Query: Two types of dopamine neuron distinctly convey positive and negative motivational signals

Relevant passages:
A framework for mesencephalic dopamine systems based on predictive Hebbian learning. <=SEP=> We develop a theoretical framework that shows how mesencephalic dopamine systems could distribute to their targets a signal that represents information about future expectations. In particular, we show how activity in the cerebral cortex can make predictions about future receipt of reward and how fluctuations in the activity levels of neurons in diffuse dopamine systems above and below baseline levels would represent errors in these predictions that are delivered to cortical and subcortical targets. We present a model for how such errors could be constructed in a real brain that is consistent with physiological results for a subset of dopaminergic neurons located in the ventral tegmental area and surrounding dopaminergic neurons. The theory also makes testable predictions about human choice behavior on a simple decision-making task. Furthermore, we show that, through a simple influence on synaptic plasticity, fluctuations in dopamine release can act to change the predictions in an appropriate manner.

Predictive reward signal of dopamine neurons. <=SEP=> The effects of lesions, receptor blocking, electrical self-stimulation, and drugs of abuse suggest that midbrain dopamine systems are involved in processing reward information and learning approach behavior. Most dopamine neurons show phasic activations after primary liquid and food rewards and conditioned, reward-predicting visual and auditory stimuli. They show biphasic, activation-depression responses after stimuli that resemble reward-predicting stimuli or are novel or particularly salient. However, only few phasic activations follow aversive stimuli. Thus dopamine neurons label environmental stimuli with appetitive value, predict and detect rewards and signal alerting and motivating events. By failing to discriminate between different rewards, dopamine neurons appear to emit an alerting message about the surprising presence or absence of rewards. All responses to rewards and reward-predicting stimuli depend on event predictability. Dopamine neurons are activated by rewarding events that are better than predicted, remain uninfluenced by events that are as good as predicted, and are depressed by events that are worse than predicted. By signaling rewards according to a prediction error, dopamine responses have the formal characteristics of a teaching signal postulated by reinforcement learning theories. Dopamine responses transfer during learning from primary rewards to reward-predicting stimuli. This may contribute to neuronal mechanisms underlying the retrograde action of rewards, one of the main puzzles in reinforcement learning. The impulse response releases a short pulse of dopamine onto many dendrites, thus broadcasting a rather global reinforcement signal to postsynaptic neurons. This signal may improve approach behavior by providing advance reward information before the behavior occurs, and may contribute to learning by modifying synaptic transmission. The dopamine reward signal is supplemented by activity in neurons in striatum, frontal cortex, and amygdala, which process specific reward information but do not emit a global reward prediction error signal. A cooperation between the different reward signals may assure the use of specific rewards for selectively reinforcing behaviors. Among the other projection systems, noradrenaline neurons predominantly serve attentional mechanisms and nucleus basalis neurons code rewards heterogeneously. Cerebellar climbing fibers signal errors in motor performance or errors in the prediction of aversive events to cerebellar Purkinje cells. Most deficits following dopamine-depleting lesions are not easily explained by a defective reward signal but may reflect the absence of a general enabling function of tonic levels of extracellular dopamine. Thus dopamine systems may have two functions, the phasic transmission of reward information and the tonic enabling of postsynaptic neurons.

FMRI Visualization of Brain Activity during a Monetary Incentive Delay Task <=SEP=> Comparative studies have implicated striatal and mesial forebrain circuitry in the generation of autonomic, endocrine, and behavioral responses for incentives. Using blood oxygen level-dependent functional magnetic resonance imaging, we sought to visualize functional activation of these regions in 12 normal volunteers as they anticipated and responded for monetary incentives. Both individual and group analyses of time-series data revealed significant activation of striatal and mesial forebrain structures (including insula, caudate, putamen, and mesial prefrontal cortex) during trials involving both monetary rewards and punishments. In addition to these areas, during trials involving punishment, group analysis revealed activation foci in the anterior cingulate and thalamus. These results corroborate comparative studies which implicate striatal and mesial forebrain circuitry in the elaboration of incentive-driven behavior. This report also introduces a new paradigm for probing the functional integrity of this circuitry in humans.

Acute Effects of Cocaine on Human Brain Activity and Emotion <=SEP=> We investigated brain circuitry mediating cocaine-induced euphoria and craving using functional MRI (fMRI). During double-blind cocaine (0.6 mg/kg) and saline infusions in cocaine-dependent subjects, the entire brain was imaged for 5 min before and 13 min after infusion while subjects rated scales for rush, high, low, and craving. Cocaine induced focal signal increases in nucleus accumbens/subcallosal cortex (NAc/SCC), caudate, putamen, basal forebrain, thalamus, insula, hippocampus, parahippocampal gyrus, cingulate, lateral prefrontal and temporal cortices, parietal cortex, striate/extrastriate cortices, ventral tegmentum, and pons and produced signal decreases in amygdala, temporal pole, and medial frontal cortex. Saline produced few positive or negative activations, which were localized to lateral prefrontal cortex and temporo-occipital cortex. Subjects who underwent repeat studies showed good replication of the regional fMRI activation pattern following cocaine and saline infusions, with activations on saline retest that might reflect expectancy. Brain regions that exhibited early and short duration signal maxima showed a higher correlation with rush ratings. These included the ventral tegmentum, pons, basal forebrain, caudate, cingulate, and most regions of lateral prefrontal cortex. In contrast, regions that demonstrated early but sustained signal maxima were more correlated with craving than with rush ratings; such regions included the NAc/SCC, right parahippocampal gyrus, and some regions of lateral prefrontal cortex. Sustained negative signal change was noted in the amygdala, which correlated with craving ratings. Our data demonstrate the ability of fMRI to map dynamic patterns of brain activation following cocaine infusion in cocaine-dependent subjects and provide evidence of dynamically changing brain networks associated with cocaine-induced euphoria and cocaine-induced craving.

Sensing spatial distribution of urban land use by integrating points-of-interest and Google Word2Vec model <=SEP=> Urban land use information plays an essential role in a wide variety of urban planning and environmental monitoring processes. During the past few decades, with the rapid technological development of remote sensing (RS), geographic information systems (GIS) and geospatial big data, numerous methods have been developed to identify urban land use at a fine scale. Points-of-interest (POIs) have been widely used to extract information pertaining to urban land use types and functional zones. However, it is difficult to quantify the relationship between spatial distributions of POIs and regional land use types due to a lack of reliable models. Previous methods may ignore abundant spatial features that can be extracted from POIs. In this study, we establish an innovative framework that detects urban land use distributions at the scale of traffic analysis zones (TAZs) by integrating Baidu POIs and a Word2Vec model. This framework was implemented using a Google open-source model of a deep-learning language in 2013. First, data for the Pearl River Delta (PRD) are transformed into a TAZ-POI corpus using a greedy algorithmby considering the spatial distributions of TAZs and inner POIs. Then, high-dimensional characteristic vectors of POIs and TAZs are extracted using the Word2Vec model. Finally, to validate the reliability of the POI/TAZ vectors, we implement a K-Means-based clustering model to analyze correlations between the POI/TAZ vectors and deploy TAZ vectors to identify urban land use types using a random forest algorithm (RFA) model. Compared with some state-of-the-art probabilistic topic models (PTMs), the proposed method can efficiently obtain the highest accuracy (OA = 0.8728, kappa = 0.8399). Moreover, the results can be used to help urban planners to monitor dynamic urban land use and evaluate the impact of urban planning schemes. ARTICLE HISTORY Received 21 March 2016 Accepted 28 September 2016

SemEval-2017 Task 7: Detection and Interpretation of English Puns <=SEP=> Apun is a form of wordplay in which aword suggests two or more meanings by exploiting polysemy, homonymy, or phonological similarity to another word, for an intended humorous or rhetorical effect. Though a recurrent and expected feature in many discourse types, puns stymie traditional approaches to computational lexical semantics because they violate their one-sense-percontext assumption. This paper describes the first competitive evaluation for the automatic detection, location, and interpretation of puns. We describe the motivation for these tasks, the evaluation methods, and the manually annotated data set. Finally, we present an overview and discussion of the participating systems’ methodologies, resources, and results.

Stippling by Example <=SEP=> In this work, we focus on stippling as an artistic style and discuss our technique for capturing and reproducing stipple features unique to an individual artist. We employ a texture synthesis algorithm based on the gray-level co-occurrence matrix (GLCM) of a texture field. This algorithm uses a texture similarity metric to generate stipple textures that are perceptually similar to input samples, allowing us to better capture and reproduce stipple distributions. First, we extract example stipple textures representing various tones in order to create an approximate tone map used by the artist. Second, we extract the stipple marks and distributions from the extracted example textures, generating both a lookup table of stipple marks and a texture representing the stipple distribution. Third, we use the distribution of stipples to synthesize similar distributions with slight variations using a numerical measure of the error between the synthesized texture and the example texture as the basis for replication. Finally, we apply the synthesized stipple distribution to a 2D grayscale image and place stipple marks onto the distribution, thereby creating a stippled image that is statistically similar to images created by the example artist.

Improved Single Image Dehazing Using Dark Channel Prior and Multi-scale Retinex <=SEP=> In this paper, we propose an improved image dehazing algorithm using dark channel prior and Multi-Scale Retinex. Main improvement lies in automatic and fast acquisition of transmission map of the scene. We implement the Multi-scale Retinex algorithm on the luminance component in YCbCr space, obtain a pseudo transmission map whose function is similar to the transmission map in original approach. Combining with the haze image model and the dark channel prior, we can recover a high quality haze-free image. Compared with the original method, our algorithm has two main advantages: (i) no user interaction is needed, and (ii) restoring the image much faster while maintaining comparable dehazing performance.

Efficient Road Detection and Tracking for Unmanned Aerial Vehicle <=SEP=> An unmanned aerial vehicle (UAV) has many applications in a variety of fields. Detection and tracking of a specific road in UAV videos play an important role in automatic UAV navigation, traffic monitoring, and ground-vehicle tracking, and also is very helpful for constructing road networks for modeling and simulation. In this paper, an efficient road detection and tracking framework in UAV videos is proposed. In particular, a graph-cut-based detection approach is given to accurately extract a specified road region during the initialization stage and in the middle of tracking process, and a fast homography-based road-tracking scheme is developed to automatically track road areas. The high efficiency of our framework is attributed to two aspects: the road detection is performed only when it is necessary and most work in locating the road is rapidly done via very fast homography-based tracking. Experiments are conducted on UAV videos of real road scenes we captured and downloaded from the Internet. The promising results indicate the effectiveness of our proposed framework, with the precision of 98.4% and processing 34 frames per second for 1046 × 595 videos on average.

Formal Design and Verification of Self-Adaptive Systems with Decentralized Control <=SEP=> Feedback control loops that monitor and adapt managed parts of a software system are considered crucial for realizing self-adaptation in software systems. The MAPE-K (Monitor-Analyze-Plan-Execute over a shared Knowledge) autonomic control loop is the most influential reference control model for self-adaptive systems. The design of complex distributed self-adaptive systems having decentralized adaptation control by multiple interacting MAPE components is among the major challenges. In particular, formal methods for designing and assuring the functional correctness of the decentralized adaptation logic are highly demanded.
 This article presents a framework for formal modeling and analyzing self-adaptive systems. We contribute with a formalism, called self-adaptive Abstract State Machines, that exploits the concept of multiagent Abstract State Machines to specify distributed and decentralized adaptation control in terms of MAPE-K control loops, also possible instances of MAPE patterns. We support validation and verification techniques for discovering unexpected interfering MAPE-K loops, and for assuring correctness of MAPE components interaction when performing adaptation.

Online Media Use of False News to Frame the 2016 Trump Presidential Campaign <=SEP=> The 2016 U.S. presidential election campaigns witnessed an unprecedented viral false news -- a type of misinformation referred to as "factitious information blend" that is motivated to discredit political rivals. Despite the different speculations of factors that might have influenced Donald Trump's surprised victory, empirical and theoretical research on the potential impacts of false news propagated by online news media during election campaigns on influencing voters' attitudes and public opinion is seriously lacking. By drawing on the literature on framing political-effects research and by developing our computational text analytics programs, we addressed questions regarding how online news media used false news to negatively frame the Trump presidential campaign. Our text analytics results indicate that although the negative frames against Trump far outnumbered those against Hillary Clinton, weak frames of unverifiable misinformation might have failed to influence the mass audience, leaving them to the power of Trump's direct political communications via Twitter.

Mersenne Twister A Pseudo-Random Number Generator <=SEP=> Pseudo random number generators have been widely used in numbers of applications, particularly simulation and cryptography. Among them is a Mersenne Twister. Introduced in 1998 by Makoto Matsumoto and Takuji Nishimura, it has been a highly preferred generator since it provides long period, high order of dimensional equidistribution, speed and reliability. In this paper, we will focus mainly on describing the theories and algorithm of the Mersenne Twister. We also briefly introduce its variants and discuss its limitations and improvements.

Seraph: an efficient, low-cost system for concurrent graph processing <=SEP=> Graph processing systems have been widely used in enterprises like online social networks to process their daily jobs. With the fast growing of social applications, they have to efficiently handle massive concurrent jobs. However, due to the inherent design for single job, existing systems incur great inefficiency in memory use and fault tolerance. Motivated by this, in this paper we introduce Seraph, a graph processing system that enables efficient job-level parallelism. Seraph is designed based on a decoupled data model, which allows multiple concurrent jobs to share graph structure data in memory. Seraph adopts a copy-on-write semantic to isolate the graph mutation of concurrent jobs, and a lazy snapshot protocol to generate consistent graph snapshots for jobs submitted at different time. Moreover, Seraph adopts an incremental checkpoint/regeneration model which can tremendously reduce the overhead of checkpointing. We have implemented Seraph, and the evaluation results show that Seraph significantly outperforms popular systems (such as Giraph and Spark) in both memory usage and job completion time, when executing concurrent graph jobs.

Person Re-identification with Deep Similarity-Guided Graph Neural Network <=SEP=> The person re-identification task requires to robustly estimate visual similarities between person images. However, existing person re-identification models mostly estimate the similarities of different image pairs of probe and gallery images independently while ignores the relationship information between different probe-gallery pairs. As a result, the similarity estimation of some hard samples might not be accurate. In this paper, we propose a novel deep learning framework, named Similarity-Guided Graph Neural Network (SGGNN) to overcome such limitations. Given a probe image and several gallery images, SGGNN creates a graph to represent the pairwise relationships between probegallery pairs (nodes) and utilizes such relationships to update the probegallery relation features in an end-to-end manner. Accurate similarity estimation can be achieved by using such updated probe-gallery relation features for prediction. The input features for nodes on the graph are the relation features of different probe-gallery image pairs. The probe-gallery relation feature updating is then performed by the messages passing in SGGNN, which takes other nodes’ information into account for similarity estimation. Different from conventional GNN approaches, SGGNN learns the edge weights with rich labels of gallery instance pairs directly, which provides relation fusion more precise information. The effectiveness of our proposed method is validated on three public person re-identification datasets.

Of, for, and by the people: the legal lacuna of synthetic persons <=SEP=> Conferring legal personhood on purely synthetic entities is a very real legal possibility, one under consideration presently by the European Union. We show here that such legislative action would be morally unnecessary and legally troublesome. While AI legal personhood may have some emotional or economic appeal, so do many superficially desirable hazards against which the law protects us. We review the utility and history of legal fictions of personhood, discussing salient precedents where such fictions resulted in abuse or incoherence. We conclude that difficulties in holding “electronic persons” accountable when they violate the rights of others outweigh the highly precarious moral interests that AI legal personhood might protect.

Grasping analysis for a 3-Finger Adaptive Robot Gripper <=SEP=> A 3-Finger Adaptive Robot Gripper is an advanced robotic research that provides a robotic hand-like capabilities due to its flexibility and versatility. However, the grasping performance has to be analyzed and monitored based on the motor encoder, motor current, and force feedback so that the finger position and grasping force can be effectively controlled. This paper provides an open-loop grasping analysis for a 3-Finger Adaptive Robot Gripper. A series of grasping tests has been conducted to demonstrate the robot capabilities and functionalities. Different stiffness levels of the grasped objects have been chosen to demonstrate the grasping ability. In the experiment, a Modbus RTU protocol and Matlab/Simulink are used as communication and control platform. A specially modified interlink FSR sensor is proposed where a special plastic cover has been developed to enhance the sensor sensitivity. The Arduino IO Package is employed to interface the sensor and Matlab/Simulink. The results show that the significant relationships between finger position, motor current, and force sensor are found and the results can be used for a proper grasping performance.

Posterior short-segment fixation with or without fusion for thoracolumbar burst fractures. a five to seven-year prospective randomized study. <=SEP=> BACKGROUND
The impact of fusion as a supplement to short-segment instrumentation for the treatment of thoracolumbar burst fractures is unclear. We conducted a controlled clinical trial to define the effect of fusion on lumbar spine and patient-related functional outcomes.


METHODS
From 2000 to 2002, seventy-three consecutive patients with a single-level Denis type-B burst fracture involving the thoracolumbar spine and a load-sharing score of <or=6 were managed with posterior pedicle screw instrumentation. The patients were randomly assigned to treatment with posterolateral fusion (fusion group, n = 37) or without posterolateral fusion (nonfusion group, n = 36). The patients were followed for at least five years after surgery and were assessed with regard to clinical and radiographic outcomes. Clinical outcomes were evaluated with use of the Frankel scale, the motor score of the American Spinal Injury Association, a visual analog scale, and the Short Form-36 (SF-36) questionnaire. Radiographic outcomes were assessed on the basis of the local kyphosis angle and loss of kyphosis correction.


RESULTS
No significant difference in radiographic or clinical outcomes was noted between the patients managed with the two techniques. Both operative time and blood loss were significantly less in the nonfusion group compared with the fusion group (p < 0.05). Twenty-five of the thirty-seven patients in the fusion group still had some degree of donor-site pain at the time of the latest examination.


CONCLUSIONS
Posterolateral bone-grafting is not necessary when a Denis type-B thoracolumbar burst fracture associated with a load-sharing score of <or=6 is treated with short-segment pedicle screw fixation.

Ask the Mutants: Mutating Faulty Programs for Fault Localization <=SEP=> We present MUSE (MUtation-baSEd fault localization technique), a new fault localization technique based on mutation analysis. A key idea of MUSE is to identify a faulty statement by utilizing different characteristics of two groups of mutants-one that mutates a faulty statement and the other that mutates a correct statement. We also propose a new evaluation metric for fault localization techniques based on information theory, called Locality Information Loss (LIL): it can measure the aptitude of a localization technique for automated fault repair systems as well as human debuggers. The empirical evaluation using 14 faulty versions of the five real-world programs shows that MUSE localizes a fault after reviewing 7.4 statements on average, which is about 25 times more precise than the state-of-the-art SBFL technique Op2.

APAC: Augmented PAttern Classification with Neural Networks <=SEP=> Deep neural networks have been exhibiting splendid accuracies in many of visual pattern classification problems. Many of the state-of-the-art methods employ a technique known as data augmentation at the training stage. This paper addresses an issue of decision rule for classifiers trained with augmented data. Our method is named as APAC: the Augmented PAttern Classification, which is a way of classification using the optimal decision rule for augmented data learning. Discussion of methods of data augmentation is not our primary focus. We show clear evidences that APAC gives far better generalization performance than the traditional way of class prediction in several experiments. Our convolutional neural network model with APAC achieved a state-of-the-art accuracy on the MNIST dataset among non-ensemble classifiers. Even our multilayer perceptron model beats some of the convolutional models with recentlyinvented stochastic regularization techniques on the CIFAR10 dataset.

Graphene-based single-bacterium resolution biodevice and DNA transistor: interfacing graphene derivatives with nanoscale and microscale biocomponents. <=SEP=> Establishing "large-contact-area" interfaces of sensitive nanostructures with microbes and mammalian cells will lead to the development of valuable tools and devices for biodiagnostics and biomedicine. Chemically modified graphene (CMG) nanostructures with their microscale area, sensitive electrical properties, and modifiable chemical functionality are excellent candidates for such biodevices at both biocellular and biomolecular scale. Here, we report on the fabrication and functioning of a novel CMG-based (i) single-bacterium biodevice, (ii) label-free DNA sensor, and (iii) bacterial DNA/protein and polyelectrolyte chemical transistor. The bacteria biodevice was highly sensitive with a single-bacterium attachment generating approximately 1400 charge carriers in a p-type CMG. Similarly, single-stranded DNA tethered on graphene hybridizes with its complementary DNA strand to reversibly increase the hole density by 5.61 x 1012 cm(-2). We further demonstrate (a) a control on the device sensitivity by manipulating surface groups, (b) switching of polarity specificity by changing surface polarity, and (c) a preferential attachment of DNA on thicker CMG surfaces and sharp CMG wrinkles.

Discriminative key-component models for interaction detection and recognition <=SEP=> Not all frames are equal – selecting a subset of discriminative frames from a video can improve performance at detecting and recognizing human interactions. In this paper we present models for categorizing a video into one of a number of predefined interactions or for detecting these interactions in a long video sequence. The models represent the interaction by a set of key temporal moments and the spatial structures they entail. For instance: two people approaching each other, then extending their hands before engaging in a “handshaking” interaction. Learning the model parameters requires only weak supervision in the form of an overall label for the interaction. Experimental results on the UT-Interaction and VIRAT datasets verify the efficacy of these structured models for human interactions.

Accuracy analysis of kinect depth data <=SEP=> This paper presents an investigation of the geometric quality of depth data obtained by the Kinect sensor. Based on the mathematical model of depth measurement by the sensor a theoretical error analysis is presented, which provides an insight into the factors influencing the accuracy of the data. Experimental results show that the random error of depth measurement increases with increasing distance to the sensor, and ranges from a few millimetres up to about 4 cm at the maximum range of the sensor. The accuracy of the data is also found to be influenced by the low resolution of the depth measurements.

Why Are Consumers Willing to Pay for Privacy? An Application of the Privacy-freemium Model to Media Companies <=SEP=> Monetizing their users’ personal information instead of charging a fee has become an established revenue model for platform operators—a new form of media companies specialized in aggregating, managing, and distributing user-generated online content. However, the commodification of privacy leads to privacy concerns that might be a risk for such businesses. Thus, a new approach is to focus on consumers’ willingness to pay for privacy, assuming that monetizing privacy protection might be an alternative revenue model. Following the freemium idea, we developed an innovative research design, offering 553 online survey participants the opportunity to subscribe to a fictional premium version of Facebook with additional privacy control features in return for a monthly fee. Based on the theory of planned behavior, we developed and tested a research model to explain actual willingness to pay for privacy behavior. Our findings show that perceived usefulness and trust significantly affect willingness to pay. In contrast, perceived internet privacy risk was not found to have a significant influence. We thus conclude that consumers are willing to pay for privacy in the form of a privacy-freemium model, provided they perceive the premium version as offering added value and as trustworthy.

Decision support system for servitization of industrial SMEs: a modelling and simulation approach <=SEP=> Decision support system for servitization of industrial SMEs: a modelling and simulation approach M. Chalal, X. Boucher & G. Marques To cite this article: M. Chalal, X. Boucher & G. Marques (2015) Decision support system for servitization of industrial SMEs: a modelling and simulation approach, Journal of Decision Systems, 24:4, 355-382, DOI: 10.1080/12460125.2015.1074836 To link to this article: http://dx.doi.org/10.1080/12460125.2015.1074836

A neurophilosophical slant on consciousness research. <=SEP=> Explaining the nature and mechanisms of conscious experience in neurobiological terms seems to be an attainable, if yet unattained, goal. Research at many levels is important, including research at the cellular level that explores the role of recurrent pathways between thalamic nuclei and the cortex, and research that explores consciousness from the perspective of action. Conceptually, a clearer understanding of the logic of expressions such as "causes" and "correlates", and about what to expect from a theory of consciousness are required. The logic of some terms, such as "qualia" and "reductionism", continues to generate misunderstandings about the scientific possibilities and limits. Experimentally, a deeper understanding of the role of the thalamus in coordinating activity across cortical levels, and a readiness to reconsider the orthodox approach to thalamocortical organization are also required.

TopicViz: interactive topic exploration in document collections <=SEP=> Existing methods for searching and exploring large document collections focus on surface-level matches to user queries, ignoring higher-level semantic structure. In this paper we show how topic modeling - a technique for identifying latent themes across a large collection of documents - can support semantic exploration. We present TopicViz: an interactive environment which combines traditional search and citation-graph exploration with a force-directed layout that links documents to the latent themes discovered by the topic model. We describe usage scenarios in which TopicViz supports rapid sensemaking on large document collections.

Moving Obstacle Detection using Monocular Vision <=SEP=> This paper proposes a method for detecting moving obstacles on roads, by using a vehicle mounted monocular camera. To detect various moving obstacles, such as vehicles and pedestrians, the ego-motion of the vehicle is initially estimated from images captured by the camera. There are two problems in ego-motion estimation. Firstly, a typical road scene contains moving obstacles. This causes false estimation of the ego-motion. Secondly, roads possess fewer features, when compared to the number associated with background structures. This reduces the accuracy of ego-motion estimation. In our approach, the ego-motion is estimated from the correspondences of dispersed feature points extracted from various regions other than those that contain moving obstacles. After estimating the ego-motion, any moving obstacles are detected by tracking the feature points over consecutive frames. In our experiments, it has been shown that the proposed method is able to detect moving obstacles

A simple and effective iterated greedy algorithm for the permutation flowshop scheduling problem <=SEP=> Over the last decade many metaheuristics have been applied t o the flowshop scheduling problem, ranging from Simulated Annealing or Tabu Search to complex hybrid techniques. Some of these methods provide excellent effectiveness and e fficiency at the expense of being utterly complicated. In fact, several published meth ods require substantial implementation efforts, exploit problem specific speed-up techniqu es that cannot be applied to slight variations of the original problem, and often re-implement ations of these methods by other researchers produce results that are quite different from t he original ones. In this work we present a new iterated greedy algorithm that applies two pha ses iteratively, namedestruction, were some jobs are eliminated from the incumbent solution, andconstruction, where the eliminated jobs are reinserted into the sequence using t he well known NEH construction Corresponding author

An Overview of Virtual Machine Architectures <=SEP=> hen early computer systems were being developed, hardware was designed first and software written specifically for that hardware followed. Each system was essentially handcrafted with its own instruction set, and software was developed for that specific instruction set. This included operating system software, assemblers (later compilers), and application programs. With a small user community, relatively simple programs, and fixed hardware, this tightly integrated approach worked quite well, especially while the basic concepts of the stored program computer were still evolving. But user communities grew, operating systems became more complex, and the number of application programs rapidly expanded, so that re-writing and distributing software for each new computer system became a major burden.