title: A Review of Last Mile Logistics Innovations in an Externalities Cost Reduction Vision

abstract: In this paper, a review of the recent scientific literature contributions on innovative strategies for last mile logistics, focusing on externalities cost reduction, is presented. Transport is causing problems in urban areas, in particular in freight transport: modern cities need solutions to reduce externalities costs such as congestion, pollution and others, which have increased in the last few years, especially due to the growth of goods delivery. Online sales and globalization lead to new trends in freight transport, and moreover, a larger quantity of goods is expected to be delivered in the next future. In this context, most of the delivered goods end up in the city centers. Last mile logistics is the least efficient stage of the supply chain and comprises up to 28% of the total delivery cost. Therefore, the improvement of last mile logistics and a significant externalities reduction are very important challenges for researchers. New technologies and transport means, innovative techniques and organizational strategies allow handling in a more effective way the last mile delivery in urban areas. Based on the Systematic Literature Review (SLR) method, recent papers that significantly contributed, with original proposals, to the reduction of externalities in urban logistics are identified and analyzed in this work. Furthermore, a classification of the papers dealing with the externality reduction problem is presented. It is consistent with a general formulation proposed to evaluate external costs in urban area. The innovative contributions are classified into five main categories: innovative vehicles, proximity stations or points, collaborative and cooperative urban logistics, optimization of transport management and routing, innovations in public policies and infrastructures. The new paradigm of smart logistics is based on the combination of these concepts and on the proposed innovations.


title: Fillers for the improvement in acne scars

abstract: Acne is a common inflammatory disease. Scarring is an unwanted end point of acne. Both atrophic and hypertrophic scar types occur. Soft-tissue augmentation aims to improve atrophic scars. In this review, we will focus on the use of dermal fillers for acne scar improvement. Therefore, various filler types are characterized, and available data on their use in acne scar improvement are analyzed.


title: Retweeting beyond expectation: Inferring interestingness in Twitter

abstract: Twitter information streams present a lot of unfiltered noisy information.The cognitive burden on users viewing these streams is increased.We propose a method for identifying and ranking interesting information.We implement the method by applying it to Twitter timelines.Our crowdsourced validations give confidence that the approach is effective. Online social networks such as Twitter have emerged as an important mechanism for individuals to share information and post user generated content. However, filtering interesting content from the large volume of messages received through Twitter places a significant cognitive burden on users. Motivated by this problem, we develop a new automated mechanism to detect personalised interestingness, and investigate this for Twitter. Instead of undertaking semantic content analysis and matching of tweets, our approach considers the human response to content, in terms of whether the content is sufficiently stimulating to get repeatedly chosen by users for forwarding (retweeting). This approach involves machine learning against features that are relevant to a particular user and their network, to obtain an expected level of retweeting for a user and a tweet. Tweets observed to be above this expected level are classified as interesting. We implement the approach in Twitter and evaluate it using comparative human tweet assessment in two forms: through aggregated assessment using Mechanical Turk, and through a web-based experiment for Twitter users. The results provide confidence that the approach is effective in identifying the more interesting tweets from a user's timeline. This has important implications for reduction of cognitive burden: the results show that timelines can be considerably shortened while maintaining a high degree of confidence that more interesting tweets will be retained. In conclusion we discuss how the technique could be applied to mitigate possible filter bubble effects.


title: Promoting Female Weight Management in 1920s Print Media: an Analysis of Ladies' Home Journal and Vogue Magazines

abstract: This study explored if and how editorial and advertising content published in 1920s women's magazines promoted the thin female body ideal of that time. The work was guided by symbolic convergence theory, which suggests that rhetorical visions within the media contribute to audience perceptions of social reality. Ladies' Home Journal and Vogue were selected for analysis. Qualitative content analysis revealed three overarching rhetorical visions concerning female weight management. Through these visions, advertisement and editorial content achieved the rhetorical aims of promoting external and internal means of body control; constructing weight loss as readily achievable, safe, and valuable; and constructing body size as indicative of youthfulness, beauty, and fashionability. The role of visual images in supporting these rhetorical visions is discussed, and findings are situated within their social context. In addition, the relevance of the findings to the recurring popularity of the slim female ideal in the 20th century is explored.


title: Re-ID done right: towards good practices for person re-identification

abstract: Training a deep architecture using a ranking loss has become standard for the person re-identification task. Increasingly, these deep architectures include additional components that leverage part detections, attribute predictions, pose estimators and other auxiliary information, in order to more effectively localize and align discriminative image regions. In this paper we adopt a different approach and carefully design each component of a simple deep architecture and, critically, the strategy for training it effectively for person re-identification. We extensively evaluate each design choice, leading to a list of good practices for person re-identification. By following these practices, our approach outperforms the state of the art, including more complex methods with auxiliary components, by large margins on four benchmark datasets. We also provide a qualitative analysis of our trained representation which indicates that, while compact, it is able to capture information from localized and discriminative regions, in a manner akin to an implicit attention mechanism.


title: CMOS Doherty Amplifier With Variable Balun Transformer and Adaptive Bias Control for Wireless LAN Application

abstract: This paper presents a novel CMOS Doherty power amplifier (PA) with an impedance inverter using a variable balun transformer (VBT) and adaptive bias control of an auxiliary amplifier. Unlike a conventional quarter-wavelength $(\lambda /4)$ transmission line impedance inverter of a Doherty PA, the proposed VBT impedance inverter can achieve load modulation without any phase delay circuit. As a result, a $\lambda /4$ phase compensation circuit at the input path of the auxiliary amplifier can be removed, and the total size of the Doherty PA can be reduced. Additionally, an enhancement of the power efficiency at backed-off power levels can successfully be achieved with an adaptive gate bias in a common gate stage of the auxiliary amplifier. The PA, fabricated with 0.13-µm CMOS technology, achieved a 1-dB compression point (P1 dB) of 31.9 dBm and a power-added efficiency (PAE) at P1 dB of 51%. When the PA is tested with 802.11g WLAN orthogonal frequency division multiplexing (OFDM) signal of 54 Mb/s, a 25-dB error vector magnitude (EVM) compliant output power of 22.8 dBm and a PAE of 30.1% are obtained, respectively.


title: Fast Genome-Wide Functional Annotation through Orthology Assignment by eggNOG-Mapper

abstract: Orthology assignment is ideally suited for functional inference. However, because predicting orthology is computationally intensive at large scale, and most pipelines are relatively inaccessible (e.g. new assignments only available through database updates), less precise homology-based functional transfer is still the default for (meta-)genome annotation. We therefore developed eggNOG-mapper, a tool for functional annotation of large sets of sequences based on fast orthology assignments using precomputed clusters and phylogenies from the eggNOG database. To validate our method, we benchmarked Gene Ontology predictions against two widely used homology-based approaches: BLAST and InterProScan. Orthology filters applied to BLAST results reduced the rate of false positive assignments by 11%, and increased the ratio of experimentally validated terms recovered over all terms assigned per protein by 15%. Compared to InterProScan, eggNOG-mapper achieved similar proteome coverage and precision while predicting, on average, 41 more terms per protein and increasing the rate of experimentally validated terms recovered over total term assignments per protein by 35%. EggNOG-mapper predictions scored within the top-5 methods in the three Gene Ontology categories using the CAFA2 NK-partial benchmark. Finally, we evaluated eggNOG-mapper for functional annotation of metagenomics data, yielding better performance than interProScan. eggNOG-mapper runs ∼15x faster than BLAST and at least 2.5x faster than InterProScan. The tool is available standalone and as an online service at http://eggnog-mapper.embl.de.


title: >21% Efficient Silicon Heterojunction Solar Cells on n- and p-Type Wafers Compared

abstract: The properties and high-efficiency potential of front- and rear-emitter silicon heterojunction solar cells on n- and p-type wafers were experimentally investigated. In the low-carrier-injection range, cells on p-type wafers suffer from reduced minority carrier lifetime, mainly due to the asymmetry in interface defect capture cross sections. This leads to slightly lower fill factors than for n-type cells. By using high-quality passivation layers, however, these losses can be minimized. High open-circuit voltages (Vocs) were obtained on both types of float zone (FZ) wafers: up to 735 mV on n-type and 726 mV on p-type. The best Voc measured on Czochralski (CZ) p-type wafers was only 692 mV, whereas it reached 732 mV on CZ n-type. The highest aperture-area certified efficiencies obtained on 4 cm2 cells were 22.14% (Voc = 727 mV , FF = 78.4%) and 21.38% (Voc = 722 mV, FF = 77.1%) on n- and p-type FZ wafers, respectively, proving that heterojunction schemes can perform almost as well on high-quality p-type as on n-type wafers. To our knowledge, this is the highest efficiency ever reported for a full silicon heterojunction solar cell on a p-type wafer, and the highest Voc on any p-type crystalline silicon device with reasonable FF.


Unmanned Aerial Vehicle-based Automobile License Plate Recognition System for Institutional Parking Lots


title: Planar Ultrawideband Antennas With Multiple Notched Bands Based on Etched Slots on the Patch and/or Split Ring Resonators on the Feed Line

abstract: Three types of ultrawideband (UWB) antennas with triple notched bands are proposed and investigated for UWB communication applications. The proposed antennas consist of a planar circular patch monopole UWB antenna and multiple etched slots on the patch and/or split ring resonators (SRRs) coupled to the feed line. Good agreement is achieved between the simulated and measured results. These techniques are significant for designing UWB antennas with multiple narrow frequency notched bands or for designing multiband antennas.


title: Systemic effect of crude extract from rhizome of Drynaria fortunei on bone formation in mice.

abstract: The structure of the bones of mice with and without consumption of Gusuibu extracts were compared. Twenty 8-week-old male BALB/c mice were divided into two groups. In the control group ten mice were daily fed a normal diet and distilled water for drinking. In the Gusuibu group ten mice were daily fed a normal diet and distilled water mixed with Gusuibu extract for drinking. The mice were kept for 5 weeks and were then killed. Using micro-computed tomography, 20 micro-tomographic slices with an increment of 0.25 mm were acquired to cover the proximal end of the left tibia of each mouse. Quantitative morphometry of the bone structure was performed. The results showed that consumption of Gusuibu extract increased the bone volume/tissue volume ratio by 6.45%. The bone trabeculae were increased by 10.00% in number so that the bone density was increased. To conclude, Gusuibu extract taken orally increased bone density.


title: Stomach strangulated in inguinal hernia presenting with hematemesis.

abstract: A case of inguinal hernia containing stomach and presenting with hematemesis is reported. Initial conservative treatment was followed by elective surgery and was successful in this case. The possibility of strangulated stomach as part of the contents of inguinal hernia should be borne in mind in cases of very large hernia, presenting with hematemesis.


title: The Stixel World- A Compact Medium Level Representation of the 3D-World

abstract: Ambitious driver assistance for complex urban scenarios demands a complete awareness of the situation, including all moving and stationary objects that limit the free space. Recent progress in real-time dense stereo vision provides precise depth information for nearly every pixel of an image. This rises new questions: How can one efficiently analyze half a million disparity values of next generation imagers? And how can one find all relevant obstacles in this huge amount of data in real-time? In this paper we build a medium-level representation named "stixel-world". It takes into account that the free space in front of vehicles is limited by objects with almost vertical surfaces. These surfaces are approximated by adjacent rectangular sticks of a certain width and height. The stixel-world turns out to be a compact but flexible representation of the three-dimensional traffic situation that can be used as the common basis for the scene understanding tasks of driver assistance and autonomous systems.


title: N.: Multi-class twitter emotion classification: A new approach

abstract: Micro blogging today has become a very popular communication tool among Internet users. Millions of users share opinions on different aspects of life every day. Therefore micro blogging web-sites are rich sources of data for opinion mining and sentiment analysis. Because micro blogging has appeared relatively recently, there are a few research works that are devoted to this topic. In this paper, we are focusing on using Twitter, the most popular micro blogging platform, for the task of Emotion analysis. We will show how to automatically collect a corpus for Emotion analysis and opinion mining purposes and then perform linguistic analysis of the collected corpus and explain discovered phenomena. Using the corpus, we will build a Emotion classifier that will be able to determine the emotion class of the person writing.


title: [Awake craniotomy and intraoperative language cortical mapping for eloquent cerebral glioma resection: preliminary clinical practice in 3.0 T intraoperative magnetic resonance imaging integrated surgical suite].

abstract: OBJECTIVES ::: To evaluate preliminary clinical experience for combining awake craniotomy and intraoperative language brain mapping within the integrated 3.0 T intraoperative magnetic resonance imaging (iMRI) suite. ::: ::: ::: METHODS ::: From December 2010 to April 2011, 11 right hand-dominant patients with left glioma were involved in, or adjacent to, eloquent cortex was carried out awake craniotomies with cortical stimulation within an integrated 3.0 T iMRI suite. Aphasia battery of Chinese was used to test the language function before the operation. During the procedure, after the occipital, temporal, and supraorbital nerves were blocked by the anesthesiologists, the head was fixed with a custom high-field MRI-compatible head holder. The skull and dura was opened as usual and language brain mapping was then performed. Language testing followed a set protocol: counting numbers from 1 to 50, naming objects, reading single words. Resection of the tumor was guided by neuronavigation system and continued until eloquent areas were encountered or the margin of assessment was reached. An interdissection MRI was acquired to evaluate the glioma removal in a movable MRI scanner after minimal draping. Meanwhile, adverse effects caused by electrical stimulation and iMRI were recorded. The follow-up speech tests were assessed on 7th day and 1 month at least after the operation. ::: ::: ::: RESULTS ::: The combined use of 3.0 T iMRI and awake craniotomy was performed safely in all patients. No adverse effects were reported. The duration of surgery was prolonged by 2 to 4 h. The patients' perception of iMRI during surgery was favorable. First-look MRI studies led to further resection attempts in 6/11 cases as well as a 3/11 increase in the number of gross-total resections. One week after surgery, baseline language function worsened in 4 cases. However, no patients had a persistent language deficit one month after surgery. ::: ::: ::: CONCLUSIONS ::: Awake craniotomy and direct cortical electrical stimulation can be performed safely and effectively within a 3.0 T iMRI suite. The combination of high-field iMRI and awake craniotomy may facilitate safe removal of eloquent glioma.


title: M3RSM: Many-to-many multi-resolution scan matching

abstract: We describe a new multi-resolution scan matching method that makes exhaustive (and thus local-minimum-proof) matching practical, even for large positional uncertainties. Unlike earlier multi-resolution methods, in which putative matches at low-resolutions can lead the matcher to an incorrect solution, our method generates exactly the same answer as a brute-force full-resolution method. We provide a proof of this. Novelly, our method allows decimation of both the look-up table and in the point cloud, yielding a 10x speedup versus contemporary correlative methods. When a robot closes a large-scale loop, it must often consider many loop-closure candidates. In this paper, we describe an approach for posing a scan matching query over these candidates jointly, finding the best match(es) between a particular pose and a set of candidate poses (“one-to-many”), or the best match between two sets of poses (“many-to-many”). This mode of operation finds the first loop closure as much as 45x faster than traditional “one-to-one” scan matching.


title: ABSTRACT Logistic Regression and Collaborative Filtering for Sponsored Search Term Recommendation

abstract: The primary of the ignition coil is tapped to provide two partial primary portions which are serially connected; during an initial current flow, only one partial portion has current flowing therethrough, the other partial portion being short-circuited. When the current through the coil has risen to a predetermined level, as sensed by current flow through a sensing resistor, the short circuit across the second partial portion is removed so that current can continue to flow through the entire primary, causing a substantial drop in current level but maintaining the stored inductive energy. Current can thus rise rapidly initially, so that the coil will store sufficient energy to initiate sparking at high speeds of the connected internal combustion engine while, at low speeds thereof, the overall current flow through the coil is decreased.


title: Perceptual effects in real-time tone mapping

abstract: Tremendous progress in the development and accessibility of high dynamic range (HDR) technology that has happened just recently results in fast proliferation of HDR synthetic image sequences and captured HDR video. When properly processed, such HDR data can lead to very convincing and realistic results even when presented on traditional low dynamic range (LDR) display devices. This requires real-time local contrast compression (tone mapping) with simultaneous modeling of important in HDR image perception effects such as visual acuity, glare, day and night vision. We propose a unified model to include all those effects into a common computational framework, which enables an efficient implementation on currently available graphics hardware. We develop a post processing module which can be added as the final stage of any real-time rendering system, game engine, or digital video player, which enhances the realism and believability of displayed image streams.


title: Immobilisation of Pseudomonas gessardii acidic lipase derived from beef tallow onto mesoporous activated carbon and its application on hydrolysis of olive oil.

abstract: Abstract The study was carried out to immobilise the acidic lipase derived from Pseudomonas gessardii onto mesoporous activated carbon (MAC 400 ) for the application of hydrolysis of olive oil. MAC 400 was prepared from rice husk by the two stages process. P. gessardii was isolated from the beef tallow acclimatised soil. The acidic lipase (ALIP) was produced from a slaughterhouse waste, namely beef tallow as a substrate and immobilised onto MAC 400 . The maximum immobilisation capacity of the MAC 400 was 3570 U/g at optimum immobilisation conditions; time (180 min), pH (5.0) and temperature (30 °C). The immobilised lipase had better thermal stability and reusability than the free lipase. The immobilisation of ALIP onto MAC 400 (MAC 400 –ALIP) followed pseudo-first-order rate kinetics with rate constant 0.012/min. The Michaelis–Menten constant of MAC 400 –ALIP was lower than that of the ALIP, which confirmed the higher affinity between enzyme and substrate. The immobilization of acidic lipase onto MAC 400 was confirmed by Fourier transform-infrared spectroscopy (FT-IR), scanning electron microscopy (SEM) and X-ray diffraction (XRD) pattern analysis. Reusability study of MAC 400 –ALIP on olive oil hydrolysis showed 82% of hydrolysis up to 13 runs and 50% of hydrolysis up to 35 cycles of reuse. This work concludes that the acidic lipase immobilised mesoporous activated carbon matrix can be considered as a potential biocatalyst for the hydrolysis of olive oil. Thus, the enzyme immobilised matrix has potential industrial applications.”


title: Recent Progress of Face Image Synthesis

abstract: Face synthesis has been a fascinating yet challenging problem in computer vision and machine learning. Its main research effort is to design algorithms to generate photo-realistic face images via given semantic domain. It has been a crucial prepossessing step of main-stream face recognition approaches and an excellent test of AI ability to use complicated probability distributions. In this paper, we provide a comprehensive review of typical face synthesis works that involve traditional methods as well as advanced deep learning approaches. Particularly, Generative Adversarial Net (GAN) is highlighted to generate photo-realistic and identity preserving results. Furthermore, the public available databases and evaluation metrics are introduced in details. We end the review with discussing unsolved difficulties and promising directions for future research.


title: Semi-Supervised Word Sense Disambiguation Using Word Embeddings in General and Specific Domains

abstract: One of the weaknesses of current supervised word sense disambiguation (WSD) systems is that they only treat a word as a discrete entity. However, a continuous-space representation of words (word embeddings) can provide valuable information and thus improve generalization accuracy. Since word embeddings are typically obtained from unlabeled data using unsupervised methods, this method can be seen as a semi-supervised word sense disambiguation approach. This paper investigates two ways of incorporating word embeddings in a word sense disambiguation setting and evaluates these two methods on some SensEval/SemEval lexical sample and all-words tasks and also a domain-specific lexical sample task. The obtained results show that such representations consistently improve the accuracy of the selected supervised WSD system. Moreover, our experiments on a domainspecific dataset show that our supervised baseline system beats the best knowledge-based systems by a large margin.


title: Is it wrong to play violent video games?

abstract: Many people have a strong intuition that there is something morally objectionable about playing violent video games, particularly with increases in the number of people who are playing them and the games' alleged contribution to some highly publicized crimes. In this paper, I use the framework of utilitarian, deontological, and virtue ethical theories to analyze the possibility that there might be some philosophical foundation for these intuitions. I raise the broader question of whether or not participating in authentic simulations of immoral acts in general is wrong. I argue that neither the utilitarian, nor the Kantian has substantial objections to violent game playing, although they offer some important insights into playing games in general and what it is morally to be a ``good sport.'' The Aristotelian, however, has a plausible and intuitive way to protest participation in authentic simulations of violent acts in terms of character: engaging in simulated immoral acts erodes one's character and makes it more difficult for one to live a fulfilled eudaimonic life.


title: Lower Extremity Landing Biomechanics in Both Sexes After a Functional Exercise Protocol

abstract: Context: Sex differences in landing biomechanics play a role in increased rates of anterior cruciate ligament (ACL) injuries in female athletes. Exercising to various states of fatigue may negatively affect landing mechanics, resulting in a higher injury risk, but research is inconclusive regarding sex differences in response to fatigue. Objective: To use the Landing Error Scoring System (LESS), a valid clinical movement-analysis tool, to determine the effects of exercise on the landing biomechanics of males and females. Design: Cross-sectional study. Setting: University laboratory. Patients or Other Participants: Thirty-six (18 men, 18 women) healthy college-aged athletes (members of varsity, club, or intramural teams) with no history of ACL injury or prior participation in an ACL injury-prevention program. Intervention(s): Participants were videotaped performing 3 jump-landing trials before and after performance of a functional, sportlike exercise protocol consisting of repetitive sprinting, jumpi...


title: Heavy-duty omni-directional Mecanum-wheeled robot for autonomous navigation: System development and simulation realization

abstract: A Mecanum-wheeled robot benefits from great omni-direction maneuverability. However it suffers from random slippage and high-speed vibration, which creates electric power safety, uncertain position errors and energy waste problems for heavy-duty tasks. A lack of Mecanum research on heavy-duty autonomous navigation demands a robot platform to conduct experiments in the future. This paper introduces AuckBot, a heavy-duty omni-directional Mecanum robot platform developed at the University of Auckland, including its hardware overview, the control system architecture and the simulation design. In particular the control system, synergistically combining the Beckhoff system as the Controller-PC to serve low-level motion execution and ROS as the Navigation-PC to accomplish highlevel intelligent navigation tasks, is developed. In addition, a computer virtual simulation based on ISG-virtuos for virtual AuckBot has been validated. The present status and future work of AuckBot are described at the end.


title: Open source structure-from-motion for aerial video

abstract: Structure-from-motion (SfM) is a well-studied problem in the computer vision field and is of particular interest for aerial imaging applications like mapping, terrain modeling, crop monitoring, etc. With the current rapid growth in the commercial UAV and small satellite markets, aerial SfM is becoming even more important. In recent years, free and open source software has enabled almost anyone to apply SfM to their data at no cost. Existing free packages are oriented toward processing unordered collections of photographs and are less efficient at processing ordered collections or video. They are also prone to failure with nearly planar scenes that often arise in aerial photography. While commercial solutions for aerial SfM exist, they are proprietary and expensive. This paper presents a new open source software toolkit named MAP-Tk that targets SfM for aerial video. It exploits temporal continuity and the aerial nature of the data to speed up and improve feature tracking, loop closure, and bundle adjustment. The system is highly configurable and modular. Dynamic plugins provide state-of-the art algorithms from other open source projects like OpenCV, VXL, and Ceres Solver. This paper presents the modular system design, user interface, novel algorithms exploiting aerial video, and results on various aerial data sets.


title: Intangible cultural heritage preservation: An exploratory study of digitization of the historical literature of Chinese Kunqu opera librettos

abstract: This article introduces the study contents and some research findings regarding digital preservation methods for Chinese Kunqu opera libretto historical literature, including historical literature electronic libretto transformation, libretto musical score image segmentation, musical information recognition, musical score information representation, musical score information storage, and libretto reconstruction on the Web. It proposes a novel editable text method to represent the multidimensional tree-like information structure of the Kunqu libretto literature and a musical semantic annotation method based on numbered musical notation to accommodate the musical features of Kunqu librettos. To maintain the characteristics of the original Kunqu musical notation, it proposes a method to reconstruct Kunqu libretto on the Web based on scalable vector graphics. Some Kunqu librettos were randomly selected for experiments, and the results demonstrated that the editable text method and the musical semantic annotation method were able to fully represent the effective information of the Kunqu libretto literature and that the method to reconstruct librettos on the Web was able to reflect the writing characteristics of the musical notation in the original librettos. Finally, it discusses the primary future research directions related to digital Kunqu, including Kunqu libretto metadata research, corpus construction for the librettos and Qupai (the unique ancient Chinese tune mode), libretto music information disambiguation research, libretto image segmentation and pattern recognition, digital Kunqu roles, digital Kunqu stages, digital Kunqu costume suitcases, virtual Kunqu, digitization and restoration of Kunqu cultural relics, and Kunqu's application prospects in conventional media such as animation, anime, and movies.


title: The Integrated Enterprise: Enterprise Architecture, Investment Process and System Development

abstract: The enterprise architecture provides benefits to the organization that utilizes it. However if the enterprise architecture is not tightly coupled with other enterprise level programs such as investment management and system development process, its overall effectiveness is compromised. This paper identifies the process integration and enterprise architecture touchpoints from the perspective of the investment management process and it outlines an overall integrated enterprise life cycle process flow. The paper also presents a case study of the implementation of the enterprise life cycle process flow


SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules


title: Measuring Individual Video QoE: A Survey, and Proposal for Future Directions Using Social Media

abstract: The next generation of multimedia services have to be optimized in a personalized way, taking user factors into account for the evaluation of individual experience. Previous works have investigated the influence of user factors mostly in a controlled laboratory environment which often includes a limited number of users and fails to reflect real-life environment. Social media, especially Facebook, provide an interesting alternative for Internet-based subjective evaluation. In this article, we develop (and open-source) a Facebook application, named YouQ1, as an experimental platform for studying individual experience for videos. Our results show that subjective experiments based on YouQ can produce reliable results as compared to a controlled laboratory experiment. Additionally, YouQ has the ability to collect user information automatically from Facebook, which can be used for modeling individual experience.


title: Road-Sign Detection and Recognition Based on Support Vector Machines

abstract: This paper presents an automatic road-sign detection and recognition system based on support vector machines (SVMs). In automatic traffic-sign maintenance and in a visual driver-assistance system, road-sign detection and recognition are two of the most important functions. Our system is able to detect and recognize circular, rectangular, triangular, and octagonal signs and, hence, covers all existing Spanish traffic-sign shapes. Road signs provide drivers important information and help them to drive more safely and more easily by guiding and warning them and thus regulating their actions. The proposed recognition system is based on the generalization properties of SVMs. The system consists of three stages: 1) segmentation according to the color of the pixel; 2) traffic-sign detection by shape classification using linear SVMs; and 3) content recognition based on Gaussian-kernel SVMs. Because of the used segmentation stage by red, blue, yellow, white, or combinations of these colors, all traffic signs can be detected, and some of them can be detected by several colors. Results show a high success rate and a very low amount of false positives in the final recognition stage. From these results, we can conclude that the proposed algorithm is invariant to translation, rotation, scale, and, in many situations, even to partial occlusions


title: Referenceless Prediction of Perceptual Fog Density and Perceptual Image Defogging

abstract: We propose a referenceless perceptual fog density prediction model based on natural scene statistics (NSS) and fog aware statistical features. The proposed model, called Fog Aware Density Evaluator (FADE), predicts the visibility of a foggy scene from a single image without reference to a corresponding fog-free image, without dependence on salient objects in a scene, without side geographical camera information, without estimating a depth-dependent transmission map, and without training on human-rated judgments. FADE only makes use of measurable deviations from statistical regularities observed in natural foggy and fog-free images. Fog aware statistical features that define the perceptual fog density index derive from a space domain NSS model and the observed characteristics of foggy images. FADE not only predicts perceptual fog density for the entire image, but also provides a local fog density index for each patch. The predicted fog density using FADE correlates well with human judgments of fog density taken in a subjective study on a large foggy image database. As applications, FADE not only accurately assesses the performance of defogging algorithms designed to enhance the visibility of foggy images, but also is well suited for image defogging. A new FADE-based referenceless perceptual image defogging, dubbed DEnsity of Fog Assessment-based DEfogger (DEFADE) achieves better results for darker, denser foggy images as well as on standard foggy images than the state of the art defogging methods. A software release of FADE and DEFADE is available online for public use: http://live.ece.utexas.edu/research/fog/index.html.


title: iTurk: Turning Passive Haptics into Active Haptics by Making Users Reconfigure Props in Virtual Reality

abstract: We present a system that complements virtual reality experiences with passive props, yet still allows modifying the virtual world at runtime. The main contribution of our system is that it does not require any actuators; instead, our system employs the user to reconfigure and actuate otherwise passive props. We demonstrate a foldable prop that users reconfigure to represent a suitcase, fuse cabinet, railing, and a seat. A second prop, suspended from a long pendulum, not only stands in for inanimate objects, but also for objects that move and demonstrate proactive behavior, such as a group of flying droids that physically attack the user. Our approach conveys a sense of a living, animate world, when in reality the user is the only animate entity present in the system, complemented with only one or two physical props. In our study, participants rated their experience as more enjoyable and realistic than a corresponding no-haptics condition.


title: Information Theoretic Measures for Clusterings Comparison: Variants, Properties, Normalization and Correction for Chance

abstract: Information theoretic measures form a fundamental class of measures for comparing clusterings, and have recently received increasing interest. Nevertheless, a number of questions concerning their properties and inter-relationships remain unresolved. In this paper, we perform an organized study of information theoretic measures for clustering comparison, including several existing popular measures in the literature, as well as some newly proposed ones. We discuss and prove their important properties, such as the metric property and the normalization property. We then highlight to the clustering community the importance of correcting information theoretic measures for chance, especially when the data size is small compared to the number of clusters present therein. Of the available information theoretic based measures, we advocate the normalized information distance (NID) as a general measure of choice, for it possesses concurrently several important properties, such as being both a metric and a normalized measure, admitting an exact analytical adjusted-for-chance form, and using the nominal [0,1] range better than other normalized variants.


title: Enforcing Location and Time-Based Access Control on Cloud-Stored Data

abstract: Recent incidents of data-breaches from the cloud suggest that users should not trust the cloud provider to enforce access control on their data. We focus on mitigating trust to the cloud in scenarios where granting access to data not only considers user identities (as in conventional access policies), but also contextual information such as the user's location and time of access. Previous work in this context assumes a fully trusted cloud that is further capable of locating users. We introduce LoTAC, a novel framework that seamlessly integrates the operation of a cloud provider and a localization infrastructure to enforce location- and time-based access control to cloud-stored data. In LoTAC, the two entities operate independently and are only trusted to offer their basic services: the cloud provider is used and trusted only to reliably store data, the localization infrastructure is used and trusted only to accurately locate users. Furthermore, neither the cloud provider nor the localization infrastructure can access the data, even if they collude. LoTAC protocols require no changes to the cloud provider and minimal changes to the localization infrastructure. We evaluate our protocols using a cellular network as the localization infrastructure and show that they incur in low communication and computation costs and scale well with a large number of users and policies.


title: A formal perspective on the view selection problem

abstract: The view selection problem is to choose a set of views to materialize over a database schema, such that the cost of evaluating a set of workload queries is minimized and such that the views fit into a prespecified storage constraint. The two main applications of the view selection problem are materializing views in a database to speed up query processing, and selecting views to materialize in a data warehouse to answer decision support queries. In addition, view selection is a core problem for intelligent data placement over a wide-area network for data integration applications and data management for ubiquitous computing. We describe several fundamental results concerning the view selection problem. We consider the problem for views and workloads that consist of equality-selection, project and join queries, and show that the complexity of the problem depends crucially on the quality of the estimates that a query optimizer has on the size of the views it is considering to materialize. When a query optimizer has good estimates of the sizes of the views, we show a somewhat surprising result, namely, that an optimal choice of views may involve a number of views that is exponential in the size of the database schema. On the other hand, when an optimizer uses standard estimation heuristics, we show that the number of necessary views and the expression size of each view are polynomially bounded.


title: Human-like walking with toe supporting for humanoids

abstract: A model of a walking pattern imitating human motion is presented. An accurate imitation of human motion and a robust bipedal walking motion are, however, hardly realized together. We therefore focus on only three characteristics of human walking motion: single toe support, knee stretching, and swing leg motion. Based on a conventional pattern generator, single toe support is added, waist height is changed in order to stretch the knees as much as possible, and swing leg motion is generated approximating the human's motion. The generated motion is then filtered to provide a feasible pattern. In addition, the stabilizer is improved in order to keep the Zero Moment Point (ZMP) within the tiny support polygon during the single support phase with toe link. Finally, we successfully demonstrate the generated walking pattern with the robot HRP-4C.


title: A Deep-Learning Based Precipitation Forecasting Approach Using Multiple Environmental Factors

abstract: Precise precipitation forecasting can better reflect the changing trend of climate and also provide timely and efficient environmental information for management decision, as well as prevent the occurrence of floods or droughts. In the era of big data, this paper proposes a novel approach for precipitation forecasting based on deep belief nets, called DBNPF (Deep Belief Network for Precipitation Forecast). Through simulating neural connecting structure of human brain, Gaussian kernel function for data conversion, and back-propagation network for fine-tuning the entire network, the features of the data in the original space are mapped into the new feature space with semantic feature through the dimensionality reduction. The proposed approach can not only learn the hierarchical representation of raw data using a highly generalized way, fully mining the information hidden in the original data, but also make a more accurate description of the rule underlying the different kind of time series of big data. Seven kinds of environmental factors, which are very closely related to precipitation, are used as input vector, and the next 24 hours precipitation is used as the output vector. A set of dedicated experiments with data from Zunyi area of Guizhou Province is conducted to validate the feasibility and useability of the model. We also compare the deep-learning based approach with other traditional machine learning approaches. The experimental results show that the proposed approach can improve the precision of precipitation forecasting.


title: Meta-analytic evidence for common and distinct neural networks associated with directly experienced pain and empathy for pain

abstract: A growing body of evidence suggests that empathy for pain is underpinned by neural structures that are also involved in the direct experience of pain. In order to assess the consistency of this finding, an image-based meta-analysis of nine independent functional magnetic resonance imaging (fMRI) investigations and a coordinate-based meta-analysis of 32 studies that had investigated empathy for pain using fMRI were conducted. The results indicate that a core network consisting of bilateral anterior insular cortex and medial/anterior cingulate cortex is associated with empathy for pain. Activation in these areas overlaps with activation during directly experienced pain, and we link their involvement to representing global feeling states and the guidance of adaptive behavior for both self- and other-related experiences. Moreover, the image-based analysis demonstrates that depending on the type of experimental paradigm this core network was co-activated with distinct brain regions: While viewing pictures of body parts in painful situations recruited areas underpinning action understanding (inferior parietal/ventral premotor cortices) to a stronger extent, eliciting empathy by means of abstract visual information about the other's affective state more strongly engaged areas associated with inferring and representing mental states of self and other (precuneus, ventral medial prefrontal cortex, superior temporal cortex, and temporo-parietal junction). In addition, only the picture-based paradigms activated somatosensory areas, indicating that previous discrepancies concerning somatosensory activity during empathy for pain might have resulted from differences in experimental paradigms. We conclude that social neuroscience paradigms provide reliable and accurate insights into complex social phenomena such as empathy and that meta-analyses of previous studies are a valuable tool in this endeavor.


Phonologically Aware Neural Model for Named Entity Recognition in Low Resource Transfer Settings


title: Learning Multiple Similarities of Users and Items in Recommender Systems

abstract: In addition to the sparse user-item (U-I) matrix, an increasing number of current recommender systems seek to improve performance by exploiting extra heterogeneous data sources (e.g., online social networks). Such rich side sources can provide very useful information about users' personal behaviors and items' properties, therefore can significantly benefit recommender systems. Most existing work can only incorporate a single side source of users or items. In many real-life applications, however, there exist multiple side sources for both users and items, which may be constructed from multiple domains. To incorporate multiple sources, we propose a flexible and robust algorithm called MSUI (Learning Multiple Similarities of Users and Items). The key idea of MSUI is to simultaneously capture the associations between latent factors in the U-I matrix and multiple side sources of users and items through joint nonnegative matrix factorization. MSUI has several advantages over existing methods. First, it seamlessly integrates information from the U-I matrix and information from multiple side sources. Second, by incorporating multiple sources that are usually in complementary, it's more robust to noise contained in each source. Finally, it provides a unified framework that can be easily extended to incorporate additional data sources.


Predictive Accuracy : A Misleading Performance Measure for Highly Imbalanced Data


Men at work: the StackOverflow case


title: Bayesian Supervised Dimensionality Reduction

abstract: Dimensionality reduction is commonly used as a preprocessing step before training a supervised learner. However, coupled training of dimensionality reduction and supervised learning steps may improve the prediction performance. In this paper, we introduce a simple and novel Bayesian supervised dimensionality reduction method that combines linear dimensionality reduction and linear supervised learning in a principled way. We present both Gibbs sampling and variational approximation approaches to learn the proposed probabilistic model for multiclass classification. We also extend our formulation toward model selection using automatic relevance determination in order to find the intrinsic dimensionality. Classification experiments on three benchmark data sets show that the new model significantly outperforms seven baseline linear dimensionality reduction algorithms on very low dimensions in terms of generalization performance on test data. The proposed model also obtains the best results on an image recognition task in terms of classification and retrieval performances.


title: Unsupervised Multiway Data Analysis: A Literature Survey

abstract: Two-way arrays or matrices are often not enough to represent all the information in the data and standard two-way analysis techniques commonly applied on matrices may fail to find the underlying structures in multi-modal datasets. Multiway data analysis has recently become popular as an exploratory analysis tool in discovering the structures in higher-order datasets, where data have more than two modes. We provide a review of significant contributions in the literature on multiway models, algorithms as well as their applications in diverse disciplines including chemometrics, neuroscience, social network analysis, text mining and computer vision.


title: THE DISTRIBUTION AND USE OF SCIENTIFIC AND TECHNICAL INFORMATION

abstract: A great deal of discussion is proceeding with regard to the distribution and use of scientific and technical information, and theories and proposals on the subject fill the air, but the facts on which these discussions are based appear to be few, or even non‐existent. This paper is the report of an effort to discover, by means of a questionnaire addressed to the readers of publications borrowed from the Science Museum Library, some information on such questions as how references to publications are obtained, what the expected information is required for, and whether, in fact, the publications contain the desired information. No earlier survey of this type has been traced.


title: Demand models for the static retail price optimization problem - A Revenue Management perspective

abstract: Revenue Management (RM) has been successfully applied to many industries and to various problem settings. While this is well reflected in research, RM literature is almost entirely focused on the dynamic pricing problem where a perishable product is priced over a finite selling horizon. In retail however, the static case, in which products are continuously replenished and therefore virtually imperishable is equally relevant and features a unique set of industry-specific problem properties. Dierent aspects of this problem have been discussed in isolation in various fields. The relevant contributions remain therefore scattered throughout Operations Research, Econometrics, and foremost Marketing and Retailing while a holistic discussion is virtually non-existent. We argue that RM with its interdisciplinary, practical, and systemic approach would provide the ideal framework to connect relevant research across fields and to narrow the gap between theory and practice. We present a review of the static retail pricing problem from an RM perspective in which we focus on the demand model as the core of the retail RM system and highlight its links to the data and the optimization model. We then define five criteria that we consider critical for the applicability of the demand model in the retail RM context. We discuss the relevant models in the light of these criteria and review literature that has connected dierent aspects of the problem. We identify several avenues for future research to illustrate the vast potential of discussing the static retail pricing problem in the RM context. 1998 ACM Subject Classification G.1.10 Applications


title: Interacting with Predictions: Visual Inspection of Black-box Machine Learning Models

abstract: Understanding predictive models, in terms of interpreting and identifying actionable insights, is a challenging task. Often the importance of a feature in a model is only a rough estimate condensed into one number. However, our research goes beyond these naive estimates through the design and implementation of an interactive visual analytics system, Prospector. By providing interactive partial dependence diagnostics, data scientists can understand how features affect the prediction overall. In addition, our support for localized inspection allows data scientists to understand how and why specific datapoints are predicted as they are, as well as support for tweaking feature values and seeing how the prediction responds. Our system is then evaluated using a case study involving a team of data scientists improving predictive models for detecting the onset of diabetes from electronic medical records.


Integrated force and distance sensing using elastomer-embedded commodity proximity sensors


title: Machine Reading: A “Killer App ” for Statistical Relational AI

abstract: Machine reading aims to automatically extract knowledge from text. It is a long-standing goal of AI and holds the promise of revolutionizing Web search and other fields. In this paper, we analyze the core challenges of machine reading and show that statistical relational AI is particularly well suited to address these challenges. We then propose a unifying approach to machine reading in which statistical relational AI plays a central role. Finally, we demonstrate the promise of this approach by presenting OntoUSP, an end-to-end machine reading system that builds on recent advances in statistical relational AI and greatly outperforms state-of-theart systems in a task of extracting knowledge from biomedical abstracts and answering questions.


title: ScholarBase: Towards a Cross-Domain Knowledgebase for Linked Scholarly Data

abstract: Linked Data has gained a significant momentum over the last years through various initiatives embraced by academia and industry as well. In this paper, we introduce ScholarBase, a research project in progress that is aimed to serve as a Linked Data repository for cross-domain scholarly data. ScholarBase can be conceived as a knowledgebase that weaves links among scholars, institutions, research areas, publications, and geographical locations in a Linked Data fashion. Initially, the primary source of data stems from Google Scholar, specifically the profile pages of scholars. Subsequently, the collected dataset is transformed into a semantic-based format (i.e. RDF). The semantified dataset will enable a machine-readable expression of entity relationships, and can in turn be linked to external knowledge bases (e.g. DBpedia). Through the paper, we discuss the architecture and implementation of the project. ScholarBase can play an important role in the Linked Data web by opening extended opportunities for interesting applications, and data exploration of scholarly data.


title: Architecture Centric Development - generating more code from your UML models

abstract: A water-dispersible epoxy modified alkyd resin produced by reacting (A) 0-50 parts by weight of oil or oil fatty acid, (B) 10-50 parts by weight of polyhydric alcohol, (C) 0-25 parts by weight of monobasic acid having 6 to 18 carbon atoms, (D) 15-70 parts by weight of polybasic acid having 4 to 10 carbon atoms or its anhydride, (E) 5-25 parts by weight of polyoxyalkylene glycol having a molecular weight of 600 to 20,000, and (F) an epoxy compound, wherein the ratio of the hydroxyl groups to the carboxyl groups in the components (A) through (E) is 0.625-1.8 and the amount of the component (F) is 3-50 parts by weight per 100 parts by weight of the total weight of the components (A) through (E), can provide water-dispersible coatings having improved drying characteristics and water resistance as well as corrosion resistance.


K-means Subject Matter Expert Refined Topic Model Methodology Topic Model Estimation via K-Means


title: Robust dual-band MWIR/LWIR infrared target tracking

abstract: We introduce an SIR particle filter for tracking civilian targets including vehicles and pedestrians in dual-band midwave/longwave infrared imagery as well as a novel dualband track consistency check for triggering appearance model updates. Because of the paucity of available dual-band data, we constructed a custom sensor to acquire the test sequences. The proposed algorithm is robust against magnification changes, aspect changes, and clutter and successfully tracked all 17 cases tested, including two partial occlusions. Future work is needed to comprehensively evaluate performance of the algorithm against state-of-the-art video trackers, especially considering the relatively small number of previous dual-band tracking results that have appeared.


title: Non-orthogonal transmission technology in LTE evolution

abstract: Non-orthogonal transmission, although not entirely new to the wireless industry, is gaining more attention due to its promised throughput gain and unique capability to support a large number of simultaneous transmissions within limited resources. In this article, several key techniques for non-orthogonal transmission are discussed. The downlink technique is featured by MUST, which is being specified in 3GPP for mobile broadband services. In the uplink, grantfree schemes such as multi-user shared access and sparse code multiple access, are promising in supporting massive machine-type communication services. The multi-antenna aspect is also addressed in the context of MUST, showing that MIMO technology and non-orthogonal transmission can be used jointly to provide combined gain.


TOWARDS NATURAL SPOKEN INTERACTION WITH ARTIFICIAL INTELLIGENT SYSTEMS


title: Ultrawideband Printed Log-Periodic Dipole Antenna With Multiple Notched Bands

abstract: A printed log-periodic dipole antenna (PLPDA) with multiple notched bands is proposed for ultrawideband (UWB) applications. The impedance bandwidth of 3.1 GHz-10.6 GHz with VSWR less than 2 is achieved based on the wideband property of the PLPDA as well as the half mode substrate integrated waveguide (HMSIW) Balun. Different from omnidirectional UWB antennas, the end-fire radiation pattern of the PLPDA is more stable within the UWB band. Multiple notched bands are generated by integrating U-shaped slots into the PLPDA for blocking the interference from other narrow band wireless communication systems. Several antennas with the notched frequencies of 3.5 GHz, 5.5 GHz, 6.8 GHz, and 8.5 GHz are designed, fabricated, and measured. The measured results are in agreement with the predicted results.


title: The Multicultural Identity Integration Scale (MULTIIS): Developing a comprehensive measure for configuring one's multiple cultural identities within the self.

abstract: OBJECTIVES ::: The research investigating how one's multiple cultural identities are configured within the self has yet to account for existing cultural identity configurations aside from integration, and for identifying with more than 2 cultural groups at once. The current research addresses these issues by constructing the Multicultural Identity Integration Scale (MULTIIS) to examine 3 different multicultural identity configurations, and their relationship to well-being based on Amiot and colleagues' (2007) cognitive-developmental model of social identity integration (CDSMII). ::: ::: ::: METHOD ::: Diverse samples of multicultural individuals completed the MULTIIS along with identity and well-being measures. (Study 1A: N = 407; 1B: N = 310; 2A = 338 and 2A = 254) RESULTS: Reliability and confirmatory factorial analyses (Studies 1A and 2A) all supported the factorial structure of the MULTIIS. Regression analyses (Studies 1B and 2B) confirmed that the integration subscale of the MULTIIS positively predicted well-being, whereas compartmentalization negatively predicted well-being. Categorization was inconsistently related to well-being. ::: ::: ::: CONCLUSIONS ::: These findings support the CDSMII and the usefulness of the MULTIIS measure, and suggest that each identity configuration is uniquely related to well-being outcomes.


title: The treasure beneath convolutional layers: Cross-convolutional-layer pooling for image classification

abstract: A number of recent studies have shown that a Deep Convolutional Neural Network (DCNN) pretrained on a large dataset can be adopted as a universal image descriptor, and that doing so leads to impressive performance at a range of image classification tasks. Most of these studies, if not all, adopt activations of the fully-connected layer of a DCNN as the image or region representation and it is believed that convolutional layer activations are less discriminative. This paper, however, advocates that if used appropriately, convolutional layer activations constitute a powerful image representation. This is achieved by adopting a new technique proposed in this paper called cross-convolutional-layer pooling. More specifically, it extracts subarrays of feature maps of one convolutional layer as local features, and pools the extracted features with the guidance of the feature maps of the successive convolutional layer. Compared with existing methods that apply DCNNs in the similar local feature setting, the proposed method avoids the input image style mismatching issue which is usually encountered when applying fully connected layer activations to describe local regions. Also, the proposed method is easier to implement since it is codebook free and does not have any tuning parameters. By applying our method to four popular visual classification tasks, it is demonstrated that the proposed method can achieve comparable or in some cases significantly better performance than existing fully-connected layer based image representations.


title: Watermarking, Tamper-Proofing, and ObfuscationÐTools for Software Protection

abstract: We identify three types of attack on the intellectual property contained in software and three corresponding technical defenses. A defense against reverse engineering is obfuscation, a process that renders software unintelligible but still functional. A defense against software piracy is watermarking, a process that makes it possible to determine the origin of software. A defense against tampering is tamper-proofing, so that unauthorized modifications to software (for example, to remove a watermark) will result in nonfunctional code. We briefly survey the available technology for each type of defense.


title: Function Interface Analysis: A Principled Approach for Function Recognition in COTS Binaries

abstract: Function recognition is one of the key tasks in binary analysis, instrumentation and reverse engineering. Previous approaches for this problem have relied on matching code patterns commonly observed at the beginning and end of functions. While early efforts relied on compiler idioms and expert-identified patterns, more recent works have systematized the process using machine-learning techniques. In contrast, we develop a novel static analysis based method in this paper. In particular, we combine a low-level technique for enumerating candidate functions with a novel static analysis for determining if these candidates exhibit the properties associated with a function interface. Both control-flow properties (e.g., returning to the location at the stack top at the function entry point) and data-flow properties (e.g., parameter passing via registers and the stack, and the degree of adherence to application-binary interface conventions) are checked. Our approach achieves an F1-score above 99% across a broad range of programs across multiple languages and compilers. More importantly, it achieves a 4x or higher reduction in error rate over best previous results.


title: Semi-autonomous electrosurgery for tumor resection using a multi-degree of freedom electrosurgical tool and visual servoing

abstract: This paper specifies a surgical robot performing semi-autonomous electrosurgery for tumor resection and evaluates its accuracy using a visual servoing paradigm. We describe the design and integration of a novel, multi-degree of freedom electrosurgical tool for the smart tissue autonomous robot (STAR). Standardized line tests are executed to determine ideal cut parameters in three different types of porcine tissue. STAR is then programmed with the ideal cut setting for porcine tissue and compared against expert surgeons using open and laparoscopic techniques in a line cutting task. We conclude with a proof of concept demonstration using STAR to semi-autonomously resect pseudo-tumors in porcine tissue using visual servoing. When tasked to excise tumors with a consistent 4mm margin, STAR can semi-autonomously dissect tissue with an average margin of 3.67 mm and a standard deviation of 0.89mm.


title: The benefits of statistical visualization in an immersive environment

abstract: We have created an immersive application for statistical graphics and have investigated what benefits it offers over more traditional data analysis tools. We present a description of both the traditional data analysis tools and our virtual environment, and results of an experiment designed to determine if an immersive environment based on the XGobi desktop system provides advantages over XGobi for analysis of high-dimensional statistical data. The experiment included two aspects of each environment: three structure detection (visualization) tasks and one ease of interaction task. The subjects were given these tasks in both the C2 virtual environment and a workstation running XGobi. The experiment results showed an improvement in participants' ability to perform structure detection tasks in the C2 to their performance in the desktop environment. However, participants were more comfortable with the interaction tools in the desktop system.


title: Mapping and Localization in 3D Environments Using a 2D Laser Scanner and a Stereo Camera

abstract: 2D laser scanners have been widely used for accomplishing a number of challenging AI and robotics tasks such as mapping of large environments and localization in highly dynamic environments. However, using only one 2D laser scanner could be insufficient and less reliable for accomplishing tasks in 3D environments. The problem could be solved using multiple 2D laser scanners or a 3D laser scanner for performing 3D perception. Unfortunately, the cost of such 3D sensing systems is still too high for enabling AI and robotics applications. In this paper, we propose to use a 2D laser scanner and a stereo camera for accomplishing simultaneous localization and mapping (SLAM) in 3D indoor environments in which the 2D laser scanner is used for SLAM and the stereo camera is used for 3D mapping. The experimental results demonstrate that the proposed system is lower cost yet effective, and the obstacle detection rate is significant improved compares to using one 2D laser scanner for mapping.


title: Poeticizing the economy: The Corsican language in a nexus of pride and profit

abstract: Abstract This article explores the carefully managed semiotic complex found in the Corsican village of Pigna with respect to the themes of pride and profit in the valuation of minority languages. This complex includes the careful coordination of color, graphics, the use of the Corsican language, as well as high-tech soundscaping of place through QR codes that tie specific locales to Corsican music and multilingual texts. This display, labeled the “poeticizing of the economy” by a key social actor, is directed both at the many tourists who visit the village as well as Corsican visitors and locals. It is linked to an effort to embed Corsican linguistic and musical heritage in new practices that both create place and integrate tradition and modernity. On the one hand, these practices can be linked to discourses of both “pride” and “profit” (Duchêne and Heller 2012) attested in many minority language contexts. On the other hand, I argue that the Pigna esthetic indexes a shift in the framework for cultural and linguistic revitalization from one that emphasizes a return to past native speaker communicative practice to one that focuses on the collective agency and identity associated with style and stylization and “transactional,” or situated authenticities.


title: Measuring the tendency of CNNs to Learn Surface Statistical Regularities

abstract: Deep CNNs are known to exhibit the following peculiarity: on the one hand they generalize extremely well to a test set, while on the other hand they are extremely sensitive to so-called adversarial perturbations. The extreme sensitivity of high performance CNNs to adversarial examples casts serious doubt that these networks are learning high level abstractions in the dataset. We are concerned with the following question: How can a deep CNN that does not learn any high level semantics of the dataset manage to generalize so well? The goal of this article is to measure the tendency of CNNs to learn surface statistical regularities of the dataset. To this end, we use Fourier filtering to construct datasets which share the exact same high level abstractions but exhibit qualitatively different surface statistical regularities. For the SVHN and CIFAR-10 datasets, we present two Fourier filtered variants: a low frequency variant and a randomly filtered variant. Each of the Fourier filtering schemes is tuned to preserve the recognizability of the objects. Our main finding is that CNNs exhibit a tendency to latch onto the Fourier image statistics of the training dataset, sometimes exhibiting up to a 28% generalization gap across the various test sets. Moreover, we observe that significantly increasing the depth of a network has a very marginal impact on closing the aforementioned generalization gap. Thus we provide quantitative evidence supporting the hypothesis that deep CNNs tend to learn surface statistical regularities in the dataset rather than higher-level abstract concepts.


title: Exploiting Semantic Role Labeling, WordNet And Wikipedia For Coreference Resolution

abstract: In this paper we present an extension of a machine learning based coreference resolution system which uses features induced from different semantic knowledge sources. These features represent knowledge mined from WordNet and Wikipedia, as well as information about semantic role labels. We show that semantic features indeed improve the performance on different referring expression types such as pronouns and common nouns.


title: Caramel: detecting and fixing performance problems that have non-intrusive fixes

abstract: Performance bugs are programming errors that slow down program execution. While existing techniques can detect various types of performance bugs, a crucial and practical aspect of performance bugs has not received the attention it deserves: how likely are developers to fix a performance bug? In practice, fixing a performance bug can have both benefits and drawbacks, and developers fix a performance bug only when the benefits outweigh the drawbacks. Unfortunately, for many performance bugs, the benefits and drawbacks are difficult to assess accurately. This paper presents C aramel , a novel static technique that detects and fixes performance bugs that have non-intrusive fixes likely to be adopted by developers. Each performance bug detected by C aramel is associated with a loop and a condition. When the condition becomes true during the loop execution, all the remaining computation performed by the loop is wasted. Developers typically fix such performance bugs because these bugs waste computation in loops and have non-intrusive fixes: when some condition becomes true dynamically, just break out of the loop. Given a program, C aramel detects such bugs statically and gives developers a potential source-level fix for each bug. We evaluate C aramel on real-world applications, including 11 popular Java applications (e.g., Groovy, Log4J, Lucene, Struts, Tomcat, etc) and 4 widely used C/C++ applications (Chromium, GCC, Mozilla, and MySQL). C aramel finds 61 new performance bugs in the Java applications and 89 new performance bugs in the C/C++ applications. Based on our bug reports, developers so far have fixed 51 and 65 performance bugs in the Java and C/C++ applications, respectively. Most of the remaining bugs are still under consideration by developers.


FASCIA AS A SENSORY ORGAN : Clinical Applications by Robert Schleip


title: Self-Adaptive Anytime Stream Clustering

abstract: Clustering streaming data requires algorithms which are capable of updating clustering results for the incoming data. As data is constantly arriving, time for processing is limited. Clustering has to be performed in a single pass over the incoming data and within the possibly varying inter-arrival times of the stream. Likewise, memory is limited, making it impossible to store all data. For clustering, we are faced with the challenge of maintaining a current result that can be presented to the user at any given time. In this work, we propose a parameter free algorithm that automatically adapts to the speed of the data stream. It makes best use of the time available under the current constraints to provide a clustering of the objects seen up to that point. Our approach incorporates the age of the objects to reflect the greater importance of more recent data. Moreover, we are capable of detecting concept drift, novelty and outliers in the stream. For efficient and effective handling, we introduce the ClusTree, a compact and self-adaptive index structure for maintaining stream summaries. Our experiments show that our approach is capable of handling a multitude of different stream characteristics for accurate and scalable anytime stream clustering.


title: Relationships between vocal characteristics and body size and shape in human males: An evolutionary explanation for a deep male voice

abstract: A deep male voice may play a role in courtship and competitive behaviours in humans by attracting female mates and indicating body size to male competitors. The current correlational study investigated the relationship between vocal measures (fundamental and formant frequencies) and both body size and shape. Vocal samples and physical measures were obtained from 50 heterosexual male volunteers. A significant negative relationship was found between fundamental frequency and measures of body shape and weight. Further, a significant negative relationship was found between formant dispersion (the relationship between successive formant frequencies) and measures of body size as well as body shape. Findings are discussed in relation to the ‘good genes’ model of sexual selection and the size exaggeration theory of laryngeal descent.


title: Sacramental psychology: Treating intimacy failure in catholic priests.

abstract: The treatment of intimacy failure in Roman Catholic priests in an outpatient program designed to treat sexual misconduct is discussed here. Three case vignettes highlight a particular feature of an intimacy deficit that sets the stage for the priests' sexual acting out. Intimacy, hypocrisy, and self-deception and the Catholic sacramental world view are discussed to shed light on the treatment of sexual addiction in the clergy. Tapping into a priest's beliefs regarding the divine power arising from his own ordination creates a nexus of healing where psychotherapy and spiritual direction join forces to produce a kind of “sacramental psychology.”


title: The Chinese Facial Emotion Recognition Database (CFERD): A computer-generated 3-D paradigm to measure the recognition of facial emotional expressions at different intensities

abstract: The Chinese Facial Emotion Recognition Database (CFERD), a computer-generated three-dimensional (3D) paradigm, was developed to measure the recognition of facial emotional expressions at different intensities. The stimuli consisted of 3D colour photographic images of six basic facial emotional expressions (happiness, sadness, disgust, fear, anger and surprise) and neutral faces of the Chinese. The purpose of the present study is to describe the development and validation of CFERD with nonclinical healthy participants (N=100; 50 men; age ranging between 18 and 50 years), and to generate normative data set. The results showed that the sensitivity index d′ [d′=Z(hit rate)–Z(false alarm rate), where function Z(p), p∈[0,1]], for all emotions was 0.94. The emotion was more readily detected in happiness, and less easily detected in surprise and sadness. In general, this study replicated the previous findings on the recognition accuracy of emotional expression with the Westerner faces. However, our paradigm extends the previous work by including a wider sensitivity range to differentiate subtle perception of emotion intensities. The CFERD will be a useful tool for emotion recognition assessment in affective neurosciences research, especially for the Chinese and cross-cultural studies.


title: A 2D-fractional derivative mask for image feature edge detection

abstract: Feature extraction is a classic problem of machine vision and image processing. Edges are often detected using integer-order differential operators. In this paper, a one-dimensional digital fractional-order Charef differentiator (1D-FCD) is introduced and extended to 2D by a multi-directional operator. The obtained 2D-fractional differentiation (2D-FCD) is a new edge detection operation. The computed multi-directional mask coefficients are computed in a way that image details are detected and preserved. Experiments on texture images have demonstrated the efficiency of the proposed filter compared to existing techniques.


title: iSkin: Flexible, Stretchable and Visually Customizable On-Body Touch Sensors for Mobile Computing

abstract: We propose iSkin, a novel class of skin-worn sensors for touch input on the body. iSkin is a very thin sensor overlay, made of biocompatible materials, and is flexible and stretchable. It can be produced in different shapes and sizes to suit various locations of the body such as the finger, forearm, or ear. Integrating capacitive and resistive touch sensing, the sensor is capable of detecting touch input with two levels of pressure, even when stretched by 30% or when bent with a radius of 0.5cm. Furthermore, iSkin supports single or multiple touch areas of custom shape and arrangement, as well as more complex widgets, such as sliders and click wheels. Recognizing the social importance of skin, we show visual design patterns to customize functional touch sensors and allow for a visually aesthetic appearance. Taken together, these contributions enable new types of on-body devices. This includes finger-worn devices, extensions to conventional wearable devices, and touch input stickers, all fostering direct, quick, and discreet input for mobile computing.


Voltage Level Shifters – Review


title: Vitamin B 6 -magnesium treatment for autism: the current status of the research

abstract: Auteur(s) : Kimberly A Murza, Stacey L Pavelko, Melissa D Malani, Chad Nye University of Central Florida, Center for Autism and Related Disabilities, Orlando, USA In 2005, a systematic review was published by the Cochrane Collaboration that reviewed the effects of vitamin B6-magnesium (vit B6-Mg) treatment for autism [1]. The focus of the review was to assess the impact of vit B6-Mg for improving the communication and behavioural responses of children and adults with [...]


title: Implicit and Explicit Stereotyping of Adolescents

abstract: Although adolescents are commonly assumed to be rebellious, risky and moody, two experiments demonstrate for the first time that these beliefs operate both explicitly and implicitly as stereotypes. In Experiment 1, participants (a) explicitly endorsed adolescent stereotypes and (b) implicitly associated adolescent stereotyped words more rapidly with the adolescent than the adult social category. Individual differences in the explicit endorsement of adolescent stereotypes predicted explicit perceptions of the rebelliousness of a 17-year-old but not a 71-year-old, although individual differences in implicit stereotyping did not. Identification with adults was associated with greater implicit stereotyping but not explicit stereotyping. In Experiment 2, subliminal exposure to adolescent stereotyped words increased subsequent perceptions of the rebelliousness of a 17-year-old but not a 71-year-old. Although individual differences in implicit adolescent stereotyping did not predict explicit evaluations of adolescents, stereotypes of adolescents nevertheless influenced explicit evaluations unconsciously and unintentionally.


title: Image Location Inference by Multisaliency Enhancement

abstract: Locations of images have been widely used in many application scenarios for large geotagged image corpora. As to images that are not geographically tagged, we estimate their locations with the help of the large geotagged image set by content-based image retrieval. Bag-of-words image representation has been utilized widely. However, the individual visual word-based image retrieval approach is not effective in expressing the salient relationships of image region. In this paper, we present an image location estimation approach by multisaliency enhancement. We first extract region-of-interests (ROIs) by mean-shift clustering on the visual words and salient map of the image based on which we further determine the importance of the ROI. Then, we describe each ROI by the spatial descriptors of visual words. Finally, region-based visual phrases are generated to further enhance the saliency in image location estimation. Experiments show the effectiveness of our proposed approach.


title: Student information consumption strategies: implications of the Google effect

abstract: Previous research in the area of distributed cognition within the educational process has revealed several new and emerging phenomena regarding student behavior. Students appear to be strategically discriminating in their learning strategies, taking the opportunity to offload learning when information is easily accessible and retrievable. This paper reviews several current research findings, and offers societal implications.


title: Music Listening Modulates Functional Connectivity and Information Flow in the Human Brain

abstract: Abstract Listening to familiar music has recently been reported to be beneficial during recovery from stroke. A better understanding of changes in functional connectivity and information flow is warranted to further optimize and target this approach through music therapy. Twelve healthy volunteers listened to seven different auditory samples during an fMRI scanning session: a musical piece chosen by the volunteer that evokes a strong emotional response (referred to as: “self-selected emotional”), two unfamiliar music pieces (Invention #1 by J. S. Bach and Gagaku — Japanese classical opera, referred to as “unfamiliar”), the Bach piece repeated with visual guidance (directed music listening [DML]), and three spoken language pieces (unfamiliar African click language, an excerpt of emotionally charged language, and an unemotional reading of a news bulletin). Functional connectivity and betweenness maps, a measure for information flow, were created with a graph-theoretical approach. Distinct variation in funct...


title: Removing Non-Uniform Motion Blur from Images

abstract: We propose a method for removing non-uniform motion blur from multiple blurry images. Traditional methods focus on estimating a single motion blur kernel for the entire image. In contrast, we aim to restore images blurred by unknown, spatially varying motion blur kernels caused by different relative motions between the camera and the scene. Our algorithm simultaneously estimates multiple motions, motion blur kernels, and the associated image segments. We formulate the problem as a regularized energy function and solve it using an alternating optimization technique. Real- world experiments demonstrate the effectiveness of the proposed method.


title: Vehicle Optimal Torque Vectoring Using State-Derivative Feedback and Linear Matrix Inequality

abstract: A controller assistant system is developed based on the closed-form solution of an offline optimization problem for a four-wheel-drive front-wheel-steerable vehicle. The objective of the controller is to adjust the actual vehicle attitude and motion according to the driver's manipulating commands. The controller takes feedback from acceleration signals, and the imposed conditions and limitations on the controller are studied through the concept of state-derivative feedback control systems. The controller gains are optimized using linear matrix inequality (LMI) and genetic algorithm (GA) techniques. Reference signals are calculated using a driver command interpreter module (DCIM) to accurately interpret the driver's intentions for vehicle motion and to allow the controller to generate proper control actions. It is shown that the controller effectively enhances the handling performance and stability of the vehicle under different road conditions and driving scenarios. Although controller performance is studied for a four-wheel-drive front-wheel-steerable vehicle, the algorithm can also be applied to other vehicle configurations with slight changes.


title: An RRT-based navigation approach for mobile robots and automated vehicles

abstract: Advances in autonomous navigation, safety, and natural-landmark based localization, are among the key objec- tives in the development of the next generation of autonomous vehicles, to be deployed in manufacturing and semi-structured environments. In this paper, autonomous navigation and collision detection will be focused, where it is proposed a novel navigation approach that incorporates a RRT-based dynamic path planning and a path-following controller. Safety issues are taken into account in the form of a laser-based object detection and tracking. Experimental results obtained in a virtual environment provide evidence that our proposed navigation method is promising for real-world applications.


title: Design of dual-frequency substrate integrated waveguide (SIW) cavity backed slot array antenna

abstract: In this paper, a design of planar, compact dual frequency slot array antenna is implemented using substrate integrated waveguide (SIW) cavity backed slot antenna as array element. The proposed design uses a pair of slots placed at the top wall of the cavity to excite higher order hybrid mode along with conventional TE 120 mode which helps to create dual resonances. A design of 1×4 array of the proposed SIW cavity backed slot pair is used to achieve dual frequency slot array antenna in planar substrate. The array is fed by SIW power divider. The proposed antenna array resonates at 9.88 GHz and 10.76 GHz with a gain of 11.3 dBi and 11.8 dBi respectively and sidelobe level 12.5 dB below at both resonances.


title: Leveraging Financial News for Stock Trend Prediction with Attention-Based Recurrent Neural Network

abstract: Stock market prediction is one of the most attractive research topic since the successful prediction on the market's future movement leads to significant profit. Traditional short term stock market predictions are usually based on the analysis of historical market data, such as stock prices, moving averages or daily returns. However, financial news also contains useful information on public companies and the market. Existing methods in finance literature exploit sentiment signal features, which are limited by not considering factors such as events and the news context. We address this issue by leveraging deep neural models to extract rich semantic features from news text. In particular, a Bidirectional-LSTM are used to encode the news text and capture the context information, self attention mechanism are applied to distribute attention on most relative words, news and days. In terms of predicting directional changes in both Standard & Poor's 500 index and individual companies stock price, we show that this technique is competitive with other state of the art approaches, demonstrating the effectiveness of recent NLP technology advances for computational finance.


title: Perceptual Distortions in the Adaptation of English Consonant Clusters: Syllable Structure or Consonantal Contact Constraints?

abstract: We present the results from an experiment that tests the perception of English consonantal sequences by Korean speakers and we confirm that perceptual epenthesis in a second languge (L2) arises from syllable structure restrictions of the first language (L1), rather than linear co-occurence restrictions. Our study replicates and extends Dupoux, Kakehi, Hirose, Pallier, & Mehler’s (1999) results that suggested that listeners perceive epenthetic vowels within consonantal sequences that violate the phonotactics of their L1. Korean employs at least two kinds of phonotactic restrictions: (i) syllable structure restrictions that prohibit the occurence of certain consonants in coda position (e.g., *[c.], *[g.]), while allowing others (e.g., [k.], [l.]), and (ii) consonantal contact restrictions that ban the co-occurrence of certain heterosyllabic consonants (e.g., *[k.m]; *[l.n]) due to various phonological processes that repair such sequences on the surface (i.e., /k.m / ! [!.m]; /l.n / ! [l.l]). The results suggest that Korean syllable structure restrictions, rather than consonantal contact restrictions, result in the perception of epenthetic vowels. Furthermore, the frequency of co-occurrence fails to explain the epenthesis effects in the percept of consonant clusters employed in the present study. We address questions regarding the interaction between speech perception and phonology and test the validity of Steriade’s (2001 a,b) Perceptual-Mapping (P-Map) hypothesis for the Korean sonorant assimilation processes. Our results indicate that Steriade’s hypothesis makes incorrect predictions about Korean phonology and that speech perception is not isomorphic to speech production.


title: Ammonia Affects Maillard Chemistry of an Extruded Autolyzed Yeast Extract: Pyrazine Aroma Generation and Brown Color Formation

abstract: The effect of ammonia was studied by extruding mixtures of autolyzed yeast extract, glucose, and ammonium bicarbonate. The resulting extrudates were compared for degree of roast aroma generation by analysis of the pyrazine volatiles formed. In addition, the overall extent of the browning reaction was measured by monitoring brown color formation. Samples which contained ammonia exhibited a greater degree of brown color formation; however, ammonia adversely affected the total amount of pyrazines generated. The reactive nature of the free ammonia molecule was suspected to be the governing causative factor.


title: A climbing motion recognition method using anatomical information for screen climbing games

abstract: Screen climbing games have made a new category of gaming experience between a human climber and a virtual game projected onto an artificial climbing wall. Here, climbing motion recognition is required to interact with the game. In existing climbing games, motion recognition is based on a simple calculation using the depth difference between the climber’s body area and the climbing wall. However, using the body area in this way is devoid of anatomical information; thus the gaming system cannot recognize which part, or parts, of the climber’s body is in contact with the artificial climbing wall. In this paper, we present a climbing motion recognition method using anatomical information obtained by parsing a climber’s body area into its constituent anatomical parts. In ensuring that game events consider anatomical information, a climbing game can provide a more immersive experience for gamers.


title: Transcranial Current Stimulation Alters the Expression of Immune-Mediating Genes

abstract: Despite its extensive use in clinical studies, the molecular mechanisms underlying the effects of transcranial direct current stimulation (tDCS) remain to be elucidated. We previously described subacute effects of tDCS on immune- and stem cells in the rat brain. To investigate the more immediate effects of tDCS regulating those cellular responses, we treated rats with a single session of either anodal or cathodal tDCS, and analyzed the gene expression by microarray; sham-stimulated rats served as control. Anodal tDCS increased expression of several genes coding for the major histocompatibility complex I (MHC I), while cathodal tDCS increased the expression of the immunoregulatory protein osteopontin (OPN). We confirmed the effects of gene upregulation by immunohistochemistry at the protein level. Thus, our data show a novel mechanism for the actions of tDCS on immune- and inflammatory processes, providing a target for future therapeutic studies.


title: Measuring auditory selective attention using frequency tagging

abstract: Frequency tagging of sensory inputs (presenting stimuli that fluctuate periodically at rates to which the cortex can phase lock) has been used to study attentional modulation of neural responses to inputs in different sensory modalities. For visual inputs, the visual steady-state response (VSSR) at the frequency modulating an attended object is enhanced, while the VSSR to a distracting object is suppressed. In contrast, the effect of attention on the auditory steady-state response (ASSR) is inconsistent across studies. However, most auditory studies analyzed results at the sensor level or used only a small number of equivalent current dipoles to fit cortical responses. In addition, most studies of auditory spatial attention used dichotic stimuli (independent signals at the ears) rather than more natural, binaural stimuli. Here, we asked whether these methodological choices help explain discrepant results. Listeners attended to one of two competing speech streams, one simulated from the left and one from the right, that were modulated at different frequencies. Using distributed source modeling of magnetoencephalography results, we estimate how spatially directed attention modulates the ASSR in neural regions across the whole brain. Attention enhances the ASSR power at the frequency of the attended stream in the contralateral auditory cortex. The attended-stream modulation frequency also drives phase-locked responses in the left (but not right) precentral sulcus (lPCS), a region implicated in control of eye gaze and visual spatial attention. Importantly, this region shows no phase locking to the distracting stream suggesting that the lPCS in engaged in an attention-specific manner. Modeling results that take account of the geometry and phases of the cortical sources phase locked to the two streams (including hemispheric asymmetry of lPCS activity) help partly explain why past ASSR studies of auditory spatial attention yield seemingly contradictory results.


Differential handling and weight gain in the albino rat.


title: Sylvester Normalizing Flows for Variational Inference

abstract: Variational inference relies on flexible approximate posterior distributions. Normalizing flows provide a general recipe to con- ::: struct flexible variational posteriors. We introduce Sylvester normalizing flows, which can be seen as a generalization of planar flows. Sylvester normalizing flows remove the well-known single-unit bottleneck from planar flows, making a single transformation much more flexible. We compare the performance of Sylvester normalizing flows against planar flows and inverse autoregressive flows and demonstrate that they compare favorably on several datasets.


title: Fast End-to-End Trainable Guided Filter

abstract: Image processing and pixel-wise dense prediction have been advanced by harnessing the capabilities of deep learning. One central issue of deep learning is the limited capacity to handle joint upsampling. We present a deep learning building block for joint upsampling, namely guided filtering layer. This layer aims at efficiently generating the high-resolution output given the corresponding low-resolution one and a high-resolution guidance map. The proposed layer is composed of a guided filter, which is reformulated as a fully differentiable block. To this end, we show that a guided filter can be expressed as a group of spatial varying linear transformation matrices. This layer could be integrated with the convolutional neural networks (CNNs) and jointly optimized through end-to-end training. To further take advantage of end-to-end training, we plug in a trainable transformation function that generates task-specific guidance maps. By integrating the CNNs and the proposed layer, we form deep guided filtering networks. The proposed networks are evaluated on five advanced image processing tasks. Experiments on MIT-Adobe FiveK Dataset demonstrate that the proposed approach runs 10-100 times faster and achieves the state-of-the-art performance. We also show that the proposed guided filtering layer helps to improve the performance of multiple pixel-wise dense prediction tasks. The code is available at this https URL


title: Use of cycloheximide on intracellular growth of Mycobacterium leprae in cultured murine macrophages.

abstract: Mycobacterium leprae is an obligate intracellular parasite and grows within mononuclear phagocytes. When murine macrophages derived from the peritoneal cavities of CBA mice were infected in vitro with M. leprae (Thai-53 strain), intracellular multiplication was observed three weeks after infection. On the other hand, there was no increase in the number of heat-killed M. leprae at the same times after infection. Morphological studies showed that the growth rate of the bacteria increased by about 20-30% in medium supplemented with cycloheximide. With the addition of cycloheximide to the culture medium, metabolic activity of macrophages was decreased but infected cells were maintained in good condition and seldom floated off from the culture flask.