Query: Autoencoders, Minimum Description Length and Helmholtz Free Energy

Relevant passages:
Neural networks and principal component analysis: Learning from examples without local minima <=SEP=> We consider the problem of learning from examples in layered linear feed-forward neural networks using optimization methods, such as back propagation, with respect to the usual quadratic error function E of the connection weights. Our main result is a complete description of the landscape attached to E in terms of principal component analysis. We show that E has a unique minimum corresponding to the projection onto the subspace generated by the first principal vectors of a covariance matrix associated with the training patterns. All the additional critical points of E are saddle points (corresponding to projections onto subspaces generated by higher order vectors). The auto-associative case is examined in detail. Extensions and implications for the learning algorithms are discussed.

From basic network principles to neural architecture: emergence of orientation columns. <=SEP=> Orientation-selective cells--cells that are selectively responsive to bars and edges at particular orientations--are a salient feature of the architecture of mammalian visual cortex. In the previous paper of this series, I showed that such cells emerge spontaneously during the development of a simple multilayered network having local but initially random feedforward connections that mature, one layer at a time, according to a simple development rule (of Hebb type). In this paper, I show that, in the presence of lateral connections between developing orientation cells, these cells self-organize into banded patterns of cells of similar orientation. These patterns are similar to the "orientation columns" found in mammalian visual cortex. No orientation preference is specified to the system at any stage, none of the basic developmental rules is specific to visual processing, and the results emerge even in the absence of visual input to the system (as has been observed in macaque monkey).

A simplified neuron model as a principal component analyzer. <=SEP=> A simple linear neuron model with constrained Hebbian-type synaptic modification is analyzed and a new class of unconstrained learning rules is derived. It is shown that the model neuron tends to extract the principal component from a stationary input vector sequence.

From basic network principles to neural architecture: emergence of orientation-selective cells. <=SEP=> This is the second paper in a series of three that explores the emergence of several prominent features of the functional architecture of visual cortex, in a "modular self-adaptive network" containing several layers of cells with parallel feedforward connections whose strengths develop according to a Hebb-type correlation-rewarding rule. In the present paper I show that orientation-selective cells, similar to the "simple" cortical cells of Hubel and Wiesel [Hubel, D. H. & Wiesel, T. N. (1962) J. Physiol. 160, 106-154], emerge in such a network. No orientation preference is specified to the system at any stage, the orientation-selective cell layer emerges even in the absence of environmental input to the system, and none of the basic developmental rules is specific to visual processing.

Auto-association by multilayer perceptrons and singular value decomposition <=SEP=> The multilayer perceptron, when working in auto-association mode, is sometimes considered as an interesting candidate to perform data compression or dimensionality reduction of the feature space in information processing applications. The present paper shows that, for auto-association, the nonlinearities of the hidden units are useless and that the optimal parameter values can be derived directly by purely linear techniques relying on singular value decomposition and low rank matrix approximation, similar in spirit to the well-known Karhunen-Loève transform. This approach appears thus as an efficient alternative to the general error back-propagation algorithm commonly used for training multilayer perceptrons. Moreover, it also gives a clear interpretation of the rôle of the different parameters.

Exploratory analysis of OpenStreetMap for land use classification <=SEP=> In the last years, volunteers have been contributing massively to what we know nowadays as Volunteered Geographic Information. This huge amount of data might be hiding a vast geographical richness and therefore research needs to be conducted to explore their potential and use it in the solution of real world problems. In this study we conduct an exploratory analysis of data from the OpenStreetMap initiative. Using the Corine Land Cover database as reference and continental Portugal as the study area, we establish a possible correspondence between both classification nomenclatures, evaluate the quality of OpenStreetMap polygon features classification against Corine Land Cover classes from level 1 nomenclature, and analyze the spatial distribution of OpenStreetMap classes over continental Portugal. A global classification accuracy around 76% and interesting coverage areas' values are remarkable and promising results that encourages us for future research on this topic.

An Empirical Study on Detecting and Fixing Buffer Overflow Bugs <=SEP=> Buffer overflow is one of the most common types of software security vulnerabilities. Although researchers have proposed various static and dynamic techniques for buffer overflow detection, buffer overflow attacks against both legacy and newly-deployed software systems are still quite prevalent. Compared with dynamic detection techniques, static techniques are more systematic and scalable. However, there are few studies on the effectiveness of state-of-the-art static buffer overflow detection techniques. In this paper, we perform an in-depth quantitative and qualitative study on static buffer overflow detection. More specifically, we obtain both the buggy and fixed versions of 100 buffer overflow bugs from 63 real-world projects totalling 28 MLoC (Millions of Lines of Code) based on the reports in Common Vulnerabilities and Exposures (CVE). Then, quantitatively, we apply Fortify, Checkmarx, and Splint to all the buggy versions to investigate their false negatives, and also apply them to all the fixed versions to investigate their false positives. We also qualitatively investigate the causes for the false-negatives and false-positives of studied techniques to guide the design and implementation of more advanced buffer overflow detection techniques. Finally, we also categorized the patterns of manual buffer overflow repair actions to guide automated repair techniques for buffer overflow. The experiment data is available at http://bo-study.github.io/Buffer-Overflow-Cases/.

Applied Nonlinear Control of Unmanned Vehicles with Uncertain Dynamics <=SEP=> The presented research concerns the control of unmanned vehicles. The results introduced in this dissertation provide a solid control framework for a wide class of nonlinear uncertain systems, with a special emphasis on issues related to implementation, such as control input amplitude and rate saturation, or partial state measurements availability. More specifically, an adaptive control framework, allowing to enforce amplitude and rate saturation of the command, is developed. The motion control component of this framework, which works in conjunction with a saturation algorithm, is then specialized to different types of vehicles. Vertical take-off and landing aerial vehicles and a general class of autonomous marine vehicles are considered. A nonlinear control algorithm addressing the tracking problem for a class of underactuated, non-minimum phase marine vehicles is then introduced. This motion controller is extended, using direct and indirect adaptive techniques, to handle parametric uncertainties in the system model. Numerical simulations are used to illustrate the efficacy of the algorithms. Next, the output feedback control problem is treated, for a large class of nonlinear and uncertain systems. The proposed solution relies on a novel nonlinear observer which uses output measurements and partial knowledge of the system’s dynamics to reconstruct the entire state for a wide class of nonlinear systems. The observer is then extended to operate in conjunction with a full state feedback control law and solve both the output feedback control problem and the state observation problem simultaneously. The resulting output feedback control algorithm is then adjusted to provide a high level of robustness to both parametric and structural model uncertainties. Finally, in a natural extension of these results from motion control of a single system to collaborative control of a group of vehicles, a cooperative control framework addressing limited communication issues is introduced.

Multiple Sensor Fusion and Classification for Moving Object Detection and Tracking <=SEP=> The accurate detection and classification of moving objects is a critical aspect of advanced driver assistance systems. We believe that by including the object classification from multiple sensor detections as a key component of the object's representation and the perception process, we can improve the perceived model of the environment. First, we define a composite object representation to include class information in the core object's description. Second, we propose a complete perception fusion architecture based on the evidential framework to solve the detection and tracking of moving objects problem by integrating the composite representation and uncertainty management. Finally, we integrate our fusion approach in a real-time application inside a vehicle demonstrator from the interactIVe IP European project, which includes three main sensors: radar, lidar, and camera. We test our fusion approach using real data from different driving scenarios and focusing on four objects of interest: pedestrian, bike, car, and truck.

On-Line New Event Detection and Tracking <=SEP=> We define and describe the related problems of new event detection and event tracking within a stream of broadcast news stories. We focus on a strict on-line setting-i.e., the system must make decisions about one story before looking at any subsequent stories. Our approach to detection uses a single pass clustering algorithm and a novel thresholding model that incorporates the properties of events as a major component. Our approach to tracking is similar to typical information filtering methods. We discuss the value of “surprising” features that have unusual occurrence characteristics, and briefly explore on-line adaptive filtering to handle evolving events in the news. New event detection and event tracking are part of the Topic Detection and Tracking (TDT) initiative.

Estimation of land surface temperature over Delhi using Landsat-7 ETM + <=SEP=> Land surface temperature (LST) is important factor in global change studies, in estimating radiation budgets in heat balance studies and as a control for climate models. The knowledge of surface temperature is important to a range of issues and themes in earth sciences central to urban climatology, global environmental change, and human-environment interactions. In the study an attempt has been made to estimate surface temperature over Delhi area using Landsat-7 ETM+ satellite data. The variability of these retrieved LSTs has been investigated with respect to different land use / land cover (LU/LC) types determined from the Landsat visible and NIR channels. The classification uncertainties over different land use land cover were assessed and it has been observed that the classification uncertainties were found to be lowest using Minimum Noise Fraction (MNF) components. The emissivity per pixel is retrieved directly from satellite data and has been estimated as narrow band emissivity at the satellite sensor channel in order to have least error in the surface temperature estimation. Strong correlation is observed between surface temperature with Normalized Difference Vegetation Index (NDVI) over different LU/LC classes and the relationship is moderate with fractional vegetation cover (FVC). A regression relation between these parameters has also been estimated indicating that surface temperatures can be predicted if NDVI values are known. The results suggest that the methodology is feasible to estimate NDVI, surface emissivity and surface temperature with reasonable accuracy over heterogeneous urban areas.

AN APPROACH TO THE PSYCHOLOGY OF INSTRUCTION <=SEP=> The relationship between a theory of learning and a theory of instruction is discussed. Examples are presented that illustrate how to proceed from a theoretical description of the learning process to the specification of an optimal strategy for carrying out instruction. The examples deal with fairly simple learning tasks and are admittedly of limited generality. Nevertheless, they clearly define the steps necessary for deriving and testing instructional strategies, thereby providing a set of procedures for analyzing more complex problems. The parameter-dependent optimization strategies are of particular importance because they take into account individual differences among learners as well as differences in difficulty among curriculum units. Experimental evaluations indicate that the parameter-dependent strategies lead to major gains in learning, when compared with strategies that do not take individual differences into account.

Change-Impact Analysis of Firewall Policies <=SEP=> Firewalls are the mainstay of enterprise security and the most widely adopted technology for protecting private networks. The quality of protection provided by a firewall directly depends on the quality of its policy (i.e., configuration). Due to the lack of tools for analyzing firewall policies, most firewalls on the Internet have been plagued with policy errors. A firewall policy error either creates security holes that will allow malicious traffic to sneak into a private network or blocks legitimate traffic and disrupts normal business processes, which in turn could lead to irreparable, if not tragic, consequences. A major source of policy errors stem from policy changes. Firewall policies often need to be changed as networks evolve and new threats emerge. In this paper, we first present the theory and algorithms for firewall policy change-impact analysis. Our algorithms take as input a firewall policy and a proposed change, then output the accurate impact of the change. Thus, a firewall administrator can verify a proposed change before committing it.

Managing Risk in Software Process Improvement: An Action Research Approach <=SEP=> Many software organizations engage in software process improvement (SPI) initiatives to increase their capability to develop quality solutions at a competitive level. Such efforts, however, are complex and very demanding. A variety of risks makes it difficult to develop and implement new processes. We studied SPI in its organizational context through collaborative practice research (CPR), a particular form of action research. The CPR program involved close collaboration between practitioners and researchers over a three-year period to understand and improve SPI initiatives in four Danish software organizations. The problem of understanding and managing risks in SPI teams emerged in one of the participating organizations and led to this research. We draw upon insights from the literature on SPI and software risk management as well as practical lessons learned from managing SPI risks in the participating software organizations. Our research offers two contributions. First, we contribute to knowledge on SPI by proposing an approach to understand and manage risks in SPI teams. This risk management approach consists of a framework for understanding risk areas and risk resolution strategies within SPI and a related Iversen et al./Managing Risk in SPI 396 MIS Quarterly Vol. 28 No. 3/September 2004 process for managing SPI risks. Second, we contribute to knowledge on risk management within the information systems and software engineering disciplines. We propose an approach to tailor risk management to specific contexts. This approach consists of a framework for understanding and choosing between different forms of risk management and a process to tailor risk management to specific contexts.

The Impact of Brand Equity and Innovation on the Long-Term Effectiveness of Promotions <=SEP=> Vol. XLV (June 2008), 293–306 293 © 2008, American Marketing Association ISSN: 0022-2437 (print), 1547-7193 (electronic) *Rebecca J. Slotegraaf is Assistant Professor of Marketing and Eli Lilly Faculty Fellow, Kelley School of Business, Indiana University (e-mail: rslotegr@indiana.edu). Koen Pauwels is Associate Professor of Business Administration, Tuck School of Business, Dartmouth College (e-mail: koen.h.pauwels@dartmouth.edu). Both authors contributed equally to this research. The authors thank Kevin Keller; Don Lehmann; the two anonymous JMR reviewers; and seminar participants at Emory University, the London Business School, Rice University, University of Michigan, University of Illinois, Georgia Institute of Technology, ANZMAC, and the Marketing Dynamics Conference for their valuable comments. The authors also appreciate the generous financial support of the 3M University Relations faculty grant awarded to the first author. Michel Wedel served as associate editor for this article. REBECCA J. SLOTEGRAAF and KOEN PAUWELS*

Interactive Visual Exploration of a Large Spatio-temporal Dataset: Reflections on a Geovisualization Mashup. <=SEP=> Exploratory visual analysis is useful for the preliminary investigation of large structured, multifaceted spatio-temporal datasets. This process requires the selection and aggregation of records by time, space and attribute, the ability to transform data and the flexibility to apply appropriate visual encodings and interactions. We propose an approach inspired by geographical 'mashups' in which freely-available functionality and data are loosely but flexibly combined using de facto exchange standards. Our case study combines MySQL, PHP and the LandSerf GIS to allow Google Earth to be used for visual synthesis and interaction with encodings described in KML. This approach is applied to the exploration of a log of 1.42 million requests made of a mobile directory service. Novel combinations of interaction and visual encoding are developed including spatial 'tag clouds', 'tag maps', 'data dials' and multi-scale density surfaces. Four aspects of the approach are informally evaluated: the visual encodings employed, their success in the visual exploration of the dataset, the specific tools used and the 'mashup' approach. Preliminary findings will be beneficial to others considering using mashups for visualization. The specific techniques developed may be more widely applied to offer insights into the structure of multifarious spatio-temporal data of the type explored here.

The synchronous data flow programming language LUSTRE <=SEP=> This paper describes the language LUSTRE which is a data flow synchronous language, designed for programming reactive systems-uch as automatic control and monitoring sy s t emsas well as for describing hardware. The data flow aspect of LUSTRE makes it very close to usual description tools in these domains (blockdiagrams, networks of operators, dynamical sample-systems, etc.), and its synchronous interpretation makes it well suited for handling time in programs. Moreover, this synchronous interpretation allows it to be compiled into an efficient sequential program. Finally, the LUSTRE formalism is very similar to temporal logics. This allows the language to be used for both writing programs and expressing program properties, which results in an original program verification methodology.

A Self-tuning Failure Detection Scheme for Cloud Computing Service <=SEP=> Cloud computing is an increasingly important solution for providing services deployed in dynamically scalable cloud networks. Services in the cloud computing networks may be virtualized with specific servers which host abstracted details. Some of the servers are active and available, while others are busy or heavy loaded, and the remaining are offline for various reasons. Users would expect the right and available servers to complete their application requirements. Therefore, in order to provide an effective control scheme with parameter guidance for cloud resource services, failure detection is essential to meet users' service expectations. It can resolve possible performance bottlenecks in providing the virtual service for the cloud computing networks. Most existing Failure Detector (FD) schemes do not automatically adjust their detection service parameters for the dynamic network conditions, thus they couldn't be used for actual application. This paper explores FD properties with relation to the actual and automatic fault-tolerant cloud computing networks, and find a general non-manual analysis method to self-tune the corresponding parameters to satisfy user requirements. Based on this general automatic method, we propose specific and dynamic Self-tuning Failure Detector, called SFD, as a major breakthrough in the existing schemes. We carry out actual and extensive experiments to compare the quality of service performance between the SFD and several other existing FDs. Our experimental results demonstrate that our scheme can automatically adjust SFD control parameters to obtain corresponding services and satisfy user requirements, while maintaining good performance. Such an SFD can be extensively applied to industrial and commercial usage, and it can also significantly benefit the cloud computing networks.

Noise Compensation using Spectrogram Morphological Filtering <=SEP=> This paper describes the application of morphological filtering to speech spectrograms for noise robust automatic speech recognition. Speech regions of the spectrogram are identified based on the proximity of high energy regions to neighbouring high energy regions in the three-dimensional space. The process of erosion can remove noise while dilation can then restore any erroneously removed speech regions. The combination of the two techniques results in a non-linear, time-frequency filter. Automatic speech recognition experiments are performed on the AURORA database and results show an average relative improvement of 10% is delivered with the morphological filtering approach. When combined with quantile-based noise estimation and non-linear spectral subtraction, the average relative performance improvement is also 10% but with a different performance profile in terms of SNR.

ACStream: Enforcing Access Control over Data Streams <=SEP=> In this demo proposal, we illustrate ACStream, a system built on top of Stream Base [1], to specify and enforce access control policies over data streams. ACStream supports a very flexible role-based access control model specifically designed to protect against unauthorized access to streaming data. The core component of ACStream is a query rewriting mechanism that, by exploiting a set of secure operators proposed by us in [2], rewrites a user query in such a way that it does not violate the specified access control policies during its execution. The demo will show how policies modelling a variety of access control requirements can be easily specified and enforced using ACStream.

Fast Anomaly Detection for Streaming Data <=SEP=> This paper introduces Streaming Half-Space-Trees (HS-Trees), a fast one-class anomaly detector for evolving data streams. It requires only normal data for training and works well when anomalous data are rare. The model features an ensemble of random HS-Trees, and the tree structure is constructed without any data. This makes the method highly efficient because it requires no model restructuring when adapting to evolving data streams. Our analysis shows that Streaming HS-Trees has constant amortised time complexity and constant memory requirement. When compared with a state-of-theart method, our method performs favourably in terms of detection accuracy and runtime performance. Our experimental results also show that the detection performance of Streaming HS-Trees is not sensitive to its parameter settings.

A method for learning matching errors for stereo computation <=SEP=> This paper describes a novel learning-based approach for improving the performance of stereo computation. It is based on the observation that whether the image matching scores lead to true or erroneous depth values is dependent on the original stereo images and the underlying scene structure. This function is learned from training data and is integrated into a depth estimation algorithm using the MAP-MRF framework. Because the resultant likelihood function is dependent on the states of a large neighboring region around each pixel, we propose to solve the high-order MRF inference problem using the simulated annealing algorithm combined with a Metropolis-Hastings sampler. A segmentation-based approach is proposed to accelerate the computational speed and improve the performance. Preliminary experimental results show that the learning process captures common errors in SSD matching including the fattening effect, the aperture effect, and mismatches in occluded or low texture regions. It is also demonstrated that the proposed approach significantly improves the accuracy of the depth computation.

From machu_picchu to "rafting the urubamba river": anticipating information needs via the entity-query graph <=SEP=> We study the problem of anticipating user search needs, based on their browsing activity. Given the current web page p that a user is visiting we want to recommend a small and diverse set of search queries that are relevant to the content of p, but also non-obvious and serendipitous.
 We introduce a novel method that is based on the content of the page visited, rather than on past browsing patterns as in previous literature. Our content-based approach can be used even for previously unseen pages.
 We represent the topics of a page by the set of Wikipedia entities extracted from it. To obtain useful query suggestions for these entities, we exploit a novel graph model that we call EQGraph (Entity-Query Graph), containing entities, queries, and transitions between entities, between queries, as well as from entities to queries. We perform Personalized PageRank computation on such a graph to expand the set of entities extracted from a page into a richer set of entities, and to associate these entities with relevant query suggestions. We develop an efficient implementation to deal with large graph instances and suggest queries from a large and diverse pool.
 We perform a user study that shows that our method produces relevant and interesting recommendations, and outperforms an alternative method based on reverse IR.

Big Data Analytics for Earth Sciences: the EarthServer approach <=SEP=> Large-Scale Scientific Information Systems, Jacobs University, Bremen, Germany; Rasdaman GmbH, Bremen, Germany; CNR-IIA, National Research Council of Italy, Institute of Atmospheric Pollution Research, Florence, Italy; EOX IT Services GmbH, Vienna, Austria; Consorzio COMETA, Catania, Italy; Division of Catania, Italian National Institute for Nuclear Physics, 15 Catania, Italy; Department of Physics and Astronomy, University of Catania, Catania, Italy; MEEO S.r.l., Ferrara, Italy; Plymouth Marine Laboratory, Plymouth, UK; Fraunhofer IGD, Darmstadt, Germany; Athena Research and Innovation Center in Information Communication & Knowledge Technologies, Athens, Greece; British Geological Survey, Edinburgh, UK; Software Engineering Italia S.r.l., Catania, Italy; British Geological Survey, Keyworth, UK; NASA Ames 20 Research Center, Moffett Field, CA, USA

Multi-criteria ABC analysis using artificial-intelligence-based classification techniques <=SEP=> ABC analysis is a popular and effective method used to classify inventory items into specific categories that can be managed and controlled separately. Conventional ABC analysis classifies inventory items three categories: A, B, or C based on annual dollar usage of an inventory item. Multi-criteria inventory classification has been proposed by a number of researchers in order to take other important criteria into consideration. These researchers have compared artificial-intelligence (AI)-based classification techniques with traditional multiple discriminant analysis (MDA). Examples of these AI-based techniques include support vector machines (SVMs), backpropagation networks (BPNs), and the k-nearest neighbor (k-NN) algorithm. To test the effectiveness of these techniques, classification results based on four benchmark techniques are compared. The results show that AI-based techniques demonstrate superior accuracy to MDA. Statistical analysis reveals that SVM enables more accurate classification than other AI-based techniques. This finding suggests the possibility of implementing AI-based techniques for multi-criteria ABC analysis in enterprise resource planning (ERP) systems. 2010 Elsevier Ltd. All rights reserved.

Improving Sub-Phone Modeling for Better Native Language Identification with Non-Native English Speech <=SEP=> Identifying a speaker’s native language with his speech in a second language is useful for many human-machine voice interface applications. In this paper, we use a sub-phone-based i-vector approach to identify non-native English speakers’ native languages by their English speech input. Time delay deep neural networks (TDNN) are trained on LVCSR corpora for improving the alignment of speech utterances with their corresponding sub-phonemic “senone” sequences. The phonetic variability caused by a speaker’s native language can be better modeled with the sub-phone models than the conventional phone model based approach. Experimental results on the database released for the 2016 Interspeech ComParE Native Language challenge with 11 different L1s show that our system outperforms the best system by a large margin (87.2% UAR compared to 81.3% UAR for the best system from the 2016 ComParE challenge).

Software reuse research: status and future <=SEP=> This paper briefly summarizes software reuse research, discusses major research contributions and unsolved problems, provides pointers to key publications, and introduces four papers selected from The Eighth International Conference on Software Reuse (ICSR8).

Hempseed as a nutritional resource: An overview <=SEP=> The seed of Cannabis sativa L. has been an important source of nutrition for thousands of years in Old World cultures. Non-drug varieties of Cannabis, commonly referred to as hemp, have not been studied extensively for their nutritional potential in recent years, nor has hempseed been utilized to any great extent by the industrial processes and food markets that have developed during the 20th century. Technically a nut, hempseed typically contains over 30% oil and about 25% protein, with considerable amounts of dietary fiber, vitamins and minerals. Hempseed oil is over 80% in polyunsaturated fatty acids (PUFAs), and is an exceptionally rich source of the two essential fatty acids (EFAs) linoleic acid (18:2 omega-6) and alpha-linolenic acid (18:3 omega-3). The omega-6 to omega-3 ratio (n6/n3) in hempseed oil is normally between 2:1 and 3:1, which is considered to be optimal for human health. In addition, the biological metabolites of the two EFAs, gamma-linolenic acid (18:3 omega-6; ‘GLA’) and stearidonic acid (18:4 omega-3; ‘SDA’), are also present in hempseed oil. The two main proteins in hempseed are edestin and albumin. Both of these high-quality storage proteins are easily digested and contain nutritionally significant amounts of all essential amino acids. In addition, hempseed has exceptionally high levels of the amino acid arginine. Hempseed has been used to treat various disorders for thousands of years in traditional oriental medicine. Recent clinical trials have identified hempseed oil as a functional food, and animal feeding studies demonstrate the long-standing utility of hempseed as an important food resource.

Are Attractive People Rewarding? Sex Differences in the Neural Substrates of Facial Attractiveness <=SEP=> The current study examined the neural substrates of facial attractiveness judgments. Based on the extant behavioral literature, it was hypothesized that brain regions involved in identifying the potential reward value of a stimulus would be more active when men viewed attractive women than when women viewed attractive men. To test this hypothesis, we conducted an event-related functional magnetic resonance imaging experiment during which participants provided explicit attractiveness judgments for faces of the opposite sex. These individual ratings were subsequently used to perform analyses aimed at identifying the brain regions preferentially responsive to attractive faces for both sex groups. The results revealed that brain regions comprising the putative reward circuitry (e.g., nucleus accumbens [NAcc], orbito-frontal cortex [OFC]) showed a linear increase in activation with increased judgments of attractiveness. However, further analysis also revealed sex differences in the recruitment of OFC, which distinguished attractive and unattractive faces only for male participants.

Compact UWB Bandpass Filter With Ultra Narrow Notched Band <=SEP=> A compact ultra-wideband (UWB) bandpass filter with an ultra narrow notched band is proposed using a hybrid microstrip and coplanar waveguide (CPW) structure. The CPW detached-mode resonator (DMR) composed of a quarter-wavelength (¿/4) nonuniform CPW resonator with a short-stub and a ¿/4 single-mode CPW resonator (SMCR) can allocate three split frequencies at the lower end, middle, higher end of the UWB passband. The conventional broadside-coupled microstrip/CPW structure is introduced to improve the bandwidth enhancement around the split frequencies, which leads to good UWB operation. To avoid the interferences such as WLAN signals, the ¿/4 meander slot-line structure embedded in the DMR is employed to obtain the notched band inside the UWB passband. The design is then verified by experiment. Good passband and stopband performances are achieved. Specifically, the fabricated filter has a 10 dB notched fractional bandwidth (FBW) of 2.06% at the notched center frequency of 5.80 GHz.