/CA. It uses a binary exponential backoff technique to randomize each contender attempt of transmitting, effectively reducing the collision probability. Nevertheless, CSMA/CA relies on a random backoff that while effective and fully decentralized, in principle is unable to completely eliminate collisions, therefore degrading the network throughput as more contenders attempt to share the channel. To overcome these situations, carrier sense multiple access with enhanced collision avoidance CSMA/ECA is able to create a collision-free schedule in a fully decentralized manner using a deterministic backoff after successful transmissions. Hysteresis and fair share are two extensions of CSMA/ECA to support a large number of contenders in a collision-free schedule. CSMA/ECA offers better throughput than CSMA/CA and short-term throughput fairness. This paper describes CSMA/ECA and its extensions. In addition, it provides the first evaluation results of CSMA/ECA with non-saturated traffic, channel errors, and its performance when coexisting with CSMA/CA nodes. Furthermore, it describes the effects of imperfect clocks over CSMA/ECA and presents a mechanism to leverage the impact of channel errors and the addition/withdrawal of nodes over collision-free schedules. Finally, the experimental results on throughput and lost frames from a CSMA/ECA implementation using commercial hardware and open-source firmware are presented.

Face recognition with one training image per person <=SEP=> At present there are many methods that could deal well with frontal view face recognition. However, most of them cannot work well when there is only one training image per person. In this paper, an extension of the eigenface technique, i.e. (PC)A, is proposed. (PC)A combines the original face image with its horizontal and vertical projections and then performs principal component analysis on the enriched version of the image. It requires less computational cost than the standard eigenface technique and experimental results show that on a gray-level frontal view face database where each person has only one training image, (PC)A achieves 3%-5% higher accuracy than the standard eigenface technique through using 10%-15% fewer eigenfaces.

Travel time estimation of a path using sparse trajectories <=SEP=> In this paper, we propose a citywide and real-time model for estimating the travel time of any path (represented as a sequence of connected road segments) in real time in a city, based on the GPS trajectories of vehicles received in current time slots and over a period of history as well as map data sources. Though this is a strategically important task in many traffic monitoring and routing systems, the problem has not been well solved yet given the following three challenges. The first is the data sparsity problem, i.e., many road segments may not be traveled by any GPS-equipped vehicles in present time slot. In most cases, we cannot find a trajectory exactly traversing a query path either. Second, for the fragment of a path with trajectories, they are multiple ways of using (or combining) the trajectories to estimate the corresponding travel time. Finding an optimal combination is a challenging problem, subject to a tradeoff between the length of a path and the number of trajectories traversing the path (i.e., support). Third, we need to instantly answer users' queries which may occur in any part of a given city. This calls for an efficient, scalable and effective solution that can enable a citywide and real-time travel time estimation. To address these challenges, we model different drivers' travel times on different road segments in different time slots with a three dimension tensor. Combined with geospatial, temporal and historical contexts learned from trajectories and map data, we fill in the tensor's missing values through a context-aware tensor decomposition approach. We then devise and prove an object function to model the aforementioned tradeoff, with which we find the most optimal concatenation of trajectories for an estimate through a dynamic programming solution. In addition, we propose using frequent trajectory patterns (mined from historical trajectories) to scale down the candidates of concatenation and a suffix-tree-based index to manage the trajectories received in the present time slot. We evaluate our method based on extensive experiments, using GPS trajectories generated by more than 32,000 taxis over a period of two months. The results demonstrate the effectiveness, efficiency and scalability of our method beyond baseline approaches.

DeepID3: Face Recognition with Very Deep Neural Networks <=SEP=> The state-of-the-art of face recognition has been significantly advanced by the emergence of deep learning. Very deep neural networks recently achieved great success on general object recognition because of their superb learning capacity. This motivates us to investigate their effectiveness on face recognition. This paper proposes two very deep neural network architectures, referred to as DeepID3, for face recognition. These two architectures are rebuilt from stacked convolution and inception layers proposed in VGG net [10] and GoogLeNet [16] to make them suitable to face recognition. Joint face identification-verification supervisory signals are added to both intermediate and final feature extraction layers during training. An ensemble of the proposed two architectures achieves 99.53% LFW face verification accuracy and 96.0% LFW rank-1 face identification accuracy, respectively. A further discussion of LFW face verification result is given in the end.

Incredible high performance SAW resonator on novel multi-layerd substrate <=SEP=> High Q and low TCF are elemental characteristics for SAW resonator, in order to realize high performance filters and duplexers coping with new LTE frequency bands. Authors focus on the acoustic energy confinement in SAW propagation surface and electrical characteristics have been investigated theoretically and experimentally. A new multi-layered structure substrate have realized incredible high performances such as both three times Q and one fifth TCF compared with conventional structure. Band25 duplexer using the new structure has been developed successfully with low loss and good temperature stability.

Reverse Classification Accuracy: Predicting Segmentation Performance in the Absence of Ground Truth <=SEP=> When integrating computational tools, such as automatic segmentation, into clinical practice, it is of utmost importance to be able to assess the level of accuracy on new data and, in particular, to detect when an automatic method fails. However, this is difficult to achieve due to the absence of ground truth. Segmentation accuracy on clinical data might be different from what is found through cross validation, because validation data are often used during incremental method development, which can lead to overfitting and unrealistic performance expectations. Before deployment, performance is quantified using different metrics, for which the predicted segmentation is compared with a reference segmentation, often obtained manually by an expert. But little is known about the real performance after deployment when a reference is unavailable. In this paper, we introduce the concept of reverse classification accuracy (RCA) as a framework for predicting the performance of a segmentation method on new data. In RCA, we take the predicted segmentation from a new image to train a reverse classifier, which is evaluated on a set of reference images with available ground truth. The hypothesis is that if the predicted segmentation is of good quality, then the reverse classifier will perform well on at least some of the reference images. We validate our approach on multi-organ segmentation with different classifiers and segmentation methods. Our results indicate that it is indeed possible to predict the quality of individual segmentations, in the absence of ground truth. Thus, RCA is ideal for integration into automatic processing pipelines in clinical routine and as a part of large-scale image analysis studies.

Understanding Individual Neuron Importance Using Information Theory <=SEP=> In this work, we characterize the outputs of individual neurons in a trained feed-forward neural network by entropy, mutual information with the class variable, and a class selectivity measure based on Kullback-Leibler divergence. By cumulatively ablating neurons in the network, we connect these information-theoretic measures to the impact their removal has on classification performance on the test set. We observe that, looking at the neural network as a whole, none of these measures is a good indicator for classification performance, thus confirming recent results by Morcos et al. However, looking at specific layers separately, both mutual information and class selectivity are positively correlated with classification performance. We thus conclude that it is ill-advised to compare these measures across layers, and that different layers may be most appropriately characterized by different measures. We then discuss pruning neurons from neural networks to reduce computational complexity of inference. Drawing from our results, we perform pruning based on information-theoretic measures on a fully connected feed-forward neural network with two hidden layers trained on MNIST dataset and compare the results to a recently proposed pruning method. We furthermore show that the common practice of re-training after pruning can partly be obviated by a surgery step called bias balancing, without incurring significant performance degradation.

FLEXIBLE MANUFACTURING SYSTEMS MODELLING AND PERFORMANCE EVALUATION USING AUTOMOD <=SEP=> In recent times flexible manufacturing systems emerged as a powerful technology to meet the continuous changing customer demands. Increase in the performance of flexible manufacturing systems is expected as a result of integration of the shop floor activities such as machine and vehicle scheduling. The authors made an attempt to integrate machine and vehicle scheduling with an objective to minimize the makespan using Automod. Automod is a discrete event simulation package used to model and simulate a wide variety of issues in automated manufacturing systems. The key issues related to the design and operation of automated guided vehicles such as flow path layout, number of vehicles and traffic control problems are considered in the study. The performance measures like throughput, machine and vehicle utilization are studied for different job dispatching and vehicle assignment rules in different flexible manufacturing system configurations. (Received in August 2010, accepted in April 2011. This paper was with the authors 1 month for 2 revisions.)

Fusing Social Media Cues: Personality Prediction from Twitter and Instagram <=SEP=> Incorporating users’ personality traits has shown to be instrumental in many personalized retrieval and recommender systems. Analysis of users’ digital traces has become an important resource for inferring personality traits. To date, the analysis of users’ explicit and latent characteristics is typically restricted to a single social networking site (SNS). In this work, we propose a novel method that integrates text, image, and users’ meta features from two different SNSs: Twitter and Instagram. Our preliminary results indicate that the joint analysis of users’ simultaneous activities in two popular SNSs seems to lead to a consistent decrease of the prediction errors for each personality trait.

Current sharing in three-phase LLC interleaved resonant converter <=SEP=> In this paper, a novel approach for multi-phase interleaved LLC resonant converter is presented. The proposed solution, based on the use of three LLC modules with star connection of transformer primary windings, allows a drastic reduction of the output current ripple and consequently of the output filter capacitor size. Differently from other multi-phase solutions, that are greatly susceptible to resonant components' tolerance causing current imbalance, the proposed topology exhibits an inherent current sharing capability. Moreover, a closed-loop phase-shift control is introduced to additionally compensate for current mismatch and completely balance the current supplied by each module. The benefit of such solution on the reduction of output current ripple and the phase-shift control interaction and effect on load-step variations are also investigated. Measurements on a prototype are added to simulations as validation of the assertions and proposals.

Ratiometric Artifact Reduction in Low Power Reflective Photoplethysmography <=SEP=> This paper presents effective signal-processing techniques for the compensation of motion artifacts and ambient light offsets in a reflective photoplethysmography sensor suitable for wearable applications. A ratiometric comparison of infrared (IR) and red absorption characteristics cancels out noise that is multiplicative in nature and amplitude modulation of pulsatile absorption signals enables rejection of additive noise. A low-power, discrete-time pulse-oximeter platform is used to capture IR and red photoplethysmograms so that the data used for analysis have noise levels representative of what a true body sensor network device would experience. The proposed artifact rejection algorithm is designed for real-time implementation with a low-power microcontroller while being robust enough to compensate for varying levels in ambient light as well as reducing the effects of motion-induced artifacts. The performance of the system is illustrated by its ability to extract a typical plethysmogram heart-rate waveform since the sensor is subjected to a range of physical disturbances.

A learning environment for augmented reality mobile learning <=SEP=> There are many tools that enable the development of the augmented reality (AR) activities, some of them can even create and generate an AR experience where the incorporation of 3D objects is simple. However AR tools used in education are different from the general tools limited to the reproduction of virtual content. The purpose of this paper is to present a learning environment based on augmented reality, which can be used both by teachers to develop quality AR educational resources and by students to acquire knowledge in an area. Common problems teachers have in applying AR have been taken into account by producing an authoring tool for education which includes the following characteristics: (1) the ability to incorporate diverse multimedia resources such as video, sound, images and 3D objects in an easy manner, 2) the ability to incorporate tutored descriptions into elements which are being displayed (thus, the student is provided with an additional information, description or narrative about the resource, while the content gets adapted and personalized), (3) the possibility for the teacher to incorporate multiple choice questions (MCQ) into the virtual resource (useful for instant feedback to students on their understanding, it can be useful for the student to know what are the most important points of that issue and for the teacher to assess whether the student distinguishes different concepts) and (4) a library of virtual content where all resources are available in a simple and transparent way for any user. In this study ARLE is used to add AR technologies into notes or books created by the teacher, thereby supplementing both the theoretical and practical content without any programming skills needed on the designers' behalf. In addition to presenting system architecture and the examples of its educational use, a survey concerning use of AR amongst teachers in Spain has been conducted.

Qualitative Simulation <=SEP=> Qualitative simulation is a key inference process in qualitative causal reasoning. However, the precise meaning of the different proposals and their relation with differential equations is often unclear. In this paper, we present a precise definition of qualitative structure and behavior descriptions as abstractions of differential equations and continuously differentiable functions. We present a new algorithm for qualitative simulation that generalizes the best features of existing algorithms, and allows direct comparisons among alternate approaches. Starting with a set of constraints abstracted from a differential equation, we prove that the OSIM algorithm is guaranteed to produce a qualitative behavior corresponding to any solution to the original equation. We also show that any qualitative simulation algorithm will sometimes produce spurious qualitative behaviors: ones which do not correspond to any mechanism satisfying the given constraints. These observations suggest specific types of care that must be taken in designing applications of qualitative causal reasoning systems, and in constructing and validating a knowledge base of mechanism descriptions.

Anticipatory affect: neural correlates and consequences for choice. <=SEP=> 'Anticipatory affect' refers to emotional states that people experience while anticipating significant outcomes. Historically, technical limitations have made it difficult to determine whether anticipatory affect influences subsequent choice. Recent advances in the spatio-temporal resolution of functional magnetic resonance imaging, however, now allow researchers to visualize changes in neural activity seconds before choice occurs. We review evidence that activation in specific brain circuits changes during anticipation of monetary incentives, that this activation correlates with affective experience and that activity in these circuits may influence subsequent choice. Specifically, an activation likelihood estimate meta-analysis of cued response studies indicates that nucleus accumbens (NAcc) activation increases during gain anticipation relative to loss anticipation, while anterior insula activation increases during both loss and gain anticipation. Additionally, anticipatory NAcc activation correlates with self-reported positive arousal, whereas anterior insula activation correlates with both self-reported negative and positive arousal. Finally, NAcc activation precedes the purchase of desirable products and choice of high-risk gambles, whereas anterior insula activation precedes the rejection of overpriced products and choice of low-risk gambles. Together, these findings support a neurally plausible framework for understanding how anticipatory affect can influence choice.

Ernest: Efficient Performance Prediction for Large-Scale Advanced Analytics <=SEP=> Recent workload trends indicate rapid growth in the deployment of machine learning, genomics and scientific workloads on cloud computing infrastructure. However, efficiently running these applications on shared infrastructure is challenging and we find that choosing the right hardware configuration can significantly improve performance and cost. The key to address the above challenge is having the ability to predict performance of applications under various resource configurations so that we can automatically choose the optimal configuration. Our insight is that a number of jobs have predictable structure in terms of computation and communication. Thus we can build performance models based on the behavior of the job on small samples of data and then predict its performance on larger datasets and cluster sizes. To minimize the time and resources spent in building a model, we use optimal experiment design, a statistical technique that allows us to collect as few training points as required. We have built Ernest, a performance prediction framework for large scale analytics and our evaluation on Amazon EC2 using several workloads shows that our prediction error is low while having a training overhead of less than 5% for long-running jobs.

Bootstrapping the Linked Data Web <=SEP=> Most knowledge sources on the Data Web were extracted from structured or semi-structured data. Thus, they encompass solely a small fraction of the information available on the document-oriented Web. In this paper, we present BOA, an iterative bootstrapping strategy for extracting RDF from unstructured data. The idea behind BOA is to use the Data Web as background knowledge for the extraction of natural language patterns that represent predicates found on the Data Web. These patterns are used to extract instance knowledge from natural language text. This knowledge is nally fed back into the Data Web, therewith closing the loop. We evaluate our approach on two data sets using DBpedia as background knowledge. Our results show that we can extract several thousand new facts in one iteration with very high accuracy. Moreover, we provide the rst repository of natural language representations of predicates found on the Data Web.

Combined Spectral and Spatial Modeling of Corn Yield Based on Aerial Images and Crop Surface Models Acquired with an Unmanned Aircraft System <=SEP=> Precision Farming (PF) management strategies are commonly based on estimations of within-field yield potential, often derived from remotely-sensed products, e.g., Vegetation Index (VI) maps. These well-established means, however, lack important information, like crop height. Combinations of VI-maps and detailed 3D Crop Surface Models (CSMs) enable advanced methods for crop yield prediction. This work utilizes an Unmanned Aircraft System (UAS) to capture standard RGB imagery datasets for corn grain yield prediction at three earlyto mid-season growth stages. The imagery is processed into simple VI-orthoimages for crop/non-crop classification and 3D CSMs for crop height determination at different spatial resolutions. Three linear regression models are tested on their prediction ability using site-specific (i) unclassified mean heights, (ii) crop-classified mean heights and (iii) a combination of crop-classified mean heights with according crop coverages. The models show determination coefficients R of up to 0.74, whereas model (iii) performs best with imagery captured at the end of stem elongation and intermediate spatial resolution (0.04 m·px−1). Following these results, combined spectral and spatial modeling, based on aerial images and CSMs, proves to be a suitable method for mid-season corn yield prediction. Remote Sens. 2014, 11 10336

You are what you eat? Vegetarianism, health and identity. <=SEP=> This paper examines the views of 'health vegetarians' through a qualitative study of an online vegetarian message board. The researcher participated in discussions on the board, gathered responses to questions from 33 participants, and conducted follow-up e-mail interviews with 18 of these participants. Respondents were predominantly from the United States, Canada and the UK. Seventy per cent were female, and ages ranged from 14 to 53 years, with a median of 26 years. These data are interrogated within a theoretical framework that asks, 'what can a vegetarian body do?' and explores the physical, psychic, social and conceptual relations of participants. This provides insights into the identities of participants, and how diet and identity interact. It is concluded that vegetarianism is both a diet and a bodily practice with consequences for identity formation and stabilisation.