Query: Sensor fusion for semantic segmentation of urban scenes

Relevant passages:
SuperParsing: Scalable Nonparametric Image Parsing with Superpixels <=SEP=> This paper presents a simple and effective nonparametric approach to the problem of image parsing, or labeling image regions (in our case, superpixels produced by bottom-up segmentation) with their categories. This approach requires no training, and it can easily scale to datasets with tens of thousands of images and hundreds of labels. It works by scene-level matching with global image descriptors, followed by superpixel-level matching with local features and efficient Markov random field (MRF) optimization for incorporating neighborhood context. Our MRF setup can also compute a simultaneous labeling of image regions into semantic classes (e.g., tree, building, car) and geometric classes (sky, vertical, ground). Our system outperforms the state-of-the-art nonparametric method based on SIFT Flow on a dataset of 2,688 images and 33 labels. In addition, we report per-pixel rates on a larger dataset of 15,150 images and 170 labels. To our knowledge, this is the first complete evaluation of image parsing on a dataset of this size, and it establishes a new benchmark for the problem.

Multiscale Combinatorial Grouping <=SEP=> We propose a unified approach for bottom-up hierarchical image segmentation and object candidate generation for recognition, called Multiscale Combinatorial Grouping (MCG). For this purpose, we first develop a fast normalized cuts algorithm. We then propose a high-performance hierarchical segmenter that makes effective use of multiscale information. Finally, we propose a grouping strategy that combines our multiscale regions into highly-accurate object candidates by exploring efficiently their combinatorial space. We conduct extensive experiments on both the BSDS500 and on the PASCAL 2012 segmentation datasets, showing that MCG produces state-of-the-art contours, hierarchical regions and object candidates.

Fast Approximate Energy Minimization via Graph Cuts <=SEP=> In this paper we address the problem of minimizing a large class of energy functions that occur in early vision. The major restriction is that the energy function’s smoothness term must only involve pairs of pixels. We propose two algorithms that use graph cuts to compute a local minimum even when very large moves are allowed. The first move we consider is an α-βswap: for a pair of labels α, β, this move exchanges the labels between an arbitrary set of pixels labeled α and another arbitrary set labeled β. Our first algorithm generates a labeling such that there is no swap move that decreases the energy. The second move we consider is an α-expansion: for a label α, this move assigns an arbitrary set of pixels the label α. Our second algorithm, which requires the smoothness term to be a metric, generates a labeling such that there is no expansion move that decreases the energy. Moreover, this solution is within a known factor of the global minimum. We experimentally demonstrate the effectiveness of our approach on image restoration, stereo and motion. 1 Energy minimization in early vision Many early vision problems require estimating some spatially varying quantity (such as intensity or disparity) from noisy measurements. Such quantities tend to be piecewise smooth; they vary smoothly at most points, but change dramatically at object boundaries. Every pixel p ∈ P must be assigned a label in some set L; for motion or stereo, the labels are disparities, while for image restoration they represent intensities. The goal is to find a labeling f that assigns each pixel p ∈ P a label fp ∈ L, where f is both piecewise smooth and consistent with the observed data. These vision problems can be naturally formulated in terms of energy minimization. In this framework, one seeks the labeling f that minimizes the energy E(f) = Esmooth(f) + Edata(f). Here Esmooth measures the extent to which f is not piecewise smooth, while Edata measures the disagreement between f and the observed data. Many different energy functions have been proposed in the literature. The form of Edata is typically

Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes <=SEP=> We present a 3-D shape-based object recognition system for simultaneous recognition of multiple objects in scenes containing clutter and occlusion. Recognition is based on matching surfaces by matching points using the spin-image representation. The spin-image is a data level shape descriptor that is used to match surfaces represented as surface meshes. We present a compression scheme for spin-images that results in efficient multiple object recognition which we verify with results showing the simultaneous recognition of multiple objects from a library of 20 models. Furthermore, we demonstrate the robust performance of recognition in the presence of clutter and occlusion through analysis of recognition trials on 100 scenes. This research was performed at Carnegie Mellon University and was supported by the US Department of Energy under contract DE-AC21-92MC29104. 1

Real-time object classification in 3D point clouds using point feature histograms <=SEP=> This paper describes a LIDAR-based perception system for ground robot mobility, consisting of 3D object detection, classification and tracking. The presented system was demonstrated on-board our autonomous ground vehicle MuCAR-3, enabling it to safely navigate in urban traffic-like scenarios as well as in off-road convoy scenarios. The efficiency of our approach stems from the unique combination of 2D and 3D data processing techniques. Whereas fast segmentation of point clouds into objects is done in a 2 D occupancy grid, classifying the objects is done on raw 3D point clouds. For fast object feature extraction, we advocate the use of statistics of local point cloud properties, captured by histograms over point features. In contrast to most existing work on 3D point cloud classification, where real-time operation is often impossible, this combination allows our system to perform in real-time at 0.1s frame-rate.

Latent Fingerprint Enhancement via Multi-Scale Patch Based Sparse Representation <=SEP=> Latent fingerprint identification plays an important role for identifying and convicting criminals in law enforcement agencies. Latent fingerprint images are usually of poor quality with unclear ridge structure and various overlapping patterns. Although significant advances have been achieved on developing automated fingerprint identification system, it is still challenging to achieve reliable feature extraction and identification for latent fingerprints due to the poor image quality. Prior to feature extraction, fingerprint enhancement is necessary to suppress various noises, and improve the clarity of ridge structures in latent fingerprints. Motivated by the recent success of sparse representation in image denoising, this paper proposes a latent fingerprint enhancement algorithm by combining the total variation model and multiscale patch-based sparse representation. First, the total variation model is applied to decompose the latent fingerprint into cartoon and texture components. The cartoon component with most of the nonfingerprint patterns is removed as the structured noise, whereas the texture component consisting of the weak latent fingerprint is enhanced in the next stage. Second, we propose a multiscale patch-based sparse representation method for the enhancement of the texture component. Dictionaries are constructed with a set of Gabor elementary functions to capture the characteristics of fingerprint ridge structure, and multiscale patch-based sparse representation is iteratively applied to reconstruct high-quality fingerprint image. The proposed algorithm cannot only remove the overlapping structured noises, but also restore and enhance the corrupted ridge structures. In addition, we present an automatic method to segment the foreground of latent image with the sparse coefficients and orientation coherence. Experimental results and comparisons on NIST SD27 latent fingerprint database are presented to show the effectiveness of the proposed algorithm and its superiority over existing algorithms.

A study on intelligent personalized push notification with user history <=SEP=> In mobile application, the push notification is an important function to make users consume an item (eg. movie, novel). If a mobile application sends too many notifications to users without considering their preferences, it can cause them to turn off the notification or even deleting the mobile application. Thus, we develop a framework that selects a set of users who are likely to react to the push notification. To the best of our knowledge, we first apply recommendation techniques (i.e., collaborative filtering and content-based models) to the problem of discovering the target of push notification.

Social media analytics and research test-bed (SMART dashboard) <=SEP=> We developed a social media analytics and research testbed (SMART) dashboard for monitoring Twitter messages and tracking the diffusion of information in different cities. SMART dashboard is an online geo-targeted search and analytics tool, including an automatic data processing procedure to help researchers to 1) search tweets in different cities; 2) filter noise (such as removing redundant retweets and using machine learning methods to improve precision); 3) analyze social media data from a spatiotemporal perspective, and 4) visualize social media data in various ways (such as weekly and monthly trends, top URLs, top retweets, top mentions, or top hashtags). By monitoring social messages in geo-targeted cities, we hope that SMART dashboard can assist researchers investigate and monitor various topics, such as flu outbreaks, drug abuse, and Ebola epidemics at the municipal level.

Racing Bib Numbers Recognition <=SEP=> Running races, such as marathons, are broadly covered by professional as well as amateur photographers. This leads to a constantly growing number of photos covering a race, making the process of identifying a particular runner in such datasets difficult. Today, such identification is often done manually. In running races, each competitor has an identification number, called the Racing Bib Number (RBN), used to identify that competitor during the race. RBNs are usually printed on a paper or cardboard tag and pined onto the competitor’s T-shirt during the race. We introduce an automatic system that receives a set of natural images taken in running sports events and outputs the participants’ RBN.

An improved Opposition-Based Sine Cosine Algorithm for global optimization <=SEP=> Real life optimization problems require techniques that properly explore the search spaces to obtain the best solutions. In this sense, it is common that traditional optimization algorithms fail in local optimal values. The Sine Cosine Algorithms (SCA) has been recently proposed; it is a global optimization approach based on two trigonometric functions. SCA uses the sine and cosine functions to modify a set of candidate solutions; such operators create a balance between exploration and exploitation of the search space. However, like other similar approaches, SCA tends to be stuck into sub-optimal regions that it is reflected in the computational effort required to find the best values. This situation occurs due that the operators used for exploration do not work well to analyze the search space. This paper presents an improved version of SCA that considers the opposition based learning (OBL) as a mechanism for a better exploration of the search space generating more accurate solutions. OBL is a machine learning strategy commonly used to increase the performance of metaheuristic algorithms. OBL considers the opposite position of a solution in the search space. Based on the objective function value, the OBL selects the best element between the original solution and its opposite position; this task increases the accuracy of the optimization process. The hybridization of concepts from different fields is crucial in intelligent and expert systems; it helps to combine the advantages of algorithms to generate more efficient approaches. The proposed method is an example of this combination; it has been tested over several benchmark functions and engineering problems. Such results support the efficacy of the proposed approach to find the optimal solutions in complex search spaces. © 2017 Elsevier Ltd. All rights reserved. e w g t S f t ( fi b M a b

The meaning of compassion fatigue to student nurses: an interpretive phenomenological study <=SEP=> Background: Compassion fatigue is a form of occupational stress which occurs when individuals are exposed to suffering and trauma on an ongoing basis. The purpose of this study was to explore the experiences of compassion fatigue among student nurses following their first clinical placement in a UK health care setting during 2015. Methods: The aim of this study was to explore students’ thoughts and feelings about compassion fatigue using reflective poems as a source of data. An interpretive phenomenological approach was taken using a purposeful sampling strategy which aimed to explore in depth meaning of the concept as experienced by the students. Results: From this study it is clear that students experience compassion fatigue and this has a psychological effect on their wellbeing and ability to learn in the clinical practice setting. Reflective poetry writing enabled articulation of feelings which were at times negative and linked to the student’s status as a novice nurse. Conclusions: Students experience compassion fatigue and educators need to find ways to provide support in both clinical and university settings. Positive practices such as shared reflection and the use of creative teaching methods might be beneficial, to support exploration of feelings, build resilience and effective ways of coping.

The dependency paradox in close relationships: accepting dependence promotes independence. <=SEP=> Using multiple methods, this investigation tested the hypothesis that a close relationship partner's acceptance of dependence when needed (e.g., sensitive responsiveness to distress cues) is associated with less dependence, more autonomous functioning, and more self-sufficiency (as opposed to more dependence) on the part of the supported individual. In two studies, measures of acceptance of dependency needs and independent functioning were obtained through couple member reports, by observing couple members' behaviors during laboratory interactions, by observing responses to experimentally manipulated partner assistance provided during an individual laboratory task, and by following couples over a period of 6 months to examine independent goal striving as a function of prior assessments of dependency acceptance. Results provided converging evidence in support of the proposed hypothesis. Implications of the importance of close relationships for optimal individual functioning are discussed.

Clustering Algorithms in Biomedical Research: A Review <=SEP=> Applications of clustering algorithms in biomedical research are ubiquitous, with typical examples including gene expression data analysis, genomic sequence analysis, biomedical document mining, and MRI image analysis. However, due to the diversity of cluster analysis, the differing terminologies, goals, and assumptions underlying different clustering algorithms can be daunting. Thus, determining the right match between clustering algorithms and biomedical applications has become particularly important. This paper is presented to provide biomedical researchers with an overview of the status quo of clustering algorithms, to illustrate examples of biomedical applications based on cluster analysis, and to help biomedical researchers select the most suitable clustering algorithms for their own applications.

Towards an automated system for short-answer assessment using ontology mapping <=SEP=> A key concern for any e-assessment tool (computer assisted assessment) is its efficiency in assessing the learner’s knowledge, skill set and ability. Multiple-choice questions are the most common means of assessment used in e-assessment systems, and are also successful. An efficient e-assessment system should use variety of question types including shortanswers, essays etc. and modes of response to assess learner’s performance. In this paper, we consider the task of assessing short-answer questions. Several researches have been performed on the evaluation and assessment of short-answer questions and many products are deployed to assess the same as part of e-learning systems. We propose an automated system for assessing short-answers using ontology mapping. We also compare our approach with some existing systems and give an overall evaluation of experiment results, which shows that our approach using ontology mapping gives an optimized result.

A Direct Least-Squares (DLS) method for PnP <=SEP=> In this work, we present a Direct Least-Squares (DLS) method for computing all solutions of the perspective-n-point camera pose determination (PnP) problem in the general case (n ≥ 3). Specifically, based on the camera measurement equations, we formulate a nonlinear least-squares cost function whose optimality conditions constitute a system of three third-order polynomials. Subsequently, we employ the multiplication matrix to determine all the roots of the system analytically, and hence all minima of the LS, without requiring iterations or an initial guess of the parameters. A key advantage of our method is scalability, since the order of the polynomial system that we solve is independent of the number of points. We compare the performance of our algorithm with the leading PnP approaches, both in simulation and experimentally, and demonstrate that DLS consistently achieves accuracy close to the Maximum-Likelihood Estimator (MLE).

A Soft Robotic Orthosis for Wrist Rehabilitation 1 <=SEP=> In the United States about 795,000 people suffer from a stroke each year [1]. Of those who survive the stroke, the majority experience paralysis on one side of their body, a condition known as hemiparesis. Physical therapy has been shown to restore functionality of the paretic limbs, especially when done early and often [2]. However, the effectiveness of therapy is limited by the availability of therapists and the amount of practice that patients do on their own. Robot-assisted therapy has been explored as a means of guiding patients through the time-intensive exercise regimes that most therapy techniques prescribe. Several wearable, robotic orthoses for the hand and wrist have been developed and are still being developed today [3]. However, few of these existing solutions allow for any significant range of motion, and those that do only offer one degree of freedom. The assisted degree of freedom is almost always flexion/extension, despite the fact that supination/ pronation is crucial in nearly all activities of daily living. In addition, current devices are often large, heavy, and uncomfortable for the wearer, presenting significant deterrents to practice. This paper presents a soft wearable device for the wrist that provides motion-specific assistance with rehabilitation for hemiparetic stroke patients at home. Unlike conventional robotassisted rehabilitation, this pneumatically actuated orthosis is portable, soft, and lightweight, making it more suitable for use outside of the clinic. In addition, this device supports all the degrees of freedom of the wrist, including supination and pronation, which are critical to many tasks.

Video enhancement using per-pixel virtual exposures <=SEP=> We enhance underexposed, low dynamic range videos by adaptively and independently varying the exposure at each photoreceptor in a post-process. This virtual exposure is a dynamic function of both the spatial neighborhood and temporal history at each pixel. Temporal integration enables us to expand the image's dynamic range while simultaneously reducing noise. Our non-linear exposure variation and denoising filters smoothly transition from temporal to spatial for moving scene elements. Our virtual exposure framework also supports temporally coherent per frame tone mapping. Our system outputs restored video sequences with significantly reduced noise, increased exposure time of dark pixels, intact motion, and improved details.

Effects of pomegranate juice consumption on oxidative stress in patients with type 2 diabetes: a single-blind, randomized clinical trial. <=SEP=> Increased free radicals production due to hyperglycemia produces oxidative stress in patients with diabetes. Pomegranate juice (PJ) has antioxidant properties. This study was conducted to determine the effects of PJ consumption in oxidative stress in type 2 diabetic patients. This study was a randomized clinical trial performed on 60, 40-65 years old diabetic patients. The patients were randomly allocated either to PJ consumption group or control. Patients in PJ group consumed 200 ml of PJ daily for six weeks. Sex distribution and the mean age were not different between two groups. After six weeks intervention, oxidized LDL and anti-oxidized LDL antibodies decreased and total serum antioxidant capacity and arylesterase activity of paraoxonase increased significantly in the PJ-treated group compared to the control group. Our data have shown that six weeks supplementation of PJ could have favorable effects on oxidative stress in patients with type 2 diabetes (T2D).

Towards Context-Aware Interaction Recognition for Visual Relationship Detection <=SEP=> Recognizing how objects interact with each other is a crucial task in visual recognition. If we define the context of the interaction to be the objects involved, then most current methods can be categorized as either: (i) training a single classifier on the combination of the interaction and its context; or (ii) aiming to recognize the interaction independently of its explicit context. Both methods suffer limitations: the former scales poorly with the number of combinations and fails to generalize to unseen combinations, while the latter often leads to poor interaction recognition performance due to the difficulty of designing a contextindependent interaction classifier.,,To mitigate those drawbacks, this paper proposes an alternative, context-aware interaction recognition framework. The key to our method is to explicitly construct an interaction classifier which combines the context, and the interaction. The context is encoded via word2vec into a semantic space, and is used to derive a classification result for the interaction. The proposed method still builds one classifier for one interaction (as per type (ii) above), but the classifier built is adaptive to context via weights which are context dependent. The benefit of using the semantic space is that it naturally leads to zero-shot generalizations in which semantically similar contexts (subject-object pairs) can be recognized as suitable contexts for an interaction, even if they were not observed in the training set. Our method also scales with the number of interaction-context pairs since our model parameters do not increase with the number of interactions. Thus our method avoids the limitation of both approaches. We demonstrate experimentally that the proposed framework leads to improved performance for all investigated interaction representations and datasets.

MAG: A Multilingual, Knowledge-base Agnostic and Deterministic Entity Linking Approach <=SEP=> Entity linking has recently been the subject of a significant body of research. Currently, the best performing approaches rely on trained mono-lingual models. Porting these approaches to other languages is consequently a difficult endeavor as it requires corresponding training data and retraining of the models. We address this drawback by presenting a novel multilingual, knowledge-base agnostic and deterministic approach to entity linking, dubbed MAG. MAG is based on a combination of context-based retrieval on structured knowledge bases and graph algorithms. We evaluate MAG on 23 data sets and in 7 languages. Our results show that the best approach trained on English datasets (PBOH) achieves a micro F-measure that is up to 4 times worse on datasets in other languages. MAG on the other hand achieves state-of-the-art performance on English datasets and reaches a micro F-measure that is up to 0.6 higher than that of PBOH on non-English languages.

A Memory-Network Based Solution for Multivariate Time-Series Forecasting <=SEP=> Multivariate time series forecasting is extensively studied throughout the years with ubiquitous applications in areas such as finance, traffic, environment, etc. Still, concerns have been raised on traditional methods for incapable of modeling complex patterns or dependencies lying in real word data. To address such concerns, various deep learning models, mainly Recurrent Neural Network (RNN) based methods, are proposed. Nevertheless, capturing extremely long-term patterns while effectively incorporating information from other variables remains a challenge for time-series forecasting. Furthermore, lack-of-explainability remains one serious drawback for deep neural network models. Inspired by Memory Network proposed for solving the question-answering task, we propose a deep learning based model named Memory Timeseries network (MTNet) for time series forecasting. MTNet consists of a large memory component, three separate encoders, and an autoregressive component to train jointly. Addtionally, the attention mechanism designed enable MTNet to be highly interpretable. We can easily tell which part of the historic data is referenced the most.

Chapter 17 Wireless Sensor Network Security : A Survey <=SEP=> As wireless sensor networks continue to grow, so does the need for effective security mechanisms. Because sensor networks may interact with sensitive data and/or operate in hostile unattended environments, it is imperative that these security concerns be addressed from the beginning of the system design. However, due to inherent resource and computing constraints, security in sensor networks poses different challenges than traditional network/computer security. There is currently enormous research potential in the field of wireless sensor network security. Thus, familiarity with the current research in this field will benefit researchers greatly. With this in mind, we survey the major topics in wireless sensor network security, and present the obstacles and the requirements in the sensor security, classify many of the current attacks, and finally list their corresponding defensive measures.

A Nonlinear Controller Based on a Discrete Energy Function for an AC/DC Boost PFC Converter <=SEP=> AC/DC converter systems generally have two stages: an input power factor correction (PFC) boost ac/dc stage that converts input ac voltage to an intermediate dc voltage while reducing the input current harmonics injected to the grid, followed by a dc/dc converter that steps up or down the intermediate dc-bus voltage as required by the output load and provides high-frequency galvanic isolation. Since a low-frequency ripple (second harmonic of the input ac line frequency) exists in the output voltage of the PFC ac/dc boost converter due to the power ripple, the voltage loop in the conventional control system must have a very low bandwidth in order to avoid distortions in the input current waveform. This results in the conventional PFC controller having a slow dynamic response against load variations with adverse overshoots and undershoots. This paper presents a new control approach that is based on a novel discrete energy function minimization control law that allows the front-end ac/dc boost PFC converter to operate with faster dynamic response than the conventional controllers and simultaneously maintain near unity input power factor. Experimental results from a 3-kW ac/dc converter built for charging traction battery of a pure electric vehicle are presented in this paper to validate the proposed control method and its superiority over conventional controllers.

Tracking Sentiment in Mail: How Genders Differ on Emotional Axes <=SEP=> With the widespread use of email, we now have access to unprecedented amounts of text that we ourselves have written. In this paper, we show how sentiment analysis can be used in tandem with effective visualizations to quantify and track emotions in many types of mail. We create a large word–emotion association lexicon by crowdsourcing, and use it to compare emotions in love letters, hate mail, and suicide notes. We show that there are marked differences across genders in how they use emotion words in work-place email. For example, women use many words from the joy–sadness axis, whereas men prefer terms from the fear–trust axis. Finally, we show visualizations that can help people track emotions in their emails.

Bidding algorithms for simultaneous auctions <=SEP=> This paper introduces RoxyBot, one of the top-scoring agents in the First International Trading Agent Competition. A TAC agent simulates one vision of future travel agents: it represents a set of clients in simultaneous auctions, trading complementary (e.g., airline tickets and hotel reservations) and substitutable (e.g., symphony and theater tickets) goods. RoxyBot faced two key technical challenges in TAC: (i) allocation---assigning purchased goods to clients at the end of a game instance so as to maximize total client utility, and (ii) completion---determining the optimal quantity of each resource to buy and sell given client preferences, current holdings, and market prices. For the dimensions of TAC, an optimal solution to the allocation problem is tractable, and RoxyBot uses a search algorithm based on A* to produce optimal allocations. An optimal solution to the completion problem is also tractable, but in the interest of minimizing bidding cycle time, RoxyBot solves the completion problem using beam search, producing approximately optimal completions. RoxyBot's completer relies on an innovative data structure called a priceline.

Bandwidth Enhancement of a Single-Feed Circularly Polarized Antenna Using a Metasurface: Metamaterial-based wideband CP rectangular microstrip antenna. <=SEP=> In this article, the authors use a 7 x 7 rectangular-ring unit metasurface and a single-feed, CP, rectangular, slotted patch antenna to enhance bandwidth. The antenna consists of a rectangular, slotted patch radiator, a metasurface with an array of rectangular-ring cells, and a coaxial feed. Using the metasurface results in a wide CP bandwidth sevenfold the size of a conventional antenna. The measured 3-dB-AR bandwidth of an antenna prototype printed onto an FR4 substrate is 28.3% (3.62 GHz-4.75 GHz) with 2:1-voltage standing wave ratio (VSWR) bandwidth of 36%. A 6.0-dBic, boresight gain with a variation of 1.5 dB is achieved across the frequency band of 3.35 GHz-4.75 GHz, with the maximum gain of 7.4 dBic at 4.1 GHz. We measure and compare the antenna prototype with a simulation using CST Microwave Studio. Parametric studies aid in understanding the proposed antenna's operation mechanism.

Dynamic Online Pricing with Incomplete Information <=SEP=> Consider the pricing decision for a manager at large online retailer, such as Amazon.com, that sells millions of products. The pricing manager must decide on real-time prices for each of these product. Due to the large number of products, the manager must set retail prices without complete demand information. A manager can run price experiments to learn about demand and maximize long run profits. There are two aspects that make the online retail pricing different from traditional brick and mortar settings. First, due to the number of products the manager must be able to automate pricing. Second, an online retailer can make frequent price changes. Pricing differs from other areas of online marketing where experimentation is common, such as online advertising or website design, as firms do not randomize prices to different customers at the same time. In this paper we propose a dynamic price experimentation policy where the firm has incomplete demand information. For this general setting, we derive a pricing algorithm that balances earning profit immediately and learning for future profits. The proposed approach marries statistical machine learning and economic theory. In particular we combine multi-armed bandit (MAB) algorithms with partial identification of consumer demand into a unique pricing policy. Our automated policy solves this problem using a scalable distribution-free algorithm. We show that our method converges to the optimal price faster than standard machine learning MAB solutions to the problem. In a series of Monte Carlo simulations, we show that the proposed approach perform favorably compared to methods in computer science and revenue management. ∗Ross School of Business, University of Michigan. Contact: ericmsch@umich.edu †Rady School of Management, University of California, San Diego. Contact: kamisra@ucsd.edu ‡Department of Electrical Engineering and Computer Science, University of Michigan. Contact: jabernet@umich.edu

The biological principles of swarm intelligence <=SEP=> The roots of swarm intelligence are deeply embedded in the biological study of self-organized behaviors in social insects. From the routing of traffic in telecommunication networks to the design of control algorithms for groups of autonomous robots, the collective behaviors of these animals have inspired many of the foundational works in this emerging research field. For the first issue of this journal dedicated to swarm intelligence, we review the main biological principles that underlie the organization of insects’ colonies. We begin with some reminders about the decentralized nature of such systems and we describe the underlying mechanisms of complex collective behaviors of social insects, from the concept of stigmergy to the theory of self-organization in biological systems. We emphasize in particular the role of interactions and the importance of bifurcations that appear in the collective output of the colony when some of the system’s parameters change. We then propose to categorize the collective behaviors displayed by insect colonies according to four functions that emerge at the level of the colony and that organize its global behavior. Finally, we address the role of modulations of individual behaviors by disturbances (either environmental or internal to the colony) in the overall flexibility of insect colonies. We conclude that future studies about self-organized biological behaviors should investigate such modulations to better understand how insect colonies adapt to uncertain worlds.

Illuminating Light: An Optical Design Tool with a Luminous-Tangible Interface <=SEP=> We describe a novel system for rapid prototyping of laserbased optical and holographic layouts. Users of this optical prototyping tool – called the Illuminating Light system – move physical representations of various optical elements about a workspace, while the system tracks these components and projects back onto the workspace surface the simulated propagation of laser light through the evolving layout. This application is built atop the Luminous Room infrastructure, an aggregate of interlinked, computer-controlled projector-camera units called I/O Bulbs. Philosophically, the work embodies the emerging ideas of the Luminous Room and builds on the notions of ‘graspable media’. We briefly introduce the I/O Bulb and Luminous Room concepts and discuss their current implementations. After an overview of the optical domain that the Illuminating Light system is designed to address, we present the overall system design and implementation, including that of an intermediary toolkit called voodoo which provides a general facility for object identification and tracking.

Congruent evidence from α-tubulin and β-tubulin gene phylogenies for a zygomycete origin of microsporidia <=SEP=> The origin of microsporidia and the evolutionary relationships among the major lineages of fungi have been examined by molecular phylogeny using a-tubulin and b-tubulin. Chytrids, basidiomycetes, ascomycetes, and microsporidia were all recovered with high support, and the zygomycetes were consistently paraphyletic. The microsporidia were found to branch within zygomycetes, and showed relationships with members of the Entomophthorales and Zoopagales. This provides support for the microsporidia having evolved from within the fungi, however, the tubulin genes are difficult to interpret unambiguously since fungal and microsporidian tubulins are very divergent. Rapid evolutionary rates a characteristic of practically all microsporidian genes studied, so determining their evolutionary history will never be completely free of such difficulties. While the tubulin phylogenies do not provide a decisive conclusion, they do further narrow the probable origin of microsporidia to a zygomycete-like ancestor. 2002 Elsevier Science (USA). All rights reserved.