title: Effects of 24-epibrassinolide on antioxidation defense and osmoregulation systems of young grapevines (V. vinifera L.) under chilling stress

abstract: The objective of this study was to investigate the influence of exogenous 24-epibrassinolide (EBR) on the substances involved in antioxidation and osmoregulation responses of young grape plants under chilling stress. The grapevine leaves were sprayed with 0 (control), 0.05, 0.10 or 0.15 mg/L of 24-epibrassinolide and then exposed to 4 and 0 °C for 24 h, respectively. The EBR treatment significantly enhanced the activities of antioxidative enzymes such as catalase, superoxide dismutase, peroxidase and ascorbate peroxidase in the plant leaves compared with the control. The contents of ascorbic acid and reduced glutathione increased after the EBR treatment, while reactive oxygen species (ROS) and lipid peroxidation were inhibited. In addition, the EBR treatment also greatly increased the contents of free proline, soluble protein, and soluble sugar. These results indicated that exogenous EBR treatment could enhance the antioxidation defense system and reduce oxidative damage caused by ROS and lipid oxidation in the young grapevine leaves. Meanwhile, it was found that the treatment could also increase the osmoregulation substance content in the grapevine leaves and improve their resistance against chilling stress.


title: Do criminally accused politicians affect economic outcomes? Evidence from India

abstract: The recent increase in the number of criminally accused politicians elected to state assemblies has caused much furor in India. Despite the potentially important consequences and the widely divergent views, the implications of their elections to state legislative assemblies on constituency-level economic performance are unknown. Using a regression discontinuity design and data on the intensity of night lights in satellite imagery at the constituency level, our results suggest that the cost of electing criminally accused politicians on measures of economic activity is quite large. Using estimates of the elasticity of GDP to light, we find that the election of criminally accused candidates lead to roughly 5 percent lower GDP growth per year on average. These estimated costs increase for candidates with serious accusations, multiple accusations, and accusations regarding financial crimes. Our result survives variety of robustness checks.


title: Network Coding Multicast in Satellite Networks

abstract: The broadcast nature of satellite networks such as DVB-RCS based systems provides a highly effective medium for content distribution, especially in the case of geographically scattered clients. Non-real-time services typically rely on application level protocols that often use forward error correction (FEC) and carousel data cycling schemes in unidirectional links. The presence of a feedback channel in satellite networks is mostly unexploited in the context of reliable multicast services. We show that network coding protocols offer a native and transparent solution for reliable multicast over satellites. When employing a feedback channel, network coding can reach near real-time performance at an efficiency level close to known theoretical bounds for lossy satellite links.


title: Are you sleeping?: sharing portrayed sleeping status within a social network

abstract: Within a group of peers, it is often useful or interesting to know whether someone in the group has gone to bed or whether they have awakened in the morning. This information, naturally integrated as a peripheral augmentation of an alarm clock, allows people to know whether it is appropriate to make a call or feel more connected with someone living remotely. In this paper, we present the design and evaluation of such an alarm clock, the BuddyClock, and describe how it enables users in a small social network to automatically share information about their sleeping behaviors with one another. Through 3-6 week deployment studies of this technology with five different social networks, we found that the alarm clock affected participant behaviors and allowed them to feel more connected to those with whom they shared their sleeping behaviors.


Synthesising isiZulu-English Code-Switch Bigrams Using Word Embeddings


title: Levelized Taxonomy Approach for the Job Seeking/Recruitment Problem

abstract: Recently, job recommendation has attracted a lot of research attention, the aim being to get a sorted list of relevant candidates for an applicant (job sekeer or recruiter). To an effective matching, the utilisation of semantic technology has shown good results. Particularly, the use of taxonomy hierarchizing the skill following a relation of inheritance. However request to the user to weighing the skills is a barrier to an usability and an efficiency of such methods on the user point of view. This paper intends to provide a first answer for such a problem.


title: Distinct autophagosomal-lysosomal fusion mechanism revealed by thapsigargin-induced autophagy arrest.

abstract: Autophagy, a catabolic pathway that delivers cellular components to lysosomes for degradation, can be activated by stressful conditions such as nutrient starvation and endoplasmic reticulum (ER) stress. We report that thapsigargin, an ER stressor widely used to induce autophagy, in fact blocks autophagy. Thapsigargin does not affect autophagosome formation but leads to accumulation of mature autophagosomes by blocking autophagosome fusion with the endocytic system. Strikingly, thapsigargin has no effect on endocytosis-mediated degradation of epidermal growth factor receptor. Molecularly, while both Rab7 and Vps16 are essential regulatory components for endocytic fusion with lysosomes, we found that Rab7 but not Vps16 is required for complete autophagy flux, and that thapsigargin blocks recruitment of Rab7 to autophagosomes. Therefore, autophagosomal-lysosomal fusion must be governed by a distinct molecular mechanism compared to general endocytic fusion.


title: Interactive 3-D Video Representation and Coding Technologies

abstract: Interactivity in the sense of being able to explore and navigate audio-visual scenes by freely choosing viewpoint and viewing direction, is an important key feature of new and emerging audio-visual media. This paper gives an overview of suitable technology for such applications, with a focus on international standards, which are beneficial for consumers, service providers, and manufacturers. We first give a general classification and overview of interactive scene representation formats as commonly used in computer graphics literature. Then, we describe popular standard formats for interactive three-dimensional (3-D) scene representation and creation of virtual environments, the virtual reality modeling language (VRML), and the MPEG-4 BInary Format for Scenes (BIFS) with some examples. Recent extensions to MPEG-4 BIFS, the Animation Framework eXtension (AFX), providing advanced computer graphics tools, are explained and illustrated. New technologies mainly targeted at reconstruction, modeling, and representation of dynamic real world scenes are further studied. The user shall be able to navigate photorealistic scenes within certain restrictions, which can be roughly defined as 3-D video. Omnidirectional video is an extension of the planar two-dimensional (2-D) image plane to a spherical or cylindrical image plane. Any 2-D view in any direction can be rendered from this overall recording to give the user the impression of looking around. In interactive stereo two views, one for each eye, are synthesized to provide the user with an adequate depth cue of the observed scene. Head motion parallax viewing can be supported in a certain operating range if sufficient depth or disparity data are delivered with the video data. In free viewpoint video, a dynamic scene is captured by a number of cameras. The input data are transformed into a special data representation that enables interactive navigation through the dynamic scene environment.


title: Juvenile generalized pustular psoriasis with IL36RN mutation treated with short-term infliximab.

abstract: A 8-year-old Chinese boy with generalized pustular psoriasis (GPP) refractory to cyclosporine and methylprednisolone was treated successfully with two infusions of infliximab 3.3 mg/kg. He remained in remission for 21 months. Direct sequencing of IL36RN gene showed a homozygous mutation, c.115 + 6T>C. Juvenile GPP is a rare severe form of psoriasis occasionally associated with life-threatening complications. Like acitretin, cyclosporine and methotrexate, infliximab has been reported to be effective for juvenile GPP in case reports. However, there is a lack of data in the optimal treatment course of infliximab for juvenile GPP. Prolonged administration of these medications may cause toxic or fatal complications. We suggest that short-term infliximab regimen should be recommended as a choice for acute juvenile GPP refractory to traditional systemic therapies. WBC count and CRP are sensitive parameters to reflect the disease activity and evaluate the effectiveness of treatment. Monitoring CD4 T lymphocyte count, preventing and correcting CD4 lymphocytopenia are important in the treatment course of juvenile GPP.


title: © 2010 INFORMS Online Demand Under Limited Consumer Search

abstract: Using aggregate product search data from Amazon.com, we jointly estimate consumer information search and online demand for consumer durable goods. To estimate the demand and search primitives, we introduce an optimal sequential search process into a model of choice and treat the observed market-level product search data as aggregations of individual-level optimal search sequences. The model builds on the dynamic programming framework by Weitzman [Weitzman, M. L. 1979. Optimal search for the best alternative. Econometrica47(3) 641--654] and combines it with a choice model. It can accommodate highly complex demand patterns at the market level. At the individual level, the model has a number of attractive properties in estimation, including closed-form expressions for the probability distribution of alternative sets of searched goods and breaking the curse of dimensionality. Using numerical experiments, we verify the model's ability to identify the heterogeneous consumer tastes and search costs from product search data. Empirically, the model is applied to the online market for camcorders and is used to answer manufacturer questions about market structure and competition and to address policy-maker issues about the effect of selectively lowered search costs on consumer surplus outcomes. We demonstrate that the demand estimates from our search model predict the actual product sales ranks. We find that consumer search for camcorders at Amazon.com is typically limited to 10--15 choice options and that this affects estimates of own and cross elasticities. In a policy simulation, we also find that the vast majority of the households benefit from Amazon.com's product recommendations via lower search costs.


title: CONTRAST ENHANCEMENT OF COLOR IMAGES USING IMPROVED RETINEX METHOD

abstract: Color images provide large information for human visual perception compared to grayscale images. Color image enhancement methods enhance the visual data to increase the clarity of the color image. It increases human perception of information. Different color image contrast enhancement methods are used to increase the contrast of the color images. The Retinex algorithms enhance the color images similar to the scene perceived by the human eye. Multiscale retinex with color restoration (MSRCR) is a type of retinex algorithm. The MSRCR algorithm results in graying out and halo artifacts at the edges of the images. So here the focus is on improving the MSRCR algorithm by combining it with contrast limited adaptive histogram equalization (CLAHE) using image.


title: Handwriting training in Parkinson’s disease: A trade-off between size, speed and fluency

abstract: BACKGROUND ::: In previous work, we found that intensive amplitude training successfully improved micrographia in Parkinson's disease (PD). Handwriting abnormalities in PD also express themselves in stroke duration and writing fluency. It is currently unknown whether training changes these dysgraphic features. ::: ::: ::: OBJECTIVE ::: To determine the differential effects of amplitude training on various hallmarks of handwriting abnormalities in PD. ::: ::: ::: METHODS ::: We randomized 38 right-handed subjects in early to mid-stage of PD into an experimental group (n = 18), receiving training focused at improving writing size during 30 minutes/day, five days/week for six weeks, and a placebo group (n = 20), receiving stretch and relaxation exercises at equal intensity. Writing skills were assessed using a touch-sensitive tablet pre- and post-training, and after a six-week retention period. Tests encompassed a transfer task, evaluating trained and untrained sequences, and an automatization task, comparing single- and dual-task handwriting. Outcome parameters were stroke duration (s), writing velocity (cm/s) and normalized jerk (i.e. fluency). ::: ::: ::: RESULTS ::: In contrast to the reported positive effects of training on writing size, the current results showed increases in stroke duration and normalized jerk after amplitude training, which were absent in the placebo group. These increases remained after the six-week retention period. In contrast, velocity remained unchanged throughout the study. ::: ::: ::: CONCLUSION ::: While intensive amplitude training is beneficial to improve writing size in PD, it comes at a cost as fluency and stroke duration deteriorated after training. The findings imply that PD patients can redistribute movement priorities after training within a compromised motor system.


title: Context-aware mobile music recommendation for daily activities

abstract: Existing music recommendation systems rely on collaborative filtering or content-based technologies to satisfy users' long-term music playing needs. Given the popularity of mobile music devices with rich sensing and wireless communication capabilities, we present in this paper a novel approach to employ contextual information collected with mobile devices for satisfying users' short-term music playing needs. We present a probabilistic model to integrate contextual information with music content analysis to offer music recommendation for daily activities, and we present a prototype implementation of the model. Finally, we present evaluation results demonstrating good accuracy and usability of the model and prototype.


title: Hippocampus segmentation through multi-view ensemble ConvNets

abstract: Automated segmentation of brain structures from MR images is an important practice in many neuroimage studies. In this paper, we explore the utilization of a multi-view ensemble approach that relies on neural networks (NN) to combine multiple decision maps in achieving accurate hippocampus segmentation. Constructed under a general convolutional NN structure, our Ensemble-Net networks explore different convolution configurations to capture the complementary information residing in the multiple label probabilities produced by our U-Seg-Net (a modified U-Net) segmentation neural network. T1-weighted MRI scans and the associated Hippocampal masks of 110 healthy subjects from the ADNI project were used as the training and testing data. The combined U-Seg-Net + Ensemble-Net framework achieves over 89% Dice ratio on the test dataset.


title: Prefab: implementing advanced behaviors using pixel-based reverse engineering of interface structure

abstract: Current chasms between applications implemented with different user interface toolkits make it difficult to implement and explore potentially important interaction techniques in new and existing applications, limiting the progress and impact of human-computer interaction research. We examine an approach based in the single most common characteristic of all graphical user interface toolkits, that they ultimately paint pixels to a display. We present Prefab, a system for implementing advanced behaviors through the reverse engineering of the pixels in graphical interfaces. Informed by how user interface toolkits paint interfaces, Prefab features a separation of the modeling of widget layout from the recognition of widget appearance. We validate Prefab in implementations of three applications: target-aware pointing techniques, Phosphor transitions, and Side Views parameter spectrums. Working only from pixels, we demonstrate a single implementation of these enhancements in complex existing applications created in different user interface toolkits running on different windowing systems.


title: Generation of multi-LOD 3D city models in CityGML with the procedural modelling engine Random3Dcity

abstract: The production and dissemination of semantic 3D city models is rapidly increasing benefiting a growing number of use cases. However, their availability in multiple LODs and in the CityGML format is still problematic in practice. This hinders applications and experiments where multi-LOD datasets are required as input, for instance, to determine the performance of different LODs in a spatial analysis. An alternative approach to obtain 3D city models is to generate them with procedural modelling, which is – as we discuss in this paper – well suited as a method to source multi-LOD datasets useful for a number of applications. However, procedural modelling has not yet been employed for this purpose. Therefore, we have developed RANDOM3DCITY, an experimental procedural modelling engine for generating synthetic datasets of buildings and other urban features. The engine is designed to produce models in CityGML and does so in multiple LODs. Besides the generation of multiple geometric LODs, we implement the realisation of multiple levels of spatiosemantic coherence, geometric reference variants, and indoor representations. As a result of their permutations, each building can be generated in 392 different CityGML representations, an unprecedented number of modelling variants of the same feature. The datasets produced by RANDOM3DCITY are suited for several applications, as we show in this paper with documented uses. The developed engine is available under an open-source licence at Github at http://github.com/tudelft3d/Random3Dcity.


title: Inventory rebalancing through pricing in public bike sharing systems

abstract: Abstract This paper presents a new conceptual approach to improve the operational performance of public bike sharing systems using pricing schemes. Its methodological developments are accompanied by experimental analyses with bike demand data from Capital Bikeshare program of Washington, DC (USA). An optimized price vector determines the incentive levels that can persuade system customers to take bikes from, or park them at, neighboring stations so as to strategically minimize the number of imbalanced stations. This strategy intentionally makes some imbalanced stations even more imbalanced, creating hub stations. This reduces the need for trucks and dedicated staff to carry out inventory repositioning. For smaller networks, a bi-level optimization model with a single level reformulation is introduced to minimize the number of imbalanced stations optimally. The results are compared with a heuristic approach that adjusts route prices by segregating the stations into different categories based on their current inventory profile, projected future demand, and maximum and minimum inventory values calculated to fulfill certain desired service level requirements. We use a routing model for repositioning trucks to show that the proposed optimization model and the latter heuristic approach, called the iterative price adjustment scheme (IPAS), reduce the overall operating cost while partially or fully obviating the need for a manual repositioning operation.


title: Criminal prediction using Naive Bayes theory

abstract: The paper introduces a solution to the criminal prediction problem using Naive Bayes theory. The criminal prediction problem is stated as finding the most likely criminal of a particular crime incident when the history of crime incidents is given with the incident-level crime data. The incident-level crime data are assumed to be given as a crime dataset where the incident date and location, crime type, criminal ID and the acquaintances are the attributes or crime parameters considered in the paper. The acquaintances are the suspects whose names are either directly involved in the incident or indirectly the acquaintances of the criminal. Acquiring the crime dataset is a difficult process in practice due to confidentiality principle. So the crime dataset is generated synthetically using the state-of-the-art methods. The proposed system is tested for the criminal prediction problem using the cross-validation, and the experimental results show that the proposed system provides high scores in finding of suspected criminals.


title: A Systematic Review of Wearable Patient Monitoring Systems – Current Challenges and Opportunities for Clinical Adoption

abstract: The aim of this review is to investigate barriers and challenges of wearable patient monitoring (WPM) solutions adopted by clinicians in acute, as well as in community, care settings. Currently, healthcare providers are coping with ever-growing healthcare challenges including an ageing population, chronic diseases, the cost of hospitalization, and the risk of medical errors. WPM systems are a potential solution for addressing some of these challenges by enabling advanced sensors, wearable technology, and secure and effective communication platforms between the clinicians and patients. A total of 791 articles were screened and 20 were selected for this review. The most common publication venue was conference proceedings (13, 54%). This review only considered recent studies published between 2015 and 2017. The identified studies involved chronic conditions (6, 30%), rehabilitation (7, 35%), cardiovascular diseases (4, 20%), falls (2, 10%) and mental health (1, 5%). Most studies focussed on the system aspects of WPM solutions including advanced sensors, wireless data collection, communication platform and clinical usability based on a specific area or disease. The current studies are progressing with localized sensor-software integration to solve a specific use-case/health area using non-scalable and 'silo' solutions. There is further work required regarding interoperability and clinical acceptance challenges. The advancement of wearable technology and possibilities of using machine learning and artificial intelligence in healthcare is a concept that has been investigated by many studies. We believe future patient monitoring and medical treatments will build upon efficient and affordable solutions of wearable technology.


title: Small Cluster in Cyber Physical Systems: Network Topology, Interdependence and Cascading Failures

abstract: In cyber physical system (CPS), computational resources and physical resources are strongly correlated and mutually dependent. Cascading failures occur between coupled networks, cause the system more fragile than single network. Besides widely used metric giant component , we study small cluster (small component) in interdependent networks after cascading failures occur. We first introduce an overview on how small clusters distribute in various single networks. Then we propose a percolation theory based mathematical method to study how small clusters be affected by the interdependence between two coupled networks. We prove that the upper bounds exist for both the fraction and the number of operating small clusters. Without loss of generality, we apply both synthetic network and real network data in simulation to study small clusters under different interdependence models and network topologies. The extensive simulations highlight our findings: except the giant component, considerable proportion of small clusters exists, with the remaining part fragmenting to very tiny pieces or even massive isolated single vertex; no matter how the two networks are tightly coupled, an upper bound exists for the size of small clusters. We also discover that the interdependent small-world networks generally have the highest fractions of operating small clusters. Three attack strategies are compared: Inter Degree Priority Attack , Intra Degree Priority Attack and Random Attack . We observe that the fraction of functioning small clusters keeps stable and is independent from the attack strategies.


title: Real-Time Hand Tracking Using a Sum of Anisotropic Gaussians Model

abstract: Real-time marker-less hand tracking is of increasing importance in human-computer interaction. Robust and accurate tracking of arbitrary hand motion is a challenging problem due to the many degrees of freedom, frequent self-occlusions, fast motions, and uniform skin color. In this paper, we propose a new approach that tracks the full skeleton motion of the hand from multiple RGB cameras in real-time. The main contributions include a new generative tracking method which employs an implicit hand shape representation based on Sum of Anisotropic Gaussians (SAG), and a pose fitting energy that is smooth and analytically differentiable making fast gradient based pose optimization possible. This shape representation, together with a full perspective projection model, enables more accurate hand modeling than a related baseline method from literature. Our method achieves better accuracy than previous methods and runs at 25 fps. We show these improvements both qualitatively and quantitatively on publicly available datasets.


Solid and Liquid Movement Characteristics in Two-dimensional Cold Model for the Dripping Zone of a Blast Furnace


title: Enriching in-person encounters through social media: A study on family connectedness for the elderly

abstract: Social media sites have become immensely popular. In 2010 it was estimated that Americans spent a quarter of their online time using social networking sites (SNSs) and blogs. Prior studies have shown how people spend more time socializing through digital communication services such as SNSs reducing face-to-face interaction. Individuals limited offline interactions cause a sense of self-perception of being less socially involved. In this paper we explore how the use of an ubiquitous system we developed, Tlatoque, is able to adapt and move the SNS's social capital outside the desktop into a domestic setting to support older adults' offline interactions with their family. The findings of a 21 week deployment study uncovered the offline practices surrounding the use of Tlatoque and its social implications toward the existing family ties (n=30). Results qualitatively indicate that the content shared in SNSs strengthens older adults' social network by enriching and complementing traditional social engagements such as those conducted over the phone or in-person.


title: Breast Cancer Histopathological Image Classification: Is Magnification Important?

abstract: Breast cancer is one of the most common cancer in women worldwide. It is typically diagnosed via histopathological microscopy imaging, for which image analysis can aid physicians for more effective diagnosis. Given a large variability in tissue appearance, to better capture discriminative traits, images can be acquired at different optical magnifications. In this paper, we propose an approach which utilizes joint colour-texture features and a classifier ensemble for classifying breast histopathology images. While we demonstrate the effectiveness of the proposed framework, an important objective of this work is to study the image classification across different optical magnification levels. We provide interesting experimental results and related discussions, demonstrating a visible classification invariance with cross-magnification training-testing. Along with magnification-specific model, we also evaluate the magnification independent model, and compare the two to gain some insights.


Evaluating the FIWARE Platform


title: Novel Printed MIMO Antenna With Pattern and Polarization Diversity

abstract: In this letter, a novel compact antenna system for diversity/multiple-input–multiple-output (MIMO) application is proposed for WLAN (5.8 GHz) band with good isolation between the two input ports. The novelty of the proposed MIMO antenna system is, though the resonators are physically separated by a distance of $0.029\lambda $ ( $\lambda $ is the free-space wavelength at 5.8 GHz) equal to the thickness of the substrate (1.524 mm) used for the fabrication of the antenna system, the isolation between them is enough for practical application of the antenna for MIMO. The MIMO system possesses good pattern diversity and polarization diversity with good isolation without the use of any isolation enhancement techniques. Moreover, the concept can be extended to the realization of an MIMO antenna system to operate at other frequencies with suitable scaling and optimization of geometrical parameters of the antenna topology.


Universal Planning Networks-Long Version + Supplementary


title: A Framework for Criminal Network Analysis Using Big Data

abstract: Big data analytics have been proposed as a disruptive technology that will reshape security and law enforcement agencies, which is a domain that relies on data analytics to achieve criminal network insights for better decision-making. Rooted in current literature, we review the landscape of criminal network analysis through the big data analytic framework in this paper. We identify the big data sources, big data platforms, tools, and applications related to criminal network analysis. We then present some real-world examples, research challenges, and future research directions of applying big data analytics to the criminal analytics domain.


title: Web media semantic concept retrieval via tag removal and model fusion

abstract: Multimedia data on social websites contain rich semantics and are often accompanied with user-defined tags. To enhance Web media semantic concept retrieval, the fusion of tag-based and content-based models can be used, though it is very challenging. In this article, a novel semantic concept retrieval framework that incorporates tag removal and model fusion is proposed to tackle such a challenge. Tags with useful information can facilitate media search, but they are often imprecise, which makes it important to apply noisy tag removal (by deleting uncorrelated tags) to improve the performance of semantic concept retrieval. Therefore, a multiple correspondence analysis (MCA)-based tag removal algorithm is proposed, which utilizes MCA's ability to capture the relationships among nominal features and identify representative and discriminative tags holding strong correlations with the target semantic concepts. To further improve the retrieval performance, a novel model fusion method is also proposed to combine ranking scores from both tag-based and content-based models, where the adjustment of ranking scores, the reliability of models, and the correlations between the intervals divided on the ranking scores and the semantic concepts are all considered. Comparative results with extensive experiments on the NUS-WIDE-LITE as well as the NUS-WIDE-270K benchmark datasets with 81 semantic concepts show that the proposed framework outperforms baseline results and the other comparison methods with each component being evaluated separately.


title: LightBox: SGX-assisted Secure Network Functions at Near-native Speed

abstract: The recent trend of outsourcing network functions, aka. middleboxes, raises confidentiality and integrity concern on redirected packet, runtime state, and processing result. The outsourced middleboxes must be protected against cyber attacks and malicious service provider. It is challenging to simultaneously achieve strong security, practical performance, complete functionality and compatibility. Prior software-centric approaches relying on customized cryptographic primitives fall short of fulfilling one or more desired requirements. In this paper, after systematically addressing key challenges brought to the fore, we design and build a secure SGX-assisted system, LightBox, which supports secure and generic middlebox functions, efficient networking, and most notably, low-overhead stateful processing. LightBox protects middlebox from powerful adversary, and it allows stateful network function to run at nearly native speed: it adds only 3us packet processing delay even when tracking 1.5M concurrent flows.


title: Efficient Disk I/O Scheduling with QoS Guarantee for Xen-based Hosting Platforms

abstract: In this paper, we address the problem of allocating disk resources to guarantee specified latency and throughput targets of VMs while keeping efficient disk I/O. Accordingly, we present two-level scheduling framework, namely Flubber, in Xen-based hosting platform that decouples latency and throughput allocation. The high-level throughput control regulates the pending requests from the VMs, in order to meet the throughput requirements of different VMs and ensure isolation. Meanwhile, the low-level latency control, by the virtue of the batch and delay EDF mechanism, reorders all pending requests from VMs based on the their deadlines, and batches them to the disk device considering the locality of accesses across VMs. We have implemented Flubber with intensive evaluations on Xen-based host. The results show that Flubber can simultaneously meet the different service requirements of VMs while improving the efficiency of the physical disk. In contrast to CFQ, besides that Flubber achieves the desired QoS of each VM, Flubber speeds up the sequential and random read by 17% and 25% due to the efficient physical disk utilization.


title: GraphChi: Large-scale graph computation on just a PC

abstract: Current systems for graph computation require a distributed computing cluster to handle very large real-world problems, such as analysis on social networks or the web graph. While distributed computational resources have become more accessible, developing distributed graph algorithms still remains challenging, especially to non-experts. ::: ::: In this work, we present GraphChi, a disk-based system for computing efficiently on graphs with billions of edges. By using a well-known method to break large graphs into small parts, and a novel parallel sliding windows method, GraphChi is able to execute several advanced data mining, graph mining, and machine learning algorithms on very large graphs, using just a single consumer-level computer. We further extend GraphChi to support graphs that evolve over time, and demonstrate that, on a single computer, GraphChi can process over one hundred thousand graph updates per second, while simultaneously performing computation. We show, through experiments and theoretical analysis, that GraphChi performs well on both SSDs and rotational hard drives. ::: ::: By repeating experiments reported for existing distributed systems, we show that, with only fraction of the resources, GraphChi can solve the same problems in very reasonable time. Our work makes large-scale graph computation available to anyone with a modern PC.


title: Security analysis of cryptographic constructions used within communications in modern transportation systems on the base of modelling

abstract: The goal of this paper is the analysis of cryptographic mechanisms utilised in VANET communication for Intelligent Transportation Systems with focus on security. The practical part of the contribution is dedicated to modelling of security properties of VANET networks in OPNET Modeler modelling tool extended by the implementation of the OpenSSL library. The designed models simulate a transmission of authorised alert messages in Car-to-Car communication with Elliptic Curve Integrated Encryption Scheme (ECIES). The OpenSSL library has also been utilised for the comparison of time demandingness of ECDSA schemes for different key-lengths.


title: A Cost-Effective Deadline-Constrained Dynamic Scheduling Algorithm for Scientific Workflows in a Cloud Environment

abstract: Cloud computing, a distributed computing paradigm, enables delivery of IT resources over the Internet and follows the pay-as-you-go billing model. Workflow scheduling is one of the most challenging problems in cloud computing. Although, workflow scheduling on distributed systems like grids and clusters have been extensively studied, however, these solutions are not viable for a cloud environment. It is because, a cloud environment differs from other distributed environment in two major ways: on-demand resource provisioning and pay-as-you-go pricing model. Thus, to achieve the true benefits of workflow orchestration onto cloud resources novel approaches that can capitalize the advantages and address the challenges specific to a cloud environment needs to be developed. This work proposes a dynamic cost-effective deadline-constrained heuristic algorithm for scheduling a scientific workflow in a public cloud. The proposed technique aims to exploit the advantages offered by cloud computing while taking into account the virtual machine (VM) performance variability and instance acquisition delay to identify a just-in-time schedule of a deadline constrained scientific workflow at lesser costs. Performance evaluation on some well-known scientific workflows exhibit that the proposed algorithm delivers better performance in comparison to the current state-of-the-art heuristics.


title: MyLifeBits: a personal database for everything

abstract: MyLifeBits is a system that began in 2001 to explore the use of SQL to store all personal information found in PCs. The system initially focused on capturing and storing scanned and encoded archival material e.g. articles, books, music, photos, and video as well as everything born digital e.g. office documents, email, digital photos. It evolved to have a goal of storing everything that could be captured. The later included web pages, phone calls, meetings, room conversations, keystrokes and mouse clicks for every active screen or document, and all the 1-2 thousand photos that SenseCam captures every day. In 2006 the software platform is used for research including real time data collection, advanced SenseCams, and particular applications e.g. health and wellness. This article expands on the January 2006, CACM publication of the same name. MyLifeBits features, functions, and use experience are given in the main body, followed by an appendix of future research and product needs that the research has identified.


title: Quality-of-experience assessment and its application to video services in lte networks

abstract: Reliable and repeatable video quality assessment is essential for performance analysis of wireless multimedia applications in the third generation (3G) Long Term Evolution (LTE) network. In this article we report on a database containing subjective assessment scores and the corresponding Quality-of-Service parameters of 70 video test sequences encoded with H.264, which are corrupted when transmitted over a wireless 3G LTE network simulator. Then, a new assessment method based on neural networks (NN) is proposed, whose weights are determined through training. The resulting pseudo-subjective assessment scores are then compared to the true MOS results in our database. Naturally, the accuracy of the NN-based prediction tool should be tested ‘outside’ of the set used for NN weight training. However, there are persistent residual errors between the predicted and subjectively evaluated MOS, which can be further reduced by particle swarm optimization, applied as post-processing of the NN weights to improve its accuracy. The proposed assessment method has potential applications, including Quality-of-Experience-aware network optimization for LTE network operators.


title: Impedance control for biped robot locomotion

abstract: This paper proposes a control method for biped robot locomotion based on impedance control and impedance modulation. Both legs are controlled by the impedance control, where the desired impedance at the hip and the swing foot are specified. The impedance parameters are changed depending on the gait phase of the biped robot. To reduce the magnitude of impact and to guarantee a stable footing at foot landings on the ground, the damping coefficient of the impedance of the landing foot is increased drastically and the reference trajectory of the landing foot is reset at its initial contact with the ground. A series of computer simulations of a 12-degree-of-freedom (DOF) biped robot with a 6-DOF environment model which consists of nonlinear and linear compliant spring-and-dampers, shows that the proposed control method works well and is superior to position-oriented control methods such as the computed-torque control method in impact regulation during foot landings and adaptation to some ground irregularity.


title: Exit, Loyalty, and Collective Action Among Workers in a Simulated Business Environment: Interactive Effects of Group Identification and Boundary Permeability

abstract: Past research on the effects of boundary permeability and tokenism (open boundaries with restricted access) suggests that when options for individual mobility exist, members of low status groups tend to exit their group and attempt to enter higher status groups. We hypothesized that the effects of boundary permeability on preferences for individual vs. collective action would depend upon prior levels of in-group identification, such that people who are more identified with their group would remain loyal and choose collective action, even under conditions of high boundary permeability. To test this hypothesis, a 2 (High vs. Low Group Identification) × 2 (High vs. Low Permeability) experimental design was employed to assess preferences for exit and loyalty in the context of a simulated business environment. For both rating measures and behavioral choices, the interaction hypothesis was supported. Implications for group loyalty and strategies of tokenism are discussed.


title: Mining closed relational graphs with connectivity constraints

abstract: Relational graphs are widely used in modeling large scale networks such as biological networks and social networks. In this kind of graph, connectivity becomes critical in identifying highly associated groups and clusters. In this paper, we investigate the issues of mining closed frequent graphs with connectivity constraints in massive relational graphs where each graph has around 10K nodes and 1M edges. We adopt the concept of edge connectivity and apply the results from graph theory, to speed up the mining process. Two approaches are developed to handle different mining requests: CloseCut , a pattern-growth approach, and splat , a pattern-reduction approach. We have applied these methods in biological datasets and found the discovered patterns interesting.


title: Blockchain Technology: Applications in Health Care

abstract: Blockchain technology has gained substantial attention in recent years with increased interest in several diverse fields, including the healthcare industry. Blockchain offers a secure, distributed database that can operate without a central authority or administrator. Blockchain uses a distributed, peer-to-peer network to make a continuous, growing list of ordered records called blocks to form a digital ledger. Each transaction, represented in a cryptographically signed block, is then automatically validated by the network itself. Blockchain has also garnered interest as a platform to improve the authenticity and transparency of healthcare data through many use cases, from maintaining permissions in electronic health records (EHR) to streamlining claims processing. In this article, we describe the basics of blockchain and illustrate current and future applications of this technology within the healthcare industry. ::: ::: Bitcoin, a cryptocurrency and payment system first introduced in 2008, is one of the most well-known implementations of blockchain.1 The transfer of digital assets, such as bitcoin, within a blockchain is initiated when a seller or payer submits a transaction (Figure [A]).2 These transactions are broadcasted to every peer connected to the blockchain network where clients, called miners, use a cryptographic algorithm to validate the transaction. This validation solves 2 key problems that previously existed with digital currency exchange: ensuring that the digital asset exists and that it has not already been spent. A transaction is said to be valid if a miner deems it is well formed (the input and output contain only the fields that are defined in the protocol), and the outputs it attempts to transfer exist. Miners are not certified and can be anyone who volunteers to invest their resources. The incentive for miners comes in the form of the bitcoin, which are generated and rewarded to the miners for every block of transactions validated. …


title: DC-DC converter design for battery-operated systems

abstract: This paper describes performance, design and optimization of DC-DC converters for energy limited, battery operated systems. Variable-frequency operation is used to achieve voltage regulation and high efficiency for an extremely wide range of load currents. An experimental 15 W, 3.3 V buck converter has been constructed to demonstrate design and optimization techniques. The converter employs synchronous rectification to reduce conduction losses, and discontinuous, variable-frequency, current-mode control with optimum peak current to maximize efficiency for a wide range of loads. Applications include portable computers, hand-held instruments, and telecommunications. >


title: Multiband Fractal PIFA (Planar Inverted F Antenna) for mobile phones

abstract: A novel Fractal planar inverted F antenna F-PIFA) based on the self affinity design is presented in this paper. The procedure for designing a Fractal Planar Inverted F Antenna is explained and the 2nd iteration of Sierpinski Carpet is chosen as an antenna for mobile phones. The F-PIFA has a total dimension of 27 mm × 27 mm and is designed and optimized to receive GSM (Global System for Mobile Communication) and UMTS (Universal Mobile Telecommunication System) and HiperLAN (High Performance Radio LAN). The frequency ranges are from 1900 MHz to 2100 MHz, 1885 to 2200 MHz for 3G and 4800 MHz to 5800 MHz for HiperLAN respectively. The antenna covered the GSM, UMTS and HiperLan frequency ranges with −6dB return loss and has an almost omni-directional radiation pattern. This antenna has been tested using a mobile phone model and the performance met all the criteria for a mobile phone application.


title: Reformat: Automatic Reverse Engineering of Encrypted Messages

abstract: Automatic protocol reverse engineering has recently received significant attention due to its importance to many security applications. However, previous methods are all limited in analyzing only plaintext communications wherein the exchanged messages are not encrypted. In this paper, we propose ReFormat, a system that aims at deriving the message format even when the message is encrypted. Our approach is based on the observation that an encrypted input message will typically go through two phases: message decryption and normal protocol processing. These two phases can be differentiated because the corresponding instructions are significantly different. Further, with the help of data lifetime analysis of run-time buffers, we can pinpoint the memory locations that contain the decrypted message generated from the first phase and are later accessed in the second phase. We have developed a prototype and evaluated it with several real-world protocols. Our experiments show that ReFormat can accurately identify decrypted message buffers and then reveal the associated message structure.


title: Microscale Strategies for Generating Cell-Encapsulating

abstract: Hydrogels in which cells are encapsulated are of great potential interest for tissue engineering applications. These gels provide a structure inside which cells can spread and proliferate. Such structures benefit from controlled microarchitectures that can affect the behavior of the enclosed cells. Microfabrication-based techniques are emerging as powerful approaches to generate such cell-encapsulating hydrogel structures. In this paper we introduce common hydrogels and their crosslinking methods and review the latest microscale approaches for generation of cell containing gel particles. We specifically focus on microfluidics-based methods and on techniques such as micromolding and electrospinning.


title: Deep Convolutional Neural Network Based Regression Approach for Estimation of Remaining Useful Life

abstract: Prognostics technique aims to accurately estimate the Remaining Useful Life (RUL) of a subsystem or a component using sensor data, which has many real world applications. However, many of the existing algorithms are based on linear models, which cannot capture the complex relationship between the sensor data and RUL. Although Multilayer Perceptron (MLP) has been applied to predict RUL, it cannot learn salient features automatically, because of its network structure. A novel deep Convolutional Neural Network (CNN) based regression approach for estimating the RUL is proposed in this paper. Although CNN has been applied on tasks such as computer vision, natural language processing, speech recognition etc., this is the first attempt to adopt CNN for RUL estimation in prognostics. Different from the existing CNN structure for computer vision, the convolution and pooling filters in our approach are applied along the temporal dimension over the multi-channel sensor data to incorporate automated feature learning from raw sensor signals in a systematic way. Through the deep architecture, the learned features are the higher-level abstract representation of low-level raw sensor signals. Furthermore, feature learning and RUL estimation are mutually enhanced by the supervised feedback. We compared with several state-of-the-art algorithms on two publicly available data sets to evaluate the effectiveness of this proposed approach. The encouraging results demonstrate that our proposed deep convolutional neural network based regression approach for RUL estimation is not only more efficient but also more accurate.


title: From Observation to Information: Data-Driven Understanding of on Farm Yield Variation

abstract: Agriculture research uses "recommendation domains" to develop and transfer crop management practices adapted to specific contexts. The scale of recommendation domains is large when compared to individual production sites and often encompasses less environmental variation than farmers manage. Farmers constantly observe crop response to management practices at a field scale. These observations are of little use for other farms if the site and the weather are not described. The value of information obtained from farmers' experiences and controlled experiments is enhanced when the circumstances under which it was generated are characterized within the conceptual framework of a recommendation domain, this latter defined by Non-Controllable Factors (NCFs). Controllable Factors (CFs) refer to those which farmers manage. Using a combination of expert guidance and a multi-stage analytic process, we evaluated the interplay of CFs and NCFs on plantain productivity in farmers' fields. Data were obtained from multiple sources, including farmers. Experts identified candidate variables likely to influence yields. The influence of the candidate variables on yields was tested through conditional forests analysis. Factor analysis then clustered harvests produced under similar NCFs, into Homologous Events (HEs). The relationship between NCFs, CFs and productivity in intercropped plantain were analyzed with mixed models. Inclusion of HEs increased the explanatory power of models. Low median yields in monocropping coupled with the occasional high yields within most HEs indicated that most of these farmers were not using practices that exploited the yield potential of those HEs. Varieties grown by farmers were associated with particular HEs. This indicates that farmers do adapt their management to the particular conditions of their HEs. Our observations confirm that the definition of HEs as recommendation domains at a small-scale is valid, and that the effectiveness of distinct management practices for specific micro-recommendation domains can be identified with the methodologies developed.


title: Industrial Automation as a Cloud Service

abstract: New cloud services are being developed to support a wide variety of real-life applications. In this paper, we introduce a new cloud service: industrial automation, which includes different functionalities from feedback control and telemetry to plant optimization and enterprise management. We focus our study on the feedback control layer as the most time-critical and demanding functionality. Today’s large-scale industrial automation projects are expensive and time-consuming. Hence, we propose a new cloud-based automation architecture, and we analyze cost and time savings under the proposed architecture. We show that significant cost and time savings can be achieved, mainly due to the virtualization of controllers and the reduction of hardware cost and associated labor. However, the major difficulties in providing cloud-based industrial automation systems are timeliness and reliability. Offering automation functionalities from the cloud over the Internet puts the controlled processes at risk due to varying communication delays and potential failure of virtual machines and/or links. Thus, we design an adaptive delay compensator and a distributed fault tolerance algorithm to mitigate delays and failures, respectively. We theoretically analyze the performance of the proposed architecture when compared to the traditional systems and prove zero or negligible change in performance. To experimentally evaluate our approach, we implement our controllers on commercial clouds and use them to control: (i) a physical model of a solar power plant, where we show that the fault-tolerance algorithm effectively makes the system unaware of faults, and (ii) industry-standard emulation with large injected delays and disturbances, where we show that the proposed cloud-based controllers perform indistinguishably from the best-known counterparts: local controllers.


title: Wideband Radar Cross Section Reduction of Slot Antennas Arrays

abstract: A comprehensive analysis aimed at reducing the radar cross section (RCS) of array antennas, preserving at the same time their radiating performance, is presented. A microstrip slot array is considered as a test case to illustrate the proposed strategy for radar cross section reduction (RCSR). It is shown that a remarkable reduction of the radar signature can be accomplished over a frequency band as wide as two octaves by employing an array of periodic resistive elements in front of the radiating apertures. The monostatic and bistatic RCS of the proposed structures are investigated both for normal and oblique incidence. Different arrangements and geometries of the periodic resistive pattern are thoroughly analyzed showing the benefits and the drawbacks in terms of antenna gain and level of the scattered fields. Furthermore, the use of metallic parasitic elements for enhancing the antenna gain is considered, and the scattering phenomena caused by their presence are addressed, taking into account the appearance of grating lobes. The antenna designs are also analyzed by resorting to a bidimensional color plot presenting the variation of the reradiated field both in frequency and spatial domain. The guidelines illustrated by the proposed examples can be easily applied to other antenna architectures.


title: Total Recall: System Support for Automated Availability Management

abstract: Availability is a storage system property that is both highly desired and yet minimally engineered. While many systems provide mechanisms to improve availability - such as redundancy and failure recovery - how to best configure these mechanisms is typically left to the system manager. Unfortunately, few individuals have the skills to properly manage the trade-offs involved, let alone the time to adapt these decisions to changing conditions. Instead, most systems are configured statically and with only a cursory understanding of how the configuration will impact overall performance or availability. While this issue can be problematic even for individual storage arrays, it becomes increasingly important as systems are distributed - and absolutely critical for the wide-area peer-to-peer storage infrastructures being explored. ::: ::: This paper describes the motivation, architecture and implementation for a new peer-to-peer storage system, called TotalRecall, that automates the task of availability management. In particular, the TotalRecall system automatically measures and estimates the availability of its constituent host components, predicts their future availability based on past behavior, calculates the appropriate redundancy mechanisms and repair policies, and delivers user-specified availability while maximizing efficiency.


Reading Touch Screen Storybooks with Mothers Negatively Affects 7-Year-Old Readers’ Comprehension but Enriches Emotional Engagement


title: A new meta-heuristic butterfly-inspired algorithm

abstract: Abstract This paper proposes a novel bio-inspired algorithm named Artificial Butterfly Optimization (ABO) algorithm. The new algorithm is based on the mate-finding strategy of some butterfly species. Two groups of artificial butterflies are employed for simulating the flight strategies. If the flight strategies of artificial butterflies are redefined, ABO can develop a new algorithm. From this point, ABO is a mimic-life algorithm in grandness. By presenting three flight strategies, we build two new algorithms named ABO1 and ABO2. We validate the two new algorithms and compare their performance with other well-known nature-inspired algorithms on twenty-two benchmark functions. The experimental results show that the proposed algorithm is able to provide very promising and competitive results on most benchmark functions. It also proves that the ABO algorithm provides a new effective computational framework for solving optimization problems.


title: The Direct and Indirect Effects of Domestic Violence on Young Children's Intellectual Functioning

abstract: This study examined the direct and indirect effects of domestic violence on preschoolers' intellectual functioning. The sample consisted of 100 women and their 3–5-year-old children (44 boys and 56 girls) recruited from the general community. Forty-three percent of mothers had experienced domestic violence within the last year, defined by at least one physically aggressive act by an intimate partner. Children who had witnessed domestic violence had significantly poorer verbal abilities than nonwitnesses after controlling for SES and child abuse, but there were no group differences on visual–spatial abilities. Domestic violence also indirectly affected both types of intellectual abilities through its impact on maternal depression and the intellectual quality of the home environment. Strengths and limitations of the study are discussed, as well as the implications for interventions for young child witnesses.


title: An Efficient Model to Improve Customer Acceptance of Mobile Banking

abstract:  Abstract— During the last ten years technological development has reshaped the banking industry, which has become one of the leading sectors in utilizing new technology on consumer markets. Today, mobile communication technologies offer vast additional value for consumers' banking transactions due to their always-on functionality and the option to access banks anytime and anywhere. Various alternative approaches have used in analyzing customer's acceptance of new technologies. In this paper, factors affect acceptance of Mobile Banking are explored and presented as a New Model.


title: Model-Based Predictive Control of Weld Penetration in Gas Tungsten Arc Welding

abstract: Skilled welders can estimate and control the weld joint penetration, which is primarily measured by the backside bead width, based on weld pool observation. This suggests that an advanced control system could be developed to control the weld joint penetration by emulating the estimation and decisionmaking process of the human welder. In this paper an innovative 3-D vision sensing system is used to measure the characteristic parameters of the weld pool in real-time in gas tungsten arc welding. The measured characteristic parameters are used to estimate the backside bead width, using an adaptive neuro-fuzzy inference system (ANFIS) as an emulation of skilled welder. Dynamic experiments are conducted to establish the model that relates the backside bead width to the welding current and speed. The dynamic linear model is first constructed and the modeling result is analyzed. The linear model is then improved by incorporating a nonlinear operating point modeled by an ANFIS. Because the weld pool needs to gradually change, being controlled by a skilled welder, a model predictive control is used to follow a trajectory to reach the desired backside bead width and the control increment is penalized. Because the weld pool is not supposed to change in an extremely large range, the resultant model predictive control is actually linear and an analytical solution is derived. Welding experiments confirm that the developed control system is effective in achieving the desired weld joint penetration under various disturbances and initial conditions.


title: Room-temperature single-photon generation from solitary dopants of carbon nanotubes.

abstract: On-demand single-photon sources capable of operating at room temperature and the telecom wavelength range of 1,300-1,500 nm hold the key to the realization of novel technologies that span from sub-diffraction imaging to quantum key distribution and photonic quantum information processing. Here, we show that incorporation of undoped (6,5) single-walled carbon nanotubes into a SiO2 matrix can lead to the creation of solitary oxygen dopant states capable of fluctuation-free, room-temperature single-photon emission in the 1,100-1,300 nm wavelength range. We investigated the effects of temperature on photoluminescence emission efficiencies, fluctuations and decay dynamics of the dopant states and determined the conditions most suitable for the observation of single-photon emission. This emission can in principle be extended to 1,500 nm by doping of smaller-bandgap single-walled carbon nanotubes. This easy tunability presents a distinct advantage over existing defect centre single-photon emitters (for example, diamond defect centres). Our SiO2-encapsulated sample also presents exciting opportunities to apply Si/SiO2-based micro/nano-device fabrication techniques in the development of electrically driven single-photon sources and integration of these sources into quantum photonic devices and networks.


title: An Efficient Dynamic Ridesharing Algorithm

abstract: In this paper, we propose a scheduling algorithm for ridesharing. It efficiently serves real-time requests sent by passengers and generates ride sharing schedules that try to meet users' demands. In our method, we first propose a taxi searching algorithm using binary search strategy to quickly and efficiently retrieve candidate taxis that are likely to satisfy a trip request. A scheduling algorithm is then proposed. It checks each candidate taxi and insert the request trip into the schedule of the taxi which satisfies the request with the maximum average satisfaction. We evaluated our system using a large scale taxi dataset containing 101,952 trips in Beijing Chaoyang district. Results showed that our approach serves 40% additional taxi users while saving 30% travel distance compared with no-ridesharing.


title: Comparison of efficacy of tray-delivered carbamide and hydrogen peroxide for at-home bleaching: a systematic review and meta-analysis

abstract: OBJECTIVES ::: The aim of the study was to compare the color change produced by tray-delivered carbamide peroxide [CP] versus hydrogen peroxide products [HP] for at-home bleaching through a systematic review and meta-analysis. ::: ::: ::: MATERIALS AND METHODS ::: MEDLINE via PubMeb, Scopus, Web of Science, Latin American and Caribbean Health Sciences Literature database (LILACS), Brazilian Library in Dentistry (BBO), and Cochrane Library and Grey literature were searched without restrictions. The abstracts of the International Association for Dental Research (IADR) and unpublished and ongoing trial registries were also searched. Dissertations and theses were explored using the ProQuest Dissertations and Periodicos Capes Theses databases. We included randomized clinical trials that compared tray-delivered CP versus HP for at-home dental bleaching. The color change in shade guide units (SGU) and ΔE were the primary outcomes, and tooth sensitivity and gingival irritation were the secondary outcomes. The risk of bias tool of the Cochrane Collaboration was used for quality assessment. ::: ::: ::: DATA ::: After duplicate removal, 1379 articles were identified. However, only eight studies were considered to be at "low" risk of bias in the key domains of the risk bias tool and they were included in the analysis. For ΔE, the standardized mean difference was -0.45 (95 % CI -0.69 to -0.21), which favored tray-delivered CP products (p < 0.001). The color change in ΔSGU (p = 0.70), tooth sensitivity (p = 0.83), and gingival irritation (p = 0.62) were not significantly different between groups. ::: ::: ::: CONCLUSIONS ::: Tray-delivered CP gels showed a slightly better whitening efficacy than HP-based products in terms of ΔE, but they were similar in terms of ΔSGU. Both whitening systems demonstrated equal level of gingival irritation and tooth sensitivity. ::: ::: ::: CLINICAL SIGNIFICANCE ::: Tray-delivered CP gels have a slightly better whitening efficacy than HP-based products in terms of ΔE. This should be interpreted with caution as the data of ΔSGU did not show statistical difference between the products.


title: Capability Hardware Enhanced RISC Instructions: CHERI Instruction-Set Architecture (Version 5)

abstract: This technical report describes CHERI ISAv5, the fifth version of the Capability Hardware Enhanced RISC Instructions (CHERI) Instruction-Set Architecture (ISA)1 being developed by SRI International and the University of Cambridge. This design captures six years of research, development, experimentation, refinement, formal analysis, and testing, and is a substantial enhancement to the ISA versions described in earlier technical reports. This version introduces the CHERI-128 “compressed” capability format, adds further capability instructions to improve code efficiency, and rationalizes a number of ISA design choices (such as system permissions) as we have come to better understand mappings from C programming-language and MMUbased operating-system models into CHERI. It also contains improvements to descriptions, explanations, and rationale. The CHERI instruction set is a hybrid capability-system architecture that adds new capabilitysystem primitives to a commodity 64-bit RISC ISA enabling software to efficiently implement fine-grained memory protection and a hardware-software object-capability security model. These extensions support incrementally adoptable, high-performance, formally based, programmerfriendly underpinnings for fine-grained software decomposition and compartmentalization, motivated by and capable of enforcing the principle of least privilege. Fine-grained memory protection provides direct mitigation of many widely deployed exploit techniques. The CHERI system architecture purposefully addresses known performance and robustness gaps in commodity ISAs that hinder the adoption of more secure programming models centered around the principle of least privilege. To this end, CHERI blends traditional paged virtual memory with a per-address-space capability model that includes capability registers, capability instructions, and tagged memory that have been added to the 64-bit MIPS ISA. CHERI learns from the C-language fat-pointer literature: its capabilities describe fine-grained regions of memory and can be substituted for data or code pointers in generated code, protecting data and also providing Control-Flow Integrity (CFI). Strong monotonicity properties allow the CHERI capability model to express a variety of protection properties, from valid C-language pointer provenance and bounds checking to implementing the isolation and controlled communication structures required for higher-level models such as software compartmentalization. CHERI’s hybrid system approach, inspired by the Capsicum security model, allows incremental adoption of capability-oriented software design: software implementations that are more robust and resilient can be deployed where they are most needed, while leaving less critical software largely unmodified, but nevertheless suitably constrained to be incapable of having adverse effects. For example, we are focusing conversion efforts on low-level TCB components of the system: separation kernels, hypervisors, operating-system kernels, language runtimes, and userspace TCBs such as web browsers. Likewise, we see early-use scenarios (such as data compression, protocol parsing, image processing, and video processing) that relate to particularly high-risk software libraries, which are concentrations of both complex and historically vulnerability-prone code combined with untrustworthy data sources, while leaving containing applications unchanged. 1We have attempted to avoid confusion among three rather different uses of the word ‘architecture’. The ISA specifies the interface between hardware and software, rather than describing either the (micro-)architecture of a particular hardware prototype, or laying out the total-system hardware-software architecture. We expect further documentation on the latter to emerge in the final year of the project.


title: Development of an interactive book with Augmented Reality for teaching and learning geometric shapes

abstract: This paper focuses on the GeoAR, an interactive book which incorporates Augmented Reality resources, developed to support teaching and learning of Geometry topics covering the main geometric shapes. The GeoAR development followed a process based on prototyping, proposed to support the creation of Augmented Reality applications. The paper points out concepts and related work, describes the development of the GeoAR, relates prototypes evaluations, and discusses the use of the GeoAR by children in a real situation.


title: Vision-based guidance and control of a hovering vehicle in unknown, GPS-denied environments

abstract: This paper describes the system architecture and core algorithms for a quadrotor helicopter that uses vision data to navigate an unknown, indoor, GPS-denied environment. Without external sensing, an estimation system that relies only on integrating inertial data will have rapidly drifting position estimates. Micro aerial vehicles (MAVs) are stringently weight-constrained, leaving little margin for additional sensors beyond the mission payload. The approach taken in this paper is to introduce an architecture that exploits a common mission payload, namely a video camera, as a dual-use sensor to aid in navigation. Several core algorithms, including a fast environment mapper and a novel heuristic for obstacle avoidance, are also presented. Finally, drift-free hover and obstacle avoidance flight tests in a controlled environment are presented and analyzed.


title: A Model of Corn and Soybean Shipments on the Ohio River

abstract: This paper estimates the demand for corn and soybean shipments on the Ohio River during the 1992-2004 period. Using monthly data, OLS (with Newewy-West standard errors) parameters estimates were obtained for the explanatory variables. Results indicate that Ohio River corn and soybean shipments are significantly and positively affected by one month lagged shipments, corn and soybean stocks in the Ohio River production region, and corn and soybean exports from lower Mississippi River Gulf ports. Some empirical support was found for a significant negative relationship between Ohio River corn and soybean shipments and ocean shipping freight rates from lower Mississippi River Gulf ports to Japan. Indiana corn prices did not significantly affect Ohio River corn and soybean shipments.


title: RF Signal Loss in Through-body Wireless Transmission and Implications for Implantable Devices

abstract: Physicians and researchers require systems that provide real-time collection of physiological data sets to explore new diagnostics and therapies. Implantable devices used to monitor a variety of physiological parameters and administer therapies lend themselves to the development of customizable platforms that can be applied to individualized disease treatment. Radio frequency (RF) communication can provide a reliable data path allowing through-body communication. Here, RF propagation is analyzed in a saline phantom to characterize signal loss through free space when varying the distance between the transmitting and receiving antennas and the type and size of antennas, to find the highest received power to the lowest transmitted power. Two testing environments were constructed: dry and 0.9% saline. Four frequencies (400 MHz, 700 MHz, 900 MHz, and 2.5 GHz) and four antenna types (quarter-wave ground plane, horizontal half-wave dipole, single loop, and coiled loop) were implemented in the system.


title: Reductions in DNAPL Longevity through Biological Flux Enhancement

abstract: Abstract : In the first phase of this project (Phase I), two 11.7 cubic meter Experimental Controlled Release Systems (ECRS), packed with sandy model aquifer material and amended with tetrachloroethene (PCE) DNAPL source zones, were operated in parallel with identical flow regimes and electron donor amendments. Hydrogen Releasing Compound-trademark (HRC), and later dissolved lactate, served as electron donors to promote dechlorination. One ECRS was bioaugmented with an anaerobic dechlorinating consortium directly into the source zone, and the other served as a control (biostimulated only) to determine the benefits of bioaugmentation. The presence of halorespiring bacteria in the aquifer matrix prior to bioaugmentation, shown by nested PCR with phylogenetic primers, suggests that dechlorinating catabolic potential may be somewhat widespread. PCR analyses demonstrated that the bacteria present in the culture used for bioaugmentation in the ECRS prevailed for almost a year. Unfortunately, even with Dehalococcoides present, complete dechlorination to ethene was achieved at minimum (<1 micrometer). Results demonstrated that the low concentration of ethene produced in this first phase was not due to washout of the dechlorinating organisms. It was also demonstrated that as long as the electron acceptor was not limiting, there was greater energy flow to the dechlorinating populations than to the methanogens. Overall, the results obtained in the Phase I corroborate that source zone reductive Dechlorinating of PCE is possible at near field scale, and that a system bioaugmented with a competent halorespiring consortium can enhance DNAPL dissolution and dechlorination processes at significantly greater rates than in a system that is biostimulated only.


title: Secure spread spectrum watermarking for multimedia

abstract: This paper presents a secure (tamper-resistant) algorithm for watermarking images, and a methodology for digital watermarking that may be generalized to audio, video, and multimedia data. We advocate that a watermark should be constructed as an independent and identically distributed (i.i.d.) Gaussian random vector that is imperceptibly inserted in a spread-spectrum-like fashion into the perceptually most significant spectral components of the data. We argue that insertion of a watermark under this regime makes the watermark robust to signal processing operations (such as lossy compression, filtering, digital-analog and analog-digital conversion, requantization, etc.), and common geometric transformations (such as cropping, scaling, translation, and rotation) provided that the original image is available and that it can be successfully registered against the transformed watermarked image. In these cases, the watermark detector unambiguously identifies the owner. Further, the use of Gaussian noise, ensures strong resilience to multiple-document, or collusional, attacks. Experimental results are provided to support these claims, along with an exposition of pending open problems.


title: Lateral flow (immuno)assay: its strengths, weaknesses, opportunities and threats. A literature survey.

abstract: Lateral flow (immuno)assays are currently used for qualitative, semiquantitative and to some extent quantitative monitoring in resource-poor or non-laboratory environments. Applications include tests on pathogens, drugs, hormones and metabolites in biomedical, phytosanitary, veterinary, feed/food and environmental settings. We describe principles of current formats, applications, limitations and perspectives for quantitative monitoring. We illustrate the potentials and limitations of analysis with lateral flow (immuno)assays using a literature survey and a SWOT analysis (acronym for “strengths, weaknesses, opportunities, threats”). Articles referred to in this survey were searched for on MEDLINE, Scopus and in references of reviewed papers. Search terms included “immunochromatography”, “sol particle immunoassay”, “lateral flow immunoassay” and “dipstick assay”.


title: BM3D filter in salt-and-pepper noise removal

abstract: There is a significant recent advance in filtering of the salt-and-pepper noise for digital images. However, almost all recent schemes for filtering of this type of noise are not taking into an account the shape of objects (in particular edges) in images. We have applied the block-matching and 3D filtering (BM3D) scheme in order to refine the output of the decision-based/adaptive median techniques. Obtained results are excellent, surpassing current state-of-the-art for about 2 dB for both grayscale and color images.


title: DAVE: A Unified Framework for Fast Vehicle Detection and Annotation

abstract: Vehicle detection and annotation for streaming video data with complex scenes is an interesting but challenging task for urban traffic surveillance. In this paper, we present a fast framework of Detection and Annotation for Vehicles (DAVE), which effectively combines vehicle detection and attributes annotation. DAVE consists of two convolutional neural networks (CNNs): a fast vehicle proposal network (FVPN) for vehicle-like objects extraction and an attributes learning network (ALN) aiming to verify each proposal and infer each vehicle’s pose, color and type simultaneously. These two nets are jointly optimized so that abundant latent knowledge learned from the ALN can be exploited to guide FVPN training. Once the system is trained, it can achieve efficient vehicle detection and annotation for real-world traffic surveillance data. We evaluate DAVE on a new self-collected UTS dataset and the public PASCAL VOC2007 car and LISA 2010 datasets, with consistent improvements over existing algorithms.


title: Question detection from acoustic features using recurrent neural network with gated recurrent unit

abstract: Question detection is of importance for many speech applications. Only parts of the speech utterances can provide useful clues for question detection. Previous work of question detection using acoustic features in Mandarin conversation is weak in capturing such proper time context information, which could be modeled essentially in recurrent neural network (RNN) structure. In this paper, we conduct an investigation on recurrent approaches to cope with this problem. Based on gated recurrent unit (GRU), we build different RNN and bidirectional RNN (BRNN) models to extract efficient features at segment and utterance level. The particular advantage of GRU is it can determine a proper time scale to extract high-level contextual features. Experimental results show that the features extracted within proper time scale make the classifier perform better than the baseline method with pre-designed lexical and acoustic feature set.


title: Manifold Regularized Correlation Object Tracking

abstract: In this paper, we propose a manifold regularized correlation tracking method with augmented samples. To make better use of the unlabeled data and the manifold structure of the sample space, a manifold regularization-based correlation filter is introduced, which aims to assign similar labels to neighbor samples. Meanwhile, the regression model is learned by exploiting the block-circulant structure of matrices resulting from the augmented translated samples over multiple base samples cropped from both target and nontarget regions. Thus, the final classifier in our method is trained with positive, negative, and unlabeled base samples, which is a semisupervised learning framework. A block optimization strategy is further introduced to learn a manifold regularization-based correlation filter for efficient online tracking. Experiments on two public tracking data sets demonstrate the superior performance of our tracker compared with the state-of-the-art tracking approaches.


title: Classes of small-world networks

abstract: We study the statistical properties of a variety of diverse real-world networks. We present evidence of the occurrence of three classes of small-world networks: (a) scale-free networks, characterized by a vertex connectivity distribution that decays as a power law; (b) broad-scale networks, characterized by a connectivity distribution that has a power law regime followed by a sharp cutoff; and (c) single-scale networks, characterized by a connectivity distribution with a fast decaying tail. Moreover, we note for the classes of broad-scale and single-scale networks that there are constraints limiting the addition of new links. Our results suggest that the nature of such constraints may be the controlling factor for the emergence of different classes of networks.


title: REVIEW Global Consequences of Land Use

abstract: Land use has generally been considered a local environmental issue, but it is becoming a force of global importance. Worldwide changes to forests, farmlands, waterways, and air are being driven by the need to provide food, fiber, water, and shelter to more than six billion people. Global croplands, pastures, plantations, and urban areas have expanded in recent decades, accompanied by large increases in energy, water, and fertilizer consumption, along with considerable losses of biodiversity. Such changes in land use have enabled humans to appropriate an increasing share of the planet’s resources, but they also potentially undermine the capacity of ecosystems to sustain food production, maintain freshwater and forest resources, regulate climate and air quality, and ameliorate infectious diseases. We face the challenge of managing trade-offs between immediate human needs and maintaining the capacity of the biosphere to provide goods and services in the long term.


title: Visual7W: Grounded Question Answering in Images

abstract: We have seen great progress in basic perceptual tasks such as object recognition and detection. However, AI models still fail to match humans in high-level vision tasks due to the lack of capacities for deeper reasoning. Recently the new task of visual question answering (QA) has been proposed to evaluate a model’s capacity for deep image understanding. Previous works have established a loose, global association between QA sentences and images. However, many questions and answers, in practice, relate to local regions in the images. We establish a semantic link between textual descriptions and image regions by object-level grounding. It enables a new type of QA with visual answers, in addition to textual answers used in previous work. We study the visual QA tasks in a grounded setting with a large collection of 7W multiple-choice QA pairs. Furthermore, we evaluate human performance and several baseline models on the QA tasks. Finally, we propose a novel LSTM model with spatial attention to tackle the 7W QA tasks.


title: A barometer-IMU fusion method for vertical velocity and height estimation

abstract: We propose a new fusing method to estimate the vertical velocity and the height of barometer-IMU systems. The method removed initialization processes of the system by calibrating accelerometer errors on-line. An EKF tracks the bias and scale error of each accelerometer, while estimating vertical velocity and height simultaneously. The method is tested on simulation and on an actual low-cost barometer-IMU sensor platform. The errors are successfully modeled before 30 to 60 seconds of system startup. Proposed method may be extensively used for low-cost, chip-scale barometer/IMU integrated devices, since on-line calibration not only removes initialization processes but also enables tracking of time/temperature dependent varying IMU errors from which MEMS based IMUs suffer.


title: A Review: Text Classification on Social Media Data

abstract: In today's world most of us depend on Social Media to communicate, express our feelings and share information with our friends. Social Media is the medium where now a day's people feel free to express their emotions. Social Media collects the data in structured and unstructured, formal and informal data as users do not care about the spellings and accurate grammatical construction of a sentence while communicating with each other using different social networking websites ( Facebook, Twitter, LinkedIn and YouTube). Gathered data contains sentiments and opinion of users which will be processed using data mining techniques and analyzed for achieving the meaningful information from it. Using Social media data we can classify the type of users by analysis of their posted data on the social web sites. Machine learning algorithms are used for text classification which will extract meaningful data from these websites. Here, in this paper we will discuss the different types of classifiers and their advantages and disadvantages.


title: Early evidence of the genus Homo in East Asia.

abstract: The timing and route of the earliest dispersal from Africa to Eastern Asia are contentious topics in the study of early human evolution because Asian hominin fossil sites with precise age constraints are very limited. Here we report new high-resolution magnetostratigraphic results that place stringent age controls on excavated hominin incisors and stone tools from the Yuanmou Basin, southwest China. The hominin-bearing layer resides in a reverse polarity magnetozone just above the upper boundary of the Olduvai subchron, yielding an estimated age of 1.7Ma. The finding represents the age of the earliest documented presence of Homo, with affinities to Homo erectus, in mainland East Asia. This age estimate is roughly the same as for H. erectus in island Southeast Asia and immediately prior to the oldest archaeological evidence in northeast Asia. Mammalian fauna and pollen obtained directly from the hominin site indicate that the Yuanmou hominins lived in a varied habitat of open vegetation with patches of bushland and forest on an alluvial fan close to a lake or swamp. The age and location are consistent with a rapid southern migration route of initial hominin populations into Eastern Asia.


title: A Holistic Approach to Cross-Channel Image Noise Modeling and Its Application to Image Denoising

abstract: Modelling and analyzing noise in images is a fundamental task in many computer vision systems. Traditionally, noise has been modelled per color channel assuming that the color channels are independent. Although the color channels can be considered as mutually independent in camera RAW images, signals from different color channels get mixed during the imaging process inside the camera due to gamut mapping, tone-mapping, and compression. We show the influence of the in-camera imaging pipeline on noise and propose a new noise model in the 3D RGB space to accounts for the color channel mix-ups. A data-driven approach for determining the parameters of the new noise model is introduced as well as its application to image denoising. The experiments show that our noise model represents the noise in regular JPEG images more accurately compared to the previous models and is advantageous in image denoising.


title: Architectures for Natural Language Generation: Problems and Perspectives

abstract: Current research in natural language generation is situated in a computational linguistics tradition that was founded several decades ago. We critically analyse some of the architectural assumptions underlying existing systems and point out some problems in the domains of text planning and lexicalization. Guided by the identification of major generation challenges viewed from the angles of knowledge-based systems and cognitive psychology, we sketch some new directions for future research.


title: A Connectionist Approach to Embodied Conceptual Metaphor

abstract: A growing body of data has been gathered in support of the view that the mind is embodied and that cognition is grounded in sensory-motor processes. Some researchers have gone so far as to claim that this paradigm poses a serious challenge to central tenets of cognitive science, including the widely held view that the mind can be analyzed in terms of abstract computational principles. On the other hand, computational approaches to the study of mind have led to the development of specific models that help researchers understand complex cognitive processes at a level of detail that theories of embodied cognition (EC) have sometimes lacked. Here we make the case that connectionist architectures in particular can illuminate many surprising results from the EC literature. These models can learn the statistical structure in their environments, providing an ideal framework for understanding how simple sensory-motor mechanisms could give rise to higher-level cognitive behavior over the course of learning. Crucially, they form overlapping, distributed representations, which have exactly the properties required by many embodied accounts of cognition. We illustrate this idea by extending an existing connectionist model of semantic cognition in order to simulate findings from the embodied conceptual metaphor literature. Specifically, we explore how the abstract domain of time may be structured by concrete experience with space (including experience with culturally-specific spatial and linguistic cues). We suggest that both EC researchers and connectionist modelers can benefit from an integrated approach to understanding these models and the empirical findings they seek to explain.


title: On the Importance of Consistency in Training Deep Neural Networks

abstract: We explain that the difficulties of training deep neural networks come from a syndrome of three consistency issues. This paper describes our efforts in their analysis and treatment. The first issue is the training speed inconsistency in different layers. We propose to address it with an intuitive, simple-to-implement, low footprint second-order method. The second issue is the scale inconsistency between the layer inputs and the layer residuals. We explain how second-order information provides favorable convenience in removing this roadblock. The third and most challenging issue is the inconsistency in residual propagation. Based on the fundamental theorem of linear algebra, we provide a mathematical characterization of the famous vanishing gradient problem. Thus, an important design principle for future optimization and neural network design is derived. We conclude this paper with the construction of a novel contractive neural network.


Pacific Asia Conference on Information Systems ( PACIS ) 2010 Self-Regulation , Individual Characteristics and Cyberloafing


title: All-metal Ku-band Luneburg lens antenna based on variable parallel plate spacing Fakir bed of nails

abstract: This paper presents an all-metal parallel plate waveguide (PPW) Luneburg lens designed using an array of subwavelength Fakir bed of nails unit-cells. Variation of the metal post height and PPW-spacing modulates the effective refractive index. The lens operates in the Ku-band of frequencies, with more than 40% bandwidth, in a quasi-TEM mode. Broadband and low loss, with high directivity and wide field-of-view, this antenna could be an excellent candidate as a lens-like beamformer for multiple beams satellite communication applications.


Biomedical Named Entity Recognition Using Neural Networks


title: Modeling and design solutions to overcome warpage challenge for fan-out wafer level packaging (FO-WLP) technology

abstract: Fan-out wafer-level-packaging (FO-WLP) technology gets more and more significant attention with its advantages of small form factor, higher I/O density, cost effective and high performance for wide range application. However, wafer warpage is still one critical issue which is needed to be addressed for successful subsequent processes for FO-WLP packaging. In this study, methodology to reduce wafer warpage of 12" wafer at different processes was proposed in terms of geometry design, material selection, and process optimization through finite element analysis (FEA) and experiment. Wafer process dependent modeling results were validated by experimental measurement data. Solutions for reducing wafer warpage were recommended. Key parameters were identified based on FEA modeling results: thickness ratio of die to total mold thickness, molding compound and support wafer materials, dielectric material and RDL design.


title: Road Network Extraction and Intersection Detection From Aerial Images by Tracking Road Footprints

abstract: In this paper, a new two-step approach (detecting and pruning) for automatic extraction of road networks from aerial images is presented. The road detection step is based on shape classification of a local homogeneous region around a pixel. The local homogeneous region is enclosed by a polygon, called the footprint of the pixel. This step involves detecting road footprints, tracking roads, and growing a road tree. We use a spoke wheel operator to obtain the road footprint. We propose an automatic road seeding method based on rectangular approximations to road footprints and a toe-finding algorithm to classify footprints for growing a road tree. The road tree pruning step makes use of a Bayes decision model based on the area-to-perimeter ratio (the A/P ratio) of the footprint to prune the paths that leak into the surroundings. We introduce a lognormal distribution to characterize the conditional probability of A/P ratios of the footprints in the road tree and present an automatic method to estimate the parameters that are related to the Bayes decision model. Results are presented for various aerial images. Evaluation of the extracted road networks using representative aerial images shows that the completeness of our road tracker ranges from 84% to 94%, correctness is above 81%, and quality is from 82% to 92%.


title: Active learning with extremely sparse labeled examples

abstract: In the setting of active learning there exists a general assumption that labeled examples are available for training a classifier, which in turn is used to examine unlabeled data to select the most 'informative' examples for manual labeling. However, in some domain applications there are a limited number of labeled examples available, such as in the most extreme cases of having a single labeled example per category. In these scenarios, the most existing active learning methodologies cannot be directly applied without initially making an assumption on label assignment. In this paper we present a method for finding high-informative examples for manual labeling based on extremely limited labeled data available during training. We propose using canonical correlation analysis to investigate the correlation between different views of the available data and demonstrate that this measure can be used as a selection criterion for the novel application of active learning using only a single labeled example from each class. We demonstrate our method with promising experimental results on text classification, advertisement removal and multi-class image classification tasks.


title: Transcriptome sequencing and phylogenetic analysis of four species of luminescent beetles

abstract: The evolution of bioluminescence has prompted scientific attention to illuminate phylogenetic relationships of luminescent beetles. However, genomic resources are virtually lacking in rhagophthalmids (Rhagophthalmidae) and their related firefly beetles lampyrids (Lampyridae). Here, we employed the Illumina Hiseq 2000 platform and sequenced the whole-body transcriptomes of the four luminescent beetles: one rhagophthalmid (Rhagophthalmus sp.) and three fireflies (Asymmetricata circumdata, Aquatica ficta, and Pyrocoelia pectoralis). We obtained 55.4, 43.4, 38.6, and 36.7 million clean reads for the four species, respectively. All reads were assembled into contigs from which unigenes were derived. All unigenes were annotated by publicly available databases, and a total of 4325 orthologous genes were identified. Using multiple phylogenetic approaches, our transcriptome data confirmed the distinctiveness of Rhagophthalmidae from Lampyridae, which was also supported by our mitogenome analysis using three newly determined mitogenome sequences and 12 previously published ones. Together, this study is the first report of whole transcriptome sequencing data in Rhagophthalmidae and Lampyridae species, representing a valuable genomic resource for studying the origin and evolution of some remarkable traits in these beetles such as bioluminescence. Moreover, our transcriptome and mitogenome data provide useful phylogenetic information that could be of importance in future studies of phylogenetic inference.